Sato Hideyuki, Fujimoto Shinichiro, Tomizawa Nobuo, Inage Hidekazu, Yokota Takuya, Kudo Hikaru, Fan Ruiheng, Kawamoto Keiichi, Honda Yuri, Kobayashi Takayuki, Minamino Tohru, Kogure Yosuke
Department of Radiological Technology, Juntendo University Hospital, Tokyo, Japan.
Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Acad Radiol. 2023 Nov;30(11):2657-2665. doi: 10.1016/j.acra.2022.12.040. Epub 2023 Jan 22.
Deep-learning-based super-resolution image reconstruction (DLSRR) is a novel image reconstruction technique that is expected to contribute to improvement in spatial resolution as well as noise reduction through learning from high-resolution computed tomography (CT). This study aims to evaluate image quality obtained with DLSRR and assess its clinical potential.
CT images of a Mercury CT 4.0 phantom were obtained using a 320-row multi-detector scanner at tube currents of 100, 200, and 300 mA. Image data were reconstructed by filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), deep-learning-based image reconstruction (DLR), and DLSRR at image reconstruction strength levels of mild, standard, and strong. Noise power spectrum (NPS), task transfer function (TTF), and detectability index were calculated.
The magnitude of the noise-reducing effect in comparison with FBP was in the order MBIR <HIR <DLR <DLSRR. The resolution property was in the order HIR <FBP <DLR <MBIR <DLSRR. The detectability index was highest for DLSRR. The maximum and mean of the NPS shifted towards lower frequencies for HIR and MBIR compared with FBP, and similar shifts were observed for DLR and DLSRR. For each image reconstruction technique, NPS decreased with increasing reconstruction strength level, but no change was observed in TTF.
The present results suggest that DLSRR can achieve greater noise reduction and improved spatial resolution in the high-contrast region compared with conventional DLR and iterative reconstruction techniques.
基于深度学习的超分辨率图像重建（DLSRR）是一种新型图像重建技术，有望通过从高分辨率计算机断层扫描（CT）中学习来提高空间分辨率并降低噪声。本研究旨在评估DLSRR获得的图像质量并评估其临床潜力。
使用320排多探测器扫描仪在100、200和300 mA的管电流下获取Mercury CT 4.0体模的CT图像。图像数据通过滤波反投影（FBP）、混合迭代重建（HIR）、基于模型的迭代重建（MBIR）、基于深度学习的图像重建（DLR）和DLSRR在轻度、标准和强度的图像重建强度水平下进行重建。计算噪声功率谱（NPS）、任务传递函数（TTF）和可检测性指数。
与FBP相比，降噪效果的大小顺序为MBIR <HIR <DLR <DLSRR。分辨率特性顺序为HIR <FBP <DLR <MBIR <DLSRR。DLSRR的可检测性指数最高。与FBP相比，HIR和MBIR的NPS的最大值和平均值向低频移动，DLR和DLSRR也观察到类似的移动。对于每种图像重建技术，NPS随着重建强度水平的增加而降低，但TTF没有变化。
目前的结果表明，与传统的DLR和迭代重建技术相比，DLSRR在高对比度区域可以实现更大的降噪和更高的空间分辨率。
