Combined cardiac and respiratory motion compensation for atrial fibrillation ablation procedures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6891 LNCS, Issue PART 1, 2011, Pages 540-547

  Brost, Alexander ^a   Wu, Wen ^b   Koch, Martin ^a   Wimmer, Andreas ^a   Chen, Terrence ^b   Liao, Rui ^b   Hornegger, Joachim ^a   Strobel, Norbert ^c  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b SIEMENS CORPORATE RESEARCH   (United States)

^c SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ATRIAL FIBRILLATION; AVERAGE ERRORS; CARDIAC MOTION; CATHETER ABLATION; CLINICAL DATA; CORONARY SINUS; ELECTRICAL ISOLATION; FALSE POSITIVE RATES; FLUOROSCOPIC IMAGE GUIDANCE; FLUOROSCOPIC IMAGES; LEFT ATRIUMS; MOTION ESTIMATES; PULMONARY VEINS; SOFT TISSUE; VOLUMETRIC DATA; XRAY IMAGING;

ABLATION; CATHETERS; DISEASES; DRUG THERAPY; HEART; MAPPING; MOTION COMPENSATION; THREE DIMENSIONAL; TISSUE; VOLUMETRIC ANALYSIS;

MEDICAL COMPUTING;

ALGORITHM; ARTICLE; BREATHING; CATHETER ABLATION; CORONARY SINUS; ELECTRODE; HEART; HEART ATRIUM FIBRILLATION; HOSPITALIZATION; HUMAN; IMAGE PROCESSING; LABORATORY DIAGNOSIS; METHODOLOGY; MOTION; PATHOLOGY; PATHOPHYSIOLOGY; PHYSIOLOGY; REPRODUCIBILITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; X RAY;

ALGORITHMS; ATRIAL FIBRILLATION; CATHETER ABLATION; CORONARY SINUS; ELECTRODES; FALSE POSITIVE REACTIONS; HEART; HOSPITALIZATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, STATISTICAL; MOTION; REPRODUCIBILITY OF RESULTS; RESPIRATION; X-RAYS;

EID: 80053524706     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23623-5_68     Document Type: Conference Paper

References (13)

1. Gage, B., Waterman, A., Shannon, W., Boechler, M., Rich, M., Radford, M.: Validation of Clinical Classification Schemes for Predicting Stroke. Journal of the American Medical Association 285(22), 2864-2870 (2001)
2. Calkins, H., Brugada, J., Packer, D., Cappato, R., Chen, S., Crijns, H., Damiano, R., Davies, D., Haines, D., Haïssaguerre, M., Iesaka, Y., Jackman,W., Jais, P., Kottkamp, H., Kuck, K., Lindsay, B., Marchlinski, F., McCarthy, P., Mont, J., Moradi, F., Nademanee, K., Natale, A., Pappone, C., Prystowsky, E., Raviele, A., Ruskin, J., Shemin, R.: HRS/EHRA/ECAS Expert Consensus Statement on Catheter and Surgical Ablation of Atrial Fibrillation: Recommendations for Personnel, Policy, Procedures and Follow-Up. Heart Rhythm 4(6), 1-46 (2007)
3. Prümmer, M., Hornegger, J.: Cardiac C-arm CT: a unified framework for motion estimation and dynamic CT. IEEE Transactions on Medical Imaging 28(11), 1836-1849 (2009)
4. Ector, J., De Buck, S., Huybrechts, W., Nuyens, D., Dymarkowski, S., Bogaert, J., Maes, F., Heidbüchel, H.: Biplane three-dimensional augmented fluoroscopy as single navigation tool for ablation of atrial fibrillation: Accuracy and clinical value. Heart Rhythm 5(7), 957-964 (2008)
5. Brost, A., Liao, R., Hornegger, J., Strobel, N.: 3-D respiratory motion compensation during EP procedures by image-based 3-D lasso catheter model generation and tracking. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 394-401. Springer, Heidelberg (2009)
6. Ma, Y., King, A.P., Gogin, N., Rinaldi, C.A., Gill, J., Razavi, R., Rhode, K.S.: Real-time respiratory motion correction for cardiac electrophysiology procedures using image-based coronary sinus catheter tracking. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 391-399. Springer, Heidelberg (2010)
7. Brost, A., Liao, R., Hornegger, J., Strobel, N.: Model-based registration for motion compensation during EP ablation procedures. In: Fischer, B., Dawant, B.M., Lorenz, C. (eds.) WBIR 2010. LNCS, vol. 6204, pp. 234-245. Springer, Heidelberg (2010)
8. Brost, A., Wimmer, A., Liao, R., Hornegger, J., Strobel, N.: Catheter tracking: Filter-based vs. Learning-based. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) Pattern Recognition. LNCS, vol. 6376, pp. 293-302. Springer, Heidelberg (2010)
9. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman & Hall, New York (1984)
10. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119-139 (1997)
11. Breu, H., Gil, J., Kirkpatrick, D., Werman, M.: Linear time Euclidean distance transform algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(5), 529-533 (1995)
12. Wu, W., Chen, T., Barbu, A., Wang, P., Strobel, N., Zhou, S., Comaniciu, D.: Learning-based Hypothesis Fusion for Robust Catheter Tracking in 2D X-ray Fluoroscopy. To Appear IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 (2011)
13. Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1589-1596. IEEE Computer Society, Los Alamitos (2005)