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Conventional Computed Tomography (CT) systems use a single X-ray source and an arc of detectors mounted on a rotating gantry to acquire a set of projection data. Novel CT systems are now being pioneered in which a complete ring of distributed X-ray sources and detectors are electronically turned on and off, without any mechanical motion, to acquire a set of projections for tomographic reconstruction. This paper discusses new sensing and reconstruction paradigms enabled by this new CT architecture.

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The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous trajectory is obtained by solving a traveling salesman problem (TSP) through these points. A feasible trajectory satisfying the gradient constraints is then obtained by parameterizing it using state-of-the-art methods. In this paper, a constrained convex optimization based method to obtain feasible trajectories is proposed.

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A novel class of k-space trajectories for magnetic resonance imaging (MRI) sampling using space-filling curves (SFCs) is presented here. More specifically, Peano, Hilbert and Sierpinski curves are used. We propose 1-shot and 4-shot variable density SFCs by utilizing the space coverage provided by SFCs in different iterations. The proposed trajectories are compared with state-of-the-art echo-planar imaging (EPI) trajectories for 128 × 128 and 256 × 256 phantom and brain images.

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Transcranial magnetic stimulation (TMS) is a non-invasive clinical technique used for treatment of several neurological diseases such as depression, Alzheimer’s disease and Parkinson’s disease. However, it is always challenging to accurately adjust the electric field on different specific brain regions due to the requirement of several stimulation parameters’ optimizations.

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One of the main difficulties in the use of deep learning strategies in medical contexts is the training set size. While these methods need large annotated training sets, this data is costly to obtain in medical contexts and suffers from intra and iter-subject variability.

In the present work, two new pre-processing techniques are introduced to improve a classifier performance. First, data augmentation based on co-registration is suggested. Then, multi-scale enhancement based on Difference of Gaussians is proposed.

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Most of the existing ultrasound image restoration methods consider a spatially-invariant point-spread function (PSF) model and circulant boundary conditions. While computationally efficient, this model is not realistic and severely limits the quality of reconstructed images. In this work, we address ultrasound image restoration under the hypothesis of vertical variation of the PSF. To regularize the solution, we use the classical elastic net constraint.

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