Simple jQuery Dropdowns
Please use this identifier to cite or link to this item:
Title: Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: Anatomic region of interest-based comparison of rigid and deformable algorithms
Authors: Abdallah S.R. Mohamed
Manee Naad Ruangskul
Musaddiq J. Awan
Charles A. Baron
Jayashree Kalpathy-Cramer
Richard Castillo
Edward Castillo
Thomas M. Guerrero
Esengul Kocak-Uzel
Jinzhong Yang
Laurence E. Court
Michael E. Kantor
G. Brandon Gunn
Rivka R. Colen
Steven J. Frank
Adam S. Garden
David I. Rosenthal
Clifton D. Fuller
University of Texas MD Anderson Cancer Center
Massachusetts General Hospital
Rice University
University of Texas Health Science Center at Houston
Alexandria University
Mahidol University
Case Western Reserve University
Jefferson Medical College
Sisli Etfal Hospital
Keywords: Medicine
Issue Date: 1-Mar-2015
Citation: Radiology. Vol.274, No.3 (2015), 752-763
Abstract: © RSNA, 2014. Purpose: To develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation computed tomography (CT) with diagnostic CT coregistration. Materials and Methods: Radiation therapy-simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison.. Results: A total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness. Conclusion: Development of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation.
ISSN: 15271315
Appears in Collections:Scopus 2011-2015

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.