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  • 1.
    Adjeiwaah, Mary
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Lundman, Josef A.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Jonsson, Joakim H.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Dosimetric Impact of MRI Distortions: A Study on Head and Neck Cancers2019In: International Journal of Radiation Oncology, Biology, Physics, ISSN 0360-3016, E-ISSN 1879-355X, Vol. 103, no 4, p. 994-1003Article in journal (Refereed)
    Abstract [en]

    Purpose: To evaluate the effect of magnetic resonance (MR) imaging (MRI) geometric distortions on head and neck radiation therapy treatment planning (RTP) for an MRI-only RTP. We also assessed the potential benefits of patient-specific shimming to reduce the magnitude of MR distortions for a 3-T scanner.

    Methods and Materials: Using an in-house Matlab algorithm, shimming within entire imaging volumes and user-defined regions of interest were simulated. We deformed 21 patient computed tomography (CT) images with MR distortion fields (gradient nonlinearity and patient-induced susceptibility effects) to create distorted CT (dCT) images using bandwidths of 122 and 488 Hz/mm at 3 T. Field parameters from volumetric modulated arc therapy plans initially optimized on dCT data sets were transferred to CT data to compute a new plan. Both plans were compared to determine the impact of distortions on dose distributions.

    Results: Shimming across entire patient volumes decreased the percentage of voxels with distortions of more than 2 mm from 15.4% to 2.0%. Using the user-defined region of interest (ROI) shimming strategy, (here the Planning target volume (PTV) was the chosen ROI volume) led to increased geometric for volumes outside the PTV, as such voxels within the spinal cord with geometric shifts above 2 mm increased from 11.5% to 32.3%. The worst phantom-measured residual system distortions after 3-dimensional gradient nonlinearity correction within a radial distance of 200 mm from the isocenter was 2.17 mm. For all patients, voxels with distortion shifts of more than 2 mm resulting from patient-induced susceptibility effects were 15.4% and 0.0% using bandwidths of 122 Hz/mm and 488 Hz/mm at 3 T. Dose differences between dCT and CT treatment plans in D-50 at the planning target volume were 0.4% +/- 0.6% and 0.3% +/- 0.5% at 122 and 488 Hz/mm, respectively.

    Conclusions: The overall effect of MRI geometric distortions on data used for RTP was minimal. Shimming over entire imaging volumes decreased distortions, but user-defined subvolume shimming introduced significant errors in nearby organs and should probably be avoided.

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  • 2.
    Adjeiwaah, Mary
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Lundman, Josef A.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Thellenberg Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Jonsson, Joakim H.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
    Quantifying the Effect of 3T Magnetic Resonance Imaging Residual System Distortions and Patient-Induced Susceptibility Distortions on Radiation Therapy Treatment Planning for Prostate Cancer2018In: International Journal of Radiation Oncology, Biology, Physics, ISSN 0360-3016, E-ISSN 1879-355X, Vol. 100, no 2, p. 317-324Article in journal (Refereed)
    Abstract [en]

    Purpose: To investigate the effect of magnetic resonance system- and patient-induced susceptibility distortions from a 3T scanner on dose distributions for prostate cancers.

    Methods and Materials: Combined displacement fields from the residual system and patient-induced susceptibility distortions were used to distort 17 prostate patient CT images. VMAT dose plans were initially optimized on distorted CT images and the plan parameters transferred to the original patient CT images to calculate a new dose distribution.

    Results: Maximum residual mean distortions of 3.19 mm at a radial distance of 25 cm and maximum mean patient-induced susceptibility shifts of 5.8 mm were found using the lowest bandwidth of 122 Hz per pixel. There was a dose difference of <0.5% between distorted and undistorted treatment plans. The 90% confidence intervals of the mean difference between the dCT and CT treatment plans were all within an equivalence interval of (−0.5, 0.5) for all investigated plan quality measures.

    Conclusions: Patient-induced susceptibility distortions at high field strengths in closed bore magnetic resonance scanners are larger than residual system distortions after using vendor-supplied 3-dimensional correction for the delineated regions studied. However, errors in dose due to disturbed patient outline and shifts caused by patient-induced susceptibility effects are below 0.5%.

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  • 3.
    Björeland, Ulrika
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Alm, Magnus
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Beckman, Lars
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Inter-fraction movements of the prostate and pelvic lymph nodes during IGRT2018In: Journal of radiation oncology, ISSN 1948-7894, Vol. 7, no 4, p. 357-366Article in journal (Refereed)
    Abstract [en]

    Objectivities: The aim of this study was to evaluate inter-fraction movements of lymph node regions that are commonly included in the pelvic clinical target volume (CTV) for high-risk prostate cancer patients. We also aimed to evaluate if the movements affect the planning target volumes. Methods: Ten prostate cancer patients were included. The patients underwent six MRI scans, from treatment planning to near end of treatment. The CTV movements were analyzed with deformable registration technique with the CTV divided into sections. The validity of the deformable registration was assessed by comparing the results for individual lymph nodes that were possible to identify in all scans. Results: Using repetitive MRI, measurements showed that areas inside the CTV (lymph nodes) in some extreme cases were as mobile as the prostate and not fixed to the bones. The lymph node volumes closest to the prostate did not tend to follow the prostate motion. The more cranial lymph node volumes moved less, but still independently, and they were not necessarily fixed to the pelvic bones. In 95% of the cases, the lymph node motion in the R-L direction was 2-4mm, in the A-P direction 2-7mm, and in the C-C direction 2-5mm depending on the CTV section. Conclusion: Lymph nodes and prostate were most mobile in the A-P direction, followed by the C-C and R-L directions. This movement should be taken into account when deciding the margins for the planning target volumes (PTV).

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  • 4.
    Björeland, Ulrika
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Notstam, Kristina
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Fransson, Per
    Umeå University, Faculty of Medicine, Department of Nursing.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Beckman, Lars
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Hyaluronic acid spacer in prostate cancer radiotherapy: dosimetric effects, spacer stability and long-term toxicity and PRO in a phase II study2023In: Radiation Oncology, E-ISSN 1748-717X, Vol. 18, no 1, article id 1Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Perirectal spacers may be beneficial to reduce rectal side effects from radiotherapy (RT). Here, we present the impact of a hyaluronic acid (HA) perirectal spacer on rectal dose as well as spacer stability, long-term gastrointestinal (GI) and genitourinary (GU) toxicity and patient-reported outcome (PRO).

    METHODS: In this phase II study 81 patients with low- and intermediate-risk prostate cancer received transrectal injections with HA before external beam RT (78 Gy in 39 fractions). The HA spacer was evaluated with MRI four times; before (MR0) and after HA-injection (MR1), at the middle (MR2) and at the end (MR3) of RT. GI and GU toxicity was assessed by physician for up to five years according to the RTOG scale. PROs were collected using the Swedish National Prostate Cancer Registry and Prostate cancer symptom scale questionnaires.

    RESULTS: There was a significant reduction in rectal V70% (54.6 Gy) and V90% (70.2 Gy) between MR0 and MR1, as well as between MR0 to MR2 and MR3. From MR1 to MR2/MR3, HA thickness decreased with 28%/32% and CTV-rectum space with 19%/17% in the middle level. The cumulative late grade ≥ 2 GI toxicity at 5 years was 5% and the proportion of PRO moderate or severe overall bowel problems at 5 years follow-up was 12%. Cumulative late grade ≥ 2 GU toxicity at 5 years was 12% and moderate or severe overall urinary problems at 5 years were 10%.

    CONCLUSION: We show that the HA spacer reduced rectal dose and long-term toxicity.

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  • 5.
    Björeland, Ulrika
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Skorpil, Mikael
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Blomqvist, Lennart
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Beckman, Lars
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Impact of neoadjuvant androgen deprivation therapy on magnetic resonance imaging features in prostate cancer before radiotherapy2021In: Physics and Imaging in Radiation Oncology, E-ISSN 2405-6316, Vol. 17, p. 117-123Article in journal (Refereed)
    Abstract [en]

    Background and purpose: In locally advanced prostate cancer (PC), androgen deprivation therapy (ADT) in combination with whole prostate radiotherapy (RT) is the standard treatment. ADT affects the prostate as well as the tumour on multiparametric magnetic resonance imaging (MRI) with decreased PC conspicuity and impaired localisation of the prostate lesion. Image texture analysis has been suggested to be of aid in separating tumour from normal tissue. The aim of the study was to investigate the impact of ADT on baseline defined MRI features in prostate cancer with the goal to investigate if it might be of use in radiotherapy planning.

    Materials and methods: Fifty PC patients were included. Multiparametric MRI was performed before, and three months after ADT. At baseline, a tumour volume was delineated on apparent diffusion coefficient (ADC) maps with suspected tumour content and a reference volume in normal prostatic tissue. These volumes were transferred to MRIs after ADT and were analysed with first-order -and invariant Haralick -features.

    Results: At baseline, the median value and several of the invariant Haralick features of ADC, showed a significant difference between tumour and reference volumes. After ADT, only ADC median value could significantly differentiate the two volumes.

    Conclusions: Invariant Haralick -features could not distinguish between baseline MRI defined PC and normal tissue after ADT. First-order median value remained significantly different in tumour and reference volumes after ADT, but the difference was less pronounced than before ADT.

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  • 6.
    Björeland, Ulrika
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Skorpil, Mikael
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Blomqvist, Lennart
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Beckman, Lars
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Diffusion-weighted MRI and 11C-acetate-PET/CT imaging in high-risk/very high-risk prostate cancerManuscript (preprint) (Other academic)
  • 7.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Holmberg, August
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Goldhaber, David
    Jian, Yiqiang
    Illerstam, Fredrik
    Engström, Mathias
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Technical note: adapting a GE SIGNA PET/MR scanner for radiotherapy2018In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 8, p. 3546-3550Article in journal (Refereed)
    Abstract [en]

    Purpose: Simultaneous collection of PET and MR data for radiotherapy purposes are useful for, for example, target definition and dose escalations. However, a prerequisite for using PET/MR in the radiotherapy workflow is the ability to image the patient in treatment position. The aim of this work was to adapt a GE SIGNA PET/MR scanner to image patients for radiotherapy treatment planning and evaluate the impact on signal-to-noise (SNR) of the MR images, and the accuracy of the PET attenuation correction. Method: A flat tabletop and a coil holder were developed to image patients in the treatment position, avoid patient contour deformation, and facilitate attenuation correction of flex coils. Attenuation corrections for the developed hardware and an anterior array flex coil were also measured and implemented to the PET/MR system to minimize PET quantitation errors. The reduction of SNR in the MR images due to the added distance between the coils and the patient was evaluated using a large homogenous saline-doped water phantom, and the activity quantitation errors in PET imaging were evaluated with and without the developed attenuation corrections. Result: We showed that the activity quantitation errors in PET imaging were within ±5% when correcting for attenuation of the flat tabletop, coil holder, and flex coil. The SNR of the MRI images were reduced to 74% using the tabletop, and 66% using the tabletop and coil holders. Conclusion: We present a tabletop and coil holder for an anterior array coil to be used with a GE SIGNA PET/MR scanner, for scanning patients in the radiotherapy work flow. Implementing attenuation correction of the added hardware from the radiotherapy setup leads to acceptable PET image quantitation. The drop in SNR in MR images may require adjustment of the imaging protocols.

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  • 8.
    Bylund, Mikael
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Lundman, Josef
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Using deep learning to generate synthetic CTs for radiotherapy treatment planning2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S283-S283Article in journal (Other academic)
  • 9.
    Grefve, Josefine
    et al.
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Gunnlaugsson, Adalsteinn
    Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund University, Lund, Sweden.
    Sandgren, Kristina
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Keeratijarut Lindberg, Angsana
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nilsson, Erik
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Bergh, Anders
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Moreau, Mathieu
    Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund University, Lund, Sweden.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Olsson, Lars.E.
    Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Blomqvist, Lennart
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Berg Loegager, Vibeke
    Department of Radiology, Copenhagen University Hospital in Herlev, Herlev, Denmark.
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Histopathology-validated gross tumor volume delineations of intraprostatic lesions using PSMA-positron emission tomography/multiparametric magnetic resonance imaging2024In: Physics and Imaging in Radiation Oncology, E-ISSN 2405-6316, Vol. 31, article id 100633Article in journal (Refereed)
    Abstract [en]

    Background and purpose: Dose escalation in external radiotherapy of prostate cancer shows promising results in terms of biochemical disease-free survival. Boost volume delineation guidelines are sparse which may cause high interobserver variability. The aim of this research was to characterize gross tumor volume (GTV) delineations based on multiparametric magnetic resonance imaging (mpMRI) and prostate specific membrane antigen-positron emission tomography (PSMA-PET) in relation to histopathology-validated Gleason grade 4 and 5 regions.

    Material and methods: The study participants were examined with [68Ga]PSMA-PET/mpMRI prior to radical prostatectomy. Four radiation oncologists delineated GTVs in 15 study participants, on four different image types; T2-weighted (T2w), diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and PSMA-PET scans separately. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to generate combined GTVs. GTVs were subsequently compared to histopathology. We analysed how Dice similarity coefficient (DSC) and lesion coverage are affected by using single versus multiple image types as well as by adding a clinical target volume (CTV) margin.

    Results: Median DSC (STAPLE) for different GTVs varied between 0.33 and 0.52. GTVPSMA-PET/mpMRI generated the highest median lesion coverage at 0.66. Combining different image types achieved similar lesion coverage as adding a CTV margin to contours from a single image type, while reducing non-malignant tissue inclusion within the target volume.

    Conclusion: The combined use of mpMRI or PSMA-PET/mpMRI shows promise, achieving higher DSC and lesion coverage while minimizing non-malignant tissue inclusion, in comparison to the use of a single image type with an added CTV margin.

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  • 10.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Different methods of creating pseudo-CT images2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S349-S349Article in journal (Other academic)
  • 11.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Integration of MRI into the radiotherapy workflow2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The modern day radiotherapy treatments are almost exclusively based on computed tomography (CT) images. The CT images are acquired using x-rays, and therefore reflect the radiation interaction properties of the material. This information is used to perform accurate dose calculation by the treatment planning system, and the data is also well suited for creating digitally reconstructed radiographs for comparing patient set up at the treatment machine where x-ray images are routinely acquired for this purpose.

    The magnetic resonance (MR) scanner has many attractive features for radiotherapy purposes. The soft tissue contrast as compared to CT is far superior, and it is possible to vary the sequences in order to visualize different anatomical and physiological properties of an organ. Both of these properties may contribute to an increase in accuracy of radiotherapy treatment.

    Using the MR images by themselves for treatment planning is, however, problematic. MR data reflects the magnetic properties of protons, and thus have no connection to the radiointeraction properties of the material. MRI also has inherent difficulty in imaging bone, which will appear in images as areas of no signal similar to air. This makes both dose calculation and patient positioning at the treatment machine troublesome.

    There are several clinics that use MR images together with CT images to perform treatment planning. The images are registered to a common coordinate system, a process often described as image fusion. In these cases, the MR images are primarily used for target definition and the CT images are used for dose calculations. This method is now not ideal, however, since the image fusion may introduce systematic uncertainties into the treatment due to the fact that the tumor is often able to move relatively freely with respect to the patients’ bony anatomy and outer contour, especially when the image registration algorithms take the entire patient anatomy in the volume of interest into account.

    The work presented in the thesis “Integration of MRI into the radiotherapy workflow” aim towards investigating the possibilities of workflows based entirely on MRI without using image registration, as well as workflows using image registration methods that are better suited for targets that can move with respect to surrounding bony anatomy, such as the prostate.

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  • 12.
    Jonsson, Joakim H.
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Akhtari, Mohammad M.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Karlsson, Magnus G.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions2015In: Radiation Oncology, E-ISSN 1748-717X, Vol. 10, article id 13Article in journal (Refereed)
    Abstract [en]

    Background: In this pilot study we evaluated the performance of a substitute CT (s-CT) image derived from MR data of the brain, as a basis for optimization of intensity modulated rotational therapy, final dose calculation and derivation of reference images for patient positioning. Methods: S-CT images were created using a Gaussian mixture regression model on five patients previously treated with radiotherapy. Optimizations were compared using D-max, D-min, D-median and D-mean measures for the target volume and relevant risk structures. Final dose calculations were compared using gamma index with 1%/1 mm and 3%/3 mm acceptance criteria. 3D geometric evaluation was conducted using the DICE similarity coefficient for bony structures. 2D geometric comparison of digitally reconstructed radiographs (DRRs) was performed by manual delineation of relevant structures on the s-CT DRR that were transferred to the CT DRR and compared by visual inspection. Results: Differences for the target volumes in optimization comparisons were small in general, e.g. a mean difference in both D-min and D-max within similar to 0.3%. For the final dose calculation gamma evaluations, 100% of the voxels passed the 1%/1 mm criterion within the PTV. Within the entire external volume between 99.4% and 100% of the voxels passed the 3%/3 mm criterion. In the 3D geometric comparison, the DICE index varied between approximately 0.8-0.9, depending on the position in the skull. In the 2D DRR comparisons, no appreciable visual differences were found. Conclusions: Even though the present work involves a limited number of patients, the results provide a strong indication that optimization and dose calculation based on s-CT data is accurate regarding both geometry and dosimetry.

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  • 13.
    Jonsson, Joakim H
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Registration accuracy for MR images of the prostate using a subvolume based registration protocol2011In: Radiation Oncology, E-ISSN 1748-717X, Vol. 6, no 1, p. 73-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate.

    METHODS: Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances.

    RESULTS: We found that prostate position was most uncertain in the anterior-posterior (AP) direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p < 0.001) in the AP direction. The optimum registration volume size was 0 mm margin added to the prostate gland as outlined in the first image series.

    CONCLUSIONS: Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD) compared to several millimeters for registration based on the whole pelvis.

  • 14.
    Jonsson, Joakim H
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Magnus G
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Internal fiducial markers and susceptibility effects in MRI: simulation and measurement of spatial accuracy2012In: International Journal of Radiation Oncology, Biology, Physics, ISSN 0360-3016, E-ISSN 1879-355X, Vol. 82, no 5, p. 1612-1618Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: It is well-known that magnetic resonance imaging (MRI) is preferable to computed tomography (CT) in radiotherapy target delineation. To benefit from this, there are two options available: transferring the MRI delineated target volume to the planning CT or performing the treatment planning directly on the MRI study. A precondition for excluding the CT study is the possibility to define internal structures visible on both the planning MRI and on the images used to position the patient at treatment. In prostate cancer radiotherapy, internal gold markers are commonly used, and they are visible on CT, MRI, x-ray, and portal images. The depiction of the markers in MRI are, however, dependent on their shape and orientation relative the main magnetic field because of susceptibility effects. In the present work, these effects are investigated and quantified using both simulations and phantom measurements.

    METHODS AND MATERIALS: Software that simulated the magnetic field distortions around user defined geometries of variable susceptibilities was constructed. These magnetic field perturbation maps were then reconstructed to images that were evaluated. The simulation software was validated through phantom measurements of four commercially available gold markers of different shapes and one in-house gold marker.

    RESULTS: Both simulations and phantom measurements revealed small position deviations of the imaged marker positions relative the actual marker positions (<1 mm).

    CONCLUSION: Cylindrical gold markers can be used as internal fiducial markers in MRI.

  • 15.
    Jonsson, Joakim H
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Magnus G
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions2010In: Radiation Oncology, E-ISSN 1748-717X, Vol. 5, p. 62-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Because of superior soft tissue contrast, the use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To keep the workflow simple and cost effective and to reduce patient dose, it is natural to strive for a treatment planning procedure based entirely on MRI. In the present study, we investigate the dose calculation accuracy for different treatment regions when using bulk density assignments on MRI data and compare it to treatment planning that uses CT data.

    METHODS: MR and CT data were collected retrospectively for 40 patients with prostate, lung, head and neck, or brain cancers. Comparisons were made between calculations on CT data with and without inhomogeneity corrections and on MRI or CT data with bulk density assignments. The bulk densities were assigned using manual segmentation of tissue, bone, lung, and air cavities.

    RESULTS: The deviations between calculations on CT data with inhomogeneity correction and on bulk density assigned MR data were small. The maximum difference in the number of monitor units required to reach the prescribed dose was 1.6%. This result also includes effects of possible geometrical distortions.

    CONCLUSIONS: The dose calculation accuracy at the investigated treatment sites is not significantly compromised when using MRI data when adequate bulk density assignments are made. With respect to treatment planning, MRI can replace CT in all steps of the treatment workflow, reducing the radiation exposure to the patient, removing any systematic registration errors that may occur when combining MR and CT, and decreasing time and cost for the extra CT investigation.

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  • 16.
    Jonsson, Joakim
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Treatment planning of intracranial targets on MRI derived substitute CT data2013In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 108, no 1, p. 118-122Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: The use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To eliminate systematic uncertainties due to image registration, a workflow based entirely on MRI may be preferable. In the present pilot study, we investigate dose calculation accuracy for automatically generated substitute CT (s-CT) images of the head based on MRI. We also produce digitally reconstructed radiographs (DRRs) from s-CT data to evaluate the feasibility of patient positioning based on MR images. METHODS AND MATERIALS: Five patients were included in the study. The dose calculation was performed on CT, s-CT, s-CT data without inhomogeneity correction and bulk density assigned MRI images. Evaluation of the results was performed using point dose and dose volume histogram (DVH) comparisons, and gamma index evaluation. RESULTS: The results demonstrate that the s-CT images improves the dose calculation accuracy compared to the method of non-inhomogeneity corrected dose calculations (mean improvement 2.0 percentage points) and that it performs almost identically to the method of bulk density assignment. The s-CT based DRRs appear to be adequate for patient positioning of intra-cranial targets, although further investigation is needed on this subject. CONCLUSIONS: The s-CT method is very fast and yields data that can be used for treatment planning without sacrificing accuracy.

  • 17.
    Jonsson, Joakim
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    The rationale for MR-only treatment planning for external radiotherapy2019In: Clinical and Translational Radiation Oncology, E-ISSN 2405-6308, Vol. 18, p. 60-65Article, review/survey (Refereed)
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  • 18.
    Kaushik, Sandeep S.
    et al.
    GE Healthcare, Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Cozzini, Cristina
    GE Healthcare, Munich, Germany.
    Shanbhag, Dattesh
    GE Healthcare, Bangalore, India.
    Petit, Steven F.
    Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, Netherlands.
    Wyatt, Jonathan J.
    Translational and Clinical Research Institute, Newcastle University and Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, United Kingdom.
    Menzel, Marion I.
    GE Healthcare, Munich, Germany; Dept. of Physics, Technical University of Munich, Munich, Germany.
    Pirkl, Carolin
    GE Healthcare, Munich, Germany.
    Mehta, Bhairav
    GE Healthcare, Bangalore, India.
    Chauhan, Vikas
    Sree Chitra Tirunal Institute of Medical Sciences and Technology (SCTIMST), Trivandrum, India.
    Chandrasekharan, Kesavadas
    Sree Chitra Tirunal Institute of Medical Sciences and Technology (SCTIMST), Trivandrum, India.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Wiesinger, Florian
    GE Healthcare, Munich, Germany.
    Menze, Bjoern
    Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
    Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network2023In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 68, no 19, article id 195003Article in journal (Refereed)
    Abstract [en]

    Objective: In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.

    Approach: We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.

    Main results: We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.

    Significance: We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.

  • 19. Mannerberg, Annika
    et al.
    Persson, Emilia
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jamtheim Gustafsson, Christian
    Gunnlaugsson, Adalsteinn
    Olsson, Lars E.
    Ceberg, Sofie
    Dosimetric effects of adaptive prostate cancer radiotherapy in an MR-linac workflow2020In: Radiation Oncology, E-ISSN 1748-717X, Vol. 15, no 1, article id 168Article in journal (Refereed)
    Abstract [en]

    Background: The purpose was to evaluate the dosimetric effects in prostate cancer treatment caused by anatomical changes occurring during the time frame of adaptive replanning in a magnetic resonance linear accelerator (MR-linac) workflow.

    Methods: Two MR images (MR1 and MR2) were acquired with 30 min apart for each of the 35 patients enrolled in this study. The clinical target volume (CTV) and organs at risk (OARs) were delineated based on MR1. Using a synthetic CT (sCT), ultra-hypofractionated VMAT treatment plans were created for MR1, with three different planning target volume (PTV) margins of 7 mm, 5 mm and 3 mm. The three treatment plans of MR1, were recalculated onto MR2 using its corresponding sCT. The dose distribution of MR2 represented delivered dose to the patient after 30 min of adaptive replanning, omitting motion correction before beam on. MR2 was registered to MR1, using deformable registration. Using the inverse deformation, the structures of MR1 was deformed to fit MR2 and anatomical changes were quantified. For dose distribution comparison the dose distribution of MR2 was warped to the geometry MR1.

    Results: The mean center of mass vector offset for the CTV was 1.92 mm [0.13 – 9.79 mm]. Bladder volume increase ranged from 12.4 to 133.0% and rectum volume difference varied between −10.9 and 38.8%. Using the conventional 7 mm planning target volume (PTV) margin the dose reduction to the CTV was 1.1%. Corresponding values for 5 mm and 3 mm PTV margin were 2.0% and 4.2% respectively. The dose to the PTV and OARs also decreased from D1 to D2, for all PTV margins evaluated. Statistically significant difference was found for CTV Dmin between D1 and D2 for the 3 mm PTV margin (p < 0.01).

    Conclusions: A target underdosage caused by anatomical changes occurring during the reported time frame for adaptive replanning MR-linac workflows was found. Volume changes in both bladder and rectum caused large prostate displacements. This indicates the importance of thorough position verification before treatment delivery and that the workflow needs to speed up before introducing margin reduction.

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  • 20.
    Nilsson, Erik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Sandgren, Kristina
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Grefve, Josefine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Keeratijarut Lindberg, Angsana
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Thellenberg Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Blomqvist, Lennart
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden.
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Bergh, Anders
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    The grade of individual prostate cancer lesions predicted by magnetic resonance imaging and positron emission tomography2023In: Communications Medicine, E-ISSN 2730-664X, Vol. 3, no 1, article id 164Article in journal (Refereed)
    Abstract [en]

    Background: Multiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) are widely used for the management of prostate cancer (PCa). However, how these modalities complement each other in PCa risk stratification is still largely unknown. We aim to provide insights into the potential of mpMRI and PET for PCa risk stratification.

    Methods: We analyzed data from 55 consecutive patients with elevated prostate-specific antigen and biopsy-proven PCa enrolled in a prospective study between December 2016 and December 2019. [68Ga]PSMA-11 PET (PSMA-PET), [11C]Acetate PET (Acetate-PET) and mpMRI were co-registered with whole-mount histopathology. Lower- and higher-grade lesions were defined by International Society of Urological Pathology (ISUP) grade groups (IGG). We used PET and mpMRI data to differentiate between grades in two cases: IGG 3 vs. IGG 2 (case 1) and IGG ≥ 3 vs. IGG ≤ 2 (case 2). The performance was evaluated by receiver operating characteristic (ROC) analysis.

    Results: We find that the maximum standardized uptake value (SUVmax) for PSMA-PET achieves the highest area under the ROC curve (AUC), with AUCs of 0.72 (case 1) and 0.79 (case 2). Combining the volume transfer constant, apparent diffusion coefficient and T2-weighted images (each normalized to non-malignant prostatic tissue) results in AUCs of 0.70 (case 1) and 0.70 (case 2). Adding PSMA-SUVmax increases the AUCs by 0.09 (p < 0.01) and 0.12 (p < 0.01), respectively.

    Conclusions: By co-registering whole-mount histopathology and in-vivo imaging we show that mpMRI and PET can distinguish between lower- and higher-grade prostate cancer, using partially discriminative cut-off values.

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  • 21.
    Nyholm, Tufve
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Counterpoint: Opportunities and Challenges of a Magnetic Resonance Imaging-Only Radiotherapy Work Flow2014In: Seminars in radiation oncology, ISSN 1053-4296, E-ISSN 1532-9461, Vol. 24, no 3, p. 175-180Article in journal (Refereed)
    Abstract [en]

    Magnetic resonance (MR) imaging plays an important role in modern radiotherapy. The benefits of MR as compared with those of computed tomography for the definition of target volumes is evident for many soft tissue tumor types. It has been suggested that for these patient groups, the computed tomography examination is unnecessary as part of the preparation for radiotherapy. Here, we review the rationale for an MR-only radiotherapy work flow, as well as the technical challenges and solutions connected to it.

  • 22.
    Nyholm, Tufve
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Bergström, Per
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Carlberg, Andreas
    Frykholm, Gunilla
    Behrens, Claus F.
    Geertsen, Poul Flemming
    Trepiakas, Redas
    Hanvey, Scott
    Sadozye, Azmat
    Ansari, Jawaher
    McCallum, Hazel
    Frew, John
    McMenemin, Rhona
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Variability in prostate and seminal vesicle delineations defined on magnetic resonance images, a multi-observer, -center and -sequence study2013In: Radiation Oncology, E-ISSN 1748-717X, Vol. 8, p. 126-Article in journal (Refereed)
    Abstract [en]

    Background: The use of magnetic resonance (MR) imaging as a part of preparation for radiotherapy is increasing. For delineation of the prostate several publications have shown decreased delineation variability using MR compared to computed tomography (CT). The purpose of the present work was to investigate the intra- and inter-physician delineation variability for prostate and seminal vesicles, and to investigate the influence of different MR sequence settings used clinically at the five centers participating in the study.

    Methods: MR series from five centers, each providing five patients, were used. Two physicians from each center delineated the prostate and the seminal vesicles on each of the 25 image sets. The variability between the delineations was analyzed with respect to overall, intra-and inter-physician variability, and dependence between variability and origin of the MR images, i.e. the MR sequence used to acquire the data.

    Results: The intra-physician variability in different directions was between 1.3 - 1.9 mm and 3 - 4 mm for the prostate and seminal vesicles respectively (1 std). The inter-physician variability for different directions were between 0.7 - 1.7 mm and approximately equal for the prostate and seminal vesicles. Large differences in variability were observed for individual patients, and also for individual imaging sequences used at the different centers. There was however no indication of decreased variability with higher field strength.

    Conclusion: The overall delineation variability is larger for the seminal vesicles compared to the prostate, due to a larger intra-physician variability. The imaging sequence appears to have a large influence on the variability, even for different variants of the T2-weighted spin-echo based sequences, which were used by all centers in the study.

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    Variability in prostate and seminal vesicle delineations defined on magnetic resonance images, a multi-observer, -center and -sequence study
  • 23.
    Nyholm, Tufve
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Svensson, Stina
    Andersson, Sebastian
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Sohlin, Maja
    Gustafsson, Christian
    Kjellén, Elisabeth
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Albertsson, Per
    Blomqvist, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Olsson, Lars E.
    Gunnlaugsson, Adalsteinn
    MR and CT data with multiobserver delineations of organs in the pelvic area: Part of the Gold Atlas project2018In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 3, p. 1295-1300Article in journal (Refereed)
    Abstract [en]

    Purpose: We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT).

    Acquisition and validation methods: T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset.

    Data format and usage notes: The dataset has been made publically available to be used for academic purposes, and can be accessed from . Potential applicationsThe dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm.

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  • 24. Persson, Emilia
    et al.
    Gustafsson, Christian
    Nordström, Fredrik
    Sohlin, Maja
    Gunnlaugsson, Adalsteinn
    Petruson, Karin
    Rintelä, Niina
    Hed, Kristoffer
    Blomqvist, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Olsson, Lars E.
    Siversson, Carl
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    MR-OPERA: a multicenter/multivendor validation of magnetic resonance imaging–only prostate treatment planning using synthetic computed tomography images2017In: International Journal of Radiation Oncology, Biology, Physics, ISSN 0360-3016, E-ISSN 1879-355X, Vol. 99, no 3, p. 692-700Article in journal (Refereed)
    Abstract [en]

    Purpose: To validate the dosimetric accuracy and clinical robustness of a commercially available software for magnetic resonance (MR) to synthetic computed tomography (sCT) conversion, in an MR imaging–only workflow for 170 prostate cancer patients.

    Methods and Materials: The 4 participating centers had MriPlanner (Spectronic Medical), an atlas-based sCT generation software, installed as a cloud-based service. A T2-weighted MR sequence, covering the body contour, was added to the clinical protocol. The MR images were sent from the MR scanner workstation to the MriPlanner platform. The sCT was automatically returned to the treatment planning system. Four MR scanners and 2 magnetic field strengths were included in the study. For each patient, a CT-treatment plan was created and approved according to clinical practice. The sCT was rigidly registered to the CT, and the clinical treatment plan was recalculated on the sCT. The dose distributions from the CT plan and the sCT plan were compared according to a set of dose-volume histogram parameters and gamma evaluation. Treatment techniques included volumetric modulated arc therapy, intensity modulated radiation therapy, and conventional treatment using 2 treatment planning systems and different dose calculation algorithms.

    Results: The overall (multicenter/multivendor) mean dose differences between sCT and CT dose distributions were below 0.3% for all evaluated organs and targets. Gamma evaluation showed a mean pass rate of 99.12% (0.63%, 1 SD) in the complete body volume and 99.97% (0.13%, 1 SD) in the planning target volume using a 2%/2-mm global gamma criteria.

    Conclusions: Results of the study show that the sCT conversion method can be used clinically, with minimal differences between sCT and CT dose distributions for target and relevant organs at risk. The small differences seen are consistent between centers, indicating that an MR imaging–only workflow using MriPlanner is robust for a variety of field strengths, vendors, and treatment techniques.

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  • 25.
    Sandgren, Kristina
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Johansson, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ögren, Mattias
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Ögren, Margareta
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Andersson, Martin
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Radiation dosimetry of [Ga-68]PSMA-11 in low-risk prostate cancer patients2019In: EJNMMI Physics, E-ISSN 2197-7364, Vol. 6, article id 2Article in journal (Refereed)
    Abstract [en]

    Background: 68Ga-labeled Glu-NH-CO-NH-Lys(Ahx)-HBED-CC ([68Ga]PSMA-11) has been increasingly used to image prostate cancer using positron emission tomography (PET)/computed tomography (CT) both during diagnosis and treatment planning. It has been shown to be of clinical value for patients both in the primary and secondary stages of prostate cancer. The aim of this study was to determine the effective dose and organ doses from injection of [68Ga]PSMA-11 in a cohort of low-risk prostate cancer patients.

    Methods: Six low-risk prostate cancer patients were injected with 133–178 MBq [68Ga]PSMA-11 and examined with four PET/CT acquisitions from injection to 255 min post-injection. Urine was collected up to 4 h post-injection, and venous blood samples were drawn at 45 min, 85 min, 175 min, and 245 min post-injection. Kidneys, liver, lungs, spleen, salivary and lacrimal glands, and total body where delineated, and cumulated activities and absorbed organ doses calculated. The software IDAC-Dose 2.1 was used to calculate absorbed organ doses according to the International Commission on Radiological Protection (ICRP) publication 107 using specific absorbed fractions published in ICRP 133 and effective dose according to ICRP Publication 103. We also estimated the absorbed dose to the eye lenses using Monte Carlo methods.

    Results: [68Ga]PSMA-11 was rapidly cleared from the blood and accumulated preferentially in the kidneys and the liver. The substance has a biological half-life in blood of 6.5 min (91%) and 4.4 h (9%). The effective dose was calculated to 0.022 mSv/MBq. The kidneys received approximately 40 mGy after an injection with 160 MBq [68Ga]PSMA-11 while the lacrimal glands obtained an absorbed dose of 0.12 mGy per administered MBq. Regarding the eye lenses, the absorbed dose was low (0.0051 mGy/MBq).

    Conclusion: The effective dose for [68Ga]PSMA-11 is 0.022 mSv/MBq, where the kidneys and lacrimal glands receiving the highest organ dose.

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  • 26.
    Sandgren, Kristina
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Ögren, Mattias
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Blomqvist, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Friedrich, Bengt
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Bergh, Anders
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Ahlström Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Histology correlation of in vivo [68Ga]PSMA-PET/MRI data of the prostate2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S541-S541Article in journal (Other academic)
  • 27.
    Sandgren, Kristina
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nilsson, Erik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Keeratijarut Lindberg, Angsana
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Blomqvist, Lennart
    Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden.
    Bergh, Anders
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Friedrich, Bengt
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ögren, Margareta
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Ögren, Mattias
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Söderström, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Registration of histopathology to magnetic resonance imaging of prostate cancer2021In: Physics and Imaging in Radiation Oncology, E-ISSN 2405-6316, Vol. 18, p. 19-25Article in journal (Refereed)
    Abstract [en]

    Background and purpose: The diagnostic accuracy of new imaging techniques requires validation, preferably by histopathological verification. The aim of this study was to develop and present a registration procedure between histopathology and in-vivo magnetic resonance imaging (MRI) of the prostate, to estimate its uncertainty and to evaluate the benefit of adding a contour-correcting registration.

    Materials and methods: For twenty-five prostate cancer patients, planned for radical prostatectomy, a 3D-printed prostate mold based on in-vivo MRI was created and an ex-vivo MRI of the specimen, placed inside the mold, was performed. Each histopathology slice was registered to its corresponding ex-vivo MRI slice using a 2D-affine registration. The ex-vivo MRI was rigidly registered to the in-vivo MRI and the resulting transform was applied to the histopathology stack. A 2D deformable registration was used to correct for specimen distortion concerning the specimen's fit inside the mold. We estimated the spatial uncertainty by comparing positions of landmarks in the in-vivo MRI and the corresponding registered histopathology stack.

    Results: Eighty-four landmarks were identified, located in the urethra (62%), prostatic cysts (33%), and the ejaculatory ducts (5%). The median number of landmarks was 3 per patient. We showed a median in-plane error of 1.8 mm before and 1.7 mm after the contour-correcting deformable registration. In patients with extraprostatic margins, the median in-plane error improved from 2.1 mm to 1.8 mm after the contour-correcting deformable registration.

    Conclusions: Our registration procedure accurately registers histopathology to in-vivo MRI, with low uncertainty. The contour-correcting registration was beneficial in patients with extraprostatic surgical margins.

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  • 28.
    Sandgren, Kristina
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Grefve, Josefine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Keeratijarut Lindberg, Angsana
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nilsson, Erik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Bergh, Anders
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Friedrich, Bengt
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Blomqvist, Lennart
    Department of Molecular Medicine and Surgery, Karolinska Institute, Solna, Sweden.
    Loegager, Vibeke Berg
    Department of Radiology, Copenhagen University Hospital in Herlev, Herlev, Denmark.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ögren, Mattias
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Ögren, Margareta
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Histopathology-validated lesion detection rates of clinically significant prostate cancer with mpMRI, [68Ga]PSMA-11-PET and [11C]Acetate-PET2023In: Nuclear medicine communications, ISSN 0143-3636, E-ISSN 1473-5628, Vol. 44, no 11, p. 997-1004Article in journal (Refereed)
    Abstract [en]

    Objective: PET/CT and multiparametric MRI (mpMRI) are important diagnostic tools in clinically significant prostate cancer (csPC). The aim of this study was to compare csPC detection rates with [68Ga]PSMA-11-PET (PSMA)-PET, [11C] Acetate (ACE)-PET, and mpMRI with histopathology as reference, to identify the most suitable imaging modalities for subsequent hybrid imaging. An additional aim was to compare inter-reader variability to assess reproducibility.

    Methods: During 2016–2019, all study participants were examined with PSMA-PET/mpMRI and ACE-PET/CT prior to radical prostatectomy. PSMA-PET, ACE-PET and mpMRI were evaluated separately by two observers, and were compared with histopathology-defined csPC. Statistical analyses included two-sided McNemar test and index of specific agreement.

    Results: Fifty-five study participants were included, with 130 histopathological intraprostatic lesions >0.05 cc. Of these, 32% (42/130) were classified as csPC with ISUP grade ≥2 and volume >0.5 cc. PSMA-PET and mpMRI showed no difference in performance (P = 0.48), with mean csPC detection rate of 70% (29.5/42) and 74% (31/42), respectively, while with ACE-PET the mean csPC detection rate was 37% (15.5/42). Interobserver agreement was higher with PSMA-PET compared to mpMRI [79% (26/33) vs 67% (24/38)]. Including all detected lesions from each pair of observers, the detection rate increased to 90% (38/42) with mpMRI, and 79% (33/42) with PSMA-PET.

    Conclusion: PSMA-PET and mpMRI showed high csPC detection rates and superior performance compared to ACE-PET. The interobserver agreement indicates higher reproducibility with PSMA-PET. The combined result of all observers in both PSMA-PET and mpMRI showed the highest detection rate, suggesting an added value of a hybrid imaging approach.

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  • 29.
    Sandgren, Kristina
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Westerlinck, Philippe
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Blomqvist, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
    Thellenberg Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Department of Immunology, Genetics, and Pathology, Medical Radiation Science, Uppsala University, Uppsala, Sweden.
    Dirix, Piet
    Imaging for the Detection of Locoregional Recurrences in Biochemical Progression After Radical Prostatectomy: A Systematic Review2019In: European Urology Focus, E-ISSN 2405-4569, Vol. 5, no 4, p. 550-560Article, review/survey (Refereed)
    Abstract [en]

    Context: Local and regional recurrence after radical prostatectomy (RP) can be treated using salvage radiotherapy (SRT). If the recurrence can be delineated on diagnostic imaging, this could allow for increasingly individualized SRT.

    Objective: This systematic review aimed at evaluating the evidence regarding the usefulness of positron emission tomography (PET) and magnetic resonance imaging (MRI) in identifying local and regional recurrences, with the aim to further individualize the SRT treatment.

    Evidence acquisition: A systematic PubMed/Medline search was conducted in December 2015. Studies included were imaging studies of post-RP patients focusing on local and/or regional recurrence where sensitivity and specificity of MRI or PET were the primary end points. Only studies using biopsy, other histological analysis, and/or treatment follow-up as reference standard were included. Quality Assessment of Diagnostic Accuracy Studies-2 was used to score the study quality. Twenty-five articles were deemed of sufficient quality and included in the review.

    Evidence synthesis: [11C]Acetate had the highest pooled sensitivity (92%), while [11C]choline and [18F]choline had pooled sensitivities of 71% and 84%, respectively. The PET tracer with highest pooled specificity was [11C]choline (86%). Regarding MRI, MR spectroscopy combined with dynamic contrast enhanced (DCE) MRI showed the highest pooled sensitivity (89%). High pooled sensitivities were also seen using multiparametric MRI (84%), diffusion-weighted MRI combined with T2-weigthed (T2w) imaging (82%), and DCE MRI combined with T2w imaging (82%). These also showed high pooled specificities (85%, 89%, and 92%, respectively).

    Conclusions: Both MRI and PET have adequate sensitivity and specificity for the detection of prostate cancer recurrences post-RP. Multiparametric MRI, using diffusion-weighted and/or DCE imaging, and the choline-labeled tracers showed high pooled sensitivity and specificity, although their ranges were broad.

    Patient summary: After reviewing imaging studies of recurrent prostate cancer after prostatectomy, we concluded that choline positron emission tomography and diffusion-weighted magnetic resonance imaging can be proposed as the current standard, with high sensitivity and specificity.

  • 30.
    Simkó, Attila
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jönsson, Gustav
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Towards MR contrast independent synthetic CT generation2024In: Zeitschrift für Medizinische Physik, ISSN 0939-3889, E-ISSN 1876-4436, Vol. 34, no 2, p. 270-277Article in journal (Refereed)
    Abstract [en]

    The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics.

    To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.

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  • 31.
    Simkó, Attila
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Reproducibility of the methods in medical imaging with deep learning2023In: Medical imaging with deep learning 2023 / [ed] Ipek Oguz; Jack Noble; Xiaoxiao Li; Martin Styner; Christian Baumgartner; Mirabela Rusu; Tobias Heinmann; Despina Kontos; Bennett Landman; Benoit Dawant, ML Research Press , 2023, p. 95-106Conference paper (Refereed)
    Abstract [en]

    Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The Medical Imaging with Deep Learning (MIDL) conference has made advancements in employing empirical rigor with regards to reproducibility by advocating open access, and recently also recommending authors to make their code public—both aspects being adopted by the majority of the conference submissions. We have evaluated all accepted full paper submissions to MIDL between 2018 and 2022 using established, but adjusted guidelines addressing the reproducibility and quality of the public repositories. The evaluations show that publishing repositories and using public datasets are becoming more popular, which helps traceability, but the quality of the repositories shows room for improvement in every aspect. Merely 22% of all submissions contain a repository that was deemed repeatable using our evaluations. From the commonly encountered issues during the evaluations, we propose a set of guidelines for machine learning-related research for medical imaging applications, adjusted specifically for future submissions to MIDL. We presented our results to future MIDL authors who were eager to continue an open discussion on the topic of code reproducibility.

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  • 32.
    Simkó, Attila
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning2021In: Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR / [ed] Mattias Heinrich; Qi Dou; Marleen de Bruijne; Jan Lellmann; Alexander Schläfer; Floris Ernst, Lübeck University; Hamburg University of Technology , 2021, Vol. 143, p. 713-727Conference paper (Refereed)
    Abstract [en]

     The contrast settings to select before acquiring magnetic resonance imaging (MRI) signal depend heavily on the subsequent tasks. As each contrast highlights different tissues, automated segmentation tools for example might be optimized for a certain contrast. While for radiotherapy, multiple scans of the same region with different contrasts can achieve a better accuracy for delineating tumours and organs at risk. Unfortunately, the optimal contrast for the subsequent automated methods might not be known during the time of signal acquisition, and performing multiple scans with different contrasts increases the total examination time and registering the sequences introduces extra work and potential errors. Building on the recent achievements of deep learning in medical applications, the presented work describes a novel approach for transferring any contrast to any other. The novel model architecture incorporates the signal equation for spin echo sequences, and hence the model inherently learns the unknown quantitative maps for proton density, 𝑇1 and 𝑇2 relaxation times (𝑃𝐷, 𝑇1 and 𝑇2, respectively). This grants the model the ability to retrospectively reconstruct spin echo sequences by changing the contrast settings Echo and Repetition Time (𝑇𝐸 and 𝑇𝑅, respectively). The model learns to identify the contrast of pelvic MR images, therefore no paired data of the same anatomy from different contrasts is required for training. This means that the experiments are easily reproducible with other contrasts or other patient anatomies. Despite the contrast of the input image, the model achieves accurate results for reconstructing signal with contrasts available for evaluation. For the same anatomy, the quantitative maps are consistent for a range of contrasts of input images. Realized in practice, the proposed method would greatly simplify the modern radiotherapy pipeline. The trained model is made public together with a tool for testing the model on example images. 

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  • 33.
    Simkó, Attila
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    MRI bias field correction with an implicitly trained CNN2022In: Proceedings of the 5th international conference on medical imaging with deep learning / [ed] Ender Konukoglu; Bjoern Menze; Archana Venkataraman; Christian Baumgartner; Qi Dou; Shadi Albarqouni, ML Research Press , 2022, p. 1125-1138Conference paper (Refereed)
    Abstract [en]

    In magnetic resonance imaging (MRI), bias fields are difficult to correct since they are inherently unknown. They cause intra-volume intensity inhomogeneities which limit the performance of subsequent automatic medical imaging tasks, \eg, tissue-based segmentation. Since the ground truth is unavailable, training a supervised machine learning solution requires approximating the bias fields, which limits the resulting method. We introduce implicit training which sidesteps the inherent lack of data and allows the training of machine learning solutions without ground truth. We describe how training a model implicitly for bias field correction allows using non-medical data for training, achieving a highly generalized model. The implicit approach was compared to a more traditional training based on medical data. Both models were compared to an optimized N4ITK method, with evaluations on six datasets. The implicitly trained model improved the homogeneity of all encountered medical data, and it generalized better for a range of anatomies, than the model trained traditionally. The model achieves a significant speed-up over an optimized N4ITK method—by a factor of 100, and after training, it also requires no parameters to tune. For tasks such as bias field correction - where ground truth is generally not available, but the characteristics of the corruption are known - implicit training promises to be a fruitful alternative for highly generalized solutions.

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  • 34.
    Simkó, Attila
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Ruiter, Simone
    Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Improving MR image quality with a multi-task model, using convolutional losses2023In: BMC Medical Imaging, E-ISSN 1471-2342, Vol. 23, no 1, article id 148Article in journal (Refereed)
    Abstract [en]

    PURPOSE: During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correction, super-resolution, motion-, and noise correction. Machine learning has achieved outstanding results in improving MR image quality for these tasks individually, yet multi-task methods are rarely explored.

    METHODS: In this study, we developed a model to simultaneously correct for all four aforementioned artefacts using multi-task learning. Two different datasets were collected, one consisting of brain scans while the other pelvic scans, which were used to train separate models, implementing their corresponding artefact augmentations. Additionally, we explored a novel loss function that does not only aim to reconstruct the individual pixel values, but also the image gradients, to produce sharper, more realistic results. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test.

    RESULTS: Our proposed model generally outperformed other commonly-used correction methods for individual artefacts, consistently achieving equal or superior results in at least one of the evaluation metrics. For images with multiple simultaneous artefacts, we show that the performance of using a combination of models, trained to correct individual artefacts depends heavily on the order that they were applied. This is not an issue for our proposed multi-task model. The model trained using our novel convolutional loss function always outperformed the model trained with a mean squared error loss, when evaluated using Visual Information Fidelity, a quality metric connected to perceptual quality.

    CONCLUSION: We trained two models for multi-task MRI artefact correction of brain, and pelvic scans. We used a novel loss function that significantly improves the image quality of the outputs over using mean squared error. The approach performs well on real world data, and it provides insight into which artefacts it detects and corrects for. Our proposed model and source code were made publicly available.

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  • 35. Siversson, Carl
    et al.
    Nordström, Fredrik
    Nilsson, Terese
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Gunnlaugsson, Adalsteinn
    Olsson, Lars E.
    Technical Note: MRI only prostate radiotherapy planning using the statistical decomposition algorithm2015In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 42, no 10, p. 6090-6097Article in journal (Refereed)
    Abstract [en]

    Purpose: In order to enable a magnetic resonance imaging (MRI) only workflow in radiotherapy treatment planning, methods are required for generating Hounsfield unit (HU) maps (i.e., synthetic computed tomography, sCT) for dose calculations, directly from MRI. The Statistical Decomposition Algorithm (SDA) is a method for automatically generating sCT images from a single MR image volume, based on automatic tissue classification in combination with a model trained using a multimodal template material. This study compares dose calculations between sCT generated by the SDA and conventional CT in the male pelvic region. Methods: The study comprised ten prostate cancer patients, for whom a 3D T2 weighted MRI and a conventional planning CT were acquired. For each patient, sCT images were generated from the acquired MRI using the SDA. In order to decouple the effect of variations in patient geometry between imaging modalities from the effect of uncertainties in the SDA, the conventional CT was nonrigidly registered to the MRI to assure that their geometries were well aligned. For each patient, a volumetric modulated arc therapy plan was created for the registered CT (rCT) and recalculated for both the sCT and the conventional CT. The results were evaluated using several methods, including mean average error (MAE), a set of dose-volume histogram parameters, and a restrictive gamma criterion (2% local dose/1 mm). Results: The MAE within the body contour was 36.5 +/- 4.1 (1 s.d.) HU between sCT and rCT. Average mean absorbed dose difference to target was 0.0% +/- 0.2% (1 s.d.) between sCT and rCT, whereas it was -0.3% +/- 0.3% (1 s.d.) between CT and rCT. The average gamma pass rate was 99.9% for sCT vs rCT, whereas it was 90.3% for CT vs rCT. Conclusions: The SDA enables a highly accurate MRI only workflow in prostate radiotherapy planning. The dosimetric uncertainties originating from the SDA appear negligible and are notably lower than the uncertainties introduced by variations in patient geometry between imaging sessions.

  • 36.
    Strandberg, Sara
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Zarei, Maryam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Aglund, Kristina
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Blomqvist, Lennart
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Baseline and early response 2-[18F]FDG-PET/MRI for prediction of radiotherapy outcome in uterine cervical squamous cell carcinoma: a prospective single-center observational cohort study2024In: EJNMMI Reports, E-ISSN 3005-074X, Vol. 8, no 1, article id 5Article in journal (Refereed)
    Abstract [en]

    Background: Should early response imaging predict tumor response to therapy, personalized treatment adaptations could be feasible to improve outcome or reduce the risk of adverse events. This prospective single-center observational study on 2-fluorine-18-fluoro-deoxy-glucose (2-[18F]FDG) positron-emission tomography/magnetic resonance imaging (PET/MRI) features aims to investigate the association between semantic 2-[18F]FDG-PET/MRI imaging parameters and outcome prediction in uterine cervical squamous cell carcinoma (CSCC) treated with radiotherapy.

    Results: Eleven study participants with previously untreated CSCC were examined with 2-[18F]FDG-PET/MRI at baseline and approximately one week after start of curative radiotherapy. All study participants had at least 24 months clinical follow-up. Two patients relapsed during the follow-up period. Reduced tumor size according to visual assessment was present in 9/11 participants (median change in sum of largest diameters (SLD) − 10.4%; range − 2.5 to − 24.6%). The size reduction was less pronounced in the relapse group compared to the no relapse group, with median change in SLD − 4.9%, versus − 10.4%. None of the reductions qualified as significantly reduced or increased in size according to RECIST 1.1., hence all participants were at this stage classified as non-responders/stable disease. Median baseline functional tumor volume (FTV) for the relapse group was 126 cm3, while for the no relapse group 9.3 cm3. Median delta FTV in the relapse group was 50.7 cm3, representing an actual increase in metabolically active volume, while median delta FTV in the no relapse group was − 2.0 cm3. Median delta apparent diffusion coefficient (ADC) was lower in the relapse group versus the no relapse group (− 3.5 mm2/s vs. 71 mm2/s).

    Conclusions: Early response assessment with 2-[18F]FDG-PET/MRI identified potentially predictive functional imaging biomarkers for prediction of radiotherapy outcome in CSCC, that could not be recognized with tumor measurements according to RECIST 1.1. These biomarkers (delta FTV and delta ADC) should be further evaluated.

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  • 37.
    Wallstén, Elin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images2020In: EJNMMI Physics, E-ISSN 2197-7364, Vol. 7, no 1, article id 68Article in journal (Refereed)
    Abstract [en]

    Background: Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region.

    Materials and method: Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth.

    Results: The mean whole-image PET uptake error was reduced from - 5.4% for Dixon-PET to - 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from - 5.6% for Dixon-PET to - 2.3% for SDA-PET.

    Conclusion: Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.

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  • 38.
    Wallstén, Elin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    PET/MRI attenuation correction in the pelvic region with a statistical decomposition method2019In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 46, no SUPPL 1, p. S289-S290Article in journal (Other academic)
    Abstract [en]

    Aim/Introduction: Quantification in PET/MRI is of importance, and its accuracy is currently limited by the MR based attenuation correction estimate. A common method for attenuation correction of the pelvic region is based on a 2-echo Dixon MRI sequence for segmentation of fat and water and does not account for bone. In this work, we evaluate a new method for attenuation correction using an algorithm based on statistical decomposition of a T2 weighted MRI scan.

    Materials and Methods: Substitute CT images (sCTs) were calculated from T2 weighted MRI scans with a statistical decomposition algorithm, originally developed for MRI-based radiotherapy dose-planning [1]. These sCTs benefits from having bone density information included, in addition to fat and water information. Prostate cancer patients from the PARAPLY study [2] were retrospectivelyselected, scanned with PET/MRI 11C-Acatate and CT the same day. The stand-alone CT images were transformed to the same geometry as the PET and MR images, using a non-rigid registration. CT images, generated sCT images, and the Dixonbased attenuation maps (MRAC), all in the same geometry, were together with the PET raw data used to reconstruct attenuation-corrected PET images using the PETrecon toolbox [GE Healthcare]. The two MR-based attenuation corrections were compared to the CT-based attenuation correction with root mean squared error (RMSE). Lesion analysis will also be reported. PET/MRI images were acquired on a Signa PET/MRI (GE Healthcare), and the CT images on a Brilliance Big Bore (Phillips Healthcare). The study will include 12 patients and a subset of 6 patients has been analyzed so far and is presented here.

    Results: Soft tissue in-between pelvic bone structures were overestimated with 13% in MRAC-PET, and the error was reduced to 5% with sCT attenuation corrected PET (sCT-PET). For the whole patient volume, an average underestimation of 6% was found in the MRAC-PET, compared to 1% for sCTPET. RMSE within the body was reduced with a factor 2.5 with sCT-PET (RMSE=3.6%), compared to MRAC-PET (RMSE=8.8%).

    Conclusion: Applying sCT from statistical decomposition as a base for calculation of attenuation maps reduces quantification errors in PET-images of the pelvic region compared to the common Dixon based method.

  • 39. Wiesinger, Florian
    et al.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yang, Jaewon
    Kaushik, Sandeep
    Shanbhag, Dattesh
    Ahn, Sangtae
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Lundman, Josef
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Hope, Thomas
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Uppsala University, Uppsala, Sweden.
    Larson, Peder
    Cozzini, Cristina
    Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning2018In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 80, no 4, p. 1440-1451Article in journal (Refereed)
    Abstract [en]

    Purpose: To describe a method for converting Zero TE (ZTE) MR images into Xray attenuation information in the form of pseudo-CT images and demonstrate its performance for (1) attenuation correction (AC) in PET/MR and (2) dose planning in MR-guided radiation therapy planning (RTP).

    Methods: Proton density-weighted ZTE images were acquired as input for MRbased pseudo-CT conversion, providing (1) efficient capture of short-lived bone signals, (2) flat soft-tissue contrast, and (3) fast and robust 3D MR imaging. After bias correction and normalization, the images were segmented into bone, soft-tissue, and air by means of thresholding and morphological refinements. Fixed Hounsfield replacement values were assigned for air (-1000 HU) and soft-tissue (142 HU), whereas continuous linear mapping was used for bone.

    Results: The obtained ZTE-derived pseudo-CT images accurately resembled the true CT images (i. e., Dice coefficient for bone overlap of 0.73 +/- 0.08 and mean absolute error of 123 +/- 25 HU evaluated over the whole head, including errors from residual registration mismatches in the neck and mouth regions). The linear bone mapping accounted for bone density variations. Averaged across five patients, ZTE-based AC demonstrated a PET error of -0.04 +/- 1.68% relative to CT-based AC. Similarly, for RTP assessed in eight patients, the absolute dose difference over the target volume was found to be 0.23 +/- 0.42%.

    Conclusion: The described method enables MR to pseudo-CT image conversion for the head in an accurate, robust, and fast manner without relying on anatomical prior knowledge. Potential applications include PET/MR-AC, and MR-guided RTP.

  • 40.
    Zarei, Maryam
    et al.
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Wallstén, Elin
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Grefve, Josefine
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Gunnlaugsson, Adalsteinn
    Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund, Sweden.
    Sandgren, Kristina
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Keeratijarut Lindberg, Angsana
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Nilsson, Erik
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Bergh, Anders
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Moreau, Mathieu
    Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund, Sweden.
    Thellenberg-Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Olsson, Lars E.
    Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Riklund, Katrine
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Blomqvist, Lennart
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF). Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden.
    Berg Loegager, Vibeke
    Department of Radiology, Copenhagen University Hospital in Herlev, Herlev, Denmark.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Strandberg, Sara
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Diagnostics and Intervention. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Accuracy of gross tumour volume delineation with [68Ga]-PSMA-PET compared to histopathology for high-risk prostate cancer2024In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 63, p. 503-510Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: The delineation of intraprostatic lesions is vital for correct delivery of focal radiotherapy boost in patients with prostate cancer (PC). Errors in the delineation could translate into reduced tumour control and potentially increase the side effects. The purpose of this study is to compare PET-based delineation methods with histopathology.

    MATERIALS AND METHODS: The study population consisted of 15 patients with confirmed high-risk PC intended for prostatectomy. [68Ga]-PSMA-PET/MR was performed prior to surgery. Prostate lesions identified in histopathology were transferred to the in vivo [68Ga]-PSMA-PET/MR coordinate system. Four radiation oncologists manually delineated intraprostatic lesions based on PET data. Various semi-automatic segmentation methods were employed, including absolute and relative thresholds, adaptive threshold, and multi-level Otsu threshold.

    RESULTS: The gross tumour volumes (GTVs) delineated by the oncologists showed a moderate level of interobserver agreement with Dice similarity coefficient (DSC) of 0.68. In comparison with histopathology, manual delineations exhibited the highest median DSC and the lowest false discovery rate (FDR) among all approaches. Among semi-automatic approaches, GTVs generated using standardized uptake value (SUV) thresholds above 4 (SUV > 4) demonstrated the highest median DSC (0.41), with 0.51 median lesion coverage ratio, FDR of 0.66 and the 95th percentile of the Hausdorff distance (HD95%) of 8.22 mm.

    INTERPRETATION: Manual delineations showed a moderate level of interobserver agreement. Compared to histopathology, manual delineations and SUV > 4 exhibited the highest DSC and the lowest HD95% values. The methods that resulted in a high lesion coverage were associated with a large overestimation of the size of the lesions.

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  • 41.
    Zborayova, Katarina
    et al.
    Umeå University, Faculty of Medicine, Department of Clinical Sciences.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Blomqvist, L.
    Flygare, Lennart
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Gebre-Medhin, M.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Söderkvist, Karin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Zackrisson, Björn
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Early changes in multiparametric imaging parameters during radiotherapy of squamous carcinoma2019In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 132, p. 63-63Article in journal (Other academic)
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