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Next generation online and real-time adaptive radiotherapy workflows requireprecise particle transport simulations in sub-second times, which is unfeasiblewith current analytical pencil beam algorithms (PBA) or stochastic Monte Carlo(MC) methods. We present a data-driven millisecond speed dose calculationalgorithm (DoTA) accurately predicting the dose deposited by mono-energeticproton pencil beams for arbitrary energies and patient geometries. Given theforward-scattering nature of protons, we frame 3D particle transport asmodeling a sequence of 2D geometries in the beam's eye view. DoTA combinesconvolutional neural networks extracting spatial features (e.g., tissue anddensity contrasts) with a transformer self-attention backbone that routesinformation between the sequence of geometry slices and a vector representingthe beam's energy, and is trained to predict low noise MC simulations of protonbeamlets using 80,000 different head and neck, lung, and prostate geometries.Predicting beamlet doses in 5 ms with a very high gamma pass rate of 99.37%(1%, 3 mm) compared to the ground truth MC calculations, DoTA significantlyimproves upon analytical pencil beam algorithms both in precision and speed.Offering MC accuracy 100 times faster than PBAs for pencil beams, our modelcalculates full treatment plan doses in 10 to 15 s depending on the number ofbeamlets, achieving a 99.70% (2%, 2 mm) gamma pass rate across 9 test patients.Outperforming all previous analytical pencil beam and deep learning basedapproaches, DoTA represents a new state of the art in data-driven dosecalculation and can directly compete with the speed of even commercial GPU MCapproaches. Providing the sub-second speed required for adaptive treatments,straightforward implementations could offer similar benefits to other steps ofthe radiotherapy workflow or other modalities such as helium or carbontreatments.

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy