Cited 9 times in
Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study
DC Field | Value | Language |
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dc.contributor.author | 김기욱 | - |
dc.contributor.author | 김명진 | - |
dc.contributor.author | 김진아 | - |
dc.contributor.author | 박채정 | - |
dc.contributor.author | 이호준 | - |
dc.date.accessioned | 2018-10-22T13:14:57Z | - |
dc.date.available | 2018-10-22T13:14:57Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1229-6929 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/163661 | - |
dc.description.abstract | Objective: The purpose of this study was to determine the diagnostic utility of low-dose CT with knowledge-based iterative model reconstruction (IMR) for the evaluation of parotid gland tumors. Materials and Methods: This prospective study included 42 consecutive patients who had undergone low-dose contrast-enhanced CT for the evaluation of suspected parotid gland tumors. Prior or subsequent non-low-dose CT scans within 12 months were available in 10 of the participants. Background noise (BN), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between non-low-dose CT images and images generated using filtered back projection (FBP), hybrid iterative reconstruction (iDose4; Philips Healthcare), and knowledge-based IMR. Subjective image quality was rated by two radiologists using five-point grading scales to assess the overall image quality, delineation of lesion contour, image sharpness, and noise. Results: With the IMR algorithm, background noise (IMR, 4.24 ± 3.77; iDose4, 8.77 ± 3.85; FBP, 11.73 ± 4.06; p = 0.037 [IMR vs. iDose4] and p < 0.001 [IMR vs. FBP]) was significantly lower and SNR (IMR, 23.93 ± 7.49; iDose4, 10.20 ± 3.29; FBP, 7.33 ± 2.03; p = 0.011 [IMR vs. iDose4] and p < 0.001 [IMR vs. FBP]) was significantly higher compared with the other two algorithms. The CNR was also significantly higher with the IMR compared with the FBP (25.76 ± 11.88 vs. 9.02 ± 3.18, p < 0.001). There was no significant difference in BN, SNR, and CNR between low-dose CT with the IMR algorithm and non-low-dose CT. Subjective image analysis revealed that IMR-generated low-dose CT images showed significantly better overall image quality and delineation of lesion contour with lesser noise, compared with those generated using FBP by both reviewers 1 and 2 (4 vs. 3; 4 vs. 3; and 3-4 vs. 2; p < 0.05 for all pairs), although there was no significant difference in subjective image quality scores between IMR-generated low-dose CT and non-low-dose CT images. Conclusion: Iterative model reconstruction-generated low-dose CT is an alternative to standard non-low-dose CT without significantly affecting image quality for the evaluation of parotid gland tumors. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Korean Society of Radiology | - |
dc.relation.isPartOf | KOREAN JOURNAL OF RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Radiology | - |
dc.contributor.googleauthor | Chae Jung Park | - |
dc.contributor.googleauthor | Ki Wook Kim | - |
dc.contributor.googleauthor | Ho-Joon Lee | - |
dc.contributor.googleauthor | Myeong-Jin Kim | - |
dc.contributor.googleauthor | Jinna Kim | - |
dc.identifier.doi | 10.3348/kjr.2018.19.5.957 | - |
dc.contributor.localId | A05089 | - |
dc.contributor.localId | A00426 | - |
dc.contributor.localId | A01022 | - |
dc.contributor.localId | A04942 | - |
dc.contributor.localId | A03329 | - |
dc.relation.journalcode | J02884 | - |
dc.identifier.eissn | 2005-8330 | - |
dc.identifier.pmid | 30174486 | - |
dc.subject.keyword | Computed tomography | - |
dc.subject.keyword | Filtered back projection | - |
dc.subject.keyword | Image quality | - |
dc.subject.keyword | Image reconstruction | - |
dc.subject.keyword | Knowledge-based iterative reconstruction | - |
dc.subject.keyword | Parotid gland | - |
dc.subject.keyword | Parotid tumor | - |
dc.subject.keyword | Radiation dosage | - |
dc.contributor.alternativeName | Kim, Ki Wook | - |
dc.contributor.alternativeName | Kim, Myeong Jin | - |
dc.contributor.alternativeName | Kim, Jinna | - |
dc.contributor.alternativeName | Park, Chae Jung | - |
dc.contributor.alternativeName | Lee, Ho Joon | - |
dc.contributor.affiliatedAuthor | Kim, Ki Wook | - |
dc.contributor.affiliatedAuthor | Kim, Myeong Jin | - |
dc.contributor.affiliatedAuthor | Kim, Jinna | - |
dc.contributor.affiliatedAuthor | Park, Chae Jung | - |
dc.contributor.affiliatedAuthor | Lee, Ho Joon | - |
dc.citation.volume | 19 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 957 | - |
dc.citation.endPage | 964 | - |
dc.identifier.bibliographicCitation | KOREAN JOURNAL OF RADIOLOGY, Vol.19(5) : 957-964, 2018 | - |
dc.identifier.rimsid | 58962 | - |
dc.type.rims | ART | - |
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