Semiconducting Polymer Dots for Dual-Wavelength Differential Background-Suppressed Photoacoustic Imaging

Photoacoustic imaging (PAI) can sensitively detect regions and substances with strong optical absorption, which means that diseased tissue can be imaged with high contrast in the presence of surrounding healthy tissue through the photoacoustic effect. However, its signal intensity and resolution may be limited by background signals generated by endogenous chromophores such as melanin and hemoglobin. A feasible method for practical application of this so-called background-suppressed PAI is still lacking. In this work, we develop a dual-wavelength differential background noise suppressed photoacoustic tomography based on organic semiconducting polymer dots (Pdots). The Pdots have a strong absorption peak at 945 nm, and then the absorption decreases sharply with the increase of wavelength, and the absorption intensity drops to only about a quarter of the original value at 1050 nm. The present system significantly suppresses the strong background noise of blood through dual-wavelength differential PAI, enabling precise monitoring of the distribution information of theranostic agents in diseased tissues. The signal-to-noise ratio of the theranostic agent distribution map is increases by about 20 dB. This work provides a platform for real-time and accurate monitoring of tumors and drugs, which helps avoid damage to healthy tissue during treatment and has clinical significance in cancer treatment. This article is protected by copyright. All rights reserved.

Dual-modality rigid endoscope for photoacoustic imaging and white light videoscopy

Significance

There has been significant interest in the development of miniature photoacoustic imaging probes for a variety of clinical uses, including the in situ assessment of tumors and minimally invasive surgical guidance. Most of the previously implemented probes are either side viewing or operate in the optical-resolution microscopy mode in which the imaging depth is limited to ∼ 1    mm . We describe a forward-viewing photoacoustic probe that operates in tomography mode and simultaneously provides white light video images.

Aim

We aim to develop a dual-modality endoscope capable of performing high-resolution PAT imaging and traditional white light videoscopy simultaneously in the forward-viewing configuration.

Approach

We used a Fabry-Pérot ultrasound sensor that operates in the 1500 to 1600 nm wavelength range and is transparent in the visible and near infrared region (580 to 1250 nm). The FP sensor was optically scanned using a miniature MEMs mirror located at the proximal end of the endoscope, resulting in a system that is sufficiently compact (10 mm outer diameter) and lightweight for practical endoscopic use.

Results

The imaging performance of the endoscope is evaluated, and dual-mode imaging capability is demonstrated using phantoms and abdominal organs of an ex vivo mouse including spleen, liver, and kidney.

Conclusions

The proposed endoscope design offers several advantages including the high acoustic sensitivity and wide detection bandwidth of the FP sensor, dual-mode imaging capability, compact footprint, and an all-optical distal end for improved safety. The dual-mode imaging capability also offers the advantage of correlating the tissue surface morphology with the underlying vascular anatomy. Potential applications include the guidance of laparoscopic surgery and other interventional procedures.

Photoacoustic imaging for cutaneous melanoma assessment: a comprehensive review

Significance

Cutaneous melanoma (CM) has a high morbidity and mortality rate, but it can be cured if the primary lesion is detected and treated at an early stage. Imaging techniques such as photoacoustic (PA) imaging (PAI) have been studied and implemented to aid in the detection and diagnosis of CM.

Aim

Provide an overview of different PAI systems and applications for the study of CM, including the determination of tumor depth/thickness, cancer-related angiogenesis, metastases to lymph nodes, circulating tumor cells (CTCs), virtual histology, and studies using exogenous contrast agents.

Approach

A systematic review and classification of different PAI configurations was conducted based on their specific applications for melanoma detection. This review encompasses animal and preclinical studies, offering insights into the future potential of PAI in melanoma diagnosis in the clinic.

Results

PAI holds great clinical potential as a noninvasive technique for melanoma detection and disease management. PA microscopy has predominantly been used to image and study angiogenesis surrounding tumors and provide information on tumor characteristics. Additionally, PA tomography, with its increased penetration depth, has demonstrated its ability to assess melanoma thickness. Both modalities have shown promise in detecting metastases to lymph nodes and CTCs, and an all-optical implementation has been developed to perform virtual histology analyses. Animal and human studies have successfully shown the capability of PAI to detect, visualize, classify, and stage CM.

Conclusions

PAI is a promising technique for assessing the status of the skin without a surgical procedure. The capability of the modality to image microvasculature, visualize tumor boundaries, detect metastases in lymph nodes, perform fast and label-free histology, and identify CTCs could aid in the early diagnosis and classification of CM, including determination of metastatic status. In addition, it could be useful for monitoring treatment efficacy noninvasively.

Accelerated model-based iterative reconstruction strategy for sparse-view photoacoustic tomography aided by multi-channel autoencoder priors

Photoacoustic tomography (PAT) commonly works in sparse view due to data acquisition limitations. However, reconstruction suffers from serious deterioration (e.g., severe artifacts) using traditional algorithms under sparse view. Here, a novel accelerated model-based iterative reconstruction strategy for sparse-view PAT aided by multi-channel autoencoder priors was proposed. A multi-channel denoising autoencoder network was designed to learn prior information, which provides constraints for model-based iterative reconstruction. This integration accelerates the iteration process, leading to optimal reconstruction outcomes. The performance of the proposed method was evaluated using blood vessel simulation data and experimental data. The results show that the proposed method can achieve superior sparse-view reconstruction with a significant acceleration of iteration. Notably, the proposed method exhibits excellent performance under extremely sparse condition (e.g., 32 projections) compared with the U-Net method, with an improvement of 48% in PSNR and 12% in SSIM for in vivo experimental data.

Improvement of LED-based photoacoustic imaging using lag-coherence factor (LCF) beamforming

BACKGROUND

Owing to its portability, affordability, and energy-efficiency, LED-based photoacoustic (PA) imaging is increasingly becoming popular when compared to its laser-based alternative, mainly for superficial vascular imaging applications. However, this technique suffers from low SNR and thereby limited imaging depth. As a result, visual image quality of LED-based PA imaging is not optimal, especially in sub-surface vascular imaging applications.

PURPOSE

Combination of linear ultrasound (US) probes and LED arrays are the most common implementation in LED-based PA imaging, which is currently being explored for different clinical imaging applications. Traditional delay-and-sum (DAS) is the most common beamforming algorithm in linear array-based PA detection. Side-lobes and reconstruction-related artifacts make the DAS performance unsatisfactory and poor for a clinical-implementation. In this work, we explored a new weighting-based image processing technique for LED-based PAs to yield improved image quality when compared to the traditional methods.

METHODS

We are proposing a lag-coherence factor (LCF), which is fundamentally based on the combination of the spatial auto-correlation of the detected PA signals. In LCF, the numerator contains lag-delay-multiply-and-sum (DMAS) beamformer instead of a conventional DAS beamformer. A spatial auto-correlation operation is performed between the detected US array signals before using DMAS beamformer. We evaluated the new method on both tissue-mimicking phantom (2D) and human volunteer imaging (3D) data acquired using a commercial LED-based PA imaging system.

RESULTS

Our novel correlation-based weighting technique showed LED-based PA image quality improvement when it is combined with conventional DAS beamformer. Both phantom and human volunteer imaging results gave a direct confirmation that by introducing LCF, image quality was improved and this method could reduce side-lobes and artifacts when compared to the DAS and coherence-factor (CF) approaches. Signal-to-noise ratio, generalized contrast-to-noise ratio, contrast ratio and spatial resolution were evaluated and compared with conventional beamformers to assess the reconstruction performance in a quantitative way. Results show that our approach offered image quality enhancement with an average signal-to-noise ratio and spatial resolution improvement of around 20% and 25% respectively, when compared with conventional CF based DAS algorithm.

CONCLUSIONS

Our results demonstrate that the proposed LCF based algorithm performs better than the conventional DAS and CF algorithms by improving signal-to-noise ratio and spatial resolution. Therefore, our new weighting technique could be a promising tool to improve the performance of LED-based PA imaging and thus accelerate its clinical translation.

Dual-Scan Photoacoustic Tomography for the Imaging of Vascular Structure on Foot

Chronic leg ulcers are affecting approximately 6.5 million Americans, and they are associated with significant mortality, reduced quality of life, and high treatment costs. Since many chronic ulcers have underlying vascular insufficiency, accurate assessment of tissue perfusion is critical to treatment planning and monitoring. This study introduces a dual-scan photoacoustic (PA) tomography (PAT) system that can simultaneously image the dorsal and plantar sides of the foot to reduce imaging time. To account for the unique shape of the foot, the system employs height-adjustable and articulating baseball stages that can scan along the foot's contour. In vivo results from healthy volunteers demonstrate the system's ability to acquire clear images of foot vasculature, and results from patients indicate that the system can image patients with various ulcer conditions. We also investigated various PA features and examined their correlation with the foot condition. Our preliminary results indicate that vessel sharpness, occupancy, intensity, and density could all be used to assess tissue perfusion. This research demonstrated the potential of PAT for routine clinical tissue perfusion assessment.

In vivo monitoring of hemodynamic changes in ischemic stroke using photoacoustic tomography

Ischemic stroke occurs when a blood vessel supplying the brain is blocked, leading to decreased blood flow. Early diagnosis and treatment are crucial. However, existing clinical imaging methods have limitations, such as safety issues and low time resolution. To address these challenges, we propose using photoacoustic tomography (PAT) with a contrast agent, known for its high resolution and contrast capabilities. Our study involved imaging brain vasculature in three groups: normal, unilateral common carotid artery ligation (UCAL), and middle cerebral artery occlusion (MCAO). On the ischemic stroke side, we observed reduced blood vessel density and hemodynamic changes were evident after injecting indocyanine green for PAT. The photoacoustic intensity was notably lower in the ligated sides of the UCAL and MCAO groups, with statistically significant differences between the three groups. This work highlights PAT's potential as a powerful tool for early diagnosis and guidance in ischemic stroke cases.

Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human

The development of high-performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image processing techniques have been developed in the area, their applications in 3D imaging are still limited due to challenges in computational cost and memory allocation. To address those limitations, this work implements a 3D fully-dense (3DFD) U-net to linear array based photoacoustic tomography and utilizes volumetric simulation and mixed precision training to increase efficiency and training size. Through numerical simulation, phantom imaging, and in vivo experiments, this work demonstrates that the trained network restores the true object size, reduces the noise level and artifacts, improves the contrast at deep regions, and reveals vessels subject to limited view distortion. With these enhancements, 3DFD U-net successfully produces clear 3D vascular images of the palm, arms, breasts, and feet of human subjects. These enhanced vascular images offer improved capabilities for biometric identification, foot ulcer evaluation, and breast cancer imaging. These results indicate that the new algorithm will have a significant impact on preclinical and clinical photoacoustic tomography.

Multiscale photoacoustic tomography using reversibly switchable thermochromics

SIGNIFICANCE

Based on acoustic detection of optical absorption, photoacoustic tomography (PAT) allows functional and molecular imaging beyond the optical diffusion limit with high spatial resolution. However, multispectral functional and molecular PAT is often limited by decreased spectroscopic accuracy and reduced detection sensitivity in deep tissues, mainly due to wavelength-dependent optical attenuation and inaccurate acoustic inversion.

AIM

Previous work has demonstrated that reversible color-shifting can drastically improve the detection sensitivity of PAT by suppressing nonswitching background signals. We aim to develop a new color switching-based PAT method using reversibly switchable thermochromics (ReST).

APPROACH

We developed a family of ReST with excellent water dispersion, biostability, and temperature-controlled color changes by surface modification of commercial thermochromic microcapsules with the hydrophilic polysaccharide alginate.

RESULTS

The optical absorbance of the ReST was switched on and off repeatedly by modulating the surrounding temperature, allowing differential photoacoustic detection that effectively suppressed the nonswitching background signal and substantially improved image contrast and detection sensitivity. We demonstrate reversible thermal-switching imaging of ReST in vitro and in vivo using three PAT modes at different length scales.

CONCLUSIONS

ReST-enabled PAT is a promising technology for high-sensitivity deep tissue imaging of molecular activity in temperature-related biomedical applications, such as cancer thermotherapy.

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.

Quantitative photoacoustic tomography with light fluence compensation based on radiance Monte Carlo model

Objective. Photoacoustic tomography (PAT) is a rapidly evolving imaging modality that provides images with high contrast and spatial resolution showing the optical properties of biological tissues. The photoacoustic pressure is proportional to the product of the optical absorption coefficient and the local light fluence. The essential challenge in reconstructing quantitative images representing spatially varying absorption coefficients is the unknown light fluence. In addition, optical attenuation induces spatial variations in the light fluence, and the heterogeneity of the fluence determines the limits of reconstruction quality and depth.Approach.In this work, a reconstruction enhancement scheme is proposed to compensate for the variation in the light fluence in the absorption coefficient recovery. The inverse problem of the radiance Monte Carlo model describing light transport through the tissue is solved by using an alternating optimization strategy. In the iteration, the absorption coefficients and photon weights are alternately updated.Main results.The method provides highly accurate quantitative images of absorption coefficients in simulations, phantoms, andin vivostudies. The results show that the method has great potential for improving the accuracy of absorption coefficient recovery compared to conventional reconstruction methods that ignore light fluence variations. Comparison with state-of-the-art fluence compensation methods shows significant improvements in root mean square error, normalized mean square absolute distance, and structural similarity metrics.Significance.This method achieves high precision quantitative imaging by compensating for nonuniform light fluence without increasing the complexity and operation of the imaging system.

An Investigation of Signal Preprocessing for Photoacoustic Tomography

Photoacoustic tomography (PAT) is increasingly being used for high-resolution biological imaging at depth. Signal-to-noise ratios and resolution are the main factors that determine image quality. Various reconstruction algorithms have been proposed and applied to reduce noise and enhance resolution, but the efficacy of signal preprocessing methods which also affect image quality, are seldom discussed. We, therefore, compared common preprocessing techniques, namely bandpass filters, wavelet denoising, empirical mode decomposition, and singular value decomposition. Each was compared with and without accounting for sensor directivity. The denoising performance was evaluated with the contrast-to-noise ratio (CNR), and the resolution was calculated as the full width at half maximum (FWHM) in both the lateral and axial directions. In the phantom experiment, counting in directivity was found to significantly reduce noise, outperforming other methods. Irrespective of directivity, the best performing methods for denoising were bandpass, unfiltered, SVD, wavelet, and EMD, in that order. Only bandpass filtering consistently yielded improvements. Significant improvements in the lateral resolution were observed using directivity in two out of three acquisitions. This study investigated the advantages and disadvantages of different preprocessing methods and may help to determine better practices in PAT reconstruction.

Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging

Linear-array-based photoacoustic computed tomography (PACT) has been widely used in vascular imaging due to its low cost and high compatibility with current ultrasound systems. However, linear-array transducers have inherent limitations for three-dimensional imaging due to the poor elevation resolution. In this study, we introduced a deep learning-assisted data process algorithm to enhance the image quality in linear-array-based PACT. Compared to our earlier study where training was performed on 2D reconstructed data, here, we utilized 2D and 3D reconstructed data to train the two networks separately. We then fused the image data from both 2D and 3D training to get features from both algorithms. The numerical and in vivo validations indicate that our approach can improve elevation resolution, recover the true size of the object, and enhance deep vessels. Our deep learning-assisted approach can be applied to translational imaging applications that require detailed visualization of vascular features.

Multiscale photoacoustic tomography of neural activities with GCaMP calcium indicators

SIGNIFICANCE

Optical imaging of responses in fluorescently labeled neurons has progressed significantly in recent years. However, there is still a need to monitor neural activities at divergent spatial scales and at depths beyond the optical diffusion limit.

AIM

To meet these needs, we aim to develop multiscale photoacoustic tomography (PAT) to image neural activities across spatial scales with a genetically encoded calcium indicator GCaMP.

APPROACH

First, using photoacoustic microscopy, we show that depth-resolved GCaMP signals can be monitored in vivo from a fly brain in response to odor stimulation without depth scanning and even with the cuticle intact. In vivo monitoring of GCaMP signals was also demonstrated in mouse brains. Next, using photoacoustic computed tomography, we imaged neural responses of a mouse brain slice at depths beyond the optical diffusion limit.

RESULTS

We provide the first unambiguous demonstration that multiscale PAT can be used to record neural activities in transgenic flies and mice with select neurons expressing GCaMP.

CONCLUSIONS

Our results indicate that the combination of multiscale PAT and fluorescent neural activity indicators provides a methodology for imaging targeted neurons at various scales.

Cross-Ray Ultrasound Tomography and Photoacoustic Tomography of Cerebral Hemodynamics in Rodents

Recent advances in functional ultrasound imaging (fUS) and photoacoustic tomography (PAT) offer powerful tools for studying brain function. Complementing each other, fUS and PAT, respectively, measure the cerebral blood flow (CBF) and hemoglobin concentrations, allowing synergistic characterization of cerebral hemodynamics. Here, cross-ray ultrasound tomography (CRUST) and its combination with PAT are presented. CRUST employs a virtual point source from a spherically focused ultrasonic transducer (SFUST) to provide widefield excitation at a 4-kHz pulse repetition frequency. A full-ring-shaped ultrasonic transducer array whose imaging plane is orthogonal to the SFUST's acoustic axis receives scattered ultrasonic waves. Superior to conventional fUS, whose sensitivity to blood flow is angle-dependent and low for perpendicular flow, the crossed transmission and panoramic detection fields of CRUST provide omnidirectional sensitivity to CBF. Using CRUST-PAT, the CBF, oxygen saturation, and hemoglobin concentration changes of the mouse brain during sensory stimulation are measured, with a field of view of ≈7 mm in diameter, spatial resolution of ≈170 µm, and temporal resolution of 200 Hz. The results demonstrate CRUST-PAT as a unique tool for studying cerebral hemodynamics.

Approaching closed spherical, full-view detection for photoacoustic tomography

SIGNIFICANCE

Photoacoustic tomography (PAT) is a widely explored imaging modality and has excellent potential for clinical applications. On the acoustic detection side, limited-view angle and limited-bandwidth are common key issues in PAT systems that result in unwanted artifacts. While analytical and simulation studies of limited-view artifacts are fairly extensive, experimental setups capable of comparing limited-view to an ideal full-view case are lacking.

AIMS

A custom ring-shaped detector array was assembled and mounted to a 6-axis robot, then rotated and translated to achieve up to 3.8π steradian view angle coverage of an imaged object.

APPROACH

Minimization of negativity artifacts and phantom imaging were used to optimize the system, followed by demonstrative imaging of a star contrast phantom, a synthetic breast tumor specimen phantom, and a vascular phantom.

RESULTS

Optimization of the angular/rotation scans found ≈212 effective detectors were needed for high-quality images, while 15-mm steps were used to increase the field of view as required depending on the size of the imaged object. Example phantoms were clearly imaged with all discerning features visible and minimal artifacts.

CONCLUSIONS

A near full-view closed spherical system has been developed, paving the way for future work demonstrating experimentally the significant advantages of using a full-view PAT setup.

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.

Randomized multi-angle illumination for improved linear array photoacoustic computed tomography in brain

One of the key challenges in linear array transducer-based photoacoustic computed tomography is to image structures embedded deep within the biological tissue with limited optical energy. Here, we utilized a manually controlled multi-angle illumination technique to allow the incident photons to interact with the imaging targets for longer periods of time and diffuse further in all directions. We have developed and optimized a compact probe that enables manual changes to the angle of illumination while acquiring photoacoustic signals. The performance has been demonstrated and evaluated by imaging complex blood vessel mimicking phantoms in-vitro and sheep brain samples ex-vivo. For effective image reconstruction from the data acquired by multi-angle illumination method, we have utilized a method based on the extraction of maximum intensity. In both cases, multi-angle illumination has out-performed the conventional fixed angle illumination technique to improve the overall image quality. Specifically, extraction of the imaging targets located at greater axial depths was possible using this multi-angle illumination technique.

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-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR.

Photoacoustic imaging for in vivo quantification of alcohol-induced structural and functional changes in cerebral vasculature in high alcohol-preferring mice (HAP)

Alcohol-induced structural and functional changes were studied in vivo by photoacoustic tomography (PAT) of the cerebrovascular system in selectively bred alcohol-preferring mice. High (HAP) and low (LAP) alcohol-preferring mice are replicate lines of mice selectively bred to prefer 10% (v/v) ethanol to water and water to ethanol, respectively, in a free-access two-bottle choice scenario. A cohort of 15 singly-housed alcohol-preferring mice (five HAP mice for the experimental group, five LAP mice for the control group, and five other LAP mice set aside) were given free-access two-bottle choice 10% ethanol (v/v) and water in 50-mL graduated drinking bottles mounted on each of their cages for 4 weeks prior to PAT brain scanning. A daily log of the volume of ethanol consumed over a 24-h period was kept. At the end of the fourth week, blood samples were collected from the HAP mice and blood ethanol concentrations (BECs) were measured to ascertain their levels of ethanol intoxication. The mice were then grouped into five weight-matched pairs of HAP and LAP for comparison purposes, and noninvasive in vivo PAT imaging was performed on each weight-matched pair. To mimic a binge drinking paradigm, mice were rearranged into four weight-matched groups of three animals each: an HAP mouse and two LAP mice. For each group, one HAP mouse and one LAP mouse received a 20% ethanol solution via intraperitoneal (i.p.) injection after 24 h of ethanol abstinence, in weight-based doses of 3 g/kg prior to imaging, while the last LAP mouse received a sham i.p. injection. PAT images of the brain were collected for 30 min thereafter. Cerebral vascular diameters for selected vessels of interest were extracted from the PAT images and compared between HAP mice and LAP mice. For the binge scenario, changes in vessel diameter and hemoglobin oxygen saturation were extracted from PAT images and studied over a 30-min duration. Vascular diameter was significantly smaller in HAP mice compared to LAP mice in weight-matched pairs. Hemoglobin-oxygen saturation and vessel diameter dropped more quickly in LAP mice than in HAP mice following a 20% ethanol i.p. injection (3 g/kg), with a 32% reduction in cerebrovascular diameter in a 30-min period. This study demonstrates the effectiveness of PAT in alcohol addiction imaging and diagnosis, and its feasibility in studying alcohol-induced changes in vascular structure and perfusion. It also adds to other bodies of evidence to suggest that the effects of binge drinking are more adverse in occasional drinkers than habitual drinkers.

Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography

Linear-array-based photoacoustic tomography has shown broad applications in biomedical research and preclinical imaging. However, the elevational resolution of a linear array is fundamentally limited due to the weak cylindrical focus of the transducer element. While several methods have been proposed to address this issue, they have all handled the problem in a less time-efficient way. In this work, we propose to improve the elevational resolution of a linear array through Deep-E, a fully dense neural network based on U-net. Deep-E exhibits high computational efficiency by converting the three-dimensional problem into a two-dimension problem: it focused on training a model to enhance the resolution along elevational direction by only using the 2D slices in the axial and elevational plane and thereby reducing the computational burden in simulation and training. We demonstrated the efficacy of Deep-E using various datasets, including simulation, phantom, and human subject results. We found that Deep-E could improve elevational resolution by at least four times and recover the object's true size. We envision that Deep-E will have a significant impact in linear-array-based photoacoustic imaging studies by providing high-speed and high-resolution image enhancement.

Analysis for Full-Field Photoacoustic Tomography with Variable Sound Speed

Photoacoustic tomography (PAT) is a non-invasive imaging modality that requires recovering the initial data of the wave equation from certain measurements of the solution outside the object. In the standard PAT measurement setup, the used data consist of time-dependent signals measured on an observation surface. In contrast, the measured data from the recently invented full-field detection technique provide the solution of the wave equation on a spatial domain at a single instant in time. While reconstruction using classical PAT data has been extensively studied, not much is known for the full field PAT problem. In this paper, we build mathematical foundations of the latter problem for variable sound speed and settle its uniqueness and stability. Moreover, we introduce an exact inversion method using time-reversal and study its convergence. Our results demonstrate the suitability of both the full field approach and the proposed time-reversal technique for high resolution photoacoustic imaging.

Development of a Compact Photoacoustic Tomography Imaging System with Dual Single-Element Transducers for Image Enhancement

OBJECTIVE

This paper proposes a new photoacoustic computed tomography (PACT) imaging system employing dual ultrasonic transducers with different frequencies. When imaging complex biological tissues, photoacoustic (PA) signals with multiple frequencies are produced simultaneously; however, due to the limited bandwidth of a single-frequency transducer, the received PA signals with specific frequencies may be missing, leading to a low imaging quality.

METHODS

In contrast to our previous work, the proposed system has a compact volume as well as specific selection of the detection center frequency of the transducer, which can provide a comprehensive range for the detection of PA signals. In this study, a series of numerical simulation and phantom experiments were performed to validate the efficacy of the developed PACT system.

RESULTS

The images generated by our system combined the advantages of both high resolution and ideal brightness/contrast.

CONCLUSION

The interchangeability of transducers with different frequencies provides potential for clinical deployment under the circumstance where a single frequency transducer cannot perform well.

Facile synthesis of near-infrared bodipy by donor engineering for in vivo tumor targeted dual-modal imaging

Bodipy is one of the most popular dyes for bioimaging, however, a complicated synthetic protocol is needed to create and isolate ideal near-infrared (NIR) emissive Bodipy derivatives for optical bioimaging. It is noticed that the donor species impact the wavelength when the π-conjugation system of green light emissive Bodipy is elongated via a one-step reaction. Herein, several Bodipy dyes bearing different common donors are synthesized. Their optical properties confirm that both absorption and emission peaks of the synthesized Bodipy could be tuned to NIR wavelength by using stronger donors via a facile reaction. The synthesized monocarboxyl Bodipy could conjugate with aminated PEG to yield an amphiphilic polymer, which further self-assembles into a NIR nanoparticle (NP). The NIR NP exhibits preferential tumor accumulation via the enhanced permeation and retention (EPR) effect, making it useful for tumor diagnosis by both fluorescence imaging and photoacoustic tomography.

Discretization of Learned NETT Regularization for Solving Inverse Problems

Deep learning based reconstruction methods deliver outstanding results for solving inverse problems and are therefore becoming increasingly important. A recently invented class of learning-based reconstruction methods is the so-called NETT (for Network Tikhonov Regularization), which contains a trained neural network as regularizer in generalized Tikhonov regularization. The existing analysis of NETT considers fixed operators and fixed regularizers and analyzes the convergence as the noise level in the data approaches zero. In this paper, we extend the frameworks and analysis considerably to reflect various practical aspects and take into account discretization of the data space, the solution space, the forward operator and the neural network defining the regularizer. We show the asymptotic convergence of the discretized NETT approach for decreasing noise levels and discretization errors. Additionally, we derive convergence rates and present numerical results for a limited data problem in photoacoustic tomography.

Comparing Deep Learning Frameworks for Photoacoustic Tomography Image Reconstruction

Conventional reconstruction methods for photoacoustic images are not suitable for the scenario of sparse sensing and geometrical limitation. To overcome these challenges and enhance the quality of reconstruction, several learning-based methods have recently been introduced for photoacoustic tomography reconstruction. The goal of this study is to compare and systematically evaluate the recently proposed learning-based methods and modified networks for photoacoustic image reconstruction. Specifically, learning-based post-processing methods and model-based learned iterative reconstruction methods are investigated. In addition to comparing the differences inherently brought by the models, we also study the impact of different inputs on the reconstruction effect. Our results demonstrate that the reconstruction performance mainly stems from the effective amount of information carried by the input. The inherent difference of the models based on the learning-based post-processing method does not provide a significant difference in photoacoustic image reconstruction. Furthermore, the results indicate that the model-based learned iterative reconstruction method outperforms all other learning-based post-processing methods in terms of generalizability and robustness.

Investigation of photoacoustic tomography reconstruction with a limited view from linear array

SIGNIFICANCE

As linear array transducers are widely used in clinical ultrasound imaging, photoacoustic tomography (PAT) with linear arrays is similarly suitable for clinical applications. However, due to the limited-view problem, a linear array has limited performance and leads to artifacts and blurring, which has hindered its broader application. There is a need to address the limited-view problem in PAT imaging with linear arrays.

AIM

We investigate potential approaches for improving PAT reconstruction from linear array, by optimizing the detection geometry and implementing iterative reconstruction.

APPROACH

PAT imaging with a single-array, dual-probe configurations in parallel-shape and L-shape, and square-shape configuration are compared in simulations and phantom experiments. An iterative model-based algorithm based on the variance-reduced stochastic gradient descent (VR-SGD) method is implemented. The optimum configuration found in simulation is validated on phantom experiments.

RESULTS

PAT imaging with dual-probe detection and VR-SGD algorithm is found to improve the limited-view problem compared to a single probe and provide comparable performance as full-view geometry in simulation. This configuration is validated in experiments where more complete structure is obtained with reduced artifacts compared with a single array.

CONCLUSIONS

PAT with dual-probe detection and iterative reconstruction is a promising solution to the limited-view problem of linear arrays.

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.

Perspective on fast-evolving photoacoustic tomography

SIGNIFICANCE

Acoustically detecting the rich optical absorption contrast in biological tissues, photoacoustic tomography (PAT) seamlessly bridges the functional and molecular sensitivity of optical excitation with the deep penetration and high scalability of ultrasound detection. As a result of continuous technological innovations and commercial development, PAT has been playing an increasingly important role in life sciences and patient care, including functional brain imaging, smart drug delivery, early cancer diagnosis, and interventional therapy guidance.

AIM

Built on our 2016 tutorial article that focused on the principles and implementations of PAT, this perspective aims to provide an update on the exciting technical advances in PAT.

APPROACH

This perspective focuses on the recent PAT innovations in volumetric deep-tissue imaging, high-speed wide-field microscopic imaging, high-sensitivity optical ultrasound detection, and machine-learning enhanced image reconstruction and data processing. Representative applications are introduced to demonstrate these enabling technical breakthroughs in biomedical research.

CONCLUSIONS

We conclude the perspective by discussing the future development of PAT technologies.

Approximate back-projection method for improving lateral resolution in circular-scanning-based photoacoustic tomography

PURPOSE

In circular-scanning-based photoacoustic tomography (PAT), the effect of finite transducer aperture has not been effectively resolved. The goal of this paper is to propose a practical reconstruction method that accounts for the finite transducer aperture to improve the lateral resolution.

METHODS

We for the first time propose to calculate the spatial-temporal response (STR) of the employed finite-sized transducer in a forward model, and then compensate the time delay and the directional sensitivity of the transducer in the framework of the back-projection method. Both simulation and phantom experiments were carried out to evaluate the lateral resolution improvement with the proposed method. The performance of this new method for imaging complicated targets was also assessed by calculating the mean image gradient.

RESULTS

Simulation results showed that with this new method the lateral resolution for off-center targets can be as good as that for the center targets. Phantom experimental results showed that this new method can improve the lateral resolution more than two times for a point target about 5 mm far from the rotation center. Phantom experimental results also showed that many blurred fine structures of a piece of leaf veins at the off-center regions were well restored with the new method, and the mean image gradient improved about 1.3 times.

CONCLUSION

The proposed new method can effectively account for the effect of finite transducer aperture for circular-scanning-based PAT in homogenous acoustic media. This new method also features its robustness and computational efficiency, so that it is a worthy replacement to the conventional back-projection algorithm in circular-scanning-based PAT. This new method can be of great importance to the design of circular-scanning or spherical-scanning-based PAT systems.

[Progress of motion artifact correction in photoacoustic microscopy and photoacoustic tomography]

Photoacoustic imaging (PAI) is a rapidly developing hybrid biomedical imaging technology, which is capable of providing structural and functional information of biological tissues. Due to inevitable motion of the imaging object, such as respiration, heartbeat or eye rotation, motion artifacts are observed in the reconstructed images, which reduce the imaging resolution and increase the difficulty of obtaining high-quality images. This paper summarizes current methods for correcting and compensating motion artifacts in photoacoustic microscopy (PAM) and photoacoustic tomography (PAT), discusses their advantages and limits and forecasts possible future work.

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi-spectral photoacoustic tomography

In photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO2 ). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi-pixel method to smooth the measurements before calculating rHbT and %sO2 . We first perform phantom studies using blood tubes of calibrated %sO2 to evaluate the accuracy of our %sO2 estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co-registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO2 and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90-0.96) on the testing data set was achieved by combining mean %sO2 , a spectral feature, and the score of the study radiologist.

Generalized spatial coherence reconstruction for photoacoustic computed tomography

SIGNIFICANCE

Coherence, a fundamental property of waves and fields, plays a key role in photoacoustic image reconstruction. Previously, techniques such as short-lag spatial coherence (SLSC) and filtered delay, multiply, and sum (FDMAS) have utilized spatial coherence to improve the reconstructed resolution and contrast with respect to delay-and-sum (DAS). While SLSC uses spatial coherence directly as the imaging contrast, FDMAS employs spatial coherence implicitly. Despite being more robust against noise, both techniques have their own drawbacks: SLSC does not preserve a relative signal magnitude, and FDMAS shows a reduced contrast-to-noise ratio.

AIM

To overcome these limitations, our aim is to develop a beamforming algorithm-generalized spatial coherence (GSC)-that unifies SLSC and FDMAS into a single equation and outperforms both beamformers.

APPROACH

We demonstrated the application of GSC in photoacoustic computed tomography (PACT) through simulation and experiments and compared it to previous beamformers: DAS, FDMAS, and SLSC.

RESULTS

GSC outperforms the imaging metrics of previous state-of-the-art coherence-based beamformers in both simulation and experiments.

CONCLUSIONS

GSC is an innovative reconstruction algorithm for PACT, which combines the strengths of FDMAS and SLSC expanding PACT's applications.

Tunable blood oxygenation in the vascular anatomy of a semi-anthropomorphic photoacoustic breast phantom

SIGNIFICANCE

Recovering accurate oxygenation estimations in the breast with quantitative photoacoustic tomography (QPAT) is not straightforward. Accurate light fluence models are required, but the unknown ground truth of the breast makes it difficult to validate them. Phantoms are often used for the validation, but most reported phantoms have a simple architecture. Fluence models developed in these simplistic objects are not accurate for application on the complex tissues of the breast.

AIM

We present a sophisticated breast phantom platform for photoacoustic (PA) and ultrasound (US) imaging in general, and specifically for QPAT. The breast phantom is semi-anthropomorphic in distribution of optical and acoustic properties and contains wall-less channels with blood.

APPROACH

3D printing approaches are used to develop the solid 3D breast phantom from custom polyvinyl chloride plastisol formulations and additives for replicating the tissue optical and acoustic properties. A flow circuit was developed to flush the channels with bovine blood with a controlled oxygen saturation level. To showcase the phantom's functionality, PA measurements were performed on the phantom with two oxygenation levels. Image reconstructions with and without fluence compensation from Monte Carlo simulations were analyzed for the accuracy of oxygen saturation estimations.

RESULTS

We present design aspects of the phantom, demonstrate how it is developed, and present its breast-like appearance in PA and US imaging. The oxygen saturations were estimated in two regions of interest with and without using the fluence models. The fluence compensation positively influenced the SO2 estimations in all cases and confirmed that highly accurate fluence models are required to minimize estimation errors.

CONCLUSIONS

This phantom allows studies to be performed in PA in carefully controlled laboratory settings to validate approaches to recover both qualitative and quantitative features sought after in in-vivo studies. We believe that testing with phantoms of this complexity can streamline the transition of new PA technologies from the laboratory to studies in the clinic.

A Deep Learning Approach for the Photoacoustic Tomography Recovery From Undersampled Measurements

Photoacoustic tomography (PAT) is a propitious imaging modality, which is helpful for biomedical study. However, fast PAT imaging and denoising is an exigent task in medical research. To address the problem, recently, methods based on compressed sensing (CS) have been proposed, which accede the low computational cost and high resolution for implementing PAT. Nevertheless, the imaging results of the sparsity-based methods strictly rely on sparsity and incoherence conditions. Furthermore, it is onerous to ensure that the experimentally acquired photoacoustic data meets CS's prerequisite conditions. In this work, a deep learning-based PAT (Deep-PAT)method is instigated to overcome these limitations. By using a neural network, Deep-PAT is not only able to reconstruct PAT from a fewer number of measurements without considering the prerequisite conditions of CS, but also can eliminate undersampled artifacts effectively. The experimental results demonstrate that Deep-PAT is proficient at recovering high-quality photoacoustic images using just 5% of the original measurement data. Besides this, compared with the sparsity-based method, it can be seen through statistical analysis that the quality is significantly improved by 30% (approximately), having average SSIM = 0.974 and PSNR = 29.88 dB with standard deviation ±0.007 and ±0.089, respectively, by the proposed Deep-PAT method. Also, a comparsion of multiple neural networks provides insights into choosing the best one for further study and practical implementation.

Internal-Illumination Photoacoustic Tomography Enhanced by a Graded-Scattering Fiber Diffuser

The penetration depth of photoacoustic imaging in biological tissues has been fundamentally limited by the strong optical attenuation when light is delivered externally through the tissue surface. To address this issue, we previously reported internal-illumination photoacoustic imaging using a customized radial-emission optical fiber diffuser, which, however, has complex fabrication, high cost, and non-uniform light emission. To overcome these shortcomings, we have developed a new type of low-cost fiber diffusers based on a graded-scattering method in which the optical scattering of the fiber diffuser is gradually increased as the light travels. The graded scattering can compensate for the optical attenuation and provide relatively uniform light emission along the diffuser. We performed Monte Carlo numerical simulations to optimize several key design parameters, including the number of scattering segments, scattering anisotropy factor, divergence angle of the optical fiber, and reflective index of the surrounding medium. These optimized parameters collectively result in uniform light emission along the fiber diffuser and can be flexibly adjusted to accommodate different applications. We fabricated and characterized the prototype fiber diffuser made of agarose gel and intralipid. Equipped with the new fiber diffuser, we performed thorough proof-of-concept studies on ex vivo tissue phantoms and an in vivo swine model to demonstrate the deep-imaging capability (~10 cm achieved ex vivo) of photoacoustic tomography. We believe that the internal light delivery via the optimized fiber diffuser is an effective strategy to image deep targets (e.g., kidney) in large animals or humans.

Non-local means improves total-variation constrained photoacoustic image reconstruction

Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging. Among the model-based schemes, total-variation (TV) constrained reconstruction scheme is a popular approach. In this work, a two-step approach was proposed for improving the TV constrained optoacoustic inversion by adding a non-local means based filtering step within each TV iteration. Compared to TV-based reconstruction, inclusion of this non-local means step resulted in signal-to-noise ratio improvement of 2.5 dB in the reconstructed optoacoustic images.

Sound Out the Deep Colors: Photoacoustic Molecular Imaging at New Depths

Photoacoustic tomography (PAT) has become increasingly popular for molecular imaging due to its unique optical absorption contrast, high spatial resolution, deep imaging depth, and high imaging speed. Yet, the strong optical attenuation of biological tissues has traditionally prevented PAT from penetrating more than a few centimeters and limited its application for studying deeply seated targets. A variety of PAT technologies have been developed to extend the imaging depth, including employing deep-penetrating microwaves and X-ray photons as excitation sources, delivering the light to the inside of the organ, reshaping the light wavefront to better focus into scattering medium, as well as improving the sensitivity of ultrasonic transducers. At the same time, novel optical fluence mapping algorithms and image reconstruction methods have been developed to improve the quantitative accuracy of PAT, which is crucial to recover weak molecular signals at larger depths. The development of highly-absorbing near-infrared PA molecular probes has also flourished to provide high sensitivity and specificity in studying cellular processes. This review aims to introduce the recent developments in deep PA molecular imaging, including novel imaging systems, image processing methods and molecular probes, as well as their representative biomedical applications. Existing challenges and future directions are also discussed.

Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.

Deep learning in photoacoustic tomography: current approaches and future directions

Biomedical photoacoustic tomography, which can provide high-resolution 3D soft tissue images based on optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of deep learning (DL), or deep neural networks, to this problem has received a great deal of attention. We review the literature on learned image reconstruction, summarizing the current trends and explain how these approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these techniques can be understood from a Bayesian perspective, providing useful insights. We also provide a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications—where data may be sparse, fast imaging critical, and priors difficult to construct by hand—that DL will have the most impact. With this in mind, we conclude with some indications of possible future research directions.

A Review of Endogenous and Exogenous Contrast Agents Used in Photoacoustic Tomography with Different Sensing Configurations

Optical-based sensing approaches have long been an indispensable way to detect molecules in biological tissues for various biomedical research and applications. The advancement in optical microscopy is one of the main drivers for discoveries and innovations in both life science and biomedical imaging. However, the shallow imaging depth due to the use of ballistic photons fundamentally limits optical imaging approaches’ translational potential to a clinical setting. Photoacoustic (PA) tomography (PAT) is a rapidly growing hybrid imaging modality that is capable of acoustically detecting optical contrast. PAT uniquely enjoys high-resolution deep-tissue imaging owing to the utilization of diffused photons. The exploration of endogenous contrast agents and the development of exogenous contrast agents further improve the molecular specificity for PAT. PAT’s versatile design and non-invasive nature have proven its great potential as a biomedical imaging tool for a multitude of biomedical applications. In this review, representative endogenous and exogenous PA contrast agents will be introduced alongside common PAT system configurations, including the latest advances of all-optical acoustic sensing techniques.

Functionalized NIR-II Semiconducting Polymer Nanoparticles for Single-cell to Whole-Organ Imaging of PSMA-Positive Prostate Cancer

Development of molecular probes holds great promise for early diagnosis of aggressive prostate cancer. Here, 2-[3-(1,3-dicarboxypropyl) ureido] pentanedioic acid (DUPA)-conjugated ligand and bis-isoindigo-based polymer (BTII) are synthesized to formulate semiconducting polymer nanoparticles (BTII-DUPA SPN) as a prostate-specific membrane antigen (PSMA)-targeted probe for prostate cancer imaging in the NIR-II window. Insights into the interaction of the imaging probes with the biological targets from single cell to whole organ are obtained by transient absorption (TA) microscopy and photoacoustic (PA) tomography. At single-cell level, TA microscopy reveals the targeting efficiency, kinetics, and specificity of BTII-DUPA SPN to PSMA-positive prostate cancer. At organ level, PA tomographic imaging of BTII-DUPA SPN in the NIR-II window demonstrates superior imaging depth and contrast. By intravenous administration, BTII-DUPA SPN demonstrates selective accumulation and retention in the PSMA-positive tumor, allowing noninvasive PA detection of PSMA overexpressing prostate tumors in vivo. The distribution of nanoparticles inside the tumor tissue is further analyzed through TA microscopy. These results collectively demonstrate BTII-DUPA SPN as a promising probe for prostate cancer diagnosis by PA tomography.

Fan-shaped scanning approach for miniaturized photoacoustic tomography

We describe a novel scanning approach for miniaturized photoacoustic tomography (PAT), based on fan-shaped scanning of a single transducer at one or two discrete positions. This approach is tested and evaluated using several phantom and animal experiments. The results obtained show that this new scanning approach provides high image quality in the configuration of miniaturized handheld or endoscopic PAT with improved effective field of view and penetration depth.

Photoacoustic imaging depth comparison at 532-, 800-, and 1064-nm wavelengths: Monte Carlo simulation and experimental validation

Photoacoustic imaging (PAI) provides high-resolution and high-optical-contrast imaging beyond optical diffusion limit. Further improvement in imaging depth has been achieved by using near-infrared window-I (NIR-I, 700 to 900 nm) for illumination, due to lower scattering and absorption by tissues in this wavelength range. Recently, near-infrared window-II (NIR-II, 900 to 1700 nm) has been explored for PAI. We studied the imaging depths in biological tissues for different illumination wavelengths in visible, NIR-I, and NIR-II regions using Monte Carlo (MC) simulations and validated with experimental results. MC simulations were done to compute fluence in tissue, absorbance in blood vessel, and in a spherical absorber (mimicking sentinel lymph node) embedded at different depths in breast tissue. Photoacoustic tomography and acoustic resolution photoacoustic microscopy experiments were conducted to validate the MC results. We demonstrate that maximum imaging depth is achieved by wavelengths in NIR-I window (∼800  nm) when the energy density deposited is same for all wavelengths. However, illumination using wavelengths around 1064 nm (NIR-II window) gives the maximum imaging depth when the energy density deposited is proportional to maximum permissible exposure (MPE) at corresponding wavelength. These results show that it is the higher MPE of NIR-II window that helps in increasing the PAI depth for chromophores embedded in breast tissue.

Development of a blood oxygenation phantom for photoacoustic tomography combined with online pO2 detection and flow spectrometry

Photoacoustic tomography (PAT) is intrinsically sensitive to blood oxygen saturation (sO2) in vivo. However, making accurate sO2 measurements without knowledge of tissue- and instrumentation-related correction factors is extremely challenging. We have developed a low-cost flow phantom to facilitate validation of PAT systems. The phantom is composed of a flow circuit of tubing partially embedded within a tissue-mimicking material, with independent sensors providing online monitoring of the optical absorption spectrum and partial pressure of oxygen in the tube. We first test the flow phantom using two small molecule dyes that are frequently used for photoacoustic imaging: methylene blue and indocyanine green. We then demonstrate the potential of the phantom for evaluating sO2 using chemical oxygenation and deoxygenation of blood in the circuit. Using this dynamic assessment of the photoacoustic sO2 measurement in phantoms in relation to a ground truth, we explore the influence of multispectral processing and spectral coloring on accurate assessment of sO2. Future studies could exploit this low-cost dynamic flow phantom to validate fluence correction algorithms and explore additional blood parameters such as pH and also absorptive and other properties of different fluids.

Listening to tissues with new light: recent technological advances in photoacoustic imaging

Photoacoustic tomography (PAT), or optoacoustic tomography, has achieved remarkable progress in the past decade, benefiting from the joint developments in optics, acoustics, chemistry, computing and mathematics. Unlike pure optical or ultrasound imaging, PAT can provide unique optical absorption contrast as well as widely scalable spatial resolution, penetration depth and imaging speed. Moreover, PAT has inherent sensitivity to tissue's functional, molecular, and metabolic state. With these merits, PAT has been applied in a wide range of life science disciplines, and has enabled biomedical research unattainable by other imaging methods. This Review article aims at introducing state-of-the-art PAT technologies and their representative applications. The focus is on recent technological breakthroughs in structural, functional, molecular PAT, including super-resolution imaging, real-time small-animal whole-body imaging, and high-sensitivity functional/molecular imaging. We also discuss the remaining challenges in PAT and envisioned opportunities.

Single-pixel camera photoacoustic tomography

Since it was first demonstrated more than a decade ago, the single-pixel camera concept has been used in numerous applications in which it is necessary or advantageous to reduce the channel count, cost, or data volume. Here, three-dimensional (3-D), compressed-sensing photoacoustic tomography (PAT) is demonstrated experimentally using a single-pixel camera. A large area collimated laser beam is reflected from a planar Fabry–Pérot ultrasound sensor onto a digital micromirror device, which patterns the light using a scrambled Hadamard basis before it is collected into a single photodetector. In this way, inner products of the Hadamard patterns and the distribution of thickness changes of the FP sensor—induced by the photoacoustic waves—are recorded. The initial distribution of acoustic pressure giving rise to those photoacoustic waves is recovered directly from the measured signals using an accelerated proximal gradient-type algorithm to solve a model-based minimization with total variation regularization. Using this approach, it is shown that 3-D PAT of imaging phantoms can be obtained with compression rates as low as 10%. Compressed sensing approaches to photoacoustic imaging, such as this, have the potential to reduce the data acquisition time as well as the volume of data it is necessary to acquire, both of which are becoming increasingly important in the drive for faster imaging systems giving higher resolution images with larger fields of view.

Photoacoustic Imaging with Capacitive Micromachined Ultrasound Transducers: Principles and Developments

Photoacoustic imaging (PAI) is an emerging imaging technique that bridges the gap between pure optical and acoustic techniques to provide images with optical contrast at the acoustic penetration depth. The two key components that have allowed PAI to attain high-resolution images at deeper penetration depths are the photoacoustic signal generator, which is typically implemented as a pulsed laser and the detector to receive the generated acoustic signals. Many types of acoustic sensors have been explored as a detector for the PAI including Fabry-Perot interferometers (FPIs), micro ring resonators (MRRs), piezoelectric transducers, and capacitive micromachined ultrasound transducers (CMUTs). The fabrication technique of CMUTs has given it an edge over the other detectors. First, CMUTs can be easily fabricated into given shapes and sizes to fit the design specifications. Moreover, they can be made into an array to increase the imaging speed and reduce motion artifacts. With a fabrication technique that is similar to complementary metal-oxide-semiconductor (CMOS), CMUTs can be integrated with electronics to reduce the parasitic capacitance and improve the signal to noise ratio. The numerous benefits of CMUTs have enticed researchers to develop it for various PAI purposes such as photoacoustic computed tomography (PACT) and photoacoustic endoscopy applications. For PACT applications, the main areas of research are in designing two-dimensional array, transparent, and multi-frequency CMUTs. Moving from the table top approach to endoscopes, some of the different configurations that are being investigated are phased and ring arrays. In this paper, an overview of the development of CMUTs for PAI is presented.

Impacts of the murine skull on high-frequency transcranial photoacoustic brain imaging

Non-invasive photoacoustic tomography (PAT) of mouse brains with intact skulls has been a challenge due to the skull's strong acoustic attenuation, aberration, and reverberation, especially in the high-frequency range (>15 MHz). In this paper, we systematically investigated the impacts of the murine skull on the photoacoustic wave propagation and on the PAT image reconstruction. We studied the photoacoustic acoustic wave aberration due to the acoustic impedance mismatch at the skull boundaries and the mode conversion between the longitudinal wave and shear wave. The wave's reverberation within the skull was investigated for both longitudinal and shear modes. In the inverse process, we reconstructed the transcranial photoacoustic computed tomography (PACT) and photoacoustic microscopy (PAM) images of a point target enclosed by the mouse skull, showing the skull's different impacts on both modalities. Finally, we experimentally validated the simulations by imaging an in vitro mouse skull phantom using representative transcranial PAM and PACT systems. The experimental results agreed well with the simulations and confirmed the accuracy of our forward and inverse models. We expect that our results will provide better understanding of the impacts of the murine skull on transcranial photoacoustic brain imaging and pave the ways for future technical improvements.