Ghane Behrooz, Karimian Alireza, Mostafapour Samaneh, Gholamiankhak Faezeh, Shojaerazavi Seyedjafar, Arabi Hossein
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Department of Radiology Technology, Faculty of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
J Med Signals Sens. 2023 May 29;13(2):118-128. doi: 10.4103/jmss.jmss_173_21. eCollection 2023 Apr-Jun.
Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images.
In this light, we set out to simulate four reduced dose levels (60% dose, 40% dose, 20% dose, and 10% dose) of standard CT imaging using Beer-Lambert's law across 49 patients infected with COVID-19. Then, three denoising filters, namely Gaussian, bilateral, and median, were applied to the different low-dose CT images, the quality of which was assessed prior to and after the application of the various filters via calculation of peak signal-to-noise ratio, root mean square error (RMSE), structural similarity index measure, and relative CT-value bias, separately for the lung tissue and whole body.
The quantitative evaluation indicated that 10%-dose CT images have inferior quality (with RMSE = 322.1 ± 104.0 HU and bias = 11.44% ± 4.49% in the lung) even after the application of the denoising filters. The bilateral filter exhibited superior performance to suppress the noise and recover the underlying signals in low-dose CT images compared to the other denoising techniques. The bilateral filter led to RMSE and bias of 100.21 ± 16.47 HU and - 0.21% ± 1.20%, respectively, in the lung regions for 20%-dose CT images compared to the Gaussian filter with RMSE = 103.46 ± 15.70 HU and bias = 1.02% ± 1.68% and median filter with RMSE = 129.60 ± 18.09 HU and bias = -6.15% ± 2.24%.
The 20%-dose CT imaging followed by the bilateral filtering introduced a reasonable compromise between image quality and patient dose reduction.
计算机断层扫描（CT）是诊断和评估新型冠状病毒肺炎（COVID-19）病情进展的主要工具之一。为避免CT成像的副作用，低剂量CT成像对于降低人群吸收剂量至关重要。然而，这种方法会在CT图像中引入相当大的噪声水平。
有鉴于此，我们利用比尔-朗伯定律对49例COVID-19感染患者的标准CT成像模拟了四种降低剂量水平（60%剂量、40%剂量、20%剂量和10%剂量）。然后，对不同的低剂量CT图像应用高斯、双边和中值三种去噪滤波器，并通过计算峰值信噪比、均方根误差（RMSE）、结构相似性指数测量值和相对CT值偏差，分别针对肺组织和全身，在应用各种滤波器之前和之后评估图像质量。
定量评估表明，即使应用了去噪滤波器，10%剂量的CT图像质量仍较差（肺组织中RMSE = 322.1 ± 104.0 HU，偏差 = 11.44% ± 4.49%）。与其他去噪技术相比，双边滤波器在抑制低剂量CT图像中的噪声和恢复潜在信号方面表现更优。对于20%剂量的CT图像，在肺区域，双边滤波器导致的RMSE和偏差分别为100.21 ± 16.47 HU和 - 0.21% ± 1.20%，而高斯滤波器的RMSE = 103.46 ± 15.70 HU，偏差 = 1.02% ± 1.68%，中值滤波器的RMSE = 129.60 ± 18.09 HU，偏差 = -6.15% ± 2.24%。
20%剂量的CT成像随后进行双边滤波在图像质量和患者剂量减少之间引入了合理的折衷方案。
