Keywords: AI/ML Image Reconstruction, Image Reconstruction
Motivation: MRI reconstruction remains unreliable due to high variance in quality, limiting its use in clinical diagnostics. Our research aims to reduce this variability, enhancing reliability for medical applications.
Goal(s): Develop a method to reduce reconstruction variance while improve their quality.
Approach: We applied Sequential Monte Carlo with a diffusion model prior, integrating a twisting function to reduce reconstruction variance.
Results: Our method successfully reduces MRI reconstruction variance, consistently achieving higher PSNR, SSIM, and LPIPS scores than baseline methods.
Impact: This method enhances MRI reconstruction consistency, increasing reliability for clinical use and establishing a foundation for broader adoption in diagnostic imaging and medical diagnostics.
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