Self-supervised neural network improves tri-exponential intravoxel incoherent motion model fitting compared to least-squares fitting in non-alcoholic fatty liver disease

Self-supervised neural network improves tri-exponential intravoxel incoherent motion model fitting compared to least-squares fitting in non-alcoholic fatty liver disease

Recent literature suggests that tri-exponential models may provide additional information and fit liver intravoxel incoherent motion (IVIM) data more accurately than conventional bi-exponential models. However, voxel-wise fitting of IVIM results in noisy and unreliable parameter maps. For bi-exponen...

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Bibliographic Details
Main Authors: Marian A. Troelstra, Anne-Marieke Van Dijk, Julia J. Witjes, Anne Linde Mak, Diona Zwirs, Jurgen H. Runge, Joanne Verheij, Ulrich H. Beuers, Max Nieuwdorp, Adriaan G. Holleboom, Aart J. Nederveen, Oliver J. Gurney-Champion
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Physiology
Subjects:
magnetic resonance imaging
diffusion magnetic resonance imaging
non-alcoholic fatty liver disease
intravoxel incoherent motion (IVIM)
deep learning
tri-exponential
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2022.942495/full

Internet

https://www.frontiersin.org/articles/10.3389/fphys.2022.942495/full

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