Abstract
In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible.
Highlights
Computed tomography (CT) is a non-invasive imaging technique which is based on reconstruction of an object from a series of projection images
Iterative reconstruction algorithms, such as the Simultaneous Iterative Reconstruction Technique (SIRT) [7], allow to incorporate prior knowledge about the object into the reconstruction such that high quality reconstructions can be obtained from even a low number of projections
We proposed a multiresolution Discrete Algebraic Reconstruction Technique (DART) (MDART) algorithm for discrete tomography
Summary
Computed tomography (CT) is a non-invasive imaging technique which is based on reconstruction of an object from a series of projection images. CT has applications on all scales, ranging from 3D imaging of nanomaterials by electron microscopy to the reconstruction of electron-density maps of the solar corona [2,3] In many of these applications, it is highly desirable to reduce the number of projections taken. Analytical reconstruction algorithms, such as Filtered Back Projection (FBP) [6], require a large number of projections acquired from a full angular range to obtain reconstructions of acceptable quality. Iterative reconstruction algorithms, such as the Simultaneous Iterative Reconstruction Technique (SIRT) [7], allow to incorporate prior knowledge about the object into the reconstruction such that high quality reconstructions can be obtained from even a low number of projections. The proposed approach can extend the area of applicability of DART, allowing its application to large experimental datasets
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