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Abstract #0749

Rigorous Uncertainty Estimation for MRI Reconstruction

Ke Wang1,2, Anastasios Angelopoulos1, Alfredo De Goyeneche1, Amit Kohli1, Efrat Shimron1, Stella Yu1,2, Jitendra Malik1, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2International Computer Science Institute, University of California, Berkeley, Berkeley, CA, United States


Deep-learning (DL)-based MRI reconstructions have shown great potential to reduce scan time while maintaining diagnostic image quality. However, their adoption has been plagued with fears that the models will hallucinate or eliminate important anatomical features. To address this issue, we develop a framework to identify when and where a reconstruction model is producing potentially misleading results. Specifically, our framework produces confidence intervals at each pixel of a reconstruction image such that 95% of these intervals contain the true pixel value with high probability. In-vivo 2D knee and brain reconstruction results demonstrate the effectiveness of our proposed uncertainty estimation framework.

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