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DC Field | Value | Language |
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dc.citation.endPage | 1744 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1736 | - |
dc.citation.title | MAGNETIC RESONANCE IN MEDICINE | - |
dc.citation.volume | 79 | - |
dc.contributor.author | Han, SoHyun | - |
dc.contributor.author | Stoyanova, R | - |
dc.contributor.author | Lee, Hansol | - |
dc.contributor.author | Carlin, Sean D. | - |
dc.contributor.author | Koutcher, Jason A. | - |
dc.contributor.author | Cho, Hyungjoon | - |
dc.contributor.author | Ackerstaff, Ellen | - |
dc.date.accessioned | 2023-12-21T21:08:34Z | - |
dc.date.available | 2023-12-21T21:08:34Z | - |
dc.date.created | 2017-07-21 | - |
dc.date.issued | 2018-03 | - |
dc.description.abstract | Purpose: To automate dynamic contrast-enhanced MRI (DCE-MRI) data analysis by unsupervised pattern recognition (PR) to enable spatial mapping of intratumoral vascular heterogeneity. Methods: Three steps were automated. First, the arrival time of the contrast agent at the tumor was determined, including a calculation of the precontrast signal. Second, four criteria-based algorithms for the slice-specific selection of number of patterns (NP) were validated using 109 tumor slices from subcutaneous flank tumors of five different tumor models. The criteria were: half area under the curve, standard deviation thresholding, percent signal enhancement, and signal-to-noise ratio (SNR). The performance of these criteria was assessed by comparing the calculated NP with the visually determined NP. Third, spatial assignment of single patterns and/or pattern mixtures was obtained by way of constrained nonnegative matrix factorization. Results: The determination of the contrast agent arrival time at the tumor slice was successfully automated. For the determination of NP, the SNR-based approach outperformed other selection criteria by agreeing >97% with visual assessment. The spatial localization of single patterns and pattern mixtures, the latter inferring tumor vascular heterogeneity at subpixel spatial resolution, was established successfully by automated assignment from DCE-MRI signal-versus-time curves. Conclusion: The PR-based DCE-MRI analysis was successfully automated to spatially map intratumoral vascular heterogeneity. |
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dc.identifier.bibliographicCitation | MAGNETIC RESONANCE IN MEDICINE, v.79, no.3, pp.1736 - 1744 | - |
dc.identifier.doi | 10.1002/mrm.26822 | - |
dc.identifier.issn | 0740-3194 | - |
dc.identifier.scopusid | 2-s2.0-85040764842 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/22475 | - |
dc.identifier.url | http://onlinelibrary.wiley.com/doi/10.1002/mrm.26822/abstract | - |
dc.identifier.wosid | 000427186000009 | - |
dc.language | 영어 | - |
dc.publisher | WILEY | - |
dc.title | Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | DCE-MRI | - |
dc.subject.keywordAuthor | pattern recognition analysis | - |
dc.subject.keywordAuthor | principal component analysis | - |
dc.subject.keywordAuthor | automation | - |
dc.subject.keywordAuthor | intratumoral vascular heterogeneity | - |
dc.subject.keywordPlus | NMR SPECTRAL QUANTITATION | - |
dc.subject.keywordPlus | PROSTATE-CANCER | - |
dc.subject.keywordPlus | DCE-MRI | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | RECOVERY | - |
dc.subject.keywordPlus | MICROENVIRONMENT | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | PROGRESSION | - |
dc.subject.keywordPlus | HYPOXIA | - |
dc.subject.keywordPlus | TUMORS | - |
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