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20192026
most citedGeometric Loss for Deep Multiple Sclerosis lesion Segmentation

21 citations · 111 across the 32 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

physics.med-ph2024

Implicit neural representation for free-breathing MR fingerprinting (INR-MRF): co-registered 3D whole-liver water T1, water T2, proton density fat fraction, and R2* mapping

Chao Li, Jiahao Li, Jinwei Zhang +6

Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled…

eess.IV2024

Unique MS Lesion Identification from MRI

Carlos A. Rivas, Jinwei Zhang, Shuwen Wei +3

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance…

eess.IV2024

Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting

Jinwei Zhang, Lianrui Zuo, Yihao Liu +4

Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brai…

physics.med-ph2024

MRI quantification of liver fibrosis using diamagnetic susceptibility: An ex-vivo feasibility study

Chao Li, Jinwei Zhang, Alexey V. Dimov +8

In chronic liver disease, liver fibrosis develops as excessive deposition of extracellular matrix macromolecules, predominantly collagens, progressively form fibrous scars that dis…

eess.SP2024

Deep learning improved autofocus for motion artifact reduction and its application in quantitative susceptibility mapping

Chao Li, Jinwei Zhang, Hang Zhang +4

Purpose: To develop a pipeline for motion artifact correction in mGRE and quantitative susceptibility mapping (QSM). Methods: Deep learning is integrated with autofocus to improve…