Estimation of the Doppler ultrasound maximal umbilical waveform envelope: I. Estimation method

Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos

OBJECTIVE

Novel applications of transcranial Doppler (TCD) ultrasonography, such as the assessment of cerebral vessel narrowing/occlusion or the non-invasive estimation of intracranial pressure (ICP), require high-quality maximal flow velocity waveforms. However, due to the low signal-to-noise ratio of TCD spectrograms, measuring the maximal flow velocity is challenging. In this work, we propose a calibration-free algorithm for estimating maximal flow velocities from TCD spectrograms and present a pertaining beat-by-beat signal quality index.

METHODS

Our algorithm performs multiple binary segmentations of the TCD spectrogram and then extracts the pertaining envelopes (maximal flow velocity waveforms) via an edge-following step that incorporates physiological constraints. The candidate maximal flow velocity waveform with the highest signal quality index is finally selected.

RESULTS

We evaluated the algorithm on 32 TCD recordings from the middle cerebral and internal carotid arteries in 6 healthy and 12 neurocritical care patients. Compared to manual spectrogram tracings, we obtained a relative error of -1.5%, when considering the whole waveform, and a relative error of -3.3% for the peak systolic velocity.

CONCLUSION

The feedback loop between the signal quality assessment and the binary segmentation yields a robust algorithm for maximal flow velocity estimation. Clinical Impact: The algorithm has already been used in our ICP estimation pipeline. By making the code and the data publicly available, we hope that the algorithm will be a useful building block for the development of novel TCD applications that require high-quality flow velocity waveforms.

A Robust Automatic Ultrasound Spectral Envelope Estimation

Accurate estimation of ultrasound Doppler spectrogram envelope is essential for clinical pathological diagnosis of various cardiovascular diseases. However, due to intrinsic spectral broadening in the power spectrum and speckle noise existing in ultrasound images, it is difficult to obtain the accurate maximum velocity. Each of the standard existing methods has their own limitations and does not work well in complicated recordings. This paper proposes a robust automatic spectral envelope estimation method that is more accurate in phantom recordings and various in-vivo recordings than the currently used methods. Comparisons were performed on phantom recordings of the carotid artery with varying noise and additional in-vivo recordings. The accuracy of the proposed method was on average 8% greater than the existing methods. The experimental results demonstrate the wide applicability under different blood conditions and the robustness of the proposed algorithm.

Adaptive Spectral Envelope Estimation for Doppler Ultrasound

Estimation of accurate maximum velocities and spectral envelope in ultrasound Doppler blood flow spectrograms are both essential for clinical diagnostic purposes. However, obtaining accurate maximum velocity is not straightforward due to intrinsic spectral broadening and variance in the power spectrum estimate. The method proposed in this paper for maximum velocity point detection has been developed by modifying an existing method-signal noise slope intersection, incorporating in it steps from an altered version of another method called geometric method. Adaptive noise estimation from the spectrogram ensures that a smooth spectral envelope is obtained postdetection of these maximum velocity points. The method has been tested on simulated Doppler signal with scatterers possessing a parabolic flow velocity profile constant in time, steady and pulsatile string phantom recordings, as well as in vivo recordings from uterine, umbilical, carotid, and subclavian arteries. The results from simulation experiments indicate a bias of less than 2.5% in maximum velocities when estimated for a range of peak velocities, Doppler angles, and SNR levels. Standard deviation in the envelope is low-less than 2% in the case of experiments done by varying the peak velocity and Doppler angle for steady phantom and simulated flow, and also less than 2% in the case of experiments done by varying SNR but keeping constant flow conditions for in vivo and simulated flow. Low variability in the envelope makes the prospect of using the envelope for automated blood flow measurements possible and is illustrated for the case of pulsatility index estimation in uterine and umbilical arteries.

Optimization of Doppler echocardiographic velocity measurements using an automatic contour detection method

Intra- and interobserver variability in Doppler echocardiographic velocity measurements (DEVM) is a significant issue. Indeed, imprecisions of DEVM can lead to diagnostic errors, particularly in the quantification of the severity of heart valve dysfunctions. To reduce the variability and rapidity of DEVM, we have developed an automatic method of Doppler velocity wave contour detection, based on active contour models. To validate our new method, results obtained with this method were compared with those obtained manually by two experienced echocardiographers on Doppler echocardiographic images of left ventricular outflow tract and transvalvular flow velocity signals recorded in 30 patients with aortic or mitral stenosis, 20 with normal sinus rhythm and 10 with atrial fibrillation. We focused on the three essential variables that are measured routinely using Doppler echocardiography in the clinical setting: the maximum velocity (Vmax), the mean velocity (Vmean) and the velocity-time integral (VTI). Comparison between the two methods has shown a very good agreement. A small bias value was found between the two methods (between -3.9% and 0.5% for Vmax, between -4.6% and -1.4% for Vmean and between -3.6% and 4.4% for VTI). Moreover, the computation time was short, approximately 5 s. This new method applied to DEVM could, therefore, provide a useful tool to eliminate the intra- and interobserver variabilities associated with DEVM and thereby to improve the accuracy of the diagnosis of cardiovascular disease. This automatic method could also allow the echocardiographer to realize these measurements within a much shorter period of time compared with the standard manual tracing method. From a practical point of view, the model developed can be easily implemented in a standard echocardiographic system.

Doppler echocardiography flow-velocity image analysis for patients with atrial fibrillation

Currently, Doppler echocardiography analysis is performed manually. An automated method that analyzes the Doppler signal can potentially improve accuracy and result in a powerful tool for noninvasive evaluation of cardiac hemodynamics, especially for patients with atrial fibrillation, where multiple samples are needed to obtain an accurate averaged measurement. The aim of this study was to develop an automated method for Doppler analysis based on image processing and computer vision algorithms. Images were obtained from the mitral valve and the tricuspid valve Doppler tracings from 45 patients, 20 with normal sinus rhythm and 25 with atrial fibrillation. The proposed algorithm automatically detects the maximal velocity envelope of the spectral Doppler ultrasound tracings. Averaged values for the time velocity integral, peak mitral inflow velocity and peak tricuspid regurgitation velocity were calculated for multiple beats available in a single screen frame. Measurements extracted automatically from the maximal velocity envelope were compared to measurements obtained manually by two expert technicians. High linear correlation (r) was found between the automatically- and the manually-extracted parameters (0.95 < r < 0.99). A smaller variation was found in most cases between the manually-calculated average beat and the automated average beat (bias value between 3.8% and 5.2%) than between the manually-calculated average beat and the selection of a representative beat (bias value between 6.2% and -2.6%). The newly-developed automated method offers a new, accurate and reliable clinical tool, particularly for the assessment of patients with irregular heart rate.

Estimation of the Doppler ultrasound umbilical maximal waveform envelope: II. Prediction of fetal distress

Blood flow variables obtained via Doppler ultrasound (US) waveform estimation have been investigated for prediction of fetal distress. The umbilical flow was assessed using a number of waveform summary statistics in addition to the currently used resistance indices. We examined the relationship between umbilical artery waveform patterns and intrauterine growth restriction, preterm delivery and hypertensive disorders. To enhance prediction, we defined waveform skewness profiles based on pivotal points of the umbilical waveform that appeared to be related to the incidence of preterm delivery and that facilitated construction of IUGR prediction models. The data comprised 204 unselected pregnancies with the umbilical artery images recorded at 18 pregnancy weeks. The sample was divided into 114 pregnancies used to estimate model parameters and 90 pregnancies to validate the model. Logistic prediction models for detection of abnormal velocity waveforms associated with intrauterine growth restriction were derived, based on the waveform information. The estimated model sensitivity and specificity on the training data were 74% and 84%, respectively. Validation of the model on independent data yielded a sensitivity of 57% and specificity of 84%. The logistic IUGR prediction model appears to have significant predictive ability and potential for clinical use, even at this early gestational age. Our data suggest that prediction of IUGR at 18 pregnancy weeks can be much improved when the waveform shape is captured with a number of summary statistics in addition to resistance indices.