In computed tomography (CT), partial volume effects impede accurate segmentation of structures that are small with respect to the pixel size. In this paper, it is shown that for objects consisting of a small number of homogeneous materials, the reconstruction resolution can be substantially increased without altering the acquisition process. A super-resolution reconstruction approach is introduced that is based on discrete tomography, in which prior knowledge about the materials in the object is assumed. Discrete tomography has already been used to create reconstructions from a low number of projection angles, but in this paper, it is demonstrated that it can also be applied to increase the reconstruction resolution. Experiments on simulated and real μCT data of bone and foam structures show that the proposed method indeed leads to significantly improved structure segmentation and quantification compared with what can be achieved from conventional reconstructions.
Additional Metadata
Keywords Computed tomography, segmentation, superresolution, discrete tomography
THEME Life Sciences (theme 5), Information (theme 2)
Publisher I.E.E.E.
Journal IEEE Transactions on Image Processing
Citation
van Aarle, W, Batenburg, K.J, van Gompel, G, van de Casteele, E, & Sijbers, J. (2014). Super-Resolution for Computed Tomography Based on Discrete Tomography. IEEE Transactions on Image Processing, 23(3), 1181–1193.