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Abstract #2632

Consistent MRI Reconstruction with Diffusion Models and Sequential Monte Carlo

Wei Jiang1, Wenjia Song1, Yang Gao2, Nan Ye1, Feng Liu1, and Hongfu Sun1
1The University of Queensland, Brisbane, Australia, 2Central South University, Changsha, China

Synopsis

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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Keywords