Purpose To develop a quality assurance (QA) workflow using a robust, curated, manually-segmented anatomic ROI library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation CT (SimCT) to diagnostic CT (DxCT) co-registration. Materials and Methods SimCTs and DxCTs of twenty patients with head and neck squamous cell carcinoma treated with curative-intent intensity modulated radiotherapy (IMRT) between August 2011 and May 2012 were retrospectively retrieved under an institutional review board approval. 68 reference anatomic regions of interest (ROIs) in addition to gross tumor and nodal targets were then manually contoured on each scan. DxCT was registered to SimCT rigidly, and through 4 different deformable image registration (DIR) algorithms; Atlas-based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs using similarity coefficient metrics (i.e. Dice similarity coefficient) and surface distance metrics (i.e. 95% maximum Hausdorff distance). Non-parametric Steel test with control was used to compare different DIR algorithms to rigid registration (RIR) with post hoc Wilcoxon rank test for stratified metric comparison. Results A total of 2720 anatomic and 50 tumor/nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for both anatomic and target ROIs conformance as shown for the majority of comparison metrics (Steel test, p-value <0.008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures/category, and SimCT image slice thickness. Conclusion Development of a formal ROI-based QA workflow for registration assessment revealed improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck DxCT-SimCT allineation, especially for target delineation.