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|a eng
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| 100 |
1 |
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|a Chou, Chen-Rui
|e verfasserin
|4 aut
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| 245 |
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|a 2D/3D Image Registration using Regression Learning
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|c 2013
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|a Date Revised 21.10.2021
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object's 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projec-tion(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region's motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method's application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof
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|a Journal Article
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|a 2D/3D Registration
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|a IGRT
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|a Machine Learning
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| 650 |
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|a Radiation Therapy
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|a Regression
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|a Frederick, Brandon
|e verfasserin
|4 aut
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| 700 |
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|a Mageras, Gig
|e verfasserin
|4 aut
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| 700 |
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|a Chang, Sha
|e verfasserin
|4 aut
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| 700 |
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|a Pizer, Stephen
|e verfasserin
|4 aut
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|t Computer vision and image understanding : CVIU
|d 1997
|g 117(2013), 9 vom: 01. Sept., Seite 1095-1106
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|x 1077-3142
|7 nnns
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