Titlebook: Makroökonomie; Lehrbuch für das vol Eva Below,Wolfram Ebinger,Ulrich Pramann Textbook 1977 Dr. Gabler-Verlag · Wiesbaden 1977 Arbeitslosigk

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Eva von Below,Wolfram Ebinger,Peter Lorenz,Ulrich Pramannnfor developmental pathologies. In this paper, we model and explore brain development by learning a discriminative representation of the cortical brain data (T1 MRI) with a class-wise non-negative dictionary learning (NDDL) approach. For each class, the proposed approach performs data modeling by fir

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Eva von Below,Wolfram Ebinger,Peter Lorenz,Ulrich Pramannn. Based on a measured system matrix, MPI reconstruction can be cast as an inverse problem that is commonly solved via regularized iterative optimization. Yet, hand-crafted regularization terms can elicit suboptimal performance. Here, we propose a novel MPI reconstruction “PP-MPI” based on a deep plu

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Eva von Below,Wolfram Ebinger,Peter Lorenz,Ulrich Pramannnnstructed images. We introduce “NPB-REC”, a non-parametric fully Bayesian framework for uncertainty assessment in MRI reconstruction from undersampled “k-space” data. We use Stochastic gradient Langevin dynamics (SGLD) during the training phase to characterize the posterior distribution of the netwo

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Eva von Below,Wolfram Ebinger,Peter Lorenz,Ulrich Pramannnlving ill-posed background field removal (BFR) and field-to-source inversion problems. Because current QSM techniques struggle to generate reliable QSM in clinical contexts, QSM clinical translation is greatly hindered. Recently, deep learning (DL) approaches for QSM reconstruction have shown impres

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and reconstruction speed. Recently, deep learning used for compressed sensing (CS) methods have been proposed to accelerate the acquisition by undersampling in the K-space and reconstruct images with neural networks. However, there are still some challenges remained: First, directly training network

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Eva von Below,Wolfram Ebinger,Peter Lorenz,Ulrich PramannnMRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the scanner, rather than simulated k-space data from magnitude MR images in LOUPE, was retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data; secondly, binary stochastic k-spac

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