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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: 2019-07-25

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Gu, XuanKnutsson, HansEklund, Anders
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