Photoacoustic ultrasound (PAUS)--reconstruction tomography

Simultaneous photoacoustic and ultrasound imaging: A review

Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that combines the advantages of optical and ultrasound imaging, enabling the generation of images with both optical resolution and acoustic penetration depth. By leveraging similar signal acquisition and processing methods, the integration of photoacoustic and ultrasound imaging has introduced a novel hybrid imaging modality suitable for clinical applications. Photoacoustic-ultrasound imaging allows for non-invasive, high-resolution, and deep-penetrating imaging, providing a wealth of image information. In recent years, with the deepening research and the expanding biomedical application scenarios of photoacoustic-ultrasound bimodal systems, the immense potential of photoacoustic-ultrasound bimodal imaging in basic research and clinical applications has been demonstrated, with some research achievements already commercialized. In this review, we introduce the principles, technical advantages, and biomedical applications of photoacoustic-ultrasound bimodal imaging techniques, specifically focusing on tomographic, microscopic, and endoscopic imaging modalities. Furthermore, we discuss the future directions of photoacoustic-ultrasound bimodal imaging technology.

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring

Radiation-induced acoustic (RA) imaging is a promising technique for visualizing the invisible radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, RA imaging signal often suffers from poor signal-to-noise ratios (SNRs), thus requiring measuring hundreds or even thousands of frames for averaging to achieve satisfactory quality. This repetitive measurement increases ionizing radiation dose and degrades the temporal resolution of RA imaging, limiting its clinical utility. In this study, we developed a general deep inception convolutional neural network (GDI-CNN) to denoise RA signals to substantially reduce the number of frames needed for averaging. The network employs convolutions with multiple dilations in each inception block, allowing it to encode and decode signal features with varying temporal characteristics. This design generalizes GDI-CNN to denoise acoustic signals resulting from different radiation sources. The performance of the proposed method was evaluated using experimental data of x-ray-induced acoustic, protoacoustic, and electroacoustic signals both qualitatively and quantitatively. Results demonstrated the effectiveness of GDI-CNN: it achieved x-ray-induced acoustic image quality comparable to 750-frame-averaged results using only 10-frame-averaged measurements, reducing the imaging dose of x-ray-acoustic computed tomography (XACT) by 98.7%; it realized proton range accuracy parallel to 1500-frame-averaged results using only 20-frame-averaged measurements, improving the range verification frequency in proton therapy from 0.5 to 37.5 Hz; it reached electroacoustic image quality comparable to 750-frame-averaged results using only a single frame signal, increasing the electric field monitoring frequency from 1 fps to 1k fps. Compared to lowpass filter-based denoising, the proposed method demonstrated considerably lower mean-squared-errors, higher peak-SNR, and higher structural similarities with respect to the corresponding high-frame-averaged measurements. The proposed deep learning-based denoising framework is a generalized method for few-frame-averaged acoustic signal denoising, which significantly improves the RA imaging's clinical utilities for low-dose imaging and real-time therapy monitoring.

Handheld interventional ultrasound/photoacoustic puncture needle navigation based on deep learning segmentation

Interventional ultrasound (US) has challenges in accurate localization of the puncture needle due to intrinsic acoustic interferences, which lead to blurred, indistinct, and even invisible needles in handheld linear array transducer-based US navigation, especially the incorrect needle tip positioning. Photoacoustic (PA) imaging can provide complementary image contrast, without additional data acquisition. Herein, we proposed an internal illumination to solely light up the needle tip in PA imaging. Then deep-learning-based feature segmentation alleviates acoustic interferences, enhancing the needle shaft-tip visibility. Further, needle shaft-tip compensation aligned the needle shaft in US image and the needle tip in the PA image. The experiments on phantom, ex vivo chicken breast, preclinical radiofrequency ablation and in vivo biopsy of sentinel lymph nodes were piloted. The target registration error can reach the submillimeter level, achieving precise puncture needle tracking ability with in-plane US/PA navigation.

Photoacoustic-guided ultrasound thermal imaging without prior knowledge of tissue composition

Thermal strain imaging (TSI) is a widely investigated ultrasound (US) thermometry technique that is based on the temperature-dependent change in speed of sound. However, a major challenge of TSI is a calibration process to account for material-dependent thermal strain. In this study, we leverage nanoparticle (NP)-mediated photoacoustic (PA) thermometry to calibrate thermal strain and guide US thermal imaging. By controlling the molecular composition of the sub-micrometer layer surrounding the NPs, PA thermometry becomes independent of the thermal characteristics of the overall background tissue where the NPs reside. Thus accurate temperature measurements are obtainable from sparse NP-mediated PA signals. These measurements are used to guide TSI, allowing US thermometry to produce an expanded temperature map over the entire region of interest without prior knowledge of tissue composition. Our feasibility study in tissue-mimicking phantoms demonstrates the potential to improve TSI by integrating a PA-based calibration method that complements and guides US thermometry.

Invariant representations in deep learning for optoacoustic imaging

Image reconstruction in optoacoustic tomography (OAT) is a trending learning task highly dependent on measured physical magnitudes present at sensing time. A large number of different settings and also the presence of uncertainties or partial knowledge of parameters can lead to reconstruction algorithms that are specifically tailored and designed to a particular configuration, which could not be the one that will ultimately be faced in a final practical situation. Being able to learn reconstruction algorithms that are robust to different environments (e.g., the different OAT image reconstruction settings) or invariant to such environments is highly valuable because it allows us to focus on what truly matters for the application at hand and discard what are considered spurious features. In this work, we explore the use of deep learning algorithms based on learning invariant and robust representations for the OAT inverse problem. In particular, we consider the application of the ANDMask scheme due to its easy adaptation to the OAT problem. Numerical experiments are conducted showing that when out-of-distribution generalization (against variations in parameters such as the location of the sensors) is imposed, there is no degradation of the performance and, in some cases, it is even possible to achieve improvements with respect to standard deep learning approaches where invariance robustness is not explicitly considered.

Hybrid photoacoustic and fast super-resolution ultrasound imaging

The combination of photoacoustic (PA) imaging and ultrasound localization microscopy (ULM) with microbubbles has great potential in various fields such as oncology, neuroscience, nephrology, and immunology. Here we developed an interleaved PA/fast ULM imaging technique that enables super-resolution vascular and physiological imaging in less than 2 seconds per frame in vivo. By using sparsity-constrained (SC) optimization, we accelerated the frame rate of ULM up to 37 times with synthetic data and 28 times with in vivo data. This allows for the development of a 3D dual imaging sequence with a commonly used linear array imaging system, without the need for complicated motion correction. Using the dual imaging scheme, we demonstrated two in vivo scenarios challenging to image with either technique alone: the visualization of a dye-labeled mouse lymph node showing nearby microvasculature, and a mouse kidney microangiography with tissue oxygenation. This technique offers a powerful tool for mapping tissue physiological conditions and tracking the contrast agent biodistribution non-invasively.

Influence of the temperature-dependent dielectric constant on the photoacoustic effect of gold nanospheres

Photoacoustic imaging techniques with gold nanoparticles as contrast agents have received a great deal of attention. The photoacoustic response of gold nanoparticles strongly depends on the far-field optical properties, which essentially depend on the dielectric constant of the material. The dielectric constant of gold not only varies with wavelength but is also affected by temperature. However, the effect of the temperature dependence of the dielectric constant on gold nanoparticles' photoacoustic response has not been fully investigated. In this work, the Drude-Lorentz model and Mie theory are used to calculate the dielectric constant and absorption efficiency of gold nanospheres in aqueous solution, respectively. Then, the finite element method is used to simulate the heat transfer process of gold nanospheres and surrounding water. Finally, the one-dimensional velocity-stress equation is solved by the finite-difference time-domain method to obtain the photoacoustic response of gold nanospheres. The results show that under the irradiation of a high-fluence nanosecond pulse laser, ignoring the temperature dependence of the dielectric constant will lead to large errors in the photothermal response and the nonlinear photoacoustic signals (it can even exceed 20% and 30%). The relative error of the photothermal and photoacoustic response caused by ignoring the temperature-dependent dielectric constant is determined from both the temperature dependence of absorption efficiency and the maximum temperature increase of gold nanospheres. This work provides a new perspective for the photothermal and photoacoustic effects of gold nanospheres, which is meaningful for the development of high-resolution photoacoustic detectors and nano/microscale temperature measurement techniques.

Photoacoustic imaging for the study of oxygen saturation and total hemoglobin in bone healing and non-union formation

Non-union formation represents a major complication in trauma surgery. Adequate vascularization has been recognized as vital for bone healing. However, the role of vascularization in the pathophysiology of non-union formation remains elusive. This is due to difficulties in studying bone microcirculation in vivo. Therefore, we herein studied in a murine osteotomy model whether photoacoustic imaging may be used to analyze vascularization in bone healing and non-union formation. We found that oxygen saturation within the callus tissue is significantly lower in non-unions compared to unions and further declines over time. Moreover, the amount of total hemoglobin (HbT) within the callus tissue was markedly reduced in non-unions. Correlation analyses showed a strong positive correlation between microvessel density and HbT, indicating that photoacoustically determined HbT is a valid parameter to assess vascularization during bone healing. In summary, photoacoustic imaging is a promising approach to study vascular function and tissue oxygenation in bone regeneration.

Contrast Agents for Photoacoustic Imaging: A Review Focusing on the Wavelength Range

Photoacoustic imaging using endogenous chromophores as a contrast has been widely applied in biomedical studies owing to its functional imaging capability at the molecular level. Various exogenous contrast agents have also been investigated for use in contrast-enhanced imaging and functional analyses. This review focuses on contrast agents, particularly in the wavelength range, for use in photoacoustic imaging. The basic principles of photoacoustic imaging regarding light absorption and acoustic release are introduced, and the optical characteristics of tissues are summarized according to the wavelength region. Various types of contrast agents, including organic dyes, semiconducting polymeric nanoparticles, gold nanoparticles, and other inorganic nanoparticles, are explored in terms of their light absorption range in the near-infrared region. An overview of the contrast-enhancing capacity and other functional characteristics of each agent is provided to help researchers gain insights into the development of contrast agents in photoacoustic imaging.

Ultrasound-guided adaptive photoacoustic tomography

Image formation in photoacoustic tomography (PAT) is generally based on the assumption that biological tissues are acoustically homogeneous. However, this does not hold, especially when strongly heterogeneous tissues, such as bones and air cavities, are present. Tissue heterogeneity can cause acoustic reflection, refraction, and scattering at interfaces, which may create distortions and artifacts in final images. To mitigate this problem, we propose an adaptive photoacoustic (PA) image reconstruction method based on prior structural information of an acoustically heterogeneous region extracted from ultrasound images. The method works in three steps: acoustic heterogeneity identification via ultrasound imaging; acoustically heterogeneous region segmentation; and adaptive time-domain raw data truncation and image reconstruction. The data truncation is based on a variable cutoff time, which can be adaptively determined according to the relative position of a transducer and an acoustically heterogeneous region. Numerical and in vivo experimental imaging results of human fingers demonstrate that the proposed ultrasound-guided adaptive image reconstruction method can effectively suppress acoustic heterogeneity-induced artifacts and substantially improve image quality. This work provides a practical way to mitigate the influence of acoustic heterogeneity in PAT.

Weighted model-based optoacoustic reconstruction for partial-view geometries

Acoustic heterogeneities in biological samples are known to cause artifacts in tomographic optoacoustic (photoacoustic) image reconstruction. A statistical weighted model-based reconstruction approach was previously introduced to mitigate such artifacts. However, this approach does not reliably provide high-quality reconstructions for partial-view imaging systems, which are common in preclinical and clinical optoacoustics. In this article, the capability of the weighted model-based algorithm is extended to generate optoacoustic reconstructions with less distortions for partial-view geometry data. This is achieved by manipulating the weighting scheme based on the detector geometry. Using partial-view optoacoustic tomography data from a tissue-mimicking phantom containing a strong acoustic reflector, tumors grafted onto mice, and a mouse brain with intact skull, the proposed partial-view-corrected weighted model-based algorithm is shown to mitigate reflection artifacts in reconstructed images without distorting structures or boundaries, compared with both conventional model-based and the weighted model-based algorithms. It is also demonstrated that the partial-view-corrected weighted model-based algorithm has the additional advantage of suppressing streaking artifacts due to the partial-view geometry itself in the presence of a very strong optoacoustic chromophore. Due to its enhanced performance, the partial-view-corrected weighted model-based algorithm may prove useful for improving the quality of partial-view multispectral optoacoustic tomography, leading to enhanced visualization of functional parameters such as tissue oxygenation.

Acoustic-resolution photoacoustic microscope based on compact and low-cost delta configuration actuator

The state-of-the-art configurations for acoustic-resolution photoacoustic (PA) microscope (AR-PAM) are large in size and expensive, hindering their democratization. While previous research on AR-PAMs introduced a low-cost light source to reduce the cost, few studies have investigated the possibility of optimizing the sensor actuation, particularly for the AR-PAM. Additionally, there is an unmet need to evaluate the image quality deterioration associated with the actuation inaccuracy. A low-cost actuation device is introduced to reduce the system size and cost of the AR-PAM while maintaining the image quality by implementing the advanced beamformers. This work proposes an AR-RAM incorporating the delta configuration actuator adaptable from a low-cost off-the-shelf 3D printer as the sensor actuation device. The image degradation due to the data acquisition positioning inaccuracy is evaluated in the simulation. We further assess the mitigation of potential actuation precision uncertainty through advanced 3D synthetic aperture focusing algorithms represented by the Delay-and-Sum (DAS) with Coherence Factor (DAS+CF) and Delay-Multiply-and-Sum (DMAS) algorithms. The simulation study demonstrated the tolerance of image quality on actuation inaccuracy and the effect of compensating the actuator motion precision error through advanced reconstruction algorithms. With those algorithms, the image quality degradation was suppressed to within 25% with the presence of 0.2 mm motion inaccuracy. The experimental evaluation using phantoms and an ex-vivo sample presented the applicability of low-cost delta configuration actuators for AR-PAMs. The measured full width at half maximum of the 0.2 mm diameter pencil-lead phantom were 0.45 ± 0.06 mm, 0.31 ± 0.04 mm, and 0.35 ± 0.07 mm, by applying the DAS, DAS+CF, and DMAS algorithms, respectively. AR-PAMs with a compact and low-cost delta configuration provide high-quality PA imaging with better accessibility for biomedical applications. The research evaluated the image degradation contributed by the actuation inaccuracy and suggested that the advanced beamformers are capable of suppressing the actuation inaccuracy.

Modeling combined ultrasound and photoacoustic imaging: Simulations aiding device development and artificial intelligence

Combined ultrasound and photoacoustic (USPA) imaging has attracted several pre-clinical and clinical applications due to its ability to simultaneously display structural, functional, and molecular information of deep biological tissue in real time. However, the depth and wavelength dependent optical attenuation and the unknown optical and acoustic heterogeneities limit the USPA imaging performance in deep tissue regions. Novel instrumentation, image reconstruction, and artificial intelligence (AI) methods are currently being investigated to overcome these limitations and improve the USPA image quality. Effective implementation of these approaches requires a reliable USPA simulation tool capable of generating US based anatomical and PA based molecular contrasts of deep biological tissue. Here, we developed a hybrid USPA simulation platform by integrating finite element models of light (NIRFast) and ultrasound (k-Wave) propagations for co-simulation of B-mode US and PA images. The platform allows optimization of different design parameters for USPA devices, such as the aperture size and frequency of both light and ultrasound detector arrays. For designing tissue-realistic digital phantoms, a dictionary-based function has been added to k-Wave to generate various levels of ultrasound speckle contrast. The feasibility of modeling US imaging combined with optical fluence dependent multispectral PA imaging is demonstrated using homogeneous as well as heterogeneous tissue phantoms mimicking human organs (e.g., prostate and finger). In addition, we also demonstrate the potential of the simulation platform to generate large scale application-specific training and test datasets for AI enhanced USPA imaging. The complete USPA simulation codes together with the supplementary user guides have been posted to an open-source repository (https://github.com/KothapalliLabPSU/US-PA_simulation_codes).

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.

Photoacoustic computed tomography for functional human brain imaging [Invited]

The successes of magnetic resonance imaging and modern optical imaging of human brain function have stimulated the development of complementary modalities that offer molecular specificity, fine spatiotemporal resolution, and sufficient penetration simultaneously. By virtue of its rich optical contrast, acoustic resolution, and imaging depth far beyond the optical transport mean free path (∼1 mm in biological tissues), photoacoustic computed tomography (PACT) offers a promising complementary modality. In this article, PACT for functional human brain imaging is reviewed in its hardware, reconstruction algorithms, in vivo demonstration, and potential roadmap.

Photoacoustic Neuroimaging - Perspectives on a Maturing Imaging Technique and its Applications in Neuroscience

A prominent goal of neuroscience is to improve our understanding of how brain structure and activity interact to produce perception, emotion, behavior, and cognition. The brain's network activity is inherently organized in distinct spatiotemporal patterns that span scales from nanometer-sized synapses to meter-long nerve fibers and millisecond intervals between electrical signals to decades of memory storage. There is currently no single imaging method that alone can provide all the relevant information, but intelligent combinations of complementary techniques can be effective. Here, we thus present the latest advances in biomedical and biological engineering on photoacoustic neuroimaging in the context of complementary imaging techniques. A particular focus is placed on recent advances in whole-brain photoacoustic imaging in rodent models and its influential role in bridging the gap between fluorescence microscopy and more non-invasive techniques such as magnetic resonance imaging (MRI). We consider current strategies to address persistent challenges, particularly in developing molecular contrast agents, and conclude with an overview of potential future directions for photoacoustic neuroimaging to provide deeper insights into healthy and pathological brain processes.

Towards in vivo Dosimetry for Prostate Radiotherapy with a Transperineal Ultrasound Array: A Simulation Study

X-ray-induced acoustic computed tomography (XACT) is a promising imaging modality to monitor the position of the radiation beam and the deposited dose during external beam radiotherapy delivery. The purpose of this study was to investigate the feasibility of using a transperineal ultrasound transducer array for XACT imaging to guide the prostate radiotherapy. A customized two-dimensional (2D) matrix ultrasound transducer array with 10000 (100×100 elements) ultrasonic sensors with a central frequency of 1 MHz was designed on a 5 cm×5 cm plane to optimize three-dimensional (3D) volumetric imaging. The CT scan and dose treatment plan for a prostate patient undergoing intensity modulated radiation therapy (IMRT) were obtained. In-house simulation was developed to model the time varying X-ray induced acoustic (XA) signals detected by the transperineal ultrasound array. A 3D filtered back projection (FBP) algorithm has been used for 3D XACT image reconstruction. Results of this study will greatly enhance the potential of XACT imaging for real time in vivo dosimetry during radiotherapy.

A review of the application of machine learning in molecular imaging

Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI. OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural network-based machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

Spatial resolution in photoacoustic computed tomography

Photoacoustic computed tomography (PACT) is a novel biomedical imaging modality and has experienced fast developments in the past two decades. Spatial resolution is an important criterion to measure the imaging performance of a PACT system. Here we survey state-of-the-art literature on the spatial resolution of PACT and analyze resolution degradation models from signal generation, propagation, reception, to image reconstruction. Particularly, the impacts of laser pulse duration, acoustic attenuation, acoustic heterogeneity, detector bandwidth, detector aperture, detector view angle, signal sampling, and image reconstruction algorithms are reviewed and discussed. Analytical expressions of point spread functions related to these impacting factors are summarized based on rigorous mathematical formulas. State-of-the-art approaches devoted to enhancing spatial resolution are also reviewed. This work is expected to elucidate the concept of spatial resolution in PACT and inspire novel image quality enhancement techniques.

Synchrotron X-ray induced acoustic imaging

X-ray induced acoustic imaging (XAI) is an emerging biomedical imaging technique that can visualize X-ray absorption contrast at ultrasound resolution with less ionizing radiation exposure than conventional X-ray computed tomography. So far, medical linear accelerators or industrial portable X-ray tubes have been explored as X-ray excitation sources for XAI. Here, we demonstrate the first feasible synchrotron XAI (sXAI). The synchrotron generates X-rays, with a dominant energy of 4 to 30 keV, a pulse-width of 30 ps, a pulse-repetition period of 2 ns, and a bunch-repetition period of 940 ns. The X-ray induced acoustic (XA) signals are processed in the Fourier domain by matching the signal frequency with the bunch-repetition frequency. We successfully obtained two-dimensional XA images of various lead targets. This novel sXAI tool could complement conventional synchrotron applications.

Real-time interleaved spectroscopic photoacoustic and ultrasound (PAUS) scanning with simultaneous fluence compensation and motion correction

For over two decades photoacoustic imaging has been tested clinically, but successful human trials have been limited. To enable quantitative clinical spectroscopy, the fundamental issues of wavelength-dependent fluence variations and inter-wavelength motion must be overcome. Here we propose a real-time, spectroscopic photoacoustic/ultrasound (PAUS) imaging approach using a compact, 1-kHz rate wavelength-tunable laser. Instead of illuminating tissue over a large area, the fiber-optic delivery system surrounding an US array sequentially scans a narrow laser beam, with partial PA image reconstruction for each laser pulse. The final image is then formed by coherently summing partial images. This scheme enables (i) automatic compensation for wavelength-dependent fluence variations in spectroscopic PA imaging and (ii) motion correction of spectroscopic PA frames using US speckle tracking in real-time systems. The 50-Hz video rate PAUS system is demonstrated in vivo using a murine model of labelled drug delivery.

Photoacoustic Tomography Opening New Paradigms in Biomedical Imaging

After the emergence of the ultrasound, X-ray CT, PET, and MRI, photoacoustic tomography (PAT) is now in the phase of its exponential growth, with its expected full maturation being another form of mainstream clinical imaging modality. By combining the high contrast benefit of optical imaging and the high-resolution deep imaging capability of ultrasound, PAT can provide unprecedented anatomical image contrasts at clinically relevant depths as well as enable the use of a variety of functional and molecular imaging information, which is not possible with conventional imaging modalities. With these strengths, PAT has achieved numerous breakthroughs in various biomedical applications and also provided new technical platforms that may be able to resolve unmet issues in clinics. In this chapter, we provide an overview of the development of PAT technology for several major biomedical applications and provide an approximate projection of the future of PAT.

Photoacoustic imaging with fiber optic technology: A review

Photoacoustic imaging (PAI) has achieved remarkable growth in the past few decades since it takes advantage of both optical and ultrasound (US) imaging. In order to better promote the wide clinical applications of PAI, many miniaturized and portable PAI systems have recently been proposed. Most of these systems utilize fiber optic technologies. Here, we overview the fiber optic technologies used in PAI. This paper discusses three different fiber optic technologies: fiber optic light transmission, fiber optic US transmission, and fiber optic US detection. These fiber optic technologies are analyzed in different PAI modalities including photoacoustic microscopy (PAM), photoacoustic computed tomography (PACT), and minimally invasive photoacoustic imaging (MIPAI).

Photoacoustic and Magnetic Resonance Imaging of Hybrid Manganese Dioxide-Coated Ultra-small NaGdF4 Nanoparticles for Spatiotemporal Modulation of Hypoxia in Head and Neck Cancer

Simple Summary

Tumor hypoxia is a documented negative prognostic factor that contributes to treatment resistance in head and neck cancer. In the present study, we use non-invasive magnetic resonance imaging (MRI) and photoacoustic imaging (PAI) to evaluate the ability of ultra-small manganese dioxide coated nanoparticles to modulate tumor oxygenation in vitro and in vivo. Our results highlight the utility of MRI and PAI in mapping tumor hypoxia and nanoparticle delivery and demonstrate the potential of image-guided nanodelivery in alleviating tumor hypoxia in head and neck cancer.

Abstract

There is widespread interest in developing agents to modify tumor hypoxia in head and neck squamous cell carcinomas (HNSCC). Here, we report on the synthesis, characterization, and potential utility of ultra-small NaYF₄:Nd³⁺/NaGdF₄ nanocrystals coated with manganese dioxide (usNP-MnO₂) for spatiotemporal modulation of hypoxia in HNSCC. Using a dual modality imaging approach, we first visualized the release of Mn²⁺ using T1-weighted magnetic resonance imaging (MRI) and modulation of oxygen saturation (%sO₂) using photoacoustic imaging (PAI) in vascular channel phantoms. Combined MRI and PAI performed in patient-derived HNSCC xenografts following local and systemic delivery of the hybrid nanoparticles enabled mapping of intratumoral nanoparticle accumulation (based on T1 contrast enhancement) and improvement in tumor oxygenation (increased %sO₂) within the tumor microenvironment. Our results demonstrate the potential of hybrid nanoparticles for the modulation of tumor hypoxia in head and neck cancer. Our findings also highlight the potential of combined MRI-PAI for simultaneous mapping nanoparticle delivery and oxygenation changes in tumors. Such imaging methods could be valuable in the precise selection of patients that are likely to benefit from hypoxia-modifying nanotherapies.

A Synthetic Total Impulse Response Characterization Method for Correction of Hand-Held Optoacoustic Images

The impulse response of optoacoustic (photoacoustic) tomographic imaging system depends on several system components, the characteristics of which can influence the quality of reconstructed images. The effect of these system components on reconstruction quality have not been considered in detail so far. Here we combine sparse measurements of the total impulse response (TIR) with a geometric acoustic model to obtain a full characterization of the TIR of a handheld optoacoustic tomography system with concave limited-view acquisition geometry. We then use this synthetic TIR to reconstruct data from phantoms and healthy human volunteers, demonstrating improvements in image resolution and fidelity. The higher accuracy of optoacoustic tomographic reconstruction with TIR correction further improves the diagnostic capability of handheld optoacoustic tomographic systems.

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging

The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, high-quality real-time intra-operative imaging remains as a challenging problem. Meanwhile, photoacoustic imaging has appeared so promising to provide images of crucial structures such as blood vessels and microvasculature of tumors. To achieve high-quality photoacoustic images of vessels regarding the artifacts caused by the incomplete data, we proposed an approach based on the combination of time-reversal (TR) and deep learning methods. The proposed method applies a TR method in the first layer of the network which is followed by the convolutional neural network with weights adjusted to a set of simulated training data for the other layers to estimate artifact-free photoacoustic images. It was evaluated using a generated synthetic database of vessels. The mean of signal to noise ratio (SNR), peak SNR, structural similarity index, and edge preservation index for the test data were reached 14.6 dB, 35.3 dB, 0.97 and 0.90, respectively. As our results proved, by using the lower number of detectors and consequently the lower data acquisition time, our approach outperforms the TR algorithm in all criteria in a computational time compatible with clinical use.

Pickering Bubbles as Dual-Modality Ultrasound and Photoacoustic Contrast Agents

Microbubbles (MBs) stabilized by particle surfactants (i.e., Pickering bubbles) have better thermodynamic stability compared to MBs stabilized by small molecules as a result of steric hindrance against coalescence, higher diffusion resistance, and higher particle desorption energy. In addition, the use of particles to stabilize MBs that are typically used as an ultrasound (US) contrast agent can also introduce photoacoustic (PA) properties, thus enabling a highly effective dual-modality US and PA contrast agent. Here, we report the use of partially reduced and functionalized graphene oxide as the sole surfactant to stabilize perfluorocarbon gas bubbles in the preparation of a dual-modality US and PA agent, with high contrast in both imaging modes and without the need for small-molecule or polymer additives. This approach offers an increase in loading of the PA agent without destabilization and increased thickness of the MB shell compared to traditional systems, in which the focus is on adding a PA agent to existing MB formulations.

Feasibility of Electroacoustic Tomography: A Simulation Study

The feasibility of electroacoustic tomography (EAT) was investigated for in situ monitoring the electric field distribution in soft tissue. EAT exploits the phenomenon that the amplitude of acoustic emission generated by an electric field is proportional to the electrical energy deposition in tissue. After detecting these acoustic waves with ultrasound transducers, an image of the electric field distribution can be reconstructed in real-time. In our computer simulations, the electric field distribution in soft tissue was generated by solving general partial differential equations (PDEs) using finite element analysis (FEA). The electric field distributions were converted into initial pressure distributions, and the propagation of the induced acoustic waves was simulated using K-Wave simulation. A circular array of 128 ultrasound transducers was placed around the target to detect the acoustic waves, and a time reversal reconstruction algorithm was used to reconstruct the EAT image. A different number of electrodes set at different distances with different voltage inputs on the electrodes were performed to simulate different electric field distributions during electroporation. It was found that the electrical energy deposition in reconstructed EAT imaging is decreased as the distance of the electrodes increases. We also have investigated the sensitivity of the EAT imaging with different voltage inputs. The minimal voltage we can detect with EAT is 970 V at the pulsewidth of 180 ns. The results of this study demonstrated that EAT is a feasible technique for monitoring the electric field distribution and guiding the electrotherapy in future clinical practice.

Recent advances in molecular imaging of atherosclerotic plaques and thrombosis

As the complications of atherosclerosis such as myocardial infarction and stroke are still one of the leading causes of mortality worldwide, the development of new diagnostic tools for the early detection of plaque instability and thrombosis is urgently needed. Advanced molecular imaging probes based on functional nanomaterials in combination with cutting edge imaging techniques are now paving the way for novel and unique approaches to monitor the inflammatory progress in atherosclerosis. This review focuses on the development of various molecular probes for the diagnosis of plaques and thrombosis in atherosclerosis, along with perspectives of their diagnostic applications in cardiovascular diseases. Specifically, we summarize the biological targets that can be used for atherosclerosis and thrombosis imaging. Then we describe the emerging molecular imaging techniques based on the utilization of engineered nanoprobes together with their challenges in clinical translation.

Iterative image reconstruction in transcranial photoacoustic tomography based on the elastic wave equation

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the measurements recorded on an aperture outside the support of the source. A major challenge in transcranial PACT of the brain is to compensate for aberrations and attenuation in the measured data due to the propagation of the photoacoustic wavefields through the skull. To properly account for these effects, a wave equation-based inversion method can be employed that can model the heterogeneous elastic properties of the medium. In this study, an optimization-based image reconstruction method for 3D transcranial PACT is developed based on the elastic wave equation. To accomplish this, a forward-adjoint operator pair based on a finite-difference time-domain discretization of the 3D elastic wave equation is utilized to compute penalized least squares estimates of the initial pressure distribution. Computer-simulation and experimental studies are conducted to investigate the robustness of the reconstruction method to model mismatch and its ability to effectively resolve cortical and superficial brain structures.

Temperature estimation of inflamed bowel by the photoacoustic inverse approach

Local temperature increase is one of the five classical signs of regions with inflammations. This work is focused on the application of the photoacoustic technique for the estimation of the temperature field in the colon, as the solution of an inverse problem, for the detection of inflamed regions. Two-dimensional cases are examined here involving a cross section of the bowel, which characterize either the inflammation of the whole mucosa layer, or three small inflamed regions. The inverse problem is solved for a rotating laser inside the intestine lumen, which imposes pulses for the generation of the acoustic waves. One single ultrasound detector, also located at the laser rotating shaft, provides the simulated measurements for the inverse analysis. The inverse problem is solved here with the minimization of the maximum a posteriori objective function. Results show that the proposed technique can be applied for accurate estimations of the temperature distribution in the region of interest, which might be used for the diagnosis of inflammatory bowel diseases (IBD).

Fully waterproof two-axis galvanometer scanner for enhanced wide-field optical-resolution photoacoustic microscopy

A large field-of-view and fast scanning of photoacoustic microscopy (PAM) relatively have been difficult to obtain due to the water-drowned structure of the system for the transmission of ultrasonic signals. Researchers have widely studied the achievement of a waterproof scanner for dynamic biological applications with a high-resolution and high signal-to-noise ratio. This Letter reports a novel, to the best of our knowledge, waterproof galvanometer scanner-based PAM system with a successfully attainable ${9.0}\;{\rm mm} \times {14.5}\;{\rm mm}$9.0mm×14.5mm scan region, amplitude scan rate of 40 kHz, and spatial resolution of 4.9 µm. The in vivo characterization of a mouse brain in intact-skull microvascular visualization demonstrated its capability in biomedical imaging and is anticipated to be an effective technique for various preclinical and clinical studies.

GPU-accelerated 3D volumetric X-ray-induced acoustic computed tomography

X-ray acoustic imaging is a hybrid biomedical imaging technique that can acoustically monitor X-ray absorption distribution in biological tissues through the X-ray induced acoustic effect. In this study, we developed a 3D volumetric X-ray-induced acoustic computed tomography (XACT) system with a portable pulsed X-ray source and an arc-shaped ultrasound array transducer. 3D volumetric XACT images are reconstructed via the back-projection algorithm, accelerated by a custom-developed graphics processing unit (GPU) software. Compared with a CPU-based software, the GPU software reconstructs an image over 40 times faster. We have successfully acquired 3D volumetric XACT images of various lead targets, and this work shows that the 3D volumetric XACT system can monitor a high-resolution X-ray dose distribution and image X-ray absorbing structures inside biological tissues.

A Partially-Learned Algorithm for Joint Photo-acoustic Reconstruction and Segmentation

In an inhomogeneously illuminated photoacoustic image, important information like vascular geometry is not readily available, when only the initial pressure is reconstructed. To obtain the desired information, algorithms for image segmentation are often applied as a post-processing step. In this article, we propose to jointly acquire the photoacoustic reconstruction and segmentation, by modifying a recently developed partially learned algorithm based on a convolutional neural network. We investigate the stability of the algorithm against changes in initial pressures and photoacoustic system settings. These insights are used to develop an algorithm that is robust to input and system settings. Our approach can easily be applied to other imaging modalities and can be modified to perform other high-level tasks different from segmentation. The method is validated on challenging synthetic and experimental photoacoustic tomography data in limited angle and limited view scenarios. It is computationally less expensive than classical iterative methods and enables higher quality reconstructions and segmentations than the state-of-the-art learned and non-learned methods.

Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

In optoacoustic tomography, image reconstruction is often performed with incomplete or noisy data, leading to reconstruction errors. Significant improvement in reconstruction accuracy may be achieved in such cases by using nonlinear regularization schemes, such as total-variation minimization and L ₁-based sparsity-preserving schemes. In this paper, we introduce a new framework for optoacoustic image reconstruction based on adaptive anisotropic total-variation regularization, which is more capable of preserving complex boundaries than conventional total-variation regularization. The new scheme is demonstrated in numerical simulations on blood-vessel images as well as on experimental data and is shown to be more capable than the total-variation-L ₁ scheme in enhancing image contrast.

Bacteriophage-mediated identification of bacteria using photoacoustic flow cytometry

Infection with resistant bacteria has become an ever increasing problem in modern medical practice. Currently, broad spectrum antibiotics are prescribed until bacteria can be identified through blood cultures, a process that can take two to three days and is unable to provide quantitative information. To detect and quantify bacteria rapidly in blood samples, we designed a method using labeled bacteriophage in conjunction with photoacoustic flow cytometry (PAFC). PAFC is the generation of ultrasonic waves created by the absorption of laser light in particles under flow. Bacteriophage is a virus that infects bacteria and possesses the ability to discriminate bacterial surface antigens, allowing the bacteriophage to bind only to their target bacteria. Bacteria can be tagged with dyed phage and processed through a photoacoustic flow cytometer where they are detected by the acoustic response. We demonstrate that E. coli; can be detected and discriminated from Salmonella; using this method. Our goal is to develop a method to determine bacterial content in blood samples. We hope to develop this technology into future clinical use and decrease the time required to identify bacterial species from 3 to 4 days to less than 1 hour.

Performance Characteristics of Photoacoustic Imaging Probes with Varying Frequencies and Light-delivery Schemes

Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that utilizes a combination of light and ultrasound to detect photoabsorbers embedded within tissues. While the clinical utility of PAI has been widely explored for several applications, limitations in light penetration and detector sensitivity have restricted these studies to mostly superficial sites. Given the importance of PA signal generation and detection on light delivery and ultrasound detector frequency, there is an ongoing effort to optimize these parameters to enhance photoabsorber detection at increased depths. With this in mind, in this study we examined performance benchmarks of a commercially available PAI/ultrasound linear array system when using different imaging frequencies and light delivery schemes. A modified light fiber jacket providing focused light delivery (FLD) at the center of the probe was compared with the built-in fiber optics lining the length of the probe. Studies were performed in vitro to compare performance characteristics such as imaging resolution, maximum imaging depth, and sensitivity to varying hematocrit concentration for each frequency and light delivery method. Monte Carlo simulations of each light delivery method revealed increased light penetration with FLD. In tissue-mimicking phantoms, vascular channels used to simulate blood vessels could be visualized at a depth of 2.4 cm when lowering imaging frequency and utilizing FLD. Imaging at lower frequencies with FLD also enabled enhanced detection of varying hematocrit concentration levels at increased depths, although lateral imaging resolution was reduced. Finally, a proof of concept in vivo probe comparison study in a mouse tumor model provided supportive evidence of our in vitro results. Collectively, our findings show that adjusting imaging frequency and applying FLD can be a straightforward approach for improving PAI performance.

A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography

Photoacoustic computed tomography (PACT), also known as optoacoustic tomography, is an emerging imaging technique that holds great promise for biomedical imaging. PACT is a hybrid imaging method that can exploit the strong endogenous contrast of optical methods along with the high spatial resolution of ultrasound methods. In its canonical form that is addressed in this article, PACT seeks to estimate the photoacoustically-induced initial pressure distribution within the object. Image reconstruction methods are employed to solve the acoustic inverse problem associated with the image formation process. When an idealized imaging scenario is considered, analytic solutions to the PACT inverse problem are available; however, in practice, numerous challenges exist that are more readily addressed within an optimization-based, or iterative, image reconstruction framework. In this article, the PACT image reconstruction problem is reviewed within the context of modern optimization-based image reconstruction methodologies. Imaging models that relate the measured photoacoustic wavefields to the sought-after object function are described in their continuous and discrete forms. The basic principles of optimization-based image reconstruction from discrete PACT measurement data are presented, which includes a review of methods for modeling the PACT measurement system response and other important physical factors. Non-conventional formulations of the PACT image reconstruction problem, in which acoustic parameters of the medium are concurrently estimated along with the PACT image, are also introduced and reviewed.

Cardiovascular optoacoustics: From mice to men - A review

Imaging has become an indispensable tool in the research and clinical management of cardiovascular disease (CVD). An array of imaging technologies is considered for CVD diagnostics and therapeutic assessment, ranging from ultrasonography, X-ray computed tomography and magnetic resonance imaging to nuclear and optical imaging methods. Each method has different operational characteristics and assesses different aspects of CVD pathophysiology; nevertheless, more information is desirable for achieving a comprehensive view of the disease. Optoacoustic (photoacoustic) imaging is an emerging modality promising to offer novel information on CVD parameters by allowing high-resolution imaging of optical contrast several centimeters deep inside tissue. Implemented with illumination at several wavelengths, multi-spectral optoacoustic tomography (MSOT) in particular, is sensitive to oxygenated and deoxygenated hemoglobin, water and lipids allowing imaging of the vasculature, tissue oxygen saturation and metabolic or inflammatory parameters. Progress with fast-tuning lasers, parallel detection and advanced image reconstruction and data-processing algorithms have recently transformed optoacoustics from a laboratory tool to a promising modality for small animal and clinical imaging. We review progress with optoacoustic CVD imaging, highlight the research and diagnostic potential and current applications and discuss the advantages, limitations and possibilities for integration into clinical routine.

3D Photoacoustic Tomography System Based on Full-View Illumination and Ultrasound Detection

A 3D photoacoustic computed tomography (3D-PACT) system based on full-view illumination and ultrasound detection was developed and applied to 3D photoacoustic imaging of several phantoms. The system utilized an optics cage design to achieve full-view uniform laser illumination and completed 3D scanning with the rotation of a dual-element transducer (5 MHz) and the vertical motion of imaging target, which obtains the best solution in the mutual restriction relation between cost and performance. The 3D-PACT system exhibits a spatial resolution on the order of 300 μm, and the imaging area can be up to 52 mm in diameter. The transducers used in the system provides tomography imaging with large fields of view. In addition, the coplanar uniform illumination and acoustic detection configuration based on a quartz bowl greatly enhances the efficiency of laser illumination and signal detection, making it available for use on samples with irregular surfaces. Performance testing and 3D photoacoustic experiments on various phantoms verify that the system can perform 3D photoacoustic imaging on targets with complex surfaces or large sizes. In future, efforts will be made to achieve full-body 3D tomography of small animals and a multimodal 3D imaging system.

Photoacoustic tomography for imaging the prostate: a transurethral illumination probe design and application

In vivo imaging of prostate cancer with photoacoustic tomography is currently limited by the lack of sufficient local fluence for deep tissue penetration and the risk of over-irradiation near the laser-tissue contact surface. We propose the design of a transurethral illumination probe that addresses those limitations. A high energy of 50 mJ/pulse is coupled into a 1000-µm-core diameter multimode fiber. A 2 cm diffusing end is fabricated, which delivers light in radial illumination. The radial illumination is then reflected and reshaped by a parabolic cylindrical mirror to obtain nearly parallel side illumination with a doubled fluence. The fiber assembly is housed in a 25 Fr cystoscope sheath to provide protection of the fiber and maintain a minimal laser-tissue contact distance of 5 mm. A large laser-tissue contact surface area of 4 cm² is obtained and the fluence on the tissue surface is kept below the maximum permissible exposure. By imaging a prostate mimicking phantom, a penetration depth of 3.5 cm at 10 mJ/cm² fluence and 700 nm wavelength is demonstrated. The results indicate that photoacoustic tomography with the proposed transurethral probe has the potential to image the entire prostate while satisfying the fluence maximum permissible exposure and delivering a high power to the tissue.

Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal

Photoacoustic imaging is an emerging imaging modality that is based upon the photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure waves are measured by an array of detectors and used to reconstruct an image of the initial pressure distribution. A common challenge faced in PAT is that the measured acoustic waves can only be sparsely sampled. Reconstructing sparsely sampled data using standard methods results in severe artifacts that obscure information within the image. We propose a modified convolutional neural network (CNN) architecture termed fully dense UNet (FD-UNet) for removing artifacts from two-dimensional PAT images reconstructed from sparse data and compare the proposed CNN with the standard UNet in terms of reconstructed image quality.

Optoacoustic mesoscopy for biomedicine

Fuelled by innovation, optical microscopy plays a critical role in the life sciences and medicine, from basic discovery to clinical diagnostics. However, optical microscopy is limited by typical penetration depths of a few hundred micrometres for in vivo interrogations in the visible spectrum. Optoacoustic microscopy complements optical microscopy by imaging the absorption of light, but it is similarly limited by penetration depth. In this Review, we summarize progress in the development and applicability of optoacoustic mesoscopy (OPAM); that is, optoacoustic imaging with acoustic resolution and wide-bandwidth ultrasound detection. OPAM extends the capabilities of optical imaging beyond the depths accessible to optical and optoacoustic microscopy, and thus enables new applications. We explain the operational principles of OPAM, its placement as a bridge between optoacoustic microscopy and optoacoustic macroscopy, and its performance in the label-free visualization of tissue pathophysiology, such as inflammation, oxygenation, vascularization and angiogenesis. We also review emerging applications of OPAM in clinical and biological imaging.

Optimized light delivery probe using ball lenses for co-registered photoacoustic and ultrasound endo-cavity subsurface imaging

An optimized hand-held photoacoustic and ultrasound probe suitable for endo-cavity tumor subsurface imaging was designed and evaluated. Compared to previous designs, the prototype probe, consisting of four 1 mm multi-mode optical fibers attached with 1.5 mm diameter ball-shaped fiber tips sandwiched between a transvaginal ultrasound transducer and a custom-made sheath, demonstrated a higher light output and better beam homogeneity on tissue subsurface. The output power and fluence profile were simulated for different design parameters. A camera recorded fluence profiles through calibrated intralipid solution at various imaging depths. The light delivery efficiency was experimentally compared with and without the ball lenses, based on ex-vivo imaging of human colorectal cancer and in-vivo imaging of a palmar vein proximal to the human wrist. The simulations and experiments demonstrated that ball-shaped fiber tip design can achieve homogeneous fluence distribution on tissue subsurface with acceptable light output efficiency, suggesting its clinical potential for in-vivo endo-cavity imaging.

Advances in acoustic monitoring and control of focused ultrasound-mediated increases in blood-brain barrier permeability

Transcranial focused ultrasound (FUS) combined with intravenously circulating microbubbles can transiently and selectively increase blood-brain barrier permeability to enable targeted drug delivery to the central nervous system, and is a technique that has the potential to revolutionize the way neurological diseases are managed in medical practice. Clinical testing of this approach is currently underway in patients with brain tumors, early Alzheimer's disease, and amyotrophic lateral sclerosis. A major challenge that needs to be addressed in order for widespread clinical adoption of FUS-mediated blood-brain barrier permeabilization to occur is the development of systems and methods for real-time treatment monitoring and control, to ensure that safe and effective acoustic exposure levels are maintained throughout the procedures. This review gives a basic overview of the oscillation dynamics, acoustic emissions, and biological effects associated with ultrasound-stimulated microbubbles in vivo, and provides a summary of recent advances in acoustic-based strategies for detecting, controlling, and mapping microbubble activity in the brain. Further development of next-generation clinical FUS brain devices tailored towards microbubble-mediated applications is warranted and required for translation of this potentially disruptive technology into routine clinical practice.