Surface flattening of the human left atrium and proof-of-concept clinical applications

Karim, Rashed, Ma, YingLiang ORCID: https://orcid.org/0000-0001-5770-5843, Jang, Munjung, Housden, R. James, Williams, Steven E., Chen, Zhong, Ataollahi, Asghar, Althoefer, Kaspar, Rinaldi, C. Aldo, Razavi, Reza, O'Neill, Mark D., Schaeftter, Tobias and Rhode, Kawal S. (2014) Surface flattening of the human left atrium and proof-of-concept clinical applications. Computerized Medical Imaging and Graphics, 38 (4). pp. 251-266. ISSN 0895-6111

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Abstract

Surface flattening in medical imaging has seen widespread use in neurology and more recently in cardiology to describe the left ventricle using the bull's-eye plot. The method is particularly useful to standardize the display of functional information derived from medical imaging and catheter-based measurements. We hypothesized that a similar approach could be possible for the more complex shape of the left atrium (LA) and that the surface flattening could be useful for the management of patients with atrial fibrillation (AF). We implemented an existing surface mesh parameterization approach to flatten and unfold 3D LA models. Mapping errors going from 2D to 3D and the inverse were investigated both qualitatively and quantitatively using synthetic data of regular shapes and computer tomography scans of an anthropomorphic phantom. Testing of the approach was carried out using data from 14 patients undergoing ablation treatment for AF. 3D LA meshes were obtained from magnetic resonance imaging and electroanatomical mapping systems. These were unfolded using the developed approach and used to demonstrate proof-of-concept applications, such as the display of scar information, electrical information and catheter position. The work carried out shows that the unfolding of complex cardiac structures, such as the LA, is feasible and has several potential clinical uses for the management of patients with AF.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://pureportal.coventry.ac.uk/en/pub...
Depositing User:	LivePure Connector
Date Deposited:	03 Jan 2023 14:33
Last Modified:	10 Dec 2024 01:40
URI:	https://ueaeprints.uea.ac.uk/id/eprint/90374
DOI:	10.1016/j.compmedimag.2014.01.004

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