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most citedSpatially Covariant Lesion Segmentation

7 citations · 20 across the 9 of their papers we have counts for

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5 papers · 1 filter

eess.IV2023

High-pass filtered fidelity-imposed network edit (HP-FINE) for robust quantitative susceptibility mapping from high-pass filtered phase

Jinwei Zhang, Alexey Dimov, Chao Li +4

Purpose: To improve the generalization ability of deep learning based predictions of quantitative susceptibility mapping (QSM) from high-pass filtered phase (HPFP) data. Methods: A…

eess.IV2023★ 5 cited

mcLARO: Multi-Contrast Learned Acquisition and Reconstruction Optimization for simultaneous quantitative multi-parametric mapping

Jinwei Zhang, Thanh D. Nguyen, Eddy Solomon +6

Purpose: To develop a method for rapid sub-millimeter T1, T2, T2* and QSM mapping in a single scan using multi-contrast Learned Acquisition and Reconstruction Optimization (mcLARO)…

eess.IV2023★ 7 cited

Spatially Covariant Lesion Segmentation

Hang Zhang, Rongguang Wang, Jinwei Zhang +3

Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by inj…

eess.IV2022★ 1 cited

LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping

Jinwei Zhang, Pascal Spincemaille, Hang Zhang +6

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time…

eess.IV2021★ 6 cited

NeRD: Neural Representation of Distribution for Medical Image Segmentation

Hang Zhang, Rongguang Wang, Jinwei Zhang +5

We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an u…