Iterative reconstruction methods with regularization become more and more popular. In the literature, amazing results are reported that are able to reconstruct images from very few views and from trajectories that do not acquire complete data sets such as would be required for analytical reconstruction methods. A large disadvantage of iterative methods is their high computational demand. Bruder...

We propose and study a block-iterative projections method for solving linear equations and/or inequalities. The method allows diagonal component-wise relaxation in conjunction with orthogonal projections onto the individual hyperplanes of the system, and is thus called diagonally-relaxed orthogonal projections (DROP). Diagonal relaxation has proven useful in accelerating the initial convergence...

A new theory named compressed sensing for simultaneous sampling and compression of signals has been becoming popular in the communities of signal processing, imaging and applied mathematics. In this paper, we present improved/accelerated iterative curvelet thresholding methods for compressed sensing reconstruction in the fields of remote sensing. Some recent strategies including Bioucas-Dias an...

We give uniqueness theorem and reconstruction algorithm for the nonlinearized problem of finding the dielectric anisotropy f of the medium from non-overdetermined polarization tomography data. We assume that the medium has uniform background parameters and that the anisotropic (dielectric permeability) perturbation is described by symmetric and sufficiently small matrix-function f . On a pure m...

PURPOSE The exciting prospect of Spectral CT (SCT) using photon-counting detectors (PCD) will lead to new techniques in computed tomography (CT) that take advantage of the additional spectral information provided. We introduce a method to reduce metal artifact in X-ray tomography by incorporating knowledge obtained from SCT into a statistical iterative reconstruction scheme. We call our method ...

This letter considers the problem of sparse signal reconstruction from the timing of its Level Crossings (LC)s. We formulate the sparse Zero Crossing (ZC) reconstruction problem in terms of a single 1-bit Compressive Sensing (CS) model. We also extend the Smoothed L0 (SL0) sparse reconstruction algorithm to the 1-bit CS framework and propose the Binary SL0 (BSL0) algorithm for iterative reconst...

OBJECTIVES: This course aims to describe the effects of colored noise in the parallel MRI [1– 3] experiment and how to perform a reconstruction, which takes the noise distribution into consideration. Techniques for SNR scaled reconstruction [4] are described for Cartesian and non-Cartesian imaging. The course will also describe iterative reconstruction algorithms and their regularized forms. So...

S. Inati, J-Y. Lee, L. Fleysher, R. Fleysher, L. Greengard Center for Neural Science and Department of Psychology, New York University, New York, NY, United States, Center for Brain Imaging, New York University, New York, NY, United States, Department of Mathematics, New York University, New York, NY, United States, Department of Mathematics, Ewha Womans University, Seoul, Korea, Republic of, C...