Umeå University's logo

umu.sePublications
Change search
Refine search result
123 1 - 50 of 120
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 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.

    Download full text (pdf)
    fulltext
  • 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%.

    Download full text (pdf)
    fulltext
  • 3.
    Adjeiwaah, Mary
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Quality Assurance for MRI in Radiotherapy: Sensitivity Analysis of Current MethodsManuscript (preprint) (Other academic)
  • 4.
    Adjeiwaah, Mary
    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.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Sensitivity analysis of different quality assurance methods for magnetic resonance imaging in radiotherapy2020In: Physics and Imaging in Radiation Oncology, E-ISSN 2405-6316, Vol. 13, p. 21-27Article in journal (Refereed)
    Abstract [en]

    Background and purpose: There are currently no standard quality assurance (QA) methods for magnetic resonance imaging (MRI) in radiotherapy (RT). This work was aimed at evaluating the ability of two QA protocols to detect common events that affect quality of MR images under RT settings.

    Materials and methods: The American College of Radiology (ACR) MRI QA phantom was repeatedly scanned using a flexible coil and action limits for key image quality parameters were derived. Using an exploratory survey, issues that reduce MR image quality were identified. The most commonly occurring events were introduced as provocations to produce MR images with degraded quality. From these images, detection sensitivities of the ACR MRI QA protocol and a commercial geometric accuracy phantom were determined.

    Results: Machine-specific action limits for key image quality parameters set at mean&#xB1;3&#x3C3;" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">mean±3σ were comparable with the ACR acceptable values. For the geometric accuracy phantom, provocations from uncorrected gradient nonlinearity effects and a piece of metal in the bore of the scanner resulted in worst distortions of 22.2 mm and 3.4 mm, respectively. The ACR phantom was sensitive to uncorrected signal variations, electric interference and a piece of metal in the bore of the scanner but could not adequately detect individual coil element failures.

    Conclusions: The ACR MRI QA phantom combined with the large field-of-view commercial geometric accuracy phantom were generally sensitive in identifying some common MR image quality issues. The two protocols when combined may provide a tool to monitor the performance of MRI systems in the radiotherapy environment.

    Download full text (pdf)
    fulltext
  • 5.
    Adjeiwaah, Mary
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Lundman, Josef A.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Technical Note: Comparison between simulated and measured B0 field maps of head and neck MRIManuscript (preprint) (Other academic)
  • 6.
    Andersson, Jonas
    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.
    Ceberg, Crister
    Almén, Anja
    Bernhardt, Peter
    Fransson, Annette
    Olsson, Lars E.
    Artificial intelligence and the medical physics profession - A Swedish perspective2021In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 88, p. 218-225Article in journal (Refereed)
    Abstract [en]

    Background: There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in the future. The aim for this work was to establish a foundation for a Swedish perspective on the potential effect of AI on the medical physics profession.

    Materials and methods: We designed a survey to gauge viewpoints regarding AI in the Swedish medical physics community. Based on the survey results and present-day situation in Sweden, a SWOT analysis was performed on the implications of AI for the medical physics profession.

    Results: Out of 411 survey recipients, 163 responded (40%). The Swedish medical physicists with a professional license believed (90%) that AI would change the practice of medical physics but did not foresee (81%) that AI would pose a risk to their practice and career. The respondents were largely positive to the inclusion of AI in educational programmes. According to self-assessment, the respondents’ knowledge of and workplace preparedness for AI was generally low.

    Conclusions: From the survey and SWOT analysis we conclude that AI will change the medical physics profession and that there are opportunities for the profession associated with the adoption of AI in healthcare. To overcome the weakness of limited AI knowledge, potentially threatening the role of medical physicists, and build upon the strong position in Swedish healthcare, medical physics education and training should include learning objectives on AI.

    Download full text (pdf)
    fulltext
  • 7.
    Asklund, Thomas
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Hauksson, Jon
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Henriksson, Roger
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Evaluation of advanced MR techniques for development of early biomarkers for treatment efficacy in malignant brain tumors2010Conference paper (Refereed)
  • 8.
    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).

    Download full text (pdf)
    fulltext
  • 9.
    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, ISSN 1748-717X, 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.

    Download full text (pdf)
    fulltext
  • 10.
    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.

    Download full text (pdf)
    fulltext
  • 11.
    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)
  • 12.
    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.

    Download full text (pdf)
    fulltext
  • 13.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    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.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Gray-level invariant Haralick texture features2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S279-S280Article in journal (Other academic)
  • 14.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nilsson, David
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Henriksson, Roger
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Hauksson, Jon
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Birgander, Richard
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    ADC texture-An imaging biomarker for high-grade glioma?2014In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 10, p. 101903-Article in journal (Refereed)
    Abstract [en]

    Purpose:

    Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers.

    Methods:

    Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression.

    Results:

    The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001.

    Conclusions:

    By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort. (C) 2014 Author(s).

  • 15.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nilsson, David
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Torheim, Turid
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Thellenberg Karlsson, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters2017In: Scientific Reports, E-ISSN 2045-2322, Vol. 7, article id 4041Article in journal (Refereed)
    Abstract [en]

    In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

    Download full text (pdf)
    fulltext
  • 16.
    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)
  • 17.
    Combs, Stephanie E.
    et al.
    Department of Radiation Oncology, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine, Department of Radiation Sciences, Helmholtz Zentrum München, Munich, Germany; German Cancer Consortium (DKTK) Partner Site (DKTK), Munich, Germany.
    Baumert, Brigitta G.
    Institute of Radiation Oncology, Cantonal Hospital Graubuenden, Chur, Switzerland.
    Bendszus, Martin
    Department of Neuroradiology, University Hospital Heidelberg, Germany.
    Bozzao, Alessandro
    Dipartimento NESMOS, Università Sapienza Roma, Azienda Ospedaliera Sant'Andrea, Rome, Italy.
    Brada, Michael
    Department of Radiation Oncology, Clatterbridge Cancer Centre NHS Foundation Trust, Bebington, United Kingdom.
    Fariselli, Laura
    Radiotherapy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
    Fiorentino, Alba
    Radiation Oncology Department, General Regional Hospital F. Miulli, Acquaviva delle fonti, Italy.
    Ganswindt, Ute
    Department of Therapeutic Radiology and Oncology, Medical University of Innsbruck, Innsbruck, Austria.
    Grosu, Anca L.
    Department of Radiation Oncology, Medical Faculty, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) Partner Site Freiburg, Germany.
    Lagerwaard, Frank L
    Department of Radiation Oncology, Amsterdam University Medical Centers, Location VUmc, Netherlands.
    Niyazi, Maximilian
    German Cancer Consortium (DKTK) Partner Site (DKTK), Munich, Germany; Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Paddick, Ian
    Queen Square Radiosurgery Centre, National Hospital for Neurology and Neurosurgery, London, United Kingdom.
    Weber, Damien Charles
    Centre for Proton Therapy, Paul Scherrer Institut, Villigen PSI, Switzerland.
    Belka, Claus
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
    Minniti, Giuseppe
    Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy; IRCCS Neuromed, Pozzilli, Italy.
    ESTRO ACROP guideline for target volume delineation of skull base tumors2021In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 156, p. 80-94Article in journal (Refereed)
    Abstract [en]

    Background and purpose: For skull base tumors, target definition is the key to safe high-dose treatments because surrounding normal tissues are very sensitive to radiation. In the present work we established a joint ESTRO ACROP guideline for the target volume definition of skull base tumors.

    Material and methods: A comprehensive literature search was conducted in PubMed using various combinations of the following medical subjects headings (MeSH) and free-text words: “radiation therapy” or “stereotactic radiosurgery” or “proton therapy” or “particle beam therapy” and “skull base neoplasms” “pituitary neoplasms”, “meningioma”, “craniopharyngioma”, “chordoma”, “chondrosarcoma”, “acoustic neuroma/vestibular schwannoma”, “organs at risk”, “gross tumor volume”, “clinical tumor volume”, “planning tumor volume”, “target volume”, “target delineation”, “dose constraints”. The ACROP committee identified sixteen European experts in close interaction with the ESTRO clinical committee who analyzed and discussed the body of evidence concerning target delineation.

    Results: All experts agree that magnetic resonance (MR) images with high three-dimensional spatial accuracy and tissue-contrast definition, both T2-weighted and volumetric T1-weighted sequences, are required to improve target delineation. In detail, several key issues were identified and discussed: i) radiation techniques and immobilization, ii) imaging techniques and target delineation, and iii) technical aspects of radiation treatments including planning techniques and dose-fractionation schedules. Specific target delineation issues with regard to different skull base tumors, including pituitary adenomas, meningiomas, craniopharyngiomas, acoustic neuromas, chordomas and chondrosarcomas are presented.

    Conclusions: This ESTRO ACROP guideline achieved detailed recommendations on target volume definition for skull base tumors, as well as comprehensive advice about imaging modalities and radiation techniques.

    Download full text (pdf)
    fulltext
  • 18. Daniel, M.
    et al.
    Kuess, P.
    Andrzejewski, P.
    Nyholm, T.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Heilmann, M.
    Helbich, T.
    Polanec, S.
    Baltzer, P.
    Georg, D.
    Pilot study: Textural features of mpMRI for response assessment in prostate cancer patients2019In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 133, p. S1113-S1113Article in journal (Other academic)
    Download full text (pdf)
    fulltext
  • 19. Daniel, M.
    et al.
    Kuess, P.
    Andrzejewski, P.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Helbich, T.
    Polanec, S.
    Dragschitz, F.
    Goldner, G.
    Georg, D.
    Baltzer, P.
    Impact of androgen deprivation therapy on apparent diffusion coefficient and T2w MRI for histogram and texture analysis with respect to focal radiotherapy of prostate cancer2019In: Strahlentherapie und Onkologie (Print), ISSN 0179-7158, E-ISSN 1439-099X, Vol. 195, no 5, p. 402-411Article in journal (Refereed)
    Abstract [en]

    Purpose: Accurate prostate cancer (PCa) detection is essential for planning focal external beam radiotherapy (EBRT). While biparametric MRI (bpMRI) including T2-weighted (T2w) and diffusion-weighted images (DWI) is an accurate tool to localize PCa, its value is less clear in the case of additional androgen deprivation therapy (ADT). The aim of this study was to investigate the value of a textural feature (TF) approach on bpMRI analysis in prostate cancer patients with and without neoadjuvant ADT with respect to future dose-painting applications.

    Methods: 28 PCa patients (54–80 years) with (n = 14) and without (n = 14) ADT who underwent bpMRI with T2w and DWI were analyzed retrospectively. Lesions, central gland (CG), and peripheral zone (PZ) were delineated by an experienced urogenital radiologist based on localized pre-therapeutic histopathology. Histogram parameters and 20 Haralick TF were calculated. Regional differences (i. e., tumor vs. PZ, tumor vs. CG) were analyzed for all imaging parameters. Receiver-operating characteristic (ROC) analysis was performed to measure diagnostic performance to distinguish PCa from benign prostate tissue and to identify the features with best discriminative power in both patient groups.

    Results: The obtained sensitivities were equivalent or superior when utilizing the TF in the no-ADT group, while specificity was higher for the histogram parameters. However, in the ADT group, TF outperformed the conventional histogram parameters in both specificity and sensitivity. Rule-in and rule-out criteria for ADT patients could exclusively be defined with the aid of TF.

    Conclusions: The TF approach has the potential for quantitative image-assisted boost volume delineation in PCa patients even if they are undergoing neoadjuvant ADT.

    Download full text (pdf)
    fulltext
  • 20. Edmund, Jens M.
    et al.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Medical Radiation Physics, Department of Immunology, Genetics and Pathology, Uppsala University.
    A review of substitute CT generation for MRI-only radiation therapy2017In: Radiation Oncology, ISSN 1748-717X, E-ISSN 1748-717X, Vol. 12, article id 28Article, review/survey (Refereed)
    Abstract [en]

    Radiotherapy based on magnetic resonance imaging as the sole modality (MRI-only RT) is an area of growing scientific interest due to the increasing use of MRI for both target and normal tissue delineation and the development of MR based delivery systems. One major issue in MRI-only RT is the assignment of electron densities (ED) to MRI scans for dose calculation and a similar need for attenuation correction can be found for hybrid PET/MR systems. The ED assigned MRI scan is here named a substitute CT (sCT). In this review, we report on a collection of typical performance values for a number of main approaches encountered in the literature for sCT generation as compared to CT. A literature search in the Scopus database resulted in 254 papers which were included in this investigation. A final number of 50 contributions which fulfilled all inclusion criteria were categorized according to applied method, MRI sequence/contrast involved, number of subjects included and anatomical site investigated. The latter included brain, torso, prostate and phantoms. The contributions geometric and/or dosimetric performance metrics were also noted. The majority of studies are carried out on the brain for 5-10 patients with PET/MR applications in mind using a voxel based method. T1 weighted images are most commonly applied. The overall dosimetric agreement is in the order of 0.3-2.5%. A strict gamma criterion of 1% and 1mm has a range of passing rates from 68 to 94% while less strict criteria show pass rates > 98%. The mean absolute error (MAE) is between 80 and 200 HU for the brain and around 40 HU for the prostate. The Dice score for bone is between 0.5 and 0.95. The specificity and sensitivity is reported in the upper 80s% for both quantities and correctly classified voxels average around 84%. The review shows that a variety of promising approaches exist that seem clinical acceptable even with standard clinical MRI sequences. A consistent reference frame for method benchmarking is probably necessary to move the field further towards a widespread clinical implementation.

    Download full text (pdf)
    fulltext
  • 21. Engvall, Gunn
    et al.
    Lindh, Viveca
    Umeå University, Faculty of Medicine, Department of Nursing.
    Mullaney, Tara
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Lindh, Jack
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Ångström-Brännström, Charlotte
    Umeå University, Faculty of Medicine, Department of Nursing.
    Children's experiences and responses towards an intervention for psychological preparation for radiotherapy2018In: Radiation Oncology, ISSN 1748-717X, E-ISSN 1748-717X, Vol. 13, article id 9Article in journal (Refereed)
    Abstract [en]

    Background: Children can experience distress when undergoing radiotherapy as a reaction to being scared of and unfamiliar with the procedure. The aim was to evaluate children's experiences and responses towards an intervention for psychological preparation for radiotherapy.

    Methods: A case control design with qualitative content analysis of semi-structured interviews and statistical analysis of anxiety ratings were used for evaluating a strategy for psychological preparation and distraction. Fifty-seven children aged 2 to 18 years and their parents participated - 30 children in the baseline group and 27 in the intervention group. Child interviews were performed and the child and their parents rated the child's anxiety.

    Results: The intervention was most appropriate for the younger children, who enjoyed the digital story, the stuffed animal and training with their parents. There were some technical problems and the digital story was not detailed enough to fit exactly with various cancer diagnoses. Children described suggestions for improvement of the intervention. The ratings of the child's anxiety during radiation treatment showed no differences between the baseline group and the intervention group.

    Conclusions: The children of all the age groups experienced their interventions as positive. The strength of the intervention was that it encouraged interaction within the family and provided an opportunity for siblings and peers to take part in what the child was going through. Future research on children's experiences to interventions should be encouraged. The intervention and the technical solutions could improve by further development.

    Trial registration: The study design was structured as an un-matched case-control study, baseline group vs. intervention group. Trial registration: ClinicalTrials.gov NCT02993978 , Protocol Record 2012-113-31 M. Retrospectively registered - 21 November 2016.

    Download full text (pdf)
    fulltext
  • 22. Engvall, Gunn
    et al.
    Ångström-Brännström, Charlotte
    Umeå University, Faculty of Medicine, Department of Nursing.
    Mullaney, Tara
    Umeå University, Faculty of Science and Technology, Umeå Institute of Design.
    Nilsson, Kristina
    Wickart-Johansson, Gun
    Svärd, Anna-Maja
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Lindh, Jack
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Lindh, Viveca
    Umeå University, Faculty of Medicine, Department of Nursing.
    It Is Tough and Tiring but It Works - Children's Experiences of Undergoing Radiotherapy2016In: PLOS ONE, E-ISSN 1932-6203, Vol. 11, no 4, article id e0153029Article in journal (Refereed)
    Abstract [en]

    Approximately 300 children ages 0 to 18 are diagnosed with cancer in Sweden every year, and 80 to 90 of them undergo radiotherapy treatment. The aim was to describe children's experiences of preparing for and undergoing radiotherapy, and furthermore to describe children's suggestions for improvement. Thirteen children between the ages of 5 and 15 with various cancer diagnoses were interviewed. Data was analyzed using qualitative content analysis. The findings revealed five categories: positive and negative experiences with hospital stays and practical arrangements; age-appropriate information, communication, and guidance to various degrees; struggle with emotions; use of distraction and other suitable coping strategies; and children's suggestions for improvement during radiotherapy. An overarching theme emerged: "It is tough and tiring but it works". Some key areas were: explanatory visits, the need for information and communication, being afraid, discomfort and suffering, the need for media distraction, dealing with emotions, and the need for support. A systematic, family-centered preparation program could possible help families prepare and individualized distraction during radiotherapy could contribute to reducing distress. Further studies with interventions could clarify successful programs.

    Download full text (pdf)
    fulltext
  • 23. Fahlström, Markus
    et al.
    Blomquist, Erik
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Elna-Marie
    Perfusion magnetic resonance imaging changes in normal appearing brain tissue after radiotherapy in glioblastoma patients may confound longitudinal evaluation of treatment response2018In: Radiology and Oncology, ISSN 1318-2099, E-ISSN 1581-3207, Vol. 52, no 2, p. 143-151Article in journal (Refereed)
    Abstract [en]

    Background: The aim of this study was assess acute and early delayed radiation-induced changes in normal-appearing brain tissue perfusion as measured with perfusion magnetic resonance imaging (MRI) and the dependence of these changes on the fractionated radiotherapy (FRT) dose level.

    Patients and methods: Seventeen patients with glioma WHO grade III-IV treated with FRT were included in this prospective study, seven were excluded because of inconsistent FRT protocol or missing examinations. Dynamic susceptibility contrast MRI and contrast-enhanced 3D-T1-weighted (3D-T1w) images were acquired prior to and in average (standard deviation): 3.1 (3.3), 34.4 (9.5) and 103.3 (12.9) days after FRT. Pre-FRT 3D-T1w images were segmented into white-and grey matter. Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were calculated and co-registered patient-wise to pre-FRT 3D-T1w images. Seven radiation dose regions were created for each tissue type: 0-5 Gy, 5-10 Gy, 10-20 Gy, 20-30 Gy, 30-40 Gy, 40-50 Gy and 50-60 Gy. Mean CBV and CBF were calculated in each dose region and normalised (nCBV and nCBF) to the mean CBV and CBF in 0-5 Gy white-and grey matter reference regions, respectively.

    Results: Regional and global nCBV and nCBF in white-and grey matter decreased after FRT, followed by a tendency to recover. The response of nCBV and nCBF was dose-dependent in white matter but not in grey matter.

    Conclusions: Our data suggest that radiation-induced perfusion changes occur in normal-appearing brain tissue after FRT. This can cause an overestimation of relative tumour perfusion using dynamic susceptibility contrast MRI, and can thus confound tumour treatment evaluation.

    Download full text (pdf)
    fulltext
  • 24. Fahlström, Markus
    et al.
    Fransson, Samuel
    Blomquist, Erik
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Larsson, Elna-Marie
    Dynamic contrast-enhanced magnetic resonance imaging may act as a biomarker for vascular damage in normal appearing brain tissue after radiotherapy in patients with glioblastoma2018In: Acta Radiologica Open, E-ISSN 2058-4601, Vol. 7, no 11, article id 2058460118808811Article in journal (Refereed)
    Abstract [en]

    Background

    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising perfusion method and may be useful in evaluating radiation-induced changes in normal-appearing brain tissue.

    Purpose

    To assess whether radiotherapy induces changes in vascular permeability (Ktrans) and the fractional volume of the extravascular extracellular space (Ve) derived from DCE-MRI in normal-appearing brain tissue and possible relationships to radiation dose given.

    Material and Methods

    Seventeen patients with glioblastoma treated with radiotherapy and chemotherapy were included; five were excluded because of inconsistencies in the radiotherapy protocol or early drop-out. DCE-MRI, contrast-enhanced three-dimensional (3D) T1-weighted (T1W) images and T2-weighted fluid attenuated inversion recovery (T2-FLAIR) images were acquired before and on average 3.3, 30.6, 101.6, and 185.7 days after radiotherapy. Pre-radiotherapy CE T1W and T2-FLAIR images were segmented into white and gray matter, excluding all non-healthy tissue. Ktrans and Ve were calculated using the extended Kety model with the Parker population-based arterial input function. Six radiation dose regions were created for each tissue type, based on each patient’s computed tomography-based dose plan. Mean Ktrans and Ve were calculated over each dose region and tissue type.

    Results

    Global Ktrans and Ve demonstrated mostly non-significant changes with mean values higher for post-radiotherapy examinations in both gray and white matter compared to pre-radiotherapy. No relationship to radiation dose was found.

    Conclusion

    Additional studies are needed to validate if Ktrans and Ve derived from DCE-MRI may act as potential biomarkers for acute and early-delayed radiation-induced vascular damages. No dose-response relationship was found.

    Download full text (pdf)
    fulltext
  • 25. Fetty, L.
    et al.
    Buschmann, M.
    Heilemann, G.
    Kuess, P.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Herrmann, H.
    Georg, D.
    Nesvacil, N.
    Solutions for MR-based RT planning with an open low field scanner using neural network tools2019In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 133, p. S567-S568Article in journal (Other academic)
    Download full text (pdf)
    fulltext
  • 26. Fetty, L.
    et al.
    Kuess, P.
    Nesvacil, N.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Georg, D.
    Furtado, H.
    Evaluating different generator networks of a conditional generative adversarial network2019In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 133, p. S555-S555Article in journal (Other academic)
    Download full text (pdf)
    fulltext
  • 27.
    Fetty, Lukas
    et al.
    Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Kuess, Peter
    Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
    Heilemann, Gerd
    Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Georg, Dietmar
    Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Latent space manipulation for high-resolution medical image synthesis via the StyleGAN2020In: Zeitschrift für Medizinische Physik, ISSN 0939-3889, E-ISSN 1876-4436, Vol. 30, no 4, p. 305-314Article in journal (Refereed)
    Abstract [en]

    Introduction: This paper explores the potential of the StyleGAN model as an high-resolution image generator for synthetic medical images. The possibility to generate sample patient images of different modalities can be helpful for training deep learning algorithms as e.g. a data augmentation technique.

    Methods: The StyleGAN model was trained on Computed Tomography (CT) and T2- weighted Magnetic Resonance (MR) images from 100 patients with pelvic malignancies. The resulting model was investigated with regards to three features: Image Modality, Sex, and Longitudinal Slice Position. Further, the style transfer feature of the StyleGAN was used to move images between the modalities. The root-mean-squard error (RMSE) and the Mean Absolute Error (MAE) were used to quantify errors for MR and CT, respectively.

    Results: We demonstrate how these features can be transformed by manipulating the latent style vectors, and attempt to quantify how the errors change as we move through the latent style space. The best results were achieved by using the style transfer feature of the StyleGAN (58.7 HU MAE for MR to CT and 0.339 RMSE for CT to MR). Slices below and above an initial central slice can be predicted with an error below 75 HU MAE and 0.3 RMSE within 4 cm for CT and MR, respectively.

    Discussion: The StyleGAN is a promising model to use for generating synthetic medical images for MR and CT modalities as well as for 3D volumes.

    Download full text (pdf)
    fulltext
  • 28. Fetty, Lukas
    et al.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Heilemann, Gerd
    Furtado, Hugo
    Nesvacil, Nicole
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Georg, Dietmar
    Kuess, Peter
    Investigating conditional GAN performance with different generator architectures, an ensemble model, and different MR scanners for MR-sCT conversion2020In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 65, no 10, article id 105004Article in journal (Refereed)
    Abstract [en]

    Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional generative adversarial networks (cGANs). However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features. In this study, we explored the generalizability of the generator models, trained on a single field strength scanner, to data acquired with higher field strengths. T2-weighted 0.35T MRIs and CTs from 51 patients treated for prostate (40) and cervical cancer (11) were included. 25 of them were used to train four different generators (SE-ResNet, DenseNet, U-Net, and Embedded Net). Further, an ensemble model was created from the four network outputs. The models were validated on 16 patients from a 0.35T MR scanner. Further, the trained models were tested on the Gold Atlas dataset, containing T2-weighted MR scans of different field strengths; 1.5T(7) and 3T(12), and 10 patients from the 0.35T scanner. The sCTs were dosimetrically compared using clinical VMAT plans for all test patients. For the same scanner (0.35T), the results from the different models were comparable on the test set, with only minor differences in the mean absolute error (MAE) (35-51HU body). Similar results were obtained for conversions of 3T GE Signa and the 3T GE Discovery images (40-62HU MAE) for three of the models. However, larger differences were observed for the 1.5T images (48-65HU MAE). The overall best model was found to be the ensemble model. All dose differences were below 1%. This study shows that it is possible to generalize models trained on images of one scanner to other scanners and different field strengths. The best metric results were achieved by the combination of all networks.

    Download full text (pdf)
    fulltext
  • 29. Fransson, Samuel
    et al.
    Tilly, David
    Ahnesjö, Anders
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Strand, Robin
    Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy2021In: Physics and Imaging in Radiation Oncology, E-ISSN 2405-6316, Vol. 20, p. 17-22Article in journal (Refereed)
    Abstract [en]

    Background and purpose: Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.

    Materials and methods: 4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields.

    Results: The registration algorithm produced accurate registration result, in general DICE overlap >0.95 across all time frames. Cumulative variance of the eigen values from the PCA showed that 50% or more of the motion is explained in the first component for all subjects. However, the size and direction for the components differed between subjects. Adding more than two components did not improve the accuracy significantly and the model was able to explain motion down to about 1 mm.

    onclusions: An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1-2 principal components.

    Download full text (pdf)
    fulltext
  • 30.
    Garpebring, Anders
    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.
    Kuess, Peter
    Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Georg, Dietmar
    Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Helbich, Thomas H.
    Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    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.
    Density Estimation of Grey-Level Co-Occurrence Matrices for Image Texture Analysis2018In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 63, no 19, p. 9-15, article id 195017Article in journal (Refereed)
    Abstract [en]

    The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI).

    The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features.

    Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes.

    The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about 20×20).

    In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.

    Download full text (pdf)
    fulltext
  • 31. Georg, Dietmar
    et al.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Olofsson, Jörgen
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Kjaer-Kristoffersen, Flemming
    Schnekenburger, Bruno
    Winkler, Peter
    Nyström, Hakan
    Ahnesjo, 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.
    Clinical evaluation of monitor unit software and the application of action levels2007In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 85, no 2, p. 306-315Article in journal (Refereed)
    Abstract [en]

    Purpose: The aim of this study was the clinical evaluation of an independent dose and monitor unit verification (MUV) software which is based on sophisticated semi-analytical modelling. The software was developed within the framework of an ESTRO project. Finally, consistent handling of dose calculation deviations applying individual action levels is discussed.

    Materials and methods: A Matlab-based software ("MUV") was distributed to five well-established treatment centres in Europe (Vienna, Graz, Basel, Copenhagen, and Umea) and evaluated as a quality assurance (QA) tool. in clinical routine. Results were acquired for 226 individual treatment plans including a total of 815 radiation fields. About 150 beam verification measurements were performed for a portion of the individual treatment plans, mainly with time variable fluence patterns. The deviations between dose calculations performed with a treatment planning system (TPS) and the MUV software were scored with respect to treatment area, treatment technique, geometrical depth, radiological depth, etc.

    Results: In general good agreement was found between calculations performed with the different TPSs and MUV, with a mean deviation per field of 0.2 +/- 3.5% (1 SD) and mean deviations of 0.2 +/- 2.2% for composite treatment plans. For pelvic treatments less than 10% of all fields showed deviations larger than 3%. In general, when using the radiological depth for verification calculations the results and the spread in the results improved significantly, especially for head-and-neck and for thorax treatments. For IMRT head-and-neck beams, mean deviations between MUV and the local TPS were -1.0 +/- 7.3% for dynamic, and -1.3 +/- 3.2% for step-and-shoot IMRT delivery. For dynamic IMRT beams in the pelvis good agreement was obtained between MUV and the local TIPS (mean: -1.6 +/- 1.5%). Treatment site and treatment technique dependent action levels between 3% and 5% seem to be clinically realistic if a radiological depth correction is performed, even for dynamic wedges and IMRT.

    Conclusion: The software MUV is well suited for patient specific treatment plan QA applications and can handle all currently available treatment techniques that can be applied with standard linear accelerators. The highly sophisticated dose calculation model implemented in MUV allows investigation of systematic TIPS deviations by performing calculations in homogeneous conditions. (c) 2007 Elsevier Ireland Ltd. All rights reserved.

  • 32. Georg, Dietmar
    et al.
    Stock, Markus
    Kroupa, Bernhard
    Olofsson, Jörgen
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ahnesjö, 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.
    Patient-specific IMRT verification using independent fluence-based dose calculation software: experimental benchmarking and initial clinical experience2007In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 52, no 16, p. 4981-4992Article in journal (Refereed)
    Abstract [en]

    Experimental methods are commonly used for patient-specific intensity-modulated radiotherapy (IMRT) verification. The purpose of this study was to investigate the accuracy and performance of independent dose calculation software ( denoted as 'MUV' ( monitor unit verification)) for patient-specific quality assurance (QA). 52 patients receiving step-and-shoot IMRT were considered. IMRT plans were recalculated by the treatment planning systems (TPS) in a dedicated QA phantom, in which an experimental 1D and 2D verification (0.3 cm(3) ionization chamber; films) was performed. Additionally, an independent dose calculation was performed. The fluence-based algorithm of MUV accounts for collimator transmission, rounded leaf ends, tongue-and-groove effect, backscatter to the monitor chamber and scatter from the flattening filter. The dose calculation utilizes a pencil beam model based on a beam quality index. DICOM RT files from patient plans, exported from the TPS, were directly used as patient-specific input data in MUV. For composite IMRT plans, average deviations in the high dose region between ionization chamber measurements and point dose calculations performed with the TPS and MUV were 1.6 +/- 1.2% and 0.5 +/- 1.1% ( 1 S. D.). The dose deviations between MUV and TPS slightly depended on the distance from the isocentre position. For individual intensity-modulated beams ( total 367), an average deviation of 1.1 +/- 2.9% was determined between calculations performed with the TPS and with MUV, with maximum deviations up to 14%. However, absolute dose deviations were mostly less than 3 cGy. Based on the current results, we aim to apply a confidence limit of 3% ( with respect to the prescribed dose) or 6 cGy for routine IMRT verification. For off-axis points at distances larger than 5 cm and for low dose regions, we consider 5% dose deviation or 10 cGy acceptable. The time needed for an independent calculation compares very favourably with the net time for an experimental approach. The physical effects modelled in the dose calculation software MUV allow accurate dose calculations in individual verification points. Independent calculations may be used to replace experimental dose verification once the IMRT programme is mature.

  • 33. Gronlund, Eric
    et al.
    Almhagen, Erik
    Johansson, Silvia
    Traneus, Erik
    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.
    Ahnesjo, Anders
    Robust treatment planning of dose painting for prostate cancer based on ADC-to-Gleason score mappings: what is the potential to increase the tumor control probability?2021In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 60, no 2, p. 199-209Article in journal (Refereed)
    Abstract [en]

    Background and purpose: The aim of this study was to evaluate the potential to increase the tumor control probability (TCP) with 'dose painting by numbers' (DPBN) plans optimized in a treatment planning system (TPS) compared to uniform dose plans. The DPBN optimization was based on our earlier published formalism for prostate cancer that is driven by dose-responses of Gleason scores mapped from apparent diffusion coefficients (ADC).

    Material and methods: For 17 included patients, a set of DPBN plans were optimized in a TPS by maximizing the TCP for an equal average dose to the prostate volume (CTVT) as for a conventional uniform dose treatment. For the plan optimizations we applied different photon energies, different precisions for the ADC-to-Gleason mappings, and different CTVT positioning uncertainties. The TCP increasing potential was evaluated by the DPBN efficiency, defined as the ratio of TCP increases for DPBN plans by TCP increases for ideal DPBN prescriptions (optimized without considering radiation transport phenomena, uncertainties of the CTVT positioning, and uncertainties of the ADC-to-Gleason mapping).

    Results: The median DPBN efficiency for the most conservative planning scenario optimized with a low precision ADC-to-Gleason mapping, and a positioning uncertainty of 0.6 cm was 10%, meaning that more than half of the patients had a TCP gain of at least 10% of the TCP for an ideal DPBN prescription. By increasing the precision of the ADC-to-Gleason mapping, and decreasing the positioning uncertainty the median DPBN efficiency increased by up to 40%.

    Conclusions: TCP increases with DPBN plans optimized in a TPS were found more likely with a high precision mapping of image data into dose-responses and a high certainty of the tumor positioning. These findings motivate further development to ensure precise mappings of image data into dose-responses and to ensure a high spatial certainty of the tumor positioning when implementing DPBN clinically.

    Download full text (pdf)
    fulltext
  • 34. Gronlund, Eric
    et al.
    Johansson, Silvia
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Thellenberg, Camilla
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Ahnesjo, Anders
    Dose painting of prostate cancer based on Gleason score correlations with apparent diffusion coefficients2018In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 57, no 5, p. 574-581Article in journal (Refereed)
    Abstract [en]

    Background: Gleason scores for prostate cancer correlates with an increased recurrence risk after radiotherapy (RT). Furthermore, higher Gleason scores correlates with decreasing apparent diffusion coefficient (ADC) data from diffusion weighted MRI (DWI-MRI). Based on these observations, we present a formalism for dose painting prescriptions of prostate volumes based on ADC images mapped to Gleason score driven dose-responses.

    Methods: The Gleason score driven dose-responses were derived from a learning data set consisting of pre-RT biopsy data and post-RT outcomes for 122 patients treated with a homogeneous dose to the prostate. For a test data set of 18 prostate cancer patients with pre-RT ADC images, we mapped the ADC data to the Gleason driven dose-responses by using probability distributions constructed from published Gleason score correlations with ADC data. We used the Gleason driven dose-responses to optimize dose painting prescriptions that maximize the tumor control probability (TCP) with equal average dose as for the learning sets homogeneous treatment dose.

    Results: The dose painting prescriptions increased the estimated TCP compared to the homogeneous dose by 0–51% for the learning set and by 4–30% for the test set. The potential for individual TCP gains with dose painting correlated with increasing Gleason score spread and larger prostate volumes. The TCP gains were also found to be larger for patients with a low expected TCP for the homogeneous dose prescription.

    Conclusions: We have from retrospective treatment data demonstrated a formalism that yield ADC driven dose painting prescriptions for prostate volumes that potentially can yield significant TCP increases without increasing dose burdens as compared to a homogeneous treatment dose. This motivates further development of the approach to consider more accurate ADC to Gleason mappings, issues with delivery robustness of heterogeneous dose distributions, and patient selection criteria for design of clinical trials.

  • 35. Gustafsson, Christian
    et al.
    Korhonen, Juha
    Persson, Emilia
    Gunnlaugsson, Adalsteinn
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Olsson, Lars E
    Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy2017In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 44, no 11, p. 5563-5574Article in journal (Refereed)
    Abstract [en]

    PURPOSE: The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration.

    METHODS: T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancer patients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images.

    RESULTS: The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 ± 1.25 mm and 1.14 ± 1.06 mm, respectively.

    CONCLUSIONS: Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.

  • 36.
    Jamtheim Gustafsson, Christian
    et al.
    Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden; Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden.
    Lempart, Michael
    Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden; Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden.
    Swärd, Johan
    Centre for Mathematical Sciences, Mathematical Statistics, Lund University, Lund, Sweden.
    Persson, Emilia
    Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden; Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden.
    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.
    Scherman, Jonas
    Department of Hematology Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden.
    Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy2021In: Journal of Applied Clinical Medical Physics, E-ISSN 1526-9914, Vol. 22, no 12, p. 51-63Article in journal (Refereed)
    Abstract [en]

    Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop a modality independent deep learning (DL) model for automatic classification and annotation of prostate RT DICOM structures.

    Delineated prostate organs at risk (OAR), support- and target structures (gross tumor volume [GTV]/clinical target volume [CTV]/planning target volume [PTV]), along with or without separate vesicles and/or lymph nodes, were extracted as binary masks from 1854 patients. An image modality independent 2D InceptionResNetV2 classification network was trained with varying amounts of training data using four image input channels. Channel 1–3 consisted of orthogonal 2D projections from each individual binary structure. The fourth channel contained a summation of the other available binary structure masks. Structure classification performance was assessed in independent CT (n = 200 pat) and magnetic resonance imaging (MRI) (n = 40 pat) test datasets and an external CT (n = 99 pat) dataset from another clinic.

    A weighted classification accuracy of 99.4% was achieved during training. The unweighted classification accuracy and the weighted average F1 score among different structures in the CT test dataset were 98.8% and 98.4% and 98.6% and 98.5% for the MRI test dataset, respectively. The external CT dataset yielded the corresponding results 98.4% and 98.7% when analyzed for trained structures only, and results from the full dataset yielded 79.6% and 75.2%. Most misclassifications in the external CT dataset occurred due to multiple CTVs and PTVs being fused together, which was not included in the training data.

    Our proposed DL-based method for automated renaming and standardization of prostate radiotherapy annotations shows great potential. Clinic specific contouring standards however need to be represented in the training data for successful use. Source code is available at https://github.com/jamtheim/DicomRTStructRenamerPublic

    Download full text (pdf)
    fulltext
  • 37.
    Johansson, Adam
    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.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Tufve, Nyholm
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    CT substitutes derived from MR images reconstructed with parallel imaging2014In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 8, p. 474-480Article in journal (Refereed)
    Abstract [en]

    Purpose: Computed tomography (CT) substitute images can be generated from ultrashort echo time (UTE) MRI sequences with radial k-space sampling. These CT substitutes can be used as ordinary CT images for PET attenuation correction and radiotherapy dose calculations. Parallel imaging allows faster acquisition of magnetic resonance (MR) images by exploiting differences in receiver coil element sensitivities. This study investigates whether non-Cartesian parallel imaging reconstruction can be used to improve CT substitutes generated from shorter examination times.

    Methods: The authors used gridding as well as two non-Cartesian parallel imaging reconstruction methods, SPIRiT and CG-SENSE, to reconstruct radial UTE and gradient echo (GE) data into images of the head for 23 patients. For each patient, images were reconstructed from the full dataset and from a number of subsampled datasets. The subsampled datasets simulated shorter acquisition times by containing fewer radial k-space spokes (1000, 2000, 3000, 5000, and 10 000 spokes) than the full dataset (30 000 spokes). For each combination of patient, reconstruction method, and number of spokes, the reconstructed UTE and GE images were used to generate a CT substitute. Each CT substitute image was compared to a real CT image of the same patient.

    Results: The mean absolute deviation between the CT number in CT substitute and CT decreased when using SPIRiT as compared to gridding reconstruction. However, the reduction was small and the CT substitute algorithm was insensitive to moderate subsampling (≥5000 spokes) regardless of reconstruction method. For more severe subsampling (≤3000 spokes), corresponding to acquisition times less than aminute long, the CT substitute quality was deteriorated for all reconstructionmethods but SPIRiT gave a reduction in the mean absolute deviation of down to 25 Hounsfield units compared to gridding.

    Conclusions: SPIRiT marginally improved the CT substitute quality for a given number of radial spokes as compared to gridding. However, the increased reconstruction time of non-Cartesian parallel imaging reconstruction is difficult to motivate from this improvement. Because the CT substitute algorithm was insensitive to moderate subsampling, data for a CT substitute could be collected in as little as minute and reconstructed with gridding without deteriorating the CT substitute quality.

  • 38.
    Johansson, Adam
    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, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    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.
    Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information: potential application for MR-only radiotherapy and attenuation correction in positron emission tomography2013In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 52, no 7, p. 1369-1373Article in journal (Refereed)
    Abstract [en]

    Background: Estimation of computed tomography (CT) equivalent data, i.e. a substitute CT (s-CT), from magnetic resonance (MR) images is a prerequisite both for attenuation correction of positron emission tomography (PET) data acquired with a PET/MR scanner and for dose calculations in an MR-only radiotherapy workflow. It has previously been shown that it is possible to estimate Hounsfield numbers based on MR image intensities, using ultra short echo-time imaging and Gaussian mixture regression (GMR). In the present pilot study we investigate the possibility to also include spatial information in the GMR, with the aim to improve the quality of the s-CT. Material and methods: MR and CT data for nine patients were used in the present study. For each patient, GMR models were created from the other eight patients, including either both UTE image intensities and spatial information on a voxel by voxel level, or only UTE image intensities. The models were used to create s-CT images for each respective patient. Results: The inclusion of spatial information in the GMR model improved the accuracy of the estimated s-CT. The improvement was most pronounced in smaller, complicated anatomical regions as the inner ear and post-nasal cavities. Conclusions: This pilot study shows that inclusion of spatial information in GMR models to convert MR data to CT equivalent images is feasible. The accuracy of the s-CT is improved and the spatial information could make it possible to create a general model for the conversion applicable to the whole body.

  • 39.
    Johansson, Adam
    et al.
    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.
    CT substitute derived from MRI sequences with ultrashort echo time2011In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 38, no 5, p. 2708-2714Article in journal (Refereed)
    Abstract [en]

    Purpose: Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET/MRI applications, as well as for patient positioning and dose planning in MRI based radiation therapy workflows. This study presents a method for generating a drop in substitute for a CT image from a set of magnetic resonance (MR)images.

    Methods:A Gaussian mixture regression model was used to link the voxel values in CT images to the voxel values in images from three MRI sequences: one T2 weighted 3D spin echo based sequence and two dual echo ultrashort echo time MRI sequences with different echo times and flip angles. The method used a training set of matched MR and CT data that after training was able to predict a substitute CT (s-CT) based entirely on the MR information for a new patient. Method validation was achieved using datasets covering the heads of five patients and applying leave-one-out cross-validation (LOOCV). During LOOCV, the model was estimated from the MR and CT data of four patients (training set) and applied to the MR data of the remaining patient (validation set) to generate an s-CT image. This procedure was repeated for all five training and validation data combinations.

    Results: The mean absolute error for the CT number in the s-CT images was 137 HU. No large differences in method accuracy were noted for the different patients, indicating a robust method. The largest errors in the s-CT images were found at air–tissue and bone–tissue interfaces. The model accurately discriminated between air and bone, as well as between soft tissues and nonsoft tissues.

    Conclusions: The s-CT method has the potential to provide an accurate estimation of CT information without risk of geometrical inaccuracies as the model is voxel based. Therefore, s-CT images could be well suited as alternatives to CT images for dose planning in radiotherapy and attenuation correction in PET/MRI.

  • 40.
    Johansson, Adam
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    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.
    Voxel-wise uncertainty in CT substitute derived from MRI2012In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 39, no 6, p. 3283-3290Article in journal (Refereed)
    Abstract [en]

    Purpose: In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences.

    Methods: An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities.

    Results: The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation.

    Conclusions: The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.

  • 41.
    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, ISSN 1748-717X, 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.

    Download full text (pdf)
    fulltext
  • 42.
    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, ISSN 1748-717X, 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.

  • 43.
    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.

  • 44.
    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, ISSN 1748-717X, 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.

    Download full text (pdf)
    fulltext
  • 45.
    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.

  • 46.
    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, ISSN 2405-6308, Vol. 18, p. 60-65Article, review/survey (Refereed)
    Download full text (pdf)
    fulltext
  • 47.
    Kadesjö, Nils
    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.
    Olofsson, Jörgen
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A practical approach to diode based in vivo dosimetry for intensity modulated radiotherapy2011In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 98, no 3, p. 378-381Article in journal (Refereed)
    Abstract [en]

    A method for in vivo entrance dosimetry, using point detectors, in intensity modulated radiotherapy has been clinically evaluated. Diode dosimetry was performed for treatments of the head and neck and prostate regions. The results were good; 92.2% of the measurements showed deviations within ±5% of the expected values.

  • 48.
    Karlsson, Mikael
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ahnesjö, Anders
    Department of Oncology, Radiology and Clinical Immunology, Uppsala.
    Georg, Dietmar
    Division Medical Radiation Physics, Department of Radiotherapy, Medical.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Olofsson, Jörgen
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Beam modelling and dose calculations2010In: Independent Dose Calculations - Concepts and Models: Booklet 10, Bryssel: ESTRO , 2010, 1, p. 39-66Chapter in book (Other academic)
  • 49.
    Karlsson, Mikael
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ahnesjö, Anders
    Department of Oncology, Radiology and Clinical Immunology, Uppsala.
    Georg, Dietmar
    Division Medical Radiation Physics, Department of Radiotherapy, Medical.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Olofsson, Jörgen
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Dosimetric tolerance limits and action limits2010In: Independent Dose Calculations - Concepts and Models: Booklet 10, Bryssel: ESTRO , 2010, 1, p. 13-24Chapter in book (Other academic)
  • 50.
    Karlsson, Mikael
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ahnesjö, Anders
    Department of Oncology, Radiology and Clinical Immunology, Uppsala.
    Georg, Dietmar
    Division Medical Radiation Physics, Department of Radiotherapy, Medical.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Olofsson, Jörgen
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Measured data for verification and dose calculations2010In: Independent Dose Calculations - Concepts and Models: Booklet 10, Bryssel: ESTRO , 2010, 1, p. 67-76Chapter in book (Other academic)
123 1 - 50 of 120
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf