Heart disease remains the biggest killer in the western world. One or multiple heart diseases can result in heart failure, which is a clinical syndrome that arises when the heart is unable to pump sufficient blood to meet the metabolic needs of the body at normal filling pressures. The goal of recording and visualizing cardiac data sets is to recognize and predict heart diseases.
In this work we use a finite element model of the human left ventricle which was obtained from a series of tagged MRI images. The deformation of the myocardium (heart muscle) is represented by the strain tensor.
Computing Volume Measures
The volume of a single finite element is obtained by integrating the identity function over the finite element in world coordinates. The calculation is simplified by using the substitution rule of multi-dimensional integration.
of the most important measures of cardiac performance is the ventricular (blood)
volume and the fraction of blood ejected during contraction. In order to apply
the volume computation introduced above the left-ventricular cavity must be
modeled by finite elements. Using our toolkit we can define centroids for
any four vertices on the endocardial surface with common longitudinal material
coordinate. Connecting these vertices to the corresponding points on the endocardial
surface results in 16 finite elements for the left ventricular cavity. The
figure below shows the finite element models of the ventricular cavity of
the healthy heart at end-diastole and end-systole.
Using these models we compute that the ventricular volume of the healthy heart at end-diastole is about 87 cm^3 and the stroke volume is 52 cm^3 resulting in an ejection fraction of about 60%. These values correspond well with data reported in the medical literature.
A one-dimensional continuous representation of the strain tensor is achieved using hyperstreamlines. The trajectory of a hyperstreamline is a streamline in the eigenvector field, ie. the trajectory is obtained by starting at a point and integrating in the direction of the selected eigenvector. The other two eigenvectors and corresponding eigenvalues define the axes and lengths of the ellipsoidal cross section of the hyperstreamline. The remaining eigenvalue is colour mapped onto the hyperstreamline.
The figure below shows hyperstreamlines in the direction of the major (left) and minor principal strain (right), respectively.
The figure above shows that the maximum compressive strain in the midmyocardium is predominantly oriented in circumferential direction with a slight downward tilt. Several interesting points exists where the strain suddenly changes direction. Results from tensor analysis show that these points are degenerate points for which at least two eigenvalue are equal. An example of such a point is indicated by the white rectangle and is shown enlarged on the right hand side of the image. We found that most of the degenerate points occur on or near the septal wall. The unusual variations in strain orientation might be caused by the right ventricular wall which is connected to the left ventricular wall at both sides of the septum.
We would like to thank Alistair A. Young from the Department of Physiology and the Department of Anatomy with Radiology of the University of Auckland, Auckland, New Zealand, for valuable discussions and for providing us with the models of the left ventricle.
Last Modified: 01/22/2003 15:02:53 by Burkhard Wünsche (Wuensche)