Where High-Performance Computing Meets Radiotherapy for Enhanced Intensity-Modulated Radiation Therapy Planning

Intensity Modulated Radiotherapy (IMRT) employs radiation beams with varying angles and intensities to precisely target cancerous tissues while sparing healthy organs. Planning methods based on the generalized Equivalent Uniform Dose (gEUD) metric achieve excellent Planning Target Volume coverage. However, computing these plans requires extensive parameter adjustments and multiple model evaluations, making the process resource-intensive and time-consuming. This study aims to enhance the computational efficiency of radiotherapy plans by automating the adjustment of gEUD parameters, reducing solution times, and facilitating clinical integration. We introduced a novel approach that combines Gradient Descent algorithms with evolutionary optimization to explore the gEUD parameter space. This hybrid methodology generates radiation plans that meet clinical constraints. To address the high computational costs, we implemented parallelization and batching strategies, leveraging multicore servers to accelerate the optimization process and enable real-time clinical applications. Benchmarking was conducted on three multicore platforms with distinct micro-architectures, testing various batch sizes and thread configurations. Using a dataset of three Head and Neck IMRT patients treated with nine beams, our approach demonstrated substantial computational speed-ups. Results confirmed the ability of the method to consistently produce high-quality radiation therapy plans that meet clinical constraints. By effectively exploiting multicore servers, this approach overcomes the computational challenges of gEUD parameter tuning, enabling its integration into clinical practice. This advancement reduces planning times, supports medical physicists, and ultimately enhances patient care in radiotherapy.