| Automatic vessel removal in gliomas from dynamic susceptibility contrast imaging. | |
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MedLine Citation:
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PMID: 19253390 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The presence of macroscopic vessels within the tumor region is a potential confounding factor in MR-based dynamic susceptibility contrast (DSC)-enhanced glioma grading. In order to distinguish between such vessels and the elevated cerebral blood volume (CBV) of brain tumors, we propose a vessel segmentation technique based on clustering of multiple parameters derived from the dynamic contrast-enhanced first-pass curve. A total of 77 adult patients with histologically-confirmed gliomas were imaged at 1.5T and glioma regions-of-interest (ROIs) were derived from the conventional MR images by a neuroradiologist. The diagnostic accuracy of applying vessel exclusion by segmentation of glioma ROIs with vessels included was assessed using a histogram analysis method and compared to glioma ROIs with vessels included. For all measures of diagnostic efficacy investigated, the highest values were observed when the glioma diagnosis was based on vessel segmentation in combination with an initial mean transit time (MTT) mask. Our results suggest that vessel segmentation based on DSC parameters may improve the diagnostic efficacy of glioma grading. The proposed vessel segmentation is attractive because it provides a mask that covers all pixels affected by the intravascular susceptibility effect. |
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Authors:
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Kyrre E Emblem; Paulina Due-Tonnessen; John K Hald; Atle Bjornerud |
Publication Detail:
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Type: Evaluation Studies; Journal Article |
Journal Detail:
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Title: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine Volume: 61 ISSN: 1522-2594 ISO Abbreviation: Magn Reson Med Publication Date: 2009 May |
Date Detail:
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Created Date: 2009-05-25 Completed Date: 2009-07-27 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8505245 Medline TA: Magn Reson Med Country: United States |
Other Details:
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Languages: eng Pagination: 1210-7 Citation Subset: IM |
Copyright Information:
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(c) 2009 Wiley-Liss, Inc. |
Affiliation:
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Department of Medical Physics, Rikshospitalet University Hospital, Oslo, Norway. kyrre.eeg.emblem@rikshospitalet.no |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Adult Aged Algorithms Artificial Intelligence Brain Neoplasms / blood supply*, pathology* Female Glioma / blood supply*, pathology* Humans Image Enhancement / methods* Image Interpretation, Computer-Assisted / methods Magnetic Resonance Imaging / methods* Male Middle Aged Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity Young Adult |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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