Advancing Ultralow-field Brain MRI through K-space Undersampling and Deep Learning Image Reconstruction

Motivation: While the emerging ULF MRI shows potential of low-cost and point-of-care imaging applications, its image quality is poor and the scan time is long. Goal(s): To reduce the ULF brain MRI scan time through deep learning image reconstruction from partial Fourier and uniformly undersampled data. Approach: We proposed a DL reconstruction method for fast 3D brain MRI at 0.055T by applying the 3D DL image reconstruction to undersampled 3D k-space data, achieving speed up of 2x over our newly developed partial Fourier reconstruction and superresolution (PF-SR) method. Results: Our preliminary results show the proposed method could reduce noise, artifacts, and enhance spatial resolution. Impact: Our model can work with uniformly undersampled data, leading to acceleration factor of 2, and a PF sampling of at a fraction of 0.7. Our development enables fast and quality whole-brain MRI at 0.055T, indicating potential for widespread biomedical applications.