Comparative review of algorithms and methods for chemical‐shift‐encoded quantitative fat‐water imaging

P Daudé, T Roussel, T Troalen, P Viout… - Magnetic …, 2024 - Wiley Online Library
Purpose To propose a standardized comparison between state‐of‐the‐art open‐source fat‐
water separation algorithms for proton density fat fraction (PDFF) and R 2* R _2^ ∗ …

Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method

JP Meneses, A Qadir, N Surendran, C Arrieta… - European …, 2024 - Springer
Objective To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE)
MR images using a deep learning (DL)-based method that is precise and robust to different …

Deep learning staging of liver Iron content from multiecho MR images

V Positano, A Meloni, MF Santarelli… - Journal of Magnetic …, 2023 - Wiley Online Library
Background MRI represents the most established liver iron content (LIC) evaluation
approach by estimation of liver T2* value, but it is dependent on the choice of the …

Artifact-free fat-water separation in Dixon MRI using deep learning

N Basty, M Thanaj, M Cule, EP Sorokin, Y Liu… - Journal of Big Data, 2023 - Springer
Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body
composition and metabolic disorders, where derived fat and water signals enable the …

Robust water–fat separation based on deep learning model exploring multi‐echo nature of mGRE

K Liu, X Li, Z Li, Y Chen, H Xiong… - Magnetic …, 2021 - Wiley Online Library
Purpose To design a new deep learning network for fast and accurate water–fat separation
by exploring the correlations between multiple echoes in multi‐echo gradient‐recalled echo …

Uncertainty‐aware physics‐driven deep learning network for free‐breathing liver fat and R2* quantification using self‐gated stack‐of‐radial MRI

SF Shih, SG Kafali, KL Calkins… - Magnetic resonance in …, 2023 - Wiley Online Library
Purpose To develop a deep learning‐based method for rapid liver proton‐density fat fraction
(PDFF) and R2* quantification with built‐in uncertainty estimation using self‐gated free …

Deep learning and its application to function approximation for MR in medicine: An overview

H Takeshima - Magnetic Resonance in Medical Sciences, 2022 - jstage.jst.go.jp
In the ImageNet large-scale visual recognition competition 2012, AlexNet showed that a
deep neural network (DNN) could significantly improve the performance of an image …

Ultrafast water–fat separation using deep learning–based single‐shot MRI

X Chen, W Wang, J Huang, J Wu… - Magnetic resonance …, 2022 - Wiley Online Library
Purpose To present a deep learning–based reconstruction method for spatiotemporally
encoded single‐shot MRI to simultaneously obtain water and fat images. Methods …

Deep learning-based water-fat separation from dual-echo chemical shift-encoded imaging

Y Wu, M Alley, Z Li, K Datta, Z Wen, C Sandino, A Syed… - Bioengineering, 2022 - mdpi.com
Conventional water–fat separation approaches suffer long computational times and are
prone to water/fat swaps. To solve these problems, we propose a deep learning-based dual …

Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse Using Root-Flipping: DeepRFSLR

D Shin, S Ji, D Lee, J Lee, SH Oh… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
A novel approach of applying deep reinforcement learning to an RF pulse design is
introduced. This method, which is referred to as DeepRF SLR, is designed to minimize the …