Segmentation-Guided Diffusion for Free-Breathing Cardiac Magnetic Resonance Image Restoration

Cardiac magnetic resonance imaging (CMR) is considered the gold standard for assessing cardiac function. However, acquiring high-quality images typically requires patients to hold their breath during scanning. Free-breathing (FB) CMR serves as an alternative for patients who cannot hold their breath; however, it often introduces motion artifacts, degrading image quality and potentially affecting diagnostic accuracy. Although deep generative models have shown promise in correcting motion artifacts, ensuring confidence in the fidelity of reconstructed artifact-free images remains a significant concern. This study quantifies the impact of segmentation masks as guidance in diffusion models to enhance anatomical structure preservation during image-conditioned generation. To that end, a standard diffusion probabilistic model (DDPM) and a segmentation-guided DDPM are trained and evaluated on a public CMR dataset and further applied to restore FB CMR using local hospital data. Quantitative evaluations using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) over the left and right ventricular regions demonstrate that the segmentation-guided approach produces higher-quality CMR and more accurately preserves anatomical structures compared to the standard DDPM.