Quantitative MRI

Quantitative MRI (qMRI) aims to extract precise, tissue-specific physical parameters from MRI scans, offering richer diagnostic information than traditional weighted images. Current research heavily emphasizes developing efficient and accurate reconstruction methods, often employing deep learning architectures like U-Nets, diffusion probabilistic models, and physics-informed neural networks, to overcome challenges posed by lengthy scan times and noisy data. These advancements are improving the accuracy and speed of qMRI parameter estimation, particularly for applications like brain and liver tissue characterization, leading to more reliable and clinically useful diagnostic tools. Furthermore, research is focusing on incorporating uncertainty quantification and task-specific experimental design to enhance the reliability and clinical impact of qMRI.