A Multiresolution Approach to Discrete Tomography Using DART

• Published in: PloS one Vol. 9; no. 9; p. e106090
• Main Authors: Dabravolski, Andrei, Batenburg, Kees Joost, Sijbers, Jan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.09.2014; Public Library of Science (PLoS)
• Subjects: Algebra; Algorithms; Artefacts; Biology and Life Sciences; Computer applications; Computer Simulation; Computing time; Datasets; Image Processing, Computer-Assisted - methods; International conferences; Iterative methods; Medical imaging; Medicine and Health Sciences; Microscopy; NMR; Nuclear magnetic resonance; Parameter estimation; Phantoms, Imaging; Pixels; Quality; Reconstruction; Research and Analysis Methods; Science; Tomography; Tomography - methods; Tomography, X-Ray Computed - methods; Belgium; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0106090

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.