Literature DB >> 11747022

FLAIR histogram segmentation for measurement of leukoaraiosis volume.

C R Jack1, P C O'Brien, D W Rettman, M M Shiung, Y Xu, R Muthupillai, A Manduca, R Avula, B J Erickson.   

Abstract

The purposes of this study were to develop a method to measure brain and white matter hyperintensity (leukoaraiosis) volume that is based on the segmentation of the intensity histogram of fluid-attenuated inversion recovery (FLAIR) images and to assess the accuracy and reproducibility of the method. Whole-head synthetic image phantoms with manually introduced leukoaraiosis lesions of varying severity were constructed. These synthetic image phantom sets incorporated image contrast and anatomic features that mimicked leukoaraiosis found in real life. One set of synthetic image phantoms was used to develop the segmentation algorithm (FLAIR-histoseg). A second set was used to measure its accuracy. Test retest reproducibility was assessed in 10 elderly volunteers who were imaged twice. The mean absolute error of the FLAIR-histoseg method was 6.6% for measurement of leukoaraiosis volume and 1.4% for brain volume. The mean test retest coefficient of variation was 1.4% for leukoaraiosis volume and 0.3% for brain volume. We conclude that the FLAIR-histoseg method is an accurate and reproducible method for measuring leukoaraiosis and whole-brain volume in elderly subjects. Copyright 2001 Wiley-Liss, Inc.

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Year:  2001        PMID: 11747022      PMCID: PMC2755497          DOI: 10.1002/jmri.10011

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  42 in total

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2.  Graphical analysis of MR feature space for measurement of CSF, gray-matter, and white-matter volumes.

Authors:  D C Bonar; K A Schaper; J R Anderson; D A Rottenberg; S C Strother
Journal:  J Comput Assist Tomogr       Date:  1993 May-Jun       Impact factor: 1.826

3.  Initial clinical experience in MR imaging of the brain with a fast fluid-attenuated inversion-recovery pulse sequence.

Authors:  J N Rydberg; C A Hammond; R C Grimm; B J Erickson; C R Jack; J Huston; S J Riederer
Journal:  Radiology       Date:  1994-10       Impact factor: 11.105

4.  Dual-echo MRI segmentation using vector decomposition and probability techniques: a two-tissue model.

Authors:  Y H Kao; J A Sorenson; M M Bahn; S S Winkler
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5.  Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study.

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7.  The role of MRI in clinical trials of multiple sclerosis: comparison of image processing techniques.

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8.  Hypertension in the elderly is associated with white matter lesions and cognitive decline.

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9.  Accuracy and reproducibility in volumetric analysis of multiple sclerosis lesions.

Authors:  E F Jackson; P A Narayana; J S Wolinsky; T J Doyle
Journal:  J Comput Assist Tomogr       Date:  1993 Mar-Apr       Impact factor: 1.826

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  78 in total

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2.  Extreme deep white matter hyperintensity volumes are associated with African American race.

Authors:  Paul A Nyquist; Murat S Bilgel; Rebecca Gottesman; Lisa R Yanek; Taryn F Moy; Lewis C Becker; Jennifer Cuzzocreo; Jerry Prince; David M Yousem; Diane M Becker; Brian G Kral; Dhananjay Vaidya
Journal:  Cerebrovasc Dis       Date:  2014-03-25       Impact factor: 2.762

3.  Aging effects on recollection and familiarity: the role of white matter hyperintensities.

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5.  White Matter Structure in Older Adults Moderates the Benefit of Sleep Spindles on Motor Memory Consolidation.

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6.  High normal fasting blood glucose is associated with dementia in Chinese elderly.

Authors:  James A Mortimer; Amy R Borenstein; Ding Ding; Charles Decarli; Qianhua Zhao; Cathleen Copenhaver; Qihao Guo; Shugang Chu; Douglas Galasko; David P Salmon; Qi Dai; Yougui Wu; Ronald Petersen; Zhen Hong
Journal:  Alzheimers Dement       Date:  2010-11       Impact factor: 21.566

7.  Anatomical mapping of white matter hyperintensities (WMH): exploring the relationships between periventricular WMH, deep WMH, and total WMH burden.

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8.  Age and education effects on relationships of cognitive test scores with brain structure in demographically diverse older persons.

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9.  Differences in brain volume, hippocampal volume, cerebrovascular risk factors, and apolipoprotein E4 among mild cognitive impairment subtypes.

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10.  Brain injury biomarkers are not dependent on β-amyloid in normal elderly.

Authors:  David S Knopman; Clifford R Jack; Heather J Wiste; Stephen D Weigand; Prashanthi Vemuri; Val J Lowe; Kejal Kantarci; Jeffrey L Gunter; Matthew L Senjem; Michelle M Mielke; Rosebud O Roberts; Bradley F Boeve; Ronald C Petersen
Journal:  Ann Neurol       Date:  2013-02-19       Impact factor: 10.422

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