Towards Quantitative Evaluation of Tissue Absorption Coefficients Using Light Fluence Correction in Optoacoustic Tomography

Dynamic synthetic-scanning photoacoustic tracking monitors hepatic and renal clearance pathway of exogeneous probes in vivo

Advancements in precision medicine necessitate understanding drug clearance pathways, especially in organs like the liver and kidneys. Traditional techniques such as PET/CT pose radiation hazards, whereas optical imaging poses challenges in maintaining both depth penetration and high resolution. Moreover, very few longitudinal studies have been performed for drug candidates for different symptoms. Leveraging non-ionizing photoacoustic tomography for deep tissue imaging, we developed a spatiotemporally resolved clearance pathway tracking (SRCPT) method, providing unprecedented insights into drug clearance dynamics within vital organs. SRCPT addresses challenges like laser fluence attenuation, enabling dynamic visualization of drug clearance pathways and essential parameter extraction. We employed a novel frequency component selection based synthetic aperture focusing technique (FCS-SAFT) with respiratory-artifacts-free weighting factors to enhance three-dimensional imaging resolutions. Inspired by this, we investigated the clearance pathway of a clinical drug, mitoxantrone, revealing reduced liver clearance when hepatic function is impaired. Furthermore, immunoglobulin G clearance analysis revealed significant differences among mice with varying renal injury degrees. The accuracy of our method was validated using a double-labeled probe [68Ga]DFO-IRDye800CW, showing a strong positive correlation between SRCPT and PET. We believe that this powerful SRCPT promises precise mapping of drug clearance pathways and enhances diagnosis and treatment of liver and kidney-related diseases.

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.

Model-based quantitative photoacoustic tomography with directional total variation

In photoacoustic tomography (PAT), acoustic inversion aims to recover the spatial distribution of light energy deposition within the imaging object from the signals captured by detectors. To achieve quantitative imaging, optical inversion is further employed to derive absorption coefficient (AC) images. However, limitations such as restricted detection angles and inherent noise lead to substantial artifacts and degradation in the quality of PAT images, consequently affecting the accuracy of optical inversion results. In this study, we propose a directional total variation constrained optical inversion model to reconstruct the AC image. By incorporating anatomy prior information into the optical inversion process, our method can effectively suppress artifacts in AC images while maintaining structural integrity. Simulation, phantom, and in vivo experimental results demonstrate that our method significantly improves the reconstructed AC image quality. Our method provides a reliable foundation for achieving high-quality quantitative PAT imaging.

Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography

Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require extensive computational resources due to repeated LF estimation based on differential light transport models. To improve LF correction efficiency, we propose to use Fourier neural operator (FNO), a neural network specially designed for estimating partial differential equations, to learn the forward projection of light transport in PAT. Trained using paired finite-element-based LF simulation data, our FNO model replaces the traditional computational heavy LF estimator during iterative correction, such that the correction procedure is considerably accelerated. Simulation and experimental results demonstrate that our method achieves comparable LF correction quality to traditional iterative methods while reducing the correction time by over 30 times.

Efficient Photoacoustic Image Synthesis with Deep Learning

Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging underlying quantification problem. While in silico studies have revealed the great potential of deep learning (DL) methodology in solving this problem, the inherent lack of an efficient gold standard method for model training and validation remains a grand challenge. This work investigates whether DL can be leveraged to accurately and efficiently simulate photon propagation in biological tissue, enabling photoacoustic image synthesis. Our approach is based on estimating the initial pressure distribution of the photoacoustic waves from the underlying optical properties using a back-propagatable neural network trained on synthetic data. In proof-of-concept studies, we validated the performance of two complementary neural network architectures, namely a conventional U-Net-like model and a Fourier Neural Operator (FNO) network. Our in silico validation on multispectral human forearm images shows that DL methods can speed up image generation by a factor of 100 when compared to Monte Carlo simulations with 5×108 photons. While the FNO is slightly more accurate than the U-Net, when compared to Monte Carlo simulations performed with a reduced number of photons (5×106), both neural network architectures achieve equivalent accuracy. In contrast to Monte Carlo simulations, the proposed DL models can be used as inherently differentiable surrogate models in the photoacoustic image synthesis pipeline, allowing for back-propagation of the synthesis error and gradient-based optimization over the entire pipeline. Due to their efficiency, they have the potential to enable large-scale training data generation that can expedite the clinical application of photoacoustic imaging.

Imaging increased metabolism in the spinal cord in mice after middle cerebral artery occlusion

Emerging evidence indicates crosstalk between the brain and hematopoietic system following cerebral ischemia. Here, we investigated metabolism and oxygenation in the spleen and spinal cord in a transient middle cerebral artery occlusion (tMCAO) model. Sham-operated and tMCAO mice underwent [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) to assess glucose metabolism. Naïve, sham-operated and tMCAO mice underwent multispectral optoacoustic tomography (MSOT) assisted by quantitative model-based reconstruction and unmixing algorithms for accurate mapping of oxygenation patterns in peripheral tissues at 24 h after reperfusion. We found increased [18F]FDG uptake and reduced MSOT oxygen saturation, indicating hypoxia in the thoracic spinal cord of tMCAO mice compared with sham-operated mice but not in the spleen. Reduced spleen size was observed in tMCAO mice compared with sham-operated mice ex vivo. tMCAO led to an increase in the numbers of mature T cells in femoral bone marrow tissues, concomitant with a stark reduction in these cell subsets in the spleen and peripheral blood. The combination of quantitative PET and MSOT thus enabled observation of hypoxia and increased metabolic activity in the spinal cord of tMCAO mice at 24 h after occlusion compared to sham-operated mice.

Quantitative photoacoustic tomography with light fluence compensation based on radiance Monte Carlo model

Objective. Photoacoustic tomography (PAT) is a rapidly evolving imaging modality that provides images with high contrast and spatial resolution showing the optical properties of biological tissues. The photoacoustic pressure is proportional to the product of the optical absorption coefficient and the local light fluence. The essential challenge in reconstructing quantitative images representing spatially varying absorption coefficients is the unknown light fluence. In addition, optical attenuation induces spatial variations in the light fluence, and the heterogeneity of the fluence determines the limits of reconstruction quality and depth.Approach.In this work, a reconstruction enhancement scheme is proposed to compensate for the variation in the light fluence in the absorption coefficient recovery. The inverse problem of the radiance Monte Carlo model describing light transport through the tissue is solved by using an alternating optimization strategy. In the iteration, the absorption coefficients and photon weights are alternately updated.Main results.The method provides highly accurate quantitative images of absorption coefficients in simulations, phantoms, andin vivostudies. The results show that the method has great potential for improving the accuracy of absorption coefficient recovery compared to conventional reconstruction methods that ignore light fluence variations. Comparison with state-of-the-art fluence compensation methods shows significant improvements in root mean square error, normalized mean square absolute distance, and structural similarity metrics.Significance.This method achieves high precision quantitative imaging by compensating for nonuniform light fluence without increasing the complexity and operation of the imaging system.

Photoacoustic image synthesis with generative adversarial networks

Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge. We propose a novel approach to PAT image synthesis that involves subdividing the challenge of generating plausible simulations into two disjoint problems: (1) Probabilistic generation of realistic tissue morphology, and (2) pixel-wise assignment of corresponding optical and acoustic properties. The former is achieved with Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data. According to a validation study on a downstream task our approach yields more realistic synthetic images than the traditional model-based approach and could therefore become a fundamental step for deep learning-based quantitative PAT (qPAT).

Pixel-wise reconstruction of tissue absorption coefficients in photoacoustic tomography using a non-segmentation iterative method

In Photoacoustic Tomography (PAT), the acquired image represents a light energy deposition map of the imaging object. For quantitative imaging, the PAT image is converted into an absorption coefficient (μ a ) map by dividing the light fluence (LF). Previous methods usually assume a uniform tissueμ a distribution, and consequently degrade the LF correction results. Here, we propose a simple method to reconstruct the pixel-wiseμ a map. Our method is based on a non-segmentation-based iterative algorithm, which alternately optimizes the LF distribution and theμ a map. Using simulation data, as well as phantom and animal data, we implemented our algorithm and compared it to segmentation-based correction methods. The results show that our method can obtain accurate estimation of the LF distribution and therefore improve the image quality and feature visibility of theμ a map. Our method may facilitate efficient calculation of the concentration distributions of endogenous and exogenous agents in vivo.

Multispectral optoacoustic tomography for the non-invasive identification of patients with severe anemia in vivo

The immediate diagnosis of severe anemia is crucial for patient outcome. However, reliable non-invasive point-of-care diagnostic tools for e.g., ICU monitoring are currently lacking. Using an advanced Multispectral Optoacoustic Tomography (MSOT) research device, we first substantiated a strong positive correlation of MSOT-signal and absolute hemoglobin concentration ex vivo in blood samples. In a clinical exploratory proof-of-concept study, we then evaluated 19 patients with different severities of anemia and controls by non-invasive in vivo measurement of hemoglobin in the radial artery. Our approach proved excellent in identifying patients with severe anemia triggering RBC transfusion based on a strong positive correlation of MSOT-signal intensity and hemoglobin concentration for 700 nm single wavelength and HbR unmixed MSOT-parameter analysis. In conclusion, our study lays the foundation to further develop MSOT-based real-time quantitative perfusion analyses in follow-up preclinical and clinical imaging studies and as a promising diagnostic tool to improve patient care in the future. DRKS00021442.

Photoacoustic imaging to monitor outcomes during hyperbaric oxygen therapy: validation in a small cohort and case study in a bilateral chronic ischemic wound

Hyperbaric oxygen therapy (HBO2) is a common therapeutic modality that drives oxygen into hypoxic tissue to promote healing. Here, ten patients undergoing HBO2 underwent PA oximetry of the left radial artery and forearm pre- and post-HBO2; this cohort validated the use of PA imaging in HBO2. There was a significant increase in radial artery oxygenation after HBO2 (p = 0.002) in the validation cohort. We also include a case study: a non-diabetic male in his 50s (HB 010) presenting with bilateral ischemic and gangrenous wounds. HB 010 showed higher perfusion and oxygen saturation on the right foot than the left after HBO2 which correlated with independent surgical observations. Imaging assisted with limb salvage treatment. Hence, this work shows that PA imaging can measure changes in arterial oxygen saturation due to HBO2; it can also produce 3D maps of tissue oxygenation and evaluate response to therapy during HBO2.

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

SIGNIFICANCE

Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect.

AIM

Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium.

APPROACH

Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets.

RESULTS

The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction.

CONCLUSIONS

The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

Modeling photoacoustic pressure generation in colloidal suspensions at different volume fractions based on a multi-scale approach

Further development of quantitative photoacoustic tomography requires understanding the photoacoustic pressure generation by modeling the generation process. This study modeled the initial photoacoustic pressure in colloidal suspensions, used as tissue phantoms, at different volume fractions on a multi-scale approach. We modeled the thermodynamic and light scattering properties on a microscopic scale with/without treating the hard-sphere interaction between colloidal particles. Meanwhile, we did the light energy density on a macroscopic scale. We showed that the hard-sphere interaction significantly influences the initial pressure and related quantities at a high volume fraction except for the thermodynamic properties. We also showed the initial pressure at the absorber inside the medium logarithmically decreases with increasing the volume fractions. This result is mainly due to the decay of the light energy density with light scattering. Our numerical results suggest that modeling light scattering and propagation is crucial over modeling thermal expansion.

MRI Information-Based Correction and Restoration of Photoacoustic Tomography

As an emerging molecular imaging modality, Photoacoustic Tomography (PAT) is capable of mapping tissue physiological metabolism and exogenous contrast agent information with high specificity. Due to its ultrasonic detection mechanism, the precise localization of targeted lesions has long been a challenge for PAT imaging. The poor soft-tissue contrast of the PAT image makes this process difficult and inaccurate. To meet this challenge, in this study, we first make use of the rich and clear structural information brought about by another advanced imaging modality, Magnetic Resonance Imaging (MRI), to assist organ segmentation and correct for the light fluence attenuation of PAT. We demonstrate improved feature visibility and enhanced localization of endogenous and exogenous agents in the fluence corrected PAT images. Compared with PAT-based methods, the contrast-to-noise ratio (CNR) of our MRI-assisted method increases by 29.1% in live animal experiments. Furthermore, we show that the co-registered MRI image can also be incorporated into PAT image restoration, and achieves improved anatomical landscape and soft-tissue contrast (CNR increased by 25.36%) while preserving similar spatial resolution. This PAT-MRI combination provides excellent structural, functional and molecular images of the subject, and may enable more comprehensive analysis of various preclinical research applications.

Superpixel spectral unmixing framework for the volumetric assessment of tissue chromophores: A photoacoustic data-driven approach

The assessment of tissue chromophores at a volumetric scale is vital for an improved diagnosis and treatment of a large number of diseases. Spectral photoacoustic imaging (sPAI) co-registered with high-resolution ultrasound (US) is an innovative technology that has a great potential for clinical translation as it can assess the volumetric distribution of the tissue components. Conventionally, to detect and separate the chromophores from sPAI, an input of the expected tissue absorption spectra is required. However, in pathological conditions, the prediction of the absorption spectra is difficult as it can change with respect to the physiological state. Besides, this conventional approach can also be hampered due to spectral coloring, which is a prominent distortion effect that induces spectral changes at depth. Here, we are proposing a novel data-driven framework that can overcome all these limitations and provide an improved assessment of the tissue chromophores. We have developed a superpixel spectral unmixing (SPAX) approach that can detect the most and less prominent absorber spectra and their volumetric distribution without any user interactions. Within the SPAX framework, we have also implemented an advanced spectral coloring compensation approach by utilizing US image segmentation and Monte Carlo simulations, based on a predefined library of optical properties. The framework has been tested on tissue-mimicking phantoms and also on healthy animals. The obtained results show enhanced specificity and sensitivity for the detection of tissue chromophores. To our knowledge, this is a unique framework that accounts for the spectral coloring and provides automated detection of tissue spectral signatures at a volumetric scale, which can open many possibilities for translational research.

The IPASC data format: A consensus data format for photoacoustic imaging

Photoacoustic imaging (PAI) is an emerging modality that has shown promise for improving patient management in a range of applications. Unfortunately, the current lack of uniformity in PAI data formats compromises inter-user data exchange and comparison, which impedes: technological progress; effective research collaboration; and efforts to deliver multi-centre clinical trials. To overcome this challenge, the International Photoacoustic Standardisation Consortium (IPASC) has established a data format with a defined consensus metadata structure and developed an open-source software application programming interface (API) to enable conversion from proprietary file formats into the IPASC format. The format is based on Hierarchical Data Format 5 (HDF5) and designed to store photoacoustic raw time series data. Internal quality control mechanisms are included to ensure completeness and consistency of the converted data. By unifying the variety of proprietary data and metadata definitions into a consensus format, IPASC hopes to facilitate the exchange and comparison of PAI data.

Self-Fluence-Compensated Functional Photoacoustic Microscopy

Optical-resolution photoacoustic microscopy (OR-PAM) can image blood oxygen saturation (sO₂) in vivo with high resolution and excellent sensitivity and offers a great tool for neurovascular study and early cancer diagnosis. OR-PAM ignores the wavelength-dependent optical attenuation in superficial tissue, which cause errors in sO₂ imaging. Monte Carlo simulation shows that variations in imaging depth, vessel diameter, and focal position can cause up to  ∼ 60 % decrease in sO₂ imaging. Here, we develop a self-fluence-compensated OR-PAM to compensate for the wavelength-dependent fluence attenuation. We propose a linearized model to estimate the fluence attenuations and use three optical wavelengths to compensate for them in sO₂ calculation. We validate the model in both numerical and physical phantoms and show that the compensation method can effectively reduce the sO₂ errors. In functional brain imaging, we demonstrate that the compensation method can effectively improve sO₂ accuracy, especially in small vessels. Compared with uncompensated ones, the sO₂ values are improved by 10~30% in the brain. We monitor ischemic-stroke-induced brain injury which demonstrates great potential for the pre-clinical study of vascular diseases.

Automatic 3-D segmentation and volumetric light fluence correction for photoacoustic tomography based on optimal 3-D graph search

Preclinical imaging with photoacoustic tomography (PAT) has attracted wide attention in recent years since it is capable of providing molecular contrast with deep imaging depth. The automatic extraction and segmentation of the animal in PAT images is crucial for improving image analysis efficiency and enabling advanced image post-processing, such as light fluence (LF) correction for quantitative PAT imaging. Previous automatic segmentation methods are mostly two-dimensional approaches, which failed to conserve the 3-D surface continuity because the image slices were processed separately. This discontinuity problem further hampers LF correction, which, ideally, should be carried out in 3-D due to spatially diffused illumination. Here, to solve these problems, we propose a volumetric auto-segmentation method for small animal PAT imaging based on the 3-D optimal graph search (3-D GS) algorithm. The 3-D GS algorithm takes into account the relation among image slices by constructing a 3-D node-weighted directed graph, and thus ensures surface continuity. In view of the characteristics of PAT images, we improve the original 3-D GS algorithm on graph construction, solution guidance and cost assignment, such that the accuracy and smoothness of the segmented animal surface were guaranteed. We tested the performance of the proposed method by conducting in vivo nude mice imaging experiments with a commercial preclinical cross-sectional PAT system. The results showed that our method successfully retained the continuous global surface structure of the whole 3-D animal body, as well as smooth local subcutaneous tumor boundaries at different development stages. Moreover, based on the 3-D segmentation result, we were able to simulate volumetric LF distribution of the entire animal body and obtained LF corrected PAT images with enhanced structural visibility and uniform image intensity.

Nonlinear iterative perturbation scheme with simplified spherical harmonics (SP3 ) light propagation model for quantitative photoacoustic tomography

When using quantitative photoacoustic tomography (q-PAT) reconstruction to recover the optical absorption coefficients of tissue, the commonly used diffusion equation has several limitations in the case of the objects that have small geometries and high-absorption or low-scattering areas. Furthermore, the conventional perturbation reconstruction strategy is unsatisfactory when the target tissue containing large heterogeneous features. We herein present a modified q-PAT implementation that employs the higher-order photon migration model achieving the tradeoff between mathematical rigidity and computational efficiency. Besides, a nonlinear iterative method is proposed to obtain the perturbations of optical absorption considering the updating of the sensitivity matrix in calculating the fluence perturbations. Consequently, the distribution of tissue optical properties can be recovered in a robust way even if the targets with high absorption are included. The proposed approach has been validated by simulation, phantom and in vivo experiments, exhibiting promising performances in image fidelity and quantitative feasibility for practical applications.

Deep learning for biomedical photoacoustic imaging: A review

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability.

Optoacoustic Imaging in Inflammation

Optoacoustic or photoacoustic imaging (OAI/PAI) is a technology which enables non-invasive visualization of laser-illuminated tissue by the detection of acoustic signals. The combination of "light in" and "sound out" offers unprecedented scalability with a high penetration depth and resolution. The wide range of biomedical applications makes this technology a versatile tool for preclinical and clinical research. Particularly when imaging inflammation, the technology offers advantages over current clinical methods to diagnose, stage, and monitor physiological and pathophysiological processes. This review discusses the clinical perspective of using OAI in the context of imaging inflammation as well as in current and emerging translational applications.

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images

The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid optoacoustic ultrasound (OPUS) combination, images retrieved by one modality may further be used to improve the reconstruction accuracy of the other. In this regard, image segmentation plays a major role as it can aid improving the image quality and quantification abilities by facilitating modeling of light and sound propagation through the imaged tissues and surrounding coupling medium. Here, we propose an automated approach for surface segmentation in whole-body mouse OPUS imaging using a deep convolutional neural network (CNN). The method has shown robust performance, attaining accurate segmentation of the animal boundary in both optoacoustic and pulse-echo ultrasound images, as evinced by quantitative performance evaluation using Dice coefficient metrics.

Multi-scale optoacoustic molecular imaging of brain diseases

The ability to non-invasively visualize endogenous chromophores and exogenous probes and sensors across the entire rodent brain with the high spatial and temporal resolution has empowered optoacoustic imaging modalities with unprecedented capacities for interrogating the brain under physiological and diseased conditions. This has rapidly transformed optoacoustic microscopy (OAM) and multi-spectral optoacoustic tomography (MSOT) into emerging research tools to study animal models of brain diseases. In this review, we describe the principles of optoacoustic imaging and showcase recent technical advances that enable high-resolution real-time brain observations in preclinical models. In addition, advanced molecular probe designs allow for efficient visualization of pathophysiological processes playing a central role in a variety of neurodegenerative diseases, brain tumors, and stroke. We describe outstanding challenges in optoacoustic imaging methodologies and propose a future outlook.

Technical considerations in the Verasonics research ultrasound platform for developing a photoacoustic imaging system

Photoacoustic imaging (PAI) is an emerging functional and molecular imaging technology that has attracted much attention in the past decade. Recently, many researchers have used the vantage system from Verasonics for simultaneous ultrasound (US) and photoacoustic (PA) imaging. This was the motivation to write on the details of US/PA imaging system implementation and characterization using Verasonics platform. We have discussed the experimental considerations for linear array based PAI due to its popularity, simple setup, and high potential for clinical translatability. Specifically, we describe the strategies of US/PA imaging system setup, signal generation, amplification, data processing and study the system performance.

Real-time interleaved spectroscopic photoacoustic and ultrasound (PAUS) scanning with simultaneous fluence compensation and motion correction

For over two decades photoacoustic imaging has been tested clinically, but successful human trials have been limited. To enable quantitative clinical spectroscopy, the fundamental issues of wavelength-dependent fluence variations and inter-wavelength motion must be overcome. Here we propose a real-time, spectroscopic photoacoustic/ultrasound (PAUS) imaging approach using a compact, 1-kHz rate wavelength-tunable laser. Instead of illuminating tissue over a large area, the fiber-optic delivery system surrounding an US array sequentially scans a narrow laser beam, with partial PA image reconstruction for each laser pulse. The final image is then formed by coherently summing partial images. This scheme enables (i) automatic compensation for wavelength-dependent fluence variations in spectroscopic PA imaging and (ii) motion correction of spectroscopic PA frames using US speckle tracking in real-time systems. The 50-Hz video rate PAUS system is demonstrated in vivo using a murine model of labelled drug delivery.

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.

Evaluating online filtering algorithms to enhance dynamic multispectral optoacoustic tomography

Multispectral optoacoustic tomography (MSOT) is an emerging imaging modality, which is able to capture data at high spatiotemporal resolution using rapid tuning of the excitation laser wavelength. However, owing to the necessity of imaging one wavelength at a time to the exclusion of others, forming a complete multispectral image requires multiple excitations over time, which may introduce aliasing due to underlying spectral dynamics or noise in the data. In order to mitigate this limitation, we have applied kinematic α and α β filters to multispectral time series, providing an estimate of the underlying multispectral image at every point in time throughout data acquisition. We demonstrate the efficacy of these methods in suppressing the inter-frame noise present in dynamic multispectral image time courses using a multispectral Shepp-Logan phantom and mice bearing distinct renal cell carcinoma tumors. The gains in signal to noise ratio provided by these filters enable higher-fidelity downstream analysis such as spectral unmixing and improved hypothesis testing in quantifying the onset of signal changes during an oxygen gas challenge.

Fluence-matching technique using photoacoustic radiofrequency spectra for improving estimates of oxygen saturation

Photoacoustic (PA) signals encode information about the optical absorption and spatial distribution of absorbing chromophores as well as the light distribution in the medium. The wavelength dependence of the latter affects the accuracy in chromophore quantification, including estimations of oxygen saturation (sO₂) with depth. We propose the use of the ratio of the PA radiofrequency (RF) spectral slopes (SS) at different optical wavelengths to generate frequency filters which can be used to match the fluence profiles across separate images generated with different optical wavelengths. Proof-of-principle experiments were carried on a plastic tube with blood of a known oxygenation inserted into a porcine tissue. The algorithm was tested in-vivo in the hind leg of six CD1 mice, each under three different breathing conditions (100 % O₂, room air and 100 % CO₂). Imaging was done using the VevoLAZR system at the wavelengths 720 and 870 nm. The SS was calculated from the linear fit of the ratio of the photoacoustic RF power spectra at the two wavelengths. An ultrasound frequency filter was designed and applied to each segmented PA signal in the frequency domain and inversely transformed into the time domain to correct for the differences in the fluence profiles at both wavelengths. Linear spectral unmixing was used to estimate sO₂ before and after applying the ultrasound frequency filter. The estimated blood sO₂ in the plastic tube for the porcine tissue experiment improved by 10.3% after applying the frequency filter when compared to the sO₂ measured by a blood gas analyzer. For the in-vivo mouse experiments, the applied sO₂ correction was 2.67, 1.33 and -3.33% for every mm of muscle tissue for mice breathing 100% O₂, room air and 100% CO₂, respectively. The approach presented here provides a new approach for fluence matching that can potentially improve the accuracy of sO₂ estimates by removing the fluence depth dependence at different optical wavelengths.

Correction of wavelength-dependent laser fluence in swept-beam spectroscopic photoacoustic imaging with a hand-held probe

Recently, we demonstrated an integrated photoacoustic (PA) and ultrasound (PAUS) system using a kHz-rate wavelength-tunable laser and a swept-beam delivery approach. It irradiates a medium using a narrow laser beam swept at high repetition rate (∼1 kHz) over the desired imaging area, in contrast to the conventional PA approach using broad-beam illumination at a low repetition rate (10-50 Hz). Here, we present a method to correct the wavelength-dependent fluence distribution and demonstrate its performance in phantom studies using a conventional limited view/bandwidth hand-held US probe. We adopted analytic fluence models, extending diffusion theory for the case of a pencil beam obliquely incident on an optically homogenous turbid medium, and developed a robust method to estimate fluence attenuation in the medium using PA measurements acquired from multiple fiber-irradiation positions swept at a kHz rate. We conducted comprehensive simulation tests and phantom studies using well-known contrast-agents to validate the reliability of the fluence model and its spectral corrections.

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.

Co-registration of optoacoustic tomography and magnetic resonance imaging data from murine tumour models

As optoacoustic tomography (OT) emerges as a mainstream pre-clinical imaging modality, understanding the relationship between optoacoustic and other imaging biomarkers in the context of the underlying tissue biology becomes vitally important. Complementary insight into tumour vasculature and hypoxia can be gained using OT alongside magnetic resonance imaging (MRI)-based techniques. To evaluate the relationship between these metrics and the relative performance of the two modalities in assessment of tumour physiology, co-registration of their output imaging data is required. Unfortunately, this poses a significant challenge due to differences in animal positioning during imaging. Here, we present an integrated framework for registration of OT and MR image data in mice. Our framework combines a novel MR animal holder, to improve animal positioning during imaging, and a landmark-based software co-registration algorithm. We demonstrate that our protocol significantly improves registration of both body and tumour contours between these modalities, enabling more precise multi-modal tumour characterisation.

Quantitative PA tomography of high resolution 3-D images: Experimental validation in a tissue phantom

Quantitative photoacoustic tomography aims to recover the spatial distribution of absolute chromophore concentrations and their ratios from deep tissue, high-resolution images. In this study, a model-based inversion scheme based on a Monte-Carlo light transport model is experimentally validated on 3-D multispectral images of a tissue phantom acquired using an all-optical scanner with a planar detection geometry. A calibrated absorber allowed scaling of the measured data during the inversion, while an acoustic correction method was employed to compensate the effects of limited view detection. Chromophore- and fluence-dependent step sizes and Adam optimization were implemented to achieve rapid convergence. High resolution 3-D maps of absolute concentrations and their ratios were recovered with high accuracy. Potential applications of this method include quantitative functional and molecular photoacoustic tomography of deep tissue in preclinical and clinical studies.

Performance Characteristics of Photoacoustic Imaging Probes with Varying Frequencies and Light-delivery Schemes

Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that utilizes a combination of light and ultrasound to detect photoabsorbers embedded within tissues. While the clinical utility of PAI has been widely explored for several applications, limitations in light penetration and detector sensitivity have restricted these studies to mostly superficial sites. Given the importance of PA signal generation and detection on light delivery and ultrasound detector frequency, there is an ongoing effort to optimize these parameters to enhance photoabsorber detection at increased depths. With this in mind, in this study we examined performance benchmarks of a commercially available PAI/ultrasound linear array system when using different imaging frequencies and light delivery schemes. A modified light fiber jacket providing focused light delivery (FLD) at the center of the probe was compared with the built-in fiber optics lining the length of the probe. Studies were performed in vitro to compare performance characteristics such as imaging resolution, maximum imaging depth, and sensitivity to varying hematocrit concentration for each frequency and light delivery method. Monte Carlo simulations of each light delivery method revealed increased light penetration with FLD. In tissue-mimicking phantoms, vascular channels used to simulate blood vessels could be visualized at a depth of 2.4 cm when lowering imaging frequency and utilizing FLD. Imaging at lower frequencies with FLD also enabled enhanced detection of varying hematocrit concentration levels at increased depths, although lateral imaging resolution was reduced. Finally, a proof of concept in vivo probe comparison study in a mouse tumor model provided supportive evidence of our in vitro results. Collectively, our findings show that adjusting imaging frequency and applying FLD can be a straightforward approach for improving PAI performance.

Development of a blood oxygenation phantom for photoacoustic tomography combined with online pO2 detection and flow spectrometry

Photoacoustic tomography (PAT) is intrinsically sensitive to blood oxygen saturation (sO2) in vivo. However, making accurate sO2 measurements without knowledge of tissue- and instrumentation-related correction factors is extremely challenging. We have developed a low-cost flow phantom to facilitate validation of PAT systems. The phantom is composed of a flow circuit of tubing partially embedded within a tissue-mimicking material, with independent sensors providing online monitoring of the optical absorption spectrum and partial pressure of oxygen in the tube. We first test the flow phantom using two small molecule dyes that are frequently used for photoacoustic imaging: methylene blue and indocyanine green. We then demonstrate the potential of the phantom for evaluating sO2 using chemical oxygenation and deoxygenation of blood in the circuit. Using this dynamic assessment of the photoacoustic sO2 measurement in phantoms in relation to a ground truth, we explore the influence of multispectral processing and spectral coloring on accurate assessment of sO2. Future studies could exploit this low-cost dynamic flow phantom to validate fluence correction algorithms and explore additional blood parameters such as pH and also absorptive and other properties of different fluids.

Optoacoustic image formation approaches-a clinical perspective

Clinical translation of optoacoustic imaging is fostered by the rapid technical advances in imaging performance as well as the growing number of clinicians recognizing the immense diagnostic potential of this technology. Clinical optoacoustic systems are available in multiple configurations, including hand-held and endoscopic probes as well as raster-scan approaches. The hardware design must be adapted to the accessible portion of the imaged region and other application-specific requirements pertaining the achievable depth, field of view or spatio-temporal resolution. Equally important is the adequate choice of the signal and image processing approach, which is largely responsible for the resulting imaging performance. Thus, new image reconstruction algorithms are constantly evolving in parallel to the newly-developed set-ups. This review focuses on recent progress on optoacoustic image formation algorithms and processing methods in the clinical setting. Major reconstruction challenges include real-time image rendering in two and three dimensions, efficient hybridization with other imaging modalitites as well as accurate interpretation and quantification of bio-markers, herein discussed in the context of ongoing progress in clinical translation.

Photoacoustic imaging for guidance of interventions in cardiovascular medicine

Imaging guidance is paramount to procedural success in minimally invasive interventions. Catheter-based therapies are the standard of care in the treatment of many cardiac disorders, including coronary artery disease, structural heart disease and electrophysiological conditions. Many of these diseases are caused by, or effect, a change in vasculature or cardiac tissue composition, which can potentially be detected by photoacoustic imaging. This review summarizes the state of the art in photoacoustic imaging approaches that have been proposed for intervention guidance in cardiovascular care. All of these techniques are currently in the preclinical phase. We will conclude with an outlook towards clinical applications.

Photoacoustic imaging as a tool to probe the tumour microenvironment

The tumour microenvironment (TME) is a complex cellular ecosystem subjected to chemical and physical signals that play a role in shaping tumour heterogeneity, invasion and metastasis. Studying the roles of the TME in cancer progression would strongly benefit from non-invasive visualisation of the tumour as a whole organ in vivo, both preclinically in mouse models of the disease, as well as in patient tumours. Although imaging techniques exist that can probe different facets of the TME, they face several limitations, including limited spatial resolution, extended scan times and poor specificity from confounding signals. Photoacoustic imaging (PAI) is an emerging modality, currently in clinical trials, that has the potential to overcome these limitations. Here, we review the biological properties of the TME and potential of existing imaging methods that have been developed to analyse these properties non-invasively. We then introduce PAI and explore the preclinical and clinical evidence that support its use in probing multiple features of the TME simultaneously, including blood vessel architecture, blood oxygenation, acidity, extracellular matrix deposition, lipid concentration and immune cell infiltration. Finally, we highlight the future prospects and outstanding challenges in the application of PAI as a tool in cancer research and as part of a clinical oncologist's arsenal.

Summary: This Review details the potential of photoacoustic imaging to visualise features of the tumour microenvironment such as blood vessels, hypoxia, fibrosis and immune infiltrate to provide unprecedented insight into tumour biology.

Maximum Entropy Based Non-Negative Optoacoustic Tomographic Image Reconstruction

OBJECTIVE

Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise, and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of this work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging.

METHODS

We present a novel method for optoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining non-negative reconstructions. The reconstruction image quality is further improved using structural prior-based fluence correction.

RESULTS

We report the performance achieved by the entropy maximization scheme on numerical simulation, experimental phantoms, and in-vivo samples.

CONCLUSION

The proposed algorithm demonstrates superior reconstruction performance by delivering non-negative pixel values with no visible distortion of anatomical structures.

SIGNIFICANCE

Our method can enable quantitative optoacoustic imaging, and has the potential to improve preclinical and translational imaging applications.

High-resolution 3D light fluence mapping for heterogeneous scattering media by localized sampling

We demonstrate an innovative concept for three-dimensional optical fluence mapping in heterogeneous highly scattering media as, e.g., biomedical tissues. We propose to use the relative light extinction analysis principle together with a miniaturized collection fiber in a direct fluence measurement setup as a method to obtain the spatially resolved light intensity distribution under transversally inhomogeneous light propagation conditions and provide local characterization of the transport medium. System performance is validated in two extreme conditions: an optically thin scattering medium and an absorption-dominated light transport. Both extremes demonstrate good agreement to theoretical expectations. Finally, we successfully prove the ability of the system to deliver high-resolution fluence maps through a model study of the light distribution induced in a scattering medium by a vertical diode laser stack with individual bars pitched only 500 μm apart.

Modalities for image- and molecular-guided cancer surgery

BACKGROUND

Surgery is the cornerstone of treatment for many solid tumours. A wide variety of imaging modalities are available before surgery for staging, although surgeons still rely primarily on visual and haptic cues in the operating environment. Image and molecular guidance might improve the adequacy of resection through enhanced tumour definition and detection of aberrant deposits. Intraoperative modalities available for image- and molecular-guided cancer surgery are reviewed here.

METHODS

Intraoperative cancer detection techniques were identified through a systematic literature search, with selection of peer-reviewed publications from January 2012 to January 2017. Modalities were reviewed, described and compared according to 25 predefined characteristics. To summarize the data in a comparable way, a three-point rating scale was applied to quantitative characteristics.

RESULTS

The search identified ten image- and molecular-guided surgery techniques, which can be divided into four groups: conventional, optical, nuclear and endogenous reflectance modalities. Conventional techniques are the most well known imaging modalities, but unfortunately have the drawback of a defined resolution and long acquisition time. Optical imaging is a real-time modality; however, the penetration depth is limited. Nuclear modalities have excellent penetration depth, but their intraoperative use is limited by the use of radioactivity. Endogenous reflectance modalities provide high resolution, although with a narrow field of view.

CONCLUSION

Each modality has its strengths and weaknesses; no single technique will be suitable for all surgical procedures. Strict selection of modalities per cancer type and surgical requirements is required as well as combining techniques to find the optimal balance.

Impact of depth-dependent optical attenuation on wavelength selection for spectroscopic photoacoustic imaging

An optical wavelength selection method based on the stability of the absorption cross-section matrix to improve spectroscopic photoacoustic (sPA) imaging was recently introduced. However, spatially-varying chromophore concentrations cause the wavelength- and depth-dependent variations of the optical fluence, which degrades the accuracy of quantitative sPA imaging. This study introduces a depth-optimized method that determines an optimal wavelength set minimizing an inverse of the multiplication of absorption cross-section matrix and fluence matrix to minimize the errors in concentration estimation. This method assumes that the optical fluence distribution is known or can be attained otherwise. We used a Monte Carlo simulation of light propagation in tissue with various depths and concentrations of deoxy-/oxy-hemoglobin. We quantitatively compared the developed and current approaches, indicating that the choice of wavelength is critical and our approach is effective especially when quantifying deeper imaging targets.

Oxygen-Enhanced and Dynamic Contrast-Enhanced Optoacoustic Tomography Provide Surrogate Biomarkers of Tumor Vascular Function, Hypoxia, and Necrosis

Measuring the functional status of tumor vasculature, including blood flow fluctuations and changes in oxygenation, is important in cancer staging and therapy monitoring. Current clinically approved imaging modalities suffer long procedure times and limited spatiotemporal resolution. Optoacoustic tomography (OT) is an emerging clinical imaging modality that may overcome these challenges. By acquiring data at multiple wavelengths, OT can interrogate hemoglobin concentration and oxygenation directly and resolve contributions from injected contrast agents. In this study, we tested whether two dynamic OT techniques, oxygen-enhanced (OE) and dynamic contrast-enhanced (DCE)-OT, could provide surrogate biomarkers of tumor vascular function, hypoxia, and necrosis. We found that vascular maturity led to changes in vascular function that affected tumor perfusion, modulating the DCE-OT signal. Perfusion in turn regulated oxygen availability, driving the OE-OT signal. In particular, we demonstrate for the first time a strong per-tumor and spatial correlation between imaging biomarkers derived from these in vivo techniques and tumor hypoxia quantified ex vivo Our findings indicate that OT may offer a significant advantage for localized imaging of tumor response to vascular-targeted therapies when compared with existing clinical DCE methods.Significance: Imaging biomarkers derived from optoacoustic tomography can be used as surrogate measures of tumor perfusion and hypoxia, potentially yielding rapid, multiparametric, and noninvasive cancer staging and therapeutic response monitoring in the clinic.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/20/5980/F1.large.jpg Cancer Res; 78(20); 5980-91. ©2018 AACR.

The Progress in Photoacoustic and Laser Ultrasonic Tomographic Imaging for Biomedicine and Industry: A Review

The current paper reviews a set of principles and applications of photoacoustic and laser ultrasonic imaging, developed in the Laser Optoacoustic Laboratories of ILIT RAS, NUST MISiS, and ILC MSU. These applications include combined photoacoustic and laser ultrasonic imaging for biological objects, and tomographic laser ultrasonic imaging of solids. Principles, algorithms, resolution of the developed methods, and related problems are discussed. The review is written in context of the current state-of-art of photoacoustic and laser ultrasonic imaging.

Intraoperative fluorescence imaging in thoracic surgery

Intraoperative fluorescence imaging (IFI) can improve real-time identification of cancer cells during an operation. Phase I clinical trials in thoracic surgery have demonstrated that IFI with second window indocyanine green (TumorGlow^® ) can identify subcentimeter pulmonary nodules, anterior mediastinal masses, and mesothelioma, while the use of a folate receptor-targeted near-infrared agent, OTL38, can improve the specificity for diagnosing tumors with folate receptor expression. Here, we review the existing preclinical and clinical data on IFI in thoracic surgery.

Recent progress in photoacoustic molecular imaging

By acoustically detecting the optical absorption contrast, photoacoustic (PA) tomography (PAT) has broken the penetration limits of traditional high-resolution optical imaging. Through spectroscopic analysis of the target's optical absorption, PAT can identify a wealth of endogenous and exogenous molecules and thus is inherently capable of molecular imaging with high sensitivity. PAT's molecular sensitivity is uniquely accompanied by non-ionizing radiation, high spatial resolution, and deep penetration in biological tissues, which other optical imaging modalities cannot achieve yet. In this concise review, we summarize the most recent technological advancements in PA molecular imaging and highlight the novel molecular probes specifically made for PAT in deep tissues. We conclude with a brief discussion of the opportunities for future advancements.

End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging

An end-to-end deep neural network, ResU-net, is developed for quantitative photoacoustic imaging. A residual learning framework is used to facilitate optimization and to gain better accuracy from considerably increased network depth. The contracting and expanding paths enable ResU-net to extract comprehensive context information from multispectral initial pressure images and, subsequently, to infer a quantitative image of chromophore concentration or oxygen saturation (sO₂). According to our numerical experiments, the estimations of sO₂ and indocyanine green concentration are accurate and robust against variations in both optical property and object geometry. An extremely short reconstruction time of 22 ms is achieved.

Statistical independence in nonlinear model-based inversion for quantitative photoacoustic tomography

The statistical independence between the distributions of different chromophores in tissue has previously been used for linear unmixing with independent component analysis (ICA). In this study, we propose exploiting this statistical property in a nonlinear model-based inversion method. The aim is to reduce the sensitivity of the inversion scheme to errors in the modelling of the fluence, and hence provide more accurate quantification of the concentration of independent chromophores. A gradient-based optimisation algorithm is used to minimise the error functional, which includes a term representing the mutual information between the chromophores in addition to the standard least-squares data error. Both numerical simulations and an experimental phantom study are conducted to demonstrate that, in the presence of experimental errors in the fluence model, the proposed inversion method results in more accurate estimation of the concentrations of independent chromophores compared to the standard model-based inversion.

Photoacoustic-based sO2 estimation through excised bovine prostate tissue with interstitial light delivery

Photoacoustic (PA) imaging is capable of probing blood oxygen saturation (sO₂), which has been shown to correlate with tissue hypoxia, a promising cancer biomarker. However, wavelength-dependent local fluence changes can compromise sO₂ estimation accuracy in tissue. This work investigates using PA imaging with interstitial irradiation and local fluence correction to assess precision and accuracy of sO₂ estimation of blood samples through ex vivo bovine prostate tissue ranging from 14% to 100% sO₂. Study results for bovine blood samples at distances up to 20 mm from the irradiation source show that local fluence correction improved average sO₂ estimation error from 16.8% to 3.2% and maintained an average precision of 2.3% when compared to matched CO-oximeter sO₂ measurements. This work demonstrates the potential for future clinical translation of using fluence-corrected and interstitially driven PA imaging to accurately and precisely assess sO₂ at depth in tissue with high resolution.

Toward whole-body quantitative photoacoustic tomography of small-animals with multi-angle light-sheet illuminations

Several attempts to achieve the quantitative photoacoustic tomography (q-PAT) have been investigated using point sources or a single-angle wide-field illumination. However, these schemes normally suffer from low signal-to-noise ratio (SNR) or poor quantification in imaging applications on large-size domains, due to the limitation of ANSI-safety incidence and incompleteness in the data acquisition. We herein present a q-PAT implementation that uses multi-angle light-sheet illuminations and calibrated recovering-and-averaging iterations. The scheme can obtain more complete information on the intrinsic absorption from the multi-angle illumination mode, and collect SNR-boosted photoacoustic signals in the selected planes from the wide-field light-sheet excitation. Therefore, the sliced absorption maps over whole body of small-animals can be recovered in a measurement-flexible, noise-robust and computation-economic way. The proposed approach is validated by phantom, ex vivo and in vivo experiments, exhibiting promising performances in image fidelity and quantitative accuracy for practical applications.