Boosting radiotherapy dose calculation accuracy with deep learning

In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium...

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Bibliographic Details
Published inarXiv.org
Main Authors Xing, Yixun, Ph D, Zhang, You, Nguyen, Dan, Mu-Han, Lin, Lu, Weiguo, Jiang, Steve
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.05.2020
Subjects
Accuracy
Algorithms
Beams (radiation)
Computed tomography
Computer simulation
Convolution
Deep learning
Machine learning
Model accuracy
Monte Carlo simulation
Radiation therapy
Workload
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Summary:In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium, is highly correlated with the dose distribution and the underlying anatomical tissue density. We hypothesize that a conversion scheme can be established to boost low-accuracy doses to high-accuracy, using intensity information obtained from computed tomography (CT) images. A deep learning-driven framework was developed to test the hypothesis by converting between two commercially-available dose calculation methods: AAA (anisotropic-analytic-algorithm) and AXB (Acuros XB).A hierarchically-dense U-Net model was developed to boost the accuracy of AAA dose towards the AXB level. The network contained multiple layers of varying feature sizes to learn their dose differences, in relationship to CT, both locally and globally. AAA and AXB doses were calculated in pairs for 120 lung radiotherapy plans covering various treatment techniques, beam energies, tumor locations, and dose levels.
ISSN:2331-8422