Rigorous Uncertainty Estimation for MRI Reconstruction

作者：Ke Wang, Anastasios N. Angelopoulos, Alfredo De Goyeneche, Amit Kohli, Efrat Shimron, Stella X. Yu, Jitendra Malik, Michael Lustig · 发表于：Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 年份：2023 · DOI：10.58530/2022/0749 · 被引用次数：1 · 研究领域：Medical Imaging Techniques and Applications、Advanced X-ray and CT Imaging、Radiomics and Machine Learning in Medical Imaging

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.