ArticleJournal of applied clinical medical physics2020
Evaluation of a commercial deformable image registration algorithm for dual-energy CT processing.
Article in Journal of applied clinical medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Multiorientation Simultaneous Computation of Back-Projection CT Image Reconstruction Algorithm in Staging Diagnosis of Bladder Cancer.Computational and mathematical methods in medicine · 2022Trial
- Geometric evaluation and quantifying dosimetric impact of diverse deformable image registration algorithms on abdomen images with biomechanically modeled deformations.Journal of applied clinical medical physics · 2024Article
- Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction.Physics in medicine and biology · 2023Article
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5 authors.
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Abstract
purposeSeveral dual-energy computed tomography (DECT) techniques require a deformable image registration to correct for motion between the acquisition of low and high energy data. However, current DECT software does not provide tools to assess registration accuracy or allow the user to export deformed images, presenting a unique challenge for image registration quality assurance (QA). This work presents a methodology to evaluate the accuracy of DECT deformable registration and to quantify the impact of registration errors on end-product images.
methodsThe deformable algorithm implemented in Siemen Healthineers's Syngo was evaluated using a deformable abdomen phantom and a rigid phantom to mimic sliding motion in the thorax. Both phantoms were imaged using sequential 80 and 140 kVp scans with motion applied between the two scans. Since Syngo does not allow the export of the deformed images, this study focused on quantifying the accuracy of various end-product, dual-energy images resulting from processing of deformed images.
resultsThe Syngo algorithm performed well for the abdomen phantom with a mean registration error of 0.4 mm for landmark analysis, Dice similarity coefficients (DSCs) > 0.90 for five organs contoured, and mean iodine concentrations within 0.2 mg/mL of values measured on static images. For rigid sliding motion, the algorithm performed poorer and resulted in noticeable registration errors toward the superior and inferior scan extents and DSCs as low as 0.41 for iodine rods imaged in the phantom. Additionally, local iodine concentration errors in areas of misregistration exceeded 3 mg/mL.
conclusionsThis work represents the first methodology for DECT image registration QA using commercial software. Our data support the clinical use of the Syngo algorithm for abdominal sites with limited motion (i.e., pancreas and liver). However, dual-energy images generated with this algorithm should be used with caution for quantitative measurements in areas with sliding motion.
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