Physics-informed neural network with adaptive loss balancing for real-time radiotherapy dose prediction and verification
Abstract
Accurate and efficient dose computation lies at the heart of modern radiotherapy planning, yet practitioners are still pulled in two opposing directions: high-fidelity solvers - Monte Carlo simulation and deterministic linear Boltzmann transport equation (LBTE) solvers such as Acuros XB - deliver the physical accuracy that adaptive workflows demand, but the runtime they impose remains hard to reconcile with on-couch decision-making. The present study introduces a physics-informed neural network (PINN) framework that embeds radiation-transport-derived constraints into a 3D encoder-decoder backbone for rapid voxel-wise dose prediction and verification. The network ingests preprocessed CT volumes together with plan-specific information - gantry, collimator and couch angles, projected MLC fluence maps, and per-beam monitor units - and outputs the full 3D dose distribution. We formulate a physics residual loss inspired by the LBTE under simplifying assumptions, applied as a soft regularizer rather than as a replacement for the supervised target, so that training data calculated by the Anisotropic Analytical Algorithm (AAA, Eclipse v15.6) remains the primary supervisory signal while physical plausibility is encouraged across the volume. To overcome the gradient competition between data-driven and physics-driven terms, a momentum-based adaptive weighting strategy continuously rebalances their contributions through training. An end-to-end verification pipeline integrating gamma…
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