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Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0002-9091-4724
Department of Clinical Sciences, Radiology, Lund UniversityLundSweden.
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
2019 (engelsk)Inngår i: Image Analysis: Lecture Notes in Computer Science / [ed] Felsberg M., Forssén PE., Sintorn IM., Unger J., Springer Publishing Company, 2019, s. 489-498Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer Publishing Company, 2019. s. 489-498
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Emneord [en]
Diffusion MRI, Generative Adversarial Networks, CycleGAN, Distortion correction
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-158662DOI: 10.1007/978-3-030-20205-7_40ISBN: 978-3-030-20204-0 (tryckt)ISBN: 978-3-030-20205-7 (digital)OAI: oai:DiVA.org:liu-158662DiVA, id: diva2:1335814
Konferanse
Scandinavian Conference on Image Analysis, SCIA
Tilgjengelig fra: 2019-07-08 Laget: 2019-07-08 Sist oppdatert: 2019-11-19
Inngår i avhandling
1. Advanced analysis of diffusion MRI data
Åpne denne publikasjonen i ny fane eller vindu >>Advanced analysis of diffusion MRI data
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2019. s. 93
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2017
Emneord
Diffusion MRI, Distortion Correction, Deep Learning, Uncertainty Estimation
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-161288 (URN)10.3384/diss.diva-161288 (DOI)9789175190037 (ISBN)
Disputas
2019-12-06, Hugo Theorell, Building 448, Campus US, Linköping, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-11-19 Laget: 2019-11-19 Sist oppdatert: 2019-11-19bibliografisk kontrollert

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