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A robust deformable image registration enhancement method based on radial basis function.

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Date
2019-07
Authors
Liang, Xiao
Yin, Fang-Fang
Wang, Chunhao
Cai, Jing
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Abstract
Background:To develop and evaluate a robust deformable image registration (DIR) enhancement method based on radial basis function (RBF) expansion. Methods:To improve DIR accuracy using sparsely available measured displacements, it is crucial to estimate the motion correlation between the voxels. In the proposed method, we chose to derive this correlation from the initial displacement vector fields (DVFs), and represent it in the form of RBF expansion coefficients of the voxels. The method consists of three steps: (I) convert an initial DVF to a coefficient matrix comprising expansion coefficients of the Wendland's RBF; (II) modify the coefficient matrix under the guidance of sparely distributed landmarks to generate the post-enhancement coefficient matrix; and (III) convert the post-enhancement coefficient matrix to the post-enhancement DVF. The method was tested on five DIR algorithms using a digital phantom. 3D registration errors were calculated for comparisons between the pre-/post-enhancement DVFs and the ground-truth DVFs. Effects of the number and locations of landmarks on DIR enhancement were evaluated. Results:After applying the DIR enhancement method, the 3D registration errors per voxel (unit: mm) were reduced from pre-enhancement to post-enhancement by 1.3 (2.4 to 1.1, 54.2%), 0.0 (0.9 to 0.9, 0.0%), 6.1 (8.2 to 2.1, 74.4%), 3.2 (4.7 to 1.5, 68.1%), and 1.7 (2.9 to 1.2, 58.6%) for the five tested DIR algorithms respectively. The average DIR error reduction was 2.5±2.3 mm (percentage error reduction: 51.1%±29.1%). 3D registration errors decreased inverse-exponentially as the number of landmarks increased, and were insensitive to the landmarks' locations in relation to the down-sampling DVF grids. Conclusions:We demonstrated the feasibility of a robust RBF-based method for enhancing DIR accuracy using sparsely distributed landmarks. This method has been shown robust and effective in reducing DVF errors using different numbers and distributions of landmarks for various DIR algorithms.
Type
Journal article
Subject
4D
Deformable image registration (DIR)
digital phantom
lung motion
Permalink
https://hdl.handle.net/10161/19360
Published Version (Please cite this version)
10.21037/qims.2019.07.05
Publication Info
Liang, Xiao; Yin, Fang-Fang; Wang, Chunhao; & Cai, Jing (2019). A robust deformable image registration enhancement method based on radial basis function. Quantitative imaging in medicine and surgery, 9(7). pp. 1315-1325. 10.21037/qims.2019.07.05. Retrieved from https://hdl.handle.net/10161/19360.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Cai

Jing Cai

Adjunct Associate Professor in the Radiation Oncology
Image-guided Radiation Therapy (IGRT), Magnetic Resonance Imaging (MRI), Tumor Motion Management, Four-Dimensional Radiation Therapy (4DRT), Stereotatic-Body Radiation Therapy (SBRT), Brachytherapy, Treatment Planning, Lung Cancer, Liver Cancer, Cervical Cancer.
Wang

Chunhao Wang

Assistant Professor of Radiation Oncology
Deep learning methods for image-based radiotherapy outcome prediction and assessment Machine learning in outcome modelling Automation in radiotherapy planning and delivery
Yin

Fang-Fang Yin

Gustavo S. Montana Distinguished Professor of Radiation Oncology
Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy, image-guided radiation therapy, oncological imaging and informatics
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