  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.
