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Abstract #0934

Accelerating Longitudinal MRI using Prior Informed Latent Posterior Sampling (PIPS)

Zachary Shah1, Yonatan Urman1, Ashwin Kumar2, Bruno P. Soares3, and Kawin Setsompop3
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Biomedical Physics, Stanford University, Stanford, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction

Motivation: Long scan times persist longitudinal MRI studies, yet lack good reconstruction priors for accelerated parallel imaging.

Goal(s): Substantially and scaleably reduce scantime by leveraging prior scans of the same patient in a data-consistent deep learning reconstruction.

Approach: Propose Prior Informed Posterior Sampling (PIPS) with latent diffusion. We train such a model on unlabeled image data, eliminating the need for a dataset of either k-space measurements or paired longitudinal scans required of other learning-based methods.

Results: Outperform baselines in effectively utilizing prior information without over-biasing prior consistency. Validated on an open-source dataset of healthy patients, as well as several longitudinal cases with clinical differentials.

Impact: We propose an unsupervised prior conditioning method to further accelerate MRI for longitudinal studies. Our method is both scalable and generalizable, as it does not require sequential k-space for training and enforces data consistency throughout the reconstruction.

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Keywords