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Biomedical subjects

S P Nudelman

Publications and source records attributed to S P Nudelman.

5 recordsLinked to original sources

PubMed1998

Evaluation of model-based processing algorithms for averaged transmitral spectral Doppler images.

In an effort to characterize more fully diastolic function using Doppler echocardiography, we have previously developed an automated method of model-based image processing for spectral Doppler images of transmitral blood flow. In this method, maximum velocity envelopes (MVEs) extracted from individual Doppler images are aligned and averaged over several cardiac cycles. The averaged waveform is fit by the solution of a kinematic model of diastolic filling. The results are estimates of the model parameters. As expected, the mean and standard deviation of the model parameter estimates depend on many factors such as noise, the number of cardiac cycles averaged, beat-to-beat variation, waveform shape, observation time and the processing methods used, among others. A comprehensive evaluation of these effects has not been performed to date. A simulation was developed to evaluate the performance of three automated processing methods and to measure the influence of noise, beat-to-beat variation and observation time on the model parameter estimates. The simulation's design and a description and analysis of the three automated processing methods are presented. Of the three methods evaluated, using the inflection point in the acceleration portion of the velocity contour as the first data point to be fit was found to be the most robust method for processing averaged E-wave MVE waveforms. Using this method under nominal conditions, the average bias was measured to be < 3% for each of the model parameters. As expected, the biases and standard deviations of the estimates increased as a result of increased noise levels, increased beat-to-beat variation and decreased observation time. Another important finding was that the effects of noise, beat-to-beat variation and waveform observation time on the parameter estimates are dependent on the location in model parameter space.

Algorithms↗

PubMed1998

Beat averaging alternatives for transmitral Doppler flow velocity images.

To characterize diastolic function from transmitral Doppler data, the image's maximum velocity envelope (MVE) is fit by a model for flow velocity. To reduce the physiologic beat-to-beat variability of best-fit determined model parameters, averaging of multiple cardiac cycles is indicated. To assess variability mathematically, we modeled physiologic noise as a random (normally-distributed) process and evaluated three methods of averaging (1, averaging model parameters from single images; 2, averaging images; and 3, averaging MVEs) using clinical datasets (50 continuous beats from 5 subjects). Method 2 generates a positive bias because low-velocity beats will not contribute to the composite MVE. The difference between Methods 3 and 1 is less than 2.0 E-5 (m/s)2 for uncorrelated model parameters. Input having 10% beat-to-beat variation yields a bias of <4% for model parameter mean. Hence, Method 1 was, in general, more robust than Method 3.

Blood Flow Velocity↗

PubMed1997

Echo machine-imposed limit on transmitral spectral Doppler velocity-profile analysis.

We have previously developed a kinematic model of ventricular filling. Its application to in vivo transmitral Doppler velocity profiles provides a quantitative characterization of filling. However, the model parameters computed by solving the "inverse problem" may depend on ultrasound machine type and setting (e.g., gain, baseline filter, dynamic range). To determine machine-based effects on the computed model parameters, we performed a flow phantom study using Acuson and HP echocardiography machines at various settings. We compared maximum velocity envelopes (MVEs), as well as the model fit to these MVEs, for 3 simulated waveforms imaged by both machines. For all 3 waveforms, the machines generated comparable MVEs, fit by the model within a mean-square difference of 5E-5 (m/s)2. The associated variations in model parameters for the 3 waveforms were not uniform. Two waveforms showed slight variation between machines, with model parameters varying by less than 6%. The shortest duration waveform showed model parameter variations of 10-15%. Analysis of the parameter space for this waveform showed a constant mean-square error contour that was larger than that for the other two, causing similar small variations in measured MVEs to result in larger differences in the parameter estimates for this waveform. Because this method completely eliminates inter- and intraobserver variability, we conclude that, within the limits established, the slight contour variations due to machine type and setting should not affect this method's applicability in clinical Doppler-flow analysis.

Blood Flow Velocity↗

PubMed1995

Relationship of the third heart sound to transmitral flow velocity deceleration.

BACKGROUND: The third heart sound (S3) occurs shortly after the early (E-wave) peak of the transmitral diastolic Doppler velocity profile (DVP). It is thought to be due to cardiohemic vibrations powered by rapid deceleration of transmitral blood flow. Although the presence, timing, and clinical correlates of the S3 have been extensively characterized, derivation and validation of a causal, mathematical relation between transmitral flow velocity and the S3 are lacking. METHODS AND RESULTS: To characterize the kinematics and physiological mechanisms of S3 production, we modeled the cardiohemic system as a forced, damped, nonlinear harmonic oscillator. The forcing term used a closed-form mathematical expression for the deceleration portion of the DVP. We tested the hypothesis that our model's predictions for amplitude, timing, and frequency of S3 accurately predict the transthoracic phonocardiogram, using the simultaneously recorded transmitral Doppler E wave as input, in three subject groups: those with audible pathological S3, those with audible physiological S3, and those with inaudible S3. CONCLUSIONS: We found excellent agreement between model prediction and the observed data for all three subject groups. We conclude that, in the presence of a normal mitral valve, the kinematics of filling requires that all hearts have oscillations of the cardiohemic system during E-wave deceleration. However, the oscillations may not have high enough amplitude or frequency to be heard as an S3 unless there is sufficiently rapid fluid deceleration (of the Doppler E-wave contour) with sufficient cardiohemic coupling.

Blood Flow Velocity↗

PubMed1994

Automated method for characterization of diastolic transmitral Doppler velocity contours: late atrial filling.

We develop an automated method of characterizing the late atrial filling phase of diastole by fitting a kinematic model for diastolic filling to the clinical Doppler A-wave contour. The result is a set of model parameters which completely characterizes the contour. We have previously derived a parameterized diastolic filling (PDF) model, which predicts the time-dependent transmitral blood flow velocity obtained by Doppler echocardiography. An automated method to determine the PDF model parameters for early rapid filling from the clinical Doppler E-wave has also been developed and validated. The method consists of digitizing the acoustic Doppler waveform, recreating the Doppler velocity profile, extracting the maximum velocity envelope, and fitting the PDF model for early filling to the envelope. In the current work, we apply the same general approach for PDF parameter determination for the late atrial filling phase of diastole. To assess the presence and significance of near-degeneracies in the model parameter set, numerical experiments (consisting of fitting the model to a model-generated contour to which Gaussian noise was added) were performed. These revealed a two-dimensional degeneracy in four-dimensional parameter space which could be removed by using two kinematic simplifications: critical damping and resonant forcing. We show that these degeneracy-eliminating approximations do not limit the ability of the model to predict clinical A-wave contours.

Atrial Function↗