Self-Supervised Denoising Model for 7T MR Angiography with Z-Directional Artery Focused DDPM

Motivation: 7T MRA imaging can offer improved SNR but still poses challenges in diagnosing small vessel abnormalities. Goal(s): To develop a self-supervised deep learning approach that effectively reduces noise while preserving vascular details in 7T MRA images. Approach: Utilizing independent noise characteristics between adjacent slices, the method creates high-SNR datasets through slice averaging and trains a conditional DDPM with surrounding slice information. Results: The proposed method outperforms conventional approaches like BM3D and DnCNN, by effectively reducing noise while preserving fine vascular details and enhancing vessel visibility, even revealing previously undetectable structures. Impact: This study advances 7T MRA imaging by providing a ground truth-free denoising method, potentially improving the diagnosis accuracy of cerebrovascular diseases and enhancing the clinical utility of 7T MRI in vascular imaging applications.