Dynamic breast MRI with Flexible Temporal Resolution Aided by Deep Learning

MRI is the most effective method for screening high-risk breast cancer patients. While current exams rely on the qualitative evaluation of morphological features before and after contrast administration and less on contrast kinetic information, recent developments in fast acquisition methods aim to combine both. However, balancing spatial resolution, temporal resolution and scan time poses a considerable challenge in dynamic MRI. Here, we introduce a radial MRI reconstruction framework for Dynamic Contrast Enhanced (DCE) imaging, termed Enhanced Locally low-rank Imaging for Tissue contrast Enhancement (ELITE), to address these limitations. ELITE combines locally low-rank subspace modeling to capture spatially localized tissue dynamics with deep learning. We evaluate its effectiveness using the publicly available fastMRI breast initiative, demonstrating substantial improvements in CNR and noise reduction while enabling flexible temporal resolution down to 1 second. ELITE also shows benefits in neck and brain imaging, making it a viable alternative for other DCE-MRI applications. Dynamic Contrast Enhanced (DCE) MRI allows highly sensitive cancer screening, but balancing between temporal and spatial resolution poses a challenge in dynamic DCE-MRI. Here, the authors develop ELITE, an image reconstruction framework powered by AI, to overcome such limitations and enhance breast, head and neck, and brain DCE-MRI screening with high spatial and temporal fidelity.