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Photoacoustic Tomography of Neural Systems

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Neural Engineering

Abstract

Neuroscience has become one of the most exciting contemporary research areas with major breakthroughs expected in the coming decades. Modern imaging techniques have enabled scientific understanding of the neural system by revealing anatomical, functional, metabolic, and molecular information about the brain. Among these techniques, photoacoustic tomography (PAT), drawing more and more attention, is playing an increasingly important role in brain studies, thanks to its rich optical absorption contrast, high spatiotemporal resolution, and deep penetration. More importantly, PAT’s unique scalability empowers neuroscientists to examine the brain at multiple spatial scales using the same contrast mechanism, bridging microscopic insights to macroscopic observations of the brain. In this chapter, we review the principles of PAT, present the major implementations, and summarize the representative neuroscience applications. We also discuss challenges in translating PAT to human brain imaging and envision its potential promise.

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Authors and Affiliations

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

    Lei Li & Lihong V. Wang

  2. Photoacoustic Imaging Laboratory, Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Junjie Yao

Authors
  1. Lei Li
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  2. Junjie Yao
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  3. Lihong V. Wang
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Correspondence to Lihong V. Wang .

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Editors and Affiliations

  1. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Bin He

Homework

Homework

  1. 1.

    Show that the units of the pressure and the energy density are the same.

  2. 2.

    Estimate the temperature and the initial pressure rises upon short-pulsed laser excitation of whole arterial blood at the body temperature, with an optical fluence of 10 mJ cm−2 at 532 nm.

  3. 3.

    Estimate the local initial pressure rise per one-degree local temperature rise at the body temperature.

  4. 4.

    In water, estimate the fractional PA amplitude change upon one-degree local temperature rise with the baseline temperature of (a) 20 °C and (b) 37 °C.

  5. 5.

    Given d c = 1 mm or 0.01 mm, compute τ th and τ s in muscles.

  6. 6.

    Derive the photoacoustic equation shown in Eq. (12.10).

  7. 7.

    Show that the time reversal of the temporal function is equivalent to the complex conjugation of the temporal spectrum.

  8. 8.

    Use Eq. (12.17) to derive and plot the PA pressure wave as a function of time observed outside a sphere excited by (a) a delta pulse and (b) a Gaussian pulse.

  9. 9.

    Use Eq. (12.17) to derive and plot the pressure wave as a function of time observed outside a line object excited by (a) a delta pulse and (b) a Gaussian pulse.

  10. 10.

    The line in Question 9 has a finite length; please simulate the PA pressure wave detected by (a) a linear transducer array and (b) a ring array (see the geometry below). Please reconstruct the PA image using the forward data from the linear array and the ring array. Hint: please use the MATLAB k-wave toolbox for both the forward and reconstruction simulations. Please download the k-wave toolbox from http://www.k-wave.org/.

    figure a

    Fig. 21.33

  11. 11.

    For the circular geometry, if the designed imaging FOV is 25 mm in diameter, to satisfy the spatial Nyquist sampling requirement, what is the minimum number of sampling channels for detection at a cutoff frequency of (a) 2.25 MHz and (b) 15 MHz?

  12. 12.

    Under the same conditions in Q.10, please calculate the minimum number of sampling channels for the full spherical geometry.

  13. 13.

    Derive Eq. (12.31), assuming the frequency response of the detector has a Gaussian profile.

  14. 14.

    Assuming you are engineering a “perfect” PA contrast agent for molecular imaging, please list all the desired key characteristics and explain the reasons.

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Li, L., Yao, J., Wang, L.V. (2020). Photoacoustic Tomography of Neural Systems. In: He, B. (eds) Neural Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-43395-6_12

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