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Titolo: Ordered subset reconstruction for xray CT
Autore: Beekman, FJ; Kamphuis, C;
 Indirizzi:
 Univ Utrecht Hosp E02 222, Image Sci Inst, NL3584 CX Utrecht, NetherlandsUniv Utrecht Hosp E02 222 Utrecht Netherlands NL3584 CX ht, Netherlands Univ Calif Los Angeles, Sch Med, Crump Inst Mol Imaging, Los Angeles, CA USA Univ Calif Los Angeles Los Angeles CA USA l Imaging, Los Angeles, CA USA
 Titolo Testata:
 PHYSICS IN MEDICINE AND BIOLOGY
fascicolo: 7,
volume: 46,
anno: 2001,
pagine: 1835  1844
 SICI:
 00319155(200107)46:7<1835:OSRFXC>2.0.ZU;2N
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 FANBEAM COLLIMATION; IMAGERECONSTRUCTION; TRANSMISSION TOMOGRAPHY; EMISSIONTOMOGRAPHY; ITERATIVE ALGORITHM; SPECT; CONVERGENCE; CAMERA; SYSTEM;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Life Sciences
 Citazioni:
 25
 Recensione:
 Indirizzi per estratti:
 Indirizzo: Beekman, FJ Univ Utrecht Hosp E02 222, Image Sci Inst, Heidelberglaan 100,NL3584 CX Utrecht, Netherlands Univ Utrecht Hosp E02 222 Heidelberglaan 100 Utrecht Netherlands NL3584 CX



 Citazione:
 F.J. Beekman e C. Kamphuis, "Ordered subset reconstruction for xray CT", PHYS MED BI, 46(7), 2001, pp. 18351844
Abstract
Statistical methods for image reconstruction such as the maximum likelihood expectation maximization are more robust and flexible than analytical inversion methods and allow for accurate modelling of the counting statistics and photon transport during acquisition of projection data. Statistical reconstruction is prohibitively slow when applied to clinical xray CT due to the large data sets and the high number of iterations required for reconstructing highresolution images. Recently, however. powerful methods for accelerating statistical reconstruction have been proposed which. instead of accessing all projections simultaneously for updating an image estimate. are based on accessing a subset of projections at the time during iterative reconstruction. In this paper we study images generated by the convex algorithm accelerated by the use of ordered subsets (the OS convex algorithm (OSC))for data sets with sizes, noise levels and spatial resolution representative of xray CT imaging. It is only in the case of extremely high acceleration factors (higher than 50, corresponding to fewer than 20 projections per subset). that areas with incorrect grey values appear in the reconstructed images. and that image noise increases compared with the standard convex algorithm. These image degradations can be adequately corrected for by running the final iteration of OSC with a reduced number of subsets. Even by applying such a relatively slow final iteration, OSC produces almost an equal resolution and lesion contrast as the standard convex algorithm, but more than two orders of magnitude faster.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 18/01/20 alle ore 01:46:50