BROWSE

Related Scientist

cnir's photo.

cnir
뇌과학이미징연구단
more info

ITEM VIEW & DOWNLOAD

Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity

DC Field Value Language
dc.contributor.authorSoHyun Han-
dc.contributor.authorRadka Stoyanova-
dc.contributor.authorHansol Lee-
dc.contributor.authorSean D. Carlin-
dc.contributor.authorJason A. Koutcher-
dc.contributor.authorHyungJoon Cho-
dc.contributor.authorEllen Ackerstaff-
dc.date.available2018-04-27T06:31:12Z-
dc.date.created2018-02-14-
dc.date.issued2018-03-
dc.identifier.issn0740-3194-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/4438-
dc.description.abstractPurpose: 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. Magn Reson Med 79:1736–1744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.-
dc.description.uri1-
dc.language영어-
dc.publisherWILEY-BLACKWELL-
dc.subjectautomation-
dc.subjectDCE-MRI-
dc.subjectintratumoral vascular heterogeneity-
dc.subjectpattern recognition analysis-
dc.subjectprincipal component analysis-
dc.titleAutomation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000427186000009-
dc.identifier.scopusid2-s2.0-85040764842-
dc.identifier.rimsid62242ko
dc.date.tcdate2018-10-01-
dc.contributor.affiliatedAuthorSoHyun Han-
dc.identifier.doi10.1002/mrm.26822-
dc.identifier.bibliographicCitationMAGNETIC RESONANCE IN MEDICINE, v.79, no.3, pp.1736 - 1744-
dc.citation.titleMAGNETIC RESONANCE IN MEDICINE-
dc.citation.volume79-
dc.citation.number3-
dc.citation.startPage1736-
dc.citation.endPage1744-
dc.date.scptcdate2018-10-01-
dc.description.scptc0-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusPRINCIPAL COMPONENT ANALYSIS-
dc.subject.keywordPlusNMR SPECTRAL QUANTITATION-
dc.subject.keywordPlusPROSTATE-CANCER-
dc.subject.keywordPlusTUMOR HYPOXIA-
dc.subject.keywordPlusDCE-MRI-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusMICROENVIRONMENT-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusRADIOGENOMICS-
dc.subject.keywordPlusANGIOGENESIS-
dc.subject.keywordAuthorDCE-MRI-
dc.subject.keywordAuthorpattern recognition analysis-
dc.subject.keywordAuthorprincipal component analysis-
dc.subject.keywordAuthorautomation-
dc.subject.keywordAuthorintratumoral vascular heterogeneity-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
Files in This Item:
Automation of Pattern Recognition Analysis of Dynamic Contrast-Enhanced MRI Data to Characterize Intratu moral Vascular Heterogeneity_한소현.pdfDownload

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse