Cardiac Magnetic Resonance-to-Computed Tomography Angiography image conversion using diffusion models for Transcatheter Aortic Valve Implantation planning

Transcatheter Aortic Valve Implantation (TAVI) has become the preferred method for treating severe aortic stenosis, especially in patients who are unsuitable for traditional surgery. Typically, preoperative imaging for TAVI involves contrast-enhanced Computed Tomography Angiography (CTA). However, for patients with contraindications to contrast agents, Cardiac Magnetic Resonance imaging (CMR) is a viable alternative, albeit with its limitations in visualizing calcifications. This study explores the application of diffusion models to enhance CMR-to-CTA contrast-free image conversion, to avoid the use of contrast agents and ionizing radiation. We developed a pipeline incorporating Denoising Diffusion Probabilistic Models (DDPMs) and Stochastic Differential Equations (SDE) models to synthesize CTA-equivalent images from CMR scans. We evaluated this approach using an in-house dataset consisting of 39 paired CTA and CMR scans. For the training process, coregistration of both modalities was required, which we achieved by performing rigid registration using the segmented aorta masks. Our results show that the synthesized CTA images maintain high fidelity to the actual scans. This is quantitatively supported by a mean Structural Similarity Index Measure (SSIM) of 0.8 and a Peak Signal-to-Noise Ratio (PSNR) of 22 dB using conditional Stochastic Differential Equations (SDE) and Prediction-Correction (PC), indicating strong structural preservation and low reconstruction error. However, the model occasionally fails to accurately detect valve calcifications, likely due to limitations in capturing subtle pathological details that are not visually discernible in CMR images. Diffusion models used to synthesize CTA images from CMR datasets achieve high accuracy, providing a contrast-free alternative for TAVI planning and potential insights into valvular calcification patterns. However, accurate visualization of valve calcification occasionally fails, and larger datasets are desirable for validation. • Diffusion models synthesize CTA-like images from contrast-free CMR scans. • We used Diffusion Probabilistic Models and Stochastic Differential Equations. • Achieved high image fidelity with SSIM of 0.8 and PSNR of 22 dB. • Enables safe TAVI planning without contrast or ionizing radiation. • Model struggles with accurate detection of valve calcifications.