Adaptive Radiotherapy Based on High Performance Computing
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: The goal of radiation oncology is to deliver a prescribed radiation dose to targets containing tumor and cancerous regions while sparing surrounding functional organs and normal tissues. The current state of the art, however, is to treat using a so-called “optimal?treatment plan that is determined based on patient geometry acquired via imaging before commencement of a sequence of 25-40 daily treatments. The current treatment paradigm therefore implicitly (and erroneously) assumes that the patient is static and ignores the fact that 1) tumor and nearby organs are mobile and distensible, so that their position and shape may vary substantially from day to day, and 2) there is significant dynamic change in tumor geometry in response to the radiotherapy. As a result, the quality of this predesigned treatment plan will deteriorate during the course of treatment. The ultimate goal of our project is to redesign, in real-time, a treatment plan for each day of treatment, thereby ensuring the optimality of the treatment and ensuring that the treatment objectives are met. We will refer to this process as real-time re-planning for on-line adaptive radiotherapy. The biggest challenge for the proposed technology is the real-time requirement of performing three computationally intensive tasks: patient modeling based on deformable image registration, radiation transport simulation and dose calculation using Monte Carlo methods, and treatment optimization as a large scale optimization problem. Successfully developing this technology requires expertise in high performance computing, Monte Carlo simulation of radiation transport, patient modeling and treatment optimization. The required expertise will be provided by a multidisciplinary team formed through the collaboration among three research groups based on UCSD campus and LLNL: 1) Jiang group at UCSD Radiation Oncology Department has clinical and research experience in cancer radiotherapy and expertise in deformable image registration and treatment optimization; 2) Majumdar group in the San Diego Supercomputer Center (SDSC) at UCSD has expertise in high performance computing; and 3) Descalle group at LLNL has expertise in Monte Carlo simulation of radiation transport and dose calculation. Specifically, the goal of the proposed project is to develop an on-line real-time re-planning software system by extending the Peregrine system developed at LLNL and by using the supercomputers at SDSC. The system will consist of three major components: (1) patient modeling based on deformable image registration, (2) dose calculation based on Monte Carlo simulation, and (3) plan optimization. The ITK package, used at the UCSD Radiation Oncology department, will be utilized for deformable image registration. The Monte Carlo module in the Peregrine system will be used for dose calculation. A treatment plan optimization algorithm developed by the PI will used for plan optimization for the proposed on-line adaptive radiotherapy. The general aim of the project is to parallelize, implement, and test the codes/algorithms of those three major components on supercomputers. The specific aims of the proposed project are: 1) implementing parallel version of the deformable registration, Monte Carlo dose calculation, and plan optimization codes on the supercomputers at SDSC, 2) building interfaces between Peregrine and supercomputers and between Peregrine and the treatment machine, and 3) testing the integrated system in the clinical environment at the UCSD Radiation Oncology Department. The next step, after finishing the project, is to implement the new technology in clinical practice and start clinical trials, which we believe should have the promise to significantly improve the cure rate of cancer radiotherapy.