MRI data consistency guided conditional diffusion probabilistic model for MR imaging acceleration

Mojtaba Safari; Xiaofeng Yang; Ali Fatemi

doi:10.1117/12.3002863

2 April 2024 MRI data consistency guided conditional diffusion probabilistic model for MR imaging acceleration

Mojtaba Safari, Xiaofeng Yang, Ali Fatemi

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 129300R (2024) https://doi.org/10.1117/12.3002863
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The long acquisition time required for high-resolution Magnetic Resonance Imaging (MRI) leads to patient discomfort, increased likelihood of voluntary and involuntary movements, and reduced throughput in imaging centers. This study proposed a novel method that leverages MRI physics to incorporate data consistency during the training of a conditional diffusion probabilistic model, which we refer to as the data consistency-guided conditional diffusion probabilistic model (DC-CDPM). This model aimed to reconstruct high-resolution contrast enhanced T1W MRI from partially sampled data. The DC-CDPM utilized the conjugate gradient optimization method to minimize data consistency loss between reconstructed MRI images and fully sampled unknown MRI images. Further, a diffusion probabilistic model conditioned on the optimization’s output was trained to reconstruct the fully sampled MRI. The publicly available dataset of 230 post-surgery patients with different brain tumors was used in this study to train the model. The equidistant under-sampling method was implemented to simulate four different under-sampling levels. The qualitative and quantitative comparisons were done between DC-CDPM and an exactly similar CDPM model except not conditioned on the optimization output. Qualitatively, the DC-CDPM could reconstruct fully sampled images compared with CDPM. Furthermore, the image profile along a tumor indicated better performance of DC-CDPM. Quantitatively, the DC-CDPM outperformed CDPM in four out of six quantitative metrics and had a consistent performance throughout the different under-sampling levels. Our method could allow us to perform brain imaging with substantially lower acquisition time while achieving similar image quality of fully sampled MRI images with a long acquisition time.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mojtaba Safari, Xiaofeng Yang, and Ali Fatemi "MRI data consistency guided conditional diffusion probabilistic model for MR imaging acceleration", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129300R (2 April 2024); https://doi.org/10.1117/12.3002863

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