Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction

2026-01-19

arXiv.org

AI Summary

This work addresses the significant degradation in MRI reconstruction quality at high acceleration factors and the absence of mechanisms to assess diagnostic reliability without fully sampled reference images. The authors propose a general framework that integrates conformal quantile regression with an end-to-end variational network to deliver statistically rigorous, pixel-wise uncertainty estimates and visualize unreliable regions. This approach achieves, for the first time, reference-free, pixel-level uncertainty quantification, enabling adaptive acquisition protocol design. Evaluated on fastMRI brain and knee datasets, the predicted uncertainties exhibit Pearson correlation coefficients exceeding 90% with actual reconstruction errors at 4× and higher acceleration rates, substantially outperforming heuristic methods based on residual magnitude.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

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• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Parallel imaging techniques reduce magnetic resonance imaging (MRI) scan time but image quality degrades as the acceleration factor increases. In clinical practice, conservative acceleration factors are chosen because no mechanism exists to automatically assess the diagnostic quality of undersampled reconstructions. This work introduces a general framework for pixel-wise uncertainty quantification in parallel MRI reconstructions, enabling automatic identification of unreliable regions without access to any ground-truth reference image. Our method integrates conformal quantile regression with image reconstruction methods to estimate statistically rigorous pixelwise uncertainty intervals. We trained and evaluated our model on Cartesian undersampled brain and knee data obtained from the fastMRI dataset using acceleration factors ranging from 2 to 10. An end-to-end Variational Network was used for image reconstruction. Quantitative experiments demonstrate strong agreement between predicted uncertainty maps and true reconstruction error. Using our method, the corresponding Pearson correlation coefficient was higher than 90% at acceleration levels at and above four-fold; whereas it dropped to less than 70% when the uncertainty was computed using a simpler a heuristic notion (magnitude of the residual). Qualitative examples further show the uncertainty maps based on quantile regression capture the magnitude and spatial distribution of reconstruction errors across acceleration factors, with regions of elevated uncertainty aligning with pathologies and artifacts. The proposed framework enables evaluation of reconstruction quality without access to fully-sampled ground-truth reference images. It represents a step toward adaptive MRI acquisition protocols that may be able to dynamically balance scan time and diagnostic reliability.

Problem

• accelerated MRI reconstruction
• uncertainty quantification
• parallel imaging
• diagnostic quality assessment
• undersampled MRI

Innovation

• uncertainty quantification
• accelerated MRI
• conformal quantile regression
• pixel-wise uncertainty
• parallel imaging