Accelerated high-resolution deep learning reconstructed turbo spin echo MRI of the knee at ultra-high field strength

Motivation: High-resolution 7T-MRI using Turbo-Spin-Echoes requires high acceleration factors for reasonable scan times. Deep-Learning (DL) algorithms enable increased data under-sampling compared to state-of-the-art reconstructions. Goal(s): To explore the feasibility of undersampled data acquisition in combination with DL-reconstruction for high-resolution T1- and PD-weighted knee MRI. Approach: Volunteers underwent twofold, threefold and fourfold-accelerated 7T knee MRI with and without DL image reconstruction. Three readers rated various aspects of image quality. Results: Image quality was rated significantly superior for fourfold-accelerated DL reconstructed images compared to images without DL reconstruction, while compared to twofold and threefold accelerated images, no image quality difference was observed. Impact: This study successfully employed DL reconstructions at ultra-high field strength with promising results regarding image quality compared to conventional image reconstruction. Therefore, DL reconstructions at fourfold acceleration allows an efficient reduction in acquisition time, while still delivering high-quality images.