20 research outputs found

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

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    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

    Computationally Efficient Forward Operator for Photoacoustic Tomography Based on Coordinate Transformations

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    IEEE Photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect. In PAT, a photoacoustic image is computed from measured data by modeling ultrasound propagation in the imaged domain and solving an inverse problem utilizing a discrete forward operator. However, in realistic measurement geometries with several ultrasound transducers and relatively large imaging volume, an explicit formation and use of the forward operator can be computationally prohibitively expensive. In this work, we propose a transformation based approach for efficient modeling of photoacoustic signals and reconstruction of photoacoustic images. In the approach, the forward operator is constructed for a reference ultrasound transducer and expanded into a general measurement geometry using transformations that map the formulated forward operator in local coordinates to the global coordinates of the measurement geometry. The inverse problem is solved using a Bayesian framework. The approach is evaluated with numerical simulations and experimental data. The results show that the proposed approach produces accurate three-dimensional photoacoustic images with a significantly reduced computational cost both in memory requirements and in time. In the studied cases, depending on the computational factors such as discretization, over 30-fold reduction in memory consumption and was achieved without a reduction in image quality compared to a conventional approach

    Mitigating the limited view problem in photoacoustic tomography for a planar detection geometry by regularised iterative reconstruction

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    The use of a planar detection geometry in photoacoustic tomography results in the so-called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularisation were evaluated using in-silico , experimental phantom, ex vivo and in vivo data. Compared to one-step reconstruction methods, it has been shown that iterative methods provide better image quality in terms of enhanced signal-to-artefact ratio, signal-to-noise ratio, amplitude accuracy and spatial fidelity. For the total variation approaches, the impact of the regularisation parameter on image feature scale and amplitude distribution was evaluated. In addition, the extent to which the use of Bregman iterations can compensate for the systematic amplitude bias introduced by total variation was studied. This investigation is expected to inform the practical application of model-based iterative image reconstruction approaches for improving photoacoustic image quality when using finite aperture planar detection geometries

    Quantitative photoacoustic tomography: experimental phantom studies

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    Photoacoustic tomography (PAT) is a promising non-invasive imaging modality exhibiting high resolution, good contrast and specificity to light-absorbing molecules (chromophores). One of the outstanding challenges the technique faces is that PAT images, though dependent on optical absorption, are not its direct representation because they are coloured by the unknown light fluence. Theoretical studies have succeeded in quantifying optical absorption and chromophore concentration by employing model-based inversions (MBI) that can deal with the non-linearity of the problem and the fluence-related distortion. However, experimental translation has been scarce. The aim was to perform quantitative PAT (qPAT) in a rigorous experimental phantom study to show that highly-resolved 3D estimation of chromophore distributions can be achieved. The first consideration was finding a tissue-relevant and stable matrix material and chromophores. Thermoplastic PVCP was fully assessed. Its stability, intrinsic optical properties, thermoelastic efficiency and low-frequency acoustic properties were suitable. The limitation was the lack of photostability of embedded pigments. Separately, we fully characterised aqueous solutions of sulphate salts and found them to be suitable chromophores for qPAT and potential surrogates for oxy- and deoxyhemoglobin. For a phantom made of sub-mm tubes filled with sulphate solutions in an intralipid-rich background, 3D high resolution estimates of chromophore concentrations were obtained through an efficient diffusion-approximation MBI. Uncertainties in optical inputs of the MBI were tackled by assessing in silico their effect on quantification accuracy and then mitigated in the designed experiment through careful measurements. A faithful representation of the multiwavelength photoacoustic tomography images was sought by employing broadband, near-omnidirectional and high-sensitivity sensors and a detection configuration and reconstruction that overcame the limited-view problem. Estimation of the chromophore ratio, analogous to the much sought-after blood oxygenation, gave a mean absolute error of 3.4 p.p., whilst normalised estimates of the two main chromophore distributions gave errors of 13.2% and 17.2%

    Multi-modal diffuse optical tomography and bioluminescence tomography system for preclinical imaging

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    The development, characterisation and testing of a novel all-optical, multi-modal preclinical biomedical imaging system is presented. The system aims to provide a new way of accurately visualising the spatial distribution and activity of molecular structures and processes in small animals by combining 3D bioluminescence tomography (BLT; reconstruction-based 3D imaging of internal bioluminescent reporter distributions), diffuse optical tomography (DOT; reconstruction-based imaging of optical parameter distributions) and optical surface capture techniques. The key principle of the imaging system is to use surface capture results to enhance the accuracy of DOT image reconstruction, and to use the results of both surface capture and DOT to enhance the accuracy of BLT. Presented experiments show that the developed system can reconstruct luminescent source distributions and optical parameters accurately and that small animal imaging is feasible with the system

    Across frequency processes involved in auditory detection of coloration

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