Total variation (TV) regularization is a technique commonly utilized to promote sparsity of image in gradient domain. In this article, we address the problem of MR brain image reconstruction from highly undersampled Fourier measurements. We define the Moreau enhanced function of L 1 norm, and introduce the minmax-concave TV (MCTV) penalty as a regularization term for MR brain image reconstruction. MCTV strongly induces the sparsity in gradient domain, and fits the frame of fast algorithms (eg, ADMM) for solving optimization problems. Although MCTV is non-convex, the cost function in each iteration step can maintain convexity by specifying the relative nonconvexity parameter properly. Experimental results demonstrate the superior performance of the proposed method in comparison with standard TV as well as nonlocal TV minimization method, which suggests that MCTV may have promising applications in the field of neuroscience in the future. K E Y W O R D S brain image reconstruction, magnetic resonance imaging, non-convex regularization, total variation Int J Imaging Syst Technol. 2018;1-8. wileyonlinelibrary.com/journal/ima V C 2017 Wiley Periodicals, Inc. | 1
This paper presents a three-dimensional multiscale structure and object-oriented model to generate a synthetic heart. This model consists of a series of elementary objects at different resolution level from the macro-to the micro-scale. Each object is described by a vector of attributes. The shapes and size of the components of the tissue are inspired from Synchrotron Radiation Phase micro-Computed Tomography (SR-PCT) of human left ventricle wall samples. To enhance the similarity between the model and the experimental data, we use a Free-Form Deformation (FFD) technique to deform each object. Our first results demonstrate that the model can simulate realistic voxel-based elementary objects and simulate experimental data and shapes. The hierarchical graph structure of the model that includes inter level relationships has a strong potential interest.
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