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.

翻译：并行成像技术可缩短磁共振成像扫描时间，但随着加速因子的增加，图像质量会下降。在临床实践中，由于缺乏自动评估欠采样重建图像诊断质量的机制，通常选择保守的加速因子。本研究提出了一种并行磁共振成像重建中像素级不确定性量化的通用框架，能够在无需任何真实参考图像的情况下自动识别不可靠区域。该方法将保形分位数回归与图像重建方法相结合，以估计统计上严格的像素级不确定性区间。我们使用fastMRI数据集中获得的笛卡尔欠采样脑部和膝盖数据（加速因子范围为2至10）对模型进行训练和评估，并采用端到端变分网络进行图像重建。定量实验表明，预测的不确定性图与真实重建误差高度一致。使用本方法时，在四倍及以上加速水平下，相应的皮尔逊相关系数高于90%；而采用更简单的启发式方法（残差幅值）计算不确定性时，该系数降至70%以下。定性分析进一步表明，基于分位数回归的不确定性图能够捕捉不同加速因子下重建误差的幅值和空间分布，其中高不确定性区域与病理特征及伪影高度吻合。所提出的框架可在无需全采样真实参考图像的情况下评估重建质量，为实现动态平衡扫描时间与诊断可靠性的自适应磁共振成像采集协议迈出了重要一步。