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Garpebring, Anders
Publications (10 of 34) Show all publications
Adjeiwaah, M., Bylund, M., Lundman, J. A., Söderström, K., Zackrisson, B., Jonsson, J. H., . . . Nyholm, T. (2019). Dosimetric Impact of MRI Distortions: A Study on Head and Neck Cancers. International Journal of Radiation Oncology, Biology, Physics, 103(4), 994-1003
Open this publication in new window or tab >>Dosimetric Impact of MRI Distortions: A Study on Head and Neck Cancers
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2019 (English)In: International Journal of Radiation Oncology, Biology, Physics, ISSN 0360-3016, E-ISSN 1879-355X, Vol. 103, no 4, p. 994-1003Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-157192 (URN)10.1016/j.ijrobp.2018.11.037 (DOI)000459153600031 ()30496879 (PubMedID)
Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-04-15Bibliographically approved
Löfstedt, T., Brynolfsson, P., Nyholm, T. & Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLoS ONE
Open this publication in new window or tab >>Gray-level invariant Haralick texture features
2019 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203Article in journal (Refereed) Epub ahead of print
Abstract [en]

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.

National Category
Medical Biotechnology
Identifiers
urn:nbn:se:umu:diva-134995 (URN)10.1371/journal.pone.0212110 (DOI)000459709100037 ()30794577 (PubMedID)
Note

Originally included in thesis in manuscript form

Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2019-04-08
Garpebring, A., Brynolfsson, P., Kuess, P., Georg, D., Helbich, T. H., Nyholm, T. & Löfstedt, T. (2018). Density Estimation of Grey-Level Co-Occurrence Matrices for Image Texture Analysis. Physics in Medicine and Biology, 63(19), 9-15, Article ID 195017.
Open this publication in new window or tab >>Density Estimation of Grey-Level Co-Occurrence Matrices for Image Texture Analysis
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2018 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
Institute of Physics and Engineering in Medicine, 2018
Keywords
Haralick features, invariant features, GLCM, density estimation, texture analysis, image analysis
National Category
Computer Vision and Robotics (Autonomous Systems) Other Mathematics
Identifiers
urn:nbn:se:umu:diva-152488 (URN)10.1088/1361-6560/aad8ec (DOI)000446205200005 ()30088815 (PubMedID)
Funder
Västerbotten County CouncilVINNOVA
Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2018-10-31Bibliographically approved
Brynolfsson, P., Löfstedt, T., Asklund, T., Nyholm, T. & Garpebring, A. (2018). Gray-level invariant Haralick texture features. Paper presented at 37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 20-24, 2018, Barcelona, SPAIN. Radiotherapy and Oncology, 127, S279-S280
Open this publication in new window or tab >>Gray-level invariant Haralick texture features
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2018 (English)In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S279-S280Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-150493 (URN)10.1016/S0167-8140(18)30837-5 (DOI)000437723401139 ()
Conference
37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 20-24, 2018, Barcelona, SPAIN
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved
Garpebring, A. (2018). MRI based radiotherapy - what can go wrong and how to QA?. Paper presented at 37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 20-24, 2018, Barcelona, SPAIN. Radiotherapy and Oncology, 127, S339-S339
Open this publication in new window or tab >>MRI based radiotherapy - what can go wrong and how to QA?
2018 (English)In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S339-S339Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-150495 (URN)10.1016/S0167-8140(18)30949-6 (DOI)000437723401242 ()
Conference
37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 20-24, 2018, Barcelona, SPAIN
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved
Garpebring, A. & Tommy, L. (2018). Parameter estimation using weighted total least squares in the two-compartment exchange model. Magnetic Resonance in Medicine, 79(1), 561-567
Open this publication in new window or tab >>Parameter estimation using weighted total least squares in the two-compartment exchange model
2018 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 79, no 1, p. 561-567Article in journal (Refereed) Published
Abstract [en]

Purpose

The linear least squares (LLS) estimator provides a fast approach to parameter estimation in the linearized two-compartment exchange model. However, the LLS method may introduce a bias through correlated noise in the system matrix of the model. The purpose of this work is to present a new estimator for the linearized two-compartment exchange model that takes this noise into account.

Method

To account for the noise in the system matrix, we developed an estimator based on the weighted total least squares (WTLS) method. Using simulations, the proposed WTLS estimator was compared, in terms of accuracy and precision, to an LLS estimator and a nonlinear least squares (NLLS) estimator.

Results

The WTLS method improved the accuracy compared to the LLS method to levels comparable to the NLLS method. This improvement was at the expense of increased computational time; however, the WTLS was still faster than the NLLS method. At high signal-to-noise ratio all methods provided similar precisions while inconclusive results were observed at low signal-to-noise ratio.

Conclusion

The proposed method provides improvements in accuracy compared to the LLS method, however, at an increased computational cost. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
dynamic contrast-enhanced magnetic resonance imaging; parameter estimation, two-compartment exchange model, weighted total least squares
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-135670 (URN)10.1002/mrm.26677 (DOI)000417926300055 ()28349618 (PubMedID)
Available from: 2017-06-02 Created: 2017-06-02 Last updated: 2018-06-09Bibliographically approved
Bayisa, F., Liu, X., Garpebring, A. & Yu, J. (2018). Statistical learning in computed tomography image estimation. Medical physics (Lancaster), 45(12), 5450-5460
Open this publication in new window or tab >>Statistical learning in computed tomography image estimation
2018 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 12, p. 5450-5460Article in journal (Refereed) Published
Abstract [en]

Purpose: There is increasing interest in computed tomography (CT) image estimations from magneticresonance (MR) images. The estimated CT images can be utilized for attenuation correction, patientpositioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introducea novel statistical learning approach for improving CT estimation from MR images and to compare theperformance of our method with the existing model-based CT image estimation methods.

Methods: The statistical learning approach proposed here consists of two stages. At the trainingstage, prior knowledge about tissue types from CT images was used together with a Gaussian mixturemodel (GMM) to explore CT image estimations from MR images. Since the prior knowledge is notavailable at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimatethe tissue types from MR images. For a new patient, the trained classifier and GMMs were used topredict CT image from MR images. The classifier and GMMs were validated by using voxel-leveltenfold cross-validation and patient-level leave-one-out cross-validation, respectively.

Results: The proposed approach has outperformance in CT estimation quality in comparison withthe existing model-based methods, especially on bone tissues. Our method improved CT image estimationby 5% and 23% on the whole brain and bone tissues, respectively.

Conclusions: Evaluation of our method shows that it is a promising method to generate CTimage substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
Computed tomography, CT image estimation, Gaussian mixture model, magnetic resonance imaging, supervised learning
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-153283 (URN)10.1002/mp.13204 (DOI)000452799400010 ()30242845 (PubMedID)2-s2.0-85056189706 (Scopus ID)
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2019-01-07Bibliographically approved
Bylund, M., Jonsson, J., Lundman, J., Brynolfsson, P., Garpebring, A., Nyholm, T. & Löfstedt, T. (2018). Using deep learning to generate synthetic CTs for radiotherapy treatment planning. Paper presented at 37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 20-24, 2018, Barcelona, SPAIN. Radiotherapy and Oncology, 127, S283-S283
Open this publication in new window or tab >>Using deep learning to generate synthetic CTs for radiotherapy treatment planning
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2018 (English)In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S283-S283Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-150491 (URN)10.1016/S0167-8140(18)30842-9 (DOI)000437723401144 ()
Conference
37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 20-24, 2018, Barcelona, SPAIN
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved
Wermer, M. J. H., van Walderveen, M. A. A., Garpebring, A., van Osch, M. J. P. & Versluis, M. J. (2017). 7 Tesla MRA for the differentiation between intracranial aneurysms and infundibula. Magnetic Resonance Imaging, 37, 16-20
Open this publication in new window or tab >>7 Tesla MRA for the differentiation between intracranial aneurysms and infundibula
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2017 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 37, p. 16-20Article in journal (Refereed) Published
Abstract [en]

Objective: The differentiation between an aneurysm and an infundibulum with time-of-flight MRA is often difficult. However, this distinction is important because it affects further patient follow-up. The purpose of this study was to assess the added value of high resolution 7 Tesla MRA for investigating small vascular lesions suspect for an aneurysm or an infundibulum.

Materials and methods: We included patients in whom an intracranial vascular lesion was detected in our University Hospital and in whom the discrimination between a true aneurysms or an infundibulum could not be made on conventional 1.5 or 3 T MRI were included in the study. All patients underwent an additional 7 T time-of-flight MRA at higher spatial resolution.

Results: We included 6 patients. The age range of the patients was 35–65 years and 5 of them were women. 1 out of 6 had a 1.5 T MRI, the other 5 patients had a 3 T MRI previous to the 7 T MRI. The lesion size varied between 0.9 mm and 2.0 mm. In 5 of the 6 patients the presence of an infundibulum could be proven using the high resolution of the 7 T MRA. All patients tolerated the 7 T MRI well.

Conclusion: Our results suggest that high resolution and contrast of 7 T MRA provides added diagnostic value in discriminating between intracranial aneurysms and infundibula. This finding may have important consequences for patient follow-up and comfort because it might reduce unnecessary follow-up exams and decrease uncertainty about the diagnosis. Larger studies, however, are needed to confirm our findings.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Intracranial aneurysms, Infundibula, High resolution MRA, 7 Tesla, Time-of-flight MRA
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Radiology
Identifiers
urn:nbn:se:umu:diva-135669 (URN)10.1016/j.mri.2016.11.006 (DOI)000396382200003 ()27840274 (PubMedID)
Available from: 2017-06-02 Created: 2017-06-02 Last updated: 2018-06-09Bibliographically approved
Wezel, J., Boer, V. O., van der Velden, T. A., Webb, A. G., Klomp, D. W. .., Versluis, M. J., . . . Garpebring, A. (2017). A comparison of navigators, snap-shot field monitoring, and probe-based field model training for correcting B0-induced artifacts in T2*-weighted images at 7 T. Magnetic Resonance in Medicine, 78, 1373-1382
Open this publication in new window or tab >>A comparison of navigators, snap-shot field monitoring, and probe-based field model training for correcting B0-induced artifacts in T2*-weighted images at 7 T
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2017 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, p. 1373-1382Article in journal (Refereed) Published
Abstract [en]

Purpose

To compare methods for estimating B0 maps used in retrospective correction of high-resolution anatomical images at ultra-high field strength. The B0 maps were obtained using three methods: (1) 1D navigators and coil sensitivities, (2) field probe (FP) data and a low-order spherical harmonics model, and (3) FP data and a training-based model.

Methods

Data from nine subjects were acquired while they performed activities inducing B0 field fluctuations. Estimated B0 fields were compared with reference data, and the reductions of artifacts were compared in corrected T2* images.

Results

Reduction of sum-of-squares difference relative to a reference image was evaluated, and Method 1 yielded the largest artifact reduction: 27 ± 15%, 20 ± 18% (mean ± 1 standard deviation) for deep breathing and combined deep breathing and hand motion activities. Method 3 performed almost as well (24 ± 18%, 15 ± 17%), provided that adequate training data were used, and Method 2 gave a similar result (21 ± 16%, 19 ± 17%).

Conclusion

This study confirms that all of the investigated methods can be used in retrospective image correction. In terms of image quality, Method 1 had a small advantage, whereas the FP-based methods measured the B0 field slightly more accurately. The specific strengths and weaknesses of FPs and navigators should therefore be considered when determining which B0-estimation method to use. 

Keywords
NMR field probes; image reconstruction; spatio-temporal field variations; navigator echoes; ultra high-field MRI
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-135678 (URN)10.1002/mrm.26524 (DOI)000411186100013 ()
Available from: 2017-06-02 Created: 2017-06-02 Last updated: 2018-06-09Bibliographically approved
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