2020
DOI: 10.1002/acm2.12987
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Evaluation of a commercial deformable image registration algorithm for dual‐energy CT processing

Abstract: Purpose Several 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 regist… Show more

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Cited by 3 publications
(2 citation statements)
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“…Jing et al [ 17 ] can clearly supervise the training process of fuzzy images by using 3D reconstructed CT images, and the intelligent algorithm used showed good results. Huang et al [ 18 ] used intelligent algorithm to ensure the quality of CT image registration and obtain the accuracy of dual-energy images. In this study, the intelligent algorithm was applied in CT imaging to explore the bladder cancer staging.…”
Section: Discussionmentioning
confidence: 99%
“…Jing et al [ 17 ] can clearly supervise the training process of fuzzy images by using 3D reconstructed CT images, and the intelligent algorithm used showed good results. Huang et al [ 18 ] used intelligent algorithm to ensure the quality of CT image registration and obtain the accuracy of dual-energy images. In this study, the intelligent algorithm was applied in CT imaging to explore the bladder cancer staging.…”
Section: Discussionmentioning
confidence: 99%
“…To address the dual-energy motion artifact, image registration has been incorporated with medical imaging techniques to reconstruct dual-energy images from measurements taken in different patient positions. Gang et al , Huang et al and Leng have all deployed deformable image registration (DIR) methods to align two sequentially scanned images at different energies before dual-energy material decomposition (Gang et al 2009 , Leng et al 2015 , Huang et al 2020 ) and demonstrated significant reduction of motion artifacts. However, these motion-compensated methods are designed for image-domain decomposition techniques, which cannot be simply applied to DECT SIR.…”
Section: Introductionmentioning
confidence: 99%