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dc.contributor.authorTseng, Hsin-Wu
dc.contributor.authorFan, Jiahua
dc.contributor.authorKupinski, Matthew A.
dc.date.accessioned2018-01-31T16:21:09Z
dc.date.available2018-01-31T16:21:09Z
dc.date.issued2017-11-21
dc.identifier.citationAssessing computed tomography image quality for combined detection and estimation tasks 2017, 4 (04):1 Journal of Medical Imagingen
dc.identifier.issn2329-4302
dc.identifier.pmid29201940
dc.identifier.doi10.1117/1.JMI.4.4.045503
dc.identifier.urihttp://hdl.handle.net/10150/626451
dc.description.abstractMaintaining or even improving image quality while lowering patient dose is always the desire in clinical computed tomography (CT) imaging. Iterative reconstruction (IR) algorithms have been designed to allow for a reduced dose while maintaining or even improving an image. However, we have previously shown that the dose-saving capabilities allowed with IR are different for different clinical tasks. The channelized scanning linear observer (CSLO) was applied to study clinical tasks that combine detection and estimation when assessing CT image data. The purpose of this work is to illustrate the importance of task complexity when assessing dose savings and to move toward more realistic tasks when performing these types of studies. Human-observer validation of these methods will take place in a future publication. Low-contrast objects embedded in body-size phantoms were imaged multiple times and reconstructed by filtered back projection (FBP) and an IR algorithm. The task was to detect, localize, and estimate the size and contrast of low-contrast objects in the phantom. Independent signal-present and signal-absent regions of interest cropped from images were channelized by the dense-difference of Gauss channels for CSLO training and testing. Estimation receiver operating characteristic (EROC) curves and the areas under EROC curves (EAUC) were calculated by CSLO as the figure of merit. The one-shot method was used to compute the variance of the EAUC values. Results suggest that the IR algorithm studied in this work could efficiently reduce the dose by similar to 50% while maintaining an image quality comparable to conventional FBP reconstruction warranting further investigation using real patient data. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
dc.description.sponsorshipGE Healthcare, Waukesha, Wisconsinen
dc.language.isoenen
dc.publisherSPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERSen
dc.relation.urlhttps://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-4/issue-04/045503/Assessing-computed-tomography-image-quality-for-combined-detection-and-estimation/10.1117/1.JMI.4.4.045503.fullen
dc.rights© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.en
dc.subjectcomputed tomographyen
dc.subjectiterative reconstructionen
dc.subjectchannelized scanning linear observeren
dc.subjectdetectionen
dc.subjectestimationen
dc.subjectestimation receiver operating characteristicen
dc.subjectEROC curvesen
dc.titleAssessing computed tomography image quality for combined detection and estimation tasksen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Coll Opt Scien
dc.identifier.journalJournal of Medical Imagingen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-12T01:08:27Z
html.description.abstractMaintaining or even improving image quality while lowering patient dose is always the desire in clinical computed tomography (CT) imaging. Iterative reconstruction (IR) algorithms have been designed to allow for a reduced dose while maintaining or even improving an image. However, we have previously shown that the dose-saving capabilities allowed with IR are different for different clinical tasks. The channelized scanning linear observer (CSLO) was applied to study clinical tasks that combine detection and estimation when assessing CT image data. The purpose of this work is to illustrate the importance of task complexity when assessing dose savings and to move toward more realistic tasks when performing these types of studies. Human-observer validation of these methods will take place in a future publication. Low-contrast objects embedded in body-size phantoms were imaged multiple times and reconstructed by filtered back projection (FBP) and an IR algorithm. The task was to detect, localize, and estimate the size and contrast of low-contrast objects in the phantom. Independent signal-present and signal-absent regions of interest cropped from images were channelized by the dense-difference of Gauss channels for CSLO training and testing. Estimation receiver operating characteristic (EROC) curves and the areas under EROC curves (EAUC) were calculated by CSLO as the figure of merit. The one-shot method was used to compute the variance of the EAUC values. Results suggest that the IR algorithm studied in this work could efficiently reduce the dose by similar to 50% while maintaining an image quality comparable to conventional FBP reconstruction warranting further investigation using real patient data. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.


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