PT  - JOURNAL ARTICLE
AU  - Liu, Zeyu
AU  - Zhou, Xiangzhi
AU  - Tao, Shengzhen
AU  - Ma, Jun
AU  - Nickel, Dominik
AU  - Liebig, Patrick
AU  - Mostapha, Mahmoud
AU  - Patel, Vishal
AU  - Westerhold, Erin M.
AU  - Mojahed, Hamed
AU  - Gupta, Vivek
AU  - Middlebrooks, Erik H.
TI  - Application of Deep Learning Accelerated Image Reconstruction in T2-Weighted Turbo Spin-Echo Imaging of the Brain at 7T
AID  - 10.3174/ajnr.A8662
DP  - 2025 Jul 01
TA  - American Journal of Neuroradiology
PG  - 1517--1520
VI  - 46
IP  - 7
4099  - http://www.ajnr.org/content/46/7/1517.short
4100  - http://www.ajnr.org/content/46/7/1517.full
SO  - Am. J. Neuroradiol.2025 Jul 01; 46
AB  - SUMMARY: 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 by 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 by 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 < .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.CNRcontrast-to-noise ratioDLdeep learningGRAPPAgeneralized autocalibrating partially parallel acquisitionsMNIMontreal Neurological InstituteSARspecific absorption rate