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 by tissue optical properties, an effect that causes spectral corruption. Predictions of the spectral variations of light fluence in tissue are challenging since the spatial distribution of optical properties in tissue cannot be resolved in high resolution or with high accuracy by current methods. Spectral corruption has fundamentally limited the quantification accuracy of optical and optoacoustic methods and impeded the long sought-after goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical but still unattainable target for the assessment of oxygenation in physiological processes and disease. We discover a new principle underlying light fluence in tissues, which describes the wavelength dependence of light fluence as an affine function of a few reference base spectra, independently of the specific distribution of tissue optical properties. This finding enables the introduction of a previously undocumented concept termed eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively account for wavelength dependent light attenuation without explicit knowledge of the tissue optical properties. We validate eMSOT in more than 2000 simulations and with phantom and animal measurements. We find that eMSOT can quantitatively image tissue sO2 reaching in many occasions a better than 10-fold improved accuracy over conventional spectral optoacoustic methods. Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor; revealing so far unattainable tissue physiology patterns. Last, we related eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2 images and tissue perfusion and hypoxia maps obtained by correlative histological analysis

Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of 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.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

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

Optoacoustic tomography is a fast developing imaging modality, combining the high contrast available from optical excitation of tissue with the high resolution and penetration depth of ultrasound detection. Light is subject to both absorption and scattering when traveling through tissue; adequate knowledge of tissue optical properties and hence the spatial fluence distribution is required to create an optoacoustic image that is directly proportional to chromophore concentrations at all depths. Using data from a commercial multispectral optoacoustic tomography (MSOT) system, we implemented an iterative optimization for fluence correction based on a finite-element implementation of the delta-Eddington approximation to the Radiative Transfer Equation (RTE). We demonstrate a linear relationship between the image intensity and absorption coefficients across multiple wavelengths and depths in phantoms. We also demonstrate improved feature visibility and spectral recovery at depth in phantoms and with in vivo measurements, suggesting our approach could in the future enable quantitative extraction of tissue absorption coefficients in biological tissue.This work was funded by the EPSRC-CRUK Cancer Imaging Centre in Cambridge and Manchester (C197/A16465); CRUK (C47594/A16267, C14303/A17197); EU FP7 framework programme (FP7-PEOPLE-2013-CIG-630729) and the University of Cambridge EPSRC Impact Acceleration Account via a Partnership Development Award.This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via http://dx.doi.org/10.1109/TMI.2016.260719

Towards in vivo characterization of thyroid nodules suspicious for malignancy using multispectral optoacoustic tomography

Purpose: Patient-tailored management of thyroid nodules requires improved risk of malignancy stratification by accurate preoperative nodule assessment, aiming to personalize decisions concerning diagnostics and treatment. Here, we perform an exploratory pilot study to identify possible patterns on multispectral optoacoustic tomography (MSOT) for thyroid malignancy stratification. For the first time, we directly correlate MSOT images with histopathology data on a detailed level. Methods: We use recently enhanced data processing and image reconstruction methods for MSOT to provide next-level image quality by means of improved spatial resolution and spectral contrast. We examine optoacoustic features in thyroid nodules associated with vascular patterns and correlate these directly with reference histopathology. Results: Our methods show the ability to resolve blood vessels with diameters of 250 μm at depths of up to 2 cm. The vessel diameters derived on MSOT showed an excellent correlation (R2-score of 0.9426) with the vessel diameters on histopathology. Subsequently, we identify features of malignancy observable in MSOT, such as intranodular microvascularity and extrathyroidal extension verified by histopathology. Despite these promising features in selected patients, we could not determine statistically relevant differences between benign and malignant thyroid nodules based on mean oxygen saturation in thyroid nodules. Thus, we illustrate general imaging artifacts of the whole field of optoacoustic imaging that reduce image fidelity and distort spectral contrast, which impedes quantification of chromophore presence based on mean concentrations. Conclusion: We recommend examining optoacoustic features in addition to chromophore quantification to rank malignancy risk. We present optoacoustic images of thyroid nodules with the highest spatial resolution and spectral contrast to date, directly correlated to histopathology, pushing the clinical translation of MSOT.</p

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

DeepMB: Deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound

Multispectral optoacoustic tomography (MSOT) is a high-resolution functional imaging modality that can non-invasively access a broad range of pathophysiological phenomena by quantifying the contrast of endogenous chromophores in tissue. Real-time imaging is imperative to translate MSOT into clinical imaging, visualize dynamic pathophysiological changes associated with disease progression, and enable in situ diagnoses. Model-based reconstruction affords state-of-the-art optoacoustic images; however, the image quality provided by model-based reconstruction remains inaccessible during real-time imaging because the algorithm is iterative and computationally demanding. Deep learning affords faster reconstruction, but the lack of ground truth training data can lead to reduced image quality for in vivo data. We introduce a framework, termed DeepMB, that achieves accurate optoacoustic image reconstruction for arbitrary input data in 31 ms per image by expressing model-based reconstruction with a deep neural network. DeepMB facilitates accurate generalization to experimental test data through training on signals synthesized from real-world images and ground truth images generated by model-based reconstruction. The framework affords in-focus images for a broad range of anatomical locations because it supports dynamic adjustment of the reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible with the data rates and image sizes of modern multispectral optoacoustic tomography scanners. We evaluate DeepMB on a diverse dataset of in vivo images and demonstrate that the framework reconstructs images 1000 times faster than the iterative model-based reference method while affording near-identical image qualities. Accurate and real-time image reconstructions with DeepMB can enable full access to the high-resolution and multispectral contrast of handheld optoacoustic tomography

Real-time blood oxygenation tomography with multispectral photoacoustics

Multispectral photoacoustics is an emerging biomedical imaging modality which combines the penetration depth and resolution of high frequency medical ultrasonography with an optical absorption contrast. This enables tomographic imaging of blood oxygen saturation, a functional biomarker with wide applications. Already, photoacoustic imaging (PAI) is widely applied for small animal imaging in preclinical research. While PAI is a multiscale modality, its translation to clinical research and interventional use remains challenging. The objective of this thesis was to investigate the usefulness of multispectral PAI as a technique for interventional tomographic imaging of blood oxygenation. This thesis presents open challenges alongside research contributions to address them. These contributions are, (1) The design and implementation of an interventional PAI system, (2) Methods for real-time photoacoustic (PA) image processing and quantification of tissue absorption and blood oxygenation, and finally (3) the application of multispectral PAI to translational neurosurgical research – performing the first high spatiotemporal resolution tomography of spreading depolarization, and at the same time the first interventional PAI on any gyrencephalic (folded) brain. Such interventional imaging in neurology is one of many promising fields of application for PAI

The optical inverse problem in quantitative photoacoustic tomography

Photoacoustic tomography relies on the generation of ultrasound due to optical absorption to produce high resolution images with rich optical absorption-based contrast. In quantitative photoacoustic tomography, the aim is to estimate the concentration of the chromophores and thus provide functional information in addition to the structural images. This is a challenging task due to the unknown and spatially and spectrally varying light fluence within the tissue, which causes the photoacoustic images to be nonlinearly related to the chromophore concentrations. This thesis approaches this problem from two perspectives: Firstly, the conditions under which two linear quantification methods, linear spectroscopic inversion (SI) and independent component analysis (ICA), provide accurate results are investigated. Secondly, the statistical independence between the chromophores is used to improve the robustness and hence the usefulness of nonlinear model-based inversion methods in experimental settings. Using simulated images of a mouse brain, SI was shown to estimate the blood oxygenation within 5% error for a large range of imaging depths (0-9mm) and oxygenation levels (60-100%) if a large number of evenly spread wavelengths (>17) from the range 670-1000nm were used. Based on simulated and experimental images of tissue mimicking phantoms, ICA was shown to estimate the relative concentrations more accurately than SI when the spectral matrix is ill-conditioned and when the absorption of vessel-like features is approximately 0.5mm-1, under the assumption that the chromophores are statistically independent and a first order fluence correction has been applied. To reduce the sensitivity of model-based inversion to model-mismatch, a measure of the statistical independence between the chromophores was included in the error functional in addition to the least-squares data error. By minimising the new error functional using a gradient-based optimisation algorithm, more accurate quantification was obtained for both simulated and experimentally acquired phantom images in the presence of experimental uncertainties