Nazanin Tahmasebi1,2,3, Pierre Boulanger1,2,3, Jihyun Yun4, Gino Fallone4, Michelle Noga1,2, Kumaradevan Punithakumar1,2,3. 1. 1Department of Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonABT6G 2R3Canada. 2. 2Servier Virtual Cardiac CentreMazankowski Alberta Heart InstituteEdmontonABT6G 2B7Canada. 3. 3Department of Computing ScienceUniversity of AlbertaEdmontonABT6G 2R3Canada. 4. 4Medical Physics DivisionDepartment of OncologyUniversity of AlbertaEdmontonABT6G 2R3Canada.
Abstract
OBJECTIVE: This study intends to develop an accurate, real-time tumor tracking algorithm for the automated radiation therapy for cancer treatment using Graphics Processing Unit (GPU) computing. Although a previous moving mesh based tumor tracking approach has been shown to be successful in delineating the tumor regions from a sequence of magnetic resonance image, the algorithm is computationally intensive and its computation time on standard Central Processing Unit (CPU) processors is too slow to be used clinically especially for automated radiation therapy system. METHOD: A re-implementation of the algorithm on a low-cost parallel GPU-based computing platform is utilized to accelerate this computation at a speed that is amicable to clinical usages. Several components in the registration algorithm such as the computation of similarity metric are inherently parallel which fits well with the GPU parallel processing capabilities. Solving a partial differential equation numerically to generate the mesh deformation is one of the computationally intensive components which has been accelerated by utilizing a much faster shared memory on the GPU. RESULTS: Implemented on an NVIDIA Tesla K40c GPU, the proposed approach yielded a computational acceleration improvement of over 5 times its implementation on a CPU. The proposed approach yielded an average Dice score of 0.87 evaluated over 600 images acquired from six patients. CONCLUSION: This study demonstrated that the GPU computing approach can be used to accelerate tumor tracking for automated radiation therapy for mobile lung tumors. Clinical Impact: Accurately tracking mobile tumor boundaries in real-time is important to automate radiation therapy and the proposed study offers an excellent option for fast tumor region tracking for cancer treatment.
OBJECTIVE: This study intends to develop an accurate, real-time tumor tracking algorithm for the automated radiation therapy for cancer treatment using Graphics Processing Unit (GPU) computing. Although a previous moving mesh based tumor tracking approach has been shown to be successful in delineating the tumor regions from a sequence of magnetic resonance image, the algorithm is computationally intensive and its computation time on standard Central Processing Unit (CPU) processors is too slow to be used clinically especially for automated radiation therapy system. METHOD: A re-implementation of the algorithm on a low-cost parallel GPU-based computing platform is utilized to accelerate this computation at a speed that is amicable to clinical usages. Several components in the registration algorithm such as the computation of similarity metric are inherently parallel which fits well with the GPU parallel processing capabilities. Solving a partial differential equation numerically to generate the mesh deformation is one of the computationally intensive components which has been accelerated by utilizing a much faster shared memory on the GPU. RESULTS: Implemented on an NVIDIA Tesla K40c GPU, the proposed approach yielded a computational acceleration improvement of over 5 times its implementation on a CPU. The proposed approach yielded an average Dice score of 0.87 evaluated over 600 images acquired from six patients. CONCLUSION: This study demonstrated that the GPU computing approach can be used to accelerate tumor tracking for automated radiation therapy for mobile lung tumors. Clinical Impact: Accurately tracking mobile tumor boundaries in real-time is important to automate radiation therapy and the proposed study offers an excellent option for fast tumor region tracking for cancer treatment.
Authors: Marc Modat; Gerard R Ridgway; Zeike A Taylor; Manja Lehmann; Josephine Barnes; David J Hawkes; Nick C Fox; Sébastien Ourselin Journal: Comput Methods Programs Biomed Date: 2009-10-08 Impact factor: 5.428
Authors: B G Fallone; B Murray; S Rathee; T Stanescu; S Steciw; S Vidakovic; E Blosser; D Tymofichuk Journal: Med Phys Date: 2009-06 Impact factor: 4.071