  The recent introduction of portable, low-field MRI (LF-MRI) into the clinical
setting has the potential to transform neuroimaging. However, LF-MRI is limited
by lower resolution and signal-to-noise ratio, leading to incomplete
characterization of brain regions. To address this challenge, recent advances
in machine learning facilitate the synthesis of higher resolution images
derived from one or multiple lower resolution scans. Here, we report the
extension of a machine learning super-resolution (SR) algorithm to synthesize 1
mm isotropic MPRAGE-like scans from LF-MRI T1-weighted and T2-weighted
sequences. Our initial results on a paired dataset of LF and high-field (HF,
1.5T-3T) clinical scans show that: (i) application of available automated
segmentation tools directly to LF-MRI images falters; but (ii) segmentation
tools succeed when applied to SR images with high correlation to gold standard
measurements from HF-MRI (e.g., r = 0.85 for hippocampal volume, r = 0.84 for
the thalamus, r = 0.92 for the whole cerebrum). This work demonstrates
proof-of-principle post-processing image enhancement from lower resolution
LF-MRI sequences. These results lay the foundation for future work to enhance
the detection of normal and abnormal image findings at LF and ultimately
improve the diagnostic performance of LF-MRI. Our tools are publicly available
on FreeSurfer (surfer.nmr.mgh.harvard.edu/).
