An AI dose-influence matrix engine for robust pencil beam scanning protons therapy

Abstract

Background

Rapid planning is of tremendous value in proton pencil beam scanning (PBS) therapy in overcoming range uncertainty. However, the dose calculation of the dose influence matrix (D_ij) in robust PBS plan optimization is time-consuming and requires substantial acceleration to enhance efficiency.

Purpose

To accelerate the D_ij calculations in PBS therapy, we developed an AI-D_ij engine integrated into our in-house treatment planning system (TPS).

Methods

The AI-D_ij engine calculates spot dose using a transformer-based spot dose calculation model (SDM), which takes CT volumes (CT-bars, 256 $\times$ 16 $\times$ 16 voxels, 3 mm resolution) and energy (a float value) as inputs and outputs the spot dose distribution (256 $\times$ 16 $\times$ 16). The SDM was trained on over 200 000 CT-bars and Monte Carlo (MC) spot dose (spanning energy levels from 70 to 225 MeV). Clinical-implemented treatment plans for the head, lung, and liver, initially created on Raystation, were replanned using our AI-D_ij engine under identical gantry angles and uncertainties settings. After optimizing the spot weight, each in-house plan was recalculated using MCsquare for MC dose evaluation. The dose-volume histogram (DVH) metrics from the in-house TPS and Raystation were compared, evaluating both the optimized and MC doses.

Results

In optimization, the differences of DVH metrics (%, Value_in-house—Value_Raystation) across all uncertainty scenarios between the in-house and Raystation plans were 0.93 ± 2.04% for clinical target volume (CTV) and −5.94 ± 12.19% for organ at risks (OARs). For the MC doses, the differences were 2.48 ± 2.78% for CTV and −5.47 ± 14.16% for OARs. The time cost of a robust AI-D_ij calculation can be within 2s on an RTX3090 GPU.

Conclusion

We conducted a feasibility study on AI-D_ij engine-based robust PBS plan optimization, demonstrating both high planning speed and quality.