MIP-Enhanced Uncertainty-Aware Network for Fast 7T Time-of-Flight MRA Reconstruction

Abstract

Time-of-flight (TOF) magnetic resonance angiography (MRA) is the dominant non-contrast MR imaging method for visualizing intracranial vascular system. The employment of 7T MRI for TOF-MRA is of great interest due to its outstanding spatial resolution and vessel-tissue contrast. However, high-resolution 7T TOF-MRA is undesirably slow to acquire. Besides, due to complicated and thin structures of brain vessels, reliability of reconstructed vessels is of great importance. In this work, we propose an uncertainty-aware reconstruction model for accelerated 7T TOF-MRA, which combines the merits of deep unrolling and evidential deep learning, such that our model not only provides promising MRI reconstruction, but also supports uncertainty quantification within a single inference. Moreover, we propose a maximum intensity projection (MIP) loss for TOF-MRA reconstruction to improve the quality of MIP images. In the experiments, we have evaluated our model on a relatively large in-house multi-coil 7T TOF-MRA dataset extensively, showing promising superiority of our model compared to state-of-the-art models in terms of both TOF-MRA reconstruction and uncertainty quantification.