Deep-Learning-Based Electrical Noise Removal Enables High Spectral Optoacoustic Contrast in Deep Tissue.

Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Previously employed signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to capture complex characteristics of signal and noise. Moreover, they often involve time-consuming processing steps that are unsuited for real-time imaging applications. In this work, we develop and demonstrate a discriminative deep learning approach to separate electrical noise from optoacoustic signals prior to image reconstruction. The proposed deep learning algorithm is based on two key features. First, it learns spatiotemporal correlations in both noise and signal by using the entire optoacoustic sinogram as input. Second, it employs training on a large dataset of experimentally acquired pure noise and synthetic optoacoustic signals. We validated the ability of the trained model to accurately remove electrical noise on synthetic data and on optoacoustic images of a phantom and the human breast. We demonstrate significant enhancements of morphological and spectral optoacoustic images reaching 19% higher blood vessel contrast and localized spectral contrast at depths of more than 2 cm for images acquired in vivo. We discuss how the proposed denoising framework is applicable to clinical multispectral optoacoustic tomography and suitable for real-time operation.

Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.

Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy.

The characteristics of tumour development and metastasis relate not only to genomic heterogeneity but also to spatial heterogeneity, associated with variations in the intratumoural arrangement of cell populations, vascular morphology and oxygen and nutrient supply. While optical (photonic) microscopy is commonly employed to visualize the tumour microenvironment, it assesses only a few hundred cubic microns of tissue. Therefore, it is not suitable for investigating biological processes at the level of the entire tumour, which can be at least four orders of magnitude larger. In this study, we aimed to extend optical visualization and resolve spatial heterogeneity throughout the entire tumour volume. We developed an optoacoustic (photoacoustic) mesoscope adapted to solid tumour imaging and, in a pilot study, offer the first insights into cancer optical contrast heterogeneity in vivo at an unprecedented resolution of <50 µm throughout the entire tumour mass. Using spectral methods, we resolve unknown patterns of oxygenation, vasculature and perfusion in three types of breast cancer and showcase different levels of structural and functional organization. To our knowledge, these results are the most detailed insights of optical signatures reported throughout entire tumours in vivo, and they position optoacoustic mesoscopy as a unique investigational tool linking microscopic and macroscopic observations.

Longitudinal imaging of T cell-based immunotherapy with multi-spectral, multi-scale optoacoustic tomography.

Most imaging studies of immunotherapy have focused on tracking labeled T cell biodistribution in vivo for understanding trafficking and homing parameters and predicting therapeutic efficacy by the presence of transferred T cells at or in the tumour mass. Conversely, we investigate here a novel concept for longitudinally elucidating anatomical and pathophysiological changes of solid tumours after adoptive T cell transfer in a preclinical set up, using previously unexplored in-tandem macroscopic and mesoscopic optoacoustic (photoacoustic) imaging. We show non-invasive in vivo observations of vessel collapse during tumour rejection across entire tumours and observe for the first time longitudinal tumour rejection in a label-free manner based on optical absorption changes in the tumour mass due to cellular decline. We complement these observations with high resolution episcopic fluorescence imaging of T cell biodistribution using optimized T cell labeling based on two near-infrared dyes targeting the cell membrane and the cytoplasm. We discuss how optoacoustic macroscopy and mesoscopy offer unique contrast and immunotherapy insights, allowing label-free and longitudinal observations of tumour therapy. The results demonstrate optoacoustic imaging as an invaluable tool in understanding and optimizing T cell therapy.

Advances in Optoacoustic Neurotomography of Animal Models.

Unlike traditional optical methods, optoacoustic imaging is less sensitive to scattering of ballistic photons, so it is capable of high-resolution interrogation at a greater depth. By integrating video-rate visualization with multiplexing and sensing a range of endogenous and exogenous chromophores, optoacoustic imaging has matured into a versatile noninvasive investigation modality with rapidly expanding use in biomedical research. We review the principal features of the technology and discuss recent advances it has enabled in structural, functional, and molecular neuroimaging in small-animal models. In extending the boundaries of noninvasive observation beyond the reach of customary photonic methods, the latest developments in optoacoustics have substantially advanced neuroimaging inquiry, with promising implications for basic and translational studies.

Synthetic data framework to estimate the minimum detectable concentration of contrast agents for multispectral optoacoustic imaging of small animals.

The concentrations of contrast agents for optoacoustic imaging of small animals must usually be optimized through extensive pilot experiments on a case-by-case basis. The present work describes a streamlined approach for determining the minimum detectable concentration (MDC) of a contrast agent given experimental conditions and imaging system parameters. The developed Synthetic Data Framework (SDF) allows estimation of MDCs of various contrast agents under different tissue conditions without extensive animal experiments. The SDF combines simulated optoacoustic signals from exogenously administered contrast agents with in vivo experimental signals from background tissue to generate realistic synthetic multispectral optoacoustic images. In this paper, the SDF is validated with in vivo measurements and demonstrates close agreement between SDF synthetic data and experimental data in terms of both image intensity and MDCs. Use of the SDF to estimate MDCs for fluorescent dyes and nanoparticles at different tissue depths and for imaging lesions of different sizes is illustrated.

Spatial and Spectral Mapping and Decomposition of Neural Dynamics and Organization of the Mouse Brain with Multispectral Optoacoustic Tomography.

In traditional optical imaging, limited light penetration constrains high-resolution interrogation to tissue surfaces. Optoacoustic imaging combines the superb contrast of optical imaging with deep penetration of ultrasound, enabling a range of new applications. We used multispectral optoacoustic tomography (MSOT) for functional and structural neuroimaging in mice at resolution, depth, and specificity unattainable by other neuroimaging modalities. Based on multispectral readouts, we computed hemoglobin gradient and oxygen saturation changes related to processing of somatosensory signals in different structures along the entire subcortical-cortical axis. Using temporal correlation analysis and seed-based maps, we reveal the connectivity between cortical, thalamic, and sub-thalamic formations. With the same modality, high-resolution structural tomography of intact mouse brain was achieved based on endogenous contrasts, demonstrating near-perfect matches with anatomical features revealed by histology. These results extend the limits of noninvasive observations beyond the reach of standard high-resolution neuroimaging, verifying the suitability of MSOT for small-animal studies.

A Bayesian Approach to Eigenspectra Optoacoustic Tomography.

The quantification of hemoglobin oxygen saturation (sO2) with multispectral optoacoustic (OA) (photoacoustic) tomography (MSOT) is a complex spectral unmixing problem, since the OA spectra of hemoglobin are modified with tissue depth due to depth (location) and wavelength dependencies of optical fluence in tissue. In a recent work, a method termed eigenspectra MSOT (eMSOT) was proposed for addressing the dependence of spectra on fluence and quantifying blood sO2 in deep tissue. While eMSOT offers enhanced sO2 quantification accuracy over conventional unmixing methods, its performance may be compromised by noise and image reconstruction artifacts. In this paper, we propose a novel Bayesian method to improve eMSOT performance in noisy environments. We introduce a spectral reliability map, i.e., a method that can estimate the level of noise superimposed onto the recorded OA spectra. Using this noise estimate, we formulate eMSOT as a Bayesian inverse problem where the inversion constraints are based on probabilistic graphical models. Results based on numerical simulations indicate that the proposed method offers improved accuracy and robustness under high noise levels due the adaptive nature of the Bayesian method.

Pushing the Boundaries of Neuroimaging with Optoacoustics.

With the central ability to visualize a variety of endogenous chromophores and biomarkers or exogenous contrast agents, optoacoustic (photoacoustic) imaging empowers new experimental capabilities for investigating brain mechanisms and functions. Here, the operational principles of optoacoustic neuroimaging are reviewed in conjunction with recent advances enabling high-resolution and real-time observation, which extend beyond the reach of optical imaging methods. Multiple implementations of optoacoustics for monitoring hemodynamics and neuro-vascular responses in the brain are showcased. The unique capabilities of optoacoustic imaging for multi-spectral cellular and molecular sensing are discussed with reference to recent application for visualizing healthy and diseased brains. Outstanding challenges in the field are considered in the context of current and future applications of optoacoustic neuroimaging for basic and translational neuroscience research. In pushing the boundaries of brain imaging, optoacoustic methods afford major insights into the neuronal mechanisms of brain functions and organization of behavior.

Hybrid multispectral optoacoustic and ultrasound tomography for morphological and physiological brain imaging.

Expanding usage of small animal models in biomedical research necessitates development of technologies for structural, functional, or molecular imaging that can be readily integrated in the biological laboratory. Herein, we consider dual multispectral optoacoustic (OA) and ultrasound tomography based on curved ultrasound detector arrays and describe the performance achieved for hybrid morphological and physiological brain imaging of mice in vivo. We showcase coregistered hemodynamic parameters resolved by OA tomography under baseline conditions and during alterations of blood oxygen saturation. As an internal reference, we provide imaging of abdominal organs. We illustrate the performance advantages of hybrid curved detector ultrasound and OA tomography and discuss immediate and long-term implications of our findings in the context of animal and human studies.

Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues.

Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation, an effect that causes spectral corruption. Spectral corruption has limited the quantification accuracy of optical and optoacoustic spectroscopic methods, and impeded the goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical goal for the assessment of oxygenation in physiological processes and disease. Here we describe light fluence in the spectral domain and introduce eigenspectra multispectral optoacoustic tomography (eMSOT) to account for wavelength-dependent light attenuation, and estimate blood sO2 within deep tissue. We validate eMSOT in simulations, phantoms and animal measurements and spatially resolve sO2 in muscle and tumours, validating our measurements with histology data. eMSOT shows substantial sO2 accuracy enhancement over previous optoacoustic methods, potentially serving as a valuable tool for imaging tissue pathophysiology.