21 citations · 111 across the 32 of their papers we have counts for
32 papers
Spectral Vision Transformer for Efficient Tokenization with Limited Data
Alexandra G. Roberts, Maneesh John, Jinwei Zhang +10
We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline convenient theoretical prope…
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…
Encoder-Only Image Registration
Xiang Chen, Renjiu Hu, Jinwei Zhang +5
Learning-based techniques have significantly improved the accuracy and speed of deformable image registration. However, challenges such as reducing computational complexity and han…
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,…
Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing
Hang Zhang, Xiang Chen, Renjiu Hu +7
Learning-based deformable image registration (DIR) accelerates alignment by amortizing traditional optimization via neural networks. Label supervision further enhances accuracy, en…
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement
Samuel W. Remedios, Shuwen Wei, Shuo Han +6
In clinical imaging, magnetic resonance (MR) image volumes are often acquired as stacks of 2D slices with decreased scan times, improved signal-to-noise ratio, and image contrasts…
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…
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…
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…
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…
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…
DAGrid: Directed Accumulator Grid
Hang Zhang, Renjiu Hu, Xiang Chen +3
Recent research highlights that the Directed Accumulator (DA), through its parametrization of geometric priors into neural networks, has notably improved the performance of medical…
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…
Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network
Chao Li, Hang Zhang, Jinwei Zhang +3
An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles t…
Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction
Jinwei Zhang, Hang Zhang, Chao Li +4
Quantitative imaging in MRI usually involves acquisition and reconstruction of a series of images at multi-echo time points, which possibly requires more scan time and specific rec…
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…