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Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
Department of Clinical Sciences, Radiology, Lund UniversityLundSweden.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
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. p. 489-498
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords [en]
Diffusion MRI, Generative Adversarial Networks, CycleGAN, Distortion correction
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-158662DOI: 10.1007/978-3-030-20205-7_40ISBN: 978-3-030-20204-0 (print)ISBN: 978-3-030-20205-7 (electronic)OAI: oai:DiVA.org:liu-158662DiVA, id: diva2:1335814
Conference
Scandinavian Conference on Image Analysis, SCIA
Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-11-19
In thesis
1. Advanced analysis of diffusion MRI data
Open this publication in new window or tab >>Advanced analysis of diffusion MRI data
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
Diffusion MRI, Distortion Correction, Deep Learning, Uncertainty Estimation
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-161288 (URN)10.3384/diss.diva-161288 (DOI)9789175190037 (ISBN)
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

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Gu, XuanKnutsson, HansEklund, Anders

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