ArticleFrontiers in physiology2022
Self-supervised neural network improves tri-exponential intravoxel incoherent motion model fitting compared to least-squares fitting in non-alcoholic fatty liver disease.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Validation of Intravoxel Incoherent Motion MRI Using Perfused Explanted Human Livers.Magnetic resonance in medicine · 2026Article
- A New Approach for Estimating Liver IVIM Fast Diffusion Coefficient in Two-Compartment Model and Fast Diffusion Fraction in Three-Compartment Model: Theory and In Vivo Validation of Two Centers' Data.NMR in biomedicine · 2026Article
- Diffusion-weighted magnetic resonance imaging of the pancreas: A narrative review.World journal of radiology · 2025Review
- Tissue classification from raw diffusion-weighted images using machine learning.Medical physics · 2025Article
- Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions.Journal of magnetic resonance imaging : JMRI · 2024Review
- Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.European radiology · 2023Article
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
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Abstract
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-exponential IVIM, neural networks (NN) were able to produce superior parameter maps than conventional least-squares (LSQ) generated images. Hence, to improve parameter map quality of tri-exponential IVIM, we developed an unsupervised physics-informed deep neural network (IVIM
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