Room: Exhibit Hall | Forum 2
Purpose: To develop a deep convolutional neural network to automatically convert doses from the analytical anisotropic algorithm (AAA) to doses of the Acuros XB algorithm (AXB) to improve dose accuracy.
Methods: AAA calculates accurate doses at homogeneous regions while falls short in inhomogeneities. In contrast, AXB algorithm provides accurate dose calculation in both situations, while its clinical usage is currently limited. We proposed a hierarchically-dense U-net (HD U-net) for automatic AAA-to-AXB dose conversion to improve the dose reporting accuracy. Patient-specific CTs and lower-accuracy AAA doses are input into the network, and a higher-accuracy AXB dose is output. The network contained multiple layers of varying feature sizes to learn both local and global features to maximize the conversion accuracy. AAA and AXB doses were calculated in pair for 120 lung patients planned in Eclipse. The network was trained on randomly-selected 72 sets and validated on another 18 sets in training. The network was evaluated on the remaining 30 sets. The mean-squared-errors (MSEs) and gamma-pass-rates (2mm/2% & 1mm/1%) were calculated between AAA-converted and true AXB dose distributions for quantitative evaluation.
Results: The volume-of-interest for MSE calculation and gamma analysis is defined by the 20% isodose line of the maximum dose on the AXB dose map. The AAA-converted AXB doses demonstrated substantially improved match to the true AXB doses, with average(Â±s.d.) gamma pass rate(1mm/1%) 98.3%(Â±1.7%), compared to 86.0%(Â± 9.8%) of the AAA dose. The corresponding average MSE was 0.16(Â± 0.10) vs 0.52(Â± 0.26).
Conclusion: The deep learning-based dose conversion scheme has substantially improved the dose accuracy of AAA. The inaccurate radiation transport model of the AAA algorithm in inhomogeneous regions, especially around the lung-tumor interface, has been successfully corrected after the conversion. The developed network enables automatic and fast AXB dose generation from AAA doses, allowing more informed plan evaluation, fine-tuning and selection.