3 years ago

A particle filter-based target tracking algorithm for MR-guided respiratory compensation: robustness and accuracy assessment

To assess overall robustness and accuracy of a modified particle filter based tracking algorithm for MR-guided radiation therapy treatments. Methods An improved particle filter based tracking algorithm is implemented, which employs a normalized cross-correlation function as the likelihood calculation. With a total of 5 healthy volunteers and 8 patients, the robustness of the algorithm is tested on 24 dynamic MRI time-series with varying resolution, contrast, and signal-to-noise ratio. The complete data set includes data acquired with different scan parameters on a number of MRI scanners with varying field strengths including the 1.5T MR-linac. Tracking errors are computed by comparing the results obtained by the particle filter algorithm to experts' delineations. Results The ameliorated tracking algorithm is able to accurately track abdominal as well as thoracic tumors, whereas the previous Bhattacharyya distance based implementation fails in over 50% of the cases. The tracking error, combined over all MRI acquisitions, is (1.1 ± 0.4) mm, which demonstrates high robustness against variations in contrast, noise and image resolution. Finally, the effect of the input/control parameters of the model is very similar across all cases suggesting a class-based optimization is possible. Conclusion The modified particle filter tracking algorithm is highly accurate and robust against varying image quality. This makes the algorithm a promising candidate for automated tracking on the MR-linac.


A modified particle filter based algorithm allows tracking of abdominal and thoracic anatomies of interest. A total of 5 healthy volunteers and 8 patients were scanned with various image contrasts and resolutions. The results demonstrate an accurate and robust technique with promising clinical potential.

Publisher URL: www.sciencedirect.com/science

DOI: S0360301617339652

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