caa a22 4500 606209824 CHVBK 20210128101030.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s11220-015-0107-2 doi (NATIONALLICENCE)springer-10.1007/s11220-015-0107-2 Herman Gabor Department of Computer Science, The Graduate Center, City University of New York, 10016, New York, NY, USA aut Basis Functions in Image Reconstruction From Projections: A Tutorial Introduction [Elektronische Daten] [Gabor Herman] The series expansion approaches to image reconstruction from projections assume that the object to be reconstructed can be represented as a linear combination of fixed basis functions and the task of the reconstruction algorithm is to estimate the coefficients in such a linear combination based on the measured projection data. It is demonstrated that using spherically symmetric basis functions (blobs), instead of ones based on the more traditional pixels, yields superior reconstructions of medically relevant objects. The demonstration uses simulated computerized tomography projection data of head cross-sections and the series expansion method ART for the reconstruction. In addition to showing the results of one anecdotal example, the relative efficacy of using pixel and blob basis functions in image reconstruction from projections is also evaluated using a statistical hypothesis testing based task oriented comparison methodology. The superiority of the efficacy of blob basis functions over that of pixel basis function is found to be statistically significant. Springer Science+Business Media New York, 2015 Image reconstruction nationallicence Computerized tomography nationallicence Pixels nationallicence Blobs nationallicence Image representation nationallicence Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-21 1557-2064 16:1<1 2015 16 11220 https://doi.org/10.1007/s11220-015-0107-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 review-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11220-015-0107-2 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Herman Gabor Department of Computer Science, The Graduate Center, City University of New York, 10016, New York, NY, USA aut NATIONALLICENCE 773 0- Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-21 1557-2064 16:1<1 2015 16 11220