Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Nam, Jeonghyeon | - |
dc.contributor.author | Lee, Okkyun | - |
dc.date.accessioned | 2021-10-13T05:30:05Z | - |
dc.date.available | 2021-10-13T05:30:05Z | - |
dc.date.created | 2021-07-02 | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 0094-2405 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/15489 | - |
dc.description.abstract | Purpose: This study aims to develop a calibration-based estimator for the photon-counting detector (PCD)-based x-ray computed tomography. Methods: We propose the nearest neighborhood (NN)-based estimator, which searches for the nearest calibration data for a given PCD output and sets the associated basis line-integrals as the estimate. Searching for the nearest neighbors can be accelerated using the pre-calculated k-d tree for the data. Results: The proposed method is compared to the model-based maximum likelihood (ML) estimator. For slab phantom study, both ML and NN-based methods achieve the Cramér-Rao lower bound and are unbiased for various combinations of three basis materials (water, bone, and gold). The proposed method is also validated for K-edge imaging and presents almost unbiased Au concentrations in the region of interest. Conclusions: The proposed NN-based method is demonstrated to be as accurate as the model-based ML estimator, but it is computationally efficient and requires only calibration measurements. © 2021 American Association of Physicists in Medicine | - |
dc.language | English | - |
dc.publisher | American Association of Physicists in Medicine | - |
dc.title | Technical Note: The nearest neighborhood-based approach for estimating basis line-integrals using photon-counting detector | - |
dc.type | Article | - |
dc.identifier.doi | 10.1002/mp.14920 | - |
dc.identifier.wosid | 000665702500001 | - |
dc.identifier.scopusid | 2-s2.0-85108514464 | - |
dc.identifier.bibliographicCitation | Medical Physics, v.48, no.10, pp.6531 - 6535 | - |
dc.description.isOpenAccess | FALSE | - |
dc.subject.keywordAuthor | k-d tree | - |
dc.subject.keywordAuthor | maximum likelihood | - |
dc.subject.keywordAuthor | nearest neighbor | - |
dc.subject.keywordAuthor | photon-counting detector | - |
dc.subject.keywordPlus | Photon counting detectors | - |
dc.subject.keywordPlus | Region of interest | - |
dc.subject.keywordPlus | X-ray computed tomography | - |
dc.subject.keywordPlus | X ray detectors | - |
dc.subject.keywordPlus | Calibration | - |
dc.subject.keywordPlus | Computerized tomography | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Maximum likelihood estimation | - |
dc.subject.keywordPlus | Photons | - |
dc.subject.keywordPlus | Trees (mathematics) | - |
dc.subject.keywordPlus | Calibration measurements | - |
dc.subject.keywordPlus | Computationally efficient | - |
dc.subject.keywordPlus | Maximum likelihood estimator | - |
dc.subject.keywordPlus | Nearest neighborhood | - |
dc.subject.keywordPlus | Nearest neighbors | - |
dc.citation.endPage | 6535 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 6531 | - |
dc.citation.title | Medical Physics | - |
dc.citation.volume | 48 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.type.docType | Article | - |
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