Liang Zhong

Effects of Surgical Ventricular Restoration on Left Ventricular Contractility Assessed by a Novel Contractility Index in Patients With Ischemic Cardiomyopathy

A pressure-normalized left ventricular (LV) wall stress (dsigma*/dt(max)) was recently reported as a load-independent index of LV contractility. We hypothesized that this novel contractility index might demonstrate improvement in LV contractile function after surgical ventricular restoration (SVR) using magnetic resonance imaging. A retrospective analysis of magnetic resonance imaging data of 40 patients with ischemic cardiomyopathy who had undergone coronary artery bypass grafting with SVR was performed. LV volumes, ejection fraction, global systolic and diastolic sphericity, and dsigma*/dt(max) were calculated. After SVR, a decrease was found in end-diastolic and end-systolic volume indexes, whereas LV ejection fraction increased from 26% +/- 7% to 31% +/- 10% (p <0.001). LV mass index and peak normalized wall stress were decreased, whereas the sphericity index (SI) at end-diastole increased, indicating that the left ventricle became more spherical after SVR. LV contractility index dsigma*/dt(max) improvement (from 2.69 +/- 0.74 to 3.23 +/- 0.73 s(-1), p <0.001) was associated with shape change as evaluated by the difference in SI between diastole and systole (r = 0.32, p <0.001, preoperative; r = 0.23, p <0.001, postoperative), but not with baseline LV SI. In conclusion, SVR excludes akinetic LV segments and decreases LV wall stress. Despite an increase in sphericity, LV contractility, as determined by dsigma*/dt(max), actually improves. A complex interaction of LV maximal flow rate and LV mass may explain the improvement in LV contractility after SVR. Because dsigma*/dt(max) can be estimated from simple noninvasive measurements, this underscores its clinical utility for assessment of contractile function with therapeutic intervention.

A curvature-based approach for left ventricular shape analysis from cardiac magnetic resonance imaging.

It is believed that left ventricular (LV) regional shape is indicative of LV regional function, and cardiac pathologies are often associated with regional alterations in ventricular shape. In this article, we present a set of procedures for evaluating regional LV surface shape from anatomically accurate models reconstructed from cardiac magnetic resonance (MR) images. LV surface curvatures are computed using local surface fitting method, which enables us to assess regional LV shape and its variation. Comparisons are made between normal and diseased hearts. It is illustrated that LV surface curvatures at different regions of the normal heart are higher than those of the diseased heart. Also, the normal heart experiences a larger change in regional curvedness during contraction than the diseased heart. It is believed that with a wide range of dataset being evaluated, this approach will provide a new and efficient way of quantifying LV regional function.

Explaining Left Ventricular Pressure Dynamics in Terms of LV Passive and Active Elastances

Abstract There has been much characterization of the heart as a pump by means of models based on elastance and compliance. The present paper puts forward the new concept of time-varying passive and active elastance. The biomechanical basis of cyclic elastances of the left ventricle (LV) is presented. Elastance is defined in terms of the relationship between ventricular pressure and volume as dP = EdV + VdE, where E includes passive elastance, Ep, and active elastance, Ea. By incorporating this concept in LV models to simulate diastolic (filling) and systolic phases, a time-varying expression has been obtained for Ea, and an LV volume dependent expression has been obtained for Ep. It is proposed to use these two elastances Ea and Ep to represent the intrinsic LV properties. The active elastance, Ea, can be used to characterize the LV contractile state and represents LV pressure variation due to LV volume variation (such as during the filling and ejection phases). The passive elastance, Ep, can serve as a measure of LV resistance to filling. Furthermore, it has been demonstrated how the LV pressure dynamics (and LV pressure response to LV volume) can be explained in terms of Ea and Ep.

Cardiac Myocardial Disease States Cause Left Ventricular Remodeling with Decreased Contractility and Lead to Heart Failure;Interventions by Coronary Arterial Bypass Grafting and Surgical Ventricular Restoration Can Reverse LV Remodeling with Improved Cont

Dhanjoo N. Ghista1, Liang Zhong2, Leok Poh Chua3, Ghassan S. Kassab4, Yi Su5 and Ru San Tan2 1Department of Graduate and Continuing Education, Framingham State University, Framingham, Massachusetts, 2Department of Cardiology, National Heart Centre, 3School of Mechanical and Aerospace Engineering, Nanyang Technological University, 4Departments of Biomedical Engineering, Surgery, Cellular and Integrative Physiology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, 5Institute of High Performance Computing, Agency for Science, Technology and Research, 1,4USA 2,3,5Singapore

Noninvasive Assessment of Left Ventricular Remodeling: Geometry, Wall Stress, and Function

Left ventricular (LV) remodeling after myocardial infarction (MI) plays an important role in the progression of heart failure (HF). Changes in the shape, size, and function of the LV are caused by altered mechanical properties of the injured myocardium. As the survival rate after MI improves with medical advances, the incidence of HF patients increases. Hence, an accurate depiction of the LV remodeling process facilitates disease surveillance and monitoring of therapeutic efficacy. It may also help determine the choice of treatment, e.g., surgery to remove the infarcted wall segment and reduce the LV cavity size. Traditionally, there are several ways of characterizing LV remodeling: changes in LV diameter, LV volume, ejection fraction, and qualitative or semi-quantitative descriptors of LV shape. In this chapter, we present a new approach to quantify LV shape (in terms of curvedness), wall stress, and function by using computational modeling.