Multispectral Photoacoustic Imaging Research Articles

Multispectral photoacoustic fluctuation imaging for full visibility SO2 imaging

Photoacoustic (PA) imaging (PAI) images blood vessels through the hemoglobin contrast. However, conventional images are affected by visibility artefacts which prevents seeing all the blood vessels morphology. We introduced PA fluctuation imaging (PAFI) exploiting the absorption fluctuations due to blood flow. Here, we demonstrate how PAFI enhances the image quality and elaborate if it can be used for quantitative multispectral (MS) imaging for 3D blood oxygenation (SO2) imaging. A spherical array (256 elements, 8 MHz, Imasonic) connected to a Verasonics Vantage scanner, was coupled to the laser (Innolas Spitlight) through a fiber bundle (Ceramoptek). The PAFI sequence consisted in acquiring 1000 frames at 100 Hz repetition rate while scanning continuously the wavelength from 680 nm to 880 nm. Image processing resulted in PAFI image per wavelength. The compensation of predictable effects of pertubations provided quantitative SO2 images with visibility and contrast enhancement. The technique was validated in glass capillaries and in the chicken embryo model by comparing the SO2 values with the one obtained in the visible structures using conventional imaging (resp. 3.5% and 9% errors). Thus, MS-PAFI can provide quantitative SO2 full-visibility 3D imaging with an intrinsic specificity to blood which simplifies spectral unmixing.

Three-Dimensional Deep-Tissue Functional and Molecular Imaging by Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT).

Non-invasive small-animal imaging technologies, such as optical imaging, magnetic resonance imaging and x -ray computed tomography, have enabled researchers to study normal biological phenomena or disease progression in their native conditions. However, existing small-animal imaging technologies often lack either the penetration capability for interrogating deep tissues (e.g., optical microscopy), or the functional and molecular sensitivity for tracking specific activities (e.g., magnetic resonance imaging). To achieve functional and molecular imaging in deep tissues, we have developed an integrated photoacoustic, ultrasound and acoustic angiographic tomography (PAUSAT) system by seamlessly combining light and ultrasound. PAUSAT can perform three imaging modes simultaneously with complementary contrast: high-frequency B-mode ultrasound imaging of tissue morphology, microbubble-enabled acoustic angiography of tissue vasculature, and multi-spectral photoacoustic imaging of molecular probes. PAUSAT can provide three-dimensional (3D) multi-contrast images that are co-registered, with high spatial resolutions at large depths. Using PAUSAT, we performed proof-of-concept in vivo experiments on various small animal models: monitoring longitudinal development of placenta and embryo during mouse pregnancy, tracking biodistribution and metabolism of near-infrared organic dye on the whole-body scale, and detecting breast tumor expressing genetically-encoded photoswitchable phytochromes. These results have collectively demonstrated that PAUSAT has broad applicability in biomedical research, providing comprehensive structural, functional, and molecular imaging of small animal models.

Mapping the architecture of the temporal artery with photoacoustic imaging for diagnosing giant cell arteritis

Photoacoustic (PA) imaging is rapidly emerging as a promising clinical diagnostic tool. One of the main applications of PA imaging is to image vascular networks in humans. This relies on the signal obtained from oxygenated and deoxygenated hemoglobin, which limits imaging of the vessel wall itself. Giant cell arteritis (GCA) is a treatable, but potentially sight- and life-threatening disease, in which the artery wall is infiltrated by leukocytes. Early intervention can prevent complications making prompt diagnosis of importance. Temporal artery biopsy is the gold standard for diagnosing GCA. We present an approach to imaging the temporal artery using multispectral PA imaging. Employing minimally supervised spectral analysis, we produce histology-like images where the artery wall is clearly discernible from the lumen and further differentiate between PA spectra from biopsies diagnosed as GCA- and GCA+ in 77 patients.

Spectral crosstalk in photoacoustic computed tomography

Multispectral photoacoustic (PA) imaging faces two major challenges: the spectral coloring effect, which has been studied extensively as an optical inversion problem, and the spectral crosstalk, which is basically a result of non-ideal acoustic inversion. So far, there is no systematic work to analyze the spectral crosstalk because acoustic inversion and spectroscopic measurement are always treated as decoupled. In this work, we theorize and demonstrate through a series of simulations and experiments how imperfect acoustic inversion induces inaccurate PA spectrum measurement. We provide detailed analysis to elucidate how different factors, including limited bandwidth, limited view, light attenuation, out-of-plane signal, and image reconstruction schemes, conspire to render the measured PA spectrum inaccurate. We found that the model-based reconstruction outperforms universal back-projection in suppressing the spectral crosstalk in some cases.

In vivo three-dimensional multispectral photoacoustic imaging of dual enzyme-driven cyclic cascade reaction for tumor catalytic therapy

Non-invasive visualization of dynamic molecular events in real-time via molecular imaging may enable the monitoring of cascade catalytic reactions in living systems, however effective imaging modalities and a robust catalytic reaction system are lacking. Here we utilize three-dimensional (3D) multispectral photoacoustic (PA) molecular imaging to monitor in vivo cascade catalytic therapy based on a dual enzyme-driven cyclic reaction platform. The system consists of a two-dimensional (2D) Pd-based nanozyme conjugated with glucose oxidase (GOx). The combination of nanozyme and GOx can induce the PA signal variation of endogenous molecules. Combined with the PA response of the nanozyme, we can simultaneously map the 3D PA signals of dynamic endogenous and exogenous molecules associated with the catalytic process, thus providing a real-time non-invasive visualization. We can also treat tumors under the navigation of the PA imaging. Therefore, our study demonstrates the imaging-guided potential of 3D multispectral PA imaging in feedback-looped cascade catalytic therapy.

Semantic segmentation of multispectral photoacoustic images using deep learning

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen

The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional devices that are critical for biomedical activities. Multispectral photoacoustic imaging that integrates the ideas of optical and ultrasonic technologies is one of the most essential instruments. At the same time, early cancer identification is becoming increasingly important in order to minimize fatality. Deep learning (DL) techniques have recently advanced to the point where they can be used to diagnose and classify cancer using biological images. This paper describes a hybrid optimization method that combines in-depth transfer learning-based cancer detection with multispectral photoacoustic imaging. The goal of the PS-ACO-RNN approach is to use ultrasound images to detect and classify the presence of cancer. Bilateral filtration (BF) is often used as a noise removal approach in image processing. In addition, lightweight LEDNet models are used to separate the biological images. A feature extractor with particle swarm with ant colony optimization (PS-ACO) paradigm can also be used. Finally, biological images assign appropriate class labels using a recurrent neural network (RNN) model. The effectiveness of the PS-ACO-RNN technique is verified using a benchmark database, and test results show that the PS-ACO-RNN approach works better than current approaches.

Chromatic-aberration-free multispectral optical-resolution photoacoustic microscopy using reflective optics and a supercontinuum light source.

A supercontinuum (SC) light source enables multispectral photoacoustic imaging at excitation wavelengths in the visible-to-near-infrared range. However, for such a broad optical wavelength range, chromatic aberration is non-negligible. We developed a multispectral optical-resolution photoacoustic microscopy (MS-OR-PAM) setup with a nanosecond pulsed SC light source and a reflective objective lens to avoid chromatic aberration. Chromatic aberrations generated by reflective and conventional objective lenses were compared, and the images acquired using the reflective objective were not affected by chromatic aberration. Hence, MS-OR-PAM with the reflective objective was used to distinguish red blood cells from melanoma cells via spectral subtraction processing.

A scalable open-source MATLAB toolbox for reconstruction and analysis of multispectral optoacoustic tomography data

Multispectral photoacoustic tomography enables the resolution of spectral components of a tissue or sample at high spatiotemporal resolution. With the availability of commercial instruments, the acquisition of data using this modality has become consistent and standardized. However, the analysis of such data is often hampered by opaque processing algorithms, which are challenging to verify and validate from a user perspective. Furthermore, such tools are inflexible, often locking users into a restricted set of processing motifs, which may not be able to accommodate the demands of diverse experiments. To address these needs, we have developed a Reconstruction, Analysis, and Filtering Toolbox to support the analysis of photoacoustic imaging data. The toolbox includes several algorithms to improve the overall quantification of photoacoustic imaging, including non-negative constraints and multispectral filters. We demonstrate various use cases, including dynamic imaging challenges and quantification of drug effect, and describe the ability of the toolbox to be parallelized on a high performance computing cluster.

Multiparametric Photoacoustic Analysis of Human Thyroid Cancers In Vivo.

Thyroid cancer is one of the most common cancers, with a global increase in incidence rate for both genders. Ultrasound-guided fine-needle aspiration is the current gold standard to diagnose thyroid cancers, but the results are inaccurate, leading to repeated biopsies and unnecessary surgeries. To reduce the number of unnecessary biopsies, we explored the use of multiparametric photoacoustic (PA) analysis in combination with the American Thyroid Association (ATA) Guideline (ATAP). In this study, we performed in vivo multispectral PA imaging on thyroid nodules from 52 patients, comprising 23 papillary thyroid cancer (PTC) and 29 benign cases. From the multispectral PA data, we calculated hemoglobin oxygen saturation level in the nodule area, then classified the PTC and benign nodules with multiparametric analysis. Statistical analyses showed that this multiparametric analysis of multispectral PA responses could classify PTC nodules. Combining the photoacoustically indicated probability of PTC and the ATAP led to a new scoring method that achieved a sensitivity of 83% and a specificity of 93%. This study is the first multiparametric analysis of multispectral PA data of thyroid nodules with statistical significance. As a proof of concept, the results show that the proposed new ATAP scoring can help physicians examine thyroid nodules for fine-needle aspiration biopsy, thus reducing unnecessary biopsies. SIGNIFICANCE: This report highlights a novel photoacoustic scoring method for risk stratification of thyroid nodules, where malignancy of the nodules can be diagnosed with 83% sensitivity and 93% specificity.

In Vitro and In Vivo Multispectral Photoacoustic Imaging for the Evaluation of Chromophore Concentration.

Multispectral photoacoustic imaging is a powerful noninvasive medical imaging technique that provides access to functional information. In this study, a set of methods is proposed and validated, with experimental multispectral photoacoustic images used to estimate the concentration of chromophores. The unmixing techniques used in this paper consist of two steps: (1) automatic extraction of the reference spectrum of each pure chromophore; and (2) abundance calculation of each pure chromophore from the estimated reference spectra. The compared strategies bring positivity and sum-to-one constraints, from the hyperspectral remote sensing field to multispectral photoacoustic, to evaluate chromophore concentration. Particularly, the study extracts the endmembers and compares the algorithms from the hyperspectral remote sensing domain and a dedicated algorithm for segmentation of multispectral photoacoustic data to this end. First, these strategies are tested with dilution and mixing of chromophores on colored 4% agar phantom data. Then, some preliminary in vivo experiments are performed. These consist of estimations of the oxygen saturation rate (sO) in mouse tumors. This article proposes then a proof-of-concept of the interest to bring hyperspectral remote sensing algorithms to multispectral photoacoustic imaging for the estimation of chromophore concentration.

Learning Optical Scattering Through Symmetrical Orthogonality Enforced Independent Components for Unmixing Deep Tissue Photoacoustic Signals

Noninvasive mapping of chromophore distribution in deep tissue is highly desired in numerous biomedical applications. Multispectral photoacoustic (PA) imaging fused with linear spectral unmixing is employed to generate 2-D and 3-D maps of tissue chromophores. However, wavelength and depth dependent attenuation of optical fluence leads to nonuniform variations in the spectral behavior of biomolecules affecting the accuracy of conventional linear unmixing methods. To address this, a modified independent component analysis (ICA) model is proposed to construct adaptive and statistically independent components for each chromophore from the given spectral PA data. The model was trained with custom designed multispectral PA imaging simulations consisting of both endogenous (oxy- and deoxy-hemoglobin) and exogenous (indocyanine green - ICG) chromophores inside 30 mm deep tissue. End-to-end unsupervised nature of the proposed approach made the process independent of human labeling and outperformed the standard linear spectral unmixing method when tested on a set of simulated phantoms, experimental phantoms, and in vivo mouse imaging data.

ASSESSMENT OF PLAQUE VULNERABILITY USING A NOVEL TECHNIQUE: MULTI-SPECTRAL PHOTOACOUSTIC IMAGING (CVENT-PAI)

Objective: Identification of intraplaque haemorrhage using magnetic resonance imaging (MRI) is expensive, technically demanding and sometimes uncomfortable. Photoacoustic imaging (PAI) is a new non-invasive technique combining multi-wavelength infrared laser light and ultrasound (US) imaging, able to discriminate blood and other components in the tissues. The measurement by the multi-spectral PAI is likely to be more applicable and therefore could be complementary to MRI. Validate a portable multimodal and multi-wavelength PAI system, for the identification of intraplaque haemorrhage and compare with MRI. Design and method: Patients underwent carotid MRI, and US. Photoacoustic (PAI) acquisitions are performed and analysed posteriori blindly of the status of the subjects and results of MRI/US. All patients gave written informed consent. Histology of plaque will be performed as gold standard. Results: We included 22 patients (71 ± 5 years old) with carotid stenosis grade of >70% (NASCET). PAI imaging was feasible in all patients. On raw images, only clutter signal can be observed. After adequate post processing, carotid wall with the intima media and plaques were clearly visible with PAI. PAI signal from the lumen can be observed, also strong PAI signals are visible inside the plaque in a reproducible manner. Correspondence between MRI and PAI will be analysed later. Conclusions: The use of the PAI technique for carotid plaque assessment is feasible, and PAI signal are observed inside the plaque. Further analyses are needed to assess the reflected PA signal inside the plaque, for haemorrhage detection in the future.

Learned spectral decoloring enables photoacoustic oximetry

The ability of photoacoustic imaging to measure functional tissue properties, such as blood oxygenation sO_2, enables a wide variety of possible applications. sO_2 can be computed from the ratio of oxyhemoglobin HbO_2 and deoxyhemoglobin Hb, which can be distuinguished by multispectral photoacoustic imaging due to their distinct wavelength-dependent absorption. However, current methods for estimating sO_2 yield inaccurate results in realistic settings, due to the unknown and wavelength-dependent influence of the light fluence on the signal. In this work, we propose learned spectral decoloring to enable blood oxygenation measurements to be inferred from multispectral photoacoustic imaging. The method computes sO_2 pixel-wise, directly from initial pressure spectra S_{text {p}_0}(lambda , mathbf {x}), which represent initial pressure values at a fixed spatial location mathbf {x} over all recorded wavelengths lambda. The method is compared to linear unmixing approaches, as well as pO_2 and blood gas analysis reference measurements. Experimental results suggest that the proposed method is able to obtain sO_2 estimates from multispectral photoacoustic measurements in silico, in vitro, and in vivo.

Polyacrylamide hydrogel phantoms for performance evaluation of multispectral photoacoustic imaging systems

As photoacoustic imaging (PAI) begins to mature and undergo clinical translation, there is a need for well-validated, standardized performance test methods to support device development, quality control, and regulatory evaluation. Despite recent progress, current PAI phantoms may not adequately replicate tissue light and sound transport over the full range of optical wavelengths and acoustic frequencies employed by reported PAI devices. Here we introduce polyacrylamide (PAA) hydrogel as a candidate material for fabricating stable phantoms with well-characterized optical and acoustic properties that are biologically relevant over a broad range of system design parameters. We evaluated suitability of PAA phantoms for conducting image quality assessment of three PAI systems with substantially different operating parameters including two commercial systems and a custom system. Imaging results indicated that appropriately tuned PAA phantoms are useful tools for assessing and comparing PAI system image quality. These phantoms may also facilitate future standardization of performance test methodology.

3D wide-field multispectral photoacoustic imaging of human melanomas in vivo: a pilot study.

The Breslow depth is an important parameter to determine the excision margin and prognosis of melanoma. However, it is difficult to accurately determine the actual Breslow depth before surgery using the existing ocular micrometer and biopsy technique. To evaluate the use of 3D wide-field multispectral photoacoustic imaging to non-invasively measure depth and outline the boundary of melanomas for optimal surgical margin selection. Six melanoma patients were examined in vivo using the 3D multispectral photoacoustic imaging system. For five cases of melanomas (one in situ, three nodular, and one acral lentiginous type melanoma), the spectrally unmixed photoacoustic depths were calculated and compared against histopathological depths. Spectrally unmixed photoacoustic depths and histopathological depths match well within a mean absolute error of 0.36mm. In particular, the measured minimum and maximum depths in the in situ and nodular type of melanoma were 0.6 and 9.1mm, respectively. In the 3D photoacoustic image of one metastatic melanoma, feeding vessels were visualized in the melanoma, suggesting the neovascularization around the tumour. The 3D multispectral photoacoustic imaging not only provides well-measured depth and sizes of various types of melanomas, it also visualizes the metastatic type of melanoma. Obtaining accurate depth and boundary information of melanoma before surgery would play a useful role in the complete excision of melanoma during surgery.

Multispectral Interlaced Sparse Sampling Photoacoustic Tomography.

Multispectral photoacoustic tomography (PAT) is capable of resolving tissue chromophore distribution based on spectral un-mixing. It works by identifying the absorption spectrum variations from a sequence of photoacoustic images acquired at multiple illumination wavelengths. Due to multispectral acquisition, this inevitably creates a large dataset. To cut down the data volume, sparse sampling methods that reduce the number of detectors have been developed. However, image reconstruction of sparse sampling PAT is challenging because of insufficient angular coverage. During spectral un-mixing, these inaccurate reconstructions will further amplify imaging artefacts and contaminate the results. To solve this problem, we present the interlaced sparse sampling (ISS) PAT, a method that involved: 1) a novel scanning-based image acquisition scheme in which the sparse detector array rotates while switching illumination wavelength, such that a dense angular coverage could be achieved by using only a few detectors; and 2) a corresponding image reconstruction algorithm that makes use of an anatomical prior image created from the ISS strategy to guide PAT image computation. Reconstructed from the signals acquired at different wavelengths (angles), this self-generated prior image fuses multispectral and angular information, and thus has rich anatomical features and minimum artefacts. A specialized iterative imaging model that effectively incorporates this anatomical prior image into the reconstruction process is also developed. Simulation, phantom, and in vivo animal experiments showed that even under 1/6 or 1/8 sparse sampling rate, our method achieved comparable image reconstruction and spectral un-mixing results to those obtained by conventional dense sampling method.

Contrast-Enhanced Multispectral Photoacoustic Imaging for Irregular Hepatectomy Navigation: A Pilot Study.

Irregular hepatectomy plays a prominent role in the treatment of small hepatocellular carcinoma (HCC) patients with severe cirrhosis and localized liver metastasis. In clinical practices, intraoperative tumor boundaries delineation facilitates to accomplish tumor resection with negative margin, remarkably decreasing the recurrence rates. Currently, ultrasound (US) and ICG fluorescence-guided surgery has been used for intraoperative navigation in irregular hepatectomy, but insufficient specificity results in a limited prevalence. Inspired by the high resolution of photoacoustic (PA) imaging and established clinical efficacy of 18F-Alfatide that is specific for integrin αvβ3-overexpressed tumors, we herein developed a fluorescent analogue IR820-E[c(RGDfK)]2, and a proof-of-concept intraoperative multispectral PA imaging navigation for precise irregular hepatectomy using hand-held PA/US imaging system. An integrin αvβ3-targeted fluorescent contrast agent IR820-E[c(RGDfK)]2 was designed, synthesized, and characterized. In vitro studies were performed to determine optical and PA properties, affinity and specificity and biocompatibility. Multispectral PA imaging, the optimal imaging time point and contrast, multispectral PA imaging-guided irregular hepatectomy, pharmacokinetics, and safety profile were evaluated in subcutaneous and orthotopic HCC tumor models. Ex vivo macroscopic three-dimensions (3D) PA imaging with IR820-E[c(RGDfK)]2 staining was also performed in surgical biospecimens from patients with HCC. IR820-E[c(RGDfK)]2 has a simple synthetic method at gram scale, high affinity, and specificity for integrin αvβ3, excellent pharmacokinetic and safety profile can effectively differentiate tumor from normal liver tissues in animal models and surgical biospecimens from HCC patients. Preoperative tumor localization, intraoperative tumor boundaries delineation, and tumor excision, and postoperative negative margin assessment were successfully achieved during irregular hepatectomy. This initial attempt allows one to preoperatively detect tumor lesions, intraoperatively delineate tumor boundaries and guide tumor resection, and postoperatively evaluate tumor margin status during irregular hepatectomy. IR820-E[c(RGDfK)]2 has the potential to be an investigational new drug for clinical use in multispectral photoacoustic imaging-guided irregular hepatectomy.

Autocatalytic polymerization of selenium/polypyrrole nanocomposites as functional theranostic agents for multi-spectral photoacoustic imaging and photothermal therapy of tumor

The synchronization of diagnosis and treatment is a new trend in cancer treatment. Photoacoustic imaging (PAI) and photothermal therapy (PTT) are recognized as one of the perfect combinations. The autocatalytic polymerization of selenium/polypyrrole (Se@PPy) nanocomposites with a wide-absorption band at near-infrared region (NIR, 800 nm) has been developed in this paper. The wide optical absorption characteristics enable Se@PPy nanocomposites to achieve multi-spectral PAI. Ex vivo experiments show desirable photoacoustic ability of the Se@PPy nanocomposites at wavelengths ranging from 700 nm to 900 nm, which is better than that of commercial indocyanine green (ICG). Se@PPy nanocomposites have high photothermal conversion efficiency up to 36.3% as well as excellent photo-thermal stability. In vitro cytotoxicity test demonstrates that the Se@PPy nanocomposites have good bio-safety. Furthermore, the feasibility of Se@PPy nanocomposites for enhancing multi-spectral PAI guided PTT was verified on 4T1 tumor-bearing nude mice. Our results indicate that Se@PPy nanocomposites could be used as an effective theranostic agent for near-infrared light-mediated PAI and PTT of tumor.

Evaluation of Fluence Correction Algorithms in Multispectral Photoacoustic Imaging

Multispectral photoacoustic imaging (MPAI) is a promising emerging diagnostic technology, but fluence artifacts can degrade device performance. Our goal was to develop well-validated phantom-based test methods for evaluating and comparing MPAI fluence correction algorithms, including a heuristic diffusion approximation, Monte Carlo simulations, and an algorithm we developed based on novel application of the diffusion dipole model (DDM). Phantoms simulated a range of breast-mimicking optical properties and contained channels filled with chromophore solutions (ink, hemoglobin, or copper sulfate) or connected to a previously developed blood flow circuit providing tunable oxygen saturation (SO2). The DDM algorithm achieved similar spectral recovery and SO2 measurement accuracy to Monte Carlo-based corrections with lower computational cost, potentially providing an accurate, real-time correction approach. Algorithms were sensitive to optical property uncertainty, but error was minimized by matching phantom albedo. The developed test methods may provide a foundation for standardized assessment of MPAI fluence correction algorithm performance.