Data-driven Image Reconstruction for Ultra-low-field Knee and Spine MRI at 0.05T

Motivation: Deep learning (DL) is a powerful tool for MR image formation tasks and MR data at ultra-low-field (ULF) strength has significantly lower SNR than high-field. Goal(s): Enhancing the image quality of ULF knee and c-spine data at 0.05T via DL reconstruction. Approach: We extend our recently developed 3D DL partial Fourier reconstruction and superresolution (PF-SR) method on PF-sampled low-resolution noisy brain data to knee and c-spine data. Results: The preliminary results demonstrate PF-SR, trained on synthetic ULF data simulated from high-field data, can reduce noise and artifacts, and enhance spatial resolution in experimental ULF knee and c-spine data, acquired from 0.05T MRI platform. Impact: Through leveraging the homogeneous human knee and spine anatomy available in high-field data to enhance the image quality of ultra-low-field knee and spine MRI at 0.05T via deep learning reconstruction in a low-cost and shielding-free 0.05T MRI platform.