Abstract
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.
Highlights
Due to its non-invasiveness, relatively low cost, and the possibility of repeated and continuous bedside measurements, Doppler ultrasonography is routinely used for assessing blood flows in different organs [1]
In neuro-monitoring, transcranial Doppler (TCD) ultrasonography has been suggested for the diagnosis of stenosis [2], vasospasms [3], and large cerebral vessel occlusions, which are characterized by alterations in blood flow velocities and waveform morphologies [4], [5]
MANUAL SPECTROGRAM TRACING AND RATING To obtain a quantitative assessment of the performance of our envelope tracing algorithm, we developed a custom-made MATLAB program for manually tracing TCD spectrograms
Summary
Due to its non-invasiveness, relatively low cost, and the possibility of repeated and continuous bedside measurements, Doppler ultrasonography is routinely used for assessing blood flows in different organs [1]. Once a sufficient number of samples in the group exceed the threshold, the maximal flow velocity is set as the highest frequency bin in the group This approach is popular due to its simplicity, but its performance depends on the choice of the preset threshold and the intensity distribution of the spectrogram samples at each time point. In [22] the spectrogram is treated as multiple time series (one for each frequency bin) and a parametric model is fit to each time series In this model-based approach, a Gamma function is used as a parametric model for the blood flow velocity waveform. In addition to parameter selection, the signal quality algorithm provides a beat-by-beat measure of confidence for the reported blood flow velocity waveform This can be useful when passing the blood flow velocity signal to downstream processing algorithms and when interpreting derived quantities such as the pulsatility index or noninvasive ICP estimates. The code and the data is available on the IEEE DataPort [23]
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