Task-based assessment of resolution properties of CT images with a new index using deep convolutional neural network.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-11-06 DOI:10.1007/s12194-023-00751-0
Aiko Hayashi, Ryohei Fukui, Shogo Kamioka, Kazushi Yokomachi, Chikako Fujioka, Eiji Nishimaru, Masao Kiguchi, Junji Shiraishi
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引用次数: 0

Abstract

In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based manner. This method applies a deep convolutional neural network (DCNN) machine learning system trained on CT images with known modulation transfer function (MTF) values to output an index representing the resolution properties of the input CT image [i.e., the resolution property index (RPI)]. Sample CT images were obtained for training and testing of the DCNN by scanning the American Radiological Society phantom. Subsequently, the images were reconstructed using a filtered back projection algorithm with different reconstruction kernels. The circular edge method was used to measure the MTF values, which were used as teacher information for the DCNN. The resolution properties of the sample CT images used to train the DCNN were created by intentionally varying the field of view (FOV). Four FOV settings were considered. The results of adapting this method to the filtered back projection (FBP) and hybrid iterative reconstruction (h-IR) images indicated highly correlated values with the MTF10% in both cases. Furthermore, we demonstrated that the RPIs could be estimated in the same manner under the same imaging conditions and reconstruction kernels, even for other CT systems, where the DCNN was trained on CT systems produced by the same manufacturer. In conclusion, the RPI, which is a new index that represents the resolution property using the proposed method, can be used to evaluate the resolution of a CT system in a task-based manner.

使用深度卷积神经网络使用新指数对CT图像的分辨率特性进行基于任务的评估。
在这项研究中,我们提出了一种获得新指标的方法,以基于任务的方式评估计算机断层扫描(CT)图像的分辨率特性。该方法应用在具有已知调制传递函数(MTF)值的CT图像上训练的深度卷积神经网络(DCNN)机器学习系统来输出表示输入CT图像的分辨率特性的指数[即,分辨率特性指数(RPI)]。通过扫描美国放射学会体模获得样本CT图像,用于DCNN的训练和测试。随后,使用具有不同重建核的滤波反投影算法来重建图像。圆边法用于测量MTF值,这些值被用作DCNN的教师信息。用于训练DCNN的样本CT图像的分辨率特性是通过有意改变视场(FOV)来创建的。考虑了四种FOV设置。将该方法应用于滤波反投影(FBP)和混合迭代重建(h-IR)图像的结果表明,在这两种情况下,MTF10%都具有高度相关性。此外,我们证明,即使对于其他CT系统,在相同的成像条件和重建内核下,也可以以相同的方式估计RPI,其中DCNN是在同一制造商生产的CT系统上训练的。总之,RPI是一个新的指标,它代表了使用所提出的方法的分辨率特性,可以用于以基于任务的方式评估CT系统的分辨率。
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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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