From Clinical Imaging to Patient-Specific Computational Model: Rapid Adaptation of the Living Heart Human Model to a Case of Aortic Stenosis

Abstract

Aortic stenosis (AS) is the most common acquired heart valve disease in the developed world. Traditional methods of grading AS have relied on the measurement of aortic valve area and transvalvular pressure gradient. Recent research has highlighted the existence of AS variants that do not meet classic criteria for severe AS such as low-flow, low-gradient AS. With the development of sophisticated multi-scale computational models, investigation into the left ventricular (LV) biomechanics of AS offers new insights into the pathophysiology that may guide treatment decisions surrounding AS. Building upon our prior study entailing LV-aortic coupling where AS conditions were applied to the idealized geometry of the Living Heart Human Model, we now describe the first patient-specific adaptation of the model to a case of low flow, low gradient AS. EKG-gated cardiac computed tomography images were segmented to provide surfaces to which the generic Living Heart model was adapted. The model was coupled to a lumped-parameter circulatory system; it was then calibrated to patient clinical data from echocardiography/cardiac catheterization with strong correlation (simulation versus clinical measurement): ascending aorta systolic pressure: 109 mmHg vs 116 mmHg, ascending aorta diastolic pressure 50 mmHg vs 45 mmHg, LV systolic pressure: 118 mmHg vs 128 mmHg, peak transvalvular gradient: 9 mmHg vs 12 mmHg, LV ejection fraction: 23% vs 25%. This work illustrates how the Living Heart Human Model geometry can be efficiently adapted to patient-specific parameters, enabling future biomechanics investigations into the LV dysfunction of AS.