Motivation

The ventricles of our heart generate tension during systole to eject blood, and release this tension during diastole to allow blood filling. An impaired diastolic function is affected by two main characteristics of the cardiac muscle, its capacity to relax and its stiffness. But these two physiological parameters are difficult to assess in the clinic, and the criterion to diagnose diastolic dysfunction is subject to limitations and controversies. There is thus a need for better biomarkers to characterise the diastolic performance of our heart.

Our approach

CMIB investigates on methods for the direct estimation of the mechanical parameters that rule ventricular blood filling, myocardial stiffness and the decay of active tension, which will allow a better characterization of diastolic performance. We develop personalise computational ventricular mechanical models of patient’s available clinical data of myocardial deformation and blood pressure. The model parameters that best explain the clinical data then become the new biomarkers.

Technologies

The extraction of diastolic biomarkers is enabled by advances in:

• Image analysis techniques to retrieve the deformation field from dynamic sequences
• Computational model personalization techniques
• Novel imaging acquisition protocols that will remove the need of invasive pressure recordings

Further reading

• The estimation of patient-specific cardiac diastolic functions from clinical measurements
• Myocardial Stiffness Estimation: A Novel Cost Function for Unique Parameter Identification