TY - GEN
T1 - Fast adaptation of pre-operative patient specific models to real-time intra-operative volumetric data streams
AU - Cameron, Bruce M.
AU - Rettmann, Maryam E.
AU - Holmes, David R.
AU - Robb, Richard A.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79953125749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79953125749&partnerID=8YFLogxK
U2 - 10.3233/978-1-60750-706-2-99
DO - 10.3233/978-1-60750-706-2-99
M3 - Conference contribution
C2 - 21335770
AN - SCOPUS:79953125749
SN - 9781607507055
T3 - Studies in Health Technology and Informatics
SP - 99
EP - 104
BT - Medicine Meets Virtual Reality 18
PB - IOS Press
ER -