EVOLVE: Learning volume-adaptive phases for fast 3D magnetic resonance scan and image reconstruction

Abstract

Compared with 2D Magnetic Resonance Imaging (MRI), 3D MRI is more powerful for generating high resolution images and visualizing small anatomical structures. However, 3D MRI acquisition is much more time-consuming due to the significantly larger number of phase encoding steps, which is directly proportional to the acquisition time. This paper proposes to select a volume-adaptive small subset of phases to accelerate 3D MRI scans and accurately reconstruct 3D images from the corresponding undersampled 3D k-space data. To avoid the delays caused by computationally expensive yet high-performance volume-adaptive phase selection, we propose a strategy of selecting multiple phases based on sampled slices from the volume during idle time within the repetition time (TR). To enhance the performance of phase selection, we propose a novel three-directional cross-attention phase selection network. Additionally, to improve the reconstruction performance, we introduce a three-directional slice-wise volume reconstruction. To the best of our knowledge, the proposed method, which we called EVOLVE (learning volume-adaptive phases), is the first work that learns volume-adaptive phases for fast 3D MRI. The extensive experimental results on a large-scale 3D MRI dataset at various acceleration factors demonstrate the substantial performance improvement in terms of image reconstruction achieved by using the EVOLVE method for phase selection compared to traditional learning free 3D MRI phase selection methods.