SignificanceTo ensure precise tumor localization and subsequent pathological examination, a metal marker clip (MC) is placed within the tumor or lymph node prior to neoadjuvant chemotherapy for breast cancer. However, as tumors decrease in size following treatment, detecting the MC using ultrasound imaging becomes challenging in some patients. Consequently, a mammogram is often required to pinpoint the MC, resulting in additional radiation exposure, time expenditure, and increased costs. Dual-modality imaging, combining photoacoustic (PA) and ultrasound (US), offers a promising solution to this issue.AimOur objective is to localize the MC without radiation exposure using PA/US dual-modality imaging.ApproachA PA/US dual-modality imaging system was developed. Utilizing this system, both phantom and clinical experiments were conducted to demonstrate that PA/US dual-modality imaging can effectively localize the MC.ResultsThe PA/US dual-modality imaging can identify and localize the MC. In clinical trials encompassing four patients and five MCs, the recognition rate was ∼80%. Three experiments to verify the accuracy of marker position recognition were successful.ConclusionsWe effectively localized the MC in real time using PA/US dual-modality imaging. Unlike other techniques, the new method enables surgeons to pinpoint nodules both preoperatively and intraoperatively. In addition, it boasts non-radioactivity and is comparatively cost-effective.

Over the past few decades, the number of patients with neurological diseases has increased significantly, posing huge challenges and opportunities for the development of brain imaging technology. As a hybrid imaging method combining optical excitation and acoustic detection techniques, photoacoustic tomography (PAT), has experienced rapid development, due to high optical contrast and spatial resolution at depth inside tissues. With the development of lasers, ultrasonic detectors, and data computations, PAT has been widely applied for the diagnosis of oncology, dermatosis, etc. However, the energy of light and ultrasound would be greatly attenuated while penetrating the skull, due to the reflection, absorption, and scattering effects, resulting in limited application of PAT in brain imaging. In this review, we summarized the achievements of PAT and its application in the detection of brain diseases including glioma, stroke, traumatic brain injury, Alzheimer's disease, epilepsy, and Parkinson's disease. Moreover, various PAT systems and multi‐modality photoacoustic imaging are introduced for potential clinical applications. Finally, the challenges and current limitations of PAT for further brain imaging are also discussed.

In many surgical procedures, the objective is to restore tissue oxygenation and achieve effective revascularization. However, methods for evaluating tissue perfusion and oxygen metabolism are limited. Current clinical approaches mostly assess the patient's overall oxygen saturation level and lack a non-invasive, real-time method to evaluate local hypoxia during surgery. In this study, New Zealand white rabbits were first anesthetized and endotracheally intubated. The experiment began with oxygen supply being suspended for five minutes to establish a rabbit model of hypoxia. Subsequently, synchronous blood gas analysis and photoacoustic imaging tests were conducted on the carotid and femoral arteries of the rabbits. Blood gas analysis showed that the carotid arterial oxygen saturation of three rabbits was 100% before oxygen deprivation; after a five-minute cessation of oxygen, the carotid arterial oxygen saturation decreased to 3.6% +/- 1.5%, with a rapid initial decline followed by a slower rate. Photoacoustic imaging results indicated that the oxygen saturation in the femoral arteries of the three rabbits dropped sharply from 100% to below 10% at the moment of oxygen deprivation, while muscle oxygen saturation fell from around 90% to below 30%. The trend of change was consistent with arterial oxygen saturation, but with a slightly smaller amplitude. This study validates that photoacoustic imaging technology can accurately reflect changes in vascular and tissue oxygen saturation within the body during hypoxic conditions, and due to its capability for localized detection and non-invasive real-time monitoring, it holds promise for future use in assessing anastomotic patency in revascularization surgeries.

Foundation models (FMs) have rapidly evolved and have achieved significant accomplishments in computer vision tasks. Specifically, the prompt mechanism conveniently allows users to integrate image prior information into the model, making it possible to apply models without any training. Therefore, we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic (PA) image processing. We employed the Segment Anything Model (SAM) by setting simple prompts and integrating the model’s outputs with prior knowledge of the imaged objects to accomplish various tasks, including: (1) removing the skin signal in three-dimensional PA image rendering; (2) dual speed-of-sound reconstruction, and (3) segmentation of finger blood vessels. Through these demonstrations, we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training. This potentially allows for a hands-on, convenient approach to achieving efficient and accurate segmentation of PA images. This paper serves as a comprehensive tutorial, facilitating the mastery of the technique through the provision of code and sample datasets.

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

This paper presents a robotic arm assisted photoacoustic tomography imager that employs a direct visual servoing-enabled repositioning strategy designed for reproducible surveillance of peripheral arterial disease (PAD) progression. The system ensures precise repositioning of the cross-sectional location of the vessel of interest, allowing consistent measurements to track PAD progression and response to treatment. This strategy overcomes the difficulty of obtaining cross-sectional images of the same vessel at different monitoring intervals due to operator variability. In addition, the acquisition of high-resolution, high-contrast vessel images helps to clearly delineate long-term changes in the vasculature. To evaluate the effectiveness of the proposed experimental configurations and algorithms, we performed two sets of vascular phantom experiments and one set of servo-imaging experiments on human lower extremity vessels. The experimental results show 100% similarity for all three sets of grid segmentation comparisons, while the pixel-wise comparison similarity is 99.3% for the first set, 97.4% for the second set, and 98.9% for the in vivo experimental set. These results are important for monitoring the progression of PAD and predicting the risk of cardiovascular disease.

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

Assessing the metastatic status of axillary lymph nodes is a common clinical practice in the staging of early breast cancers. Yet sentinel lymph nodes (SLNs) are the regional lymph nodes believed to be the first stop along the lymphatic drainage path of the metastasizing cancer cells. Compared to axillary lymph node dissection, sentinel lymph node biopsy (SLNB) helps reduce morbidity and side effects. Current SLNB methods, however, still have suboptimum properties, such as restrictions due to nuclide accessibility and a relatively low therapeutic efficacy when only a single contrast agent is used. To overcome these limitations, researchers have been motivated to develop a non-radioactive SLN mapping method to replace or supplement radionuclide mapping. We proposed and demonstrated a clinical procedure using a dual-modality photoacoustic (PA)/ultrasound (US) imaging system to locate the SLNs to offer surgical guidance. In our work, the high contrast of PA imaging and its specificity to SLNs were based on the accumulation of carbon nanoparticles (CNPs) in the SLNs. A machine-learning model was also trained and validated to distinguish stained SLNs based on single-wavelength PA images. In the pilot study, we imaged 11 patients in vivo, and the specimens from 13 patients were studied ex vivo. PA/US imaging identified stained SLNs in vivo without a single false positive (23 SLNs), yielding 100% specificity and 52.6% sensitivity based on the current PA imaging system. Our machine-learning model can automatically detect SLNs in real time. In the new procedure, single-wavelength PA/US imaging uses CNPs as the contrast agent. The new system can, with that contrast agent, noninvasively image SLNs with high specificity in real time based on the unique features of the SLNs in the PA images. Ultimately, we aim to use our systems and approach to substitute or supplement nuclide tracers for a non-radioactive, less invasive SLN mapping method in SLNB for the axillary staging of breast cancer.

Photoacoustic computed tomography (PACT), also known as optoacoustic or thermoacoustic tomography, is a rapidly emerging hybrid imaging modality that combines optical image contrast with ultrasound detection. Most currently available PACT image reconstruction algorithms are based on idealized imaging models that assume a lossless and acoustically homogeneous medium. However, in many applications of PACT, if the non-uniform acoustic properties of objects are not considered in the reconstruction algorithm, the reconstructed images may contain significant distortions and artifacts. This paper proposes a fully automatic double-SoS (Speed-of-sound) reconstruction algorithm which runs at 10Hzfortwo-dimensional images of (512)(512) pixels. The algorithm uses a deep learning model to segment the animal’s outer profile from the water background. We adaptively calculate the most appropriate sound speed and assign different sound speeds to the two regions for PA image reconstruction. The reconstructed images were compared to those reconstructed using a uniform SoS quantitatively.

Photoacoustic imaging (PAI, also called optoacoustic imaging) combines light excitation and ultrasound detection for deep-tissue imaging with light absorption contrast. PAI can map the distribution of endogenous chromophores such as oxy-hemoglobin, deoxy-hemoglobin, lipids, water, and melanin. PAI performs especially well for structural and functional imaging of blood vessels. Its centimeter-deep imaging depth and ultrasound-defined resolution make it well suited for clinical application, where systems employing a linear array ultrasound probe are most commonly used due to simplicity, flexibility, and easy integration with standard ultrasonic imaging. The existing linear array based imaging systems typically employ optical fiber bundles for light delivery, such a scheme enjoys mechanical flexibility, optical stability, and simple light coupling. However, major drawbacks associated with fiber illumination include suboptimal transmission efficiency and a lack of control of the illumination pattern. Articulated arms provide an alternative light delivery option which potentially offer high transmission efficiency, stable and flexible operation, and low cost. Despite its wide applications in cosmetology, articulated arms for light delivery were understudied in the PAI community. In this paper, we reported the fabrication and experimental evaluation of an articulated arm specifically designed for linear-array-based PAI. Without losing the flexibility provided by the linear probe. Moreover, the articulated arm can be equipped with spatial positioning devices to perform three-dimensional reconstructions.

Photoacoustic (PA) imaging provides morphological and functional information about angiogenesis and thus is potentially suitable for breast cancer diagnosis. However, the development of PA breast imaging has been hindered by inadequate patients and a lack of ground truth images. Here, we report a digital breast phantom with realistic acoustic and optical properties, with which a digital PA-ultrasound imaging pipeline is developed to create a diverse pool of virtual patients with three types of masses: ductal carcinoma in situ, invasive breast cancer, and fibroadenoma. The experimental results demonstrate that our model is realistic, flexible, and can be potentially useful for accelerating the development of PA breast imaging technology.

Photoacoustic imaging (PAI) is a biomedical imaging modality that can provide structural, functional, and molecular information. In PAI, laser pulses illuminate the tissue, and transient light absorption leads to instant thermal expansion and succeeding ultrasound emission. Since oxy- and deoxyhemoglobin are the major light-absorbing chromophores in biological tissue, PAI has very high contrast and is intrinsically suitable for the imaging of blood vessels. Meanwhile, superb microvascular imaging (SMI) is an emerging ultrasound imaging technique for angiography. In comparison to traditional color Doppler and power Doppler techniques which rely on the suppression of low-velocity components, SMI works by an intelligent algorithm that renders small vessels with low flow velocity visible. To date, there is no work to compare PAI and SMI in terms of vascular imaging capabilities. In this paper, we provide our recent evaluation results in imaging depths, speeds, sensitivities, and resolutions of these two modalities through phantom experiments and in-vivo studies. We used PAI and SMI to image the human forearm, and our preliminary data show that PAI is superior in imaging speeds, and sensitivities for superficial blood vessels. We acknowledge that more work needs to be done to compare the two techniques in diverse clinical applications more quantitatively, and we hope our work can pave the way for such systematic studies.

Abstract. Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding. Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities. Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI. Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis. Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

Photoacoustic imaging is an emerging optical imaging modality which provides optical absorption contrasts and high resolution in the optical diffusive regime. In photoacoustic computed tomography (PACT), often times the detection of the photoacoustic signal only covers a partial solid angle less than 4π, due to experimental or economic constraints. Incomplete spatial coverage always jeopardizes image quality and resolution, and results in significant artifacts and missing of image features. This problem is referred to as “limited view” and has remained unsolved for decades. In this work, we present a new machine-learning-based method that is specifically designed to compensate for the missing information due to limited view. The robustness and effectiveness of our method were demonstrated using numerical, phantom, and in vivo experiments.