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Ultrafast longitudinal imaging of haemodynamics via single-shot volumetric photoacoustic tomography with a single-element detector

Nature Biomedical Engineering (2023)Cite this article

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Abstract

Techniques for imaging haemodynamics use ionizing radiation or contrast agents or are limited by imaging depth (within approximately 1 mm), complex and expensive data-acquisition systems, or low imaging speeds, system complexity or cost. Here we show that ultrafast volumetric photoacoustic imaging of haemodynamics in the human body at up to 1 kHz can be achieved using a single laser pulse and a single element functioning as 6,400 virtual detectors. The technique, which does not require recalibration for different objects or during long-term operation, enables the longitudinal volumetric imaging of haemodynamics in vasculature a few millimetres below the skin’s surface. We demonstrate this technique in vessels in the feet of healthy human volunteers by capturing haemodynamic changes in response to vascular occlusion. Single-shot volumetric photoacoustic imaging using a single-element detector may facilitate the early detection and monitoring of peripheral vascular diseases and may be advantageous for use in biometrics and point-of-care testing.

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Fig. 1: The PACTER system.
Fig. 2: Single-shot 3D reconstruction in PACTER.
Fig. 3: Spatiotemporal characterization of PACTER.
Fig. 4: PACTER analysis of mouse haemodynamics in vivo.
Fig. 5: PACTER analysis of human hand haemodynamics in vivo.
Fig. 6: PACTER analysis of haemodynamic changes in human foot vessels.

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Data availability

The data supporting the findings of this study are provided within the Article and its Supplementary Information. The raw and analysed datasets generated during the study are available for research purposes from the corresponding authors on reasonable request.

Code availability

The reconstruction code, the system-control software and the data-collection software are proprietary and used in licensed technologies, yet they are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Y. Zhao for contributing to the universal calibration. This work was supported in part by National Institutes of Health grants R01 EB028277, U01 EB029823 and R35 CA220436 (Outstanding Investigator Award). The computations presented here were conducted at the Resnick High Performance Computing Center, a facility supported by the Resnick Sustainability Institute at the California Institute of Technology.

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Author notes

  1. These authors contributed equally: Yide Zhang, Peng Hu.

Authors and Affiliations

  1. Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA

    Yide Zhang, Peng Hu, Lei Li, Rui Cao, Anjul Khadria, Konstantin Maslov, Xin Tong & Lihong V. Wang

  2. USC Roski Eye Institute, University of Southern California, Los Angeles, CA, USA

    Yushun Zeng, Laiming Jiang & Qifa Zhou

  3. Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA

    Yushun Zeng, Laiming Jiang & Qifa Zhou

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Contributions

Y. Zhang and L.V.W. conceived and designed the study. Y. Zhang, L.L., R.C. and K.M. built the imaging system. Y. Zhang developed the data acquisition program. P.H. developed the 3D reconstruction algorithm. Y. Zhang, L.L., R.C. and A.K. performed the experiments. Y. Zhang, P.H. and X.T. processed and analysed the data. Y. Zeng, L.J. and Q.Z. fabricated the ultrasonic transducer. L.V.W. supervised the study. All of the authors contributed to writing the paper.

Corresponding author

Correspondence to Lihong V. Wang.

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Competing interests

L.V.W. has a financial interest in Microphotoacoustics, CalPACT and Union Photoacoustic Technologies. These companies did not provide support for this work. K.M. has a financial interest in Microphotoacoustics. All other authors declare no competing interests.

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Nature Biomedical Engineering thanks Xose Luis Dean-Ben, Wenfeng Xia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 PACTER of mouse haemodynamics in vivo.

a, 3D PACTER image of the abdominal vasculature of mouse 3. Norm., normalized. b, Cross-sectional 2D images corresponding to the yellow rectangle in a at four different time instances from the 4D PACTER datasets. t0 = 0.49 s. The white solid curve represents a two-term Gaussian fit of the vessels’ profile denoted by the yellow dashed line. Differences from the first image are highlighted in colour. c, PA amplitudes along the yellow dashed line (1D image) in b versus time, where the time instances in b are labelled with vertical grey lines. d, Blue solid and orange dash-dotted curves represent the centre positions and widths of the vessel on the left (based on the first term of the Gaussian fit in b) versus time. Shaded areas denote the standard deviations (n = 5). e, Fourier transforms of the centre positions and widths of the vessel in d, showing the respiratory frequency from the vessel widths only. Scale bars, 1 mm.

Extended Data Fig. 2 PACTER of human hand vasculature in vivo.

a,c–e, 3D PACTER images of the vasculature in a middle finger (a), an index finger (c), a proximal phalanx region (d), and a thenar region (e). Norm., normalized. b, Photograph of a human hand showing the imaged regions. Scale bars, 1 mm.

Extended Data Fig. 3 PACTER of human hand haemodynamics in vivo.

a, 3D PACTER image of the thenar vasculature of participant 1, in a region different from that in Fig. 5b. Norm., normalized. b, Maximum amplitude projections of the 3D volumes from the 4D PACTER datasets along the z axis in a at the time instances before, during, and after cuffing. t0 = 15.34 s. The solid lines flank the vessel under investigation. Differences from the t0 image are highlighted in colour. c, PA amplitudes along the vessel (1D image) in b versus time, where the time instances in b are labelled with vertical grey lines. The blue and orange arrows indicate peak responses in the occlusion and recovery phases, respectively. d, Positions of the blood front along the blood vessel during the occlusion (left) and recovery (right) phases in c. The left blue curve is an exponential fit with an occlusion rate of 0.6 ± 0.1 m/s, whereas the right orange curve is a linear fit showing the blood flow speed of 21.6 ± 7.9 m/s. e, Comparison between the durations of the occlusion and recovery phases in d. P < 0.001, calculated by the two-sample t-test. Error bars, means ± standard errors of the means (n = 9). Scale bars, 1 mm.

Extended Data Fig. 4 PACTER of haemodynamic changes in human foot vessels.

a, 3D PACTER images of the foot vessels of participant 3, in a region different from that in Fig. 6b, before (left) and after (right) vascular occlusion. Norm., normalized. b, Maximum amplitude projections of the 3D volumes from the 4D PACTER datasets along the z axis in a. t0 = 0 s. The blue and orange circles indicate regions of a vein and an artery, respectively. c, Difference between the two images in b. d, Relative PA signals of the venous and arterial regions indicated by the blue and orange circles in b and c. The shaded areas denote the standard deviations (n = 5). The arrows and vertical lines indicate the start times of occlusion and recovery. Scale bars, 1 mm.

Supplementary information

Supplementary Information

Supplementary notes, figures, tables and video captions.

Reporting Summary

Peer Review File

Supplementary Video 1

Principle and implementation of PACTER (with narration).

Supplementary Video 2

Simulation of PATER and PACTER signals.

Supplementary Video 3

4D PACTER image and its maximum amplitude projection of bovine blood flushing through an S-shaped tube.

Supplementary Video 4

4D PACTER images of bovine blood flowing through a tube at different speeds.

Supplementary Video 5

4D PACTER image and its maximum amplitude projection of bovine blood flowing through a tube with a speed of 272.5 mm s−1, captured at 1,000 volumes per second.

Supplementary Video 6

4D in vivo PACTER image of the abdominal vasculature of mouse 1.

Supplementary Video 7

4D in vivo PACTER image of the abdominal vasculature of mouse 2.

Supplementary Video 8

4D in vivo PACTER image of the abdominal vasculature of mouse 3.

Supplementary Video 9

4D in vivo PACTER image of the thenar vasculature of participant 1.

Supplementary Video 10

4D in vivo PACTER image of the thenar vasculature of participant 2.

Supplementary Video 11

4D in vivo PACTER image of the thenar vasculature of participant 1 in a different region.

Supplementary Video 12

4D in vivo PACTER image of the foot vessels of participant 3.

Supplementary Video 13

4D in vivo PACTER image of the foot vessels of participant 3 in a different region.

Supplementary Video 14

PACTER signals affected by temperature fluctuations.

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Zhang, Y., Hu, P., Li, L. et al. Ultrafast longitudinal imaging of haemodynamics via single-shot volumetric photoacoustic tomography with a single-element detector. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01149-4

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