Given its direct relationship to tissue metabolism and various pathological processes, 3D mapping of blood oxygen saturation (sO2) is essential for advancing our knowledge on oxygen delivery to tissues and evaluating therapeutic efficacy. Optoacoustic microscopy has enabled label-free estimation of sO2 values by exploiting the spectrally distinctive absorption of hemoglobin in its oxygenated and deoxygenated forms. However, quantitative 3D mapping of sO2 distribution over large heterogenous tissue regions is commonly hindered due to the strong spatial and spectral variability of the excitation light fluence. Herein, we capitalize on hybridization between pulse-echo ultrasound and large-scale spectroscopic optoacoustic microscopy readings to accurately delineate the tissue surface, achieve depth-resolved tissue layer segmentation, and comprehensively evaluate the main causes behind inaccurate sO2 estimations with optoacoustic microscopy. Compensation for wavelength-dependent light fluence variations due to relative reflectance and attenuation through multiple tissue layers is further shown to remove spectral noise and restore physiologically relevant sO2 values in the images recorded from the mouse ear and the dorsal murine skin. The ultrasound-aided large-scale optoacoustic microscopy (uLSOM) approach is thus expected to enhance applicability of optoacoustic microscopy for quantitative label-free imaging of tissue oxygenation and metabolism.

Optoacoustic (OA) imaging is based on optical excitation of biological tissues with nanosecond-duration laser pulses and detection of ultrasound (US) waves generated by thermoelastic expansion following light absorption. The image quality and fidelity of OA images critically depend on the extent of tomographic coverage provided by the US detector arrays. However, full tomographic coverage is not always possible due to experimental constraints. One major challenge concerns an efficient integration between OA and pulse-echo US measurements using the same transducer array. A common approach toward the hybridization consists in using standard linear transducer arrays, which readily results in arc-type artifacts and distorted shapes in OA images due to the limited angular coverage. Deep learning methods have been proposed to mitigate limited-view artifacts in OA reconstructions by mapping artifactual to artifact-free (ground truth) images. However, acquisition of ground truth data with full angular coverage is not always possible, particularly when using handheld probes in a clinical setting. Deep learning methods operating in the image domain are then commonly based on networks trained on simulated data. This approach is yet incapable of transferring the learned features between two domains, which results in poor performance on experimental data. Here, we propose a signal domain adaptation network (SDAN) consisting of i) a domain adaptation network to reduce the domain gap between simulated and experimental signals and ii) a sides prediction network to complement the missing signals in limited-view OA datasets acquired from a human forearm by means of a handheld linear transducer array. The proposed method showed improved performance in reducing limited-view artifacts without the need for ground truth signals from full tomographic acquisitions.

Optoacoustic tomography (OAT) has recently been advanced toward ultrafast volumetric imaging frame rates in the kilohertz range. As a result, excessive data processing and storage capacity requirements are increasingly being imposed on the imaging systems. OAT data commonly exhibit significant sparsity across the spatial, temporal or spectral domains, which facilitated the development of compressed sensing algorithms exploiting various sparse acquisition and under-sampling schemes to reduce data rates. However, performance of compressed sensing critically depends on a priori knowledge on the type of acquired data and/or imaged object, commonly resulting in lack of general applicability and unpredictable image quality. In this work, we report on a fast adaptive OAT data compression framework operating on fully sampled tomographic data. It is based on a wavelet packet transform that maximizes the data compression ratio according to the desired signal energy loss. A dedicated reconstruction method was further developed that efficiently renders images directly from the compressed data. Up to 1000x compression ratios were achieved while providing efficient control over the resulting image quality from arbitrary datasets exhibiting diverse spatial, temporal and spectral characteristics. Our approach enables faster and longer acquisitions and facilitates long-term storage of large OAT datasets.

Spherical matrix arrays represent an advantageous tomographic detection geometry for non-invasive deep tissue mapping of vascular networks and oxygenation with volumetric optoacoustic tomography (VOT). Hybridization of VOT with ultrasound (US) imaging remains difficult with this configuration due to the relatively large inter-element pitch of spherical arrays. We suggest a new approach for combining VOT and US contrast-enhanced 3D imaging employing injection of clinically-approved microbubbles. Power Doppler (PD) and US localization imaging were enabled with a sparse US acquisition sequence and model-based inversion based on infimal convolution of total variation (ICTV) regularization. In vitro experiments in tissue-mimicking phantoms and in living mouse brain demonstrate the powerful capabilities of the new dual-mode imaging approach attaining 80 μm spatial resolution and a more than 10 dB signal to noise improvement with respect to a classical delay and sum beamformer. Microbubble localization and tracking allowed for flow velocity mapping up to 40 mm/s.

Wide-scale adoption of optoacoustic imaging in biology and medicine critically depends on availability of affordable scanners combining ease of operation with optimal imaging performance. Here we introduce LightSpeed: a low-cost real-time volumetric handheld optoacoustic imager based on a new compact software-defined ultrasound digital acquisition platform and a pulsed laser diode. It supports the simultaneous signal acquisition from up to 192 ultrasound channels and provides a hig-bandwidth direct optical link (2x 100G Ethernet) to the host-PC for ultra-high frame rate image acquisitions. We demonstrate use of the system for ultrafast (500Hz) 3D human angiography with a rapidly moving handheld probe. LightSpeed attained image quality comparable with a conventional optoacoustic imaging systems employing bulky acquisition electronics and a Q-switched pulsed laser. Our results thus pave the way towards a new generation of compact, affordable and high-performance optoacoustic scanners.

Iterative model-based algorithms are known to enable more accurate and quantitative optoacoustic (photoacoustic) tomographic reconstructions than standard back-projection methods. However, three-dimensional (3D) model-based inversion is often hampered by high computational complexity and memory overhead. Parallel implementations on a graphics processing unit (GPU) have been shown to efficiently reduce the memory requirements by on-the-fly calculation of the actions of the optoacoustic model matrix, but the high complexity still makes these approaches impractical for large 3D optoacoustic datasets. Herein, we show that the computational complexity of 3D model-based iterative inversion can be significantly reduced by splitting the model matrix into two parts: one maximally sparse matrix containing only one entry per voxel-transducer pair and a second matrix corresponding to cyclic convolution. We further suggest reconstructing the images by multiplying the transpose of the model matrix calculated in this manner with the acquired signals, which is equivalent to using a very large regularization parameter in the iterative inversion method. The performance of these two approaches is compared to that of standard back-projection and a recently introduced GPU-based model-based method using datasets from in vivo experiments. The reconstruction time was accelerated by approximately an order of magnitude with the new iterative method, while multiplication with the transpose of the matrix is shown to be as fast as standard back-projection.

Optoacoustic tomography systems have attained unprecedented volumetric imaging speeds, thus enabling insights into rapid biological dynamics and marking a milestone in the clinical translation of this modality. Fast imaging performance often comes at the cost of limited field-of-view, which may hinder potential applications looking at larger tissue volumes. The imaged field-of-view can potentially be expanded via scanning and using additional hardware to track the position of the imaging probe. However, this approach turns impractical for high-resolution volumetric scans performed in a freehand mode along arbitrary trajectories. We have developed an accurate framework for spatial compounding of time-lapse optoacoustic data. The method exploits the frequency-domain properties of vascular networks in optoacoustic images and estimates the relative motion and orientation of the imaging probe. This allows rapidly combining sequential volumetric frames into large area scans without additional tracking hardware. The approach is universally applicable for compounding volumetric data acquired with calibrated scanning systems but also in a freehand mode with up to six degrees of freedom. Robust performance is demonstrated for whole-body mouse imaging with spiral volumetric optoacoustic tomography and for freehand visualization of vascular networks in humans using volumetric imaging probes. The newly introduced capability for angiographic observations at multiple spatial and temporal scales is expected to greatly facilitate the use of optoacoustic imaging technology in pre-clinical research and clinical diagnostics. The technique can equally benefit other biomedical imaging modalities, such as scanning fluorescence microscopy, optical coherence tomography or ultrasonography, thus optimizing their trade-offs between fast imaging performance and field-of-view.

The recently developed optoacoustic tomography systems have attained volumetric frame rates exceeding 100 Hz, thus opening up new venues for studying previously invisible biological dynamics. Further gains in temporal resolution can potentially be achieved via partial data acquisition, though a priori knowledge on the acquired data is essential for rendering accurate reconstructions using compressed sensing approaches. In this work, we suggest a machine learning method based on principal component analysis for high-frame-rate volumetric cardiac imaging using only a few tomographic optoacoustic projections. The method is particularly effective for discerning periodic motion, as demonstrated herein by non-invasive imaging of a beating mouse heart. A training phase enables efficiently compressing the heart motion information, which is subsequently used as prior information for image reconstruction from sparse sampling at a higher frame rate. It is shown that image quality is preserved with a 64-fold reduction in the data flow. We demonstrate that, under certain conditions, the volumetric motion could effectively be captured by relying on time-resolved data from a single optoacoustic detector. Feasibility of capturing transient (non-periodic) events not registered in the training phase is further demonstrated by visualizing perfusion of a contrast agent in vivo. The suggested approach can be used to significantly boost the temporal resolution of optoacoustic imaging and facilitate development of more affordable and data efficient systems.

Accurate extraction of physical and biochemical parameters from optoacoustic images is often impeded due to the use of unrigorous inversion schemes, incomplete tomographic detection coverage, or other experimental factors that cannot be readily accounted for during the image acquisition and reconstruction process. For instance, inaccurate assumptions in the physical forward model may lead to negative optical absorption values in the reconstructed images. Any artifacts present in the single wavelength optoacoustic images can be significantly aggravated when performing a two-step reconstruction consisting in acoustic inversion and spectral unmixing aimed at rendering the distributions of spectrally distinct absorbers. We investigate a number of algorithmic strategies with non-negativity constraints imposed at the different phases of the reconstruction process. Performance is evaluated in cross-sectional multispectral optoacoustic tomography recordings from tissue-mimicking phantoms and in vivo mice embedded with varying concentrations of contrast agents. Additional in vivo validation is subsequently performed with molecular imaging data involving subcutaneous tumors labeled with genetically expressed iRFP proteins and organ perfusion by optical contrast agents. It is shown that constrained reconstruction is essential for reducing the critical image artifacts associated with inaccurate modeling assumptions. Furthermore, imposing the non-negativity constraint directly on the unmixed distribution of the probe of interest was found to maintain the most robust and accurate reconstruction performance in all experiments.

Optimal optoacoustic tomographic sampling is often hindered by the frequency-dependent directivity of ultrasound sensors, which can only be accounted for with an accurate 3-D model. Herein, we introduce a 3-D model-based reconstruction method applicable to optoacoustic imaging systems employing detection elements with arbitrary size and shape. The computational complexity and memory requirements are mitigated by introducing an efficient graphic processing unit (GPU)-based implementation of the iterative inversion. On-the-fly calculation of the entries of the model-matrix via a small look-up table avoids otherwise unfeasible storage of matrices typically occupying more than 300GB of memory. Superior imaging performance of the suggested method with respect to standard optoacoustic image reconstruction methods is first validated quantitatively using tissue-mimicking phantoms. Significant improvements in the spatial resolution, contrast to noise ratio and overall 3-D image quality are also reported in real tissues by imaging the finger of a healthy volunteer with a hand-held volumetric optoacoustic imaging system.

The high complementarity of ultrasonography and optoacoustic tomography has prompted the development of combined approaches that utilize the same transducer array for detecting both optoacoustic and pulse-echo ultrasound responses from tissues. Yet, due to the fundamentally different physical contrast and image formation mechanisms, the development of detection technology optimally suited for image acquisition in both modalities remains a major challenge. Herein, we introduce a multi-segment detector array approach incorporating array segments of linear and concave geometry to optimally support both ultrasound and optoacoustic image acquisition. The various image rendering strategies are tested and optimized in numerical simulations and calibrated tissue-mimicking phantom experiments. We subsequently demonstrate real-time hybrid optoacoustic ultrasound image acquisition in a healthy volunteer. The new approach enables the acquisition of high-quality anatomical data by both modalities complemented by functional information on blood oxygenation status provided by the multispectral optoacoustic tomography.

Analytical (closed-form) inversion schemes have been the standard approach for image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led to the development of more accurate inversion approaches, which rely on accurate forward modeling of the optoacoustic wave generation and propagation. In this way, multiple experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal response of the transducers, and acoustic heterogeneities. The model-based inversion commonly results in very large sparse matrix formulations that require computationally extensive and memory demanding regularization schemes for image reconstruction, hindering their effective implementation in real-time imaging applications. Herein, we introduce a new discretization procedure for efficient model-based reconstructions in two-dimensional optoacoustic tomography that allows for parallel implementation on a graphics processing unit (GPU) with a relatively low numerical complexity. By on-the-fly calculation of the model matrix in each iteration of the inversion procedure, the new approach results in imaging frame rates exceeding 10 Hz, thus enabling real-time image rendering using the model-based approach.

High fidelity optoacoustic (photoacoustic) tomography requires dense spatial sampling of optoacoustic signals using point acoustic detectors. However, in practice, spatial resolution of the images is often limited by limited sampling either due to coarse multi-element arrays or time in raster scan measurements. Herein, we investigate a method that integrates information from multiple optoacoustic images acquired at sub-diffraction steps into one high resolution image by means of an iterative registration algorithm. Experimental validations performed in target phantoms and ex vivo tissue samples confirm that the suggested approach renders significant improvements in terms of optoacoustic image resolution and quality without introducing significant alterations into the signal acquisition hardware or inversion algorithms.

The concept of sparsity is extensively exploited in the fields of data acquisition and image processing, contributing to better signal-to-noise and spatio-temporal performance of the various imaging methods. In the field of optoacoustic tomography, the image reconstruction problem is often characterized by computationally extensive inversion of very large datasets, for instance when acquiring volumetric multispectral data with high temporal resolution. In this article we seek to accelerate accurate model-based optoacoustic inversions by identifying various sources of sparsity in the forward and inverse models as well as in the single- and multi-frame representation of the projection data. These sources of sparsity are revealed through appropriate transformations in the signal, model and image domains and are subsequently exploited for expediting image reconstruction. The sparsity-based inversion scheme was tested with experimental data, offering reconstruction speed enhancement by a factor of 40 to 700 times as compared with the conventional iterative model-based inversions while preserving similar image quality. The demonstrated results pave the way for achieving real-time performance of model-based reconstruction in multi-dimensional optoacoustic imaging.

Segmentation of biomedical images is essential for studying and characterizing anatomical structures as well as for detection and evaluation of tissue pathologies. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities in the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.

Optoacoustic (photoacoustic) imaging based on cylindrically focused 1-D transducer arrays comes with powerful characteristics in visualizing optical contrast. Parallel reading of multiple detectors arranged around a tissue cross section enables capturing data for generating images of this plane within micro-seconds. Dedicated small animals scanners and handheld systems using 1-D cylindrically focused ultrasound transducer arrays have demonstrated real-time cross-sectional imaging and high in-plane resolution. Yet, the resolution achieved along the axis perpendicular to the focal plane, i.e., the elevation resolution, is determined by the focusing capacities of the detector and is typically lower than the in-plane resolution. Herein, we investigated whether deconvolution of the sensitivity field of the transducer could lead to tangible image improvements. We showcase the findings on experimental measurements from phantoms and animals and discuss the features and the limitations of the approach in improving resolution along the elevation dimension.

Optoacoustic tomography provides a unique possibility for ultra-high-speed 3-D imaging by acquiring complete volumetric datasets from interrogation of tissue by a single nanosecond-duration laser pulse. Yet, similarly to ultrasound, optoacoustics is a time-resolved imaging method, thus, fast 3-D imaging implies real-time acquisition and processing of high speed data from hundreds of detectors simultaneously, which presents significant technological challenges. Herein we present a highly efficient graphical processing unit (GPU) framework for real-time reconstruction and visualization of 3-D tomographic optoacoustic data. By utilizing a newly developed 3-D optoacoustic scanner, which simultaneously acquires signals with a handheld 256-element spherical ultrasonic array system, we further demonstrate tracking of deep tissue human vasculature rendered at a rate of 10 volumetric frames per second. The flexibility provided by the handheld hardware design, combined with the real-time operation, makes the developed platform highly usable for both clinical imaging practice and small animal research applications.

In many practical optoacoustic imaging implementations, dimensionality of the tomographic problem is commonly reduced into two dimensions or 1-D scanning geometries in order to simplify technical implementation, improve imaging speed or increase signal-to-noise ratio. However, this usually comes at a cost of significantly reduced quality of the tomographic data, out-of-plane image artifacts, and overall loss of image contrast and spatial resolution. Quantitative optoacoustic image reconstruction implies therefore collection of point 3-D (volumetric) data from as many locations around the object as possible. Here, we propose and validate an accurate model-based inversion algorithm for 3-D optoacoustic image reconstruction. Superior performance versus commonly-used backprojection inversion algorithms is showcased by numerical simulations and phantom experiments.

Optoacoustic tomography has recently demonstrated powerful performance in small animal imaging and initial clinical trials in terms of the high spatial resolution, versatile contrast, and dynamic imaging capabilities it can provide. Yet, the current optoacoustic image reconstruction methods are usually based on inaccurate forward modelling approaches or otherwise demand a high computational cost, which imposes certain practical limitations and hinders image quantification. Herein, we introduce a new method for accelerating optoacoustic reconstructions, based on angular image discretization of the forward model solution. The method is particularly suitable for accurate image reconstruction with arbitrary meshes and space-dependent resolution, while it can also readily account for small speed of sound variations without compromising the calculation speed. It is further anticipated that the new approach will greatly facilitate development of high performance 3-D optoacoustic reconstruction methods.

The use of model-based algorithms in tomographic imaging offers many advantages over analytical inversion methods. However, the relatively high computational complexity of model-based approaches often restricts their efficient implementation. In practice, many modern imaging modalities, such as computed-tomography, positron-emission tomography, or optoacoustic tomography, normally use a very large number of pixels/voxels for image reconstruction. Consequently, the size of the forward-model matrix hinders the use of many inversion algorithms. In this paper, we present a new framework for model-based tomographic reconstructions, which is based on a wavelet-packet representation of the imaged object and the acquired projection data. The frequency localization property of the wavelet-packet base leads to an approximately separable model matrix, for which reconstruction at each spatial frequency band is independent and requires only a fraction of the projection data. Thus, the large model matrix is effectively separated into a set of smaller matrices, facilitating the use of inversion schemes whose complexity is highly nonlinear with respect to matrix size. The performance of the new methodology is demonstrated for the case of 2-D optoacoustic tomography for both numerically generated and experimental data.

A method is presented to reduce artefacts produced in optoacoustic tomography images due to internal reflection or scattering of the acoustic waves. It is based on weighting the tomographic contribution of each detector with the probability that a signal affected by acoustic mismatches is measured at that position. The correction method does not require a priori knowledge of the acoustic or optical properties of the imaged sample. Performance tests were made with agar phantoms that included air gaps for mimicking strong acoustic reflections as well as with an acoustically heterogeneous adult Zebrafish. The results obtained with the method proposed show a clear reduction of the artefacts with respect to the original images reconstructed with filtered back-projection algorithm. This performance is directly related to in vivo small animal imaging applications involving imaging in the presence of bones, lungs, and other highly mismatched organs.

We present a fast model-based inversion algorithm for quantitative 2-D and 3-D optoacoustic tomography. The algorithm is based on an accurate and efficient forward model, which eliminates the need for regularization in the inversion process while providing modeling flexibility essential for quantitative image formation. The resulting image-reconstruction method eliminates stability problems encountered in previously published model-based techniques and, thus, enables performing image reconstruction in real time. Our model-based framework offers a generalization of the forward solution to more comprehensive optoacoustic propagation models, such as including detector frequency response, without changing the inversion procedure. The reconstruction speed and other algorithmic performances are demonstrated using numerical simulation studies and experimentally on tissue-mimicking optically heterogeneous phantoms and small animals. In the experimental examples, the model-based reconstructions manifested correctly the effect of light attenuation through the objects and did not suffer from the artifacts which usually afflict the commonly used filtered backprojection algorithms, such as negative absorption values.

We report on a new quantification methodology of optoacoustic tomographic reconstructions under heterogeneous illumination conditions representative of realistic whole-body imaging scenarios. Our method relies on the differences in the spatial characteristics of the absorption coefficient and the optical energy density within the medium. By using sparse-representation based decomposition, we exploit these different characteristics to extract both the absorption coefficient and the photon density within the imaged object from the optoacoustic image. In contrast to previous methods, this algorithm is not based on the solution of theoretical light transport equations and it does not require explicit knowledge of the illumination geometry or the optical properties of the object and other unknown or loosely defined experimental parameters, leading to highly robust performance. The method was successfully examined with numerically and experimentally generated data and was found to be ideally suited for practical implementations in tomographic schemes of varying complexity, including multiprojection illumination systems and multispectral optoacoustic tomography (MSOT) studies of tissue biomarkers.