Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction

TL;DR

This paper presents a novel pixel-wise uncertainty quantification framework for accelerated MRI reconstruction, enabling automatic identification of unreliable regions without ground-truth images, thus aiding adaptive scan protocols.

Contribution

It introduces a conformal quantile regression-based method integrated with MRI reconstruction to estimate statistically rigorous pixel-wise uncertainty intervals.

Findings

Uncertainty maps strongly correlate with true reconstruction errors, with Pearson correlation over 90% at high acceleration.

The method accurately captures error distribution and pathology regions in MRI images.

Quantitative results outperform heuristic uncertainty measures in reliability assessment.

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 pixel-wise 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.