Purpose: This study aims to quantitatively evaluate the performance of various noise reduction algorithms on markerless tumor tracking using dual-energy (DE) imaging.
Methods: DE images were obtained using the fast-kV switching capabilities of the on-board imager (OBI) of a commercial linear accelerator with alternating acquisitions at 60 and 120 kVp. Weighted log-subtraction was then used on paired 60/120 kVp images to generate soft tissue images. Both the anti-correlated noise reduction (ACNR) technique, and 2D-median filter were applied to subsequent DE images. Additionally, the adaptive histogram equalization (AHE) algorithm was also evaluated for comparison. The tumor motion on DE-unfiltered and DE-filtered image sequences was tracked using a template-based matching algorithm. Ground truth positions from the DE-unfiltered and DE-filtered images were estimated using Bayesian inference.
Results: A total of 1145 DE-unfiltered and DE-filtered image frames were evaluated for 5 patients. The DE-filtered images showed slight variation in tracking results vs. DE-unfiltered images, with reduced tracking error > 0.1 mm in 30.2% (346/1145), 13.8% (159/1145), 12.5% (144/1145) of image frames using median filter, ACNR, and AHE methods respectively. However, DE-filtered images only showed improved results for tracking error >1.0 mm in 1.57% (18/1145), 1.57% (18/1145), 2.18% (25/1145) image frames, with median filter, ACNR, and AHE techniques respectively.
Conclusion: These noise reduction techniques do not significantly affect tumor tracking on DE images. Thus, DE images are suitable to track the tumor without using any additional post-processing. This is advantageous since the addition of noise reduction techniques increases image processing times and affects system latency.
Funding Support, Disclosures, and Conflict of Interest: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA207483. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.