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Concept# Iterative reconstruction

Summary

Iterative reconstruction refers to iterative algorithms used to reconstruct 2D and 3D images in certain imaging techniques.
For example, in computed tomography an image must be reconstructed from projections of an object. Here, iterative reconstruction techniques are usually a
better, but computationally more expensive alternative to the common filtered back projection (FBP) method, which directly calculates the image in
a single reconstruction step. In recent research works, scientists have shown that extremely fast computations and massive parallelism is possible for iterative reconstruction, which makes iterative reconstruction practical for commercialization.
Basic concepts
The reconstruction of an image from the acquired data is an inverse problem. Often, it is not possible to exactly solve the inverse
problem directly. In this case, a direct algorithm has to approximate the solution, which might cause visible reconstruction artifacts in the image. Iterative algorith

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Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-André Simeoni

The present invention is notably directed to a computer-implemented method for image reconstruction. The method comprises: accessing elements that respectively correspond to measurement values, which can be respectively mapped to measurement nodes; and performing message passing estimator operations to obtain estimates of random variables associated with variable nodes, according to a message passing method in a bipartite factor graph. In this message passing method: the measurement values are, each, expressed as a term that comprises linear combinations of the random variables; each message exchanged between any of the measurement nodes and any of the variable nodes is parameterized by parameters of a distribution of the random variables; and performing the message passing estimator operations further comprises randomly mapping measurement values to the measurement nodes, at one or more iterations of the message passing method. Finally, image data are obtained from the obtained estimates of the random variables, which image data are adapted to reconstruct an image. The present invention is further directed to related systems and methods using the above image reconstruction method.

2017Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-André Simeoni

The present invention is notably directed to computer-implemented methods and systems for recovering an image. Present methods comprise: accessing signal data representing signals; identifying subsets of points arranged so as to span a region of interest as current subsets of points; reconstructing an image based on current subsets of points, by combining signal data associated to the current subsets of points; detecting one or more signal features in a last image reconstructed; for each of the detected one or more signal features, modifying one or more subsets of the current subsets, so as to increase, for each of the modified one or more subsets, a relative number of points at a location of said each of the detected one or more signal features. The relative number of points of a given subset at a given location may be defined as the number of points of said given subset at the given location divided by the total number of points of said given subset, whereby new current subsets of points are obtained; and repeating the above steps of reconstructing, detecting and modifying, as necessary to obtain a reconstructed image that satisfies a given condition.

2017Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-André Simeoni

Image reconstruction techniques from signals received by sensors find application in several fields, including radio interferometry for astronomical investigations and magnetic resonance imaging for medical applications. This paper presents a novel method for image reconstruction based on the iterative scanning of a region of interest. A modified approximate message passing (AMP) algorithm is adopted to extract relevant image information with low computational complexity from signals received by sensors. The method is illustrated by simulations, with reference to the LOFAR radio interferometer, and compared in the case of radio astronomy with the CLEAN algorithm.

2015