Guiding Quantitative MRI Reconstruction with Phase-wise Uncertainty
Journal:
arXiv
Published Date:
Feb 28, 2025
Abstract
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/.
