使用自监督去噪算法处理的低剂量 CT 图像的主观和客观图像质量。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-06-01 Epub Date: 2024-02-27 DOI:10.1007/s12194-024-00786-x
Yuya Kimura, Takeru Q Suyama, Yasuteru Shimamura, Jun Suzuki, Masato Watanabe, Hiroshi Igei, Yuya Otera, Takayuki Kaneko, Maho Suzukawa, Hirotoshi Matsui, Hiroyuki Kudo
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引用次数: 0

摘要

本研究旨在评估使用深度学习自监督去噪算法处理的低剂量计算机断层扫描(CT)图像的主观和客观图像质量。我们使用 40 名患者的低剂量 CT 图像训练了自监督去噪模型,并将该模型应用于另外 30 名患者的 CT 图像。两位放射科医生对图像质量的噪声和边缘清晰度进行了 5 级评分。计算了变异系数、对比度-噪声比(CNR)和信噪比(SNR)。将自我监督去噪模型的值与原始低剂量 CT 图像和使用其他传统去噪算法(非局部均值、块匹配和三维滤波以及基于总变异最小化的算法)处理的 CT 图像的值进行了比较。自我监督去噪算法的局部和整体噪声水平平均分(标准差)分别为 3.90 (0.40) 和 3.93 (0.51),优于原始图像和其他算法。同样,自监督去噪算法的局部和整体边缘锐度的平均得分分别为 3.90 (0.40) 和 3.75 (0.47),超过了原始图像和其他算法的得分。自我监督去噪算法的 CNR 和 SNR 均高于原始图像,但略低于其他算法。我们的研究结果表明,低剂量 CT 图像的自监督去噪算法具有潜在的临床应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm.

This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.

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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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