| Literature DB >> 22670205 |
Nic Smith1, Adelaide de Vecchi, Matthew McCormick, David Nordsletten, Oscar Camara, Alejandro F Frangi, Hervé Delingette, Maxime Sermesant, Jatin Relan, Nicholas Ayache, Martin W Krueger, Walther H W Schulze, Rod Hose, Israel Valverde, Philipp Beerbaum, Cristina Staicu, Maria Siebes, Jos Spaan, Peter Hunter, Juergen Weese, Helko Lehmann, Dominique Chapelle, Reza Rezavi.
Abstract
The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.Entities:
Keywords: cardiac modelling; multi-scale; patient specific; virtual physiological human
Year: 2011 PMID: 22670205 PMCID: PMC3262448 DOI: 10.1098/rsfs.2010.0048
Source DB: PubMed Journal: Interface Focus ISSN: 2042-8898 Impact factor: 3.906