Name
Title | ||
|---|---|---|
Automatic image-to-model framework for patient-specific electromechanical modeling of the heart. |
Abstract | ||
|---|---|---|
A key requirement for recent advances in computational modeling to be clinically applicable is the ability to fit models to patient data. Various personalization techniques have been proposed for isolated sub-components of complex models of heart physiology. However, no work has been presented that focuses on personalizing full electromechanical (EM) models in a streamlined, consistent and automatic fashion, which has been evaluated on a large population. We present an integrated system for full EM personalization from routinely acquired clinical data. The importance of mechanical parameters is analyzed in a comprehensive sensitivity study, revealing that myocyte contraction and Young's modulus are the main determinants of model output variation, what lead to the proposed personalization strategy. On a large, physiologically diverse set of 15 patients, we demonstrate the effectiveness of our framework by comparing measured and calculated parameters, yielding left ventricular ejection fraction and stroke volume errors of 6.6% and 9.2 mL, respectively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBI.2014.6868025 | ISBI |
DocType | ISSN | Citations |
Conference | 1945-7928 | 5 |
PageRank | References | Authors |
0.49 | 4 | 14 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominik Neumann | 1 | 83 | 9.40 |
| Tommaso Mansi | 2 | 454 | 45.94 |
| Sasa Grbic | 3 | 82 | 13.77 |
| Ingmar Voigt | 4 | 160 | 18.48 |
| Bogdan Georgescu | 5 | 1638 | 138.49 |
| Elham Kayvanpour | 6 | 44 | 5.40 |
| Ali Amr | 7 | 42 | 5.35 |
| Farbod Sedaghat-Hamedani | 8 | 44 | 5.40 |
| Jan Haas | 9 | 70 | 8.23 |
| Hugo A. Katus | 10 | 5 | 0.49 |
| Benjamin Meder | 11 | 53 | 6.96 |
| Joachim Hornegger | 12 | 1734 | 190.62 |
| Ali Kamen | 13 | 208 | 25.52 |
| Dorin Comaniciu | 14 | 8389 | 601.83 |