Computer-Aided Detection of Metastatic Brain Tumors Using Magnetic Resonance Black-Blood Imaging
DC Field | Value | Language |
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dc.contributor.author | Yang, Seungwook | - |
dc.contributor.author | Nam, Yoonho | - |
dc.contributor.author | Kim, Min-Oh | - |
dc.contributor.author | Kim, Eung Yeop | - |
dc.contributor.author | Park, Jaeseok | - |
dc.contributor.author | Kim, Dong-Hyun | - |
dc.date.accessioned | 2021-09-06T04:55:50Z | - |
dc.date.available | 2021-09-06T04:55:50Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-02 | - |
dc.identifier.issn | 0020-9996 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/104140 | - |
dc.description.abstract | Objectives: The objective of this study was to develop a computer-aided detection system for automated brain metastases detection using magnetic resonance black-blood imaging and compare its applicability with conventional magnetization-prepared rapid gradient echo (MP-RAGE) imaging. Materials and Methods: Twenty-six patients with brain metastases were imaged with a contrast-enhanced, 3-dimensional, whole-brain magnetic resonance black-blood pulse sequence. Approval from the institutional review board and informed consent from the patients were obtained. Preprocessing steps included B1 inhomogeneity correction and brain extraction. The computer-aided detection system used 3-dimensional template matching, which measured normalized cross-correlation coefficient to generate possible metastases candidates. An artificial neural network was used for classification after various volume features were extracted. The same detection procedure was tested with contrast-enhanced MP-RAGE, which was also acquired from the same patients. Results: The performance of the proposed detection method was measured by the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. In the black-blood case, detection process displayed an AUROC of 0.9355, a sensitivity value of 81.1%, and a specificity value of 98.2%. Magnetization-prepared rapid gradient echo data showed an AUROC of 0.6508, a sensitivity value of 30.2%, and a specificity value of 99.97%. Conclusions: The results demonstrate that accurate automated detection of metastatic brain tumors using contrast-enhanced black-blood imaging sequence is possible compared with using conventional contrast-enhanced MP-RAGE sequence. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | LIPPINCOTT WILLIAMS & WILKINS | - |
dc.subject | CANCER-DETECTION | - |
dc.subject | WHOLE-BRAIN | - |
dc.subject | MRI | - |
dc.subject | SEGMENTATION | - |
dc.subject | DIAGNOSIS | - |
dc.subject | 3T | - |
dc.title | Computer-Aided Detection of Metastatic Brain Tumors Using Magnetic Resonance Black-Blood Imaging | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Jaeseok | - |
dc.identifier.doi | 10.1097/RLI.0b013e318277f078 | - |
dc.identifier.scopusid | 2-s2.0-84872361694 | - |
dc.identifier.wosid | 000313419800009 | - |
dc.identifier.bibliographicCitation | INVESTIGATIVE RADIOLOGY, v.48, no.2, pp.113 - 119 | - |
dc.relation.isPartOf | INVESTIGATIVE RADIOLOGY | - |
dc.citation.title | INVESTIGATIVE RADIOLOGY | - |
dc.citation.volume | 48 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 113 | - |
dc.citation.endPage | 119 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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.subject.keywordPlus | CANCER-DETECTION | - |
dc.subject.keywordPlus | WHOLE-BRAIN | - |
dc.subject.keywordPlus | MRI | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | 3T | - |
dc.subject.keywordAuthor | brain metastases | - |
dc.subject.keywordAuthor | computer-aided detection | - |
dc.subject.keywordAuthor | black-blood imaging | - |
dc.subject.keywordAuthor | MP-RAGE | - |
dc.subject.keywordAuthor | artificial neural network | - |
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