PAI

Moderated by Prof. Dr. Gijs van Soest, professor in Invasive Imaging, Thoraxcentre BME group, Erasmus MC, Rotterdam, The Netherlands

Photo-Acoustic Imaging (PAI) is an optical imaging techniques which relies on different absorption spectra of (de-)oxygenated and total hemoglobin (HbO, HbR and HbT). Through the mechanism of Neurovascular Coupling (NVC), these measurements allow for an indirect detection of functional activation (1-3).

Technical Parameters

In PAI, optical excitation is combined with acoustic detection (1-9). After illuminating the tissue with a laser pulse, chromophores such as RBCs absorb the optical energy depending on the oxygenation status of their hemoglobin and the wavelength, which leads to a transient, localized rise in temperature. (4-7). Due to thermoelastic expansion, the absorbing RBCs generate an acoustic wave, which can be picked up by an ultrasound transducer. Because PAI can combine the high-contrast benefits of optical imaging, with potential for chemical specificity, while bypassing the optical diffusion limits on penetration by detecting ultrasound instead, it can reach significant penetrative depths, in the range of centimeters. Here again, there is a scalable trade-off between spatial resolution and depth penetration, depending on the ultrasonic center frequency used. In fPAM, one aims primarily for high spatial resolution (<50 µm) using a raster scanning method, with as a consequence relatively low penetration depths (millimeters) (8). In functional Photoacoustic Tomography (fPAT), also known as Photoacoustic Computed Tomography (PACT), the acoustic waves emitted from the tissue can be detected by an array of ultrasonic transducers at multiple angles simultaneously, allowing for cross-sectional or volumetric imaging (8,10,11). Here, penetrative depths of several centimeters have been described (2). So far, the literature reports cases of 5D-fPAT, which involves real-time, multispectral visualization, allowing for volumetric detection of multiple spectrally distinctive – and functionally informative – hemodynamic changes in the brain (11).

Biological Substrate

Using multispectral PAI and exploiting the fact that different functionally informative absorbers are sensitive to specific wavelengths, functional neuroimaging can be achieved. As displayed in Figures 1-3, distributions of e.g. HbO en HbR can be retrieved by spectral processing of images using discrete wavelengths. From there, other parameters such a HbT (HbO + HbR), SO2 (HbO/(HbO+HbR)) and CBV (the number of voxels for which the HbT signal is higher than a given threshold) can be determined (7,9,11). As discussed previously in the chapters on fNIRS, fUS and OCT, being able to detect these metabolic and/or hemodynamic-related changes can be an indirect measure to detect functional neuronal activation.

Intra-operative applicability

Compared to other optical techniques, PAI has the strong advantage of being able to combine the contrast of optical imaging, the relative depth penetration of ultrasound, while remaining contrast-free. Much has been written about the application of PAI for structural as well as functional brain imaging in a pre-clinical setting (7,8), however, actual intra-operative applications have not been described as of yet (12). The main bottleneck for this technique in the intra-operative setting will most likely be the still limited penetrative depth, which will allow for superficial cortical imaging only (12).

Recently, the first successful in-human brain imaging application was described in literature using a new 3D fPACT system, validated to fMRI (13,14). In this study, Na et al. (13) demonstrate the 1k3D-fPACT system which uses >1000 parallel ultrasonic transducer elements which are evenly distributed on a hemispherical bowl (Figure 4). In their application on hemicraniectomy patients, the study group was able to produce tomographic images of the brain with a wide field of view (10 cm), high spatial resolution (<500 µm) and reasonable penetration depth of around 1 cm. Both their angiography images (Figure 5) as well as their functional cortical maps during language and motor tasks were compared to similar images as produced in a 7T MRI system. The authors report how the technique was able ‘to detect functional activation faster than BOLD fMRI’ with ‘potentially greater specificity’ (13).

Figure 1 - Taken from Cao et al. (7). Head-restrained PAM of CHb, sO2, and blood flow speed in the normoxic mouse brain in the absence (0 MAC) and presence of different concentrations (0.5, 1.0, and 1.5 MAC) of isoflurane. The white arrows in the 2nd and 3rd rows highlight the isoflurane-induced changes in svO2 and blood flow speed. Scale bar, 500 μm.
Figure 2 - Taken from Ovsepian et al. (9). a) Graphical representation of the illumination and detection schemes of optoacoustic macroscopy (a), mesoscopy (b), and microscopy (c). Note the modality-dependent variations in the imaging resolution and depth (right schematic). DLS, diffuse light source; UST, ultrasound transducer; IF, imaging field; AF, acoustic focus; OF, optical focus. Double arrows indicate the scanning module of the imaging system. d) Macroscopic (MSOT) images of the changes in Hb and HbO2 levels in the mouse brain induced by hypoxia and hyperoxia. From top to bottom: pseudocolored Hb and HbO2 maps acquired during the supply of a normal air followed by a mixture of air with 10% CO2 (green arrow), 100% O2 (red arrow), and 100% CO2 (blue arrow) overlaid with the anatomical image. Scale bars, 1 mm. e) The time course of the changes in Hb and HbO2 signals in response to alterations of the breathing gas mixtures (Burton et al., 2013). f) Noninvasive in vivo optoacoustic mesoscopic image of the mouse cortex vasculature (1) with a photograph of the same area after removal of the skull (2). SS, sagittal sinus. Scale bars, 1 mm. 3D optoacoustic image of the vasculature in the mouse brain obtained using an excitation wavelength of 590 nm (3). Parasagittal view, maximum intensity projection. Scale bar, 0.5 mm. g) Dynamic blood vessel responses acquired through a hypoxic challenge shown, reflective of changes of ratiometric optoacoustic signals at 561 and 570 nm. Each colored trace represents a respective cortical vessel. Adapted with permission from Laufer et al. (2009), Stein et al. (2009b), and Xia et al. (2013a). h) Representative microscopic sO2 image of the mouse visual cortex (top) with sequential snapshots of single red blood cell (SRBC) releasing oxygen in a mouse cortex (bottom) (1 and 2). Scale bar, 200 mm. Blood flows from left to right. The dashed arrow follows the trajectory of a single flowing red blood cell. i) Graphical representation of the transient responses of SRBCs within the visual cortex to a single stimulus (semi-rectangle, bottom inset).
Figure 3 - Taken from Gottschalk et al. (11). Five-dimensional (5D) imaging of mouse brain oxygenation under hyperoxia and normoxia. a) Experimental setup. The mouse head is fixed inside a custom-made stereotactical frame to minimize motion artifacts and the mouse is placed in a supine position on top of the spherical array optoacoustic probe for volumetric imaging. DAQ: Data acquisition system. b) Extinction spectra of oxygenized (HbO) and deoxygenized (HbR) hemoglobin in the near-infrared range. Indicated are the eight wavelengths (dotted lines) that were used to acquire multispectral data sets. c) Postmortem photograph of a mouse brain. Rostral rhinal vein (rrv), superior sagittal sinus (sss), confluence of sinuses (cs), transverse sinus (ts). d) Maximum intensity projection images (along the depth direction) of HbO and HbR distribution in the mouse brain under hyperoxia and normoxia. The head of the second mouse was angled in relation to the surface of the array probe, revealing more lateral vasculature of the brain. The main identifiable veins are indicated. Scale bars are 2 mm. e) 3D visualization of HbO under hyperoxia. Volumes of interest (VOIs) were placed inside the anterior part of the sss and the longitudinal hippocampal vein (lhv). Time courses of HbO and HbR inside the VOIs are shown normalized to their respective total hemoglobin in all vessel voxels of a given frame (dots) along with the moving average over 5 frames (lines). The concentrations of HbO and HbR follow the changes from hyperoxia (white background) to normoxia (gray background) and vice versa.
Figure 4 - Taken from Na et al. (13). Representations of the 1K3D-fPaCt. a) Perspective view of the system. b) Perspective cut-away view of the imager.
Figure 5 - Taken from Na et al. (13). PaCt angiography and MRa of the same brains. a–d) Vasculatures imaged in participants 1–4 (P1–4; a–d, respectively) using the baseline PACT (left) and MRA (right). The images were segmented into the scalp (green) and cortical (red for PACT, 2D colour map for MRA) regions. The scalp and cortical images were normalized to their maximum voxel PA or MRA values (arbitrary units). e) The diameters of the selected scalp vessel (Vs) and cortical vessel (Vc) of participant 1 were quantified as the full width at half maximum (red arrows). The y axes represent the voxel amplitudes in a. The labelled scalp vessel, cortical vessel and superficial temporal arteries (STA) in a–d can be referred to for visuospatial correlation. Norm., normalized; amp., amplitude. For a–d, scale bars, 1 cm.

References

  1. Lin L, Hu P, Tong X, et al. High-speed three-dimensional photoacoustic computed tomography for preclinical research and clinical translation. Nat Commun. 2021
  2. Yao J, Wang L V. Photoacoustic tomography: Fundamentals, advances and prospects. Contrast Media and Molecular Imaging. 2011.
  3. Wang L V., Hu S. Photoacoustic tomography: In vivo imaging from organelles to organs. Science. 2012
  4. Li L, Zhu L, Ma C, Lin L, Yao J, Wang L, et al. Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution. Nat Biomed Eng. 2017
  5. Wang L V., Hu S. Photoacoustic tomography: In vivo imaging from organelles to organs. Science. 2012
  6. Zhang P, Li L, Lin L, Shi J, Wang L V. In vivo superresolution photoacoustic computed tomography by localization of single dyed droplets. Light Sci Appl. 2019
  7. Cao R, Li J, Ning B, Sun N, Wang T, Zuo Z, et al. Functional and oxygen-metabolic photoacoustic microscopy of the awake mouse brain. 2017;
  8. Wang L V., Gao L. Photoacoustic microscopy and computed tomography: From bench to bedside. Annual Review of Biomedical Engineering. 2014
  9. Ovsepian S V., Olefir I, Westmeyer G, Razansky D, Ntziachristos V. Pushing the Boundaries of Neuroimaging with Optoacoustics. 2017.
  10. Tang J, Coleman JE, Dai X, Jiang H. Wearable 3-D Photoacoustic Tomography for Functional Brain Imaging in Behaving Rats. Sci Rep. 2016
  11. Gottschalk S, Felix Fehm T, Luís Deán-Ben X, Razansky D. Noninvasive real-time visualization of multiple cerebral hemodynamic parameters in whole mouse brains using five-dimensional optoacoustic tomography. J Cereb Blood Flow Metab. 2015
  12. Steinberg I, Huland DM, Vermesh O, Frostig HE, Tummers WS, Gambhir SS. Photoacoustic clinical imaging. Photoacoustics. 2019
  13. Na S, Russin JJ, Lin L, et al. Massively parallel functional photoacoustic computed tomography of the human brain. Nat Biomed Eng. May 2021
  14. Na S, Russin J, Lin L, et al. Mapping human brain function with massively parallel high speed three-dimensional photoacoustic computed tomography. SPIE-Intl Soc Optical Eng. 2021
OpenNeurosurgery.com