Single shot full plan deep learning dose computation for radiation therapy using spherical harmonics

Dose Computation is a key component of radiotherapy planning. As the number of cases in radiation therapy grows, the need for fast planning increases. However, existing dose computation approaches based on physics can be slow or not accurate enough. Deep learning based approaches offer a potential solution for fast and accurate dose calculation.

We propose a novel physics-informed deep learning based AI dose calculation method that is able to calculate the plan dose of clinical many field VMAT and IMRT plans to high accuracy and with high speed.

A novel two-stage approach is introduced. In stage one, incoming fluence from all fields is propagated using a Beer-Lambert law approach and accumulated per voxel in spherical harmonics coefficients. These coefficients together with the CT volume constitute the input to the stage two image-to-image neural network that predicts the actual dose. We performed large scale data generation on 1641 clinical plans from three different body sites. Using a special data augmentation scheme, over 100 000 training input/outputs were used for training.

The model was evaluated on clinical plans from multiple body regions using several gamma pass rates, relative and absolute errors as well as dose profiles. At a run-time of 1.6 s on a RTX 4090, the average gamma pass rate over all sites was 99.1% for 2%/2 mm and 94.4% for 1%/1 mm.

Few to many field plan doses can be calculated quickly and to high accuracy.