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The limited view of a planar detector array results in artefacts in the reconstructed images. To address this, iterative image reconstruction methods have been proposed. However, there remains a need for a rigorous assessment of their performance in terms of artefact reduction, small structure detection, spatial resolution and robustness to noise. Additionally, an understanding of the impact of regularisation parameters on image fidelity and SNR as a function of the target geometry and instrument noise is required. The aim of this study is to explore these factors and advance the practical application of iterative reconstruction methods for in vivo images acquired using a planar detection geometry.

A new approach to photoacoustic tomography is presented in which a number of spatially overlapping photoacoustic time series measurements are aligned and registered to extend the effective imaging aperture. This approach not only improves the lateral resolution and imaging depth in tissues but also enables imaging of targets that are much larger than the physical size of the detector array.

A 3D high resolution scanner has been developed specifically for clinical use. The novel scanner architecture employing multiple interrogation beams can acquire a 3D image in less than 1 second. An initial technical validation study has been undertaken in human volunteers to determine repeatability, reproducibility and patient acceptability. Thereafter, a first-in-man clinical study aimed at assessing diagnostic accuracy in patients with inflammatory diseases has been completed.

k-Wave is an open-source MATLAB toolbox designed for the time-domain simulation of propagating acoustic waves in 1D, 2D, or 3D. The first release was in 2009, and focused on the simulation of photoacoustic initial value problems and the reconstruction of photoacoustic images from simulated or experimental data. In the ten years since, there have been eight major releases, extending both the functionality and the computational performance of the toolbox. There are now more than 10,000 registered users worldwide, and the toolbox has become the defacto standard for simulation studies in photoacoustic imaging. The development team responsible for k-Wave has also grown, with expertise now spanning physics, mathematics, inverse problems, numerical methods, software engineering, and high-performance computing. In this presentation, the major theoretical, algorithmic, and computational developments of k-Wave will be described, along with the underlying design inputs and decisions that led to these developments. A roadmap for the future development of k-Wave will also be presented. This includes new transducer classes, stair-case free sources, native support for multiple GPUs, adaptive grid refinement using moving mesh methods, gradient-based iterative photoacoustic image reconstruction, performance and accuracy improvements for the elastic wave models, and automatic job-submission to run k-Wave simulations remotely using HPC-as-a-service.

Photoacoustic imaging instruments based on the Fabry Perot ultrasound sensing concept have been used extensively for the preclinical assessment of mouse models and shown to provide high fidelity images to sub-cm depths. In a new development, a 3D high resolution scanner based on the same technology has now been specifically engineered into a format comprising a mobile platform and a convenient hand-held imaging probe for clinical use. A number of key engineering developments designed to advance the clinical translation of the technology have been implemented. The system now employs a novel 32-channel optical scanning architecture and a 1kHz PRF excitation laser providing an order-of-magnitude faster acquisition than previous pre-clinical embodiments. 3D images can now be acquired within 1 second, and video rate 2D synthetic aperture imaging is achievable. Image acquisition speed can be further accelerated by employing sub-sampling techniques based on total variation and deep learning image reconstruction, e.g. 3D images can be obtained at the rate of 4Hz with a typical 25% sub-sampling factor. To further aid clinical utility, the scanner allows rapid switching between the two imaging modes. This enables the ROI to be searched for and located in real-time using the 2D video rate mode prior to 3D image acquisition. Additional recent technical developments include bias wavelength tracking for temperature compensation, synthetic 1.5D array based receive beam forming for out-of-plane signal rejection, fast image reconstruction and visualisation and the implementation of an intuitive user-friendly interface. To confirm clinical applicability, proof-of-concept studies both in healthy volunteers and patients have been conducted using the system. Following ethical and local regulatory approval, consenting patients were recruited from a single tertiary care hospital. Participants had previously been diagnosed with peripheral vascular disease (PVD), head and neck malignant tumours (including nodal deposits), inflammatory arthritis, or were under active clinical investigation for these conditions. We obtained mutliwavelength 3D images of the superficial vasculature in critically-ischaemic and normally perfused regions in patients with PVD. In both cases, the photoacoustic images were compared to clinical B-mode and Doppler ultrasound scans. The results show that the scanner is able to visualise the spatial-temporal changes in human microvasculature and thus may be able to identify regions of ischaemia otherwise undetectable using existing modalities. Images of small joint arthopathies, and malignant lymph nodes were also obtained, and compared with contemporaneous high resolution ultrasound. Patients found the use of the scanner highly acceptable, both in degree of comfort and the duration of the scan procedure. This exploratory phase clinical study represents an initial step towards establishing the clinical utility of photoacoustic imaging in a range of clinical conditions.

Compared to piezoelectric based photoacoustic (PA) scanners, the planar Fabry-Perot (FP) scanner has several advantages. It can provide small element size with high sensitivity, a smooth broadband frequency response, and is transparent to excitation light. This enables the FP scanner to provide excellent high-resolution in vivo PA images of soft tissue to depths up to approximately 10 mm. However, unlike piezoelectric scanners, the FP scanner in its current form cannot provide a pulse-echo ultrasound (US) as well as a PA image, which is useful because of the additional tissue contrast it provides. To address this, a dual mode FP scanner-based system that, for the first time, can acquire co-registered 3D PA and US images has been developed. In order to provide an optical US generation capability, the FP ultrasound sensor was coated with a novel Gold-Nanoparticle-PDMS composite which was excited with nanosecond laser pulses to generate plane wave US pulses. By modifying the FP sensor in this way, it now acts as an US transmitter as well as a receiver. The coating is highly absorbing at the US generation wavelength (>95%) but transparent at the PA excitation wavelength, the latter to allow the system to also operate in PA imaging mode as before. The generated US pulses exhibited peak pressures in the MPa range, which is comparable to the output of conventional piezoelectric based medical US scanners. The pulses had a broad bandwidth (>40 MHz) and the emitted wavefront was planar to within λ/10 at 10 MHz. PA and pulse-echo US signals were mapped in turn by the FP scanner over centimetre scale areas with a step size of 100 μm and an element size of 64 μm. The -3dB bandwidth of the FP sensor was 30 MHz. Reconstruction methods using a k-space formulation recovered co-registered 3D PA and US images. The system’s lateral spatial resolution was evaluated by imaging a line target at depths up to 10 mm and ranged between 50 and 120 μm for both modes. Arbitrarily shaped 3D objects were imaged to demonstrate the volumetric US imaging capability of the scanner. Tissue mimicking phantoms, with impedance mismatches representative of soft tissues, and ex vivo tissue samples were imaged with the system as well as a conventional clinical US scanner for comparison. Finally, the system obtained promising high-resolution 3D dual mode PA-US images for a variety of phantoms with contrast based on both optical absorption and acoustic impedance. This novel all-optical system has the potential to add complementary morphological contrast to photoacoustic vascular images which could aid the clinical assessment of superficial tumours, lymph node disease and other conditions.

There are occasions, perhaps due to hardware constraints, or to speed-up data acquisition, when it is helpful to be able to reconstruct a photoacoustic image from an under-sampled or incomplete data set. Here, we will show how Deep Learning can be used to improve image reconstruction in such cases. Deep Learning is a type of machine learning in which a multi-layered neural network is trained from a set of examples to perform a task. Convolutional Neural Networks (CNNs), a type of deep neural network in which one or more layers perform convolutions, have seen spectacular success in recent years in tasks as diverse as image classification, language processing and game playing. In this work, a series of CNNs were trained to perform the steps of an iterative, gradient-based, image reconstruction algorithm from under-sampled data. This has two advantages: first, the iterative reconstruction is accelerated by learning more efficient updates for each iterate; second, the CNNs effectively learn a prior from the training data set, meaning that it is not necessary to make potentially unrealistic regularising assumptions about the image sparsity or smoothness, for instance. In addition, we show an example in which the CNNs learn to remove artifacts that arise when a slow but accurate acoustic model is replaced by a fast but approximate model. Reconstructions from simulated as well as in vivo data will be shown.

To unlock the full capability of photoacoustic tomography as a quantitative, high resolution, molecular imaging modality, the problem of quantitative photoacoustic tomography must be solved. The aim in this is to extract clinically relevant functional information from photoacoustic images by finding the concentrations of the chromophores in the tissue. This is a challenging task due to the effect of the unknown but spatially and spectrally varying light fluence within the tissue. Many inversion schemes that include a model of the fluence have been proposed, but these have yet to make an impact in pre-clinical or clinical imaging. In this study, the statistical independence of the chromophore's distributions is proposed as a means of improving the robustness and hence the usefulness of the model-based inversion methods. This was achieved by minimising the mutual information between the estimated chromophore distributions in addition to the least squares data error within a gradient-based optimisation scheme. By applying the proposed inversion scheme to simulated multiwavelength photoacoustic images, it was shown that more accurate estimates for the concentrations of independent chromophores could be obtained in the presence of errors in the model parameters.

Planar Fabry-Perot (FP) ultrasound sensor arrays have been used to produce in-vivo photoacoustic images of high quality due to their broad detection bandwidth, small element size, and dense spatial sampling. However like all planar arrays, FP sensors suffer from the limited view problem. Here, a multi-angle FP sensor system is described that mitigates the partial view effects of a planar FP sensor while retaining its detection advantages. The possibility of improving data acquisition speed through the use of sub-sampling techniques is also explored. The capabilities of the system are demonstrated with 3D images of pre-clinical targets

The planar Fabry Perot (FP) photoacoustic scanner provides exquisite high resolution 3D images of soft tissue structures for sub-cm penetration depths. However, as the FP sensor is optically addressed by sequentially scanning an interrogation laser beam over its surface, the acquisition speed is low. To address this, a novel scanner architecture employing 8 interrogation beams and an optimised sub-sampling framework have been developed that increase the data acquisition speed significantly. With a 200Hz repetition rate excitation laser, full 3D images can be obtained within 10 seconds. Further increases in imaging speed with only minor decreases in image quality can be obtained by applying sub-sampling techniques with rates as low as 12.5%. This paper shows 3D images reconstructed from sub-sampled data for an ex vivo dataset, and results from a dynamic phantom imaging experiment.

Quantitative photoacoustic tomography seeks to estimate the optical parameters of a target given photoacoustic measurements as a data. Conventionally the problem is split into two steps: 1) the acoustical inverse problem of estimating the acoustic initial pressure distribution from the acoustical time series data; 2) the optical inverse problem of estimating the optical absorption and scattering from the initial pressure distributions. In this work, an approach for estimating the optical absorption and scattering directly from the acoustical time series is investigated with simulations. The work combines a homogeneous acoustical forward model, based on the Green's function solution of the wave equation, and a finite element method based diffusion approximation model of light propagation into a single forward model. This model maps the optical parameters of interest into a time domain signal. The model is used with a Bayesian approach to ill-posed inverse problems to form estimates of the posterior distributions for the parameters of interest. In addition to being able to provide point estimates of the parameters of interest, i.e. reconstruct the absorption and scattering distributions, the approach can be used to derive information on the uncertainty associated with the estimates.

Photoacoustic tomography can, in principle, provide quantitatively accurate, high-resolution, images of chromophore distributions in 3D in vivo. However, achieving this goal requires not only dealing with the optical fluence-related spatial and spectral distortion but also having access to high quality, calibrated, measurements and using image reconstruction algorithms free from inaccurate assumptions. Furthermore, accurate knowledge of experimental parameters, such as the positions of the ultrasound detectors and the illumination pattern, is necessary for the reconstruction step. A meticulous and rigorous experimental phantom study was conducted to show that highly-resolved 3D estimation of chromophore distributions can be achieved: a crucial step towards in vivo implementation. The phantom consisted of four 580 μm diameter tubes with different ratios of copper sulphate and nickel sulphate as hemoglobin analogues, submersed in a background medium of intralipid and india ink. The optical absorption, scattering, photostability, and Gruneisen parameter were characterised for all components independently. A V-shaped imaging scanner enabled 3D imaging with the high resolution, high sensitivity, and wide bandwidth characteristic of Fabry-Perot ultrasound sensors, but without the limited-view disadvantage of single-plane scanners. The optical beam profile and position were determined experimentally. Nine wavelengths between 750 and 1110 nm were used. The images of the chromophore concentrations were obtained using a model-based, two-step, procedure, that did not require image segmentation. First, the acoustic reconstruction was solved with an iterative time-reversal algorithm to obtain images of the initial acoustic pressure at each of the nine wavelengths for an 18×17×13 mm3 volume with 50μm voxels. Then, 3D high resolution estimates of the chromophore concentrations were obtained by using a diffusion model of light transport in an iterative nonlinear optimisation scheme. Among the lessons to be drawn from this study, one is fundamental: in order to obtain accurate estimates of chromophores (or their ratios) it is not only necessary to model the light fluence accurately, but it is just as crucial to obtain accurate estimates of the initial acoustic pressure distributions, and to account for variations in the thermoelastic efficiency (Gruneisen parameter).

Independent component analysis (ICA) is a blind source unmixing method that may be used under certain circumstances to decompose multi-wavelength photoacoustic (PA) images into separate components representing individual chromophores. It has the advantages of being fast, easy to implement and computationally inexpensive. This study uses simulated multi-wavelength PA images to investigate the conditions required for ICA to be an accurate unmixing method and compares its performance to linear inversion. An approximate fluence adjustment based on spatially homogeneous optical properties equal to that of the background region was applied to the PA images before unmixing with ICA or LI. ICA is shown to provide accurate separation of the chromophores in cases where the absorption coefficients are lower than certain thresholds, some of which are comparable to physiologically relevant values. However, the results also show that the performance of ICA abruptly deteriorates when the absorption is increased beyond these thresholds. In addition, the accuracy of ICA decreases in the presence of spatially inhomogeneous absorption in the background.

The planar Fabry Perot (FP) photoacoustic scanner has been shown to provide exquisite high resolution 3D images of soft tissue structures in vivo to depths up to approximately 10mm. However a significant limitation of current embodiments of the concept is low image acquisition speed. To increase acquisition speed, a novel multi-beam scanner architecture has been developed. This enables a line of equally spaced 8 interrogation beams to be scanned simultaneously across the FP sensor and the photoacoustic signals detected in parallel. In addition, an excitation laser operating at 200Hz was used. The combination of parallelising the detection and the high pulse repetition frequency (PRF) of the excitation laser has enabled dramatic reductions in image acquisition time to be achieved. A 3D image can now be acquired in 10 seconds and 2D images at video rates are now possible. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Reconstructing images from measured time domain signals is an essential step in tomography-mode photoacoustic imaging. However, in practice, there are many complicating factors that make it difficult to obtain high-resolution images. These include incomplete or undersampled data, filtering effects, acoustic and optical attenuation, and uncertainties in the material parameters. Here, the processing and image reconstruction steps routinely used by the Photoacoustic Imaging Group at University College London are discussed. These include correction for acoustic and optical attenuation, spatial resampling, material parameter selection, image reconstruction, and log compression. The effect of each of these steps is demonstrated using a representative in vivo dataset. All of the algorithms discussed form part of the open-source k-Wave toolbox (available from http://www.k-wave.org).

The ability to accurately quantify chromophore concentration from photoacoustic images would have a major impact on pre-clinical and clinical imaging. Recent years have seen significant advances in the theoretical understanding of quantitative photoacoustic imaging and in the development of model-based inversion strategies that overcome issues such as non-uniqueness and non-linearity. Nevertheless, their full in vivo implementation has not successfully been achieved, partially because experimental uncertainties complicate the transition. In this study, a sensitivity analysis is performed to assess the impact on accuracy of having uncertainty in critical experimental parameters such as scattering, beam diameter, beam position and calibration factor. This study was performed using two virtual phantoms, at one illumination and four optical wavelengths. The model-based inversion was applied in 3 variants - one just inverting for chromophores and two others further inverting for either a scaling factor or the scatterer concentration. The performance of these model-based inversions is also compared to linear unmixing strategies - with and without fluence correction. The results show that experimental uncertainties in a priori fixed parameters - especially calibration factor and scatterer concentration - significantly affect accuracy of model-based inversions and therefore measures to ameliorate this uncertainty should be considered. Including a scaling parameter in the inversion appears to improve quantification estimates. Furthermore, even with realistic levels of experimental uncertainty in model-based input parameters, they outperform linear unmixing approaches. If parameter uncertainty is large and has significant impact on accuracy, the parameter can be included as an unknown in model-based schemes.

Photoacoustic tomography (PAT) is an imaging technique combining strong contrast of optical imaging to high spatial resolution of ultrasound imaging. These strengths are achieved via photoacoustic effect, where a spatial absorption of light pulse is converted into a measurable propagating ultrasound wave. The method is seen as a potential tool for small animal imaging, pre-clinical investigations, study of blood vessels and vasculature, as well as for cancer imaging. The goal in PAT is to form an image of the absorbed optical energy density field via acoustic inverse problem approaches from the measured ultrasound data. Quantitative PAT (QPAT) proceeds from these images and forms quantitative estimates of the optical properties of the target. This optical inverse problem of QPAT is illposed. To alleviate the issue, spectral QPAT (SQPAT) utilizes PAT data formed at multiple optical wavelengths simultaneously with optical parameter models of tissue to form quantitative estimates of the parameters of interest. In this work, the inverse problem of SQPAT is investigated. Light propagation is modelled using the diffusion equation. Optical absorption is described with chromophore concentration weighted sum of known chromophore absorption spectra. Scattering is described by Mie scattering theory with an exponential power law. In the inverse problem, the spatially varying unknown parameters of interest are the chromophore concentrations, the Mie scattering parameters (power law factor and the exponent), and Gruneisen parameter. The inverse problem is approached with a Bayesian method. It is numerically demonstrated, that estimation of all parameters of interest is possible with the approach.

We propose a novel multispectral reconstruction-classification method for simultaneously recovering absorption and scattering coefficients from images of absorbed optical energy. In contrast with pre-existing chromophore reconstruction methods, this approach does not require prior knowledge of the characteristic spectra of the absorbers, which is not always available. Numerical experiments performed on anatomically realistic 3D phantoms show that this approach allows for improved recovery of both the optical absorption and scattering with respect to reconstruction-only methods, and accurate classification of chromophores of clinical interest.

Quantitative photoacoustic tomography is an emerging imaging technique aimed at estimating the optical parameters inside tissue from photoacoustic images. The method proceeds from photoacoustic tomography by taking the estimated initial pressure distributions as data and estimating the absolute values of the optical parameters. Therefore, both the data and the noise of the second (optical) inverse problem are affected by the method applied to solve the first (acoustic) inverse problem. In this work, the Bayesian approach for quantitative photoacoustic tomography is taken. Modelling of noise and errors and incorporating their statistics into the solution of the inverse problem are investigated.

Fabry-Perot (FP) sensors have been used to produce in-vivo photoacoustic images of exquisite quality. However, for simplicity of construction FP sensors are produced in a planar form. Planar sensors suffer from a limited detection aperture, due to their planarity. We present a novel sensor geometry that allowed a greater field of view by placing a second sensor orthogonal to the first. This captured data from the deeper lying regions of interest and mitigated the limited view.

Photoacoustic imaging can provide high resolution images of tissue structure, pathology and function. As these images can be obtained at multiple wavelengths, quantitatively accurate, spatially resolved, estimates for chromophore concentration, for example, may be obtainable. Such a capability would find a wide range of clinical and pre-clinical applications. However, despite a growing body of theoretical papers on how this might be achieved, there is a noticeable lack of studies providing validated evidence that it can be achieved experimentally, either in vitro or in vivo. Well-defined, versatile and stable phantom materials are essential to assess the accuracy, robustness and applicability of multispectral Quantitative Photoacoustic Imaging (qPAI) algorithms in experimental scenarios. This study assesses the potential of polyvinyl chloride plastisol (PVCP) as a phantom material for qPAI, building on previous work that focussed on using PVCP for quality control. Parameters that might be controlled or tuned to assess the performance of qPAI algorithms were studied: broadband acoustic properties, multiwavelength optical properties with added absorbers and scatterers, and photoacoustic effciency. The optical and acoustic properties of PVCP can be tuned to be broadly representative of soft tissue. The Gruneisen parameter is larger than expected in tissue, which is an advantage as it increases the signal-to-noise ratio of the photoacoustic measurements. Interestingly, when the absorption was altered by adding absorbers, the absorption spectra measured using high peak power nanosecond-pulsed sources (typical in photoacoustics) were repeatably different from the ones measured using the low power source in the spectrophotometer, indicative of photochemical reactions taking place.

Photoacoustic images of exquisite quality have previously been obtained using planar Fabry-Perot ultrasound sensors, as they can synthesize detection arrays with small, highly sensitive, elements. However, their planarity prevents reconstruction of structures perpendicular to the sensor plane, which gives rise to limited-view artifacts. Here, a novel FP sensor array configuration is described that incorporates two orthogonal planar arrays in order to overcome this limitation. Three dimensional photoacoustic images of suitably structured phantoms, obtained using a time reversal reconstruction algorithm, are used to demonstrate the significant improvement in the reconstructed images.

In photoacoustic tomography (PAT) the image contrast is due to optical absorption, and because of this PAT images are sensitive to changes in blood oxygen saturation ( s O 2 ). However, this is not a linear relationship due to the presence of a non-uniform light fluence distribution. In this paper we systematically evaluate the conditions in which an approximate linear inversion scheme–which assumes the internal fluence distribution is unchanged when the absorption coefficient changes–can give accurate estimates of s O 2 . A numerical phantom of highly vascularised tissue is used to test this assumption. It is shown that using multiple wavelengths over a broad range of the near-infrared spectrum yields inaccurate estimates of oxygenation, while a careful selection of wavelengths in the 620-920nm range is likely to yield more accurate oxygenation values. We demonstrate that a 1D fluence correction obtained by fitting a linear function to the average decay rate in the image can further improve the estimates. However, opting to use these longer wavelengths involves sacrificing signal-to-noise ratio in the image, as the absorption of blood is low in this range. This results in an inherent trade-off between error in the s O 2 estimates due to fluence variation and error due to noise. This study shows that the depth to which s O 2 can be estimated accurately using a linear approximation is limited in vivo, even with idealised measurements, to at most 3mm. In practice, there will be even greater uncertainties affecting the estimates, e.g., due to bandlimited or partial-view acoustic detection.

A system for dynamic mapping of broadband ultrasound fields has been designed, with high frame rate photoacoustic imaging in mind. A Fabry-Perot interferometric ultrasound sensor was interrogated using a coherent light single-pixel camera. Scrambled Hadamard measurement patterns were used to sample the acoustic field at the sensor, and either a fast Hadamard transform or a compressed sensing reconstruction algorithm were used to recover the acoustic pressure data. Frame rates of 80 Hz were achieved for 32x32 images even though no specialist hardware was used for the on-thefly reconstructions. The ability of the system to obtain photocacoustic images with data compressions as low as 10% was also demonstrated.

In quantitative photoacoustic imaging, the aim is to recover physiologically relevant tissue parameters such as chromophore concentrations or oxygen saturation. Obtaining accurate estimates is challenging due to the non-linear relationship between the concentrations and the photoacoustic images. Nonlinear least squares inversions designed to tackle this problem require a model of light transport, the most accurate of which is the radiative transfer equation. This paper presents a highly scalable Monte Carlo model of light transport that computes the radiance in 2D using a Fourier basis to discretise in angle. The model was validated against a 2D finite element model of the radiative transfer equation, and was used to compute gradients of an error functional with respect to the absorption and scattering coefficient. It was found that adjoint-based gradient calculations were much more robust to inherent Monte Carlo noise than a finite difference approach. Furthermore, the Fourier angular discretisation allowed very efficient gradient calculations as sums of Fourier coefficients. These advantages, along with the high parallelisability of Monte Carlo models, makes this approach an attractive candidate as a light model for quantitative inversion in photoacoustic imaging.

Planar sensor arrays have many advantages including ease of manufacture and low cost. However, when used for photoacoustic (PA) imaging, planar sensors have a limited view of the acoustic field, which means some of the waves from the PA source are not recorded. This results in artifacts in the reconstructed image of the PA source (the initial acoustic pressure distribution). In this paper, we describe novel sensor array configurations based on the Fabry Perot (FP) sensor and acoustic reflectors that retain its detection advantages while improving the visibility of the reconstructed PA image.

Five numerical phantoms were developed to investigate the accuracy of approximate inversion schemes in the reconstruction of oxygen saturation in photoacoustic imaging. In particular, two types of inversion are considered: Type I, an inversion that assumes fluence is unchanged between illumination wavelengths, and Type II, a method that assumes known background absorption and scattering coefficients to partially correct for the fluence. These approaches are tested in tomography (PAT) and acoustic-resolution microscopy mode (AR-PAM). They are found to produce accurate values of oxygen saturation in a blood vessel of interest at shallow depth - less than 3mm for PAT and less than 1mm for AR-PAM.

Photoacoustic tomography (PAT) has become a powerful tool for biomedical imaging, particularly pre-clinical small animal imaging. Several different measurement systems have been demonstrated, in particular, optically addressed Fabry-Perot interferometer (FPI) sensors have been shown to provide exquisite images when a planar geometry is suitable. However, in its current incarnation the measurements must be made at each point sequentially, so these devices therefore suffer from slow data acquisition time. An alternative to this point-by-point interrogation scheme, is to interrogate the whole sensor with a series of independent patterns, so each measurement is the spatial integral of the product of the pattern and the acoustic field (as in the single-pixel Rice camera). Such an interrogation scheme allows compressed sensing to be used. This enables the number of measurements to be reduced significantly, leading to much faster data acquisition. An experimental implementation will be described, which employs a wide NIR tunable laser beam to interrogate the FPI sensor. The reflected beam is patterned by a digital micro-mirror device, and then focused to a single photodiode. To demonstrate the idea of patterned and compressed sensing for ultrasound detection, a scrambled Hadamard operator is used in the experiments. Photoacoustic imaging experiments of phantoms shows good reconstructed results with 20% compression.

Quantitative photoacoustic tomography is an emerging imaging technique aiming at estimating the distribution of optical parameters inside tissue from photoacoustic image which is formed by combining optical information and ultrasound propagation. In this paper reconstruction of absorption and scattering distributions using the radiative transfer equation and the diffusion approximation as forward models for light propagation is investigated. Data is simulated using Monte Carlo method and different size target domains are considered. The results show that the radiative transfer equation can estimate both absorption and scattering distributions with good accuracy. Furthermore, in the simulated test cases, the diffusion approximation can produce as good estimates for absorption as the radiative transfer equation.

Quantitative photoacoustic tomography involves the construction of a photoacoustic image from surface measurements of photoacoustic wave pulses and the recovery of the optical properties of the imaged region. This is a nonlinear, ill-posed inverse problem, for which model-based inversion techniques have been proposed. Here, the radiative transfer equation is used to model the light propagation, and the acoustic propagation and image reconstruction are included. In other words, the full quantitative inversion is tackled. Since Newton-based minimisations are impractical when dealing with three-dimensional images, an adjoint-assisted gradient-based inversion was used as a practical alternative to determining the optical coefficients.

Almost all known photoacoustic image reconstruction algorithms are based on the assumption that the acoustic waves leave the object (the imaged region) after a finite time. This assumption is fulfilled if the measurements are made in free space and reflections from the detectors are negligible. However, when the object is surrounded by acoustically hard detectors arrays (and/or by additional acoustic mirrors), the acoustic waves will bounce around in such a reverberant cavity many times (in the absence of absorption, forever). This paper proposes fast reconstruction algorithms for the measurements made from the walls of a rectangular reverberant cavity. The algorithms are tested using numerical simulations.

A numerical inversion scheme for recovering a map of the absolute conductivity from the absorbed power density map that is conventionally reconstructed in thermacoustic imaging is described. This offers the prospect of obtaining an image that is more closely related to the underlying tissue structure and physiology. The inversion scheme employs a full 3D full wave model of electromagnetic propagation in tissue which is iteratively fitted to the measured absorbed power density map using a simple recursive method. The reconstruction is demonstrated numerically using three examples of absorbers of varying geometries, tissue realistic complex permittivity values and noise. In these examples, the reconstruction is shown to rapidly converge to within good estimates of the true conductivity in less than 20 iterations.

Photoacoustic imaging is a promising technique combining high ultrasonic resolution and high optical contrast. However, quantification has proved rather challenging. In this paper, we present a non-iterative reconstruction strategy with multiple-optical-sources for reconstruction of absorption, scattering perturbations as well as the spatially varying Grueneisen parameter from a known turbid background. We term this method the multiple-illumination photoacoustic tomography (MI-PAT). While numerical challenges still exist, we demonstrated that the linearized MI-PAT framework we propose has orders of magnitude improved condition number compared with Continuous-Wave Diffuse Optical Tomography (CW-DOT).

Photoacoustic tomography can provide high resolution 3D images of vascular networks, making it well suited to characterising the development of tumour vasculature and its response to treatment. In this study, photoacoustic images to depths of up to 9 mm were obtained using an all optical ultrasound detection scheme. Two type of colorectal tumours (LS174T and SW1222) implanted subcutaneously in a mouse were studied. 3D photoacoustic images were obtained in vivo revealing the different vascular architectures of each tumour type and their evolution over a period of several days. The results suggest that photoacoustic imaging could play a role in providing essential pre-clinical information on tumour pathophysiology and eliciting the biological mechanisms underlying anti-angiogenic therapies and other treatments.

Most image reconstruction algorithms for biomedical photoacoustic tomography make the assumption that the optically-generated ultrasonic waves are recorded by pressure detectors with an omni-directional response. In other words, the detectors are assumed to sample the pressure field exactly at a point. In practice this is rarely the case as real detectors have a finite size and often respond not purely to pressure changes but to some combination of acoustic pressure and pressure gradient (or other derivatives). This can make them less sensitive to pressure waves at some angles. The effect of this sensor directionality on photoacoustic tomography was considered here for the case of time-reversal image reconstruction. The ultrasound simulation toolbox k-Wave was used to perform the study.

In recent years, some of the promised potential of biomedical photoacoustic imaging has begun to be realised. It has been used to produce good, three-dimensional, images of blood vasculature in mice and other small animals, and in human skin in vivo, to depths of several mm, while maintaining a spatial resolution of

In biomedical photoacoustic tomography (PAT), ultrasonic pulses generated by the absorption of near-infrared light are recorded over an array of detectors, and the measured pressure time series are used to recover an image of the initial acoustic pressure distribution within the tissue, which is related to the tissue optical coefficients and therefore to tissue physiology. For high resolution imaging, large-area detector arrays with a high density of sensitive, small elements are required. Such arrays can be expensive, so reverberant-field PAT has been suggested as a means of obtaining PAT images using arrays with a smaller number of detectors or even a single detector. We propose that by recording the reflections from a reverberant cavity in addition to the primary acoustic waves, sufficient information can be captured to allow a PAT image to be reconstructed, without the requirement for a large-area array. A pilot study using simple 2D simulations, backprojections and modal inversions was undertaken to assess the feasibility of this approach to PAT.

In biomedical photoacoustic tomography of soft tissue, the initial acoustic pressure distribution following the absorption of a short excitation laser pulse, is recovered as a function of position. This initial pressure distribution is proportional to the absorbed optical energy density, and is thus related (albeit indirectly) to the tissue optical coefficients. When imaging soft tissue which contains several absorbing chromophores (such as oxy- and deoxy-haemoglobin, water, etc.), the primary quantity of interest is the concentrations of the chromophores at each point in the tissue, and not the absorbed optical energy density, which is nonlinearly related to the chromophore concentrations, and also depends on the distribution of scattering. Estimating the distribution of the concentration of a chromophore therefore requires the recovery of two unknown functions (chromophore concentration and scattering distributions) from measurements of one (absorbed energy density). For measurements made at a single optical wavelength, this problem suffers from nonuniqueness, and cannot be solved without additional information being incorporated. A simulated example is used here to demonstrate that, in principle, by using multi-wavelength data and incorporating the known wavelength dependence of the chromophore absorption and the scattering as prior information, a chromophore concentration and spatial dependence of the scattering can be recovered simultaneously. This step opens the way to physiological and molecular imaging using multispectral photoacoustic tomography.

Photoacoustic spectroscopy has been shown to be capable of making non-invasive, spatially resolved measurements of haemodynamic parameters, such as the concentrations of oxy- and deoxyhaemoglobin and blood oxygen saturation. The development of photoacoustic techniques for molecular imaging that go beyond the measurement of haemodynamic parameters has recently become an area of interest. These techniques are aimed at the detection and quantification of for example contrast agents targeted at pathologies such as tumours for diagnostic or therapeutic purposes. This study aimed to validate a model-based inversion scheme by recovering chromophore concentrations from 2D multiwavelength images obtained using a tissue phantom. The inversion scheme employed a complete photoacoustic forward model, which incorporates a model of light transport, a model of acoustic propagation and Fourier transform image reconstruction algorithm. Using the structural information from the measured images, the photoacoustic forward model was used to calculate theoretical multiwavelength photoacoustic images as a function of the concentrations of spatially distributed tissue chromophores and scatters. The chromophore concentrations were determined by fitting the model to the measured images. It was found that concentration ratios of reasonable accuracy were recovered while the absolute concentrations showed significant errors due to light-induced instabilities in the nearinfrared dyes used in the tissue phantom.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The aim in quantitative photoacoustic imaging (QPI) is to recover the spatial distributions of the optical absorption and scattering coefficients from an absorbed energy density distribution (a conventional photoacoustic image). This paper proposes a gradient-based minimisation approach and demonstrates that functional gradients may be calculated efficiently by using a finite element model of light transport based on the diffusion approximation, in conjunction with a related adjoint model. The gradients calculated using this adjoint method are tested against finite-difference estimates, and inversions for the absorption or scattering coefficient distributions (from simulated data) are shown for the case where the other coefficient is known a priori. Simultaneous estimation for both absorption and scattering is ill-posed, and so multiwavelength inversion, in which the specific absorption spectra of the constituent chromophores and the wavelength-dependence of the scatter are known, is proposed as a means of ameliorating the ill-posedness. The unknown parameters are now the spatial variation of the chromphore concentrations and scattering coefficient. It is shown that the functional gradients for both of these can be obtained straightforwardly from the gradients for absorption and scatter and require no significant additional calculations. A simulated example is given in which the distributions of two chromophores with different spectra are recovered from absorbed energy images obtained at multiple wavelengths, when the scattering is assumed known.

Biomedical photoacoustic imaging produces a map of the initial acoustic pressure distribution, or absorbed energy density, in tissue following a short laser pulse. Quantitative photoacoustic imaging (QPI) takes the reconstruction process one stage further to produce a map of the tissue optical coefficients. This has two important advantages. Firstly, it removes the distorting effect of the internal light distribution on image contrast. Secondly, by obtaining images at multiple wavelengths, it enables standard spectroscopic techniques to be used to quantify the concentrations of specific chromophores, for instance, oxy and deoxy haemoglobin for the measurement of blood oxygenation - applying such techniques directly to "conventionally" reconstructed absorbed energy maps is problematic due to the spectroscopic 'spatial crosstalk' effects between different tissue chromophores. As well as naturally-occurring chromophores, dye-labelled molecular markers can be used to tag specific molecules, such as cell surface receptors, enzymes or pharmaceutical agents. In QPI, a diffusion-based finite element model of light transport in scattering media, with δ-Eddington scattering coefficients, is fitted to the absorbed energy distribution to estimate the optical coefficient maps. The approach described here uses a recursive algorithm and converges quickly on the absorption coefficient distribution, when the scattering is known. By adding an area of known absorption, an unknown constant scattering coefficient may also be recovered. With optical coefficient maps estimated in this way, QPI has the potential to be a powerful tool for quantifying the concentration of molecular markers in photoacoustic molecular imaging.

Photoacoustic signals are usually generated using bulky and expensive Q-switched Nd:YAG lasers, with limited scope for varying the pulse repetition frequency, wavelength and pulse width. An alternative would be to use laser diodes as excitation sources; these devices are compact, relatively inexpensive, and available in a wide variety of NIR wavelengths. Their pulse duration and repetition rates can also be varied arbitrarily enabling a wide range of time and frequency domain excitation methods to be employed. The main difficulty to overcome when using laser diodes for pulsed photoacoustic excitation is their low peak power compared to Q-switched lasers. However, the much higher repetition rate of laser diodes (~ kHz) compared to many Q-switched laser systems (~ tens of Hz) enables a correspondingly greater number of events to be acquired and signal averaged over a fixed time period. This offers the prospect of significantly increasing the signal-to-noise ratio (SNR) of the detected photoacoustic signal. Choosing the wavelength of the laser diode to be lower than that of the water absorption peak at 940nm, may also provide a significant advantage over a system lasing at 1064nm for measurements in tissue. If the output of a number of laser diodes is combined it then becomes possible, in principle, to obtain a SNR approaching that achievable with a Q-switched laser. It is also suggested that optimising the pulse duration of the laser diode may reduce the effects of frequency-dependent acoustic attenuation in tissue on the photoacoustic signal. To investigate this, a numerical model based on the Poisson solution to the wave equation was developed. To validate the model, a high peak power pulsed laser diode system was built. It was composed of a 905nm stacked array laser diode coupled to an optical fibre and driven by a high current laser diode driver. Measurements of the SNR of photoacoustic signals generated in a purely absorbing medium (ink) were made as a function of pulse duration. This preliminary study shows the potential for using laser diodes as excitation sources for photoacoustic applications in the biomedical field.

Photoacoustic imaging, which generates a map of the initial acoustic pressure distribution generated by a short laser pulse, has been demonstrated by several authors. Quantitative photoacoustic imaging takes this one stage further to produce a map of the distribution of an optical property of the tissue, in this case absorption, which can then be related to a physiological parameter. In this technique, the initial pressure distribution is assumed to be proportional to the absorbed laser energy density. A model of light transport in scattering media is then used to estimate the distribution of optical properties that would result in such a pattern of absorbed energy. The light model used a finite element implementation of the diffusion equation (with the delta-E(3) approximation included to improve the accuracy at short distances inside the scattering medium). An algorithm which applies this model iteratively and converges on a quantitative estimate of the optical absorption distribution is described. 2D examples using simulated data (initial pressure maps) with and without noise are shown to converge quickly and accurately.

The International Photoacoustic Standardisation Consortium (IPASC) emerged from SPIE 2018, established to drive consensus on photoacoustic system testing. As photoacoustic imaging (PAI) matures from research laboratories into clinical trials, it is essential to establish best-practice guidelines for photoacoustic image acquisition, analysis and reporting, and a standardised approach for technical system validation. The primary goal of the IPASC is to create widely accepted phantoms for testing preclinical and clinical PAI systems. To achieve this, the IPASC has formed five working groups (WGs). The first and second WGs have defined optical and acoustic properties, suitable materials, and configurations of photoacoustic image quality phantoms. These phantoms consist of a bulk material embedded with targets to enable quantitative assessment of image quality characteristics including resolution and sensitivity across depth. The third WG has recorded details such as illumination and detection configurations of PAI instruments available within the consortium, leading to proposals for system-specific phantom geometries. This PAI system inventory was also used by WG4 in identifying approaches to data collection and sharing. Finally, WG5 investigated means for phantom fabrication, material characterisation and PAI of phantoms. Following a pilot multi-centre phantom imaging study within the consortium, the IPASC settled on an internationally agreed set of standardised recommendations and imaging procedures. This leads to advances in: (1) quantitative comparison of PAI data acquired with different data acquisition and analysis methods; (2) provision of a publicly available reference data set for testing new algorithms; and (3) technical validation of new and existing PAI devices across multiple centres.