Transformer residual cross (T-REX) networks for volumetric super-resolution.

Motivation: Higher temporal resolution is needed for many MRI-guidance applications. Reducing matrix sizes can increase temporal resolution at the cost of lower spatial resolution. Deep learning-based super-resolution could mitigate the trade-off in spatiotemporal resolution. Goal(s): Develop and evaluate a unified deep learning-based algorithm that can up-sample thick single-slice low spatial resolution MRI to a thin multi-slice high spatial resolution MRI. Approach: Developed a transformer residual cross (T-REX) neural network that simultaneously increased the spatial resolution and decreased the slice thickness providing high spatial resolution multi-slice MRI. Results: T-REX was successfully trained and evaluated showing promising results for a variety of field strengths and sequences. Impact: The ability to acquire high spatial resolution volumetric MRI quicky has applications to low-field MRI and MRI-guided radiation therapy. Here, we present our initial findings of a unified neural network that applies volumetric super-resolution.