Towards performant and reliable undersampled MR reconstruction via diffusion model sampling

• URL: http://arxiv.org/abs/2203.04292v1
• Date: Tue, 8 Mar 2022 02:25:38 GMT
• Title: Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
• Authors: Cheng Peng, Pengfei Guo, S. Kevin Zhou, Vishal Patel, Rama Chellappa
• Abstract summary: DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
• Score: 67.73698021297022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time. To this end, current State-of-The-Art (SoTA) approaches leverage deep neural networks and supervised training to learn a recovery model. While these approaches achieve impressive performances, the learned model can be fragile on unseen degradation, e.g. when given a different acceleration factor. These methods are also generally deterministic and provide a single solution to an ill-posed problem; as such, it can be difficult for practitioners to understand the reliability of the reconstruction. We introduce DiffuseRecon, a novel diffusion model-based MR reconstruction method. DiffuseRecon guides the generation process based on the observed signals and a pre-trained diffusion model, and does not require additional training on specific acceleration factors. DiffuseRecon is stochastic in nature and generates results from a distribution of fully-sampled MR images; as such, it allows us to explicitly visualize different potential reconstruction solutions. Lastly, DiffuseRecon proposes an accelerated, coarse-to-fine Monte-Carlo sampling scheme to approximate the most likely reconstruction candidate. The proposed DiffuseRecon achieves SoTA performances reconstructing from raw acquisition signals in fastMRI and SKM-TEA.

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