A robust deformable image registration enhancement method based on radial basis function.
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.
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https://hdl.handle.net/10161/19360Published Version (Please cite this version)
10.21037/qims.2019.07.05Publication 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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Show full item recordScholars@Duke
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.
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
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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