Dynamic Characteristics of the Renal Arterial Microvasculature of the Dog Obtained by Simulation

To develop a measurement technique for microvascular stiffness and resistance, an iterative algorithm (NOLESQ) combining steepest descent and Taylor series (Gauss-Newton) methods was used to estimate model parameters to provide a minimal least squares error at 5-ms intervals of the predicted blood flow to the measured flow. Renal arterial pressure and flow data of high dynamic and static accuracy were used. The data were in blocks of 2500 values each (12.5-s samples). A reasonable fit was obtained with only three parameters: arterial conductance, compliance, and a steady pressure at or just beyond the glomerulae. Other models, incorporating large artery resistance, blood mass, pressure dependent conductance, and pressure dependent compliance were studied. Criteria for acceptance were reasonable values from prior knowledge of biological materials, similarity of parameter values between consecutive samples and between animals, convergence, and error of fit reduced to the noise level of the input data.