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 rati...
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2020
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| Online Access: | https://hdl.handle.net/1721.1/128878 |
| _version_ | 1826217162780442624 |
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| author | Wadehn, Federico Heldt, Thomas |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Wadehn, Federico Heldt, Thomas |
| author_sort | Wadehn, Federico |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:59:02Z |
| format | Article |
| id | mit-1721.1/128878 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:59:02Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1288782022-10-03T09:36:29Z Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos Wadehn, Federico Heldt, Thomas Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Research Laboratory of Electronics 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. 2020-12-21T18:51:59Z 2020-12-21T18:51:59Z 2020-07 2020-12-17T18:45:54Z Article http://purl.org/eprint/type/JournalArticle 2168-2372 https://hdl.handle.net/1721.1/128878 Wadehn, Federico and Thomas Heldt."Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos." IEEE Journal of Translational Engineering in Health and Medicine 8 (July 2020): 1800511 en http://dx.doi.org/10.1109/jtehm.2020.3011562 IEEE Journal of Translational Engineering in Health and Medicine Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) IEEE |
| spellingShingle | Wadehn, Federico Heldt, Thomas Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos |
| title | Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos |
| title_full | Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos |
| title_fullStr | Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos |
| title_full_unstemmed | Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos |
| title_short | Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos |
| title_sort | adaptive maximal blood flow velocity estimation from transcranial doppler echos |
| url | https://hdl.handle.net/1721.1/128878 |
| work_keys_str_mv | AT wadehnfederico adaptivemaximalbloodflowvelocityestimationfromtranscranialdopplerechos AT heldtthomas adaptivemaximalbloodflowvelocityestimationfromtranscranialdopplerechos |