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ETS2 repressor factor (ERF) insufficiency causes craniosynostosis (CRS4) in humans and mice. ERF is an ETS domain transcriptional repressor regulated by Erk1/2 phosphorylation via nucleo-cytoplasmic shuttling. Here, we analyze the onset and development of the craniosynostosis phenotype in an Erf-insufficient mouse model and evaluate the potential of the residual Erf activity augmented by pharmacological compounds to ameliorate the disease. Erf insufficiency appears to cause an initially compromised frontal bone formation and subsequent multisuture synostosis, reflecting distinct roles of Erf on the cells that give rise to skull and facial bones. We treated animals with Mek1/2 and nuclear export inhibitors, U0126 and KPT-330, respectively, to increase Erf activity by two independent pathways. We implemented both a low dosage locally over the calvaria and a systemic drug administration scheme to evaluate the possible indirect effects from other systems and minimize toxicity. The treatment of mice with either the inhibitors or the administration scheme alleviated the synostosis phenotype with minimal adverse effects. Our data suggest that the ERF level is an important regulator of cranial bone development and that pharmacological modulation of its activity may represent a valid intervention approach both in CRS4 and in other syndromic forms of craniosynostosis mediated by the FGFR-RAS-ERK-ERF pathway.

Objective. The aim of our article is to highlight the im­portance of the immediate treatment of lower extrem­ity degloving injuries, in order to prevent complica­tions. Cases Presentation. Here we present two cases of degloving injury of the lower extremity, both result­ing from motorway accidents. The first one concerned a 65-year-old man suffering from multiple limb frac­tures and a degloving injury of the right thigh, which was immediately treated with extensive debridement and primary full-thickness skin graft re-approxima­tion. The second case involved a 63-year-old woman who presented with cervical vertebrae fractures and a degloving injury of the left posterior leg, which, due to the severity of her condition, was treated with a delayed approach resulting in skin necrosis, which required surgical debridement, alginate dressing and foam cover. Conclusions. The optimal approach to treatment of degloving injuries is challenging and they warrant immediate surgical attention. An early diag­nosis and the evaluation of tissue viability are impor­tant in order to prevent limb-threatening situations.

We explore wavefront shaping techniques to produce and control illumination patterns for use on Light-Sheet Fluorescence Microcopy (LSFM). We demonstrate the use of these patterns to perform in vivo imaging using fluorescently labeled specimens.

Breast cancer and Glioblastoma brain cancer are aggressive malignancies with poor prognosis. In this study primary Glioblastoma and secondary breast cancer spheroids are formed and treated with the well-known Temozolomide and Doxorubicin chemotherapeutics, respectively. A custom multi-angle Light Sheet Fluorescence Microscope is employed for high resolution imaging of both cancer cell spheroids. Such a technique is successful in realizing pre-clinical drug screening, while enables the discrimination among physiologic tumor parameters. LSFM technique, parameters and method followed are also presented.

Optical microscopy constitutes, one of the most fundamental paradigms for the understanding of complex biological mechanisms in the whole-organism and live-tissue context. Novel imaging techniques such as light sheet fluorescence microscopy (LSFM) and optical projection tomography (OPT) combined with phase-retrieval algorithms (PRT) can produce highly resolved 3D images in multiple transport-mean-free-path scales. Our study aims to exemplify the microscopic capabilities of LSFM when imaging protein dynamics in Caenorhabditis elegans and the distribution of necrotic cells in cancer cell spheroids. To this end, we apply LSFM to quantify the spatio-temporal localization of the GFP-tagged aging and stress response factor DAF-16/FOXO in transgenic C. elegans. Our analysis reveals a linear nuclear localization of DAF-16::GFP across tissues in response to heat stress, using a system that outperforms confocal scanning fluorescent microscopy in imaging speed, 3D resolution and reduced photo-toxicity. Furthermore, we present how PRT can improve the depth-to-resolution-ratio when applied to image the far-red fluorescent dye DRAQ7 which stains dead cells in a T47D cancer cell spheroid recorded with a customized OPT/LSFM system. Our studies demonstrate that LSFM combined with our novel approaches enables higher resolution and more accurate 3D quantification than previously applied technologies, proving its advance as new gold standard for fluorescence microscopy.

Optical tomography in biomedical imaging is a highly dynamic field in which non-invasive optical and computational techniques are combined to obtain a three dimensional representation of the specimen we are interested to image. Although at optical wavelengths scattering is the main obstacle to reach diffraction limited resolution, recently several studies have shown the possibility to image even objects fully hidden behind a turbid layer exploiting the information contained in the speckle autocorrelation via an iterative phase retrieval algorithm. In this work we explore the possibility of blind three dimensional reconstruction approach based on the Optical Projection Tomography principles, a widely used tool to image almost transparent model organism such as C. Elegans and D. Rerio. By using autocorrelation information rather than projections at each angle we prove, both numerically and experimentally, the possibility to perform exact three dimensional reconstructions via a specifically designed phase retrieval algorithm, extending the capability of the projection-based tomographic methods to image behind scattering curtains. The reconstruction scheme we propose is simple to implement, does not require post-processing data alignment and moreover can be trivially implemented in parallel to fully exploit the computing power offered by modern GPUs, further reducing the need for costly computational resources.

Diffuse Optical Tomography commonly neglects or assumes as insignificant the presence of optically clear regions in biological tissues, estimating their contribution as a small perturbation to light transport. The inaccuracy introduced by this practice is examined in detail in the context of a complete, based on realistic geometry, virtual fluorescence Diffuse Optical Tomography experiment where a mouse head is imaged in the presence of cerebral spinal fluid. Despite the small thickness of such layer, we point out that an error is introduced when neglecting it from the model with possibly reduction in the accuracy of the reconstruction and localization of the fluorescence distribution within the brain. The results obtained in the extensive study presented here suggest that fluorescence diffuse neuroimaging studies can be improved in terms of quantitative and qualitative reconstruction by accurately taking into account optically transparent regions especially in the cases where the reconstruction is aided by the prior knowledge of the structural geometry of the specimen. Thus, this has only recently become an affordable choice, thanks to novel computation paradigms that allow to run Monte Carlo photon propagation on a simple graphic card, hence speeding up the process a thousand folds compared to CPU-based solutions.

Optical Neuroimaging is a highly dynamical field of research owing to the combination of many advanced imaging techniques and computational tools that uncovered unexplored paths through the functioning of the brain. Light propagation modelling through such complicated structures has always played a crucial role as the basis for a high resolution and quantitative imaging where even the slightest improvement could lead to significant results. Fluorescence Diffuse Optical Tomography (fDOT), a widely used technique for three dimensional imaging of small animals and tissues, has been proved to be inaccurate for neuroimaging the mouse head without the knowledge of a-priori anatomical information of the subject. Commonly a normalized Born approximation model is used in fDOT reconstruction based on forward photon propagation using Diffusive Equation (DE) which has strong limitations in the optically clear regime. The presence of the Cerebral Spinal Fluid (CSF) instead, a thin optically clear layer surrounding the brain, can be more accurately taken into account using Monte Carlo approaches which nowadays is becoming more usable thanks to parallelized GPU algorithms. In this work we discuss the results of a synthetic experimental comparison, resulting to the increase of the accuracy for the Born approximation by introducing the CSF layer in a realistic mouse head structure with respect to the current model. We point out the importance of such clear layer for complex geometrical models, while for simple slab phantoms neglecting it does not introduce a significant error.

Recently great progress has been made in biological and biomedical imaging by combining non-invasive optical methods, novel adaptive light manipulation and computational techniques for intensity-based phase recovery and three dimensionalimage reconstruction. In particular and in relation to the work presented here, Optical Projection Tomography (OPT) is awell-established technique for imaging mostly transparent absorbing biological models such as C. Elegans and Danio Rerio. On the contrary, scattering layers like the cocoon surrounding the Drosophila during the pupae stage constitutes achallenge for three dimensional imaging through such a complex structure. However, recent studies enabled image reconstruction through scattering curtains up to few transport mean free paths via phase retrieval iterative algorithms allowing to uncover objects hidden behind complex layers. By combining these two techniques we explore the possibility to perform a three dimensional image reconstruction of fluorescent objects embedded between scattering layers without compromising its structural integrity. Dynamical cross correlation registration was implemented for the registration process due to translational and flipping ambiguity of the phase retrieval problem, in order to provide the correct aligned set of data to perform the back-projection reconstruction. We have thus managed to reconstruct a hidden complex object between static scattering curtains and compared with the effective reconstruction to fully understand the process before the in-vivo biological implementation. Keywords: Phase Retrieval , Scattering, Optical Projection Tomography, Three Dimensional Imaging, BiomedicalImaging, Image Reconstruction , Turbid Lens , Memory Effect.

One of the major challenges within Optical Imaging, photon propagation through clear layers embedded between scattering tissues, can be now efficiently modelled in real-time thanks to the Monte Carlo approach based on GPU. Because of its nature, the photon propagation problem can be very easily parallelized and ran on low cost hardware, avoiding the need for expensive Super Computers. A comparison between Diffusion and MC photon propagation theory is presented in this work with application to neuroimaging, investigating low scattering regions in a mouse-like phantom. Regions such as the Cerebral Spinal Fluid, are currently not taken into account in the classical computational models because of the impossibility to accurately simulate light propagation using fast Diffusive Equation approaches, leading to inaccuracies during the reconstruction process. The goal of the study presented here, is to reduce and further improve the computation accuracy of the reconstructed solution in a highly realistic scenario in the case of neuroimaging in preclinical mouse models.

We describe a customizable and cost-effective light sheet microscopy (LSM) platform for rapid three-dimensional imaging of protein dynamics in small model organisms. The system is designed for high acquisition speeds and enables extended time-lapse in vivo experiments when using fluorescently labeled specimens. We demonstrate the capability of the setup to monitor gene expression and protein localization during ageing and upon starvation stress in longitudinal studies in individual or small groups of adult Caenorhabditis elegans nematodes. The system is equipped to readily perform fluorescence recovery after photobleaching (FRAP), which allows monitoring protein recovery and distribution under low photobleaching conditions. Our imaging platform is designed to easily switch between light sheet microscopy and optical projection tomography (OPT) modalities. The setup permits monitoring of spatio-temporal expression and localization of ageing biomarkers of subcellular size and can be conveniently adapted to image a wide range of small model organisms and tissue samples. MR, GZ and AZ acknowledge funding from the Projects “Skin-DOCTor” Grant No. 1778 and “Neureka!” Grant No. LSF7-341 implemented under the "ARISTEIA" and "Supporting Postdoctoral Researchers" Actions respectively, of the "OPERATIONAL PROGRAMME EDUCATION AND LIFELONG LEARNING", (http://www.espa.gr/en/pages/staticOPEducationandLifelongLearning.aspx), which is co-funded by the European Social Fund and National Resources and from the EU Marie Curie Initial Training Network “OILTEBIA”, Grant No. PITNGA-2012-317526 (http://ec.europa.eu/research/mariecurieactions/, http://gdo.uc3m.es/oiltebia/index. php?option = com_content&view = frontpage). JR acknowledges support from EC FP7 CIG grant HIGHTHROUGHPUT TOMO PICIG12-GA-2012-333632, (http://cordis.europa.eu/projects/333632), and Spanish MINECO grant MESO-IMAGING FIS2013-41802-R, (https://sede.micinn.gob.es/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

In this study we consider the reconstruction of the smooth subdomain boundaries of piecewise constant coefficients of the radiative transfer equation (RTE) from optical tomography data. The assumption is that the values of the absorption and scattering coefficients (μa, μs) of the different subdomains are known a priori but the smooth subdomain boundaries where μa and μs are discontinuous are unknown. For the reconstruction of (μa, μs) it is then sufficient to find the subdomain boundaries separating different values of the coefficients. This results in a nonlinear ill-posed inverse problem. In this study we propose a numerical algorithm for this inverse problem. The approach is based on the finite element discretization of the RTE. We formulate the forward problem as a mapping from a set of shape coefficients representing the shapes of the subdomain boundaries to optical tomography data, and derive the Jacobian of this forward mapping. Then an iterative Newton-type algorithm which seeks a boundary configuration minimizing the residual norm between measured and predicted data is implemented. The performance of the method is tested with simulated frequency domain optical tomography data from diffusive domains containing low-scattering (void) subdomains.

We consider the recovery of smooth 3D region boundaries with piecewise constant coefficients in optical tomography. The method is based on a parametrization of the closed boundaries of the regions by spherical harmonic coefficients, and a Newton type optimization process. A boundary integral formulation is used for the forward modelling. The calculation of the Jacobian is based on an adjoint scheme for calculating the corresponding shape derivatives. We show reconstructions for 3D situations. In addition we show the extension of the method for cases where the constant optical coefficients are also unknown. An advantage of the proposed method is the implicit regularization effect arising from the reduced dimensionality of the inverse problem.

We consider the recovery of smooth 3D region boundaries with piecewise constant coefficients in Optical Tomography (OT). The method is based on a parametrisation of the closed boundaries of the regions by spherical harmonic coefficients, and a Newton type optimisation process. A boundary integral formulation is used for the forward modelling. An advantage of the proposed method is the implicit regularisation effect arising from the reduced dimensionality of the inverse problem. Results of a numerical experiment are shown which demonstrate the performance of the new method in a realistic situation.

In vivo optical imaging of biological tissue not only requires the development of new theoretical models and experimental procedures, but also the design and construction of realistic tissue-mimicking phantoms. However, most of the phantoms available currently in literature or the market, have either simple geometrical shapes (cubes, slabs, cylinders) or when realistic in shape they use homogeneous approximations of the tissue or animal under investigation. The goal of this study is to develop a non-homogeneous realistic phantom that matches the anatomical geometry and optical characteristics of the mouse head in the visible and near-infrared spectral range. The fabrication of the phantom consisted of three stages. Initially, anatomical information extracted from either mouse head atlases or structural imaging modalities (MRI, XCT) was used to design a digital phantom comprising of the three main layers of the mouse head; the brain, skull and skin. Based on that, initial prototypes were manufactured by using accurate 3D printing, allowing complex objects to be built layer by layer with sub-millimeter resolution. During the second stage the fabrication of individual molds was performed by embedding the prototypes into a rubber-like silicone mixture. In the final stage the detailed phantom was constructed by loading the molds with epoxy resin of controlled optical properties. The optical properties of the resin were regulated by using appropriate quantities of India ink and intralipid. The final phantom consisted of 3 layers, each one with different absorption and scattering coefficient (μ a ,μ s ) to simulate the region of the mouse brain, skull and skin.

Even though surface morphology is always taken into account when assessing clinically pigmented skin lesions, it is not captured by most modern imaging systems using digital imaging. Our aim is to develop a novel three-dimensional (3D) imaging technique to record detailed information of the surface anatomy of melanocytic lesions that will enable improved classification through digital imaging. The apparatus consists of three high-resolution cameras, a light source, and accompanying software. Volume measurements of specific phantoms using volumetric tubes render slightly lower values than those obtained by our 3D imaging system (mean%± SD, 3.8%± 0.98, P0.05). To examine the reproducibility of the method, sequential imaging of melanocytic lesions is carried out. The mean%± SD differences of area, major axis length, volume, and maximum height are 2.1%± 1.1, 0.9%± 0.8, 3.8%± 2.9, and 2.5%± 3.5, respectively. Thirty melanocytic lesions are assessed, including common and dysplastic nevi and melanomas. There is a significant difference between nevi and melanomas in terms of variance in height and boundary asymmetry (P0.001). Moreover, dysplastic nevi have significantly higher variances in pigment density values than common nevi (P0.001). Preliminary data suggest that our instrument has great potential in the evaluation of the melanocytic lesions. However, these findings should be confirmed in larger-scale studies.

Diffuse optical tomography (DOT) aims at recovering three-dimensional images of absorption and scattering parameters inside diffusive body based on small number of transmission measurements at the boundary of the body. This image reconstruction problem is known to be an ill-posed inverse problem, which requires use of prior information for successful reconstruction. We present a shape based method for DOT, where we assume a priori that the unknown body consist of disjoint subdomains with different optical properties. We utilize spherical harmonics expansion to parameterize the reconstruction problem with respect to the subdomain boundaries, and introduce a finite element (FEM) based algorithm that uses a novel 3D mesh subdivision technique to describe the mapping from spherical harmonics coefficients to the 3D absorption and scattering distributions inside a unstructured volumetric FEM mesh. We evaluate the shape based method by reconstructing experimental DOT data, from a cylindrical phantom with one inclusion with high absorption and one with high scattering. The reconstruction was monitored, and we found a 87% reduction in the Hausdorff measure between targets and reconstructed inclusions, 96% success in recovering the location of the centers of the inclusions and 87% success in average in the recovery for the volumes.

The level set technique is an implicit shape-based image reconstruction method that allows the recovery of the location, size and shape of objects of distinct contrast with well-defined boundaries embedded in a medium of homogeneous or moderately varying background parameters. In the case of diffuse optical tomography, level sets can be employed to simultaneously recover inclusions that differ in their absorption or scattering parameters from the background medium. This paper applies the level set method to the three-dimensional reconstruction of objects from simulated model data and from experimental frequency-domain data of light transmission obtained from a cylindrical phantom with tissue-like parameters. The shape and contrast of two inclusions, differing in absorption and diffusion parameters from the background, respectively, are reconstructed simultaneously. We compare the performance of level set reconstruction with results from an image-based method using a Gauss-Newton iterative approach, and show that the level set technique can improve the detection and localisation of small, high-contrast targets.

In this paper we consider the recovery of ellipsoidal 3D shapes with piecewise constant coefficients in Diffuse Optical Tomography (DOT). We use an adjoint scheme for calculating gradients for the shape parameters defining the unknown ellipsoids, and a Newton-type optimisation process for the minimization of a least squares data misfit functional. A boundary integral formulation is used for the forward modelling. An advantage of the proposed method is the implicit regularisation effect arising from the reduced dimensionality of the inverse problem. Results of a numerical experiment in 3D are shown which demonstrate the performance of the method.

We present a novel multispectral scheme for fluorescence enhanced diffuse optical tomography. Reconstructions are performed using a weight matrix free algorithm. Initial multispectral reconstructions are shown.

Detection of multiple fluorophores in conditions of low signal represents a limiting factor for the application of in vivo optical imaging techniques in immunology where fluorescent labels report for different functional characteristics. A noninvasive in vivo Multi-Spectral Normalized Epifluorescence Laser scanning (M-SNELS) method was developed for the simultaneous and quantitative detection of multiple fluorophores in low signal to noise ratios and used to follow T-cell activation and clonal expansion. Colocalized DsRed- and GFP-labeled T cells were followed in tandem during the mounting of an immune response. Spectral unmixing was used to distinguish the overlapping fluorescent emissions representative of the two distinct cell populations and longitudinal data reported the discrete pattern of antigen-driven proliferation. Retrieved values were validated both in vitro and in vivo with flow cytometry and significant correlation between all methodologies was achieved. Noninvasive M-SNELS successfully quantified two colocalized fluorescent populations and provides a valid alternative imaging approach to traditional invasive methods for detecting T cell dynamics.

Non-contact Fluorescence Molecular Tomography is an emerging technique for imaging of fluorescent probes or proteins in live animals. One of the main characteristics of the non-contact acquisition systems in comparison to the usual fibre-based systems is the much denser boundary data sets that are created. When model-based reconstruction methods are used that rely on the inversion of a derivative operator, the large number of measurements poses a challenge since the explicit formulation and storage of the Jacobian matrix could be in general not feasible. In this paper we test a matrix-free method that addresses the problems of large data sets and reduces the computational cost and memory requirements for the reconstruction. More specifically we challenged the Matrix-Free method with in-vivo measurements from mice where fluorescence tubes of different but controlled concentrations are inserted, to assess the quantification performance of the method. We extended the test with simulations, using realistic geometries extracted from a mouse-atlas and including prior known information about the optical properties of tissue into the forward model.

In the recent years, there has been an increase in applications of non-contact diffusion optical tomography. Especially when the objective is the recovery of fluorescence targets. The non-contact acquisition systems with the use of a CCD-camera produce much denser sampled boundary data sets than fibre-based systems. When model-based reconstruction methods are used, that rely on the inversion of a derivative operator, the large number of measurements poses a challenge since the explicit formulation and storage of the Jacobian matrix could be in general not feasible. This problem is aggravated further in applications, where measurements at multiple wavelengths are used. We present a matrix-free model-based reconstruction method, that addresses the problems of large data sets and reduces the computational cost and memory requirements for the reconstruction. The idea behind the matrix-free method is that information about the Jacobian matrix could be available through matrix times vector products so that the creation and storage of big matrices can be avoided. We tested the method for multiple wavelength fluorescence tomography with simulated and experimental data from phantom experiments, and we found substantial benefits in computational times and memory requirements. (C) 2009 Optical Society of America

Many reconstruction problems in optical tomography, such as the imaging of haematoma in the brain, or breast tumour screening, require the detection and localisation of well-defined objects on a homogeneous or weakly varying background. A conventional parameter identification approach is to represent the image in a finite dimensional set of basis functions and to find the coefficients of this set using an optimisation strategy. Shape-based reconstruction techniques on the other hand, seek to find the boundaries of objects and possibly a representation of the interior either as constant or slowly varying functions. In this paper we compare both explicit and implicit shape reconstruction methods for the simultaneous recovery of absorption and diffusion inclusions in a three-dimensional scattering medium. Images reconstructed from simulated frequency-domain boundary measurements are compared to a voxel-based conjugate gradient method. The results demonstrate the feasibility of the shape based methods.

A shape reconstruction algorithm for optical tomography is introduced that uses a level-set formulation for the shapes. Evolution laws based on gradient directions for a cost functional are derived for two different level-set functions, one describing the absorption and one the diffusion parameter, as well as for the parameter values inside these shapes. Numerical experiments are presented in 2D that show that the new method is able to simultaneously recover shapes and contrast values of absorbing and scattering objects embedded in a moderately heterogeneous background medium from simulated noisy data.