Leveraging Multicore Servers for Enhanced IMRT Radiotherapy Planning

Abstract

Abstract This study enhances the computational efficiency of Radiotherapy Plans (RP) utilized in Intensity Modulated Radiotherapy (IMRT). IMRT is a technique that employs radiation beams with varying angles and intensities to deliver radiation to cancerous tissues while safeguarding healthy organs. The planning methods reliant on the gEUD metric yield radiation plans with exceptional PTV (Planning Target Volume) coverage.Nevertheless, computing these plans is a resource-intensive task that entails adjusting numerous parameters and conducting multiple model evaluations. To address this, we have introduced a novel approach that automates the adjustment of gEUD parameters. This is achieved by combining the EUD model, solved through a gradient descent algorithm, with an evolutionary optimization method that explores the EUD parameter space.Given the high computational demands of this approach, integrating it into clinical settings poses a challenge. Our goal is to tackle this challenge by introducing parallelization and batching strategies that leverage the capabilities of multicore servers, aiming to significantly accelerate the optimization process.To evaluate our proposal, we conducted extensive benchmarking on three distinct multicore platforms with varying micro-architectures, assessed across different batch sizes and threads configurations. Our testing dataset consisted of three Head and Neck (H\&N) patients who were treated using IMRT with nine beams.The results showcase that our approach provides substantial computational speed improvements while consistently generating high-quality RT (Radiation Therapy) plans that conform to clinical constraints.