7 citations · 26 across the 22 of their papers we have counts for
17 papers · 1 filter
Large-Scale Deployment and Analytical Implications of Structured Quality Control in Diffusion Magnetic Resonance Imaging
Michael E. Kim, Chenyu Gao, Karthik Ramadass +17
Purpose: Diffusion MRI (dMRI) provides a diverse set of quantitative measures and derived datatypes to assess white matter microstructure and macrostructure. Coupled with the incre…
CATNUS: Coordinate-Aware Thalamic Nuclei Segmentation Using T1-Weighted MRI
Anqi Feng, Zhangxing Bian, Samuel W. Remedios +6
Accurate segmentation of thalamic nuclei from magnetic resonance images is important due to the distinct roles of these nuclei in overall brain function and to their differential i…
MedForj: An open, large-scale foundational generative prior for high-resolution 3D brain MRI
Samuel W. Remedios, Aaron Carass, Jerry L. Prince +1
This work introduces MedForj, a suite of 3D foundational generative priors based on diffusion models. The MedForj models were trained on 1~mm isotropic 3D -weighted…
Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution
Anqi Feng, Zhangxing Bian, Samuel W. Remedios +5
Accurate thalamic nuclei segmentation is crucial for understanding neurological diseases, brain functions, and guiding clinical interventions. However, the optimal inputs for segme…
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,…
Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures
Savannah P. Hays, Lianrui Zuo, Anqi Feng +7
Purpose: Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning.While multi-inver…
RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs
Anqi Feng, Zhangxing Bian, Blake E. Dewey +3
Accurate segmentation of thalamic nuclei is important for better understanding brain function and improving disease treatment. Traditional segmentation methods often rely on a sing…
Beyond MR Image Harmonization: Resolution Matters Too
Savannah P. Hays, Samuel W. Remedios, Lianrui Zuo +6
Magnetic resonance (MR) imaging is commonly used in the clinical setting to non-invasively monitor the body. There exists a large variability in MR imaging due to differences in sc…
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
Ho Hin Lee, Adam M. Saunders, Michael E. Kim +15
Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing…
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution
Zejun Wu, Samuel W. Remedios, Blake E. Dewey +2
Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs…
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…
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…
A latent space for unsupervised MR image quality control via artifact assessment
Lianrui Zuo, Yuan Xue, Blake E. Dewey +3
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images. Existin…
HACA3: A Unified Approach for Multi-site MR Image Harmonization
Lianrui Zuo, Yihao Liu, Yuan Xue +10
The lack of standardization is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in ha…
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
Lianrui Zuo, Blake E. Dewey, Aaron Carass +4
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis. However, contrast variation from site to site caused by lack of standardiz…
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis
Dzung L. Pham, Yi-Yu Chou, Blake E. Dewey +3
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images. However, a continu…
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
Can Zhao, Aaron Carass, Blake E. Dewey +1
High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise.…