Comparison of data-driven respiratory signal extraction methods from cone-beam CT (CBCT)

Abstract

Abstract In Cone-Beam CT (CBCT) imaging, respiratory motion needs to be considered to mitigate motion artifacts thus increasing the accuracy of reconstructed images. Data driven methods can be used to extract respiratory signal directly from projection data without requiring any additional equipment or surrogate devices. Digital phantoms provide an adequate option to evaluate developing methods prior to clinical implementation. In this study, four data driven methods are used to extract respiratory signal from simulated projections. An in-house 4D MRI-based CBCT digital phantom is used, where actual respiratory signal is available as ground truth. In comparing all four data driven methods, the respiratory signal extracted using the Local Principal Component Analysis (LPCA) method is found to be robust and yielded the highest correlation coefficient of 0.8644 compared to the ground truth.