Kunyi Zhang

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.

Abstract High-quality radiotherapy dose distribution prediction for linear accelerators remains a labor-intensive process constrained by inter-planner variability and institutional data silos. While deep learning has shown promise in automating dose distribution prediction, most existing methods are trained on single-center datasets, limiting their generalizability. Direct multi-center data pooling is hindered by stringent privacy regulations and heterogeneous clinical protocols.

1. Introduction Traditional commercial streets are precious heritage of urban culture and history and have an indispensable significance for urban development. As one of the traditional commercial districts in Dalian, Tianjin Street has experienced nearly a hundred years of history and development and has witnessed the evolution of Dalian City (Xia & Wang, 2003, Chen, He, & Wang, 2014) .