A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption

Broadband plasmon waveguide resonance sensing for photoacoustic spectroscopic analysis

Sensitive detection of incident acoustic waves over a broad frequency band offers a faithful representation of photoacoustic pressure transients of biological microstructures. Here, we propose a plasmon waveguide resonance sensor for responding to the photoacoustic impulses. By sequentially depositing Au, MgF2, and SiO2 films on a coverslip, a composite waveguide layer produces a tightly confined optical evanescent field at the SiO2-water interface with extremely strong electric field intensity, enabling the retrieval of photoacoustic signals with an estimated noise-equivalent-pressure (NEP) sensitivity of ∼92 Pa and a -6-dB bandwidth of ∼208 MHz. An ultraviolet spectroscopically resolved photoacoustic detection system integrating our sensor allows for label-free spectral measurements of human glioma xenografts from mice brains ex vivo, in which photoacoustic measurement at the frequency domain differentiates the glioma from a healthy tissue that agrees with standard H&E-staining histologic examinations. We expect that our sensitive broadband sensor could potentially empower photoacoustic histopathological assessments of neoplasms.

Nanoporous Submicron Gold Particles Enable Nanoparticle-Based Localization Optoacoustic Tomography (nanoLOT)

Localization optoacoustic tomography (LOT) has recently emerged as a transformative super-resolution technique breaking through the acoustic diffraction limit in deep-tissue optoacoustic (OA) imaging via individual localization and tracking of particles in the bloodstream. However, strong light absorption in red blood cells has previously restricted per-particle OA detection to relatively large microparticles, ≈5 µm in diameter. Herein, it is demonstrated that submicron-sized porous gold nanoparticles, ≈600 nm in diameter, can be individually detected for noninvasive super-resolution imaging with LOT. Ultra-high-speed bright-field microscopy revealed that these nanoparticles generate microscopic plasmonic vapor bubbles, significantly enhancing opto-acoustic energy conversion through a nano-to-micro size transformation. Comprehensive in vitro and in vivo tests further demonstrated the biocompatibility and biosafety of the particles. By reducing the detectable particle size by an order of magnitude, nanoLOT enables microangiographic imaging with a significantly reduced risk of embolisms from particle aggregation and opens new avenues to visualize how nanoparticles reach vascular and potentially extravascular targets. The performance of nanoLOT for non-invasive imaging of microvascular networks in the murine brain anticipates new insights into neurovascular coupling mechanisms and longitudinal microcirculatory changes associated with neurodegenerative 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.

Ex vivo human teeth imaging with various photoacoustic imaging systems

Dental caries cause pain and if not diagnosed, it may lead to the loss of teeth in extreme cases. Dental X-ray imaging is the gold standard for caries detection; however, it cannot detect hidden caries. In addition, the ionizing nature of X-ray radiation is another concern. Hence, other alternate imaging modalities like photoacoustic (PA) imaging are being explored for dental imaging. Here, we demonstrate the feasibility of acoustic resolution photoacoustic microscopy (ARPAM) to image a tooth with metal filling, circular photoacoustic computed tomography (cPACT) to acquire images of teeth with caries and pigmentation, and linear array-based photoacoustic imaging (lPACT) of teeth with caries and pigmentation. The cavity measured with lPACT imaging is compared with the X-ray computed tomography image. The metal filling and its boundaries are clearly seen in the ARPAM image. cPACT images at 1064 nm were a better representative of the tooth surface compared to the images acquired at 532 nm. It was possible to detect the cavities present in the dentine when lPACT imaging was used. The PA signal from the pigmented caries on the lateral surface (occlusion view) of the tooth was high when imaged using the lPACT system.

Deep learning combined with attention mechanisms to assist radiologists in enhancing breast cancer diagnosis: a study on photoacoustic imaging

Accurate prediction of breast cancer (BC) is essential for effective treatment planning and improving patient outcomes. This study proposes a novel deep learning (DL) approach using photoacoustic (PA) imaging to enhance BC prediction accuracy. We enrolled 334 patients with breast lesions from Shenzhen People's Hospital between January 2022 and January 2024. Our method employs a ResNet50-based model combined with attention mechanisms to analyze photoacoustic ultrasound (PA-US) images. Experiments demonstrated that the PAUS-ResAM50 model achieved superior performance, with an AUC of 0.917 (95% CI: 0.884 -0.951), sensitivity of 0.750, accuracy of 0.854, and specificity of 0.920 in the training set. In the testing set, the model maintained high performance with an AUC of 0.870 (95% CI: 0.778-0.962), sensitivity of 0.786, specificity of 0.872, and accuracy of 0.836. Our model significantly outperformed other models, including PAUS-ResNet50, BMUS-ResAM50, and BMUS-ResNet50, as validated by the DeLong test (p < 0.05 for all comparisons). Additionally, the PAUS-ResAM50 model improved radiologists' diagnostic specificity without reducing sensitivity, highlighting its potential for clinical application. In conclusion, the PAUS-ResAM50 model demonstrates substantial promise for optimizing BC diagnosis and aiding radiologists in early detection of BC.

Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning

Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels. To address this, an unsupervised image-to-image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient-derived breast cancer xenograft models and 3D clinical angiograms, VAN-GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN-GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high-quality blood vessel segmentation (F1 score: VAN-GAN vs. U-Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure and function.

Teachability of multispectral optoacoustic tomography

To date, the appropriate training required for the reproducible operation of multispectral optoacoustic tomography (MSOT) is poorly discussed. Therefore, the aim of this study was to assess the teachability of MSOT imaging. Five operators (two experienced and three inexperienced) performed repositioning imaging experiments. The inexperienced received the following introductions: personal supervision, video meeting, or printed introduction. The task was to image the exact same position on the calf muscle for seven times on five volunteers in two rounds of investigations. In the first session, operators used ultrasound guidance during measurements while using only photoacoustic data in the second session. The performance comparison was carried out with full-reference image quality measures to quantitatively assess the difference between repeated scans. The study demonstrates that given a personal supervision and hybrid ultrasound real-time imaging in MSOT measurements, inexperienced operators are able to achieve the same level as experienced operators in terms of repositioning accuracy.

Semiconducting polymer dots for multifunctional integrated nanomedicine carriers

The expansion applications of semiconducting polymer dots (Pdots) among optical nanomaterial field have long posed a challenge for researchers, promoting their intelligent application in multifunctional nano-imaging systems and integrated nanomedicine carriers for diagnosis and treatment. Despite notable progress, several inadequacies still persist in the field of Pdots, including the development of simplified near-infrared (NIR) optical nanoprobes, elucidation of their inherent biological behavior, and integration of information processing and nanotechnology into biomedical applications. This review aims to comprehensively elucidate the current status of Pdots as a classical nanophotonic material by discussing its advantages and limitations in terms of biocompatibility, adaptability to microenvironments in vivo, etc. Multifunctional integration and surface chemistry play crucial roles in realizing the intelligent application of Pdots. Information visualization based on their optical and physicochemical properties is pivotal for achieving detection, sensing, and labeling probes. Therefore, we have refined the underlying mechanisms and constructed multiple comprehensive original mechanism summaries to establish a benchmark. Additionally, we have explored the cross-linking interactions between Pdots and nanomedicine, potential yet complete biological metabolic pathways, future research directions, and innovative solutions for integrating diagnosis and treatment strategies. This review presents the possible expectations and valuable insights for advancing Pdots, specifically from chemical, medical, and photophysical practitioners' standpoints.

Multispectral Imaging-Based System for Detecting Tissue Oxygen Saturation With Wound Segmentation for Monitoring Wound Healing

OBJECTIVE

Blood circulation is an important indicator of wound healing. In this study, a tissue oxygen saturation detecting (TOSD) system that is based on multispectral imaging (MSI) is proposed to quantify the degree of tissue oxygen saturation (StO2) in cutaneous tissues.

METHODS

A wound segmentation algorithm is used to segment automatically wound and skin areas, eliminating the need for manual labeling and applying adaptive tissue optics. Animal experiments were conducted on six mice in which they were observed seven times, once every two days. The TOSD system illuminated cutaneous tissues with two wavelengths of light - red ([Formula: see text] nm) and near-infrared ([Formula: see text] nm), and StO2 levels were calculated using images that were captured using a monochrome camera. The wound segmentation algorithm using ResNet34-based U-Net was integrated with computer vision techniques to improve its performance.

RESULTS

Animal experiments revealed that the wound segmentation algorithm achieved a Dice score of 93.49%. The StO2 levels that were determined using the TOSD system varied significantly among the phases of wound healing. Changes in StO2 levels were detected before laser speckle contrast imaging (LSCI) detected changes in blood flux. Moreover, statistical features that were extracted from the TOSD system and LSCI were utilized in principal component analysis (PCA) to visualize different wound healing phases. The average silhouette coefficients of the TOSD system with segmentation (ResNet34-based U-Net) and LSCI were 0.2890 and 0.0194, respectively.

CONCLUSION

By detecting the StO2 levels of cutaneous tissues using the TOSD system with segmentation, the phases of wound healing were accurately distinguished. This method can support medical personnel in conducting precise wound assessments. Clinical and Translational Impact Statement-This study supports efforts in monitoring StO2 levels, wound segmentation, and wound healing phase classification to improve the efficiency and accuracy of preclinical research in the field.

Metabolic and molecular imaging in inflammatory arthritis

It is known that metabolic shifts and tissue remodelling precede the development of visible inflammation and structural organ damage in inflammatory rheumatic diseases such as the inflammatory arthritides. As such, visualising and measuring metabolic tissue activity could be useful to identify biomarkers of disease activity already in a very early phase. Recent advances in imaging have led to the development of so-called 'metabolic imaging' tools that can detect these changes in metabolism in an increasingly accurate manner and non-invasively.Nuclear imaging techniques such as 18F-D-glucose and fibroblast activation protein inhibitor-labelled positron emission tomography are increasingly used and have yielded impressing results in the visualisation (including whole-body staging) of inflammatory changes in both early and established arthritis. Furthermore, optical imaging-based bedside techniques such as multispectral optoacoustic tomography and fluorescence optical imaging are advancing our understanding of arthritis by identifying intra-articular metabolic changes that correlate with the onset of inflammation with high precision and without the need of ionising radiation.Metabolic imaging holds great potential for improving the management of patients with inflammatory arthritis by contributing to early disease interception and improving diagnostic accuracy, thereby paving the way for a more personalised approach to therapy strategies including preventive strategies. In this narrative review, we discuss state-of-the-art metabolic imaging methods used in the assessment of arthritis and inflammation, and we advocate for more extensive research endeavours to elucidate their full field of application in rheumatology.

Multispectral optoacoustic tomography enables assessment of disease activity in paediatric inflammatory bowel disease

Multispectral optoacoustic tomography (MSOT) allows non-invasive molecular disease activity assessment in adults with inflammatory bowel disease (IBD). In this prospective pilot-study, we investigated, whether increased levels of MSOT haemoglobin parameters corresponded to inflammatory activity in paediatric IBD patients, too. 23 children with suspected IBD underwent MSOT of the terminal ileum and sigmoid colon with standard validation (e.g. endoscopy). In Crohn`s disease (CD) and ulcerative colitis (UC) patients with endoscopically confirmed disease activity, MSOT total haemoglobin (HbT) signals were increased in the terminal ileum of CD (72.1 ± 13.0 a.u. vs. 32.9 ± 15.4 a.u., p = 0.0049) and in the sigmoid colon of UC patients (62.9 ± 13.8 a.u. vs. 35.1 ± 16.3 a.u., p = 0.0311) as compared to controls, respectively. Furthermore, MSOT haemoglobin parameters correlated well with standard disease activity assessment (e.g. SES-CD and MSOT HbT (rs =0.69, p = 0.0075). Summarizing, MSOT is a novel technology for non-invasive molecular disease activity assessment in paediatric patients with inflammatory bowel disease.

Hybrid ultrasound and single wavelength optoacoustic imaging reveals muscle degeneration in peripheral artery disease

Peripheral arterial disease (PAD) leads to chronic vascular occlusion and results in end organ damage in critically perfused limbs. There are currently no clinical methods available to determine the muscular damage induced by chronic mal-perfusion. This monocentric prospective cross-sectional study investigated n = 193 adults, healthy to severe PAD, in order to quantify the degree of calf muscle degeneration caused by PAD using a non-invasive hybrid ultrasound and single wavelength optoacoustic imaging (US/SWL-OAI) approach. While US provides morphologic information, SWL-OAI visualizes the absorption of pulsed laser light and the resulting sound waves from molecules undergoing thermoelastic expansion. US/SWL-OAI was compared to multispectral data, clinical disease severity, angiographic findings, phantom experiments, and histological examinations from calf muscle biopsies. We were able to show that synergistic use of US/SWL-OAI is most likely to map clinical degeneration of the muscle and progressive PAD.

Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation

Significance

Photoacoustic (PA) imaging is an imaging modality that integrates anatomical, functional, metabolic, and histologic insights. It has been a hot topic of medical research and draws extensive attention.

Aim

This review aims to explore the applications of PA clinical imaging in human diseases, highlighting recent advancements.

Approach

A systemic survey of the literature concerning the clinical utility of PA imaging was conducted, with a particular focus on its application in tumors, autoimmune diseases, inflammatory conditions, and endocrine disorders.

Results

PA imaging is emerging as a valuable tool for human disease investigation. Information provided by PA imaging can be used for diagnosis, grading, and prognosis in multiple types of tumors including breast tumors, ovarian neoplasms, thyroid nodules, and cutaneous malignancies. PA imaging facilitates the monitoring of disease activity in autoimmune and inflammatory diseases such as rheumatoid arthritis, systemic sclerosis, arteritis, and inflammatory bowel disease by capturing dynamic functional alterations. Furthermore, its unique capability of visualizing vascular structure and oxygenation levels aids in assessing diabetes mellitus comorbidities and thyroid function.

Conclusions

Despite extant challenges, PA imaging offers a promising noninvasive tool for precision disease diagnosis, long-term evaluation, and prognosis anticipation, making it a potentially significant imaging modality for clinical practice.

Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.