Conditional diffusion-generated super-resolution for myocardial perfusion MRI

作者：Changyu Sun, Neha Goyal, Yu Wang, Darla L. Tharp, Senthil Kumar, Talissa A. Altes · 发表于：Frontiers in Cardiovascular Medicine · 年份：2025 · DOI：10.3389/fcvm.2025.1499593 · 被引用次数：5 · 研究领域：Advanced Neuroimaging Techniques and Applications、Advanced Image Processing Techniques、Cardiac Imaging and Diagnostics

Introduction Myocardial perfusion MRI is important for diagnosing coronary artery disease, but current clinical methods face challenges in balancing spatial resolution, temporal resolution, and slice coverage. Achieving broader slice coverage and higher temporal resolution is essential for accurately detecting abnormalities across different slice locations but remains difficult due to constraints in acquisition speed and heart rate variability. While techniques like parallel imaging and compressed sensing have significantly advanced perfusion imaging, they still suffer from noise amplification, residual artifacts, and potential temporal blurring due to the rapid transit of dynamic contrast vs. the temporal constraints of the reconstruction. Methods This study introduces a conditional diffusion-based generative model for myocardial perfusion MRI super resolution, addressing the trade-offs between spatiotemporal resolution and slice coverage. We adapted Denoising Diffusion Probabilistic Models (DDPM) to enhance low-resolution perfusion images into high-resolution outputs without requiring temporal regularization. The forward diffusion process introduces Gaussian noise incrementally, while the reverse process employs a U-Net architecture to progressively denoise the images, conditioned on the low-resolution input image. Results We trained and validated the model on a retrospective dataset of dynamic contrast-enhanced (DCE) perfusion MRI, consisting of both stress and rest images...