Iterative reconstruction

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

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (100)

Related publications

Related people (37)

Related people

Related units (17)

Related units

Related concepts (9)

Related concepts

Related courses (9)

Related courses

Related lectures (7)

Related lectures

Reconstruction using approximate message passing methods

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.

2017

Iterative image subset processing for image reconstruction

Giovanni 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.

2017

Iterative image subset scanning for image reconstruction from sensor signals

Giovanni 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

Tomography

Tomography is imaging by sections or sectioning that uses any kind of penetrating wave. The method is used in radiology, archaeology, biology, atmospheric science, geophysics, oceanography, plasma p

Single-photon emission computed tomography

Single-photon emission computed tomography (SPECT, or less commonly, SPET) is a nuclear medicine tomographic imaging technique using gamma rays. It is very similar to conventional nuclear medicine p

Positron emission tomography

Positron emission tomography (PET) is a functional imaging technique that uses radioactive substances known as radiotracers to visualize and measure changes in metabolic processes, and in other physio

MICRO-512: Image processing II

Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in JAVA; application to real-world examples in industrial vision and biomedical imaging.

MICRO-310(b): Signals and systems I (for SV)

Présentation des concepts et des outils de base pour l'analyse et la caractérisation des signaux, la conception de systèmes de traitement et la modélisation linéaire de systèmes pour les étudiants en sciences de la vie. Application de ces techniques au traitement et à la transmission de signaux.

BIO-410: Bioimage informatics

The course provides a comprehensive overview of methods, algorithms, and computer tools used in in computational bioimaging and bioimage analysis. It exposes the fundamental concepts and the practical computer solutions to extract quantitative information from multidimensional images.