Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.

Fluorescence molecular endoscopy (FME) is emerging as a "red-flag" technique with potential to deliver earlier, faster, and more personalized detection of disease in the gastrointestinal tract, including cancer, and to gain insights into novel drug distribution, dose finding, and response prediction. However, to date, the performance of FME systems is assessed mainly by endoscopists during a procedure, leading to arbitrary, potentially biased, and heavily subjective assessment. This approach significantly affects the repeatability of the procedures and the interpretation or comparison of the acquired data, representing a major bottleneck towards the clinical translation of the technology. Herein, we propose a robust methodology for FME performance assessment and quality control that is based on a novel multi-parametric rigid standard. This standard enables the characterization of an FME system's sensitivity through a single acquisition, performance comparison of multiple systems, and, for the first time, quality control of a system as a function of time and number of usages. We show the photostability of the standard experimentally and demonstrate how it can be used to characterize the performance of an FME system. Moreover, we showcase how the standard can be employed for quality control of a system. In this study, we find that the use of composite fluorescence standards before endoscopic procedures can ensure that an FME system meets the performance criteria and that components prone to performance degradation are replaced in time, avoiding disruption of clinical endoscopy logistics. This will help overcome a major barrier for the translation of FME into the clinics.

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.

Signals acquired by optoacoustic tomography systems have broadband frequency content that encodes information about structures on different physical scales. Concurrent processing and rendering of such broadband signals may result in images with poor contrast and fidelity due to a bias towards low frequency contributions from larger structures. This problem cannot be addressed by filtering different frequency bands and reconstructing them individually, as this procedure leads to artefacts due to its incompatibility with the entangled frequency content of signals generated by structures of different sizes. Here we introduce frequency-band model-based (fbMB) reconstruction to separate frequency-band-specific optoacoustic image components during image formation, thereby enabling structures of all sizes to be rendered with high fidelity. In order to disentangle the overlapping frequency content of image components, fbMB uses soft priors to achieve an optimal trade-off between localization of the components in frequency bands and their structural integrity. We demonstrate that fbMB produces optoacoustic images with improved contrast and fidelity, which reveal anatomical structures in in vivo images of mice in unprecedented detail. These enhancements further improve the accuracy of spectral unmixing in small vasculature. By offering a precise treatment of the frequency components of optoacoustic signals, fbMB improves the quality, accuracy, and quantification of optoacoustic images and provides a method of choice for optoacoustic reconstructions.

Optoacoustic signals are typically reconstructed into images using inversion algorithms applied in the time-domain. However, time-domain reconstructions can be computationally intensive and therefore slow when large amounts of raw data are collected from an optoacoustic scan. Here we considered a fast weighted ω-k (FWOK) algorithm operating in the frequency domain to accelerate the inversion in raster-scan optoacoustic mesoscopy (RSOM), while seamlessly incorporating impulse response correction with minimum computational burden. We investigated the FWOK performance with RSOM measurements from phantoms and mice in vivo and obtained 360-fold speed improvement over inversions based on the back-projection algorithm in the time-domain. This previously unexplored inversion of in vivo optoacoustic data with impulse response correction in frequency domain reconstructions points to a promising strategy of accelerating optoacoustic imaging computations, toward video-rate tomography.

The impulse response of optoacoustic (photoacoustic) tomographic imaging system depends on several system components, the characteristics of which can influence the quality of reconstructed images. The effect of these system components on reconstruction quality have not been considered in detail so far. Here we combine sparse measurements of the total impulse response (TIR) with a geometric acoustic model to obtain a full characterization of the TIR of a handheld optoacoustic tomography system with concave limited-view acquisition geometry. We then use this synthetic TIR to reconstruct data from phantoms and healthy human volunteers, demonstrating improvements in image resolution and fidelity. The higher accuracy of optoacoustic tomographic reconstruction with TIR correction further improves the diagnostic capability of handheld optoacoustic tomographic systems.

Optoacoustic (photoacoustic) mesoscopy offers unique capabilities in skin imaging and resolves skin features associated with detection, diagnosis, and management of disease. A critical first step in the quantitative analysis of clinical optoacoustic images is to identify the skin surface in a rapid, reliable, and automated manner. Nevertheless, most common edge- and surface-detection algorithms cannot reliably detect the skin surface on 3D raster-scan optoacoustic mesoscopy (RSOM) images, due to discontinuities and diffuse interfaces in the image. We present herein a novel dynamic programming approach that extracts the skin boundary as a 2D surface in one single step, as opposed to consecutive extraction of several independent 1D contours. A domain-specific energy function is introduced, taking into account the properties of volumetric optoacoustic mesoscopy images. The accuracy of the proposed method is validated on scans of the volar forearm of 19 volunteers with different skin complexions, for which the skin surface has been traced manually to provide a reference. In addition, the robustness and the limitations of the method are demonstrated on data where the skin boundaries are low-contrast or ill-defined. The automatic skin surface detection method can improve the speed and accuracy in the analysis of quantitative features seen on the RSOM images and accelerate the clinical translation of the technique. Our method can likely be extended to identify other types of surfaces in the RSOM and other imaging modalities.

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.

Raster-scan optoacoustic mesoscopy (RSOM) offers high-resolution non-invasive insights into skin pathophysiology, which holds promise for disease diagnosis and monitoring in dermatology and other fields. However, RSOM is quite vulnerable to vertical motion of the skin, which can depend on the part of the body being imaged. Motion correction algorithms have already been proposed, but they are not fully automated, they depend on anatomical segmentation pre-processing steps that might not be performed successfully, and they are not site- specific. Here, we determined for the first time the magnitude of the micrometric vertical skin displacements at different sites on the body that affect RSOM. The quantifi- cation of motion allowed us to develop a site-specific correction algorithm. The algorithm is fully automated and does not need prior anatomical information. We found that the magnitude of the vertical motion depends strongly on the site of imaging and is caused by breathing, heart beating, and arterial pulsation. The developed algorithm resulted in more than 2-fold improvement in the signal-to-noise ratio of the reconstructed images at every site tested. Proposing an effective automated motion correction algorithm paves the way for realizing the full clinical potential of RSOM.

Optoacoustic (photoacoustic) endoscopy has shown potential to reveal complementary contrast to optical endoscopy methods, indicating clinical relevance. However operational parameters for accurate optoacoustic endoscopy must be specified for optimal performance. Recent support from the EU Horizon 2020 program ESOTRAC to develop a next-generation optoacoustic esophageal endoscope directs the interrogation of the optimal frequency required for accurate implementation. We simulated the frequency response of the esophagus wall and then validated the simulation results with experimental measurements of pig esophagus. Phantoms and fresh pig esophagus samples were measured using two detectors with central frequencies of 15 or 50 MHz, and the imaging performance of both detectors was compared. We analyzed the frequency bandwidth of optoacoustic signals in relation to morphological layer structures of the esophagus and found the 50 MHz detector to differentiate layer structures better than the 15 MHz detector. Furthermore, we identify the necessary detection bandwidth for visualizing esophagus morphology and selecting ultrasound transducers for future optoacoustic endoscopy of the esophagus.

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.

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.

Optoacoustic (photoacoustic) dermoscopy offers two principal advantages over conventional optical imaging applied in dermatology. First, it yields high-resolution cross-sectional images of the skin at depths not accessible to other non-invasive optical imaging methods. Second, by resolving absorption spectra at multiple wavelengths, it enables label-free 3D visualization of morphological and functional features. However, the relation of pulse energy to generated bandwidth and imaging depth remains poorly defined. In this paper, we apply computer models to investigate the optoacoustic frequency response generated by simulated skin. We relate our simulation results to experimental measurements of the detection bandwidth as a function of optical excitation energy in phantoms and human skin. Using raster-scan optoacoustic mesoscopy, we further compare the performance of two broadband ultrasonic detectors (a bandwidth of 20-180 and 10-90MHz) in acquiring optoacoustic readouts. Based on the findings of this paper, we propose energy ranges required for skin imaging with considerations of laser safety standards.

High fidelity optoacoustic (photoacoustic) tomography requires dense spatial sampling of optoacoustic signals using point acoustic detectors. However, in practice, spatial resolution of the images is often limited by limited sampling either due to coarse multi-element arrays or time in raster scan measurements. Herein, we investigate a method that integrates information from multiple optoacoustic images acquired at sub-diffraction steps into one high resolution image by means of an iterative registration algorithm. Experimental validations performed in target phantoms and ex vivo tissue samples confirm that the suggested approach renders significant improvements in terms of optoacoustic image resolution and quality without introducing significant alterations into the signal acquisition hardware or inversion algorithms.

The concept of sparsity is extensively exploited in the fields of data acquisition and image processing, contributing to better signal-to-noise and spatio-temporal performance of the various imaging methods. In the field of optoacoustic tomography, the image reconstruction problem is often characterized by computationally extensive inversion of very large datasets, for instance when acquiring volumetric multispectral data with high temporal resolution. In this article we seek to accelerate accurate model-based optoacoustic inversions by identifying various sources of sparsity in the forward and inverse models as well as in the single- and multi-frame representation of the projection data. These sources of sparsity are revealed through appropriate transformations in the signal, model and image domains and are subsequently exploited for expediting image reconstruction. The sparsity-based inversion scheme was tested with experimental data, offering reconstruction speed enhancement by a factor of 40 to 700 times as compared with the conventional iterative model-based inversions while preserving similar image quality. The demonstrated results pave the way for achieving real-time performance of model-based reconstruction in multi-dimensional optoacoustic imaging.

The imaging performance of fluorescence molecular tomography (FMT) improves when information from the underlying anatomy is incorporated into the inversion scheme, in the form of priors. The requirement for incorporation of priors has recently driven the development of hybrid FMT systems coupled to other modalities, such as X-ray CT and MRI. A critical methodological aspect in this modality relates to the particular method selected to incorporate prior information obtained from the anatomical imaging modality into the FMT inversion. We propose herein a new approach for utilizing prior information, which preferentially minimizes residual errors associated with measurements that better describe the anatomical segments considered. This preferential minimization was realized using a weighted least square (WLS) approach, where the weights were optimized using a Mamdani-type fuzzy inference system. The method of priors introduced herein was deployed as a two-step structured regularization approach and was verified with experimental measurements from phantoms as well as ex vivo and in vivo animal studies. The results demonstrate accurate performance and minimization of reconstruction bias, without requiring user input for setting the regularization parameters. As such, the proposed method offers significant progress in incorporation of anatomical priors in FMT and, as a result, in realization of the full potential of hybrid FMT.

Statistical sub-pixel detection via the adaptive matched filter (AMF) has been shown to improve the molecular imaging sensitivity and specificity of optoacoustic (photoacoustic) imaging. Applied to multispectral optoacoustic tomography (MSOT), AMF assumes that the spatially-varying tissue spectra follow a multivariate Gaussian distribution, that the spectrum of the target molecule is precisely known and that the molecular target lies in "low probability" within the data. However, when these assumptions are violated, AMF may result in considerable performance degradation. The objective of this work is to develop a robust statistical detection framework that is appropriately suited to the characteristics of MSOT molecular imaging. Using experimental imaging data, we perform a statistical characterization of MSOT tissue images and conclude to a detector that is based on the t-distribution. More importantly, we introduce a method for estimating the covariance matrix of the background-tissue statistical distribution, which enables robust detection performance independently of the molecular target size or intensity. The performance of the statistical detection framework is assessed through simulations and experimental in vivo measurements and compared to previously used methods.

Raster-scan optoacoustic mesoscopy (RSOM) comes with high potential for in vivo diagnostic imaging in dermatology, since it allows for high resolution imaging of the natural chromophores melanin, and hemoglobin at depths of several millimeters. We have applied ultra-wideband RSOM, in the 10-160 MHz frequency band, to image healthy human skin at distinct locations. We analyzed the anatomical information contained at different frequency ranges of the optoacoustic (photoacoustic) signals in relation to resolving features of different skin layers in vivo. We further compared results obtained from glabrous and hairy skin and identify that frequencies above 60 MHz are necessary for revealing the epidermal thickness, a prerequisite for determining the invasion depth of melanoma in future studies. By imaging a benign nevus we show that the applied RSOM system provides strong contrast of melanin-rich structures. We further identify the spectral bands responsible for imaging the fine structures in the stratum corneum, assessing dermal papillae, and resolving microvascular structures in the horizontal plexus.

Fluorescence diffuse optical tomography (FDOT) is an emerging molecular imaging modality that uses near infrared light to excite the fluorophore injected into tissue; and to reconstruct the fluorophore concentration from boundary measurements. The FDOT image reconstruction is a highly ill-posed inverse problem due to a large number of unknowns and limited number of measurements. However, the fluorophore distribution is often very sparse in the imaging domain since fluorophores are typically designed to accumulate in relatively small regions. In this paper, we use compressive sensing (CS) framework to design light illumination and detection patterns to improve the reconstruction of sparse fluorophore concentration. Unlike the conventional FDOT imaging where spatially distributed light sources illuminate the imaging domain one at a time and the corresponding boundary measurements are used for image reconstruction, we assume that the light sources illuminate the imaging domain simultaneously several times and the corresponding boundary measurements are linearly filtered prior to image reconstruction. We design a set of optical intensities (illumination patterns) and a linear filter (detection pattern) applied to the boundary measurements to improve the reconstruction of sparse fluorophore concentration maps. We show that the FDOT sensing matrix can be expressed as a columnwise Kronecker product of two matrices determined by the excitation and emission light fields. We derive relationships between the incoherence of the FDOT forward matrix and these two matrices, and use these results to reduce the incoherence of the FDOT forward matrix. We present extensive numerical simulation and the results of a real phantom experiment to demonstrate the improvements in image reconstruction due to the CS-based light illumination and detection patterns in conjunction with relaxation and greedy-type reconstruction algorithms.

Optoacoustic (photoacoustic) imaging based on cylindrically focused 1-D transducer arrays comes with powerful characteristics in visualizing optical contrast. Parallel reading of multiple detectors arranged around a tissue cross section enables capturing data for generating images of this plane within micro-seconds. Dedicated small animals scanners and handheld systems using 1-D cylindrically focused ultrasound transducer arrays have demonstrated real-time cross-sectional imaging and high in-plane resolution. Yet, the resolution achieved along the axis perpendicular to the focal plane, i.e., the elevation resolution, is determined by the focusing capacities of the detector and is typically lower than the in-plane resolution. Herein, we investigated whether deconvolution of the sensitivity field of the transducer could lead to tangible image improvements. We showcase the findings on experimental measurements from phantoms and animals and discuss the features and the limitations of the approach in improving resolution along the elevation dimension.

Image quality in 3-D optoacoustic (photoacoustic) tomography is greatly influenced by both the measurement system, in particular the number and spatial arrangement of ultrasound sensors, and the ability to account for the spatio-temporal response of the sensor element(s) in the reconstruction algorithm. Herein we present a reconstruction procedure based on the inversion of a time-domain forward model incorporating the spatial impulse response due to the shape of the transducer, which is subsequently applied in a tomographic system based on a translation-rotation scan of a linear detector array. The proposed method was also adapted to cope with the data-intensive requirements of high-resolution volumetric optoacoustic imaging. The processing of 2 · 10 (4) individual signals resulted in well-resolved images of both ~ 200 μm absorbers in phantoms and complex vascular structures in biological tissue. The results reported herein demonstrate that the introduced model-based methodology exhibits a better contrast and resolution than standard back-projection and model-based algorithms that assume point detectors. Moreover, the capability of handling large datasets anticipates that model-based methods incorporating the sensor properties can become standard practice in volumetric opto acoustic image formation.

The implementation of hybrid fluorescence molecular tomography (FMT) and X-ray computed tomography (CT) has been shown to be a necessary development, not only for combining anatomical with functional and molecular contrast, but also for generating optical images of high accuracy. FMT affords highly sensitive 3-D imaging of fluorescence bio-distribution, but in stand-alone form it offers images of low resolution. It was shown that FMT accuracy significantly improves by considering anatomical priors from CT. Conversely, CT generally suffers from low soft tissue contrast. Therefore utilization of CT data as prior information in FMT inversion is challenging when different internal organs are not clearly differentiated. Instead, we combined herein FMT with emerging X-ray phase-contrast CT (PCCT). PCCT relies on phase shift differences in tissue to achieve soft tissue contrast superior to conventional CT. We demonstrate for the first time FMT-PCCT imaging of different animal models, where FMT and PCCT scans were performed in vivo and ex vivo, respectively. The results show that FMT-PCCT expands the potential of FMT in imaging lesions with otherwise low or no CT contrast, while retaining the cost benefits of CT and simplicity of hybrid device realizations. The results point to the most accurate FMT performance to date.

Multispectral optoacoustic tomography (MSOT) utilizes broadband ultrasound detection for imaging biologically-relevant optical absorption features at a range of scales. Due to the multiscale and multispectral features of the technology, MSOT comes with distinct requirements in implementation and data analysis. In this work, we investigate the interplay between scale, which depends on ultrasonic detection frequency, and optical multispectral spectral analysis, two dimensions that are unique to MSOT and represent a previously unexplored challenge. We show that ultrasound frequency-dependent artifacts suppress multispectral features and complicate spectral analysis. In response, we employ a wavelet decomposition to perform spectral unmixing on a per-scale basis (or per ultrasound frequency band) and showcase imaging of fine-scale features otherwise hidden by low frequency components. We explain the proposed algorithm by means of simple simulations and demonstrate improved performance in imaging data of blood vessels in human subjects.

Detection of intrinsic or extrinsically administered chromophores and photo-absorbing nanoparticles has been achieved by multi-spectral optoacoustic tomography (MSOT). The detection sensitivity of MSOT depends not only on the signal to noise ratio considerations, as in conventional optoacoustic (photoacoustic) tomography implementations, but also on the ability to resolve the molecular targets of interest from the absorbing tissue background by means of spectral unmixing or sub-pixel detection methods. However, it is not known which unmixing methods are optimally suited for the characteristics of multispectral optoacoustic images. In this work we investigated the performance of different sub-pixel detection methods, typically used in remote sensing hyperspectral imaging, within the context of MSOT. A quantitative comparison of the different algorithmic approaches was carried out in an effort to identify methods that operate optimally under the particulars of molecular imaging applications. We find that statistical sub-pixel detection methods can demonstrate a unique detection performance with up to five times enhanced sensitivity as compared to linear unmixing approximations, under the condition that the optical agent of interest is sparsely present within the tissue volume, as common when using targeted agents and reporter genes.

Optoacoustic (photoacoustic) imaging uniquely visualizes optical contrast in high resolution and comes with very attractive characteristics for clinical imaging applications. In this paper, we showcase the performance of a scanner based on a 24 MHz center-frequency 128 element array, developed for applications in dermatology. We perform system characterization to examine the imaging performance achieved. We then showcase its imaging ability on healthy tissue and cancer. Finally, we image burns and human lesions in vivo and gain insights on the benefits and challenges of this approach as it is considered for diagnostic and treatment follow-up applications in dermatology and beyond.

The aim of this study was the development of a simulator for Multispectral Optoacoustic Tomography (MSOT). The modelling pathway of the simulator was separated into the optical, the acoustic and the reconstruction part in generating finally a photoacoustic image. In this paper, the presented simulation geometry was based on a recently developed MSOT imaging system, but it can be easily modified to other imaging geometries. Through comparison between experimental and simulated data, a validation of the model as well as its limitations, perspectives and modifications are presented.

In many practical optoacoustic imaging implementations, dimensionality of the tomographic problem is commonly reduced into two dimensions or 1-D scanning geometries in order to simplify technical implementation, improve imaging speed or increase signal-to-noise ratio. However, this usually comes at a cost of significantly reduced quality of the tomographic data, out-of-plane image artifacts, and overall loss of image contrast and spatial resolution. Quantitative optoacoustic image reconstruction implies therefore collection of point 3-D (volumetric) data from as many locations around the object as possible. Here, we propose and validate an accurate model-based inversion algorithm for 3-D optoacoustic image reconstruction. Superior performance versus commonly-used backprojection inversion algorithms is showcased by numerical simulations and phantom experiments.

Optoacoustic tomography has recently demonstrated powerful performance in small animal imaging and initial clinical trials in terms of the high spatial resolution, versatile contrast, and dynamic imaging capabilities it can provide. Yet, the current optoacoustic image reconstruction methods are usually based on inaccurate forward modelling approaches or otherwise demand a high computational cost, which imposes certain practical limitations and hinders image quantification. Herein, we introduce a new method for accelerating optoacoustic reconstructions, based on angular image discretization of the forward model solution. The method is particularly suitable for accurate image reconstruction with arbitrary meshes and space-dependent resolution, while it can also readily account for small speed of sound variations without compromising the calculation speed. It is further anticipated that the new approach will greatly facilitate development of high performance 3-D optoacoustic reconstruction methods.

The use of model-based algorithms in tomographic imaging offers many advantages over analytical inversion methods. However, the relatively high computational complexity of model-based approaches often restricts their efficient implementation. In practice, many modern imaging modalities, such as computed-tomography, positron-emission tomography, or optoacoustic tomography, normally use a very large number of pixels/voxels for image reconstruction. Consequently, the size of the forward-model matrix hinders the use of many inversion algorithms. In this paper, we present a new framework for model-based tomographic reconstructions, which is based on a wavelet-packet representation of the imaged object and the acquired projection data. The frequency localization property of the wavelet-packet base leads to an approximately separable model matrix, for which reconstruction at each spatial frequency band is independent and requires only a fraction of the projection data. Thus, the large model matrix is effectively separated into a set of smaller matrices, facilitating the use of inversion schemes whose complexity is highly nonlinear with respect to matrix size. The performance of the new methodology is demonstrated for the case of 2-D optoacoustic tomography for both numerically generated and experimental data.

A method is presented to reduce artefacts produced in optoacoustic tomography images due to internal reflection or scattering of the acoustic waves. It is based on weighting the tomographic contribution of each detector with the probability that a signal affected by acoustic mismatches is measured at that position. The correction method does not require a priori knowledge of the acoustic or optical properties of the imaged sample. Performance tests were made with agar phantoms that included air gaps for mimicking strong acoustic reflections as well as with an acoustically heterogeneous adult Zebrafish. The results obtained with the method proposed show a clear reduction of the artefacts with respect to the original images reconstructed with filtered back-projection algorithm. This performance is directly related to in vivo small animal imaging applications involving imaging in the presence of bones, lungs, and other highly mismatched organs.

Compressive sensing (CS) has seen impressive successes and fast growth over the past ten years, including applications in medical imaging. Applications of CS to magnetic resonance imaging (MRI) have been the earliest, most numerous, and most diverse, owing to the tremendous flexibility in designing the acquisition process and the pressing need that MRI has, as a slow acquisition modality, to reduce the sampling requirements.

Fluorescence molecular tomography (FMT) allows in vivo localization and quantification of fluorescence biodistributions in whole animals. The ill-posed nature of the tomographic reconstruction problem, however, limits the attainable resolution. Improvements in resolution and overall imaging performance can be achieved by forming image priors from geometric information obtained by a secondary anatomical or functional high-resolution imaging modality such as X-ray computed tomography or magnetic resonance imaging. A particular challenge in using image priors is to avoid the use of assumptions that may bias the solution and reduced the accuracy of the inverse problem. This is particularly relevant in FMT inversions where there is not an evident link between secondary geometric information and the underlying fluorescence biodistribution. We present here a new, two step approach to incorporating structural priors into the FMT inverse problem. By using the anatomic information to define a low dimensional inverse problem, we obtain a solution which we then use to determine the parameters defining a spatially varying regularization matrix for the full resolution problem. The regularization term is thus customized for each data set and is guided by the data rather than depending only on user defined a priori assumptions. Results are presented for both simulated and experimental data sets, and show significant improvements in image quality as compared to traditional regularization techniques.

We present a fast model-based inversion algorithm for quantitative 2-D and 3-D optoacoustic tomography. The algorithm is based on an accurate and efficient forward model, which eliminates the need for regularization in the inversion process while providing modeling flexibility essential for quantitative image formation. The resulting image-reconstruction method eliminates stability problems encountered in previously published model-based techniques and, thus, enables performing image reconstruction in real time. Our model-based framework offers a generalization of the forward solution to more comprehensive optoacoustic propagation models, such as including detector frequency response, without changing the inversion procedure. The reconstruction speed and other algorithmic performances are demonstrated using numerical simulation studies and experimentally on tissue-mimicking optically heterogeneous phantoms and small animals. In the experimental examples, the model-based reconstructions manifested correctly the effect of light attenuation through the objects and did not suffer from the artifacts which usually afflict the commonly used filtered backprojection algorithms, such as negative absorption values.

A hybrid imaging system for simultaneous fluorescence tomography and X-ray computed tomography (XCT) of small animals has been developed and presented. The system capitalizes on the imaging power of a 360 ( degrees )-projection free-space fluorescence tomography system, implemented within a microcomputed tomography scanner. Image acquisition is based on techniques that automatically adjust a series of imaging parameters to offer a high dynamic range dataset. Image segmentation further allows the incorporation of structural priors in the optical reconstruction problem to improve the imaging performance. The functional system characteristics are showcased, and images from a brain imaging study are shown, which are reconstructed using XCT-derived priors into the optical forward problem.

We report on a new quantification methodology of optoacoustic tomographic reconstructions under heterogeneous illumination conditions representative of realistic whole-body imaging scenarios. Our method relies on the differences in the spatial characteristics of the absorption coefficient and the optical energy density within the medium. By using sparse-representation based decomposition, we exploit these different characteristics to extract both the absorption coefficient and the photon density within the imaged object from the optoacoustic image. In contrast to previous methods, this algorithm is not based on the solution of theoretical light transport equations and it does not require explicit knowledge of the illumination geometry or the optical properties of the object and other unknown or loosely defined experimental parameters, leading to highly robust performance. The method was successfully examined with numerically and experimentally generated data and was found to be ideally suited for practical implementations in tomographic schemes of varying complexity, including multiprojection illumination systems and multispectral optoacoustic tomography (MSOT) studies of tissue biomarkers.

While the mathematics, physics, and technology behind magnetic resonance (MR) and fluorescence image formation are distinctively different, the two modalities have significant complementary features to impart strong preclinical and clinical application synergies. Traditionally, hybrid MR and fluorescence imaging implied the use of a system where optical and MR signals can be concurrently acquired. In this case, the common geometry allows for the superposition of fluorescence images of cellular and subcellular processes onto anatomical and functional MR images. More recently, a different hybrid imaging paradigm is strongly evolving by utilizing hybrid MR-fluorescence nanoparticles. This approach offers a second paradigm of hybrid visualization where the common underlying contrast enables the coregistration of MR and fluorescence images acquired under different geometries. We review herein progress with the evolving field of multimodality MR and fluorescence imaging and discuss how these strategies offer a highly promising outlook in established and in novel preclinical and clinical applications.

It is difficult to directly coregister the 3-D fluorescence molecular tomography (FMT) image of a small tumor in a mouse whose maximal diameter is only a few millimeters with a larger CT image of the entire animal that spans about 10 cm. This paper proposes a new method to register 2-D flat and 3-D CT image first to facilitate the registration between small 3-D FMT images and large 3-D CT images. A novel algorithm combining differential evolution and improved simplex method for the registration between the 2-D flat and 3-D CT images is introduced and validated with simulated images and real images of mice. The visualization of the alignment of the 3-D FMT and CT image through 2-D registration shows promising results.

Complete projection (360 degrees ) free-space fluorescence tomography of opaque media is poised to enable 3-D imaging through entire small animals in vivo with improved depth resolution compared to 360 degrees -projection fiber-based systems or limited-view angle systems. This approach can lead to a new generation of Fluorescence Molecular Tomography (FMT) performance since it allows high spatial sampling of photon fields propagating through tissue at any projection, employing nonconstricted animal surfaces. Herein, we employ a volume carving method to capture 3-D surfaces of diffusive objects and register the captured surface in the geometry of an FMT 360 degrees -projection acquisition system to obtain 3-D fluorescence image reconstructions. Using experimental measurements we evaluate the accuracy of the surface capture procedure by reconstructing the surfaces of phantoms of known dimensions. We then employ this methodology to characterize the animal movement of anaesthetized animals. We find that the effects of animal movement on the FMT reconstructed image were within system resolution limits (approximately 0.07 cm).

We examine the problem of fluorescence molecular tomography using the normalized Born approximation, termed herein the Born ratio, from a statistical perspective. Experimentally verified noise models for received signals at the excitation and emission wavelengths are combined to generate a stochastic model for the Born ratio. This model is then utilized within a maximum likelihood framework to obtain an inverse solution based on a fixed point iteration. Results are presented for three experimental scenarios: phantom data with a homogeneous background, phantoms implanted within a small animal, and in vivo data using an exogenous probe.

Fluorescence tomography is an emerging tool for functional and molecular imaging of tissues. Inversion is however complicated by the high scattering that photons experience when propagating through tissue that reduces resolution. While continuous wave systems offer implementation simplicity, time-resolved systems offer the ability to improve resolution and reduce the ill-posed nature of the inverse problem by using early arriving photons. We present an overview of our current progress in complete-angle illumination early photon tomography and present the associated data processing steps employed for early photon inversions.

In vivo fluorescence molecular imaging and tomography has facilitated monitoring of genomics and proteomics over time and on the same animal. A highly important issue, however, has been the robust registration of animals imaged at different time points to obtain accurate description of activity and location. This paper presents a method for aligning temporal data of small animals based on surface anatomical features and improving the accuracy of monitoring fluorophore distribution. The method can account for differences in the positioning and compression of small animals and can be extended to three-dimensional as well as to other imaging modalities.

We studied the performance of three-dimensional fluorescence tomography of diffuse media in the presence of heterogeneities. Experimental measurements were acquired using an imaging system consisting of a parallel plate-imaging chamber and a lens coupled charge coupled device camera, which enables conventional planar imaging as well as fluorescence tomography. To simulate increasing levels of background heterogeneity, we employed phantoms made of a fluorescent tube surrounded by several absorbers in different combinations of absorption distribution. We also investigated the effect of low absorbing thin layers (such as membranes). We show that the normalized Born approach accurately retrieves the position and shape of the fluorochrome even at high background heterogeneity. We also demonstrate that the quantification is relatively insensitive to a varying degree of heterogeneity and background optical properties. Findings are further contrasted to images obtained with the standard Born expansion and with a normalized approach that divides the fluorescent field with excitation measurements through a homogeneous medium.

Microscopy of fluorescent proteins has enabled unprecedented insights into visualizing gene expression in living systems. Imaging deeper into animals, however, has been limited due to the lack of accurate imaging methods for the visible. We present a novel system designed to perform tomographic imaging of fluorescent proteins through whole animals. The tomographic method employed a multiangle, multiprojection illumination scheme, while detection was achieved using a highly sensitive charge-coupled device camera with appropriate filters. Light propagation was modeled using a modified solution to the diffusion equation to account for the high absorption and high scattering of tissue at the visible wavelengths. We show that the technique can quantitatively detect fluorescence with sub millimeter spatial resolution both in phantoms and in tissues. We conclude that the method could be applied in tomographic imaging of fluorescent proteins for in vivo targeting of different diseases and abnormalities.

Noncontact optical measurements from diffuse media could facilitate the use of large detector arrays at multiple angles that are well suited for diffuse optical tomography applications. Such imaging strategy could eliminate the need for individual fibers in contact with tissue, restricted geometries, and matching fluids. Thus, it could significantly improve experimental procedures and enhance our ability to visualize functional and molecular processes in vivo. In this paper, we describe the experimental implementation of this novel concept and demonstrate capacity to perform small animal imaging.

We investigate the performance of diffuse optical tomography to image highly heterogeneous media, such as breast tissue, as a function of background heterogeneity. To model the background heterogeneity, we have employed the functional information derived from Gadolinium-enhanced magnetic resonance images of the breast. We demonstrate that overall image quality and quantification accuracy worsens as the background heterogeneity increases. Furthermore we confirm the appearance of characteristic artifacts at the boundaries that scale with background heterogeneity. These artifacts are very similar to the ones seen in clinical examinations and can be misinterpreted as actual objects if not accounted for. To eliminate the artifacts and improve the overall image reconstruction, we apply a data-correction algorithm that yields superior reconstruction results and is virtually independent of the degree of the background heterogeneity.