Deep learning image formation for fast and high-resolution brain MRI at 0.055 tesla

Motivation: High, isotropic resolution (e.g., 1mm) is desirable for lesion detection and biomarkers extraction for cognitive disorders. However, ultra-low-field (ULF) MRI severely suffers from low spatial resolution and signal-to-noise ratio. Goal(s): To investigate the potential of 3D deep learning in generating <=1mm isotropic resolution results from 2D partial Fourier-sampled, low-resolution noisy brain images acquired from our custom-made 0.055T scanner. Approach: We advanced 3D deep learning partial Fourier reconstruction and super-resolution method (PF-SR) to achieve 3x/4x super-resolution factors. Results: Preliminary results indicate possibility of PF-SR with higher super-resolution factors on reconstructing experimental ULF T1w/T2w data to 1/0.75mm3 with reduced artefacts and noise. Impact: Enhancing image resolution and fidelity for fast ultra-low-field brain imaging at 0.055T using data-driven 3D deep learning approach to <=1mm3 resolution potentially enables image-guided therapies and valuable neuroimaging analysis for assessing aging and cognitive conditions.