Special Section Guest Editorial: Seeing Inside Tissue with Optical Molecular Probes.

The editorial introduces the Special Section on Seeing Inside Tissue with Optical Molecular Probes.

A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption.

Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, has shown promise in early-stage clinical trials in a range of applications from inflammatory diseases to cancer. While the first PAI systems have recently received regulatory approvals, successful adoption of PAI technology into healthcare systems for clinical decision making must still overcome a range of barriers, from education and training to data acquisition and interpretation. The International Photoacoustic Standardisation Consortium (IPASC) undertook an community exercise in 2022 to identify and understand these barriers, then develop a roadmap of strategic plans to address them. Here, we outline the nature and scope of the barriers that were identified, along with short-, medium- and long-term community efforts required to overcome them, both within and beyond the IPASC group.

Physics-guided neural network for tissue optical properties estimation.

Finding the optical properties of tissue is essential for various biomedical diagnostic/therapeutic applications such as monitoring of blood oxygenation, tissue metabolism, skin imaging, photodynamic therapy, low-level laser therapy, and photo-thermal therapy. Hence, the research for more accurate and versatile optical properties estimation techniques has always been a primary interest of researchers, especially in the field of bioimaging and bio-optics. In the past, most of the prediction methods were based on physics-based models such as the pronounced diffusion approximation method. In more recent years, with the advancement and growing popularity of machine learning techniques, most of the prediction methods are data-driven. While both methods have been proven to be useful, each of them suffers from several shortcomings that could be complemented by their counterparts. Thus, there is a need to bring the two domains together to obtain superior prediction accuracy and generalizability. In this work, we proposed a physics-guided neural network (PGNN) for tissue optical properties regression which integrates physics prior and constraint into the artificial neural network (ANN) model. With this method, we have demonstrated superior generalizability of PGNN compared to its pure ANN counterpart. The prediction accuracy and generalizability of the network were evaluated on single-layered tissue samples simulated with Monte Carlo simulation. Two different test datasets, the in-domain test dataset and out-domain dataset were used to evaluate in-domain generalizability and out-domain generalizability, respectively. The physics-guided neural network (PGNN) showed superior generalizability for both in-domain and out-domain prediction compared to pure ANN.

Android mobile-platform-based image reconstruction for photoacoustic tomography.

Significance: In photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications. Aim: To implement PAT image reconstruction in Android-based mobile platforms. Approach: For implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones. Results: The performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of in vivo small animal brain dataset in 2.4 s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation. Conclusions: We proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4 s is achieved without hampering the quality of the reconstructed image.

Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior.

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.

Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth.

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.

Looking deep inside tissue with photoacoustic molecular probes: a review.

Significance: Deep tissue noninvasive high-resolution imaging with light is challenging due to the high degree of light absorption and scattering in biological tissue. Photoacoustic imaging (PAI) can overcome some of the challenges of pure optical or ultrasound imaging to provide high-resolution deep tissue imaging. However, label-free PAI signals from light absorbing chromophores within the tissue are nonspecific. The use of exogeneous contrast agents (probes) not only enhances the imaging contrast (and imaging depth) but also increases the specificity of PAI by binding only to targeted molecules and often providing signals distinct from the background. Aim: We aim to review the current development and future progression of photoacoustic molecular probes/contrast agents. Approach: First, PAI and the need for using contrast agents are briefly introduced. Then, the recent development of contrast agents in terms of materials used to construct them is discussed. Then, various probes are discussed based on targeting mechanisms, in vivo molecular imaging applications, multimodal uses, and use in theranostic applications. Results: Material combinations are being used to develop highly specific contrast agents. In addition to passive accumulation, probes utilizing activation mechanisms show promise for greater controllability. Several probes also enable concurrent multimodal use with fluorescence, ultrasound, Raman, magnetic resonance imaging, and computed tomography. Finally, targeted probes are also shown to aid localized and molecularly specific photo-induced therapy. Conclusions: The development of contrast agents provides a promising prospect for increased contrast, higher imaging depth, and molecularly specific information. Of note are agents that allow for controlled activation, explore other optical windows, and enable multimodal use to overcome some of the shortcomings of label-free PAI.

High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.

Significance: In circular scanning photoacoustic tomography (PAT), it takes several minutes to generate an image of acceptable quality, especially with a single-element ultrasound transducer (UST). The imaging speed can be enhanced by faster scanning (with high repetition rate light sources) and using multiple-USTs. However, artifacts arising from the sparse signal acquisition and low signal-to-noise ratio at higher scanning speeds limit the imaging speed. Thus, there is a need to improve the imaging speed of the PAT systems without hampering the quality of the PAT image. Aim: To improve the frame rate (or imaging speed) of the PAT system by using deep learning (DL). Approach: For improving the frame rate (or imaging speed) of the PAT system, we propose a novel U-Net-based DL framework to reconstruct PAT images from fast scanning data. Results: The efficiency of the network was evaluated on both single- and multiple-UST-based PAT systems. Both phantom and in vivo imaging demonstrate that the network can improve the imaging frame rate by approximately sixfold in single-UST-based PAT systems and by approximately twofold in multi-UST-based PAT systems. Conclusions: We proposed an innovative method to improve the frame rate (or imaging speed) by using DL and with this method, the fastest frame rate of ∼ 3 Hz imaging is achieved without hampering the quality of the reconstructed image.

Deep and Domain Transfer Learning Aided Photoacoustic Microscopy: Acoustic Resolution to Optical Resolution.

Acoustic resolution photoacoustic micros- copy (AR-PAM) can achieve deeper imaging depth in biological tissue, with the sacrifice of imaging resolution compared with optical resolution photoacoustic microscopy (OR-PAM). Here we aim to enhance the AR-PAM image quality towards OR-PAM image, which specifically includes the enhancement of imaging resolution, restoration of micro-vasculatures, and reduction of artifacts. To address this issue, a network (MultiResU-Net) is first trained as generative model with simulated AR-OR image pairs, which are synthesized with physical transducer model. Moderate enhancement results can already be obtained when applying this model to in vivo AR imaging data. Nevertheless, the perceptual quality is unsatisfactory due to domain shift. Further, domain transfer learning technique under generative adversarial network (GAN) framework is proposed to drive the enhanced image's manifold towards that of real OR image. In this way, perceptually convincing AR to OR enhancement result is obtained, which can also be supported by quantitative analysis. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values are significantly increased from 14.74 dB to 19.01 dB and from 0.1974 to 0.2937, respectively, validating the improvement of reconstruction correctness and overall perceptual quality. The proposed algorithm has also been validated across different imaging depths with experiments conducted in both shallow and deep tissue. The above AR to OR domain transfer learning with GAN (AODTL-GAN) framework has enabled the enhancement target with limited amount of matched in vivo AR-OR imaging data.

Multi-angle data acquisition to compensate transducer finite size in photoacoustic tomography.

In photoacoustic tomography (PAT) systems, the tangential resolution decreases due to the finite size of the transducer as the off-center distance increases. To address this problem, we propose a multi-angle detection approach in which the transducer used for data acquisition rotates around its center (with specific angles) as well as around the scanning center. The angles are calculated based on the central frequency and diameter of the transducer and the radius of the region-of-interest (ROI). Simulations with point-like absorbers (for point-spread-function evaluation) and a vasculature phantom (for quality assessment), and experiments with ten 0.5 mm-diameter pencil leads and a leaf skeleton phantom are used for evaluation of the proposed approach. The results show that a location-independent tangential resolution is achieved with 150 spatial sampling and central rotations with angles of ±8°/±16°. With further developments, the proposed detection strategy can replace the conventional detection (rotating a transducer around ROI) in PAT.

Photoacoustic imaging aided with deep learning: a review.

Photoacoustic imaging (PAI) is an emerging hybrid imaging modality integrating the benefits of both optical and ultrasound imaging. Although PAI exhibits superior imaging capabilities, its translation into clinics is still hindered by various limitations. In recent years, deeplearning (DL), a new paradigm of machine learning, is gaining a lot of attention due to its ability to improve medical images. Likewise, DL is also widely being used in PAI to overcome some of the limitations of PAI. In this review, we provide a comprehensive overview on the various DL techniques employed in PAI along with its promising advantages.

Accuracy of peak-power compensation in fiber-guided and free-space acoustic-resolution photoacoustic microscopy.

Acoustic resolution photoacoustic microscopy (AR-PAM) has gained much attention in the past two decades due to its high contrast, scalable resolution, and relatively higher imaging depth. Multimode optical fibers (MMF) are extensively used to transfer light to AR-PAM imaging scan-head from the laser source. Typically, peak-power-compensation (PPC) is used to reduce the effect of pulse-to-pulse peak-power variation in generated photoacoustic (PA) signals. In MMF, the output intensity profile fluctuates due to the coherent nature of light and mode exchange caused by variations in the bending of the fibers during scanning. Therefore, using a photodiode (PD) to capture a portion of the total power of pulses as a measure of illuminated light on the sample may not be appropriate for accurate PPC. In this study, we have investigated the accuracy of PPC in fiber-guided and free-space AR-PAM systems. Experiments were conducted in the transparent and highly scattering medium. Based on obtained results for the MMF-based system, to apply PPC to the generated PA signals, tightly focused light confocal with the acoustic focus in a transparent medium must be used. In the clear medium and highly focused illumination, enhancement of about 45% was obtained in the homogeneity of an optically homogeneous sample image. In addition, it is shown that, as an alternative, free-space propagation of the laser pulses results in more accurate PPC in both transparent and highly scattering mediums. In free-space light transmission, enhancement of 25-75% was obtained in the homogeneity of the optically homogeneous sample image.

Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration.

Pulsed laser diodes are used in photoacoustic tomography (PAT) as excitation sources because of their low cost, compact size, and high pulse repetition rate. In combination with multiple single-element ultrasound transducers (SUTs) the imaging speed of PAT can be improved. However, during PAT image reconstruction, the exact radius of each SUT is required for accurate reconstruction. Here we developed a novel deep learning approach to alleviate the need for radius calibration. We used a convolutional neural network (fully dense U-Net) aided with a convolutional long short-term memory block to reconstruct the PAT images. Our analysis on the test set demonstrates that the proposed network eliminates the need for radius calibration and improves the peak signal-to-noise ratio by ∼73% without compromising the image quality. In vivo imaging was used to verify the performance of the network.

Binary photoacoustic tomography for improved vasculature imaging.

SIGNIFICANCE: The proposed binary tomography approach was able to recover the vasculature structures accurately, which could potentially enable the utilization of binary tomography algorithm in scenarios such as therapy monitoring and hemorrhage detection in different organs. AIM: Photoacoustic tomography (PAT) involves reconstruction of vascular networks having direct implications in cancer research, cardiovascular studies, and neuroimaging. Various methods have been proposed for recovering vascular networks in photoacoustic imaging; however, most methods are two-step (image reconstruction and image segmentation) in nature. We propose a binary PAT approach wherein direct reconstruction of vascular network from the acquired photoacoustic sinogram data is plausible. APPROACH: Binary tomography approach relies on solving a dual-optimization problem to reconstruct images with every pixel resulting in a binary outcome (i.e., either background or the absorber). Further, the binary tomography approach was compared against backprojection, Tikhonov regularization, and sparse recovery-based schemes. RESULTS: Numerical simulations, physical phantom experiment, and in-vivo rat brain vasculature data were used to compare the performance of different algorithms. The results indicate that the binary tomography approach improved the vasculature recovery by 10% using in-silico data with respect to the Dice similarity coefficient against the other reconstruction methods. CONCLUSION: The proposed algorithm demonstrates superior vasculature recovery with limited data both visually and based on quantitative image metrics.

Nanotechnology Facilitated Cultured Neuronal Network and Its Applications.

The development of a biomimetic neuronal network from neural cells is a big challenge for researchers. Recent advances in nanotechnology, on the other hand, have enabled unprecedented tools and techniques for guiding and directing neural stem cell proliferation and differentiation in vitro to construct an in vivo-like neuronal network. Nanotechnology allows control over neural stem cells by means of scaffolds that guide neurons to reform synaptic networks in suitable directions in 3D architecture, surface modification/nanopatterning to decide cell fate and stimulate/record signals from neurons to find out the relationships between neuronal circuit connectivity and their pathophysiological functions. Overall, nanotechnology-mediated methods facilitate precise physiochemical controls essential to develop tools appropriate for applications in neuroscience. This review emphasizes the newest applications of nanotechnology for examining central nervous system (CNS) roles and, therefore, provides an insight into how these technologies can be tested in vitro before being used in preclinical and clinical research and their potential role in regenerative medicine and tissue engineering.

Axial accuracy and signal enhancement in acoustic-resolution photoacoustic microscopy by laser jitter effect correction and pulse energy compensation.

In recent years, photoacoustic imaging has found vast applications in biomedical imaging. Photoacoustic imaging has high optical contrast and high ultrasound resolution allowing deep tissue non-invasive imaging beyond the optical diffusion limit. Q-switched lasers are extensively used in photoacoustic imaging due to the availability of high energy and short laser pulses, which are essential for high-resolution photoacoustic imaging. In most cases, this type of light source suffers from pulse peak-power energy variations and timing jitter noise, resulting in uncertainty in the output power and arrival time of the laser pulses. These problems cause intensity degradation and temporal displacement of generated photoacoustic signals which in turn deteriorate the quality of the acquired photoacoustic images. In this study, we used a high-speed data acquisition system in combination with a fast photodetector and a software-based approach to capture laser pulses precisely in order to reduce the effect of timing jitter and normalization of the photoacoustic signals based on pulse peak-powers simultaneously. In the experiments, maximum axial accuracy enhancement of 14 µm was achieved in maximum-amplitude projected images on XZ and YZ planes with ±13.5 ns laser timing jitter. Furthermore, photoacoustic signal enhancement of 77% was obtained for 75% laser pulses peak-power stability.

Ultrasonic Implantation and Imaging of Sound-Sensitive Theranostic Agents for the Treatment of Arterial Inflammation.

For site-specific diseases such as atherosclerosis, it is desirable to noninvasively and locally deliver therapeutics for extended periods of time. High-intensity focused ultrasound (HIFU) provides targeted drug delivery, yet remains unable to sustain delivery beyond the HIFU treatment time. Furthermore, methods to validate HIFU-enhanced drug delivery remain limited. In this study, we report on HIFU-targeted implantation of degradable drug-loaded sound-sensitive multicavity PLGA microparticles (mcPLGA MPs) as a theranostic agent for the treatment of arterial lesions. Once implanted into the targeted tissue, mcPLGA MPs eluted dexamethasone for several days, thereby reducing inflammatory markers linked to oxidized lipid uptake in a foam cell spheroid model. Furthermore, implanted mcPLGA MPs created hyperechoic regions on diagnostic ultrasound images, and thus noninvasively verified that the target region was treated with the theranostic agents. This novel and innovative multifunctional theranostic platform may serve as a promising candidate for noninvasive imaging and treatment for site-specific diseases such as atherosclerosis.

Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data.

The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data. The method was shown to be superior as compared to total variation regularization, basis pursuit deconvolution and Lanczos Tikhonov based regularization and provided improved performance in case of noisy data. The numerical and experimental cases show that the improvement can be as high as 8.1 dB in signal to noise ratio of the reconstructed image and 67.98% in root mean square error in comparison to the state of the art methods.

Investigating the Acoustic Response and Contrast Enhancement of Drug-Loadable PLGA Microparticles with Various Shapes and Morphologies.

Polymer nanoparticles and microparticles have been used primarily for drug delivery. There is now growing interest in further developing polymer-based solid cavitation agents to also enhance ultrasound imaging. We previously reported on a facile method to produce hollow poly(lactic-co-glycolic acid) (PLGA) microparticles with different diameters and degrees of porosity. Here, we investigate the cavitation response from these PLGA microparticles with both therapeutic and diagnostic ultrasound transducers. Interestingly, all formulations exhibited stable cavitation; larger porous and multicavity particles also provided inertial cavitation at elevated acoustic pressure amplitudes. These larger particles also achieved contrast enhancement comparable to that of commercially available ultrasound contrast agents, with a maximum recorded contrast-to-tissue ratio of 28 dB. Therefore, we found that multicavity PLGA microparticles respond to both therapeutic and diagnostic ultrasound and may be applied as a theranostic agent.

Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging.

The rapidly evolving field of photoacoustic tomography utilizes endogenous chromophores to extract both functional and structural information from deep within tissues. It is this power to perform precise quantitative measurements in vivo-with endogenous or exogenous contrast-that makes photoacoustic tomography highly promising for clinical translation in functional brain imaging, early cancer detection, real-time surgical guidance, and the visualization of dynamic drug responses. Considering photoacoustic tomography has benefited from numerous engineering innovations, it is of no surprise that many of photoacoustic tomography's current cutting-edge developments incorporate advances from the equally novel field of artificial intelligence. More specifically, alongside the growth and prevalence of graphical processing unit capabilities within recent years has emerged an offshoot of artificial intelligence known as deep learning. Rooted in the solid foundation of signal processing, deep learning typically utilizes a method of optimization known as gradient descent to minimize a loss function and update model parameters. There are already a number of innovative efforts in photoacoustic tomography utilizing deep learning techniques for a variety of purposes, including resolution enhancement, reconstruction artifact removal, undersampling correction, and improved quantification. Most of these efforts have proven to be highly promising in addressing long-standing technical obstacles where traditional solutions either completely fail or make only incremental progress. This concise review focuses on the history of applied artificial intelligence in photoacoustic tomography, presents recent advances at this multifaceted intersection of fields, and outlines the most exciting advances that will likely propagate into promising future innovations.