GPU-based ultra fast IMRT plan optimization

TL;DR

This paper presents a GPU-accelerated IMRT optimization algorithm that significantly speeds up treatment planning, enabling real-time adaptive radiotherapy with high accuracy and efficiency.

Contribution

We developed a GPU-based implementation of an IMRT optimization algorithm that achieves 20-40x speedup over CPU methods, facilitating real-time adaptive radiotherapy.

Findings

Achieved 20-40x speedup with GPU implementation.

Generated IMRT plans in under 3 seconds for prostate cases.

Maintained accuracy comparable to CPU-based solutions.

Abstract

The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient's geometry. Such efforts face major technical challenges to perform treatment planning in real-time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo's line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evaluate our implementation. On an NVIDIA Tesla C1060 GPU card, we have achieved speedup factors of 20-40 without losing accuracy, compared to the results from an Intel Xeon 2.27 GHz CPU. For a specific 9-field prostate IMRT case with 5x5 mm^2 beamlet size and 2.5x2.5x2.5 mm^3 voxel size, our GPU implementation takes only 2.8 seconds to generate an optimal IMRT plan. Our work has therefore solved a major problem in developing online re-planning technologies for adaptive radiotherapy.