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Registering preoperative CT images to intraoperative TEE images provides surgeons with richer and more useful anatomical information. Yet, CT and TEE images differ substantially in terms of scale and geometry. In the present research, we propose a novel method for the registration of CT and TEE images for navigation during surgical repair of large defects in patients with congenital heart disease. Valve data was used for the coarse registration to determine the basic location. This was followed by the use of an enhanced probability model map to overcome gray-level differences between the two imaging modalities. Finally, the rapid optimization of mutual information was achieved by migrating parameters. This method was tested on a dataset of 240 images from 12 infant, children (\u2264 3 years old), and adult patients with congenital heart disease. Compared to the \u201cbronze standard\u201d registration, the proposed method was more accurate with an average Dice coefficient of 0.91 and an average root mean square of target registration error of 1.2655 mm.<\/jats:p>","DOI":"10.1155\/2018\/4687376","type":"journal-article","created":{"date-parts":[[2018,7,30]],"date-time":"2018-07-30T19:31:21Z","timestamp":1532979081000},"page":"1-11","source":"Crossref","is-referenced-by-count":7,"title":["Large-Deformation Image Registration of CT-TEE for Surgical Navigation of Congenital Heart Disease"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2619-6481","authenticated-orcid":true,"given":"Shuiping","family":"Gou","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an 710071, 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