Feasibility of Brain Imaging Using a Digital Surround Technology Body Coil: A Study Based on SRGAN-VGG Convolutional Neural Networks

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 3734 - 3737
• Main Authors: Liu, Ya-Wen, Niu, Hai-Jun, Yin, Hong-Xia, Xia, Jing-Jing, Ren, Peng-Ling, Zhang, Ting-Ting, Li, Jing, Lv, Han, Ding, He-Yu, Ren, Jian-Liang, Wang, Zhen-Chang
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Brain - diagnostic imaging; Coils; Feasibility Studies; Head; Humans; Image Processing, Computer-Assisted; Image quality; Magnetic resonance imaging; Neural Networks, Computer; Neuroimaging; PSNR; Statistical analysis; Technology
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630816

Brain imaging using conventional head coils presents several problems in routine magnetic resonance (MR) examination, such as anxiety and claustrophobic reactions during scanning with a head coil, photon attenuation caused by the MRI head coil in positron emission tomography (PET)/MRI, and coil constraints in intraoperative MRI or MRI-guided radiotherapy. In this paper, we propose a super resolution generative adversarial (SRGAN-VGG) network-based approach to enhance low-quality brain images scanned with body coils. Two types of T1 fluid-attenuated inversion recovery (FLAIR) images scanned with different coils were obtained in this study: joint images of the head-neck coil and digital surround technology body coil (H+B images) and body coil images (B images). The deep learning (DL) model was trained using images acquired from 36 subjects and tested in 4 subjects. Both quantitative and qualitative image quality assessment methods were performed during evaluation. Wilcoxon signed-rank tests were used for statistical analysis. Quantitative image quality assessment showed an improved structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) in gray matter and cerebrospinal fluid (CSF) tissues for DL images compared with B images (P <.01), while the mean square error (MSE) was significantly decreased (P <.05). The analysis also showed that the natural image quality evaluator (NIQE) and blind image quality index (BIQI) were significantly lower for DL images than for B images (P <.0001). Qualitative scoring results indicated that DL images showed an improved SNR, image contrast and sharpness (P<.0001). The outcomes of this study preliminarily indicate that body coils can be used in brain imaging, making it possible to expand the application of MR-based brain imaging.