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.

Universal Real-Time Adaptive Signal Compression for High-Frame-Rate Optoacoustic Tomography

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.

Expediting Image Acquisition in Reflection Ultrasound Computed Tomography

Reflection ultrasound computed tomography (RUCT) attains optimal image quality from objects that can be fully accessed from multiple directions, such as the human breast or small animals. Owing to the full-view tomography approach based on the compounding of images taken from multiple angles, RUCT effectively mitigates several deficiencies afflicting conventional pulse-echo ultrasound (US) systems, such as speckle patterns and interuser variability. On the other hand, the small interelement pitch required to fulfill the spatial sampling criterion in the circular transducer configuration used in RUCT typically implies the use of an excessive number of independent array elements. This increases the system's complexity and costs, and limits the achievable imaging speed. Here, we explore acquisition schemes that enable RUCT imaging with the reduced number of transmit/receive elements. We investigated the influence of the element size in transmission and reception in a ring array geometry. The performance of a sparse acquisition approach based on partial acquisition from a subset of the elements has been further assessed. A larger element size is shown to preserve contrast and resolution at the center of the field of view (FOV), while a reduced number of elements is shown to cause uniform loss of contrast and resolution across the entire FOV. The tradeoffs of achievable FOV, contrast-to-noise ratio, and temporal and spatial resolutions are assessed in phantoms and in vivo mouse experiments. The experimental analysis is expected to aid the development of optimized hardware and image acquisition strategies for RUCT and, thus, result in more affordable imaging systems facilitating wider adoption.

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.

Dual-Mode Volumetric Optoacoustic and Contrast Enhanced Ultrasound Imaging With Spherical Matrix Arrays

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.

In-vitro and in-vivo characterization of CRANAD-2 for multi-spectral optoacoustic tomography and fluorescence imaging of amyloid-beta deposits in Alzheimer mice

The abnormal deposition of fibrillar beta-amyloid (Aβ) deposits in the brain is one of the major histopathological hallmarks of Alzheimer's disease (AD). Here, we characterized curcumin-derivative CRANAD-2 for multi-spectral optoacoustic tomography and fluorescence imaging of brain Aβ deposits in the arcAβ mouse model of AD cerebral amyloidosis. CRANAD-2 showed a specific and quantitative detection of Aβ fibrils in vitro, even in complex mixtures, and it is capable of distinguishing between monomeric and fibrillar forms of Aβ. In vivo epi-fluorescence microscopy and optoacoustic tomography after intravenous CRANAD-2 administration demonstrated higher cortical retention in arcAβ compared to non-transgenic littermate mice. Immunohistochemistry showed co-localization of CRANAD-2 and Aβ deposits in arcAβ mouse brain sections, thus verifying the specificity of the probe. In conclusion, we demonstrate suitability of CRANAD-2 for optical detection of Aβ deposits in animal models of AD pathology, which facilitates mechanistic studies and the monitoring of putative treatments targeting Aβ deposits.

LightSpeed: A Compact, High-Speed Optical-Link-Based 3D Optoacoustic Imager

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.

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.

Compressed Optoacoustic Sensing of Volumetric Cardiac Motion

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.

Model-Based Reconstruction of Large Three-Dimensional Optoacoustic Datasets

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.

Spatial Compounding of Volumetric Data Enables Freehand Optoacoustic Angiography of Large-Scale Vascular Networks

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.

Noninvasive Anatomical and Functional Imaging of Orthotopic Glioblastoma Development and Therapy using Multispectral Optoacoustic Tomography

PURPOSE

Here we demonstrate the potential of multispectral optoacoustic tomography (MSOT), a new non-invasive structural and functional imaging modality, to track the growth and changes in blood oxygen saturation (sO2) in orthotopic glioblastoma (GBMs) and the surrounding brain tissues upon administration of a vascular disruptive agent (VDA).

METHODS

Nude mice injected with U87MG tumor cells were longitudinally monitored for the development of orthotopic GBMs up to 15 days and observed for changes in sO2 upon administration of combretastatin A4 phosphate (CA4P, 30 mg/kg), an FDA approved VDA for treating solid tumors. We employed a newly-developed non-negative constrained approach for combined MSOT image reconstruction and unmixing in order to quantitatively map sO2 in whole mouse brains.

RESULTS

Upon longitudinal monitoring, tumors could be detected in mouse brains using single-wavelength data as early as 6 days post tumor cell inoculation. Fifteen days post-inoculation, tumors had higher sO2 of 63 ± 11% (n = 5, P < .05) against 48 ± 7% in the corresponding contralateral brain, indicating their hyperoxic status. In a different set of animals, 42 days post-inoculation, tumors had lower sO2 of 42 ± 5% against 49 ± 4% (n = 3, P < .05) in the contralateral side, indicating their hypoxic status. Upon CA4P administration, sO2 in 15 days post-inoculation tumors dropped from 61 ± 9% to 36 ± 1% (n = 4, P < .01) within one hour, then reverted to pre CA4P treatment values (63 ± 6%) and remained constant until the last observation time point of 6 hours.

CONCLUSION

With the help of advanced post processing algorithms, MSOT was capable of monitoring the tumor growth and assessing hemodynamic changes upon administration of VDAs in orthotopic GBMs.

Combined Pulse-Echo Ultrasound and Multispectral Optoacoustic Tomography With a Multi-Segment Detector Array

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.

Efficient 3-D Model-Based Reconstruction Scheme for Arbitrary Optoacoustic Acquisition Geometries

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.

Constrained Inversion and Spectral Unmixing in Multispectral Optoacoustic Tomography

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.

Visual Quality Enhancement in Optoacoustic Tomography Using Active Contour Segmentation Priors

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.

Real-Time Model-Based Inversion in Cross-Sectional 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.

Multiscale edge detection and parametric shape modeling for boundary delineation in optoacoustic images

In this article, we present a novel scheme for segmenting the image boundary (with the background) in optoacoustic small animal in vivo imaging systems. The method utilizes a multiscale edge detection algorithm to generate a binary edge map. A scale dependent morphological operation is employed to clean spurious edges. Thereafter, an ellipse is fitted to the edge map through constrained parametric transformations and iterative goodness of fit calculations. The method delimits the tissue edges through the curve fitting model, which has shown high levels of accuracy. Thus, this method enables segmentation of optoacoutic images with minimal human intervention, by eliminating need of scale selection for multiscale processing and seed point determination for contour mapping.