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dc.contributor.authorDo, Synhoen_US
dc.contributor.authorKarl, William Clemen_US
dc.contributor.authorSingh, Sarabjeeten_US
dc.contributor.authorKalra, Mannudeepen_US
dc.contributor.authorBrady, Tomen_US
dc.contributor.authorShin, Ellieen_US
dc.contributor.authorPien, Homeren_US
dc.date.accessioned2014-12-02T21:28:35Z
dc.date.issued2014en_US
dc.identifier.citationDo, Synho, William Clem Karl, Sarabjeet Singh, Mannudeep Kalra, Tom Brady, Ellie Shin, and Homer Pien. 2014. “High Fidelity System Modeling for High Quality Image Reconstruction in Clinical CT.” PLoS ONE 9 (11): e111625. doi:10.1371/journal.pone.0111625. http://dx.doi.org/10.1371/journal.pone.0111625.en
dc.identifier.issn1932-6203en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:13454749
dc.description.abstractToday, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional. We first isolated the fidelity term and analyzed the importance of using focal spot area modeling, flying focal spot location modeling, and active detector area modeling as opposed to just flying focal spot motion. We then compared images using different permutations of all three factors. Next, we tested the ability of the fidelity terms to retain signals upon application of the regularization term with all three factors. We then compared the differences between images generated by the proposed method and Filtered-Back-Projection. Lastly, we compared images of low-dose in vivo data using Filtered-Back-Projection, Iterative Reconstruction in Image Space, and the proposed method using raw data. The initial comparison of difference maps of images constructed showed that the focal spot area model and the active detector area model also have significant impacts on the quality of images produced. Upon application of the regularization term, images generated using all three factors were able to substantially decrease model mismatch error, artifacts, and noise. When the images generated by the proposed method were tested, conspicuity greatly increased, noise standard deviation decreased by 90% in homogeneous regions, and resolution also greatly improved. In conclusion, the improvement of the fidelity term to model clinical scanners is essential to generating higher quality images in low-dose imaging.en
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofdoi:10.1371/journal.pone.0111625en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229099/pdf/en
dash.licenseLAAen_US
dc.subjectBiology and Life Sciencesen
dc.subjectNeuroscienceen
dc.subjectNeuroimagingen
dc.subjectComputed Axial Tomographyen
dc.subjectMedicine and Health Sciencesen
dc.subjectDiagnostic Medicineen
dc.subjectDiagnostic Radiologyen
dc.subjectTomographyen
dc.subjectImaging Techniquesen
dc.titleHigh Fidelity System Modeling for High Quality Image Reconstruction in Clinical CTen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPLoS ONEen
dash.depositing.authorDo, Synhoen_US
dc.date.available2014-12-02T21:28:35Z
dc.identifier.doi10.1371/journal.pone.0111625*
dash.contributor.affiliatedSingh, Sarabjeet
dash.contributor.affiliatedDo, Synho


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