Photoacoustic imaging plus X: a review

Abstract

Significance: Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability.

Aim: The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications.

Approach: An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities.

Results: After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively.

Conclusions: The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

Keywords: circuit design; deep learning; laser source; multimodal imaging; photoacoustic imaging; treatment; ultrasound sensor.

Fig. 1

(a) Principle of PAI. (b) Overview of PAI plus X.

Fig. 2

PAI plus treatment. (a) A schematic and photograph of miniaturized integrated US/PA-guided laser ablation theranostic system. (b) PA imaging result of the ablated swine liver sample in Ref. . (c) PA guided US therapy with optimal benefits. Yellow: wavefronts of PA waves sensed by the transducers, and blue: wavefronts of US transmitted by the transducers. PA: PA and US: ultrasound. (d) Schematic of the tri-modal system for HIFU therapy. (e) Schematic of PA transmitter probe by Yu et al. (f) The overall system architecture of TRUS + PA image-guided surgical guidance system.

Fig. 3

PAI plus electrical and mechanical hardware. (a) System diagram of QuACL chip-based PA detection. (b) The MAP PAM imaging result by a peak holding circuit. (c) The setup of the 3D fsPAT imaging system and human blood vessel 3D imaging result. (d) Schematic of the multiscale PAM with polygon-scanning method and its OR-PAM imaging result. (e) Working principle of the TBS mentioned in Ref.  and the PAM imaging result by the scanner; the fast-axis scanning rate is up to 400 Hz.

Fig. 4

Novel laser sources plus PAI. (a) Principle of a DOE combining M foci to form the needle-shaped beam (OR-PAM with a needle-shaped beam). (b) The system schematic of the dual-wavelength LD-based PAM. (c) A schematic diagram of dual-compressed PA single-pixel imaging. (d) Depth-resolved imaging of carbon fibers with a needle-shaped beam (OR-PAM with a needle-shaped beam).

Fig. 5

Advance ultrasound sensors for PAI. (a) Cross-sectional structure of the broadband transducer and a photograph of the transducer reported in Ref. . (b) Design and structure of the sensitive ultrawideband transparent ultrasound detector in Ref. . (c) Photographs of the fabricated flexible transparent CMUT arrays and bond with flexible PCB. (d) Schematic showing the cross-section and ultrasound detection mechanism of a surface-micromachined optical ultrasound transducer element reported in Ref. . (e) Schematic diagram and photograph of the fiber optic ultrasound sensor. (f) Sequential in vivo OR-OAM mouse ear edge images and corresponding MIP representation with the design in Ref. .

Fig. 6

Brief comparison of end-to-end neural network methods (a) and GAN methods (b). End-to-end methods rely on a well-designed neural network to learn the forward mapping defined by the paired dataset, whereas GAN needs a generator and a discriminator to perform adversarial learning.

Fig. 7

Demonstration of two deep learning methods in PAI. (a) 3D U-Net proposed by Jongbeom Kim et al. for reconstruction of high-density superresolution images from fewer raw frames and (b) the structure of the CycleGAN proposed by Rui Cao et al., where the CycleGAN is composed of two symmetric generators and corresponding adversarial discriminators.

Fig. 8

Left: imaging results of carbon fibers and fluorescent microspheres using PARS and fluorescence microscopy by Tianrui et al. Right: imaging results of ICG-labeled ARPE-19 cells transplanted into live rabbits using multimodal imaging techniques combining PAI, OCT, and fluorescence microscopy on day one by Van et al. (a) Optical microscopy imaging result. (b) PAM imaging result. (c) Fluorescence imaging result. (d) Dual-modal imaging result. (e) Fundus color photography. (f) Fluorescence imaging. (g) PAM imaging result at 578 nm (red) and 700 nm (blue) wavelengths. (h) OCT imaging result, with white arrows indicating the transplanted cells. (i) Three-dimensional OCT reconstruction results, with color representing different depths of the retina.

Fig. 9

(a)–(f) the results of transcranial brain imaging by Na et al. (g)–(j) demonstrates the imaging result of human finger joints with VF-USPACT by Zhang et al. (a) The regions imaged in transcranial brain imaging of mice. (b) Power Doppler images (PDI) of the mouse brain. (c) Velocity amplitude map of the cerebral blood flow (CBF) in the major cortical vessels. (d) Flow vectors in region 2 of the velocity map. (e) PAT-measured functional responses, which show the contralateral functional responses to the hindlimb electrical stimulation. (f) Fractional changes of PD, hemoglobin concentrations, and sO2 signal in response to stimulation. (g) Illustration of the finger joint imaging locations. (h) PA and US image reconstructed with optimal SoS at middle finger cross section. The high PA signals from blood vessels corresponding to anechoic regions in the US images. (i) PA and US image of the thumb cross section. (j) The comparison of the image reconstructed with optimized SoS ($1496\mathrm{m}/\mathrm{s}$) and the one from the little finger ($1500\mathrm{m}/\mathrm{s}$).

Fig. 10

Outlook of PAI plus X.