T1 Weighted
T1-weighted magnetic resonance imaging (T1W MRI) provides crucial information about tissue properties, primarily based on the longitudinal relaxation time (T1). Current research focuses on improving T1W MRI acquisition and analysis through techniques like motion correction (using model-based and deep learning approaches such as diffusion probabilistic models and convolutional neural networks), and generating synthetic T1-weighted images from other modalities to reduce the need for contrast agents. These advancements enhance the accuracy and efficiency of T1W MRI for various applications, including diagnosing diseases like endometriosis and gliomas, and improving cardiac and brain mapping. The resulting quantitative T1 maps offer objective biomarkers for improved disease characterization and monitoring.
Papers
Human-AI Collaborative Multi-modal Multi-rater Learning for Endometriosis Diagnosis
Hu Wang, David Butler, Yuan Zhang, Jodie Avery, Steven Knox, Congbo Ma, Louise Hull, Gustavo Carneiro
T1-contrast Enhanced MRI Generation from Multi-parametric MRI for Glioma Patients with Latent Tumor Conditioning
Zach Eidex, Mojtaba Safari, Richard L.J. Qiu, David S. Yu, Hui-Kuo Shu, Hui Mao, Xiaofeng Yang