Effect of Statistical Fluctuation in Monte Carlo Based Photon Beam Dose Calculation on Gamma Index Evaluation

The gamma-index test has been commonly adopted to quantify the degree of agreement between a reference dose distribution and an evaluation dose distribution. Monte Carlo (MC) simulation has been widely used for the radiotherapy dose calculation for both clinical and research purposes. The goal of this work is to investigate both theoretically and experimentally the impact of the MC statistical fluctuation on the gamma-index test when the fluctuation exists in the reference, the evaluation, or both dose distributions. To the first order approximation, we theoretically demonstrated in a simplified model that the statistical fluctuation tends to overestimate gamma-index values when existing in the reference dose distribution and underestimate gamma-index values when existing in the evaluation dose distribution given the original gamma-index is relatively large for the statistical fluctuation. Our numerical experiments using clinical photon radiation therapy cases have shown that 1) when performing a gamma-index test between an MC reference dose and a non-MC evaluation dose, the average gamma-index is overestimated and the passing rate decreases with the increase of the noise level in the reference dose; 2) when performing a gamma-index test between a non-MC reference dose and an MC evaluation dose, the average gamma-index is underestimated when they are within the clinically relevant range and the passing rate increases with the increase of the noise level in the evaluation dose; 3) when performing a gamma-index test between an MC reference dose and an MC evaluation dose, the passing rate is overestimated due to the noise in the evaluation dose and underestimated due to the noise in the reference dose. We conclude that the gamma-index test should be used with caution when comparing dose distributions computed with Monte Carlo simulation

Extracting respiratory signals from thoracic cone beam CT projections

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.Comment: 21 pages, 11 figures, submitted to Phys. Med. Bio