Towards a fast and efficient approach for modelling the patient-specific ventricular haemodynamics
| Autor: | A. de Vecchi, John M. Simpson, Tobias Schaeffter, Graeme P. Penney, David Nordsletten, Alberto Gomez, Nicolas P. Smith, Kuberan Pushparajah |
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| Rok vydání: | 2014 |
| Předmět: |
Patient-Specific Modeling
Computer science Process (engineering) Biophysics Blood Pressure Image processing Tracking (particle physics) Machine learning computer.software_genre Imaging Three-Dimensional Humans Ventricular Function Computer Simulation Molecular Biology Simulation Measure (data warehouse) business.industry Models Cardiovascular Myocardial Contraction Pipeline (software) Complement (complexity) Workflow Artificial intelligence Rheology business computer Blood Flow Velocity Energy (signal processing) |
| Zdroj: | Progress in Biophysics and Molecular Biology. 116:3-10 |
| ISSN: | 0079-6107 |
| DOI: | 10.1016/j.pbiomolbio.2014.08.010 |
| Popis: | Computer modelling of the heart has emerged over the past decade as a powerful technique to explore the cardiovascular pathophysiology and inform clinical diagnosis. The current state-of-the-art in biophysical modelling requires a wealth of, potentially invasive, clinical data for the parametrisation and validation of the models, a process that is still too long and complex to be compatible with the clinical decision-making time. Therefore, there remains a need for models that can be quickly customised to reconstruct physical processes difficult to measure directly in patients. In this paper, we propose a less resource-intensive approach to modelling, whereby computational fluid-dynamics (CFD) models are constrained exclusively by boundary motion derived from imaging data through a validated wall tracking algorithm. These models are generated and parametrised based solely on ultrasound data, whose acquisition is fast, inexpensive and routine in all patients. To maximise the time and computational efficiency, a semi-automated pipeline is embedded in an image processing workflow to personalise the models. Applying this approach to two patient cases, we demonstrate this tool can be directly used in the clinic to interpret and complement the available clinical data by providing a quantitative indication of clinical markers that cannot be easily derived from imaging, such as pressure gradients and the flow energy. |
| Databáze: | OpenAIRE |
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