Improving magnetic resonance resolution with supervised learning

Despite ongoing improvements in magnetic resonance (MR) imaging (MRI), considerable clinical and, to a lesser extent, research data is acquired at lower resolutions. For example 1 mm isotropic acquisition of T₁-weighted (T₁-w) Magnetization Prepared Rapid Gradient Echo (MPRAGE) is standard practice, however T₂-weighted (T₂-w) - because of its longer relaxation times (and thus longer scan time) - is still routinely acquired with slice thicknesses of 2-5 mm and in-plane resolution of 2-3 mm. This creates obvious fundamental problems when trying to process T₁-w and T₂-w data in concert. We present an automated supervised learning algorithm to generate high resolution data. The framework is similar to the brain hallucination work of Rousseau, taking advantage of new developments in regression based image reconstruction. We present validation on phantom and real data, demonstrating the improvement over state-of-the-art super-resolution techniques.