SRflow: Deep learning based super-resolution of 4D-flow MRI data - Shit, Suprosanna; Zimmermann, Judith; Ezhov, Ivan; Paetzold, Johannes C.; Sanches, Augusto F.; Pirkl, Carolin; Menze, Bjoern H. (Frontiers in Artificial Intelligence, 2022)
Exploiting 4D-flow MRI data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A novel, direction-sensitive, and robust loss function is crucial to learning vector-field data. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Our method improves the peak-velocity to noise ratio of the flow field by 10% and 30% for invivo cardiovascular and cerebrovascular data, respectively, for 4x super-resolution over the state-of-the-art cubic B-spline. Significantly, our method offers 10x faster inference over the cubic B-spline. The proposed approach for super-resolution of 4D-flow data would potentially improve the subsequent calculation of hemodynamic quantities.