Pushing the limits of low‐cost ultra‐low‐field MRI by dual‐acquisition deep learning 3D superresolution

Abstract

PurposeRecent development of ultra‐low‐field (ULF) MRI presents opportunities for low‐power, shielding‐free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large‐scale publicly available 3T brain data.MethodsA dual‐acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross‐scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T1‐weighted and T2‐weighted imaging were trained with 3D ULF image data sets synthesized from the high‐resolution 3T brain data from the Human Connectome Project. They were applied to 0.055T brain MRI with two repetitions and isotropic 3‐mm acquisition resolution in healthy volunteers, young and old, as well as patients.ResultsThe proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5‐mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI.ConclusionThe proposed dual‐acquisition 3D superresolution approach advances ULF MRI for quality brain imaging through deep learning of high‐field brain data. Such strategy can empower ULF MRI for low‐cost brain imaging, especially in point‐of‐care scenarios or/and in low‐income and mid‐income countries.