2 citations · 5 across the 6 of their papers we have counts for
6 papers
SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Min Li, Chen Chen, Zhenghao Li +9
Quantitative susceptibility mapping (QSM) provides a valuable tool for quantifying susceptibility distributions in human brains; however, two types of opposing susceptibility sourc…
IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules
Min Li, Chen Chen, Zhuang Xiong +6
Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potentia…
Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems
Hossein Askari, Fred Roosta, Hongfu Sun
In the realm of medical imaging, inverse problems aim to infer high-quality images from incomplete, noisy measurements, with the objective of minimizing expenses and risks to patie…
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
Zhuang Xiong, Wei Jiang, Yang Gao +2
Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While super…
Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)
Zhuang Xiong, Yang Gao, Yin Liu +4
The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan paramet…
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network
Yu Ren, Guoli Wang, Pingping Wang +5
Background and Objective: Bladder cancer is a common malignant urinary carcinoma, with muscle-invasive and non-muscle-invasive as its two major subtypes. This paper aims to achieve…