Quantitative Analysis Tools and Digital Phantoms for Deformable Image Registration Quality Assurance. (August 2015)

• Record Type: Journal Article
• Authors: Kim, Haksoo; Park, Samuel B.; Monroe, James I.; Traughber, Bryan J.; Zheng, Yiran; Lo, Simon S.; Yao, Min; Mansur, David; Ellis, Rodney; Machtay, Mitchell; Sohn, Jason W.
• Is Part Of: Technology in cancer research & treatment. Volume 14:Number 4(2015:Aug.)
• Page Start: 428
• Page End: 439
• Publication Date: 2015-08
• Subjects: deformable image registration -- quality assurance; Oncology -- Periodicals; Cancer -- Diagnosis -- Periodicals; Cancer -- Treatment -- Technological innovations -- Periodicals; 616.994
• DOI: 10.1177/1533034614553891
• Languages: English
• ISSNs: 1533-0346

Abstract

This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T ). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref . Applying DVFref to the original reference image deforms T into a new image R′ . The data set, R′, T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest . For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck.