Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
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.
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