Evaluation Methodology for Respiratory Signal Extraction from Clinical Cone-Beam CT (CBCT) using Data-Driven Methods

Author:

Mohd Amin Adam Tan, ,Mokri Siti Salasiah,Ahmad Rozilawati,Ismail Fuad,Abd Rahni Ashrani Aizzuddin, , , ,

Abstract

The absence of a ground truth for internal motion in clinical studies has always been a challenge to evaluate developed methods to extract respiratory motion especially during a 60-second cone-beam CT (CBCT) scan in Image-Guided Radiotherapy Treatment (IGRT). The unavailability of a gold standard has led this study to present a methodology to manually track respiratory motion on a clinically acquired CBCT projection data set over a 360° view angle. The tracked signal is then used as a reference to assess the performance of four data-driven methods in respiratory motion extraction, namely: the Amsterdam Shroud (AS), Local Principal Component Analysis (LPCA), Intensity Analysis (IA), and Fourier Transform (FT)-based methods that do not require additional equipment nor protocol to the existing treatment delivery. The assessment using this reference signal includes both quantitative and qualitative analysis. It is found out quantitatively that all four methods managed to extract respiratory signals that are highly correlated with the reference signal, with the LPCA method displaying the highest correlation coefficient value at 0.9108. Furthermore, the normalized root-mean-squared amplitude error of detected peaks and troughs within the signal from the LPCA method is also lowest at 1.6529 % compared to the other methods. This result is further supported by qualitative analysis via visual inspection of each extracted signal plotted with the reference signal on the same axes.

Publisher

Penerbit UTHM

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,Materials Science (miscellaneous),Civil and Structural Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3