Faculty Collaboration Database

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. Radiology 2015 Mar;274(3):752-63

Date

11/08/2014

Pubmed ID

25380454

Pubmed Central ID

PMC4358813

DOI

10.1148/radiol.14132871

Scopus ID

2-s2.0-84924301770 (requires institutional sign-in at Scopus site)   53 Citations

Abstract

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.

Author List

Mohamed AS, Ruangskul MN, Awan MJ, Baron CA, Kalpathy-Cramer J, Castillo R, Castillo E, Guerrero TM, Kocak-Uzel E, Yang J, Court LE, Kantor ME, Gunn GB, Colen RR, Frank SJ, Garden AS, Rosenthal DI, Fuller CD

Author

Musaddiq J. Awan MD Assistant Professor in the Radiation Oncology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Aged
Algorithms
Benchmarking
Carcinoma, Squamous Cell
Computer Simulation
Female
Head and Neck Neoplasms
Humans
Male
Middle Aged
Quality Assurance, Health Care
Retrospective Studies
Tomography, X-Ray Computed