Learning anatomy changes from patient populations to create artificial CT images for voxel-level validation of deformable image registration-Reference-Cited by-同舟云学术

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Learning anatomy changes from patient populations to create artificial CT images for voxel-level validation of deformable image registration

Published:2016-01 Issue:1 Volume:17 Page:246-258
ISSN:1526-9914
Container-title:Journal of Applied Clinical Medical Physics
language:en
Short-container-title:Journal of Applied Clinical Medical Physics

Author:

Yu Z. Henry¹²,Kudchadker Rajat¹,Dong Lei³,Zhang Yongbin³,Court Laurence E.¹,Mourtada Firas²,Yock Adam¹,Tucker Susan L.⁴,Yang Jinzhong¹

Affiliation:

1. Departments of Radiation Physics; The University of Texas MD Anderson Cancer Center; Houston TX

2. Department of Radiation Oncology; Christiana Health Care Systems; Newark DE USA

3. Scripps Proton Therapy Center; San Diego CA

4. Bioinformatics & Computational Biology; The University of Texas MD Anderson Cancer Center; Houston TX

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1120/jacmp.v17i1.5888/fullpdf

Reference42 articles.

1. Image registration and data fusion in radiation therapy;Kessler;Br J Radiol.,2006

2. Deformable registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy;Lu;Phys Med Biol.,2006

3. Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy;Wang;Phys Med Biol.,2005

4. Deformable registration for image-guided radiation therapy;Sarrut;Z Med Phys.,2006

5. Deformable image registration for radiation therapy planing: algorithms and applications;Kaus,2007

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