Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study. Issue 5 (8th April 2019) - Grob, Dagmar; Oostveen, Luuk; Rühaak, Jan; Heldmann, Stefan; Mohr, Brian; Michielsen, Koen; Dorn, Sabrina; Prokop, Mathias; Kachelrieβ, Marc; Brink, Monique; Sechopoulos, Ioannis
Abstract : Purpose: The purpose of this study was to assess, using an anthropomorphic digital phantom, the accuracy of algorithms in registering precontrast and contrast‐enhanced computed tomography (CT) chest images for generation of iodine maps of the pulmonary parenchyma via temporal subtraction. Materials and methods: The XCAT phantom, with enhanced airway and pulmonary vessel structures, was used to simulate precontrast and contrast‐enhanced chest images at various inspiration levels and added CT simulation for realistic system noise. Differences in diaphragm position were varied between 0 and 20 mm, with the maximum chosen to exceed the 95th percentile found in a dataset of 100 clinical subtraction CTs. In addition, the influence of whole body movement, degree of iodine enhancement, beam hardening artifacts, presence of nodules and perfusion defects in the pulmonary parenchyma, and variation in noise on the registration were also investigated. Registration was performed using three lung registration algorithms — a commercial (algorithm A) and a prototype (algorithm B) version from Canon Medical Systems and an algorithm from the MEVIS Fraunhofer institute (algorithm C). For each algorithm, we calculated the voxel‐by‐voxel difference between the true deformation and the algorithm‐estimated deformation in the lungs. Results: The median absolute residual error for all three algorithms was smaller than the voxel size (1.0 × 1.0 × 1.0 mm 3 ) for up to an 8 mm diaphragm difference, which is the average difference in diaphragm levels found clinically, and increased with increasing difference in diaphragm position. At 20 mm diaphragm displacement, the median absolute residual error after registration was 0.85 mm (interquartile range, 0.51–1.47 mm) for algorithm A, 0.82 mm (0.50–1.40 mm) for algorithm B, and 0.91 mm (0.54–1.52 mm) for algorithm C. The largest errors were seen in the paracardiac regions and close to the diaphragm. The impact of all other evaluated conditions on the residual error varied, resulting in an increase in the median residual error lower than 0.1 mm for all algorithms, except in the case of whole body displacements for algorithm B, and with increased noise for algorithm C. Conclusion: Motion correction software can compensate for respiratory and cardiac motion with a median residual error below 1 mm, which was smaller than the voxel size, with small differences among the tested registration algorithms for different conditions. Perfusion defects above 50 mm will be visible with the commercially available subtraction CT software, even in poorly registered areas, where the median residual error in that area was 7.7 mm.