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Transcranial volumetric imaging using a conformal ultrasound patch

Nature volume 629, pages 810–818 (2024)Cite this article

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Abstract

Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as −1.51 ± 4.34 cm s−1, −0.84 ± 3.06 cm s−1 and −0.50 ± 2.55 cm s−1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.

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Fig. 1: Overview of the conformal ultrasound patch for TCD.
Fig. 2: Volumetric ultrafast power Doppler imaging.
Fig. 3: Validation of cerebral blood flow measurements.
Fig. 4: Monitoring of cerebral haemodynamics under different scenarios.

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Data availability

The data in this study are available at Figshare (https://doi.org/10.6084/m9.figshare.25448254.v1)54.

Code availability

The code used in this study is available at GitHub (https://github.com/Yup0626/TCD).

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Acknowledgements

We thank R. Aaslid for the discussions on the experiments, S. Olson and P. Corey for the guidance on the clinical applications of the device and S. Xiang for the feedback on the Article preparation. This work was supported by the National Institutes of Health (1R21EB025521-01, 1R21EB027303-01A1, 3R21EB027303-02S1, 1R01EB033464-01 and 1R01HL171652-01). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. All bio-experiments were conducted in accordance with the ethical guidelines of the National Institutes of Health and with the approval of the Institutional Review Board of the University of California San Diego. The mention of commercial products, their sources or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of these products by the Department of Health and Human Services.

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  1. These authors contributed equally: Sai Zhou, Xiaoxiang Gao, Geonho Park, Xinyi Yang

Authors and Affiliations

  1. Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA

    Sai Zhou, Xinyi Yang, Baiyan Qi, Xiangjun Chen & Sheng Xu

  2. Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA

    Xiaoxiang Gao, Geonho Park, Muyang Lin, Hao Huang, Yizhou Bian, Hongjie Hu, Ray S. Wu, Boyu Liu, Wentong Yue, Ruotao Wang & Sheng Xu

  3. Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA

    Chengchangfeng Lu, Pranavi Bheemreddy, Siyu Qin & Sheng Xu

  4. Department of Anesthesiology and Critical Care, University of California San Diego, La Jolla, CA, USA

    Arthur Lam

  5. U.S. Food and Drug Administration, Silver Spring, MD, USA

    Keith A. Wear

  6. Department of Radiology, University of California San Diego, La Jolla, CA, USA

    Michael Andre, Erik B. Kistler & Sheng Xu

  7. Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Erik B. Kistler & Sheng Xu

  8. Department of Neurosurgery, Seattle Neuroscience Institute, Seattle, WA, USA

    David W. Newell

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Contributions

S.Z. and S.X. conceived the project. S.Z., X.G., G.P., X.Y. and B.Q. performed the experiments. S.Z. and X.G. performed the data processing and simulations. S.Z. and G.P. analysed the data. S.Z., G.P., X.G. and S.X. wrote the paper. All authors provided constructive and valuable feedback on the Article.

Corresponding author

Correspondence to Sheng Xu.

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Nature thanks Roger Zemp and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 1D, 2D, and 3D TCD sonography.

TCD sonography can be performed in different modes. The conventional TCD probe with a single transducer insonates target arteries in 1D, and the power M-mode results show collected blood flow signals55. The conventional phased array probe with a linear transducer array insonates target arteries in two dimensions. The acquired duplex mode (that is, combined B-mode and color Doppler mode) results show the collected tissue signals and blood flow directions in the plane (https://www.medison.ru/ultrasound/gal641.htm). The conformal ultrasound patch with a matrix array insonates the target arteries in 3D, and the power Doppler mode results show the collected volumetric blood flow signals. A much larger computation power will be needed to reconstruct volumetric duplex mode images. Because we only consider the morphology of the vasculature rather than the surrounding tissues and blood flow directions, we focus on the power Doppler mode in this study. Note that conventional probes require handholding, which is impractical for long-term monitoring and generates results that are operator-dependent. The conformal ultrasound patch is self-adherent and overcomes these two challenges.

Extended Data Fig. 2 Ultrasound exposure safety.

a, System set-up for characterizing ultrasound exposure safety. The hydrophone is controlled by a 3D linear motor in a water tank. A formalin-fixed human skull sample is used to evaluate skull induced attenuation. b, Ultrasound intensity measured by the hydrophone. The maximum derated intensities of both diverging and focused beamforming strategies before derating are set to around 370 mW cm−2. The average intensity loss of the ultrasound beams after skull penetration is around 83% for both beamforming strategies. All of the measured results are lower than the maximum level recommended by the Food and Drug Administration (that is, 720 mW cm−2)27.

Extended Data Fig. 3 Blood flow spectra of compressing the left common carotid artery.

a, Schematics of before, during, and after the compression test. b, The circle of Willis can be divided into four parts, including ipsilateral anterior, contralateral anterior, ipsilateral posterior, and contralateral posterior networks. These four parts are connected by one anterior communicating artery and two posterior communicating arteries48. c, The blood flow spectra of ACA, MCA M2, MCA M1, PCA, and TICA segments on the left side before, during, and after the compression test. The red dashed boxes label the period during the compression. d, The blood flow spectra of ACA, MCA M2, MCA M1, PCA, and TICA segments on the right side before, during, and after the compression test. The red dashed boxes label the period during the compression. The spectra share the same scale bars.

Extended Data Fig. 4 Autonomous envelope tracking and parameter calculation.

a, Spectrum Doppler of blood flow in one cardiac cycle. b, The spectrum Doppler is normalized first. After that, the spectrum with an amplitude higher than 0.2 is set 1, while the spectrum with an amplitude lower than 0.1 is set 0. This enhances the contrast between spectrum Doppler and noise. c, The orange curve is the amplitude snapshot of the enhanced spectrum in b, as labelled by the orange line. The enhanced spectrum has a similar shape like a step function. Therefore, we fit the spectrum using a step function to extract the envelope. The dashed black curve is one example of a step function. Changing fstep will form different step functions. d, To find the step function that fits the spectrum the best, the sum of absolute errors is defined to quantify the difference between the spectrum curve and the step function. fstep sweeps from 0 to 2,850 Hz. The fstep corresponding to the minimum sum of absolute errors is the desired fenvelope. e, fenvelope is the envelope corresponding to the spectrum at one moment. f, The entire envelope is extracted using the above method and labelled by a red line. The peak systolic velocity, mean flow velocity, end diastolic velocity, pulsatility index, and resistance index are calculated based on the tracked envelope. The spectra share the same timescale bar.

Extended Data Fig. 5 Optical images of using different devices for TCD sonography.

a, Optical images of a participant during and after using the conventional TCD probe for 30 min. The pressing results in discomfort and redness patterns on the skin. b, Optical images of the participant during and after using a conventional TCD headset for 30 min. The screwing and pressing result in discomfort and redness patterns on the skin. c, Optical images of the participant during and after using a customized TCD headset for 30 min. This headset is designed for monitoring cerebral blood flow during brain procedures. The screwing and pressing result in discomfort and redness patterns on the skin. d, Optical images of the participant during and after using the conformal ultrasound patch for 30 min. This mechanical design eliminates the need for uncomfortable pressure and substantially reduces skin irritation. The images share the same scale bar. The inset images share the same scale bar.

Extended Data Fig. 6 Doppler spectra acquired from all transcranial windows by using different mechanical indices and thermal indices.

As the mechanical index and thermal index decrease, the signal quality correspondingly declines. The optimal mechanical indices and thermal indices were chosen to be as low as reasonably achievable during blood flow monitoring, balancing safety and signal quality. For the temporal and suboccipital windows, the optimal mechanical index and thermal index were around 0.3; for the orbital window, we selected mechanical index around 0.13 and thermal index around 0.08; and for the submandibular window, the ideal mechanical index and thermal index were approximately 0.2 and 0.11, respectively. Importantly, these thresholds could be subject to individual variations due to physiological and anatomical differences. The spectra share the same scale bars. MI, mechanical index. TIC, cranium thermal index. TIS, soft tissue thermal index.

Extended Data Table 1 Comparison of different techniques for cerebral blood flow monitoring
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Extended Data Table 2 Exemplary large group studies on TCD success rates
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Supplementary information

Supplementary Information

This file contains Supplementary Discussions 1–19, Supplementary Figures 1–61, Supplementary Tables 1 and 2, and the caption of Supplementary Video 1.

Supplementary Video 1

Twenty-five seconds of MCA blood flow spectrum recording with audio.

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Zhou, S., Gao, X., Park, G. et al. Transcranial volumetric imaging using a conformal ultrasound patch. Nature 629, 810–818 (2024). https://doi.org/10.1038/s41586-024-07381-5

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