Automated registration of 3D TEE datasets of the descending aorta for improved examination and quantification of atheromas burden

M. C. Carminati, C. Piazzese, L. Weinert, W. Tsang, G. Tamborini, M. Pepi, R. M. Lang, E. G. Caiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a robust and efficient approach for the reconstruction of the descending aorta from contiguous 3D transesophageal echocardiographic (TEE) images. It is based on an ad hoc protocol, designed to acquire ordered and partially overlapped 3D TEE datasets, followed by automated image registration that relies on this a priori knowledge. The method was validated using artificially derived misaligned images, and then applied to 14 consecutive patients. Both qualitative and quantitative results demonstrated the potential feasibility and accuracy of the proposed approach. Its clinical applicability could improve the assessment of aortic total plaque burden from 3D TEE images.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages83-92
Number of pages10
Volume8545 LNCS
ISBN (Print)9783319085531
DOIs
Publication statusPublished - 2014
Event6th International Workshop on Biomedical Image Registration, WBIR 2014 - London, United Kingdom
Duration: Jul 7 2014Jul 8 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8545 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Workshop on Biomedical Image Registration, WBIR 2014
CountryUnited Kingdom
CityLondon
Period7/7/147/8/14

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Carminati, M. C., Piazzese, C., Weinert, L., Tsang, W., Tamborini, G., Pepi, M., Lang, R. M., & Caiani, E. G. (2014). Automated registration of 3D TEE datasets of the descending aorta for improved examination and quantification of atheromas burden. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8545 LNCS, pp. 83-92). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8545 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-08554-8_9