We consider a reconstruction formula for the internal conductivity and uniqueness of conductivity in magnetic resonance electrical impedance tomography (MREIT) which aims to reconstruct the conductivity distribution using internal current distribution. We provide a counter-example of uniqueness for a single measurement of current density with Neumann boundary data and show that at least two mea...

• Most non-iterative reconstruction methods can be separated into a calibration step that generates channel combination and unaliasing coefficients followed by a step that applies these coefficients to create reconstructed images. • Maps of image shading, noise amplification and aliasing energy can be created to better assess the image quality achieved by a reconstruction pipeline. • Complete c...

A novel method for iterative reconstruction of images from undersampled MRI data acquired by multiple receiver coil systems is presented. Based on parallel Projection onto Convex Sets (POCS) formalism, the method for SENSitivity Encoded data reconstruction (POCSENSE) can be readily modified to include various linear and non-linear reconstruction constraints. Such constraints may be beneficial f...

We address the problem of localizing point sources in 3D from boundary measurements of a wave field. Recently, we proposed the sensing principle which allows extracting volumetric samples of the unknown source distribution from the boundary measurements. The extracted samples allow a non-iterative reconstruction algorithm that can recover the parameters of the source distribution projected on a...

3D information of the scene can be extracted from images acquired by cameras. Before the actual reconstruction camera calibration has to be done. Reconstruction accuracy is highly dictated by the calibration. Two typical demands, which are not easily simultaneously satisfied, are: calibration has to be done in fast and convenient manner and yet assure high degree of reconstruction accuracy. Com...

A convex programming approach to discrete tomographic image reconstruction in noisy environments is proposed. Conventional constraints are mixed with noise-based constraints on the sinogram and a binarity-promoting total variation constraint. The noise-based constraints are modeled as confidence regions that are constructed under a Poisson noise assumption. A convex objective is then minimized ...

Over the past two decades, rapid system and hardware development of x-ray computed tomography (CT) technologies has been accompanied by equally exciting advances in image reconstruction algorithms. The algorithmic development can generally be classified into three major areas: analytical reconstruction, model-based iterative reconstruction, and application-specific reconstruction. Given the lim...

Background Cardiac MR (CMR) has emerged as the gold standard in assessing biventricular size and systolic function with segmented balanced steady-state free-precession (bSSFP) cine acquisitions. The application of a novel prototype iterative reconstruction technique to sparsely undersampled segmented bSSFP cine acquisitions may enable higher acceleration factors while shortening image acquisiti...

We discuss a coherent diffractive imaging experiment wherein the illuminating X-ray beam is divergent, rather than planar. By reconstructing the illumination at the sample, it is possible to recover the exit surface wave in that plane. With this geometry, the usual iterative reconstruction scheme converges very quickly, without finding the typical ambiguous solutions. © 2007 Elsevier B.V. All r...

Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. We propose to use recurrent neural networks for both analysis and synthesis. The networks have a hierarchical architecture that represents images in multiple scales with different degrees of abstraction. The mapping between these representations is mediated by a local connectio...