High Performance Computing for the Optimization of Radiation Therapy Treatment Plans

QC 20140423

Abstract

Radiation therapy is a clinical field in which computer simulations play a crucial role. Before patients undergo radiation therapy, an individual treatment plan for each patient needs to be created based on the specifics of their case (a process often referred to as treatment planning). The main aspect of this is setting the control parameters for the treatment machine so that the radioactive dose delivered to the patient is as concentrated to the tumor volume as possible. The inverse problem of determining such appropriate control parameters is typically formulated as an optimization problem, which, considering the complexity of modern treatment machines, requires computerized algorithms to solve accurately. Solving this optimization problem can be a key computational bottleneck in the treatment planning workflow. In many cases, finding a suitable treatment plan is a trial-and-error process, requiring multiple solutions of the optimization problems with different weights and parameters.

This thesis proposes different methods to enable the use of high performance computing (HPC) hardware to accelerate the optimization process in radiation therapy. We deal with two main computational aspects of the optimization workflow, the calculation of dose, gradients and objective functions; as well as the optimization solver itself. For dose calculation during optimization, we propose a CUDA kernel for sparse matrix-vector products tuned to dose deposition matrices from proton therapy. For the evaluation of the objective function -- which is often constructed as a weighted sum -- we develop a method to distribute the calculation of the objective function and its gradient across computational nodes using message passing.

For the optimization solver itself we first propose a task-based parallel implementation for Cholesky factorization of banded matrices. This can be an important kernel in interior point methods (IPM) for optimization, depending on the structure of the optimization constraints. The final two papers in this thesis deal with the adoption of iterative linear algebra in IPMs to enable GPU acceleration of the optimization solve. We develop an IPM using a doubly augmented formulation of the KKT linear system and Jacobi preconditioned conjugate gradient method, which we show can solve problems to acceptable accuracy as well as benefit substantially from GPU acceleration. Benchmarks against the commercial treatment planning system RayStation shows that our solver can improve performance by up to 4.4 times on some realistic cases.

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-345836