Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty
This work addresses the challenge of accelerating scans in quantitative MRI for medical imaging applications, representing an incremental improvement by leveraging uncertainty in a new way.
The paper tackles the ill-posed inverse problem in quantitative MRI reconstruction by introducing PUQ, a novel approach that uses phase-wise uncertainty to guide reconstruction and parameter fitting, achieving state-of-the-art performance on T1 and T2 mapping datasets.
Quantitative magnetic resonance imaging (qMRI) requires multi-phase acqui-sition, often relying on reduced data sampling and reconstruction algorithms to accelerate scans, which inherently poses an ill-posed inverse problem. While many studies focus on measuring uncertainty during this process, few explore how to leverage it to enhance reconstruction performance. In this paper, we in-troduce PUQ, a novel approach that pioneers the use of uncertainty infor-mation for qMRI reconstruction. PUQ employs a two-stage reconstruction and parameter fitting framework, where phase-wise uncertainty is estimated during reconstruction and utilized in the fitting stage. This design allows uncertainty to reflect the reliability of different phases and guide information integration during parameter fitting. We evaluated PUQ on in vivo T1 and T2 mapping datasets from healthy subjects. Compared to existing qMRI reconstruction methods, PUQ achieved the state-of-the-art performance in parameter map-pings, demonstrating the effectiveness of uncertainty guidance. Our code is available at https://anonymous.4open.science/r/PUQ-75B2/.