Recent advances toward preclinical and clinical translation of photoacoustic tomography: a review

Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view

Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal in vivo experimental data. The results demonstrate that PAT-ADN exhibits excellent performance in effectively removing artifacts. In particular, under extremely sparse view (e.g., 16 projections), structural similarity index and peak signal-to-noise ratio are improved by ∼188 % and ∼85 % in in vivo experimental data using the proposed method compared to traditional reconstruction methods. PAT-ADN improves the imaging performance of PAT, opening up possibilities for its application in multiple domains.

Recent advances in DNA-based probes for photoacoustic imaging

Photoacoustic imaging(PAI) is a widely developing imaging modality that has seen tremendous evolvement in the last decade. PAI has gained the upper hand in the imaging field as it takes advantage of optical absorption and ultrasound detection that imparts higher resolution, rich contrast and elevated penetration depth. Unlike other imaging techniques, PAI does not use ionising radiation and is a better, cost-effective and healthier alternative to other imaging techniques. It offers greater specificity than conventional ultrasound imaging with the ability to detect haemoglobin, lipids, water and other light-absorbing chromophores. These properties of PAI have led to its extended applications in the biomedical field in the treatment of diseases such as cancer. This paper reviews how DNA probes have been used in PAI, the various techniques by which it has been modified, and their role in the process. We also focus on different nanocomposites containing DNA having PAI and photothermal therapy(PTT) properties for detection, diagnosis and therapy, its constituents and the role of DNA in it.

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.

Photoacoustic Spectral Response using Ultrasound and Interferometric Sensors: A Correlation Study for a High Bandwidth Real-Time Blood Vasculature Monitoring Application in a Chick-Embryo Chorioallantoic Membrane (CAM) Model

Photoacoustic (PA) spectral response technique has shown good promise in efficient preclinical tissue diagnosis by depicting mechano-biological properties due to high spatial resolution and penetration depth. The conventional PA-based system is a pump-probe technique that utilizes neodymium-doped yttrium aluminum garnet pulsed laser as a pump and an ultrasound sensor as a probe. For biomedical studies, high-speed PA signals need to be acquired, requiring higher bandwidth ultrasound sensors. While the bandwidth increases, they exhibit a very low signal-to-noise ratio that inhibits acquiring PA signals of biomedical samples. An interferometer-based probe has recently been investigated as a potential ultrasound probe for obtaining PA signals as an alternative. This optical PA detection technique offers high sensitivity by combining low acoustic impedance with high electromechanical coupling. However, there is a lack of exploration of the same for real-time biomedical studies. This work shows the development of a homodyne Mach-Zehnder interferometer-based PA spectral response (PASR) followed by a correlation study between the conventional ultrasound sensor and the interferometer-based sensor. Further, this study demonstrates the capability of continuous monitoring of vascular growth and the effect of an antidrug (Cisplatin) on the vasculature tested on a chick-embryo chorioallantoic membrane model. PASR was able to monitor growth changes within one day, which was not possible with conventional methods. This opens up potential possibilities for using this technique in biomedical applications.

Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration

As a non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction under sparse view could result in low-quality image in photoacoustic tomography. Here, a novel model-based sparse reconstruction method for photoacoustic tomography via diffusion model was proposed. A score-based diffusion model is designed for learning the prior information of the data distribution. The learned prior information is utilized as a constraint for the data consistency term of an optimization problem based on the least-square method in the model-based iterative reconstruction, aiming to achieve the optimal solution. Blood vessels simulation data and the animal in vivo experimental data were used to evaluate the performance of the proposed method. The results demonstrate that the proposed method achieves higher-quality sparse reconstruction compared with conventional reconstruction methods and U-Net. In particular, under the extreme sparse projection (e.g., 32 projections), the proposed method achieves an improvement of ∼ 260 % in structural similarity and ∼ 30 % in peak signal-to-noise ratio for in vivo data, compared with the conventional delay-and-sum method. This method has the potential to reduce the acquisition time and cost of photoacoustic tomography, which will further expand the application range.

Biomedical instrumentation of photoacoustic imaging and quantitative sensing for clinical applications

Photoacoustic (PA) imaging has been well researched over the last couple of decades and has found many applications in biomedical engineering. This has evinced interest among many scientists in developing this as a compact instrument for biomedical diagnosis. This review discusses various instrumentation developments for PA experimental setups and their applications in the biomedical diagnostic field. It also covers the PA spectral response or PA sensing technique, which uses the spectral information of the PA signal and performs sensing to deliver a fast, cost-effective, and compact screening tool instead of imaging. Primarily, this review provides an overview of PA imaging concepts and the development of hardware instrumentation systems in both the excitation and acquisition stages of this technique. Later, the paper discusses PA sensing, the quantitative spectral parameter extraction from the PA spectrum, and the correlation study of the spectral parameters with the physical parameters of the tissue. This PA sensing technique was used to diagnose various diseases, such as thyroid nodules, breast cancer, renal disorders, and zoonotic diseases, based on the mechanical and biological characteristics of the tissues. The paper culminates with a discussion section that provides future developments that are necessary to take this technique into clinical applications as a quantitative PA imaging technique.

Combined Doxorubicin Mesoporous Carbon Nanospheres for Effective Tumor Lymphatic Metastasis by Multi-Modal Chemo-Photothermal Treatment in vivo

Introduction

Sentinel lymph node (SLN) is the first regional lymph node where tumor cells metastasize, and its identification and treatment are of great significance for the prevention of tumor metastasis. However, the current clinical modalities for identification and treatment of SLN are still far from satisfactory owing to their high cost, invasiveness and low accuracy. We aim to design a novel nanomedicine system for SLN imaging and treatment with high efficacy.

Methods

We designed and prepared hollow mesoporous carbon spheres (HMCS) and loaded with the chemotherapeutic drug doxorubicin (DOX), which is then modified with polyvinyl pyrrolidone (PVP) to obtain nanomedicine: HMCS-PVP-DOX.

Results

HMCS-PVP with a size of about 150 nm could retain in the lymph nodes for a long time and stain the lymph nodes, which could be easily observed by the naked eye. At the same time, HMCS-PVP exhibited excellent photoacoustic and photothermal imaging capabilities, realizing multimodal imaging to locate lymph nodes precisely. Due to its high specific surface area, HMCS could be largely loaded with the chemotherapeutic drug doxorubicin (DOX). HMCS-PVP-DOX displayed highly efficient synergistic chemotherapy-photothermal therapy for lymphatic metastases in both cellular and animal experiments due to its significant photothermal effect under 1064 nm laser irradiation. HMCS-PVP-DOX also displayed great stability and biosafety.

Discussion

Multifunctional nanomedicine HMCS-PVP-DOX is expected to provide a novel paradigm for designing nanomedicine to the diagnosis and treatment of lymphatic metastases because of its good stability and safety.

Imaging of Gastrointestinal Tract Ailments

Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel's diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments.

Adaptive denoising of photoacoustic signal and image based on modified Kalman filter

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal-to-noise ratio, and reconstructed PA images are of low quality. Despite that conventional Kalman filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real-time estimation due to its fixed model. Moreover, KF-based denoising algorithm has not been applied in PAI before. In this paper, we propose an adaptive modified KF (MKF) targeted at PAI denoising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by MKF, we cascade the backward part of Rauch-Tung-Striebel smoother, which utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in-band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove validity of the algorithm.

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

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

Simultaneous dual-modal photoacoustic and harmonic ultrasound microscopy with an optimized acoustic combiner

Simultaneous photoacoustic (PA) and ultrasound (US) imaging provides rich optical and acoustic contrasts with high sensitivity, specificity, and resolution, making it a promising tool for diagnosing and assessing various diseases. However, the resolution and penetration depth tend to be contradictory due to the increased attenuation of high-frequency ultrasound. To address this issue, we present simultaneous dual-modal PA/US microscopy with an optimized acoustic combiner that can maintain high resolution while improving the penetration of ultrasound imaging. A low-frequency ultrasound transducer is used for acoustic transmission, and a high-frequency transducer is used for PA and US detection. An acoustic beam combiner is utilized to merge the transmitting and receiving acoustic beams with a predetermined ratio. By combining the two different transducers, harmonic US imaging and high-frequency photoacoustic microscopy are implemented. In vivo experiments on the mouse brain demonstrate the simultaneous PA and US imaging ability. The harmonic US imaging of the mouse eye reveals finer iris and lens boundary structures than conventional US imaging, providing a high-resolution anatomical reference for co-registered PA imaging.

Improving photoacoustic imaging in low signal-to-noise ratio by using spatial and polarity coherence

To suppress the noise and sidelobe of photoacoustic images, a method is proposed combined with spatial coherence and polarity coherence. In this method, PA signals are delayed, multiplied, then performed polarity coherence, and finally summed. The polarity of delayed-and-multiplied signals rather than the amplitude is considered in polarity coherence operation. The polarity coherence factor is calculated based on the standard deviation of the polarity. Then, the factor as weights is applied to the coherent sum output after spatial autocorrelation to finally obtain the image. The simulated and experimental results prove that the noise level can be effectively suppressed due to its relatively low polarity coherence factor. Compared with the delay-and-sum method, the quantitative results in simulations show that the image contrast and full-width at half-maximum of the proposed method increase by about 227.0 % and 56.5 % when the signal-to-noise ratio of the raw signal is 0 dB, respectively. Besides achieving a better image contrast, this method obtains improvements in sidelobe attenuation and has a narrow main lobe.

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

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

Clinical photoacoustic/ultrasound dual-modal imaging: Current status and future trends

Photoacoustic tomography (PAT) is an emerging biomedical imaging modality that combines optical and ultrasonic imaging, providing overlapping fields of view. This hybrid approach allows for a natural integration of PAT and ultrasound (US) imaging in a single platform. Due to the similarities in signal acquisition and processing, the combination of PAT and US imaging creates a new hybrid imaging for novel clinical applications. Over the recent years, particular attention is paid to the development of PAT/US dual-modal systems highlighting mutual benefits in clinical cases, with an aim of substantially improving the specificity and sensitivity for diagnosis of diseases. The demonstrated feasibility and accuracy in these efforts open an avenue of translating PAT/US imaging to practical clinical applications. In this review, the current PAT/US dual-modal imaging systems are discussed in detail, and their promising clinical applications are presented and compared systematically. Finally, this review describes the potential impacts of these combined systems in the coming future.

Noninvasive hemoglobin sensing and imaging: optical tools for disease diagnosis

SIGNIFICANCE

Measurement and imaging of hemoglobin oxygenation are used extensively in the detection and diagnosis of disease; however, the applied instruments vary widely in their depth of imaging, spatiotemporal resolution, sensitivity, accuracy, complexity, physical size, and cost. The wide variation in available instrumentation can make it challenging for end users to select the appropriate tools for their application and to understand the relative limitations of different methods.

AIM

We aim to provide a systematic overview of the field of hemoglobin imaging and sensing.

APPROACH

We reviewed the sensing and imaging methods used to analyze hemoglobin oxygenation, including pulse oximetry, spectral reflectance imaging, diffuse optical imaging, spectroscopic optical coherence tomography, photoacoustic imaging, and diffuse correlation spectroscopy.

RESULTS

We compared and contrasted the ability of different methods to determine hemoglobin biomarkers such as oxygenation while considering factors that influence their practical application.

CONCLUSIONS

We highlight key limitations in the current state-of-the-art and make suggestions for routes to advance the clinical use and interpretation of hemoglobin oxygenation information.

3D Ultrasound-Guided Photoacoustic Imaging to Monitor the Effects of Suboptimal Tyrosine Kinase Inhibitor Therapy in Pancreatic Tumors

Pancreatic cancer is a disease with an incredibly poor survival rate. As only about 20% of patients are eligible for surgical resection, neoadjuvant treatments that can relieve symptoms and shrink tumors for surgical resection become critical. Many forms of treatments rely on increased vulnerability of cancerous cells, but tumors or regions within the tumors that may be hypoxic could be drug resistant. Particularly for neoadjuvant therapies such as the tyrosine kinase inhibitors utilized to shrink tumors, it is critical to monitor changes in vascular function and hypoxia to predict treatment efficacy. Current clinical imaging modalities used to obtain structural and functional information regarding hypoxia or oxygen saturation (StO2) do not provide sufficient depth penetration or require the use of exogenous contrast agents. Recently, ultrasound-guided photoacoustic imaging (US-PAI) has garnered significant popularity, as it can noninvasively provide multiparametric information on tumor vasculature and function without the need for contrast agents. Here, we built upon existing literature on US-PAI and demonstrate the importance of changes in StO2 values to predict treatment response, particularly tumor growth rate, when the outcomes are suboptimal. Specifically, we image xenograft mouse models of pancreatic adenocarcinoma treated with suboptimal doses of a tyrosine kinase inhibitor cabozantinib. We utilize the US-PAI data to develop a multivariate regression model that demonstrates that a therapy-induced reduction in tumor growth rate can be predicted with 100% positive predictive power and a moderate (58.33%) negative predictive power when a combination of pretreatment tumor volume and changes in StO2 values pretreatment and immediately posttreatment was employed. Overall, our study indicates that US-PAI has the potential to provide label-free surrogate imaging biomarkers that can predict tumor growth rate in suboptimal therapy.

High-precision neural stimulation through optoacoustic emitters

Neuromodulation poses an invaluable role in deciphering neural circuits and exploring clinical treatment of neurological diseases. Optoacoustic neuromodulation is an emerging modality benefiting from the merits of ultrasound with high penetration depth as well as the merits of photons with high spatial precision. We summarize recent development in a variety of optoacoustic platforms for neural modulation, including fiber, film, and nanotransducer-based devices, highlighting the key advantages of each platform. The possible mechanisms and main barriers for optoacoustics as a viable neuromodulation tool are discussed. Future directions in fundamental and translational research are proposed.

Looking deep inside tissue with photoacoustic molecular probes: a review

Significance

Deep tissue noninvasive high-resolution imaging with light is challenging due to the high degree of light absorption and scattering in biological tissue. Photoacoustic imaging (PAI) can overcome some of the challenges of pure optical or ultrasound imaging to provide high-resolution deep tissue imaging. However, label-free PAI signals from light absorbing chromophores within the tissue are nonspecific. The use of exogeneous contrast agents (probes) not only enhances the imaging contrast (and imaging depth) but also increases the specificity of PAI by binding only to targeted molecules and often providing signals distinct from the background.

Aim

We aim to review the current development and future progression of photoacoustic molecular probes/contrast agents.

Approach

First, PAI and the need for using contrast agents are briefly introduced. Then, the recent development of contrast agents in terms of materials used to construct them is discussed. Then, various probes are discussed based on targeting mechanisms, in vivo molecular imaging applications, multimodal uses, and use in theranostic applications.

Results

Material combinations are being used to develop highly specific contrast agents. In addition to passive accumulation, probes utilizing activation mechanisms show promise for greater controllability. Several probes also enable concurrent multimodal use with fluorescence, ultrasound, Raman, magnetic resonance imaging, and computed tomography. Finally, targeted probes are also shown to aid localized and molecularly specific photo-induced therapy.

Conclusions

The development of contrast agents provides a promising prospect for increased contrast, higher imaging depth, and molecularly specific information. Of note are agents that allow for controlled activation, explore other optical windows, and enable multimodal use to overcome some of the shortcomings of label-free PAI.

The IPASC data format: A consensus data format for photoacoustic imaging

Photoacoustic imaging (PAI) is an emerging modality that has shown promise for improving patient management in a range of applications. Unfortunately, the current lack of uniformity in PAI data formats compromises inter-user data exchange and comparison, which impedes: technological progress; effective research collaboration; and efforts to deliver multi-centre clinical trials. To overcome this challenge, the International Photoacoustic Standardisation Consortium (IPASC) has established a data format with a defined consensus metadata structure and developed an open-source software application programming interface (API) to enable conversion from proprietary file formats into the IPASC format. The format is based on Hierarchical Data Format 5 (HDF5) and designed to store photoacoustic raw time series data. Internal quality control mechanisms are included to ensure completeness and consistency of the converted data. By unifying the variety of proprietary data and metadata definitions into a consensus format, IPASC hopes to facilitate the exchange and comparison of PAI data.

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

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

Synchronization of RF Data in Ultrasound Open Platforms (UOPs) for High-Accuracy and High-Resolution Photoacoustic Tomography Using the "Scissors" Programming Method

Synchronization is important for photoacoustic (PA) tomography, but some fixed delays between the data acquisition (DAQ) and the light pulse are a common problem degrading imaging quality. Here, we present a simple yet versatile method named "Scissors" to help synchronize ultrasound open platforms (UOPs) for PA imaging. Scissors is a programed function that can cut or add a fixed delay to radio frequency (RF) data and, thus, synchronize it before reconstruction. Scissors applies the programmable metric of UOPs and has several advantages. It is compatible with many setups regardless of the synchronization methods, light sources, transducers, and delays. The synchronization is adjustable in steps reciprocal to the UOPs' sampling rate (20-ns step with a 50-MHz sampling rate). Scissors works in real-time PA imaging, and no extra hardware is needed. We programed Scissors in Vantage UOP (Verasonics, Inc., Kirkland, WA, USA) and then imaged two 30- [Formula: see text] nichrome wires with a 20.2-MHz central frequency transducer. The PA image was severely distorted by an 828-ns delay; over 90% delay was caused by our Q -switch laser. The axial and lateral resolutions are 112 and [Formula: see text], respectively, after using Scissors. We imaged a human finger in vivo, and the imaging quality is tremendously improved after solving the 828-ns delay by using Scissors.

Interferon-γ detection in point of care diagnostics: Short review

Interferon (IFN)-γ is a cytokine secreted by immune cells. The elevated levels of IFN-γ are an early indicator of multiple diseases such as tuberculosis and autoimmune diseases. This short review focuses on different sensing methods based on optical, electrochemical, and mechanical principles. We explain how specific biorecognition molecules such as antibodies and aptamers are employed in the sensing methods. We also compare different surface functionalization methods and their details. Although the review gives an overview of only IFN-γ sensing, the same strategies can be applied to sensing other analytes with appropriate modifications.

The emerging role of photoacoustic imaging in clinical oncology

Clinical oncology can benefit substantially from imaging technologies that reveal physiological characteristics with multiscale observations. Complementing conventional imaging modalities, photoacoustic imaging (PAI) offers rapid imaging (for example, cross-sectional imaging in real time or whole-breast scanning in 10-15 s), scalably high levels of spatial resolution, safe operation and adaptable configurations. Most importantly, this novel imaging modality provides informative optical contrast that reveals details on anatomical, functional, molecular and histological features. In this Review, we describe the current state of development of PAI and the emerging roles of this technology in cancer screening, diagnosis and therapy. We comment on the performance of cutting-edge photoacoustic platforms, and discuss their clinical applications and utility in various clinical studies. Notably, the clinical translation of PAI is accelerating in the areas of macroscopic and mesoscopic imaging for patients with breast or skin cancers, as well as in microscopic imaging for histopathology. We also highlight the potential of future developments in technological capabilities and their clinical implications, which we anticipate will lead to PAI becoming a desirable and widely used imaging modality in oncological research and practice.

Characterisation of breast cancer molecular signature and treatment assessment with vibrational spectroscopy and chemometric approach

Triple negative breast cancer (TNBC) is regarded as the most aggressive breast cancer subtype with poor overall survival and lack of targeted therapies, resulting in many patients with recurrent. The insight into the detailed biochemical composition of TNBC would help develop dedicated treatments. Thus, in this study Fourier Transform Infrared microspectroscopy combined with chemometrics and absorbance ratios investigation was employed to compare healthy controls with TNBC tissue before and after chemotherapy within the same patient. The primary spectral differences between control and cancer tissues were found in proteins, polysaccharides, and nucleic acids. Amide I/Amide II ratio decrease before and increase after chemotherapy, whereas DNA, RNA, and glycogen contents increase before and decrease after the treatment. The chemometric results revealed discriminatory features reflecting a clinical response scheme and proved the chemotherapy efficacy assessment with infrared spectroscopy is possible.

Chemical Design of Activatable Photoacoustic Probes for Precise Biomedical Applications

Photoacoustic (PA) imaging technology, a three-dimensional hybrid imaging modality that integrates the advantage of optical and acoustic imaging, has great application prospects in molecular imaging due to its high imaging depth and resolution. To endow PA imaging with the ability for real-time molecular visualization and precise biomedical diagnosis, numerous activatable molecular PA probes which can specifically alter their PA intensities upon reacting with the targets or biological events of interest have been developed. This review highlights the recent developments of activatable PA probes for precise biomedical applications including molecular detection of the biotargets and imaging of the biological events. First, the generation mechanism of PA signals will be given, followed by a brief introduction to contrast agents used for PA probe design. Then we will particularly summarize the general design principles for the alteration of PA signals and activatable strategies for developing precise PA probes. Furthermore, we will give a detailed discussion of activatable PA probes in molecular detection and biomedical imaging applications in living systems. At last, the current challenges and outlooks of future PA probes will be discussed. We hope that this review will stimulate new ideas to explore the potentials of activatable PA probes for precise biomedical applications in the future.

Characterization of multi-biomarkers for bone health assessment based on photoacoustic physicochemical analysis method

Photoacoustic (PA) techniques are potential alternatives to histopathology. The physicochemical spectrogram (PCS) generated by the PA measurement at multiple wavelengths can presents the morphology and chemical composition target at multi-biomarkers simultaneously. In this work, via multi-wavelength PA measurements performed on rabbit bone models, we investigated the feasibility of using PCSs for bone health assessment. A comprehensive analysis of the PCSs, termed PA physicochemical analysis (PAPCA), was conducted. The "slope" and "relative content" were used as the PAPCA-quantified parameters to characterize the changes in the physical and chemical properties of bone tissue, respectively. The findings are consistent well with the gold-standard imaging results. It demonstrated that the PAPCA can be used to characterize both the microstructure and content of multi-biomarkers which highly related with bone health. Considering the PA technique is noninvasive and radiation-free, it has great potential in the implementation and monitoring of bone diseases progression.

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

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

Detection of collagen by multi-wavelength photoacoustic analysis as a biomarker for bone health assessment

Collagen is an important biomarker of osteoporosis progression. Noninvasive, multispectral, photoacoustic (PA) techniques use pulsed laser light to induce PA signals to facilitate the visualization of chemical components that are strongly related to tissue health. In this study, the feasibility of multi-wavelength PA (MWPA) measurement of the collagen in bone, using the wavelength range of 1300-1800 nm, was investigated. First, the feasibility of this approach for detecting the collagen content of bone was demonstrated by means of numerical simulation. Then, ex vivo experiments were conducted on both animal and human bone specimens with different bone densities using the MWPA method. The relative collagen content was extracted and compared with the results of micro-computed tomography (micro-CT) and histology. The results showed that the "relative collagen content" parameter obtained using the MWPA approach correlated well with the bone volume ratio obtained from micro-CT images and histological analysis results. This study highlights the potential of the proposed PA technique for determining the collagen content of bones as a biomarker for bone health assessment.

SVD-based filtering to detect intraplaque hemorrhage using single wavelength photoacoustic imaging

SIGNIFICANCE

Intraplaque hemorrhage (IPH) is an important indicator of plaque vulnerability. Early detection could aid the prevention of stroke.

AIM

We aim to detect IPH with single wavelength PA imaging in vivo and to improve image quality.

APPROACH

We developed a singular value decomposition (SVD)-based filter to detect the nonstationary and stationary components in ultrasound data. A PA mask was created to detect stationary (IPH) sources. The method was tested ex vivo using phantoms and in vivo in patients.

RESULTS

The flow and IPH channels were successfully separated in the phantom data. We can also detect the PA signals from IPH and reject signals from the carotid lumen in vivo. Generalized contrast-to-noise ratio improved in both ex vivo and in vivo in US imaging.

CONCLUSIONS

SVD-based filtering can successfully detect IPH using a single laser wavelength, opening up opportunities for more economical and cost-effective laser sources.

Artifact removal in photoacoustic tomography with an unsupervised method

Photoacoustic tomography (PAT) is an emerging biomedical imaging technology that can realize high contrast imaging with a penetration depth of the acoustic. Recently, deep learning (DL) methods have also been successfully applied to PAT for improving the image reconstruction quality. However, the current DL-based PAT methods are implemented by the supervised learning strategy, and the imaging performance is dependent on the available ground-truth data. To overcome the limitation, this work introduces a new image domain transformation method based on cyclic generative adversarial network (CycleGAN), termed as PA-GAN, which is used to remove artifacts in PAT images caused by the use of the limited-view measurement data in an unsupervised learning way. A series of data from phantom and in vivo experiments are used to evaluate the performance of the proposed PA-GAN. The experimental results show that PA-GAN provides a good performance in removing artifacts existing in photoacoustic tomographic images. In particular, when dealing with extremely sparse measurement data (e.g., 8 projections in circle phantom experiments), higher imaging performance is achieved by the proposed unsupervised PA-GAN, with an improvement of ∼14% in structural similarity (SSIM) and ∼66% in peak signal to noise ratio (PSNR), compared with the supervised-learning U-Net method. With an increasing number of projections (e.g., 128 projections), U-Net, especially FD U-Net, shows a slight improvement in artifact removal capability, in terms of SSIM and PSNR. Furthermore, the computational time obtained by PA-GAN and U-Net is similar (∼60 ms/frame), once the network is trained. More importantly, PA-GAN is more flexible than U-Net that allows the model to be effectively trained with unpaired data. As a result, PA-GAN makes it possible to implement PAT with higher flexibility without compromising imaging performance.

Development of Dual-Frequency PMUT Arrays Based on Thin Ceramic PZT for Endoscopic Photoacoustic Imaging

This paper presents a dual-frequency piezoelectric micromachined ultrasonic transducer (pMUT) array based on thin ceramic PZT for endoscopic photoacoustic imaging (PAI) applications. With a chip size of 7 × 7 mm², the pMUT array consists of 256 elements, half of which have a lower resonant frequency of 1.2 MHz and the other half have a higher resonant frequency of 3.4 MHz. Ceramic PZT, with outstanding piezoelectric coefficients, has been successfully thinned down to a thickness of only 4 μ by using wafer bonding and chemical mechanical polishing (CMP) techniques and employed as the piezoelectric layer of the pMUT elements. The diaphragm diameters of the lower-frequency and higher-frequency elements are 220 μm and 120 μm, respectively. The design methodology, multiphysics modeling, fabrication process, and characterization of the pMUTs are presented in detail. The fabricated pMUT array has been fully characterized via electrical, mechanical, and acoustic measurements. The measured maximum responsivities of the lower- and higher- frequency elements reach 110 nm/V and 30 nm/V at their respective resonances. The measured cross-couplings of the lower-frequency elements and higher-frequency elements are about 9% and 5%, respectively. Furthermore, PAI experiments with pencil leads embedded into an agar phantom have been conducted, which clearly shows the advantages of using dual-frequency pMUT arrays to provide comprehensive photoacoustic images with high spatial resolution and large signal-to-noise ratio simultaneously.

The feasibility study of the transmission mode photoacoustic measurement of human calcaneus bone in vivo

The photoacoustic (PA) technique is uniquely positioned for biomedical applications primarily due to its ability to visualize optical absorption contrast in deep tissue at ultrasound resolution. In this work, via both three-dimensional (3D) numerical simulations and in vivo experiments on human subjects, we investigated the possibility of PA measurement of human calcaneus bones in vivo in a non-invasive manner, as well as its feasibility to differentiate osteoporosis patients from normal subjects. The results from the simulations and the experiments both demonstrated that, when one side of the heel is illuminated by laser with light fluence under the ANSI safety limit, the PA signal generated in the human calcaneus bone can be detected by an ultrasonic transducer at the other side of the heel (i.e. transmission mode). Quantitative power spectral analyses of the calcaneus bone PA signals were also conducted, demonstrating that the microarchitectural changes in calcaneus bone due to osteoporosis can be detected, as reflected by enhanced high frequency components in detected PA bone signal. Further statistical analysis of the experimental results from 10 osteoporosis patients and 10 healthy volunteers showed that the weighted frequency as a quantified PA spectral parameter can differentiate the two subject groups with statistical significance.

An Adjustable Dark-Field Acoustic-Resolution Photoacoustic Imaging System with Fiber Bundle-Based Illumination

Photoacoustic (PA) imaging has become one of the major imaging methods because of its ability to record structural information and its high spatial resolution in biological tissues. Current commercialized PA imaging instruments are limited to varying degrees by their bulky size (i.e., the laser or scanning stage) or their use of complex optical components for light delivery. Here, we present a robust acoustic-resolution PA imaging system that consists of four adjustable optical fibers placed 90° apart around a 50 MHz high-frequency ultrasound (US) transducer. In the compact design concept of the PA probe, the relative illumination parameters (i.e., angles and fiber size) can be adjusted to fit different imaging applications in a single setting. Moreover, this design concept involves a user interface built in MATLAB. We first assessed the performance of our imaging system using in vitro phantom experiments. We further demonstrated the in vivo performance of the developed system in imaging (1) rat ear vasculature, (2) real-time cortical hemodynamic changes in the superior sagittal sinus (SSS) during left-forepaw electrical stimulation, and (3) real-time cerebral indocyanine green (ICG) dynamics in rats. Collectively, this alignment-free design concept of a compact PA probe without bulky optical lens systems is intended to satisfy the diverse needs in preclinical PA imaging studies.

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.

Waterproof Galvanometer Scanner-Based Handheld Photoacoustic Microscopy Probe for Wide-Field Vasculature Imaging In Vivo

Photoacoustic imaging (PAI) is a hybrid non-invasive imaging technique used to merge high optical contrast and high acoustic resolution in deep tissue. PAI has been extensively developed by utilizing its advantages that include deep imaging depth, high resolution, and label-free imaging. As a representative implementation of PAI, photoacoustic microscopy (PAM) has been used in preclinical and clinical studies for its micron-scale spatial resolution capability with high optical absorption contrast. Several handheld and portable PAM systems have been developed that improve its applicability to several fields, making it versatile. In this study, we developed a laboratory-customized, two-axis, waterproof, galvanometer scanner-based handheld PAM (WP-GVS-HH-PAM), which provides an extended field of view (14.5 × 9 mm2) for wide-range imaging. The fully waterproof handheld probe enables free movement for imaging regardless of sample shape, and volume rate and scanning region are adjustable per experimental conditions. Results of WP-GVS-HH-PAM-based phantom and in vivo imaging of mouse tissues (ear, iris, and brain) confirm the feasibility and applicability of our system as an imaging modality for various biomedical applications.

Hyperspectral absorption microscopy using photoacoustic remote sensing

An improved method of remote optical absorption spectroscopy and hyperspectral optical absorption imaging is described which takes advantage of the photoacoustic remote sensing detection architecture. A wide collection of photoacoustic excitation wavelengths ranging from 210 nm to 1550 nm was provided by a nanosecond tunable source allowing access to various salient endogenous chromophores such as DNA, hemeproteins, and lipids. Sensitivity of the device was demonstrated by characterizing the infrared absorption spectrum of water. Meanwhile, the efficacy of the technique was explored by recovering cell nuclei and oxygen saturation from a live chicken embryo model and by recovering adipocytes from freshly resected murine adipose tissue. This represents a continued investigation into the characteristics of the hyperspectral photoacoustic remote sensing technique which may represent an effective means of non-destructive endogenous contrast characterization and visualization.

Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS _CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS _CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS _CF in vasculature simulations) and improved clutter suppression.

Solving time-independent inhomogeneous optoacoustic wave equation numerically with a modified Green's function approach

The purpose of the paper is twofold. First, a modified Green's function (MGF) approach is described for solving the time-independent inhomogeneous optoacoustic (OA) wave equation. The performance of this technique has been assessed with respect to the exact, traditional Born series and convergent Born series methods for an acoustically inhomogeneous spherical source. Second, we apply the same approach for calculating time domain signal from a blood vessel network consisting of an ensemble of acoustically homogeneous/inhomogeneous randomly positioned disks resembling cells. The predicted signals have been compared with those generated by the exact method and a freely available standard software. The OA spectra for a spherical source demonstrated excellent agreement with the exact results when sound-speed for the source was varied from -20% to 30% compared to that of the surrounding medium. The simulated OA signals also followed the same trend as that of the exclusively used software for the acoustically homogeneous blood vessel network. Future work will focus inclusion of a suitable phase factor within the MGF facilitating OA pulses building up at correct temporal locations for an acoustically inhomogeneous source.

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

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

Deep learning for biomedical photoacoustic imaging: A review

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability.

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

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

Photoacoustic Signal Simulation Using Discrete Particle Approach and its Application in Tomography

A theoretical framework for photoacoustic (PA) signal simulation using a discrete particle approach is discussed, and the tomographic image reconstruction using such signals is reported. Various numerical phantoms in two dimensions were constructed by inserting monodisperse/polydisperse solid circles/disks of uniform strength occupying regular or random locations within the imaging region. In particular, a blood vessel network phantom was simulated by positioning solid circles mimicking red blood cells randomly within the vessel using a Monte Carlo method. The PA signal from a single disk was obtained by numerically evaluating the analytical formula, and then, such signals from many disks were summed up linearly to generate the resultant signals at detector locations. Classical backprojection and time-reversal algorithms were employed to form reconstructed images. Two model-based approaches, namely impulse response-based (IRB) and interpolation-based (IPB) methods, were also deployed for image reconstruction. Some standard parameters were calculated to assess the performance of these reconstruction algorithms. The simulation results demonstrate that the Monte Carlo method can be applied in practice for the fast simulation of tissue realization keeping microscopic details intact, and accordingly, PA signals can be calculated for photoacoustic tomography (PAT) imaging. Furthermore, the IRB technique produces images with superior quality and outperforms other algorithms.

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

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

Transrectal Ultrasound and Photoacoustic Imaging Probe for Diagnosis of Prostate Cancer

A combined transrectal ultrasound and photoacoustic (TRUS-PA) imaging probe was developed for the clear visualization of morphological changes and microvasculature distribution in the prostate, as this is required for accurate diagnosis and biopsy. The probe consisted of a miniaturized 128-element 7 MHz convex array transducer with 134.5° field-of-view (FOV), a bifurcated optical fiber bundle, and two optical lenses. The design goal was to make the size of the TRUS-PA probe similar to that of general TRUS probes (i.e., about 20 mm), for the convenience of the patients. New flexible printed circuit board (FPCB), acoustic structure, and optical lens were developed to meet the requirement of the probe size, as well as to realize a high-performance TRUS-PA probe. In visual assessment, the PA signals obtained with the optical lens were 2.98 times higher than those without the lens. Moreover, the in vivo experiment with the xenograft BALB/c (Albino, Immunodeficient Inbred Strain) mouse model showed that TRUS-PA probe was able to acquire the entire PA image of the mouse tight behind the porcine intestine about 25 mm depth. From the ex vivo and in vivo experimental results, it can be concluded that the developed TRUS-PA probe is capable of improving PA image quality, even though the TRUS-PA probe has a cross-section size and an FOV comparable to those of general TRUS probes.

Another decade of photoacoustic imaging

Photoacoustic imaging - a hybrid biomedical imaging modality finding its way to clinical practices. Although the photoacoustic phenomenon was known more than a century back, only in the last two decades it has been widely researched and used for biomedical imaging applications. In this review we focus on the development and progress of the technology in the last decade (2010-2020). From becoming more and more user friendly, cheaper in cost, portable in size, photoacoustic imaging promises a wide range of applications, if translated to clinic. The growth of photoacoustic community is steady, and with several new directions researchers are exploring, it is inevitable that photoacoustic imaging will one day establish itself as a regular imaging system in the clinical practices.

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).