Boosting radiotherapy dose calculation accuracy with deep learning

Abstract

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: Anisotropic analytic algorithm (AAA) and Acuros XB (AXB). A hierarchically dense U-Net model was developed to boost the accuracy of AAA dose toward 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. Anisotropic analytic algorithm and AXB doses were calculated in pairs for 120 lung radiotherapy plans covering various treatment techniques, beam energies, tumor locations, and dose levels. For each case, the CT and the AAA dose were used as the input and the AXB dose as the "ground-truth" output, to train and test the model. The mean squared errors (MSEs) and gamma passing rates (2 mm/2% & 1 mm/1%) were calculated between the boosted AAA doses and the "ground-truth" AXB doses. The boosted AAA doses demonstrated substantially improved match to the "ground-truth" AXB doses, with average (± s.d.) gamma passing rate (1 mm/1%) 97.6% (±2.4%) compared to 87.8% (±9.0%) of the original AAA doses. The corresponding average MSE was 0.11(±0.05) vs 0.31(±0.21). Deep learning is able to capture the differences between dose calculation algorithms to boost the low-accuracy algorithms. By combining a less accurate dose calculation algorithm with a trained deep learning model, dose calculation can potentially achieve both high accuracy and efficiency.

Keywords: AAA; AXB; CT; deep learning; dose calculation; inhomogeneous regions.

Fig. 1

(a) General framework of the Hierarchically Dense U‐Net (HD U‐Net) model for the proposed dose accuracy boosting technique. (b) General training and testing processes where the patient‐specific computed tomography (CT) and low‐accuracy anisotropic‐analytic‐algorithm (AAA) doses serve as the input into the HD U‐Net structure, and the high‐accuracy Acuros XB (AXB) doses serve as the ‘ground‐truth’ output for supervised training/validation. Using the trained framework, a new patient‐specific CT and low‐accuracy AAA dose can be input to obtain a high‐accuracy, boosted AAA dose as the output, with its accuracy matching the AXB dose level.

Fig. 2

(a) The “ground‐truth” Acuros XB (AXB) dose maps and relative differences between (b) the original anisotropic‐analytic‐algorithm (AAA) and the AXB dose maps; (c) the boosted AAA and the AXB dose maps, for a three‐dimensional non‐coplanar static beam plan. (d) The “ground‐truth” AXB dose maps and relative differences between (e) the original AAA and the AXB dose maps; (f) the boosted AAA and the AXB dose maps, for a volumetric‐modulated arc plan. The “ground‐truth” AXB doses were shown in absolute quantities (Gy). The dose differences were normalized to the plan prescription dose (%).

Fig. 3

Gamma index (1%/1 mm) maps between the original anisotropic‐analytic‐algorithm (AAA) and the Acuros XB (AXB) dose distributions, and between the boosted AAA and the AXB dose distributions for (a) a three‐dimensional non‐coplanar static beam plan and (b) a volumetric‐modulated arc plan. The color bar on the right shows the scale of gamma index and the dashed lines indicate the planning target volumes. A gamma index > 1 indicates failed gamma test.

Fig. 4

The dose volume histograms (DVH) curves of planning target volume and lungs for (a) a three‐dimensional non‐coplanar static beam plan, and (b) a volumetric‐modulated arc plan. (c) shows the zoomed‐in lung DVH curves for the volumetric‐modulated arc plan. The solid, dashed, and dotted lines correspond with the Acuros XB, the boosted anisotropic‐analytic‐algorithm (AAA), and the original AAA dose maps, respectively