21 citations · 111 across the 32 of their papers we have counts for
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Harmonizing MR Images Across 100+ Scanners: Multi-site Validation with Traveling Subjects and Real-world Protocols
Savannah P. Hays, Lianrui Zuo, Muhammad Faizyab Ali Chaudhary +13
Reliable harmonization of heterogeneous magnetic resonance~(MR) image datasets, especially those acquired in pragmatic clinical trials, is critical to advance multi-center neuroima…
MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis
Jinwei Zhang, Lianrui Zuo, Yihao Liu +10
In multiple sclerosis, lesions interfere with automated magnetic resonance imaging analyses such as brain parcellation and deformable registration, while lesion segmentation models…
UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey +9
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation,…
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…
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…
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey +4
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual deline…
RimSet: Quantitatively Identifying and Characterizing Chronic Active Multiple Sclerosis Lesion on Quantitative Susceptibility Maps
Jinwei Zhang, Thanh D. Nguyen, Renjiu Hu +3
Background: Rim+ lesions in multiple sclerosis (MS), detectable via Quantitative Susceptibility Mapping (QSM), correlate with increased disability. Existing literature lacks quanti…
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey +5
Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite the…
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…
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)…
DeDA: Deep Directed Accumulator
Hang Zhang, Rongguang Wang, Renjiu Hu +2
Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. Th…
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…
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…
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…
Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation
Hang Zhang, Jinwei Zhang, Rongguang Wang +4
Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been fu…
Probabilistic Dipole Inversion for Adaptive Quantitative Susceptibility Mapping
Jinwei Zhang, Hang Zhang, Mert Sabuncu +3
A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve the quantitative susceptibility mapping (QSM) inverse problem…
Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI
Jinwei Zhang, Hang Zhang, Alan Wang +5
The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three fold…
Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping
Jinwei Zhang, Hang Zhang, Mert Sabuncu +3
A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve quantitative susceptibility mapping (QSM) inverse problem in M…
Deep Neural Network (DNN) for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training
R. Jafari, P. Spincemaille, J. Zhang +6
Purpose: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. Methods: The current T…
RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation
Hang Zhang, Jinwei Zhang, Qihao Zhang +7
Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS). Manual delineation of MS lesions is a time-consuming and…
Fidelity Imposed Network Edit (FINE) for Solving Ill-Posed Image Reconstruction
Jinwei Zhang, Zhe Liu, Shun Zhang +5
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in imaging, such as reconstruction from noisy or incomplete data, as DL offers advantages over explicit…