Fast simulated annealing and adaptive Monte Carlo sampling based parameter optimization for dense optical-flow deformable image registration of 4DCT lung anatomy
| Autor: | Yugang Min, Anand P. Santhanam, John Neylon, T Dou, Patrick A. Kupelian, David Thomas |
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| Rok vydání: | 2016 |
| Předmět: |
Optimization problem
business.industry Computer science Numerical analysis medicine.medical_treatment Physics::Medical Physics Monte Carlo method ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Optical flow Image registration 030218 nuclear medicine & medical imaging Set (abstract data type) Radiation therapy 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis Simulated annealing Metric (mathematics) medicine Computer vision Artificial intelligence business |
| Zdroj: | Medical Imaging: Image-Guided Procedures |
| ISSN: | 0277-786X |
| Popis: | Deformable image registration (DIR) is an important step in radiotherapy treatment planning. An optimal input registration parameter set is critical to achieve the best registration performance with the specific algorithm. Methods In this paper, we investigated a parameter optimization strategy for Optical-flow based DIR of the 4DCT lung anatomy. A novel fast simulated annealing with adaptive Monte Carlo sampling algorithm (FSA-AMC) was investigated for solving the complex non-convex parameter optimization problem. The metric for registration error for a given parameter set was computed using landmark-based mean target registration error (mTRE) between a given volumetric image pair. To reduce the computational time in the parameter optimization process, a GPU based 3D dense optical-flow algorithm was employed for registering the lung volumes. Numerical analyses on the parameter optimization for the DIR were performed using 4DCT datasets generated with breathing motion models and open-source 4DCT datasets. Results showed that the proposed method efficiently estimated the optimum parameters for optical-flow and closely matched the best registration parameters obtained using an exhaustive parameter search method. |
| Databáze: | OpenAIRE |
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