Application of Deep Learning Accelerated Image Reconstruction in T2-weighted Turbo Spin Echo Imaging of the Brain at 7T

ABSTRACT

Prolonged imaging times and motion sensitivity at 7T necessitate advancements in image acceleration techniques. This study evaluates a 7T deep-learning (DL)-based image reconstruction using a deep neural network trained on 7T data, applied to T2-weighted turbo spin echo imaging. Raw k-space data from 30 consecutive clinical 7T brain MRI patients was reconstructed using both DL and standard methods. Qualitative assessments included overall image quality, artifacts, sharpness, structural conspicuity, and noise level, while quantitative metrics evaluated contrast-to-noise ratio (CNR) and image noise. DL-based reconstruction consistently outperformed standard methods across all qualitative metrics (p<0.001), with a mean CNR increase of 50.8% [95% CI: 43.0–58.6%] and a mean noise reduction of 35.1% [95% CI: 32.7–37.6%]. These findings demonstrate that DL-based reconstruction at 7T significantly enhances image quality without introducing adverse effects, offering a promising tool for addressing the challenges of ultra-high-field MRI.

ABBREVIATIONS: CNR = contrast-to-noise ratio; DL = deep learning; GRAPPA = GeneRalized Autocalibrating Partially Parallel Acquisitions; IQR = interquartile range; MNI = Montreal Neurological Institute; SD = standard deviation