Deep Learning Enhances Multiparametric Dynamic Volumetric Photoacoustic Computed Tomography In Vivo (DL-PACT)

Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson's disease

PURPOSE

Metabolism and bioenergetics in the central nervous system play important roles in the pathophysiology of Parkinson's disease (PD). Here, we employed a multimodal imaging approach to assess oxygenation changes in the spinal cord of the transgenic M83 murine model of PD overexpressing the mutated A53T alpha-synuclein form in comparison with non-transgenic littermates.

METHODS

In vivo spiral volumetric optoacoustic tomography (SVOT) was performed to assess oxygen saturation (sO2) in the spinal cords of M83 mice and non-transgenic littermates. Ex vivo high-field T1-weighted (T1w) magnetic resonance imaging (MRI) at 9.4T was used to assess volumetric alterations in the spinal cord. 3D SVOT analysis and deep learning-based automatic segmentation of T1w MRI data for the mouse spinal cord were developed for quantification. Immunostaining for phosphorylated alpha-synuclein (pS129 α-syn), as well as vascular organization (CD31 and GLUT1), was performed after MRI scan.

RESULTS

In vivo SVOT imaging revealed a lower sO2SVOT in the spinal cord of M83 mice compared to non-transgenic littermates at sub-100 μm spatial resolution. Ex vivo MRI-assisted by in-house developed deep learning-based automatic segmentation (validated by manual analysis) revealed no volumetric atrophy in the spinal cord of M83 mice compared to non-transgenic littermates at 50 μm spatial resolution. The vascular network was not impaired in the spinal cord of M83 mice in the presence of pS129 α-syn accumulation.

CONCLUSION

We developed tools for deep-learning-based analysis for the segmentation of mouse spinal cord structural MRI data, and volumetric analysis of sO2SVOT data. We demonstrated non-invasive high-resolution imaging of reduced sO2SVOT in the absence of volumetric structural changes in the spinal cord of PD M83 mouse model.

Review on Photoacoustic Monitoring after Drug Delivery: From Label-Free Biomarkers to Pharmacokinetics Agents

Photoacoustic imaging (PAI) is an emerging noninvasive and label-free method for capturing the vasculature, hemodynamics, and physiological responses following drug delivery. PAI combines the advantages of optical and acoustic imaging to provide high-resolution images with multiparametric information. In recent decades, PAI's abilities have been used to determine reactivity after the administration of various drugs. This study investigates photoacoustic imaging as a label-free method of monitoring drug delivery responses by observing changes in the vascular system and oxygen saturation levels across various biological tissues. In addition, we discuss photoacoustic studies that monitor the biodistribution and pharmacokinetics of exogenous contrast agents, offering contrast-enhanced imaging of diseased regions. Finally, we demonstrate the crucial role of photoacoustic imaging in understanding drug delivery mechanisms and treatment processes.

Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens

In pathological diagnostics, histological images highlight the oncological features of excised specimens, but they require laborious and costly staining procedures. Despite recent innovations in label-free microscopy that simplify complex staining procedures, technical limitations and inadequate histological visualization are still problems in clinical settings. Here, we demonstrate an interconnected deep learning (DL)-based framework for performing automated virtual staining, segmentation, and classification in label-free photoacoustic histology (PAH) of human specimens. The framework comprises three components: (1) an explainable contrastive unpaired translation (E-CUT) method for virtual H&E (VHE) staining, (2) an U-net architecture for feature segmentation, and (3) a DL-based stepwise feature fusion method (StepFF) for classification. The framework demonstrates promising performance at each step of its application to human liver cancers. In virtual staining, the E-CUT preserves the morphological aspects of the cell nucleus and cytoplasm, making VHE images highly similar to real H&E ones. In segmentation, various features (e.g., the cell area, number of cells, and the distance between cell nuclei) have been successfully segmented in VHE images. Finally, by using deep feature vectors from PAH, VHE, and segmented images, StepFF has achieved a 98.00% classification accuracy, compared to the 94.80% accuracy of conventional PAH classification. In particular, StepFF's classification reached a sensitivity of 100% based on the evaluation of three pathologists, demonstrating its applicability in real clinical settings. This series of DL methods for label-free PAH has great potential as a practical clinical strategy for digital pathology.

3D Multiparametric Photoacoustic Computed Tomography of Primary and Metastatic Tumors in Living Mice

Photoacoustic computed tomography (PACT), an emerging imaging modality in preclinical cancer research, can provide multiparametric 3D information about structures, physiological functions, and pharmacokinetics. Here, we demonstrate the use of high-definition 3D multiparametric PACT imaging of both primary and metastatic tumors in living mice to noninvasively monitor angiogenesis, carcinogenesis, hypoxia, and pharmacokinetics. The high-definition PACT system with a 1024-element hemispherical ultrasound transducer array provides an isotropic spatial resolution of 380 μm, an effective volumetric field-of-view of 12.8 mm × 12.8 mm × 12.8 mm without scanning, and an acquisition time of <30 s for a whole mouse body. Initially, we monitor the structural progression of the tumor microenvironment (e.g., angiogenesis and vessel tortuosity) after tumor cell inoculation. Then, we analyze the change in oxygen saturation of the tumor during carcinogenesis, verifying induced hypoxia in the tumor's core region. Finally, the whole-body pharmacokinetics are photoacoustically imaged after intravenous injection of micelle-loaded IR780 dye, and the in vivo PACT results are validated in vivo and ex vivo by fluorescence imaging. By employing the premium PACT system and applying multiparametric analyses to subcutaneous primary tumors and metastatic liver tumors, we demonstrate that this PACT system can provide multiparametric analyses for comprehensive cancer research.

Challenges and advances in two-dimensional photoacoustic computed tomography: a review

Significance

Photoacoustic computed tomography (PACT), a hybrid imaging modality combining optical excitation with acoustic detection, has rapidly emerged as a prominent biomedical imaging technique.

Aim

We review the challenges and advances of PACT, including (1) limited view, (2) anisotropy resolution, (3) spatial aliasing, (4) acoustic heterogeneity (speed of sound mismatch), and (5) fluence correction of spectral unmixing.

Approach

We performed a comprehensive literature review to summarize the key challenges in PACT toward practical applications and discuss various solutions.

Results

There is a wide range of contributions from both industry and academic spaces. Various approaches, including emerging deep learning methods, are proposed to improve the performance of PACT further.

Conclusions

We outline contemporary technologies aimed at tackling the challenges in PACT applications.

Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN) for cross-modal individual analysis of the whole brain

Cross-modal analysis of the same whole brain is an ideal strategy to uncover brain function and dysfunction. However, it remains challenging due to the slow speed and destructiveness of traditional whole-brain optical imaging techniques. Here we develop a new platform, termed Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN), for non-destructive, high-speed, 3D imaging of ex vivo rodent, ferret, and non-human primate brains. Using an optimally designed image acquisition scheme and an accompanying machine-learning algorithm, PATTERN extracts signals of genetically-encoded probes from photobleaching-based temporal modulation and enables reliable visualization of neural projection in the whole central nervous system with 3D isotropic resolution. Without structural and biological perturbation to the sample, PATTERN can be combined with other whole-brain imaging modalities to acquire the whole-brain image with both high resolution and morphological fidelity. Furthermore, cross-modal transcriptome analysis of an individual brain is achieved by PATTERN imaging. Together, PATTERN provides a compatible and versatile strategy for brain-wide cross-modal analysis at the individual level.

Hybrid spherical array for combined volumetric optoacoustic and B-mode ultrasound imaging

Optoacoustic (OA) imaging has achieved tremendous progress with state-of-the-art systems providing excellent functional and molecular contrast, centimeter scale penetration into living tissues, and ultrafast imaging performance, making it highly suitable for handheld imaging in the clinics. OA can greatly benefit from efficient integration with ultrasound (US) imaging, which remains the routine method in bedside clinical diagnostics. However, such integration has not been straightforward since the two modalities typically involve different image acquisition strategies. Here, we present a new, to our knowledge, hybrid optoacoustic ultrasound (OPUS) imaging approach employing a spherical array with dedicated segments for each modality to enable volumetric OA imaging merged with conventional B-mode US. The system performance is subsequently showcased in healthy human subjects. The new OPUS approach hence represents an important step toward establishing OA in point-of-care diagnostic settings.

Video-rate endocavity photoacoustic/harmonic ultrasound imaging with miniaturized light delivery

Significance

Endocavity ultrasound (US) imaging is a frequently employed diagnostic technique in gynecology and urology for the assessment of male and female genital diseases that present challenges for conventional transabdominal imaging. The integration of photoacoustic (PA) imaging with clinical US imaging has displayed promising outcomes in clinical research. Nonetheless, its application has been constrained due to size limitations, restricting it to spatially confined locations such as vaginal or rectal canals.

Aim

This study presents the development of a video-rate (20 Hz) endocavity PA/harmonic US imaging (EPAUSI) system.

Approach

The approach incorporates a commercially available endocavity US probe with a miniaturized laser delivery unit, comprised of a single large-core fiber and a line beamshaping engineered diffuser. The system facilitates real-time image display and subsequent processing, including angular energy density correction and spectral unmixing, in offline mode.

Results

The spatial resolutions of the concurrently acquired PA and harmonic US images were measured at 318    μ m and 291    μ m in the radial direction, respectively, and 1.22 deg and 1.50 deg in the angular direction, respectively. Furthermore, the system demonstrated its capability in multispectral PA imaging by successfully distinguishing two clinical dyes in a tissue-mimicking phantom. Its rapid temporal resolution enabled the capture of kinetic dye perfusion into an ex vivo porcine ovary through the depth of porcine uterine tissue. EPAUSI proved its clinical viability by detecting pulsating hemodynamics in the male rat's prostate in vivo and accurately classifying human blood vessels into arteries and veins based on sO 2 measurements.

Conclusions

Our proposed EPAUSI system holds the potential to unveil previously overlooked indicators of vascular alterations in genital cancers or endometriosis, addressing pressing requirements in the fields of gynecology and urology.

Photoacoustic imaging plus X: a review

Significance

Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability.

Aim

The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications.

Approach

An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities.

Results

After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively.

Conclusions

The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

A novel maternal thyroid disease prediction using multi-scale vision transformer architecture with improved linguistic hedges neural-fuzzy classifier

BACKGROUND

Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established.

OBJECTIVE

We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used. Even though binary classification is a common goal of current disease diagnosis techniques, the data sets are small, and the outcomes are not validated. Most current approaches concentrate on model optimization, focusing less on feature engineering.

METHODS

The suggested method can predict increased protein binding, non-thyroid syndrome (NTIS) (simultaneous non-thyroid disease), autoimmune thyroiditis (compensated hypothyroidism), and Hashimoto's thyroiditis (primary hypothyroidism). In this paper, we develop an automatic thyroid nodule classification system using a multi-scale vision transformer and image enhancement. Graph equalization is the chosen technique for image enhancement, and in our experiments, we used neural networks with four-layer network nodes. This work presents an enhanced linguistic coverage neuro-fuzzy classifier with chosen features for thyroid disease feature selection diagnosis. The training procedure is optimized, and a multi-scale vision transformer network is employed. Each hop connection in Dense Net now has trainable weight parameters, altering the architecture. Images of thyroid nodules from 508 patients make up the data set for this article. Sets of 80% training and 20% validation and 70% training and 30% validation are created from the data. Simultaneously, we take into account how the number of training iterations, network structure, activation function of network nodes, and other factors affect the classification outcomes.

RESULTS

According to the experimental results, the best number of training iterations is 500, the logistic function is the best activation function, and the ideal network structure is 2500-40-2-1.

CONCLUSION

K-fold validation and performance comparison with previous research validate the suggested methodology's enhanced effectiveness.

Transcytosis-Inducing Multifunctional Albumin Nanomedicines with Deep Penetration Ability for Image-Guided Solid Tumor Treatment

Transcytosis is an active transcellular transportation pathway that has garnered interest for overcoming the limited deep penetration of nanomedicines in solid tumors. In this study, a charge-convertible nanomedicine that facilitates deep penetration into solid tumors via transcytosis is designed. It is an albumin-based calcium phosphate nanomedicine loaded with IR820 (mAlb-820@CaP) for high-resolution photoacoustic imaging and enhanced photothermal therapy. Biomineralization on the surface stabilizes the albumin-IR820 complex during circulation and provides calcium ions (Ca2+ ) for tissue penetration on degradation in an acidic environment. pH-triggered transcytosis of the nanomedicine enabled by caveolae-mediated endocytosis and calcium ion-induced exocytosis in 2D cellular, 3D spheroid, and in vivo tumor models is demonstrated. Notably, the extravasation and penetration ability of the nanomedicine is observed in vivo using a high-resolution photoacoustic system, and nanomedicine shows the most potent photothermal antitumor effect in vivo. Overall, the strategy provides a versatile theragnosis platform for both noninvasive photoacoustic imaging and high therapeutic efficiency resulting from deep penetration of nanomedicine.

Contrast Agent-Free 3D Renal Ultrafast Doppler Imaging Reveals Vascular Dysfunction in Acute and Diabetic Kidney Diseases

To combat the irreversible decline in renal function associated with kidney disease, it is essential to establish non-invasive biomarkers for assessing renal microcirculation. However, the limited resolution and/or vascular sensitivity of existing diagnostic imaging techniques hinders the visualization of complex cortical vessels. Here, a 3D renal ultrafast Doppler (UFD) imaging system that uses a high ultrasound frequency (18 MHz) and ultrahigh frame rate (1 KHz per slice) to scan the entire volume of a rat's kidney in vivo is demonstrated. The system, which can visualize the full 3D renal vascular branching pyramid at a resolution of 167 µm without any contrast agent, is used to chronically and noninvasively monitor kidneys with acute kidney injury (AKI, 3 days) and diabetic kidney disease (DKD, 8 weeks). Multiparametric UFD analyses (e.g., vessel volume occupancy (VVO), fractional moving blood volume (FMBV), vessel number density (VND), and vessel tortuosity (VT)) describe rapid vascular rarefaction from AKI and long-term vascular degeneration from DKD, while the renal pathogeneses are validated by in vitro blood serum testing and stained histopathology. This work demonstrates the potential of 3D renal UFD to offer valuable insights into assessing kidney perfusion levels for future research in diabetes and kidney transplantation.

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

BACKGROUND AND AIM

Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models.

METHODS

A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines.

RESULTS

Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging.

CONCLUSION

DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom

In recent years, photoacoustic (PA) imaging has rapidly grown as a non-invasive screening technique for breast cancer detection using three-dimensional (3D) hemispherical arrays due to their large field of view. However, the development of breast imaging systems is hindered by a lack of patients and ground truth samples, as well as under-sampling problems caused by high costs. Most research related to solving these problems in the PA field were based on 2D transducer arrays or simple regular shape phantoms for 3D transducer arrays or images from other modalities. Therefore, we demonstrate an effective method for removing under-sampling artifacts based on deep neural network (DNN) to reconstruct high-quality PA images using numerical digital breast simulations. We constructed 3D digital breast phantoms based on human anatomical structures and physical properties, which were then subjected to 3D Monte-Carlo and K-wave acoustic simulations to mimic acoustic propagation for hemispherical transducer arrays. Finally, we applied a 3D delay-and-sum reconstruction algorithm and a Res-UNet network to achieve higher resolution on sparsely-sampled data. Our results indicate that when using a 757 nm laser with uniform intensity distribution illuminated on a numerical digital breast, the imaging depth can reach 3 cm with 0.25 mm spatial resolution. In addition, the proposed DNN can significantly enhance image quality by up to 78.4%, as measured by MS-SSIM, and reduce background artifacts by up to 19.0%, as measured by PSNR, even at an under-sampling ratio of 10%. The post-processing time for these improvements is only 0.6 s. This paper suggests a new 3D real time DNN method addressing the sparse sampling problem based on numerical digital breast simulations, this approach can also be applied to clinical data and accelerate the development of 3D photoacoustic hemispherical transducer arrays for early breast cancer diagnosis.

Automated Laser-Fiber Coupling Module for Optical-Resolution Photoacoustic Microscopy

Photoacoustic imaging has emerged as a promising biomedical imaging technique that enables visualization of the optical absorption characteristics of biological tissues in vivo. Among the different photoacoustic imaging system configurations, optical-resolution photoacoustic microscopy stands out by providing high spatial resolution using a tightly focused laser beam, which is typically transmitted through optical fibers. Achieving high-quality images depends significantly on optical fluence, which is directly proportional to the signal-to-noise ratio. Hence, optimizing the laser-fiber coupling is critical. Conventional coupling systems require manual adjustment of the optical path to direct the laser beam into the fiber, which is a repetitive and time-consuming process. In this study, we propose an automated laser-fiber coupling module that optimizes laser delivery and minimizes the need for manual intervention. By incorporating a motor-mounted mirror holder and proportional derivative control, we successfully achieved efficient and robust laser delivery. The performance of the proposed system was evaluated using a leaf-skeleton phantom in vitro and a human finger in vivo, resulting in high-quality photoacoustic images. This innovation has the potential to significantly enhance the quality and efficiency of optical-resolution photoacoustic microscopy.

Functional photoacoustic imaging: from nano- and micro- to macro-scale

Functional photoacoustic imaging is a promising biological imaging technique that offers such unique benefits as scalable resolution and imaging depth, as well as the ability to provide functional information. At nanoscale, photoacoustic imaging has provided super-resolution images of the surface light absorption characteristics of materials and of single organelles in cells. At the microscopic and macroscopic scales. photoacoustic imaging techniques have precisely measured and quantified various physiological parameters, such as oxygen saturation, vessel morphology, blood flow, and the metabolic rate of oxygen, in both human and animal subjects. This comprehensive review provides an overview of functional photoacoustic imaging across multiple scales, from nano to macro, and highlights recent advances in technology developments and applications. Finally, the review surveys the future prospects of functional photoacoustic imaging in the biomedical field.

Review on ultrasound-guided photoacoustic imaging for complementary analyses of biological systems in vivo

Photoacoustic imaging has been developed as a new biomedical molecular imaging modality. Due to its similarity to conventional ultrasound imaging in terms of signal detection and image generation, dual-modal photoacoustic and ultrasound imaging has been applied to visualize physiological and morphological information in biological systems in vivo. By complementing each other, dual-modal photoacoustic and ultrasound imaging showed synergistic advances in photoacoustic imaging with the guidance of ultrasound images. In this review, we introduce our recent progresses in dual-modal photoacoustic and ultrasound imaging systems at various scales of study, from preclinical small animals to clinical humans. A summary of the works reveals various strategies for combining the structural information of ultrasound images with the molecular information of photoacoustic images.

In vivo photoacoustic monitoring of vasoconstriction induced by acute hyperglycemia

Postprandial hyperglycemia, blood glucose spikes, induces endothelial dysfunction, increasing cardiovascular risks. Endothelial dysfunction leads to vasoconstriction, and observation of this phenomenon is important for understanding acute hyperglycemia. However, high-resolution imaging of microvessels during acute hyperglycemia has not been fully developed. Here, we demonstrate that photoacoustic microscopy can noninvasively monitor morphological changes in blood vessels of live animals' extremities when blood glucose rises rapidly. As blood glucose level rose from 100 to 400 mg/dL following intraperitoneal glucose injection, heart/breath rate, and body temperature remained constant, but arterioles constricted by approximately -5.7 ± 1.1% within 20 min, and gradually recovered for another 40 min. In contrast, venular diameters remained within about 0.6 ± 1.5% during arteriolar constriction. Our results experimentally and statistically demonstrate that acute hyperglycemia produces transitory vasoconstriction in arterioles, with an opposite trend of change in blood glucose. These findings could help understanding vascular glucose homeostasis and the relationship between diabetes and cardiovascular diseases.

Photoacoustic maximum amplitude projection microscopy by ultra-low data sampling

Photoacoustic microscopy (PAM) has attracted increasing research interest in the biomedical field due to its unique merit of combining light and sound. In general, the bandwidth of a photoacoustic signal reaches up to tens or even hundreds of MHz, which requires a high-performance acquisition card to meet the high requirement of precision of sampling and control. For most depth-insensitive scenes, it is complex and costly to capture the photoacoustic maximum amplitude projection (MAP) images. Herein, we propose a simple and low-cost MAP-PAM system based on a custom-made peak holding circuit to obtain the extremum values by Hz data sampling. The dynamic range of the input signal is 0.01-2.5 V, and the -6-dB bandwidth of the input signal can be up to 45 MHz. Through in vitro and in vivo experiments, we have verified that the system has the same imaging ability as conventional PAM. Owing to its compact size and ultra-low price (approximately $18), it provides a new performance paradigm for PAM and opens up a new way for an optimal photoacoustic sensing and imaging device.

Theoretical and experimental comparison of the performance of gold, titanium, and platinum nanodiscs as contrast agents for photoacoustic imaging

Exogenous contrast agents in photoacoustic imaging help improve spatial resolution and penetration depth and enable targeted molecular imaging. To screen efficient photoacoustic signaling materials as contrast agents, we propose a light absorption-weighted figure of merit (FOM) that can be calculated using material data from the literature and numerically simulated light absorption cross-sections. The calculated light absorption-weighted FOM shows that a Ti nanodisc has a photoacoustic conversion performance similar to that of an Au nanodisc and better than that of a Pt nanodisc. The photoacoustic imaging results of Ti, Au, and Pt nanodiscs, which are physically synthesized with identical shapes and dimensions, experimentally demonstrated that the Ti nanodisc could be a highly efficient contrast agent.

Bimetallic Hyaluronate-Modified Au@Pt Nanoparticles for Noninvasive Photoacoustic Imaging and Photothermal Therapy of Skin Cancer

Although spherical gold (Au) nanoparticles have remarkable photothermal conversion efficiency and photostability, their weak absorption in the near-infrared (NIR) region and poor penetration into deep tissues have limited further applications to NIR light-mediated photoacoustic (PA) imaging and noninvasive photothermal cancer therapy. Here, we developed bimetallic hyaluronate-modified Au-platinum (HA-Au@Pt) nanoparticles for noninvasive cancer theranostics by NIR light-mediated PA imaging and photothermal therapy (PTT). The growth of Pt nanodots on the surface of spherical Au nanoparticles enhanced the absorbance in the NIR region and broadened the absorption bandwidth of HA-Au@Pt nanoparticles by the surface plasmon resonance (SPR) coupling effect. In addition, HA facilitated the transdermal delivery of HA-Au@Pt nanoparticles through the skin barrier and enabled clear tumor-targeted PA imaging. Compared to conventional PTT via injection, HA-Au@Pt nanoparticles were noninvasively delivered into deep tumor tissues and completely ablated the targeted tumor tissues by NIR light irradiation. Taken together, we could confirm the feasibility of HA-Au@Pt nanoparticles as a NIR light-mediated biophotonic agent for noninvasive skin cancer theranostics.

Recent Advances in Photoacoustic Agents for Theranostic Applications

Photoacoustic agents are widely used in various theranostic applications. By evaluating the biodistribution obtained from photoacoustic images, the effectiveness of theranostic agents in terms of their delivery efficiency and treatment responses can be analyzed. Through this study, we evaluate and summarize the recent advances in photoacoustic-guided phototherapy, particularly in photothermal and photodynamic therapy. This overview can guide the future directions for theranostic development. Because of the recent applications of photoacoustic imaging in clinical trials, theranostic agents with photoacoustic monitoring have the potential to be translated into the clinical world.

Motion Compensation for 3D Multispectral Handheld Photoacoustic Imaging

Three-dimensional (3D) handheld photoacoustic (PA) and ultrasound (US) imaging performed using mechanical scanning are more useful than conventional 2D PA/US imaging for obtaining local volumetric information and reducing operator dependence. In particular, 3D multispectral PA imaging can capture vital functional information, such as hemoglobin concentrations and hemoglobin oxygen saturation (sO₂), of epidermal, hemorrhagic, ischemic, and cancerous diseases. However, the accuracy of PA morphology and physiological parameters is hampered by motion artifacts during image acquisition. The aim of this paper is to apply appropriate correction to remove the effect of such motion artifacts. We propose a new motion compensation method that corrects PA images in both axial and lateral directions based on structural US information. 3D PA/US imaging experiments are performed on a tissue-mimicking phantom and a human wrist to verify the effects of the proposed motion compensation mechanism and the consequent spectral unmixing results. The structural motions and sO₂ values are confirmed to be successfully corrected by comparing the motion-compensated images with the original images. The proposed method is expected to be useful in various clinical PA imaging applications (e.g., breast cancer, thyroid cancer, and carotid artery disease) that are susceptible to motion contamination during multispectral PA image analysis.