Uncertainty Quantification in Deep MRI Reconstruction

Understand

Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks.

• However, undersampling during MRI acquisition as well as the overparameterized and non-transparent nature of deep learning (DL) leaves substantial uncertainty about the accuracy of DL reconstruction.
• With this in mind, this study aims to quantify the uncertainty in image recovery with DL models.
• To this end, we first leverage variational autoencoders (VAEs) to develop a probabilistic reconstruction scheme that maps out (low-quality) short scans with aliasing artifacts to the diagnostic-quality ones.