Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features
Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features
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DOI：
10.1118/1.4922692
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发表时间：
2015-07-01
期刊：
影响因子：
3.8
通讯作者：
Beddar, Sam
中科院分区：
文献类型：
--
作者：
Hui, Cheukkai;Suh, Yelin;Beddar, Sam
目的：本研究的目的是开发一种新的算法来创建一个强大的内部呼吸信号（IRS）的回顾性排序的四维（4D）计算机断层扫描（CT）images.Methods：该算法结合了信息的CT图像的傅立叶变换和内部解剖特征，形成IRS。该算法首先从傅立叶空间中的低频分量和图像空间中的选定解剖特征提取潜在的呼吸信号。然后，聚类算法构建具有相似时间振荡模式的潜在呼吸信号组。选择具有最大数量的相似信号的聚类组来形成最终的IRS。为了评估所提出的算法的性能，IRS计算和外部呼吸信号从实时位置管理（RPM）系统80 patients.Results：在72（90%）的4D CT数据集测试，IRS计算作者的算法与RPM信号的基础上，他们的归一化互相关匹配。对于具有匹配呼吸信号的这些数据集，IRS和RPM信号中的吸气末时间（Δ t（ins））之间的平均差异为0.11 s，只有2.1%的Δ t（ins）间隔大于0.5 s。在IRS和RPM信号不匹配的8个（10%）4D CT数据集中，不匹配治疗床位置的平均Delta t（ins）为0.73 s，其中35.4%的Delta t（ins）大于0.5 s。在IRS与RPM信号不匹配的治疗床位置，基于相关性的度量表明RPM分类图像中相邻治疗床位置的匹配较差。这意味着，当IRS不匹配的RPM信号，使用IRS排序的图像显示较少的伪影比使用RPM signal.Conclusions排序的临床图像：作者提出的算法可以产生强大的IRS，可用于回顾性排序的4D CT数据。该算法是完全自动的，需要很少的处理时间。该算法具有成本效益，可以很容易地用于日常临床使用。(C)2015年美国医学物理学家协会。
Purpose: The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four-dimensional (4D) computed tomography (CT) images.Methods: The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low-frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signals is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real-time position management (RPM) system on 80 patients.Results: In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors' proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Delta t(ins)) in the IRS and RPM signal was 0.11 s, and only 2.1% of Delta t(ins) were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Delta t(ins) was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Delta t(ins) greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation-based metric indicated poorer matching of neighboring couch positions in the RPM-sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal.Conclusions: The authors' proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use. (C) 2015 American Association of Physicists in Medicine.