Photoacoustic imaging plus X: a review.

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.

S-Wave Accelerates Optimization-based Photoacoustic Image Reconstruction in vivo.

OBJECTIVE: Photoacoustic imaging has undergone rapid development in recent years. To simulate photoacoustic imaging on a computer, the most popular MATLAB toolbox currently used for the forward projection process is k-Wave. However, k-Wave suffers from significant computation time. Here we propose a straightforward simulation approach based on superposed Wave (s-Wave) to accelerate photoacoustic simulation. METHODS: In this study, we consider the initial pressure distribution as a collection of individual pixels. By obtaining standard sensor data from a single pixel beforehand, we can easily manipulate the phase and amplitude of the sensor data for specific pixels using loop and multiplication operators. The effectiveness of this approach is validated through an optimization-based reconstruction algorithm. RESULTS: The results reveal significantly reduced computation time compared with k-Wave. Particularly in a sparse 3-D configuration, s-Wave exhibits a speed improvement >2000 times compared with k-Wave. In terms of optimization-based image reconstruction, in vivo imaging results reveal that using the s-Wave method yields images highly similar to those obtained using k-Wave, while reducing the reconstruction time by approximately 50 times. CONCLUSION: Proposed here is an accelerated optimization-based algorithm for photoacoustic image reconstruction, using the fast s-Wave forward projection simulation. Our method achieves substantial time savings, particularly in sparse system configurations. Future work will focus on further optimizing the algorithm and expanding its applicability to a broader range of photoacoustic imaging scenarios.

Programmable Acoustic Delay-Line Enabled Low-Cost Photoacoustic Tomography System.

Photoacoustic tomography (PAT) is an emerging technology for biomedical imaging that combines the superiorities of high optical contrast and acoustic penetration. In the PAT system, more PA signals are preferred to be detected from full field of view to reconstruct the PA images with higher fidelity. However, the requirement for more PA signals' detection leads to more time consumption for single-channel scanning-based PAT system or higher cost of data acquisition (DAQ) module for an array-based PAT system. To address this issue, we proposed a programmable acoustic delay-line (PADL) module to reduce DAQ cost and accelerate imaging speed for PAT system. The module is based on bidirectional conversion between acoustic signals and electrical signals, including ultrasound transmission in between to provide sufficient time delay. The acoustic delay-line module achieves tens or hundreds of microseconds' delay for each channel and is controlled by a programmable control unit. In this work, it achieves to merge four inputs of PA signals into one output signal, which can be recovered into original four PA signals in the digital domain after DAQ. The imaging experiments of pencil leads embedded in agar phantom are conducted by the PAT system equipped with the proposed PADL module, which demonstrated its feasibility to reduce the cost of the PAT system. An in vivo study of human finger PAT imaging with delay-line module verified its feasibility for biomedical imaging applications.

Size-adjustable ring-shape photoacoustic tomography imager in vivo.

Photoacoustic tomography (PAT) has become a novel biomedical imaging modality for scientific research and clinical diagnosis. It combines the advantages of spectroscopic optical absorption contrast and acoustic resolution with deep penetration. In this article, an imaging size-adjustable PAT system is proposed for potential clinical applications such as breast cancer detection and screening, which can adapt to imaging targets with various sizes. Comparing with the conventional PAT setup with a fixed radius ring shape ultrasound transducer (UT) array, the proposed system is more flexible for imaging diverse size targets based on sectorial ultrasound transducer arrays (SUTAs). Four SUTAs form a 128-channel UT array for photoacoustic detection, where each SUTA has 32 elements. Such four SUTAs are controlled by four stepper motors, respectively, and can change their distribution layout position to adapt for various imaging applications. In this proposed system, the radius of the imaging region of interest (ROI) can be adjusted from 50 to 100 mm, which is much more flexible than the conventional PAT system with a full ring UT array. The simulation experiments using the MATLAB k-wave toolbox demonstrate the feasibility of the proposed system. To further validate the proposed system, imaging of pencil leads made phantom, ex-vivo pork breast with indocyanine green (ICG) injected, and in-vivo human wrist, finger and ankle are conducted to prove its feasibility for potential clinical applications.

A Signal Domain Object Segmentation Method for Ultrasound and Photoacoustic Computed Tomography.

Image segmentation is important in improving the diagnostic capability of ultrasound computed tomography (USCT) and photoacoustic computed tomography (PACT), as it can be included in the image reconstruction process to improve image quality and quantification abilities. Segmenting the imaged object out of the background using image domain methods is easily complicated by low contrast, noise, and artifacts in the reconstructed image. Here, we introduce a new signal domain object segmentation method for USCT and PACT which does not require image reconstruction beforehand and is automatic, robust, computationally efficient, accurate, and straightforward. We first establish the relationship between the time-of-flight of the received first arrival waves and the object's boundary which is described by ellipse equations. Then, we show that the ellipses are tangent to the boundary. By looking for tangent points on the common tangent of neighboring ellipses, the boundary can be approximated with high fidelity. Imaging experiments of human fingers and mice cross-sections showed that our method provided equivalent or better segmentations than the optimal ones by active contours. In summary, our method greatly reduces the overall complexity of object segmentation and shows great potential in eliminating user dependency without sacrificing segmentation accuracy. The method can be further seamlessly incorporated into algorithms for other processing purposes in USCT and PACT, such as high-quality image reconstruction.

Beyond fundamental resonance mode: high-order multi-band ALN PMUT for in vivo photoacoustic imaging.

This paper reports on an aluminum nitride (AlN) piezoelectric micromachined ultrasound transducer (PMUT) array for photoacoustic (PA) imaging, where the high-order resonance modes of the PMUT are utilized to improve imaging resolution. A flexural vibration mode (FVM) PMUT is fabricated and applied in a photoacoustic imaging (PAI) system. Specifically, the microelectromechanical system (MEMS)-based PMUT is suitable for PA endoscopic imaging of blood vessels and bronchi due to its miniature size and high sensitivity. More importantly, AlN is a nontoxic material, which makes it harmless for biomedical applications. In the PAI system, the AlN PMUT array is used to detect PA signals, and the acousto-mechanical response is designed and optimized at the PMUT's fundamental resonance. In this work, we focus on the high-order resonance performance of the PMUT PAI beyond the fundamental resonance. The acoustic and electrical responses of the PMUT's high-order resonance modes are characterized and analyzed. The fundamental and three high-order resonance bandwidths are 2.2, 8.8, 18.5, and 48.2 kHz. Compared with the resolution at the fundamental resonance mode, the resolutions at third- and fourth-order resonance modes increase by 38.7% and 76.9% in a phantom experiment. The high-order resonance modes of the AlN PMUT sensor array provide higher central frequency and wider bandwidth for PA signal detection, which increase the resolution of PAI compared to the PMUT working at the fundamental resonance mode.

Deep learning enabled real-time photoacoustic tomography system via single data acquisition channel.

Photoacoustic computed tomography (PACT) combines the optical contrast of optical imaging and the penetrability of sonography. In this work, we develop a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel. To reduce the channel number of DAQ, we superimpose 30 nearby channels' signals together in the analog domain, and shrinking to 4 channels of data (120/30 = 4). Furthermore, a four-to-one delay-line module is designed to combine these four channels' data into one channel before entering the single-channel DAQ, followed by decoupling the signals after data acquisition. To reconstruct the image from four superimposed 30-channels' PA signals, we train a dedicated deep learning model to reconstruct the final PA image. In this paper, we present the preliminary results of phantom and in-vivo experiments, which manifests its robust real-time imaging performance. The significance of this novel PACT system is that it dramatically reduces the cost of multi-channel DAQ module (from 120 channels to 1 channel), paving the way to a portable, low-cost and real-time PACT system.

Y-Net: Hybrid deep learning image reconstruction for photoacoustic tomography in vivo.

Conventional reconstruction algorithms (e.g., delay-and-sum) used in photoacoustic imaging (PAI) provide a fast solution while many artifacts remain, especially for limited-view with ill-posed problem. In this paper, we propose a new convolutional neural network (CNN) framework Y-Net: a CNN architecture to reconstruct the initial PA pressure distribution by optimizing both raw data and beamformed images once. The network combines two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. We compared our result with some ablation studies, and the results of the test set show better performance compared with conventional reconstruction algorithms and other deep learning method (U-Net). Both in-vitro and in-vivo experiments are used to validated our method, which still performs better than other existing methods. The proposed Y-Net architecture also has high potential in medical image reconstruction for other imaging modalities beyond PAI.

Low-Cost Multi-Wavelength Photoacoustic Imaging Based on Portable Continuous-Wave Laser Diode Module.

Photoacoustic imaging (PAI), an emerging imaging technique, exploits the merits of both optical and ultrasound imaging, equipped with optical contrast and deep penetration. Typical linear PAI relies on a nanosecond laser pulse to induce photoacoustic signals. To construct a multi-wavelength PAI system, a multi-wavelength nano-second laser source is required, which greatly increases the cost of the PAI system. However, according to the nonlinear photoacoustic effect, the amplitude of the photoacoustic signals will vary with different base temperatures of the tissue. Therefore, using continuous-wave lasers with different wavelengths to induce different temperature variations at the same point of the tissue, and then using a single-wavelength pulsed laser to induce photoacoustic signals has been an alternative method to achieve multi-wavelength PAI. In this paper, based on the nonlinear photoacoustic effect, we developed a continuous-wave multi-wavelength laser source to cut down the cost of the conventional multi-wavelength PAI system. The principle will be introduced firstly, followed by qualitative and quantitative experiments.

Frequency-domain Dual-contrast Photoacoustic Imaging with Chirp Modulation.

Photoacoustic imaging has shown its great potential in biomedical imaging. A variety of imaging applications, like blood oxygenation for functional imaging, have been widely studied during the past few decades. Most of the previous works are based on the tissue's endogenous or nanoprobe's extraneous optical absorbance. In this paper, we proposed frequency-domain dual-contrast photoacoustic imaging aiming at exploring both optical absorption and mechanical property (e.g., viscoelasticity) of tissue. Instead of conventionally used pulsed excitation, a chirp-modulated laser signal is used to excite the sample to induce photoacoustic signals. On one hand, the optical absorption contrast is obtained by cross-correlating the PA signals with the chirp pattern. On the other hand, mechanical property is obtained by performing the Fourier transform to analyze the frequency spectrum. Experimental results revealed that samples with higher density-to-viscoelasticity ratio show larger quality factor in the received PA signals' spectrum. Both theoretical analysis and experimental demonstrations are performed to prove the feasibility of the proposed method.

Reconstruct the Photoacoustic Image Based On Deep Learning with Multi-frequency Ring-shape Transducer Array.

Photoacoustic tomography (PAT) combines the superiorities of both optical imaging and ultrasound imaging, which provides rich optical absorption contrast with 3D spatial information by applying reconstruction algorithms. Classical reconstruction algorithms, e.g. delay-and-sum, have been widely used in photoacoustic imaging. Recently, the deep neural networks have showed the potential to be used to reconstruct the PA images from raw photoacoustic data. In this paper, a framework of the neural network is proposed to approach the PA imaging reconstruction using multi-frequency ultrasound sensor data. Specifically, we trained an end-to-end network to compare the performance when the transducers surround the region of interest with three different center frequencies, which receive PA signals containing different frequency spectrum information from the target. In particular, we trained and tested the network using the factitious segmented vessels' PA images from fundus oculi CT imaging after converting to PA data. From the results of the numerical simulations, the proposed frameworks have shown much better performance compared with conventional reconstruction algorithms. Moreover, the time consumption of the proposed reconstruction method outperforms other conventional reconstruction algorithms, which enables its potential to apply in real-time imaging.