ArticlePhysics in medicine and biology2012
A finite element method to correct deformable image registration errors in low-contrast regions.
Article in Physics in medicine and biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A hybrid biomechanical intensity based deformable image registration of lung 4DCT.Physics in medicine and biology · 2015Trial
- Application of radiomics-based image filtering to improve deformable image registration accuracy in thoracic images.Physics and imaging in radiation oncology · 2026Article
- Review and recommendations on deformable image registration uncertainties for radiotherapy applications.Physics in medicine and biology · 2023Review
- Artificial general intelligence for radiation oncology.Meta-radiology · 2023Article
- Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer.Medical physics · 2023Article
- Applicability and usage of dose mapping/accumulation in radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Review
- Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer.ArXiv · 2023Article
- Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).Medical physics · 2021Article
- The evaluation of a hybrid biomechanical deformable registration method on a multistage physical phantom with reproducible deformation.Radiation oncology (London, England) · 2018Article
- A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction.Physics in medicine and biology · 2018Article
- Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system.Medical physics · 2017Article
- Three-dimensional finite-element based deformable image registration for evaluation of pleural cavity irradiation during photodynamic therapy.Medical physics · 2017Article
- Adaptive radiotherapy for NSCLC patients: utilizing the principle of energy conservation to evaluate dose mapping operations.Physics in medicine and biology · 2017Article
- A Biomechanical Modeling Guided CBCT Estimation Technique.IEEE transactions on medical imaging · 2017Article
- Deformable image registration for adaptive radiotherapy with guaranteed local rigidity constraints.Radiation oncology (London, England) · 2016Article
- Deformable medical image registration of pleural cavity for photodynamic therapy by using finite-element based method.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- Sensitivity of tumor motion simulation accuracy to lung biomechanical modeling approaches and parameters.Physics in medicine and biology · 2015Article
- An adaptive MR-CT registration method for MRI-guided prostate cancer radiotherapy.Physics in medicine and biology · 2015Article
- A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy.Zeitschrift fur medizinische Physik · 2013Article
- Using patient-specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy.Journal of applied clinical medical physics · 2013Article
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6 authors.
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Abstract
Image-guided adaptive radiotherapy requires deformable image registration to map radiation dose back and forth between images. The purpose of this study is to develop a novel method to improve the accuracy of an intensity-based image registration algorithm in low-contrast regions. A computational framework has been developed in this study to improve the quality of the 'demons' registration. For each voxel in the registration's target image, the standard deviation of image intensity in a neighborhood of this voxel was calculated. A mask for high-contrast regions was generated based on their standard deviations. In the masked regions, a tetrahedral mesh was refined recursively so that a sufficient number of tetrahedral nodes in these regions can be selected as driving nodes. An elastic system driven by the displacements of the selected nodes was formulated using a finite element method (FEM) and implemented on the refined mesh. The displacements of these driving nodes were generated with the 'demons' algorithm. The solution of the system was derived using a conjugated gradient method, and interpolated to generate a displacement vector field for the registered images. The FEM correction method was compared with the 'demons' algorithm on the computed tomography (CT) images of lung and prostate patients. The performance of the FEM correction relating to the 'demons' registration was analyzed based on the physical property of their deformation maps, and quantitatively evaluated through a benchmark model developed specifically for this study. Compared to the benchmark model, the 'demons' registration has the maximum error of 1.2 cm, which can be corrected by the FEM to 0.4 cm, and the average error of the 'demons' registration is reduced from 0.17 to 0.11 cm. For the CT images of lung and prostate patients, the deformation maps generated by the 'demons' algorithm were found unrealistic at several places. In these places, the displacement differences between the 'demons' registrations and their FEM corrections were found in the range of 0.4 and 1.1 cm. The mesh refinement and FEM simulation were implemented in a single thread application which requires about 45 min of computation time on a 2.6 GHz computer. This study has demonstrated that the FEM can be integrated with intensity-based image registration algorithms to improve their registration accuracy, especially in low-contrast regions.
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