RT Journal Article
SR Electronic
T1 Application of Deep Learning Accelerated Image Reconstruction in T2-Weighted Turbo Spin-Echo Imaging of the Brain at 7T
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1517
OP 1520
DO 10.3174/ajnr.A8662
VO 46
IS 7
A1 Liu, Zeyu
A1 Zhou, Xiangzhi
A1 Tao, Shengzhen
A1 Ma, Jun
A1 Nickel, Dominik
A1 Liebig, Patrick
A1 Mostapha, Mahmoud
A1 Patel, Vishal
A1 Westerhold, Erin M.
A1 Mojahed, Hamed
A1 Gupta, Vivek
A1 Middlebrooks, Erik H.
YR 2025
UL http://www.ajnr.org/content/46/7/1517.abstract
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