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.
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 algorithms approach the correct solution using multiple iteration steps, which allows to obtain a better reconstruction at the cost of a higher computation time.
There are a large variety of algorithms, but each starts with an assumed image, computes projections from the image, compares the original projection data and updates the image based upon the difference between the calculated and the actual projections.
Algebraic reconstruction technique
The Algebraic Reconstruction Technique (ART) was the first iterative reconstruction technique used for computed tomography by Hounsfield.
SAMV (algorithm)
The iterative Sparse Asymptotic Minimum Variance algorithm is an iterative, parameter-free superresolution tomographic reconstruction method inspired by compressed sensing, with applications in synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI).
There are typically five components to statistical iterative image reconstruction algorithms, e.g.
An object model that expresses the unknown continuous-space function that is to be reconstructed in terms of a finite series with unknown coefficients that must be estimated from the data.
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The principles of 3D surface (SEM) reconstruction and its limitations will be explained. 3D volume reconstruction and tomography methods by electron microscopy (SEM/FIB and TEM) will be explained and
Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in Jupyter Notebooks; application to real-world examples in industrial vision and bio
Advanced 3D forming techniques for high throughput and high resolution (nanometric) for large scale production. Digital manufacturing of functional layers, microsystems and smart systems.
The course provides a comprehensive overview of digital signal processing theory, covering discrete time, Fourier analysis, filter design, sampling, interpolation and quantization; it also includes a
Adaptive signal processing, A/D and D/A. This module provides the basic
tools for adaptive filtering and a solid mathematical framework for sampling and
quantization
vignette|Principe de base de la tomographie par projections : les coupes tomographiques transversales S1 et S2 sont superposées et comparées à l’image projetée P. La tomographie est une technique d’, très utilisée dans l’, ainsi qu’en géophysique, en astrophysique et en mécanique des matériaux. Cette technique permet de reconstruire le volume d’un objet à partir d’une série de mesures effectuées depuis l’extérieur de cet objet.
Tomographic reconstruction is a type of multidimensional inverse problem where the challenge is to yield an estimate of a specific system from a finite number of projections. The mathematical basis for tomographic imaging was laid down by Johann Radon. A notable example of applications is the reconstruction of computed tomography (CT) where cross-sectional images of patients are obtained in non-invasive manner.
Le théorème de projection de Radon établit la possibilité de reconstituer une fonction réelle à deux variables (assimilable à une image) à l'aide de la totalité de ses projections selon des droites concourantes. L'application la plus courante de ce théorème est la reconstruction d'images médicales en tomodensitométrie, c'est-à-dire dans les scanneurs à rayon X. Il doit son nom au mathématicien Johann Radon. En pratique, il est impossible de disposer de toutes les projections d'un objet solide, seulement un échantillonnage.
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