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Multi-GPU implementation of a VMAT treatment plan optimization algorithm.

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Area of Science:

  • Medical Physics
  • Computational Science
  • Radiotherapy Optimization

Background:

  • Volumetric Modulated Arc Therapy (VMAT) optimization is computationally intensive due to large datasets and complex constraints.
  • High-performance Graphics Processing Units (GPUs) accelerate VMAT computations but face memory limitations with large dose-deposition coefficient (DDC) matrices.
  • Existing VMAT optimization methods struggle with memory constraints in complex cases, such as large or multiple targets and arcs.

Purpose of the Study:

  • To implement a column-generation-based VMAT algorithm on a multi-GPU platform to overcome memory limitations.
  • To detail the techniques used for efficient multi-GPU implementation of VMAT optimization.
  • To demonstrate the feasibility of multi-GPU platforms for large-scale medical physics problems using VMAT optimization as a case study.

Main Methods:

  • A column-generation approach iteratively solves pricing and master problems for VMAT aperture generation.
  • The sparse DDC matrix is distributed across four GPUs in compressed sparse row format, utilizing peer-to-peer access for fast data transfer.
  • The Barzilai and Borwein algorithm with a subspace step scheme is employed to solve the master problem.

Main Results:

  • The multi-GPU VMAT optimization completed a head and neck cancer case in approximately 1 minute, significantly faster than single-GPU methods (4-6 minutes).
  • Compared to single-GPU strategies, the multi-GPU approach maintained superior plan quality while achieving high computational efficiency across various patient cases.
  • VMAT plans for six patient cases were generated within 23-46 seconds, comparable to or faster than commercial Treatment Planning Systems (TPS) on CPUs.

Conclusions:

  • The multi-GPU implementation effectively handles large-scale VMAT optimization problems, delivering efficient computation without compromising plan quality.
  • This approach serves as a model for applying multi-GPU techniques to other computationally demanding problems in medical physics.
  • The study highlights the potential of multi-GPU platforms to significantly improve radiotherapy treatment planning efficiency.