Division of Medical Technology, Department of Radiology, Tohoku University Hospital, 1-1 Seiryomachi, Aoba, Sendai, Miyagi, 980-8574, Japan.
Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachidori, Chuou, Niigata, Niigata, 951-8518, Japan.
Radiol Phys Technol. 2020 Dec;13(4):336-347. doi: 10.1007/s12194-020-00586-z. Epub 2020 Sep 28.
Bedside radiography has increasingly attracted attention because it allows for immediate image diagnosis after X-ray imaging. Currently, wireless flat-panel detectors (FPDs) are used for digital radiography. However, adjustment of the X-ray tube and FPD alignment are extremely difficult tasks. Furthermore, to prevent a poor image quality caused by scattered X-rays, scatter removal grids are commonly used. In this study, we proposed a scatter-correction processing method to reduce the radiation dose when compared with that required by the X-ray grid for the segmentation of a mass region using deep learning during bedside chest radiography. A chest phantom and an acrylic cylinder simulating the mass were utilized to verify the image quality of the scatter-corrected chest X-rays with a low radiation dose. In addition, we used the peak signal-to-noise ratio and structural similarity to quantitatively assess the quality of the low radiation dose images compared with normal grid images. Furthermore, U-net was used to segment the mass region during the scatter-corrected chest X-ray with a low radiation dose. Our results showed that when scatter correction is used, an image with a quality equivalent to that obtained by grid radiography is produced, even when the imaging dose is reduced by approximately 20%. In addition, image contrast was improved using scatter radiation correction as opposed to using scatter removal grids. Our results can be utilized to further develop bedside chest radiography systems with reduced radiation doses.
床边放射摄影术越来越受到关注，因为它可以在 X 射线成像后立即进行图像诊断。目前，无线平板探测器（FPD）用于数字放射摄影术。然而，调整 X 射线管和 FPD 对准是极其困难的任务。此外，为了防止散射 X 射线导致图像质量差，通常使用散射去除格栅。在这项研究中，我们提出了一种散射校正处理方法，与床边胸部放射摄影术使用深度学习进行肿块区域分割时 X 射线格栅所需的辐射剂量相比，可降低辐射剂量。利用胸部体模和模拟肿块的亚克力圆柱，验证了低剂量散射校正胸部 X 射线的图像质量。此外，我们使用峰值信噪比和结构相似性来定量评估低剂量辐射图像与正常格栅图像的质量。此外，使用 U-net 对低剂量散射校正胸部 X 射线中的肿块区域进行分割。结果表明，使用散射校正时，即使将成像剂量降低约 20%，也可以产生与格栅放射摄影术相当的图像质量。此外，使用散射辐射校正可以提高图像对比度，而不是使用散射去除格栅。我们的研究结果可用于进一步开发低剂量辐射的床边胸部放射摄影系统。
