Fast adaptation of pre-operative patient specific models to real-time intra-operative volumetric data streams

Bruce M. Cameron, Maryam E. Rettmann, David R. Holmes, Richard A. Robb

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

Abstract

Image-guided catheter ablation therapy is becoming an increasingly popular treatment option for atrial fibrillation. Successful treatment relies on accurate guidance of the treatment catheter. Integration of high-resolution, preoperative data with electrophysiology data and positional data from tracked catheters improves targeting, but lacks the means to monitor changes in the atrial wall. Intra-operative ultrasound provides a method for imaging the atrial wall, but the real-time, dynamic nature of the data makes it difficult to seamlessly integrate with the static pre-operative patient-specific model. In this work, we propose a technique which uses a self-organizing map (SOM) for dynamically adapting a pre-operative model to surface patch data. The surface patch would be derived from a segmentation of the anatomy in a real-time, intra-operative ultrasound data stream. The method is demonstrated on two regular geometric shapes as well as data simulated from a real, patient computed tomography dataset.

Original languageEnglish (US)
Title of host publicationMedicine Meets Virtual Reality 18
Subtitle of host publicationNextMed, MMVR18
PublisherIOS Press
Pages99-104
Number of pages6
ISBN (Print)9781607507055
DOIs
StatePublished - Jan 1 2011

Publication series

NameStudies in Health Technology and Informatics
Volume163
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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  • Cite this

    Cameron, B. M., Rettmann, M. E., Holmes, D. R., & Robb, R. A. (2011). Fast adaptation of pre-operative patient specific models to real-time intra-operative volumetric data streams. In Medicine Meets Virtual Reality 18: NextMed, MMVR18 (pp. 99-104). (Studies in Health Technology and Informatics; Vol. 163). IOS Press. https://doi.org/10.3233/978-1-60750-706-2-99