A Comparative Evaluation of 7T MRI for Epilepsy with Deep Learning–Based Image Reconstruction and Dynamic Parallel Transmission

OBJECTIVES: 7T MRI enhances lesion detection in epilepsy but is limited by radiofrequency transmission field (B1+) inhomogeneity and long scan times. Recent advancements in dynamic parallel transmission and deep-learning–based reconstructions offer promising solutions. We aimed to optimize an enhanced 7T epilepsy protocol incorporating these innovations and evaluate real-world benefits compared to standard 7T epilepsy protocol. MATERIALS AND METHODS: We retrospectively compared 40 consecutive brain MRIs acquired using a standard 7T epilepsy protocol to 40 MRIs obtained with an enhanced protocol with dynamic parallel transmission and deep-learning-based k-space reconstructions. Quantitative metrics for comparison included image noise, signal homogeneity (coefficient of variation), and resolution/time trade-offs. RESULTS: The enhanced protocol demonstrated significant improvements in resolution, scan time, noise levels, and image homogeneity. Edge-enhancing gradient echo and magnetization-prepared rapid gradient echo with 2 inversions sequence exhibited a 57.8% reduction in voxel volume while reducing scan time by 33.0% and improving image homogeneity (P=.002) without a significant change in noise (P=0.09). Deep-learning-based reconstruction of coronal T2 turbo spin echo resulted in a 25.7% reduction in noise (P