Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence
Motivation: Increasing the SNR in MRI requires extended scan times, which are impractical in clinical. Beyond simple denoising, enhancing SNR by incorporating additional signal is essential.
Goal(s): To generate virtual single acquisition images that can simulate the effect of multiple acquisitions.
Approach: MIRAGE, a diffusion model-based algorithm, generates virtual multiple-NEX-like images by iteratively refining noise patterns and averaging outputs.
Results: MIRAGE-average images, reconstructed using MIRAGE-acquired images, demonstrated SNR comparable to actual multiple acquisition images.
Impact: MIRAGE offers a promising solution for efficient SNR enhancement, with potential applications across various configurations, including different anatomical regions, imaging protocols, field strengths, and x-nuclei imaging.
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