CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI

2026-08-09

AI Summary

This work addresses the degradation in T2* quantification accuracy in accelerated MRI caused by artifacts and noise from undersampled reconstruction, compounded by the lack of effective modeling of uncertainty propagation to downstream fitting. The study introduces the first explicit covariance-aware uncertainty propagation framework from reconstruction to T2* fitting in accelerated T2* mapping. It estimates voxel-wise multi-echo reconstruction uncertainty via Monte Carlo Dropout, propagates this uncertainty to T2* fitting using a covariance-aware sampling strategy, and aligns predicted variances with reconstruction uncertainties through a heteroscedastic MLP and a correlation-based regularization term. Experiments demonstrate that the proposed method significantly improves T2* fitting performance in white matter under high acceleration in brain MRI, enhances uncertainty consistency, and yields interpretable voxel-level uncertainty maps.

Abstract

Quantitative T2* maps have strong potential for biomarker discovery but are limited by long scan times, rendering them impractical in clinical settings. Significant acceleration can be achieved through undersampling in k-space combined with learning-based reconstruction. However, reconstruction artifacts and noise can propagate into downstream T2* fitting, degrading its accuracy. We introduce CUPA-T2*, a framework that explicitly propagates voxel-wise inter-echo uncertainty from stochastic Monte Carlo dropout reconstructions to downstream T2* fitting via covariance-aware sampling. T2* fitting is performed with a heteroscedastic MLP and a correlation-based regularizer that encourages alignment between predicted variance and reconstruction uncertainty. Experiments on accelerated brain MRI data show tissue-dependent behavior: CUPA-T2* achieves competitive overall T2* fitting performance and improves white-matter performance at higher accelerations. Compared with a heteroscedastic baseline, the proposed framework substantially increases alignment between reconstruction uncertainty and predicted T2* variance, while also revealing a trade-off with calibration (ECE) and selective prediction performance (AURC). CUPA-T2* enables reconstruction uncertainty-aware T2* fitting and delivers voxel-wise uncertainty maps to support the interpretation of quantitative T2* estimates.

Problem

• T2* mapping
• accelerated MRI
• reconstruction artifacts
• uncertainty propagation
• quantitative MRI

Innovation

• uncertainty propagation
• covariance-aware sampling
• heteroscedastic MLP
• T2* mapping
• accelerated MRI