5D Networks for Physics-Guided Deep Learning Reconstruction of 3D Non-Cartesian MRI from Limited Training Data

Although recent studies enabled physics-guided deep learning (PG-DL) reconstruction of 3D non-Cartesian MRI, it suffers from blurring, partially due to limited training data. In this study we propose 2.5D PG-DL using three 2D CNNs on orthogonal views for 3D reconstruction to efficiently exploit the limited training data. Results on 3D kooshball coronary MRI show the proposed strategy noticeably improves image sharpness.