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Registration of 3D trans-esophageal echocardiography to X-ray fluoroscopy using image-based probe tracking

Research output: Contribution to journalArticle

Gang Gao, Graeme Penney, Yingliang Ma, Nicolas Gogin, Pascal Cathier, Aruna Arujuna, Geraint Morton, Dennis Caulfield, Jaswinder Gill, Christopher Aldo Rinaldi, Jane Hancock, Simon Redwood, Martyn Thomas, Reza Razavi, Geert Gijsbers, Kawal Rhode

Original languageEnglish
Pages (from-to)38 - 49
Number of pages12
JournalMedical Image Analysis
Volume16
Issue number1
DOIs
PublishedJan 2012

King's Authors

Abstract

Two-dimensional (2D) X-ray imaging is the dominant imaging modality for cardiac interventions. However, the use of X-ray fluoroscopy alone is inadequate for the guidance of procedures that require soft-tissue information, for example, the treatment of structural heart disease. The recent availability of three-dimensional (3D) trans-esophageal echocardiography (TEE) provides cardiologists with real-time 3D imaging of cardiac anatomy. Increasingly X-ray imaging is now supported by using intra-procedure 3D TEE imaging. We hypothesize that the real-time co-registration and visualization of 3D TEE and Xray fluoroscopy data will provide a powerful guidance tool for cardiologists. In this paper, we propose a novel, robust and efficient method for performing this registration. The major advantage of our method is that it does not rely on any additional tracking hardware and therefore can be deployed straightforwardly into any interventional laboratory. Our method consists of an image-based TEE probe localization algorithm and a calibration procedure. While the calibration needs to be done only once, the GPU-accelerated registration takes approximately from 2 to 15s to complete depending on the number of X-ray images used in the registration and the image resolution. The accuracy of our method was assessed using a realistic heart phantom. The target registration error (TRE) for the heart phantom was less than 2 mm. In addition, we assess the accuracy and the clinical feasibility of our method using five patient datasets, two of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures. The registration results showed our technique had mean registration errors of 1.5-4.2 mm and 95% capture range of 8.7-11.4 mm in terms of TRE.

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