TH‐A‐9A‐01: Active Optical Flow Model: Predicting Voxel‐Level Dose Prediction in Spine SBRT

Abstract

Purpose:To predict voxel‐level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT.Methods:We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel‐level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model.Results:The DVHs predicted by the AOFM‐based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively.Conclusion:The AOFM is effective in predicting voxel‐level dose distribution for spine SBRT.Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.