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  • 1.
    Cheddad, Abbas
    et al.
    Umeå University, Faculty of Medicine, Umeå Centre for Molecular Medicine (UCMM).
    Svensson, Christoffer
    Umeå University, Faculty of Medicine, Umeå Centre for Molecular Medicine (UCMM).
    Sharpe, James
    Georgsson, Fredrik
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Ahlgren, Ulf
    Umeå University, Faculty of Medicine, Umeå Centre for Molecular Medicine (UCMM).
    Image processing assisted algorithms for optical projection tomography2012In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 31, no 1, p. 1-15Article in journal (Refereed)
    Abstract [en]

    Since it was first presented in 2002, optical projection tomography (OPT) has emerged as a powerful tool for the study of biomedical specimen on the mm to cm scale. In this paper, we present computational tools to further improve OPT image acquisition and tomographic reconstruction. More specifically, these methods provide: semi-automatic and precise positioning of a sample at the axis of rotation and a fast and robust algorithm for determination of postalignment values throughout the specimen as compared to existing methods. These tools are easily integrated for use with current commercial OPT scanners and should also be possible to implement in "home made" or experimental setups for OPT imaging. They generally contribute to increase acquisition speed and quality of OPT data and thereby significantly simplify and improve a number of three-dimensional and quantitative OPT based assessments.

  • 2.
    de Pierrefeu, Amicie
    et al.
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Hadj-Selem, Fouad
    Energy Transition Institute: VeDeCoM, 78 000 Versailles, France.
    Dubois, Mathieu
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Jardri, Renaud
    Univ. Lille, CNRS UMR 9193, SCALab, CHU Lille, Pôle de Psychiatrie (unit CURE), 59000 Lille, France.
    Fovet, Thomas
    Univ. Lille, CNRS UMR 9193, SCALab, CHU Lille, Pôle de Psychiatrie (unit CURE), 59000 Lille, France.
    Ciuciu, Philippe
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Frouin, Vincent
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Duchesnay, Edouard
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty2018In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 2, p. 396-407Article in journal (Refereed)
    Abstract [en]

    Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA’s interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered andunstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., l1 and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV’s effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., N-dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach (such as GraphNet PCA) are significant, since SPCA-TV reveals the variability within a data set in the form of intelligible brain patterns that are easier to interpret and more stable across different samples.

  • 3.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Östlund, Nils
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A novel estimation method for physiological parameters in dynamic contrast-enhanced MRI: application of a distributed parameter model using Fourier-domain calculations2009In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 28, no 9, p. 1375-1383Article in journal (Refereed)
    Abstract [en]

    Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.

  • 4.
    Hadj-Selem, Fouad
    et al.
    Energy Transition Institute VeDeCoM, Versailles, France.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Dohmatob, Elvis
    PARIETAL Team, INRIA/CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Frouin, Vincent
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Dubois, Mathieu
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Guillemot, Vincent
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Duchesnay, Edouard
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging2018In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 11, p. 2403-2413Article in journal (Refereed)
    Abstract [en]

    Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov’s smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov’s smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.

  • 5. Naik, Naren
    et al.
    Patil, Nishigandha
    Yadav, Yamini
    Eriksson, Jerry
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Pradhan, Asima
    Fully Nonlinear SP3 Approximation Based Fluorescence Optical Tomography2017In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 36, no 11, p. 2308-2318Article in journal (Refereed)
    Abstract [en]

    In fluorescence optical tomography, many works in the literature focus on the linear reconstruction problem to obtain the fluorescent yield or the linearized reconstruction problem to obtain the absorption coefficient. The nonlinear reconstruction problem, to reconstruct the fluorophore absorption coefficient, is of interest in imaging studies as it presents the possibility of better reconstructions owing to a more appropriate model. Accurate and computationally efficient forward models are also critical in the reconstruction process. The SPN approximation to the radiative transfer equation (RTE) is gaining importance for tomographic reconstructions owing to its computational advantages over the full RTE while being more accurate and applicable than the commonly used diffusion approximation. This paper presents Gauss-Newton-based fully nonlinear reconstruction for the SP3 approximated fluorescence optical tomography problem with respect to shape as well as the conventional finite-element method-based representations. The contribution of this paper is the Frechet derivative calculations for this problem and demonstration of reconstructions in both representations. For the shape reconstructions, radial-basis-function represented level-set-based shape representations are used. We present reconstructions for tumor-mimicking test objects in scattering and absorption dominant settings, respectively, for moderately noisy data sets in order to demonstrate the viability of the formulation. Comparisons are presented between the nonlinear and linearized reconstruction schemes in an element wise setting to illustrate the benefits of using the former especially for absorption dominant media.

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