Open AccessEvaluation Methodology for Respiratory Signal Extraction from Clinical Cone-Beam CT (CBCT) using Data-Driven Methods
Author(s) -
Adam Tan Mohd Amin,
Siti Salasiah Mokri,
Rozilawati Ahmad,
Fuad Ismail,
Ashrani Aizzuddin Abd Rahni
Publication year - 2021
Publication title -
international journal of integrated engineering/international journal of integrated engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.215
H-Index - 10
eISSN - 2600-7916
pISSN - 2229-838X
DOI - 10.30880/ijie.2021.13.05.001
Subject(s) - signal (programming language) , cone beam computed tomography , computer science , artificial intelligence , image guided radiation therapy , computer vision , nuclear medicine , medical imaging , medicine , radiology , computed tomography , programming language
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.