Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1859
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWen, L-
dc.creatorEberl, S-
dc.creatorFulham, MJ-
dc.creatorFeng, DD-
dc.creatorBai, J-
dc.date.accessioned2014-12-11T08:25:35Z-
dc.date.available2014-12-11T08:25:35Z-
dc.identifier.issn0018-9294-
dc.identifier.urihttp://hdl.handle.net/10397/1859-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectLeast square methodsen_US
dc.subjectParameter estimationen_US
dc.subjectSimulationen_US
dc.subjectSingle photon emission computed tomography (SPECT)en_US
dc.titleConstructing reliable parametric images using enhanced GLLS for dynamic SPECTen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: (David) Dagan Fengen_US
dc.identifier.spage1117-
dc.identifier.epage1126-
dc.identifier.volume56-
dc.identifier.issue4-
dc.identifier.doi10.1109/TBME.2008.2009998-
dcterms.abstractThe generalized linear least square (GLLS) method can successfully construct unbiased parametric images from dynamic positron emission tomography data. Quantitative dynamic single photon emission computed tomography (SPECT) also has the potential to generate physiological parametric images. However, the high level of noise, intrinsic in SPECT, can give rise to unsuccessful voxelwise fitting using GLLS, resulting in physiologically meaningless estimates. In this paper, we systematically investigated the applicability of our recently proposed approaches to improve the reliability of GLLS to parametric image generation from noisy dynamic SPECT data. The proposed approaches include use of a prior estimate of distribution volume (V[sub d]), a bootstrap Monte Carlo (BMC) resampling technique, as well as a combination of both techniques. Full Monte Carlo simulations were performed to generate dynamic projection data, which were then reconstructed with and without resolution recovery, before generating parametric images with the proposed methods. Four experimental clinical datasets were also included in the analysis. The GLLS methods incorporating BMC resampling could successfully and reliably generate parametric images. For high signal-to-noise ratio (SNR) imaging data, the BMC-aided GLLS provided the best estimates of K₁, while the BMC-V[sub d]-aided GLLS proved superior for estimating V[sub d]. The improvement in reliability gained with BMC-aided GLLS in low SNR image data came at the expense of some overestimation of V[sub d] and increased computation time.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on biomedical engineering, Apr. 2009, v. 56, no. 4, p. 1117-1126-
dcterms.isPartOfIEEE transactions on biomedical engineering-
dcterms.issued2009-04-
dc.identifier.isiWOS:000265937200022-
dc.identifier.scopus2-s2.0-67149130142-
dc.identifier.pmid19068420-
dc.identifier.eissn1558-2531-
dc.identifier.rosgroupidr44853-
dc.description.ros2008-2009 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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