activity
20172026
most citedLearning MRI Artifact Removal With Unpaired Data

50 citations · 137 across the 28 of their papers we have counts for

collaborators

38 papers

eess.IV2026

RelA-Diffusion: Relativistic Adversarial Diffusion for Multi-Tracer PET Synthesis from Multi-Sequence MRI

Minhui Yu, Yongheng Sun, David S. Lalush +3

Multi-tracer positron emission tomography (PET) provides critical insights into diverse neuropathological processes such as tau accumulation, neuroinflammation, and -amyloid dep…

eess.IV2024

Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion

Mengqi Wu, Minhui Yu, Shuaiming Jing +3

Multi-site structural MRI is increasingly used in neuroimaging studies to diversify subject cohorts. However, combining MR images acquired from various sites/centers may introduce…

q-bio.NC2024★ 4 cited

Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model

Danilo Bernardo, Xihe Xie, Parul Verma +8

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unkno…

eess.IV2024

Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework

Mengqi Wu, Lintao Zhang, Pew-Thian Yap +2

Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological…

eess.IV2023

Reconstruction of Cortical Surfaces with Spherical Topology from Infant Brain MRI via Recurrent Deformation Learning

Xiaoyang Chen, Junjie Zhao, Siyuan Liu +2

Cortical surface reconstruction (CSR) from MRI is key to investigating brain structure and function. While recent deep learning approaches have significantly improved the speed of…

eess.IV2023

Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization

Yilin Liu, Yunkui Pang, Jiang Li +2

Untrained networks inspired by deep image priors have shown promising capabilities in recovering high-quality images from noisy or partial measurements without requiring training s…