Abstract
In this article the Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox, a MATLAB/CUDA toolbox for fast and accurate 3D x-ray image reconstruction, is presented. One of the key features is the implementation of a wide variety of iterative algorithms as well as FDK, including a range of algorithms in the SART family, the Krylov subspace family and a range of methods using total variation regularization. Additionally, the toolbox has GPU-accelerated projection and back projection using the latest techniques and it has a modular design that facilitates the implementation of new algorithms. We present an overview of the structure and techniques used in the creation of the toolbox, together with two usage examples. The TIGRE Toolbox is released under an open source licence, encouraging people to contribute.
Highlights
Among the techniques for x-ray computed tomography (CT) in widespread use, cone beam (CB) geometry is getting increasing attention nowadays, from medical imaging to material science
In order to reduce the gap between algorithm research and end use, we have developed the Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox, a MATLAB/GPU toolbox featuring a wide range of iterative algorithms
A 3D tomographic reconstruction toolbox has been developed with fast GPU-based algorithms and a wide variety of tools and image reconstruction algorithms
Summary
Among the techniques for x-ray computed tomography (CT) in widespread use, cone beam (CB) geometry is getting increasing attention nowadays, from medical imaging to material science. In all applications of CBCT, the working principle is the same: 2D x-ray images of the ‘sample’ are obtained from different angles and a tomographic reconstruction algorithm is used to create an image from the data. The fact that in circular CBCT the original image is mathematically impossible to obtain [6, 7] and other factors, such as the high dimensionality of the problem or the inconsistency created by different physical effects with photons, make the image reconstruction problem what mathematicians define as illposed. Advanced mathematics is needed to generate a solution. This has led to extended research in image reconstruction algorithms, with a wide range of published approaches that give differing results.
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