Key result
A semi-automated method for patient-specific computational flow modeling of the left ventricle yielded results comparable to other clinically validated models using volunteer data.
A novel semi-automated method for patient-specific computational fluid dynamics modeling of the left ventricle reduces operator dependency while maintaining comparable results to existing validated models.
May facilitate semi-automated LV flow modeling; leaves open clinical adoption pending prospective patient validation.
Patient-specific computational fluid dynamics (CFD) modelling of the left ventricle (LV) is a promising technique for the visualisation of ventricular flow patterns throughout a cardiac cycle. While significant progress has been made in improving the physiological quality of such simulations, the methodologies involved for several key steps remain significantly operator-dependent to this day. This dependency limits both the efficiency of the process as well as the consistency of CFD results due to the labour-intensive nature of current methods as well as operator introduced uncertainties in the modelling process. In order to mitigate this dependency, we propose a semi-automated method for patient-specific computational flow modelling of the LV. Using magnetic resonance imaging derived coarse geometry data of a patient's LV endocardium shape throughout a cardiac cycle, we then proceed to refine the geometry to eliminate rough edges before reconstructing meshes for all time frames and finally numerically solving for the intra-ventricular flow. Using a sample of patient-specific volunteer data, we demonstrate that our semi-automated, minimal operator involvement approach is capable of yielding CFD results of the LV that are comparable to other clinically validated LV flow models in the literature.
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Nguyen et al. (2013) studied this question. Semi-automated method for patient-specific computational flow modelling vs. Other clinically validated LV flow models was evaluated on Computational fluid dynamics (CFD) results of the left ventricle. A semi-automated method for patient-specific computational flow modeling of the left ventricle yielded results comparable to other clinically validated models using volunteer data.
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