Deep Learning Image Reconstruction for Enhancing Image Quality and Acquisition Speed in 3T EDGE-MP2RAGE

BACKGROUND AND PURPOSE: The three-dimensional edge-enhancing gradient echo (3D EDGE) MRI sequence is valuable in epilepsy imaging due to improved visualization of cortical lesions by enhancing the gray matter-white matter boundary contrast. However, EDGE suffers from low SNR and long acquisition times at 3T. This study evaluated a 3T deep learning (DL)-based reconstruction implemented in the EDGE-MP2RAGE sequence and compared its image quality with standard 3T and ultra-high-field 7T EDGE-MP2RAGE MRI. MATERIALS AND METHODS: In this retrospective study, 117 patients underwent 3T DL, standard 3T, or 7T EDGE-MP2RAGE MRI (n=39 per group). The EDGE images underwent quantitative and qualitative comparison of image quality. Quantitative analysis included contrast-to-noise ratio (CNR) and image noise. Qualitative image quality was independently and blindly scored by two neuroradiologists using a Likert scale. Statistical comparisons used Mann-Whitney U tests with Bonferroni correction. RESULTS: <.001). CONCLUSIONS: DL-based reconstruction improves image quality in 3T EDGE-MP2RAGE while simultaneously decreasing scan time by nearly half versus conventional 3T EDGE-MP2RAGE.