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Björnfot, C., Eklund, A., Larsson, J., Hansson, W., Birnefeld, J., Garpebring, A., . . . Wåhlin, A. (2024). Cerebral arterial stiffness is linked to white matter hyperintensities and perivascular spaces in older adults: a 4D flow MRI study. Journal of Cerebral Blood Flow and Metabolism
Open this publication in new window or tab >>Cerebral arterial stiffness is linked to white matter hyperintensities and perivascular spaces in older adults: a 4D flow MRI study
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2024 (English)In: Journal of Cerebral Blood Flow and Metabolism, ISSN 0271-678X, E-ISSN 1559-7016Article in journal (Refereed) Epub ahead of print
Abstract [en]

White matter hyperintensities (WMH), perivascular spaces (PVS) and lacunes are common MRI features of small vessel disease (SVD). However, no shared underlying pathological mechanism has been identified. We investigated whether SVD burden, in terms of WMH, PVS and lacune status, was related to changes in the cerebral arterial wall by applying global cerebral pulse wave velocity (gcPWV) measurements, a newly described marker of cerebral vascular stiffness. In a population-based cohort of 190 individuals, 66–85 years old, SVD features were estimated from T1-weighted and FLAIR images while gcPWV was estimated from 4D flow MRI data. Additionally, the gcPWV’s stability to variations in field-of-view was analyzed. The gcPWV was 10.82 (3.94) m/s and displayed a significant correlation to WMH and white matter PVS volume (r = 0.29, p < 0.001; r = 0.21, p = 0.004 respectively from nonparametric tests) that persisted after adjusting for age, blood pressure variables, body mass index, ApoB/A1 ratio, smoking as well as cerebral pulsatility index, a previously suggested early marker of SVD. The gcPWV displayed satisfactory stability to field-of-view variations. Our results suggest that SVD is accompanied by changes in the cerebral arterial wall that can be captured by considering the velocity of the pulse wave transmission through the cerebral arterial network.

Place, publisher, year, edition, pages
Sage Publications, 2024
Keywords
4D flow MRI, cerebral small vessel disease, perivascular spaces, pulse wave velocity, white matter hyperintensities
National Category
Cardiac and Cardiovascular Systems Neurology Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-221120 (URN)10.1177/0271678X241230741 (DOI)001157963000001 ()38315044 (PubMedID)2-s2.0-85184419786 (Scopus ID)
Funder
Swedish Foundation for Strategic Research, RMX18-0152Swedish Heart Lung Foundation, 20180513Swedish Heart Lung Foundation, 20210653The Swedish Brain Foundation, F2022-0216Swedish Research Council, 2017-04949Swedish Research Council, 2022-04263Region Västerbotten
Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-22
Hellström, M., Löfstedt, T. & Garpebring, A. (2023). Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors. Magnetic Resonance in Medicine, 90(6), 2557-2571
Open this publication in new window or tab >>Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors
2023 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 90, no 6, p. 2557-2571Article in journal (Refereed) Published
Abstract [en]

Purpose: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

Methods: We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping.

Results: We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior.

Conclusion: DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
Keywords
deep image prior, denoising, parameter mapping, quantitative MRI, uncertainty estimation
National Category
Medical Image Processing Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:umu:diva-213711 (URN)10.1002/mrm.29823 (DOI)001049833500001 ()37582257 (PubMedID)2-s2.0-85168117341 (Scopus ID)
Funder
Swedish Research Council, 2019‐0432Region Västerbotten, RV‐970119Cancerforskningsfonden i Norrland, AMP 18‐912
Available from: 2023-09-14 Created: 2023-09-14 Last updated: 2023-12-20Bibliographically approved
Simkó, A., Ruiter, S., Löfstedt, T., Garpebring, A., Nyholm, T., Bylund, M. & Jonsson, J. (2023). Improving MR image quality with a multi-task model, using convolutional losses. BMC Medical Imaging, 23(1), Article ID 148.
Open this publication in new window or tab >>Improving MR image quality with a multi-task model, using convolutional losses
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2023 (English)In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 23, no 1, article id 148Article in journal (Refereed) Published
Abstract [en]

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

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

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

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

Place, publisher, year, edition, pages
BioMed Central (BMC), 2023
Keywords
Image artefact correction, Machine learning, Magnetic resonance imaging
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-215277 (URN)10.1186/s12880-023-01109-z (DOI)37784039 (PubMedID)2-s2.0-85173046817 (Scopus ID)
Funder
Cancerforskningsfonden i Norrland, LP 18-2182Cancerforskningsfonden i Norrland, AMP 18-912Cancerforskningsfonden i Norrland, AMP 20-1014Cancerforskningsfonden i Norrland, LP 22-2319Region Västerbotten
Available from: 2023-10-17 Created: 2023-10-17 Last updated: 2023-10-25Bibliographically approved
Simkó, A., Garpebring, A., Jonsson, J., Nyholm, T. & Löfstedt, T. (2023). Reproducibility of the methods in medical imaging with deep learning. In: Ipek Oguz; Jack Noble; Xiaoxiao Li; Martin Styner; Christian Baumgartner; Mirabela Rusu; Tobias Heinmann; Despina Kontos; Bennett Landman; Benoit Dawant (Ed.), Medical imaging with deep learning 2023: . Paper presented at Medical Imaging with Deep Learning 2023, MIDL, Nashville, July 10-12, 2023 (pp. 95-106). ML Research Press
Open this publication in new window or tab >>Reproducibility of the methods in medical imaging with deep learning
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2023 (English)In: Medical imaging with deep learning 2023 / [ed] Ipek Oguz; Jack Noble; Xiaoxiao Li; Martin Styner; Christian Baumgartner; Mirabela Rusu; Tobias Heinmann; Despina Kontos; Bennett Landman; Benoit Dawant, ML Research Press , 2023, p. 95-106Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
ML Research Press, 2023
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 227
Keywords
Reproducibility, Reproducibility of the Methods, Deep Learning, Medical Imaging, Open Science, Transparent Research
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-215692 (URN)2-s2.0-85189322413 (Scopus ID)
Conference
Medical Imaging with Deep Learning 2023, MIDL, Nashville, July 10-12, 2023
Note

Originally included in thesis in manuscript form. 

Available from: 2023-10-25 Created: 2023-10-25 Last updated: 2024-04-17Bibliographically approved
Simkó, A., Bylund, M., Jönsson, G., Löfstedt, T., Garpebring, A., Nyholm, T. & Jonsson, J. (2023). Towards MR contrast independent synthetic CT generation. Zeitschrift für Medizinische Physik
Open this publication in new window or tab >>Towards MR contrast independent synthetic CT generation
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2023 (English)In: Zeitschrift für Medizinische Physik, ISSN 0939-3889, E-ISSN 1876-4436Article in journal (Refereed) Epub ahead of print
Abstract [en]

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

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

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
MRI contrast, Robust machine learning, Synthetic CT generation
National Category
Computer Sciences Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-214270 (URN)10.1016/j.zemedi.2023.07.001 (DOI)37537099 (PubMedID)2-s2.0-85169824488 (Scopus ID)
Funder
Cancerforskningsfonden i Norrland, LP 18-2182Cancerforskningsfonden i Norrland, AMP 18-912Cancerforskningsfonden i Norrland, AMP 20-1014Cancerforskningsfonden i Norrland, LP 22-2319Region VästerbottenSwedish National Infrastructure for Computing (SNIC)
Available from: 2023-09-12 Created: 2023-09-12 Last updated: 2024-05-13
Simkó, A., Löfstedt, T., Garpebring, A., Nyholm, T. & Jonsson, J. (2022). MRI bias field correction with an implicitly trained CNN. In: Ender Konukoglu; Bjoern Menze; Archana Venkataraman; Christian Baumgartner; Qi Dou; Shadi Albarqouni (Ed.), Proceedings of the 5th international conference on medical imaging with deep learning: . Paper presented at International Conference on Medical Imaging with Deep Learning, Zurich, Switzerland, July 6-8, 2022 (pp. 1125-1138). ML Research Press
Open this publication in new window or tab >>MRI bias field correction with an implicitly trained CNN
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2022 (English)In: Proceedings of the 5th international conference on medical imaging with deep learning / [ed] Ender Konukoglu; Bjoern Menze; Archana Venkataraman; Christian Baumgartner; Qi Dou; Shadi Albarqouni, ML Research Press , 2022, p. 1125-1138Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
ML Research Press, 2022
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 172
Keywords
Self-supervised learning, Implicit Training, Magnetic Resonance Imaging, Bias Field Correction, Image Restoration
National Category
Radiology, Nuclear Medicine and Medical Imaging Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:umu:diva-205226 (URN)2-s2.0-85169103625 (Scopus ID)
Conference
International Conference on Medical Imaging with Deep Learning, Zurich, Switzerland, July 6-8, 2022
Available from: 2023-02-27 Created: 2023-02-27 Last updated: 2023-11-10Bibliographically approved
Björnfot, C., Garpebring, A., Qvarlander, S., Malm, J., Eklund, A. & Wahlin, A. (2021). Assessing cerebral arterial pulse wave velocity using 4D flow MRI. Journal of Cerebral Blood Flow and Metabolism, 41(10), 2769-2777
Open this publication in new window or tab >>Assessing cerebral arterial pulse wave velocity using 4D flow MRI
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2021 (English)In: Journal of Cerebral Blood Flow and Metabolism, ISSN 0271-678X, E-ISSN 1559-7016, Vol. 41, no 10, p. 2769-2777Article in journal (Refereed) Published
Abstract [en]

Intracranial arterial stiffening is a potential early marker of emerging cerebrovascular dysfunction and could be mechanistically involved in disease processes detrimental to brain function via several pathways. A prominent consequence of arterial wall stiffening is the increased velocity at which the systolic pressure pulse wave propagates through the vasculature. Previous non-invasive measurements of the pulse wave propagation have been performed on the aorta or extracranial arteries with results linking increased pulse wave velocity to brain pathology. However, there is a lack of intracranial “target-organ” measurements. Here we present a 4D flow MRI method to estimate pulse wave velocity in the intracranial vascular tree. The method utilizes the full detectable branching structure of the cerebral vascular tree in an optimization framework that exploits small temporal shifts that exists between waveforms sampled at varying depths in the vasculature. The method is shown to be stable in an internal consistency test, and of sufficient sensitivity to robustly detect age-related increases in intracranial pulse wave velocity.

Place, publisher, year, edition, pages
Sage Publications, 2021
Keywords
arterial stiffness, arteriosclerosis, Atherosclerosis, magnetic resonance imaging, neurovascular dysfunction
National Category
Cardiac and Cardiovascular Systems Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-183012 (URN)10.1177/0271678X211008744 (DOI)000681011400001 ()33853409 (PubMedID)2-s2.0-85104375387 (Scopus ID)
Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2023-09-14Bibliographically approved
Simkó, A., Löfstedt, T., Garpebring, A., Bylund, M., Nyholm, T. & Jonsson, J. (2021). Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning. In: Mattias Heinrich; Qi Dou; Marleen de Bruijne; Jan Lellmann; Alexander Schläfer; Floris Ernst (Ed.), Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR: . Paper presented at Medical Imaging with Deep Learning (MIDL), Online, 7-9 July, 2021. (pp. 713-727). Lübeck University; Hamburg University of Technology, 143
Open this publication in new window or tab >>Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning
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2021 (English)In: Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR / [ed] Mattias Heinrich; Qi Dou; Marleen de Bruijne; Jan Lellmann; Alexander Schläfer; Floris Ernst, Lübeck University; Hamburg University of Technology , 2021, Vol. 143, p. 713-727Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
Lübeck University; Hamburg University of Technology, 2021
Series
Proceedings of Machine Learning Research, ISSN 2640-3498
National Category
Radiology, Nuclear Medicine and Medical Imaging Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:umu:diva-190497 (URN)2-s2.0-85162848187 (Scopus ID)
Conference
Medical Imaging with Deep Learning (MIDL), Online, 7-9 July, 2021.
Available from: 2021-12-16 Created: 2021-12-16 Last updated: 2023-10-25Bibliographically approved
Wang, J., Garpebring, A., Brynolfsson, P. & Yu, J. (2021). Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI. Frontiers in Signal Processing, 1, Article ID 727387.
Open this publication in new window or tab >>Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI
2021 (English)In: Frontiers in Signal Processing, E-ISSN 2673-8198, Vol. 1, p. 12article id 727387Article in journal (Refereed) Published
Abstract [en]

The purpose of this work is to investigate spatial statistical modelling approaches to improve contrast agent quantification in dynamic contrast enhanced MRI, by utilising the spatial dependence among image voxels. Bayesian hierarchical models (BHMs), such as Besag model and Leroux model, were studied using simulated MRI data. The models were built on smaller images where spatial dependence can be incorporated, and then extended to larger images using the maximum a posteriori (MAP) method. Notable improvements on contrast agent concentration estimation were obtained for both smaller and larger images. For smaller images: the BHMs provided substantial improved estimates in terms of the root mean squared error (rMSE), compared to the estimates from the existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperformed Besag models with two different dependence structures. Specifically, the Besag models increased the estimation precision by 27% around the peak of the dynamic curve, while the Leroux model improved the estimation by 40% at the peak, compared with the existing estimation method. For larger images: the proposed MAP estimators showed clear improvements on rMSE for vessels, tumor rim and white matter.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2021. p. 12
Keywords
Contrast agent quantication, BHM, Besag, Leroux, INLA, MAP
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics; Radiology
Identifiers
urn:nbn:se:umu:diva-141525 (URN)10.3389/frsip.2021.727387 (DOI)
Funder
Swedish Research Council, 2013-5342
Note

Originally included in thesis in manuscript form.

Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2023-09-14Bibliographically approved
Löfstedt, T., Hellström, M., Bylund, M. & Garpebring, A. (2020). Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation. Physics in Medicine and Biology, 65(22), Article ID 225036.
Open this publication in new window or tab >>Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation
2020 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 65, no 22, article id 225036Article in journal (Refereed) Published
Abstract [en]

Purpose. To develop a method that can reduce and estimate uncertainty in quantitative MR parameter maps without the need for hand-tuning of any hyperparameters.

Methods. We present an estimation method where uncertainties are reduced by incorporating information on spatial correlations between neighbouring voxels. The method is based on a Bayesian hierarchical non-linear regression model, where the parameters of interest are sampled, using Markov chain Monte Carlo (MCMC), from a high-dimensional posterior distribution with a spatial prior. The degree to which the prior affects the model is determined by an automatic hyperparameter search using an information criterion and is, therefore, free from manual user-dependent tuning. The samples obtained further provide a convenient means to obtain uncertainties in both voxels and regions. The developed method was evaluated on T1 estimations based on the variable flip angle method.

Results. The proposed method delivers noise-reduced T1 parameter maps with associated error estimates by combining MCMC sampling, the widely applicable information criterion, and total variation-based denoising. The proposed method results in an overall decrease in estimation error when compared to conventional voxel-wise maximum likelihood estimation. However, this comes with an increased bias in some regions, predominately at tissue interfaces, as well as an increase in computational time.

Conclusions. This study provides a method that generates more precise estimates compared to the conventional method, without incorporating user subjectivity, and with the added benefit of uncertainty estimation.

Place, publisher, year, edition, pages
Institute of Physics (IOP), 2020
Keywords
Bayesian statistics, quantitative MRI, noise reduction, tissue parameter estimation, WAIC
National Category
Radiology, Nuclear Medicine and Medical Imaging Computer Vision and Robotics (Autonomous Systems) Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-177258 (URN)10.1088/1361-6560/abb9f5 (DOI)000591796100001 ()32947277 (PubMedID)2-s2.0-85097228803 (Scopus ID)
Funder
Swedish Research Council, 2019-0432Region Västerbotten, RV-738491
Available from: 2020-12-03 Created: 2020-12-03 Last updated: 2023-10-13Bibliographically approved
Projects
Next generation quantitative magnetic resonance imaging for individualized radiotherapy [2019-04302_VR]; Umeå University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0532-232X

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