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^ ∗ …
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
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 …
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
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 …
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 …
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 …
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
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 …
(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 …
deep neural network (DNN) could significantly improve the performance of an image …
Ultrafast water–fat separation using deep learning–based single‐shot MRI
Purpose To present a deep learning–based reconstruction method for spatiotemporally
encoded single‐shot MRI to simultaneously obtain water and fat images. Methods …
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
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 …
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
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 …
introduced. This method, which is referred to as DeepRF SLR, is designed to minimize the …