  Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in
widespread use in low- and middle-income countries (LMICs) and are commonly
used for some applications in higher income countries e.g. for small child
patients with obesity, claustrophobia, implants, or tattoos. However, low-field
MR images commonly have lower resolution and poorer contrast than images from
high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT)
to enhance low-field structural MRI by estimating from a low-field image the
image we would have obtained from the same subject at high field. Our approach
uses (i) a stochastic low-field image simulator as the forward model to capture
uncertainty and variation in the contrast of low-field images corresponding to
a particular high-field image, and (ii) an anisotropic U-Net variant
specifically designed for the IQT inverse problem. We evaluate the proposed
algorithm both in simulation and using multi-contrast (T1-weighted,
T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical
low-field MRI data from an LMIC hospital. We show the efficacy of IQT in
improving contrast and resolution of low-field MR images. We demonstrate that
IQT-enhanced images have potential for enhancing visualisation of anatomical
structures and pathological lesions of clinical relevance from the perspective
of radiologists. IQT is proved to have capability of boosting the diagnostic
value of low-field MRI, especially in low-resource settings.
