Extracting respiratory signals from thoracic cone beam CT projections

TL;DR

This paper introduces a novel local principal component analysis (LPCA) method to extract respiratory signals directly from CBCT projections, outperforming existing methods in clinical lung cancer radiotherapy scenarios.

Contribution

The paper presents a new LPCA technique that effectively distinguishes respiration-induced content changes from gantry rotation effects in CBCT projections, improving respiratory signal extraction.

Findings

LPCA outperforms three state-of-the-art methods in clinical data.

LPCA demonstrates the best overall performance across tested cases.

The study clarifies the applicability of existing methods.

Abstract

Patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principle component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely, the Amsterdam Shroud (AS) method, the intensity analysis (IA) method, and the Fourier-transform based phase analysis (FT-p) method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting respiratory signal. We also identified the applicability of each existing method.