Accelerating Proton Therapy Dose Calculation: A Comprehensive Study of GPU-Based Pencil Beam and Monte Carlo Approaches for Clinical Adaptivity

Abstract

Background: The demand for fast, accurate dose calculation methods in proton therapy has spurred research into leveraging Graphics Processing Units (GPUs) for both pencil beam algorithms and full Monte Carlo simulations. This paper synthesizes methodological advances and evaluates the computational and clinical trade-offs of GPU-accelerated approaches, outlining pathways to integrate sub-second dose calculation into adaptive clinical workflows.

Methods: Building on foundational work in GPU programming and parallel computing, this study constructs a conceptual pipeline that integrates contemporary pencil beam models and GPU Monte Carlo techniques. The pipeline emphasizes runtime code generation, memory-conscious data structures, and validated physical models for proton transport and scattering. Methodological design choices are aligned with historical GPU acceleration practices and modern clinical constraints. Performance and accuracy trade-offs are analyzed qualitatively and quantitatively in relation to published benchmarks: fast pencil beam approximations (Da Silva et al., 2015; Fujimoto et al., 2011), GPU Monte Carlo platforms (Perl et al., 2012; Lee et al., 2022), and validated commissioning procedures (Azcona et al., 2023).

Results: The articulated GPU pipeline demonstrates that sub-second pencil beam computations are attainable for individual fields using a combination of double-Gaussian beam models, hierarchical memory access patterns, and optimized reduction strategies—consistent with previously reported sub-second results (Da Silva et al., 2015). When contrasted with GPU Monte Carlo methods, pencil beam techniques offer orders-of-magnitude speed advantages at the cost of limited modeling fidelity for heterogeneous media and complex scattering scenarios (Perl et al., 2012; Lee et al., 2022). GPU Monte Carlo implementations, while more computationally demanding, provide superior dosimetric fidelity that is advantageous for commissioning and adaptive replanning when clinical constraints permit longer computation windows (Paganetti et al., 2021; Azcona et al., 2023).

Discussion: We provide a deep interpretive analysis of algorithmic design, including the theoretical underpinnings of pencil beam decompositions and Monte Carlo sampling strategies, the implications of parallel hardware architectures on numerical stability and reproducibility, and the practical considerations of clinical integration—quality assurance, commissioning, and workflow compatibility. Limitations include the challenge of balancing speed, accuracy, and validation demands; potential numerical artifacts introduced by aggressive optimization; and the need for robust clinical validation across tissue heterogeneities (Schreuder et al., 2019; Goma et al., 2018). Future directions center on hybrid models that fuse fast pencil beam precomputations with targeted Monte Carlo refinement, automated commissioning pipelines, and exploitation of modern GPU programming paradigms and run-time code generation (Klöckner et al., 2012; Lulla, 2025).

Conclusions: GPU-accelerated dose calculation is mature enough to dramatically improve the feasibility of adaptive proton therapy. Through careful algorithmic design, validation, and clinical integration, facilities can realize near real-time planning for selected adaptive scenarios while retaining Monte Carlo accuracy where it matters most.