Purpose: To optimize dynamically modulated fluence fields for proton computed tomography (pCT) resulting in desired spatial distributions of imaging dose and image variance, with an application for adaptive particle therapy treatment planning based on frequent low-dose and high-quality imaging.
Methods: We present an optimization algorithm for fluence-modulated pCT (FMpCT), which divides the image into a region-of-interest (ROI), in which high image quality is required and a non-ROI part for which imaging dose should be reduced. The algorithm makes use of concepts of treatment plan optimization to find modulated imaging fluences that minimize imaging dose outside of the ROI and achieve a target image variance inside the ROI using a joint cost function. Imaging doses to susceptible organs can be further reduced. Using Monte Carlo simulation, FMpCT plans were optimized for three pediatric patients with tumors in the head region for which robust proton treatment plans were used to define the ROI. Imaging doses were scored, and the accuracy of low-dose images was evaluated by re-calculating the ground truth proton plans on simulated FMpCT images.
Results: Using the optimization, the imaging dose could be reduced by 74% outside of the ROI on average over all patients, bringing it down to only 0.3mGy per tomography. Imaging doses to susceptible structures were further reduced with up to 87% dose saving. The quality of FMpCT scans for treatment planning was maintained with treatment dose passing rates for a 1% criterion on the difference to a uniform fluence pCT scan above 98% and range passing rates for a 1mm criterion above 97%.
Conclusion: A fluence modulation optimization algorithm for FMpCT was evaluated for its ability to save imaging dose while maintaining sufficient quality for treatment planning using three pediatric patients. Imaging doses of only 0.3mGy could pave the way towards daily adaptive particle therapy.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the German Research Foundation (DFG) project #388731804 and the DFG's Cluster of Excellence Munich-Centre for Advanced Photonics (MAP) and by the Bavaria-California Technology Center (BaCaTeC). Additional funding from the Zusatzfinanzierung 2019 fuer DFG-Sachbeihilfen from the Forschungsdekanat of the Faculty of Medicine of the LMU Munich.