Deep learning for biomedical photoacoustic imaging: A review

Generative priors-constraint accelerated iterative reconstruction for extremely sparse photoacoustic tomography boosted by mean-reverting diffusion model: Towards 8 projections

As a novel non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines the advantages of high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction methods under sparse view may lead to low-quality image in photoacoustic tomography. To address this problem, an advanced sparse reconstruction method for photoacoustic tomography based on the mean-reverting diffusion model is proposed. By modeling the degradation process from a high-quality image under full-view scanning (512 projections) to a sparse image with stable Gaussian noise (i.e., mean state), a mean-reverting diffusion model is trained to learn prior information of the data distribution. Then the learned prior information is employed to generate a high-quality image from the sparse image by iteratively sampling the noisy state. 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 method. In addition, the proposed method dramatically speeds up the sparse reconstruction and achieves better reconstruction results for extremely sparse images compared with the method based on conventional diffusion model. The proposed method achieves an improvement of 0.52 (∼289 %) in structural similarity and 10.01 dB (∼59 %) in peak signal-to-noise ratio for extremely sparse projections (8 projections), compared with the conventional delay-and-sum method. This method is expected to shorten the acquisition time and reduce the cost of photoacoustic tomography, thus further expanding the range of applications.

Shortcut learning leads to sex bias in deep learning models for photoacoustic tomography

PURPOSE

Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact on photoacoustic tomography (PAT), particularly concerning sex bias, remains underexplored. This study investigates this issue using peripheral artery disease (PAD) diagnosis as a specific clinical application.

METHODS

To examine the potential for sex bias due to shortcut learning in convolutional neural network (CNNs) and assess how such biases might affect diagnostic predictions, we created training and test datasets with varying PAD prevalence between sexes. Using these datasets, we explored (1) whether CNNs can classify the sex from imaging data, (2) how sex-specific prevalence shifts impact PAD diagnosis performance and underdiagnosis disparity between sexes, and (3) how similarly CNNs encode sex and PAD features.

RESULTS

Our study with 147 individuals demonstrates that CNNs can classify the sex from calf muscle PAT images, achieving an AUROC of 0.75. For PAD diagnosis, models trained on data with imbalanced sex-specific disease prevalence experienced significant performance drops (up to 0.21 AUROC) when applied to balanced test sets. Additionally, greater imbalances in sex-specific prevalence within the training data exacerbated underdiagnosis disparities between sexes. Finally, we identify evidence of shortcut learning by demonstrating the effective reuse of learned feature representations between PAD diagnosis and sex classification tasks.

CONCLUSION

CNN-based models trained on PAT data may engage in shortcut learning by leveraging sex-related features, leading to biased and unreliable diagnostic predictions. Addressing demographic-specific prevalence imbalances and preventing shortcut learning is critical for developing models in the medical field that are both accurate and equitable across diverse patient populations.

Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting

Photoacoustic tomography (PAT) is an emerging imaging modality with widespread applications in both preclinical and clinical studies. Despite its promising capabilities to provide high-resolution images, the visualization of vessels might be hampered by skin signals and attenuation in tissues. In this study, we have introduced a framework to retrieve deep vessels. It combines a deep learning network to segment skin layers and an adaptive weighting algorithm to compensate for attenuation. Evaluation of enhancement using vessel occupancy metrics and signal-to-noise ratio (SNR) demonstrates that the proposed method significantly recovers deep vessels across various body positions and skin tones. These findings indicate the method's potential to enhance quantitative analysis in preclinical and clinical photoacoustic research.

Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation

In practical applications within the human body, it is often challenging to fully encompass the target tissue or organ, necessitating the use of limited-view arrays, which can lead to the loss of crucial information. Addressing the reconstruction of photoacoustic sensor signals in limited-view detection spaces has become a focal point of current research. In this study, we introduce a self-supervised network termed HIgh-quality Self-supervised neural representation (HIS), which tackles the inverse problem of photoacoustic imaging to reconstruct high-quality photoacoustic images from sensor data acquired under limited viewpoints. We regard the desired reconstructed photoacoustic image as an implicit continuous function in 2D image space, viewing the pixels of the image as sparse discrete samples. The HIS's objective is to learn the continuous function from limited observations by utilizing a fully connected neural network combined with Fourier feature position encoding. By simply minimizing the error between the network's predicted sensor data and the actual sensor data, HIS is trained to represent the observed continuous model. The results indicate that the proposed HIS model offers superior image reconstruction quality compared to three commonly used methods for photoacoustic image reconstruction.

Method for Selecting the Down-Sampling Factor of Photoacoustic Image by Using Cumulative Power Difference in Frequency Domain

As a novel non-invasive imaging technology, a constraint of photoacoustic microscopy (PAM) is its imaging speed. Often, PAM utilizes sparse spatial sampling, which necessitates extensive prior experimentation to accurately select the down-sampling factors. To overcome this limitation, this study proposes a frequency-domain evaluation index, cumulative power difference (CPD), for rapid selection of the optimal down-sampling factor. We apply the proposed CPD to photoacoustic images of the ear and brain of the mouse. The result shows that as the down-sampling factor increases, there is a similar decreasing trend in the quality of the 20 images. CPD was significantly correlated with PCC/MSE/SSIM (p < 0.001). The findings suggest that CPD, with its ability to evaluate the quality of photoacoustic images and quickly quantify the quality loss of down-sampled images without prior inspection. This study contributes to expanding the application range of PAM and supporting its clinical prospects.

Application of a CYP1B1-Targeted NIR Probe for Breast Cancer Diagnosis, Surgical Navigation, and CYP1B1-Associated Chemotherapy Resistance Monitoring

Early detection and precise treatment for breast cancer are crucial, given its high global incidence rate. Hence, the development of novel imaging targets is essential for diagnosing and monitoring resistance to chemotherapy, which is pivotal for achieving precise and personalized treatment for breast cancer patients. In our previous work, we successfully developed a near-infrared (NIR) probe 1 for CYP1B1-targeted imaging. In this study, we aimed to investigate the utility of the probe as a NIR fluorescence and photoacoustic dual-mode imaging probe for the detection and surveillance of breast cancer. Western blotting of cancer cell lines has confirmed that CYP1B1 is widely expressed in breast cancer and gynecological cancer. In vitro NIR fluorescence imaging capability of the probe for tracking CYP1B1-positive tumor cells was validated by using confocal microscopy. Further studies, including in vivo fluorescence and photoacoustic dual-model imaging and ex vivo biological distribution analysis on a triple-negative breast cancer xenograft mouse model, demonstrated that the probe selectively accumulated in tumor tissue within as early as 0.5 h postinjection. The results of the surgical resection experiment revealed that the tumor could be entirely removed under the guidance of NIR imaging, thereby indicating the probe's efficacy in surgical navigation. CYP1B1 expression was found to be upregulated in adriamycin (ADR)-resistant breast cancer cells, MCF-7/ADR. Consequently, the sensitivity of CYP1B1 overexpressed cells, MCF-7/1B1, to ADR was significantly reduced, with an IC50 value of 0.586 ± 0.0934 μM, compared to the parental MCF-7 cells with an IC50 value of 0.183 ± 0.0444 μM. In vivo and ex vivo imaging assays conducted on MCF-7/ADR tumor-bearing mice demonstrated that the probe was specifically enriched in tumor sites, suggesting its potential for monitoring chemotherapy resistance in breast cancer. This study expands the scope of application for NIR probe 1, establishing its utility in breast cancer diagnosis through fluorescence-photoacoustic dual-model imaging, monitoring of chemotherapy resistance, and guidance for surgical resection. This strategy paves the way for novel approaches to precise and personalized treatment for breast cancer patients.

3D protoacoustic radiography: A proof of principle study

We propose protoacoustic radiography (PAR), an imaging modality combining proton excitation and acoustic detection for three-dimensional (3D) imaging from a single proton projection. PAR avoids the effect of multiple Coulomb scattering in imaging by detecting ultrasound. Proton-induced acoustic waves propagate spherically, enabling 3D imaging from a single projection. Additionally, the distinctive feature of proton beams-concentrating energy deposition primarily at the Bragg peak-allows for precise depth-selectivity through proton energy tuning. We performed PAR using clinical proton machines, and our results demonstrate the capability of PAR to reconstruct targets at various depths (between ∼20 and 23 cm) with an axial resolution of 1.3 mm by fully leveraging the Bragg peak and by tuning the kinetic energy of the proton beam. PAR offers opportunities for precise structural determination with protons both in biomedicine and nondestructive testing.

MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data

Photoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detection. These limitations often result in severe streak artifacts and blurring when using standard methods to reconstruct images from incomplete data. In this work, we propose an improved convolutional neural network (CNN) architecture, called multi-scale dense UNet (MSD-Net), to correct artifacts in 2D photoacoustic tomography (PAT). MSD-Net exploits the advantages of multi-scale information fusion and dense connections to improve the performance of CNN. Experimental validation with both simulated and in vivo datasets demonstrates that our method achieves better reconstructions with improved speed.

Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures

Recent advances in Light Emitting Diode (LED) technology have enabled a more affordable high frame rate photoacoustic imaging (PA) alternative to traditional laser-based PA systems that are costly and have slow pulse repetition rate. However, a major disadvantage with LEDs is the low energy outputs that do not produce high signal-to-noise ratio (SNR) PA images. There have been recent advancements in integrating deep learning methodologies aimed to address the challenge of improving SNR in LED-PA images, yet comprehensive evaluations across varied datasets and architectures are lacking. In this study, we systematically assess the efficacy of various Encoder-Decoder-based CNN architectures for enhancing SNR in real-time LED-based PA imaging. Through experimentation with in vitro phantoms, ex vivo mouse organs, and in vivo tumors, we compare basic convolutional autoencoder and U-Net architectures, explore hierarchical depth variations within U-Net, and evaluate advanced variants of U-Net. Our findings reveal that while U-Net architectures generally exhibit comparable performance, the Dense U-Net model shows promise in denoising different noise distributions in the PA image. Notably, hierarchical depth variations did not significantly impact performance, emphasizing the efficacy of the standard U-Net architecture for practical applications. Moreover, the study underscores the importance of evaluating robustness to diverse noise distributions, with Dense U-Net and R2 U-Net demonstrating resilience to Gaussian, salt and pepper, Poisson, and Speckle noise types. These insights inform the selection of appropriate deep learning architectures based on application requirements and resource constraints, contributing to advancements in PA imaging technology.

Progress on Carbon Dots with Intrinsic Bioactivities for Multimodal Theranostics

Carbon dots (CDs) with intrinsic bioactivities are candidates for bioimaging and disease therapy due to their diverse bioactivities, high biocompatibility, and multiple functionalities in multimodal theranostics. It is a multidisciplinary research hotspot that includes biology, physics, materials science, and chemistry. This progress report discusses the CDs with intrinsic bioactivities and their applications in multimodal theranostics. The relationship between the synthesis and structure of CDs is summarized and analyzed from a material and chemical perspective. The bioactivities of CDs including anti-tumor, antibacterial, anti-inflammatory etc. are discussed from biological points of view. Subsequently, the optical and electronic properties of CDs that can be applied in the biomedical field are summarized from a physical perspective. Based on the functional review of CDs, their applications in the biomedical field are reviewed, including optical diagnosis and treatment, biological activity, etc. Unlike previous reviews, this review combines multiple disciplines to gain a more comprehensive understanding of the mechanisms, functions, and applications of CDs with intrinsic bioactivities.

Advances in Injectable Hydrogels Based on Diverse Gelation Methods for Biomedical Imaging

The injectable hydrogels can deliver the loads directly to the predetermined sites and form reservoirs to increase the enrichment and retention of the loads in the target areas. The preparation and injection of injectable hydrogels involve the sol-gel transformation of hydrogels, which is affected by factors such as temperature, ions, enzymes, light, mechanics (self-healing property), and pH. However, tracing the injection, degradation, and drug release from hydrogels based on different ways of gelation is a major concern. To solve this problem, contrast agents are introduced into injectable hydrogels, enabling the hydrogels to be imaged under techniques such as fluorescence imaging, photoacoustic imaging, magnetic resonance imaging, and radionuclide imaging. This review details methods for causing the gelation of imageable hydrogels; discusses the application of injectable hydrogels containing contrast agents in various imaging techniques, and finally explores the potential and challenges of imageable hydrogels based on different modes of gelation.

Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning

Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.

Advancements in photoacoustic imaging for cancer diagnosis and treatment

Photoacoustic imaging provides in vivo morphological and functional information about tumors within surrounding tissue. By integrating ultrasound guidance, this technique enables precise localization and characterization of tumors. Moreover, the introduction of targeted contrast agents has further expanded the capabilities of photoacoustic imaging in the realm of in vivo molecular imaging. These contrast agents facilitate enhanced molecular and cellular characterization of cancer, enabling detailed insights into the disease. This review aims to provide a concise summary of the extensive research conducted in the field of Photoacoustic imaging for cancer management. It encompasses the development of the technology, its applications in clinical settings, and the advancements made in molecular imaging. By consolidating and synthesizing the existing knowledge, this review contributes to a better understanding of the potential of photoacoustic imaging in cancer care. In conclusion, photoacoustic imaging has emerged as a non-ionizing and noninvasive modality with the ability to visualize tissue's optical absorption properties while maintaining ultrasound's spatial resolution. Its integration with targeted contrast agents has enhanced molecular and cellular characterization of cancer. This review serves as a succinct overview of the extensive research conducted in the field, shedding light on the potential of photoacoustic imaging in the management of cancer.

Unsupervised adversarial neural network for enhancing vasculature in photoacoustic tomography images using optical coherence tomography angiography

Photoacoustic tomography (PAT) is a powerful imaging modality for visualizing tissue physiology and exogenous contrast agents. However, PAT faces challenges in visualizing deep-seated vascular structures due to light scattering, absorption, and reduced signal intensity with depth. Optical coherence tomography angiography (OCTA) offers high-contrast visualization of vasculature networks, yet its imaging depth is limited to a millimeter scale. Herein, we propose OCPA-Net, a novel unsupervised deep learning method that utilizes the rich vascular feature of OCTA to enhance PAT images. Trained on unpaired OCTA and PAT images, OCPA-Net incorporates a vessel-aware attention module to enhance deep-seated vessel details captured from OCTA. It leverages a domain-adversarial loss function to enforce structural consistency and a novel identity invariant loss to mitigate excessive image content generation. We validate the structural fidelity of OCPA-Net on simulation experiments, and then demonstrate its vascular enhancement performance on in vivo imaging experiments of tumor-bearing mice and contrast-enhanced pregnant mice. The results show the promise of our method for comprehensive vessel-related image analysis in preclinical research applications.

In vivo spatiotemporal characterizing diverse body transportation of optical labeled high immunity aluminium adjuvants with photoacoustic tomography

Vaccine development requires high-resolution, in situ, and visual adjuvant technology. To address this need, this work proposed a novel adjuvant labeling that involved indocyanine green (ICG) and bovine serum albumin (BSA) with self-assembled aluminium adjuvant (Alum), which was called BSA@ICG@Alum. This compound exhibited excellent photoacoustic properties and has been confirmed its safety, biocompatibility, high antigen binding efficiency, and superior induction of immune response. Photoacoustic tomography (PAT) tracked the distribution of Alum in lymph nodes (LNs) and lymphatic vessels in real time after diverse injection modalities. The non-invasive imaging approach revealed that BSA@ICG@Alum was transported to the draining LNs 60 min after intramuscular injection and to distal LNs within 30 min after lymph node injection. In conclusion, PAT enabled real-time three-dimensional and quantitative visualization, thus offering a powerful tool for advancing vaccine design by providing critical insights into adjuvant transport and immune system activation.

Adaptive complementary neighboring sub-aperture beamforming for thermoacoustic imaging

BACKGROUND

When applied to thermoacoustic imaging (TAI), the delay-and-sum (DAS) algorithm produces strong sidelobes due to its disadvantages of uniform aperture weighting. As a result, the quality of TAI images recovered by DAS is often severely degraded by strong non-coherent clutter, which restricts the development and application of TAI.

PURPOSE

To address this issue, we propose an adaptive complementary neighboring sub-aperture (NSA) beamforming algorithm for TAI.

METHODS

In NSA, we introduce a coordinate system transformation when calculating the normalized cross-correlation (NCC) matrix. This approach enables the computation of the NCC coefficient within the specified kernel without complex coordinate calculations. We first conducted the numerical simulation experiment to validate NSA using a tree branch phantom. In addition, we also conducted phantom (five sauce tubes), ex vivo (ablation needle in ex vivo porcine liver), and in vivo (human arm) TAI experiments using our TAI system with a center frequency of 3 GHz.

RESULTS

In the numerical simulation experiment, the structural similarity index (SSIM) value for NSA is increased from 0.37828 for DAS to 0.75492. In the point target phantom TAI experiment, the generalized contrast-to-noise ratio (gCNR) value for NSA is increased from 0.936 for DAS to 0.962. The experimental results show that NSA can recover clearer thermoacoustic images compared to DAS. In the ex vivo TAI experiment, the full width at half maxima (FWHM) of an ablation needle (diameter = 1.5 mm) for coherence factor (CF) weighted DAS and NSA are 0.9 and 1.3 mm, respectively. Furthermore, in the in vivo TAI experiment, CF reduces the signals within the arm compared to NSA. Therefore, compared with CF, NSA can maintain the integrity of target information in TAI while effectively suppressing non-coherent background clutter.

CONCLUSIONS

NSA can effectively reduce non-coherent background noise while ensuring the completeness of the target information. So, NSA offers the potential to provide high-quality thermoacoustic images and further advance their clinical application.

Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques

Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.

Non-invasive optoacoustic imaging of glycogen-storage and muscle degeneration in late-onset Pompe disease

Pompe disease (PD) is a rare autosomal recessive glycogen storage disorder that causes proximal muscle weakness and loss of respiratory function. While enzyme replacement therapy (ERT) is the only effective treatment, biomarkers for disease monitoring are scarce. Following ex vivo biomarker validation in phantom studies, we apply multispectral optoacoustic tomography (MSOT), a laser- and ultrasound-based non-invasive imaging approach, in a clinical trial (NCT05083806) to image the biceps muscles of 10 late-onset PD (LOPD) patients and 10 matched healthy controls. MSOT is compared with muscle magnetic resonance imaging (MRI), ultrasound, spirometry, muscle testing and quality of life scores. Next, results are validated in an independent LOPD patient cohort from a second clinical site. Our study demonstrates that MSOT enables imaging of subcellular disease pathology with increases in glycogen/water, collagen and lipid signals, providing higher sensitivity in detecting muscle degeneration than current methods. This translational approach suggests implementation in the complex care of these rare disease patients.

Enhanced Electroacoustic Tomography with Supervised Learning for Real-time Electroporation Monitoring

Background

Nanosecond pulsed electric fields (nsPEF)-based electroporation is a new therapy modality potentially synergized with radiation therapy to improve treatment outcomes. To verify its treatment accuracy intraoperatively, electroacoustic tomography (EAT) has been developed to monitor in-vivo electric energy deposition by detecting ultrasound signals generated by nsPEFs in real-time. However, utility of EAT is limited by image distortions due to the limited-angle view of ultrasound transducers.

Methods

This study proposed a supervised learning-based workflow to address the ill-conditioning in EAT reconstruction. Electroacoustic signals were detected by a linear array and initially reconstructed into EAT images, which were then fed into a deep learning model for distortion correction. In this study, 56 distinct electroacoustic data sets from nsPEFs of different intensities and geometries were collected experimentally, avoiding simulation-to-real-world variations. Forty-six data were used for model training and 10 for testing. The model was trained using supervised learning, enabled by a custom rotating platform to acquire paired full-view and single-view signals for the same electric field.

Results

The proposed method considerably improved the image quality of linear array-based EAT, generating pressure maps with accurate and clear structures. Quantitatively, the enhanced single-view images achieved a low-intensity error (RMSE: 0.018), high signal-to-noise ratio (PSNR: 35.15), and high structural similarity (SSIM: 0.942) compared to the reference full-view images.

Conclusions

This study represented a pioneering stride in achieving high-quality EAT using a single linear array in an experimental environment, which improves EAT's utility in real-time monitoring for nsPEF-based electroporation therapy.

Photoacoustic Quantification of Tissue Oxygenation Using Conditional Invertible Neural Networks

Intelligent systems in interventional healthcare depend on the reliable perception of the environment. In this context, photoacoustic tomography (PAT) has emerged as a non-invasive, functional imaging modality with great clinical potential. Current research focuses on converting the high-dimensional, not human-interpretable spectral data into the underlying functional information, specifically the blood oxygenation. One of the largely unexplored issues stalling clinical advances is the fact that the quantification problem is ambiguous, i.e. that radically different tissue parameter configurations could lead to almost identical photoacoustic spectra. In the present work, we tackle this problem with conditional Invertible Neural Networks (cINNs). Going beyond traditional point estimates, our network is used to compute an approximation of the conditional posterior density of tissue parameters given the photoacoustic spectrum. To this end, an automatic mode detection algorithm extracts the plausible solution from the sample-based posterior. According to a comprehensive validation study based on both synthetic and real images, our approach is well-suited for exploring ambiguity in quantitative PAT.

Microbe-material hybrids for therapeutic applications

As natural living substances, microorganisms have emerged as useful resources in medicine for creating microbe-material hybrids ranging from nano to macro dimensions. The engineering of microbe-involved nanomedicine capitalizes on the distinctive physiological attributes of microbes, particularly their intrinsic "living" properties such as hypoxia tendency and oxygen production capabilities. Exploiting these remarkable characteristics in combination with other functional materials or molecules enables synergistic enhancements that hold tremendous promise for improved drug delivery, site-specific therapy, and enhanced monitoring of treatment outcomes, presenting substantial opportunities for amplifying the efficacy of disease treatments. This comprehensive review outlines the microorganisms and microbial derivatives used in biomedicine and their specific advantages for therapeutic application. In addition, we delineate the fundamental strategies and mechanisms employed for constructing microbe-material hybrids. The diverse biomedical applications of the constructed microbe-material hybrids, encompassing bioimaging, anti-tumor, anti-bacteria, anti-inflammation and other diseases therapy are exhaustively illustrated. We also discuss the current challenges and prospects associated with the clinical translation of microbe-material hybrid platforms. Therefore, the unique versatility and potential exhibited by microbe-material hybrids position them as promising candidates for the development of next-generation nanomedicine and biomaterials with unique theranostic properties and functionalities.

Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography

Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data acquisition limitations. The results using traditional methods in limited-view PAT exhibit distortions and numerous artifacts. Here, a novel limited-view PAT reconstruction strategy that combines model-based iteration with score-based generative model was proposed. By incrementally adding noise to the training samples, prior knowledge can be learned from the complex probability distribution. The acquired prior is then utilized as constraint in model-based iteration. The information of missing views can be gradually compensated by cyclic iteration to achieve high-quality reconstruction. The performance of the proposed method was evaluated with the circular phantom and in vivo experimental data. Experimental results demonstrate the outstanding effectiveness of the proposed method in limited-view cases. Notably, the proposed method exhibits excellent performance in limited-view case of 70° compared with traditional method. It achieves a remarkable improvement of 203% in PSNR and 48% in SSIM for the circular phantom experimental data, and an enhancement of 81% in PSNR and 65% in SSIM for in vivo experimental data, respectively. The proposed method has capability of reconstructing PAT images in extremely limited-view cases, which will further expand the application in clinical scenarios.

Image restoration for ring-array photoacoustic tomography system based on blind spatially rotational deconvolution

Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.

Extractor-attention-predictor network for quantitative photoacoustic tomography

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting-expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.

Noninvasive Photothermal Therapy of Nasopharyngeal Cancer Guided by High Efficiency Optical-Absorption Nanomaterial Enhanced by NIR-II Photoacoustic Imaging

Background

Photothermal therapy (PTT) guided by photoacoustic imaging (PAI) using nanoplatforms has emerged as a promising strategy for cancer treatment due to its efficiency and accuracy. This study aimed to develop and synthesize novel second near-infrared region (NIR-II) absorption-conjugated polymer acceptor acrylate-substituted thiadiazoloquinoxaline-diketopyrrolopyrrole polymers (PATQ-DPP) designed specifically as photothermal and imaging contrast agents for nasopharyngeal carcinoma (NPC).

Methods

The PATQ-DPP nanoparticles were synthesized and characterized for their optical properties, including low optical band gaps. Their potential as PTT agents and imaging contrast agents for NPC was evaluated both in vitro and in vivo. The accumulation of nanoparticles at tumor sites was assessed post-injection, and the efficacy of PTT under near-infrared laser irradiation was investigated in a mouse model of NPC.

Results

Experimental results indicated that the PATQ-DPP nanoparticles exhibited significant photoacoustic contrast enhancement and favorable PTT performance. Safety and non-toxicity evaluations confirmed the biocompatibility of these nanoparticles. In vivo studies showed that PATQ-DPP nanoparticles effectively accumulated at NPC tumor sites and demonstrated excellent tumor growth inhibition upon exposure to near-infrared laser irradiation. Notably, complete elimination of nasopharyngeal tumors was observed within 18 days following PTT.

Discussion

The findings suggest that PATQ-DPP nanoparticles are a promising theranostic agent for NIR-II PAI and PTT of tumors. This innovative approach utilizing PATQ-DPP nanoparticles provides a powerful tool for the early diagnosis and precise treatment of NPC, offering a new avenue in the management of this challenging malignancy.

Single Mesenchymal Stromal Cell Migration Tracking into Glioblastoma Using Photoconvertible Vesicles

Reliable cell labeling and tracking techniques are imperative for elucidating the intricate and ambiguous interactions between mesenchymal stromal cells (MSCs) and tumors. Here, we explore fluorescent photoconvertible nanoengineered vesicles to study mMSC migration in brain tumors. These 3 μm sized vesicles made of carbon nanoparticles, Rhodamine B (RhB), and polyelectrolytes are readily internalized by cells. The dye undergoes photoconversion under 561 nm laser exposure with a fluorescence blue shift upon demand. The optimal laser irradiation duration for photoconversion was 0.4 ms, which provided a maximal blue shift of the fluorescent signal label without excessive laser exposure on cells. Vesicles modified with an extra polymer layer demonstrated enhanced intracellular uptake without remarkable effects on cell viability, motility, or proliferation. The optimal ratio of 20 vesicles per mMSC was determined. Moreover, the migration of individual mMSCs within 2D and 3D glioblastoma cell (EPNT-5) colonies over 2 days and in vivo tumor settings over 7 days were traced. Our study provides a robust nanocomposite platform for investigating MSC-tumor dynamics and offers insights into envisaged therapeutic strategies. Photoconvertible vesicles also present an indispensable tool for studying complex fundamental processes of cell-cell interactions for a wide range of problems in biomedicine.

High-frequency photoacoustic and ultrasound imaging for skin evaluation: Pilot study for the assessment of a chemical burn

Skin architecture and its underlying vascular structure could be used to assess the health status of skin. A non-invasive, high resolution and deep imaging modality able to visualize skin subcutaneous layers and vasculature structures could be useful for determining and characterizing skin disease and trauma. In this study, a multispectral high-frequency, linear array-based photoacoustic/ultrasound (PAUS) probe is developed and implemented for the imaging of rat skin in vivo. The study seeks to demonstrate the probe capabilities for visualizing the skin and its underlying structures, and for monitoring changes in skin structure and composition during a 5-day course of a chemical burn. We analayze composition of lipids, water, oxy-hemoglobin, and deoxy-hemoglobin (for determination of oxygen saturation) in the skin tissue. The study successfully demonstrated the high-frequency PAUS imaging probe was able to provide 3D images of the rat skin architecture, underlying vasculature structures, and oxygen saturation, water, lipids and total hemoglobin.

New insights into red blood cells in tumor precision diagnosis and treatment

Red blood cells (RBCs), which function as material transporters in organisms, are rich in materials that are exchanged with metabolically active tumor cells. Recent studies have demonstrated that tumor cells can regulate biological changes in RBCs, including influencing differentiation, maturation, and morphology. RBCs play an important role in tumor development and immune regulation. Notably, the novel scientific finding that RBCs absorb fragments of tumor-carrying DNA overturns the conventional wisdom that RBCs do not contain nucleic acids. RBC membranes are excellent biomimetic materials with significant advantages in terms of their biocompatibility, non-immunogenicity, non-specific adsorption resistance, and biodegradability. Therefore, RBCs provide a new research perspective for the development of tumor liquid biopsies, molecular imaging, drug delivery, and other tumor precision diagnosis and treatment technologies.

Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography

Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modelling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and in vivo measurements in 3D.

Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning

PURPOSE

Vascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photoacoustic imaging, a hemispherical array sensor is especially suitable for measuring blood vessels running in various directions. However, as a hemispherical array sensor, a sparse array sensor is often used due to technical and cost issues, which causes artifacts in photoacoustic images. Therefore, in this study, we reduce these artifacts using deep learning technology to generate signals of virtual dense array sensors.

METHODS

Generating 2D virtual array sensor signals using a 3D convolutional neural network (CNN) requires huge computational costs and is impractical. Therefore, we installed virtual sensors between the real sensors along the spiral pattern in three different directions and used a 2D CNN to generate signals of the virtual sensors in each direction. Then we reconstructed a photoacoustic image using the signals from both the real sensors and the virtual sensors.

RESULTS

We evaluated the proposed method using simulation data and human palm measurement data. We found that these artifacts were significantly reduced in the images reconstructed using the proposed method, while the artifacts were strong in the images obtained only from the real sensor signals.

CONCLUSION

Using the proposed method, we were able to significantly reduce artifacts, and as a result, it became possible to recognize deep blood vessels. In addition, the processing time of the proposed method was sufficiently applicable to clinical measurement.

Photoacoustic imaging of squirrel monkey cortical responses induced by peripheral mechanical stimulation

Non-human primates (NHPs) are crucial models for studies of neuronal activity. Emerging photoacoustic imaging modalities offer excellent tools for studying NHP brains with high sensitivity and high spatial resolution. In this research, a photoacoustic microscopy (PAM) device was used to provide a label-free quantitative characterization of cerebral hemodynamic changes due to peripheral mechanical stimulation. A 5 × 5 mm area within the somatosensory cortex region of an adult squirrel monkey was imaged. A deep, fully connected neural network was characterized and applied to the PAM images of the cortex to enhance the vessel structures after mechanical stimulation on the forelimb digits. The quality of the PAM images was improved significantly with a neural network while preserving the hemodynamic responses. The functional responses to the mechanical stimulation were characterized based on the improved PAM images. This study demonstrates capability of PAM combined with machine learning for functional imaging of the NHP brain.

Dual-modal Photoacoustic and Ultrasound Imaging: from preclinical to clinical applications

Photoacoustic imaging is a novel biomedical imaging modality that has emerged over the recent decades. Due to the conversion of optical energy into the acoustic wave, photoacoustic imaging offers high-resolution imaging in depth beyond the optical diffusion limit. Photoacoustic imaging is frequently used in conjunction with ultrasound as a hybrid modality. The combination enables the acquisition of both optical and acoustic contrasts of tissue, providing functional, structural, molecular, and vascular information within the same field of view. In this review, we first described the principles of various photoacoustic and ultrasound imaging techniques and then classified the dual-modal imaging systems based on their preclinical and clinical imaging applications. The advantages of dual-modal imaging were thoroughly analyzed. Finally, the review ends with a critical discussion of existing developments and a look toward the future.

Application of micro/nanorobot in medicine

The development of micro/nanorobots and their application in medical treatment holds the promise of revolutionizing disease diagnosis and treatment. In comparison to conventional diagnostic and treatment methods, micro/nanorobots exhibit immense potential due to their small size and the ability to penetrate deep tissues. However, the transition of this technology from the laboratory to clinical applications presents significant challenges. This paper provides a comprehensive review of the research progress in micro/nanorobotics, encompassing biosensors, diagnostics, targeted drug delivery, and minimally invasive surgery. It also addresses the key issues and challenges facing this technology. The fusion of micro/nanorobots with medical treatments is poised to have a profound impact on the future of medicine.

Sounding out the dynamics: a concise review of high-speed photoacoustic microscopy

Significance

Photoacoustic microscopy (PAM) offers advantages in high-resolution and high-contrast imaging of biomedical chromophores. The speed of imaging is critical for leveraging these benefits in both preclinical and clinical settings. Ongoing technological innovations have substantially boosted PAM's imaging speed, enabling real-time monitoring of dynamic biological processes.

Aim

This concise review synthesizes historical context and current advancements in high-speed PAM, with an emphasis on developments enabled by ultrafast lasers, scanning mechanisms, and advanced imaging processing methods.

Approach

We examine cutting-edge innovations across multiple facets of PAM, including light sources, scanning and detection systems, and computational techniques and explore their representative applications in biomedical research.

Results

This work delineates the challenges that persist in achieving optimal high-speed PAM performance and forecasts its prospective impact on biomedical imaging.

Conclusions

Recognizing the current limitations, breaking through the drawbacks, and adopting the optimal combination of each technology will lead to the realization of ultimate high-speed PAM for both fundamental research and clinical translation.

Photoacoustic imaging for cutaneous melanoma assessment: a comprehensive review

Significance

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

Aim

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

Approach

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

Results

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

Conclusions

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

Quantitative photoacoustic tomography: modeling and inverse problems

Significance

Quantitative photoacoustic tomography (QPAT) exploits the photoacoustic effect with the aim of estimating images of clinically relevant quantities related to the tissue's optical absorption. The technique has two aspects: an acoustic part, where the initial acoustic pressure distribution is estimated from measured photoacoustic time-series, and an optical part, where the distributions of the optical parameters are estimated from the initial pressure.

Aim

Our study is focused on the optical part. In particular, computational modeling of light propagation (forward problem) and numerical solution methodologies of the image reconstruction (inverse problem) are discussed.

Approach

The commonly used mathematical models of how light and sound propagate in biological tissue are reviewed. A short overview of how the acoustic inverse problem is usually treated is given. The optical inverse problem and methods for its solution are reviewed. In addition, some limitations of real-life measurements and their effect on the inverse problems are discussed.

Results

An overview of QPAT with a focus on the optical part was given. Computational modeling and inverse problems of QPAT were addressed, and some key challenges were discussed. Furthermore, the developments for tackling these problems were reviewed. Although modeling of light transport is well-understood and there is a well-developed framework of inverse mathematics for approaching the inverse problem of QPAT, there are still challenges in taking these methodologies to practice.

Conclusions

Modeling and inverse problems of QPAT together were discussed. The scope was limited to the optical part, and the acoustic aspects were discussed only to the extent that they relate to the optical aspect.

Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system

Significance

Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs).

Aim

Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner.

Approach

We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery.

Results

We implement a CS PAPI system for a compression factor of 4:3, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results.

Conclusions

CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

Engineered aptamers for molecular imaging

Molecular imaging, including quantification and molecular interaction studies, plays a crucial role in visualizing and analysing molecular events occurring within cells or organisms, thus facilitating the understanding of biological processes. Moreover, molecular imaging offers promising applications for early disease diagnosis and therapeutic evaluation. Aptamers are oligonucleotides that can recognize targets with a high affinity and specificity by folding themselves into various three-dimensional structures, thus serving as ideal molecular recognition elements in molecular imaging. This review summarizes the commonly employed aptamers in molecular imaging and outlines the prevalent design approaches for their applications. Furthermore, it highlights the successful application of aptamers to a wide range of targets and imaging modalities. Finally, the review concludes with a forward-looking perspective on future advancements in aptamer-based molecular imaging.

Photoacoustic imaging on its way toward clinical utility: a tutorial review focusing on practical application in medicine

Significance

Photoacoustic imaging (PAI) enables the visualization of optical contrast with ultrasonic imaging. It is a field of intense research, with great promise for clinical application. Understanding the principles of PAI is important for engineering research and image interpretation.

Aim

In this tutorial review, we lay out the imaging physics, instrumentation requirements, standardization, and some practical examples for (junior) researchers, who have an interest in developing PAI systems and applications for clinical translation or applying PAI in clinical research.

Approach

We discuss PAI principles and implementation in a shared context, emphasizing technical solutions that are amenable to broad clinical deployment, considering factors such as robustness, mobility, and cost in addition to image quality and quantification.

Results

Photoacoustics, capitalizing on endogenous contrast or administered contrast agents that are approved for human use, yields highly informative images in clinical settings, which can support diagnosis and interventions in the future.

Conclusion

PAI offers unique image contrast that has been demonstrated in a broad set of clinical scenarios. The transition of PAI from a "nice-to-have" to a "need-to-have" modality will require dedicated clinical studies that evaluate therapeutic decision-making based on PAI and consideration of the actual value for patients and clinicians, compared with the associated cost.

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging

Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has the potential to become a routine clinical modality bringing the molecular sensitivity of optics to medical US imaging. For applications where the full capabilities of clinical US scanners must be maintained in PAUS, conventional limited view and bandwidth transducers must be used. This approach, however, cannot provide high-quality maps of PA sources, especially vascular structures. Deep learning (DL) using data-driven modeling with minimal human design has been very effective in medical imaging, medical data analysis, and disease diagnosis, and has the potential to overcome many of the technical limitations of current PAUS imaging systems. The primary purpose of this article is to summarize the background and current status of DL applications in PAUS imaging. It also looks beyond current approaches to identify remaining challenges and opportunities for robust translation of PAUS technologies to the clinic.

Molecular imaging of tumour-associated pathological biomarkers with smart nanoprobe: From "Seeing" to "Measuring"

Although the extraordinary progress has been made in molecular biology, the prevention of cancer remains arduous. Most solid tumours exhibit both spatial and temporal heterogeneity, which is difficult to be mimicked in vitro. Additionally, the complex biochemical and immune features of tumour microenvironment significantly affect the tumour development. Molecular imaging aims at the exploitation of tumour-associated molecules as specific targets of customized molecular probe, thereby generating image contrast of tumour markers, and offering opportunities to non-invasively evaluate the pathological characteristics of tumours in vivo. Particularly, there are no "standard markers" as control in clinical imaging diagnosis of individuals, so the tumour pathological characteristics-responsive nanoprobe-based quantitative molecular imaging, which is able to visualize and determine the accurate content values of heterogeneous distribution of pathological molecules in solid tumours, can provide criteria for cancer diagnosis. In this context, a variety of "smart" quantitative molecular imaging nanoprobes have been designed, in order to provide feasible approaches to quantitatively visualize the tumour-associated pathological molecules in vivo. This review summarizes the recent achievements in the designs of these nanoprobes, and highlights the state-of-the-art technologies in quantitative imaging of tumour-associated pathological molecules.

Characterization of adnexal lesions using photoacoustic imaging to improve sonographic O-RADS risk assessment

OBJECTIVE

To assess the impact of photoacoustic imaging (PAI) on the assessment of ovarian/adnexal lesion(s) of different risk categories using the sonographic ovarian-adnexal imaging-reporting-data system (O-RADS) in women undergoing planned oophorectomy.

METHOD

This prospective study enrolled women with ovarian/adnexal lesion(s) suggestive of malignancy referred for oophorectomy. Participants underwent clinical ultrasound (US) examination followed by coregistered US and PAI prior to oophorectomy. Each ovarian/adnexal lesion was graded by two radiologists using the US O-RADS scale. PAI was used to compute relative total hemoglobin concentration (rHbT) and blood oxygenation saturation (%sO2 ) colormaps in the region of interest. Lesions were categorized by histopathology into malignant ovarian/adnexal lesion, malignant Fallopian tube only and several benign categories, in order to assess the impact of incorporating PAI in the assessment of risk of malignancy with O-RADS. Malignant and benign histologic groups were compared with respect to rHbT and %sO2 and logistic regression models were developed based on tumor marker CA125 alone, US-based O-RADS alone, PAI-based rHbT with %sO2 , and the combination of CA125, O-RADS, rHbT and %sO2. Areas under the receiver-operating-characteristics curve (AUC) were used to compare the diagnostic performance of the models.

RESULTS

There were 93 lesions identified on imaging among 68 women (mean age, 52 (range, 21-79) years). Surgical pathology revealed 14 patients with malignant ovarian/adnexal lesion, two with malignant Fallopian tube only and 52 with benign findings. rHbT was significantly higher in malignant compared with benign lesions. %sO2 was lower in malignant lesions, but the difference was not statistically significant for all benign categories. Feature analysis revealed that rHbT, CA125, O-RADS and %sO2 were the most important predictors of malignancy. Logistic regression models revealed an AUC of 0.789 (95% CI, 0.626-0.953) for CA125 alone, AUC of 0.857 (95% CI, 0.733-0.981) for O-RADS only, AUC of 0.883 (95% CI, 0.760-1) for CA125 and O-RADS and an AUC of 0.900 (95% CI, 0.815-0.985) for rHbT and %sO2 in the prediction of malignancy. A model utilizing all four predictors (CA125, O-RADS, rHbT and %sO2 ) achieved superior performance, with an AUC of 0.970 (95% CI, 0.932-1), sensitivity of 100% and specificity of 82%.

CONCLUSIONS

Incorporating the additional information provided by PAI-derived rHbT and %sO2 improves significantly the performance of US-based O-RADS in the diagnosis of adnexal lesions. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Multifunctional Nanoplatform for NIR-II Imaging-Guided Synergistic Oncotherapy

Tumors are a major public health issue of concern to humans, seriously threatening the safety of people's lives and property. With the increasing demand for early and accurate diagnosis and efficient treatment of tumors, noninvasive optical imaging (including fluorescence imaging and photoacoustic imaging) and tumor synergistic therapies (phototherapy synergistic with chemotherapy, phototherapy synergistic with immunotherapy, etc.) have received increasing attention. In particular, light in the near-infrared second region (NIR-II) has triggered great research interest due to its penetration depth, minimal tissue autofluorescence, and reduced tissue absorption and scattering. Nanomaterials with many advantages, such as high brightness, great photostability, tunable photophysical properties, and excellent biosafety offer unlimited possibilities and are being investigated for NIR-II tumor imaging-guided synergistic oncotherapy. In recent years, many researchers have tried various approaches to investigate nanomaterials, including gold nanomaterials, two-dimensional materials, metal sulfide oxides, polymers, carbon nanomaterials, NIR-II dyes, and other nanomaterials for tumor diagnostic and therapeutic integrated nanoplatform construction. In this paper, the application of multifunctional nanomaterials in tumor NIR-II imaging and collaborative therapy in the past three years is briefly reviewed, and the current research status is summarized and prospected, with a view to contributing to future tumor therapy.

Targeted delivery of organic small-molecule photothermal materials with engineered extracellular vesicles for imaging-guided tumor photothermal therapy

Imaging-guided photothermal therapy (PTT) for cancers recently gathered increasing focus thanks to its precise diagnosis and potent therapeutic effectiveness. Croconaine (CR) dyes demonstrate potential in expanding utility for near infrared (NIR) dyes in bio-imaging/theranostics. However, reports on CR dyes for PTT are scarce most likely due to the short of the efficacious delivery strategies to achieve specific accumulation in diseased tissues to induce PTT. Extracellular vesicles (EVs) are multifunctional nanoparticle systems that function as safe platform for disease theragnostics, which provide potential benefits in extensive biomedical applications. Here, we developed a novel delivery system for photothermal molecules based on a CR dye that exerts photothermal activity through CDH17 nanobody-engineered EVs. The formed CR@E8-EVs showed strong NIR absorption, excellent photothermal performance, good biological compatibility and superb active tumor-targeting capability. The CR@E8-EVs can not only visualize and feature the tumors through CR intrinsic property as a photoacoustic imaging (PAI) agent, but also effectively retard the tumor growth under laser irradiation to perform PTT. It is expected that the engineered EVs will become a novel delivery vehicle of small organic photothermal agents (SOPTAs) in future clinical PTT applications.

Near-Infrared Transillumination for Macroscopic Functional Imaging of Animal Bodies

The classical transillumination technique has been revitalized through recent advancements in optical technology, enhancing its applicability in the realm of biomedical research. With a new perspective on near-axis scattered light, we have harnessed near-infrared (NIR) light to visualize intricate internal light-absorbing structures within animal bodies. By leveraging the principle of differentiation, we have extended the applicability of the Beer-Lambert law even in cases of scattering-dominant media, such as animal body tissues. This approach facilitates the visualization of dynamic physiological changes occurring within animal bodies, thereby enabling noninvasive, real-time imaging of macroscopic functionality in vivo. An important challenge inherent to transillumination imaging lies in the image blur caused by pronounced light scattering within body tissues. By extracting near-axis scattered components from the predominant diffusely scattered light, we have achieved cross-sectional imaging of animal bodies. Furthermore, we have introduced software-based techniques encompassing deconvolution using the point spread function and the application of deep learning principles to counteract the scattering effect. Finally, transillumination imaging has been elevated from two-dimensional to three-dimensional imaging. The effectiveness and applicability of these proposed techniques have been validated through comprehensive simulations and experiments involving human and animal subjects. As demonstrated through these studies, transillumination imaging coupled with emerging technologies offers a promising avenue for future biomedical applications.

Comprehensive advances in the synthesis, fluorescence mechanism and multifunctional applications of red-emitting carbon nanomaterials

Red emitting fluorescent carbon nanomaterials have drawn significant scientific interest in recent years due to their high quantum yield, water-dispersibility, photostability, biocompatibility, ease of surface functionalization, low cost and eco-friendliness. The red emissive characteristics of fluorescent carbon nanomaterials generally depend on the carbon source, reaction time, synthetic approach/methodology, surface functional groups, average size, and other reaction environments, which directly or indirectly help to achieve red emission. The importance of several factors to achieve red fluorescent carbon nanomaterials is highlighted in this review. Numerous plausible theories have been explained in detail to understand the origin of red fluorescence and tunable emission in these carbon-based nanostructures. The above advantages and fluorescence in the red region make them a potential candidate for multifunctional applications in various current fields. Therefore, this review focused on the recent advances in the synthesis approach, mechanism of fluorescence, and electronic and optical properties of red-emitting fluorescent carbon nanomaterials. This review also explains the several innovative applications of red-emitting fluorescent carbon nanomaterials such as biomedicine, light-emitting devices, sensing, photocatalysis, energy, anticounterfeiting, fluorescent silk, artificial photosynthesis, etc. It is hoped that by choosing appropriate methods, the present review can inspire and guide future research on the design of red emissive fluorescent carbon nanomaterials for potential advancements in multifunctional applications.

Noise insensitive volumetric fusion method for enhanced photoacoustic microscopy

Significance

Photoacoustic imaging is an emerging imaging modality that combines the high contrast of optical imaging and the high penetration of acoustic imaging. However, the strong focusing of the laser beam in optical-resolution photoacoustic microscopy (OR-PAM) leads to a limited depth-of-field (DoF).

Aim

Here, a volumetric photoacoustic information fusion method was proposed to achieve large volumetric photoacoustic imaging at low cost.

Approach

First, the initial decision map was built through the focus detection based on the proposed three-dimensional Laplacian operator. Majority filter-based consistency verification and Gaussian filter-based map smoothing were then utilized to generate the final decision map for the construction of photoacoustic imaging with extended DoF.

Results

The performance of the proposed method was tested to show that our method can expand the limited DoF by a factor of 1.7 without the sacrifice of lateral resolution. Four sets of multi-focus vessel data at different noise levels were fused to verify the effectiveness and robustness of the proposed method.

Conclusions

The proposed method can efficiently extend the DoF of OR-PAM under different noise levels.

Deep learning assisted classification of spectral photoacoustic imaging of carotid plaques

Spectral photoacoustic imaging (sPAI) is an emerging modality that allows real-time, non-invasive, and radiation-free assessment of tissue, benefiting from their optical contrast. sPAI is ideal for morphology assessment in arterial plaques, where plaque composition provides relevant information on plaque progression and its vulnerability. However, since sPAI is affected by spectral coloring, general spectroscopy unmixing techniques cannot provide reliable identification of such complicated sample composition. In this study, we employ a convolutional neural network (CNN) for the classification of plaque composition using sPAI. For this study, nine carotid endarterectomy plaques were imaged and were then annotated and validated using multiple histological staining. Our results show that a CNN can effectively differentiate constituent regions within plaques without requiring fluence or spectra correction, with the potential to eventually support vulnerability assessment in plaques.

Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human

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

Optical Breast Imaging: A Review of Physical Principles, Technologies, and Clinical Applications

Optical imaging involves the propagation of light through tissue. Current optical breast imaging technologies, including diffuse optical spectroscopy, diffuse optical tomography, and photoacoustic imaging, capitalize on the selective absorption of light in the near-infrared spectrum by deoxygenated and oxygenated hemoglobin. They provide information on the morphological and functional characteristics of different tissues based on their varied interactions with light, including physiologic information on lesion vascular content and anatomic information on tissue vascularity. Fluorescent contrast agents, such as indocyanine green, are used to visualize specific tissues, molecules, or proteins depending on how and where the agent accumulates. In this review, we describe the physical principles, spectrum of technologies, and clinical applications of the most common optical systems currently being used or developed for breast imaging. Most notably, US co-registered photoacoustic imaging and US-guided diffuse optical tomography have demonstrated efficacy in differentiating benign from malignant breast masses, thereby improving the specificity of diagnostic imaging. Diffuse optical tomography and diffuse optical spectroscopy have shown promise in assessing treatment response to preoperative systemic therapy, and photoacoustic imaging and diffuse optical tomography may help predict tumor phenotype. Lastly, fluorescent imaging using indocyanine green dye performs comparably to radioisotope mapping of sentinel lymph nodes and appears to improve the outcomes of autologous tissue flap breast reconstruction.