SU-E-J-207: Compensation of Target Distortion of Pancreatic Tumor in Free-Breathing CT Using 4D Contour Propagation.

PURPOSE Due to lack of soft-tissue contrast, target distortion for the upper-abdomen targets such as pancreatic tumors is complicated and requiring sufficient remedy. By applying automatic contour propagation, the authors use the information obtained from 4D CT to test if the deformable image registration compensates the respiration-induced distortion of pancreatic tumor in free breathing (FB) CT images. METHODS Ten patients with unresected pancreatic cancer treated with either preoperative or definitive chemoradiation were studied. Pancreas GTVs were delineated on the FB CT. Using deformable image registration, the FB GTV contours were propagated to each phase of the 4D CT images taken right after the FB CT, and were compared with the FB GTV to see difference in tumor volume and tumor size along individual dimensions. A one-dimensional tumor motion in proportion to cos4(ωt) was simulated to calculate the probability distribution function for different magnitude of distortions during FB CT scans, and a binary classification test was conducted to analyze the observed results. RESULTS The probability distribution function predicted that four out of the ten cases would have substantial target distortion given the variation in target motion amplitudes. Three of these four cases show substantial difference in the superior-inferior size of FB GTV compared to the average 4D GTV, taking into account the uncertainties caused by motions perpendicular to the scanning axis and resolution of the CT scanner. The binary classification test yielded a precision of 75% and an accuracy of 90%. CONCLUSIONS Pancreatic GTV distorted due to respiration-induced tumor motion is effectively compensated by contour propagation from free-breathing CT to 4D CT using DIR. Union of GTVs of all breathing phases or IGTV can be genreated from 4D set of GTVs propagated from that of free breathing. This study is partially supported by NIH grant 1R01CA133539-01A2. I do not have conflict of interest.