Name
Abstract | ||
|---|---|---|
Disorder of electrical propagation pattern might lead to serious heart attacks, so a variety of efforts have been developed to identify pathological patterns. Among those works, the most promising attempt is 3D model-based imaging of cardiac electrical activities. However, current models are computationally expensive and often too complicated to be adopted into clinical data. In this paper, we propose a meshfree framework, which can build a computational model from the image-derived geometry straightforwardly, without burden mesh generation. This image-derived framework opens great possibilities, including the ability to be directly integrated into our previous cardiac information recovery framework or explore cardiac electrical activities with clinical data. Experiments have been conducted on synthetic data to show the ability of the framework, and on real human data to show its practical potential. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISBI.2008.4541280 | 2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4 |
Keywords | Field | DocType |
computational model, image-derived, meshfree, clinical data | Pattern recognition,Computer science,Simulation,Synthetic data,Artificial intelligence,Machine learning,Mesh generation,Information recovery | Conference |
ISSN | Citations | PageRank |
1945-7928 | 1 | 0.40 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heye Zhang | 1 | 397 | 52.64 |
| Linwei Wang | 2 | 170 | 39.65 |
| Hunter P J | 3 | 1352 | 177.64 |
| Pengcheng Shi | 4 | 549 | 85.38 |