Room: Exhibit Hall | Forum 7
Purpose: To implement a framework for dose prediction using a deep convolutional neural network (CNN) based on the concept of isodose feature-preserving voxelization (IFPV) in simplifying the representation of the dose distribution.
Methods: The concept of IFPV was introduced for concise representation of a treatment plan. IFPV is a sparse voxelization scheme that groups the voxels into clusters according to their geometric, anatomical and dosimetric features, which reduces the scale of computation and improves the efficiency by removing the redundant dosimetric information of voxels for a concise representation of dose distribution. In this study a deep CNN was constructed to build up a dose prediction model in IFPV domain based on 60 volumetric modulated arc therapy (VMAT) treatment plans from a database of previously treated 70 prostate cancer patients. The dose prediction model learns the contour to dose relationship and predicts the dose distribution in IFPV domain given the input contours. Additional 10 independent prostate cases were selected as testing data. DVH comparison, dose difference maps and residual analysis with the sum of absolute residual (SAR) were used to evaluate the performance of the proposed method.
Results: The proposed IFPV-based method achieved good prediction performance in terms of DVH comparison and dose difference maps. Statistical results of SARs showed that the IFPV-based method is comparable with voxel-based method even though the number of dose representation points used in the IFPV-based method was substantially reduced. The proposed approach achieved mean SARs of 0.029Â±0.020 and 0.077Â±0.030 for bladder and rectum, respectively, compared with mean SARs of 0.039Â±0.029 and 0.069Â±0.028 in the conventional voxel-based method.
Conclusion: A novel deep CNN-based dose prediction method in IFPV domain was proposed. The proposed approach has great potential to significantly improve the efficiency of dose prediction and facilitate the treatment planning workflow.
Funding Support, Disclosures, and Conflict of Interest: This work was partially supported by Varian Medical Systems, a gift fund from Huiyihuiying Medical Co, and a Faculty Research Award from Google Inc.