Assessment of iterative image reconstruction techniques for small-animal PET imaging applications

Karali, E; Pavlopoulos, S; Koutsouris, D

dc.contributor.author	Karali, E	en
dc.contributor.author	Pavlopoulos, S	en
dc.contributor.author	Koutsouris, D	en
dc.date.accessioned	2014-03-01T02:45:09Z
dc.date.available	2014-03-01T02:45:09Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32167
dc.subject	Cross Correlation	en
dc.subject	Evaluation Studies	en
dc.subject	Image Quality	en
dc.subject	Image Reconstruction	en
dc.subject	Iterative Algorithm	en
dc.subject	Iterative Reconstruction	en
dc.subject	Small Animal Pet	en
dc.subject	Visual Inspection	en
dc.subject	Contrast To Noise Ratio	en
dc.subject	Field of View	en
dc.subject	Region of Interest	en
dc.subject.other	Contrast to noise ratio	en
dc.subject.other	Cross-correlation coefficient	en
dc.subject.other	Evaluation study	en
dc.subject.other	Field of views	en
dc.subject.other	Iterative algorithm	en
dc.subject.other	Iterative image reconstruction	en
dc.subject.other	Iterative reconstruction	en
dc.subject.other	Phantom data	en
dc.subject.other	Reconstructed image	en
dc.subject.other	Region of interest	en
dc.subject.other	Small animal PET	en
dc.subject.other	Visual inspection	en
dc.subject.other	Bioinformatics	en
dc.subject.other	Edge detection	en
dc.subject.other	Image quality	en
dc.subject.other	Image reconstruction	en
dc.subject.other	Positron emission tomography	en
dc.subject.other	Restoration	en
dc.subject.other	Visual communication	en
dc.subject.other	Iterative methods	en
dc.title	Assessment of iterative image reconstruction techniques for small-animal PET imaging applications	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIBE.2008.4696810	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIBE.2008.4696810	en
heal.identifier.secondary	4696810	en
heal.publicationDate	2008	en
heal.abstract	The purpose of this study is to assess the performance of iterative reconstruction methods, using phantom data from a prototype small-animal PET system. The algorithms compared are the simultaneous versions of ART (SART), EM-ML, ISRA WLS and a new iterative algorithm we have introduced under the short name ISWLS. The evaluation study was based on reconstructed image quality, as it is derived from visual inspection, cross-correlation coefficient and CNRs (contrast-to-noise ratios) of specific ROIs (region-of-interest). In general EM-ML and ISRA present similar reconstruction time and minor differences in reconstructed image quality. Slightly superior performances show WLS and SART while ISWLS improves reconstruction resolution at the edges of the field of view.	en
heal.journalName	8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008	en
dc.identifier.doi	10.1109/BIBE.2008.4696810	en