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Advanced analysis of diffusion MRI data
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive imaging modality which can measure diffusion of water molecules, by making the MRI acquisition sensitive to diffusion. Diffusion MRI provides unique possibilities to study structural connectivity of the human brain, e.g. how the white matter connects different parts of the brain. Diffusion MRI enables a range of tools that permit qualitative and quantitative assessments of many neurological disorders, such as stroke and Parkinson.

This thesis introduces novel methods for diffusion MRI data analysis. Prior to estimating a diffusion model in each location (voxel) of the brain, the diffusion data needs to be preprocessed to correct for geometric distortions and head motion. A deep learning approach to synthesize diffusion scalar maps from a T1-weighted MR image is proposed, and it is shown that the distortion-free synthesized images can be used for distortion correction. An evaluation, involving both simulated data and real data, of six methods for susceptibility distortion correction is also presented in this thesis.

A common problem in diffusion MRI is to estimate the uncertainty of a diffusion model. An empirical evaluation of tractography, a technique that permits reconstruction of white matter pathways in the human brain, is presented in this thesis. The evaluation is based on analyzing 32 diffusion datasets from a single healthy subject, to study how reliable tractography is. In most cases only a single dataset is available for each subject. This thesis presents methods based on frequentistic (bootstrap) as well as Bayesian inference, which can provide uncertainty estimates when only a single dataset is available. These uncertainty measures can then, for example, be used in a group analysis to downweight subjects with a higher uncertainty.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. , p. 93
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2017
Keywords [en]
Diffusion MRI, Distortion Correction, Deep Learning, Uncertainty Estimation
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-161288DOI: 10.3384/diss.diva-161288ISBN: 9789175190037 (print)OAI: oai:DiVA.org:liu-161288DiVA, id: diva2:1371118
Public defence
2019-12-06, Hugo Theorell, Building 448, Campus US, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-11-19Bibliographically approved
List of papers
1. Repeated Tractography of a Single Subject: How High Is the Variance?
Open this publication in new window or tab >>Repeated Tractography of a Single Subject: How High Is the Variance?
2017 (English)In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan, Ingrid Hotz, Springer, 2017, p. 331-354Chapter in book (Other academic)
Abstract [en]

We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.

Place, publisher, year, edition, pages
Springer, 2017
Series
Mathematics and Visualization (MATHVISUAL), ISSN 1612-3786, E-ISSN 2197-666X
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-142047 (URN)10.1007/978-3-319-61358-1_14 (DOI)978-3-319-61357-4 (ISBN)978-3-319-61358-1 (ISBN)
Available from: 2017-10-19 Created: 2017-10-19 Last updated: 2019-11-19Bibliographically approved
2. Bayesian Diffusion Tensor Estimation with Spatial Priors
Open this publication in new window or tab >>Bayesian Diffusion Tensor Estimation with Spatial Priors
Show others...
2017 (English)In: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, p. 372-383Conference paper, Published paper (Refereed)
Abstract [en]

Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Keywords
Spatial regularization, Diffusion tensor, Spatial priors Markov chain, Monte Carlo Fractional anisotropy
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-139844 (URN)10.1007/978-3-319-64689-3_30 (DOI)000432085900030 ()978-3-319-64689-3 (ISBN)978-3-319-64688-6 (ISBN)
Conference
International Conference on Computer Analysis of Images and Patterns
Note

Funding agencies: Information Technology for European Advancement (ITEA) 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy); Swedish Research Council [2015-05356, 2013-5229]; National Institute of Dental and Craniof

Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2019-11-19
3. Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions
Open this publication in new window or tab >>Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions
Show others...
2017 (English)In: VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine, The Eurographics Association , 2017, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers.

Place, publisher, year, edition, pages
The Eurographics Association, 2017
Series
Eurographics Workshop on Visual Computing for Biology and Medicine, ISSN 2070-5778, E-ISSN 2070-5786 ; 2017
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-140739 (URN)10.2312/vcbm.20171244 (DOI)9783038680369 (ISBN)
Conference
Eurographics Workshop on Visual Computing for Biology and Medicine, September 7-8, 2017, Bremen, Germany
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2019-11-19Bibliographically approved
4. Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI
Open this publication in new window or tab >>Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI
2019 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 43Article in journal (Refereed) Published
Abstract [en]

Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.

Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).

Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.

Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2019
Keywords
Bootstrap, diffusion MRI, MAP-MRI, uncertainty, RtoP, NG, PA
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-157089 (URN)10.3389/fninf.2019.00043 (DOI)000471589200001 ()31244637 (PubMedID)
Note

Funding agencies:  Swedish Research Council [2015-05356]; Linkoping University Center for Industrial Information Technology (CENIIT); Knut and Alice Wallenberg Foundation project Seeing Organ Function; National Institute of Dental and Craniofacial Research (NIDCR); National

Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-11-19
5. Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
Open this publication in new window or tab >>Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
2019 (English)In: Image Analysis: Lecture Notes in Computer Science / [ed] Felsberg M., Forssén PE., Sintorn IM., Unger J., Springer Publishing Company, 2019, p. 489-498Conference paper, Published paper (Refereed)
Abstract [en]

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.

Place, publisher, year, edition, pages
Springer Publishing Company, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Diffusion MRI, Generative Adversarial Networks, CycleGAN, Distortion correction
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-158662 (URN)10.1007/978-3-030-20205-7_40 (DOI)978-3-030-20204-0 (ISBN)978-3-030-20205-7 (ISBN)
Conference
Scandinavian Conference on Image Analysis, SCIA
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-11-19

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