The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from CT scans using a deterministic Linear Boltzmann Transport Equation (LBTE) solver (Acuros CTD) and deep-learning CT segmentation algorithms. This study evaluated the accuracy of deep learning segmentation for estimating organ dose from dose maps generated by Acuros. The study focused on pediatric CT due to increased radiation concerns and segmentation challenges for the pediatric population. Organs relevant to CT dosimetry were manually contoured by experts in 246 pediatric chest-abdomen-pelvis CT datasets to serve as ground truth. A fully convolutional network based on a modified V-net architecture was trained and tuned using 226 pediatric datasets ranging in age from 1 to 16 years. An additional twenty datasets were used for preliminary evaluation. The accuracy of organ dose estimates obtained from deep learning segmentation was evaluated relative to doses obtained from the ground truth contours. The deep learning segmentation algorithm resulted in low dose errors for all organs, with a mean absolute error across test patients of 1% or less and a maximum error of 3.5% for the heart. There was high similarity between deep learning and expert contours, with mean Dice coefficients across patients greater than or equal to 0.95. There was no correlation between organ dose error or Dice coefficient with the patient age. Based on statistical analysis of students paired T-test, there was no statistically significant difference between organ doses estimated using the deep learning contours as compared to the expert ground truth contours (p>0.2). Overall, the deep learning segmentation models applied to dose maps generated by the LBTE solver (Acuros CTD) resulted in high organ dose accuracy. Additional evaluation is planned for more organ structures and patient datasets.
Cherenkov imaging during radiation therapy has been developed as a tool for dosimetry, which could have applications in patient delivery verification or in regular quality audit. The cameras used are intensified imaging sensors, either ICCD or ICMOS cameras, which allow important features of imaging, including: (1) nanosecond time gating, (2) amplification by 103-104, which together allow for imaging which has (1) real time capture at 10-30 frames per second, (2) sensitivity at the level of single photon event level, and (3) ability to suppress background light from the ambient room. However, the capability to achieve single photon imaging has not been fully analyzed to date, and as such was the focus of this study. The ability to quantitatively characterize how a single photon event appears in amplified camera imaging from the Cherenkov images was analyzed with image processing. The signal seen at normal gain levels appears to be a blur of about 90 counts in the CCD detector, after going through the chain of photocathode detection, amplification through a microchannel plate PMT, excitation onto a phosphor screen and then imaged on the CCD. The analysis of single photon events requires careful interpretation of the fixed pattern noise, statistical quantum noise distributions, and the spatial spread of each pulse through the ICCD.