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Feasibility of Brain Imaging Using a Digital Surround Technology Body Coil: A Study Based on SRGAN-VGG Convolutional Neural Networks.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 3734-3737, 2021 11.
Article em En | MEDLINE | ID: mdl-34892048
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Annu Int Conf IEEE Eng Med Biol Soc Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo Tipo de estudo: Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Annu Int Conf IEEE Eng Med Biol Soc Ano de publicação: 2021 Tipo de documento: Article