Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging

Mahsa Dadar, Josefina Maranzano, Simon Ducharme, Owen T. Carmichael, Charles DeCarli, D. Louis Collins

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Introduction: Fluid-attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints. Methods: In this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information. Results: The WMHLs obtained from T1w-Only segmentations correlated with the manual WMHLs (ADNI1: r=.743, p<.001, ADNI2/GO: r=.904, p<.001), segmentations obtained from T1w+T2w+PD for ADNI1 (r=.888, p<.001) and T1w+FLAIR for ADNI2/GO (r=.969, p<.001), age (ADNI1: r=.391, p<.001, ADNI2/GO: r=.466, p<.001), and ADAS13 (ADNI1: r=.227, p<.001, ADNI2/GO: r=.190, p<0.001), and NPI (ADNI1: r=.290, p<.001, ADNI2/GO: r=0.144, p<.001), controlling for age. Conclusion: Our results suggest that while T2w/PD and FLAIR provide more accurate estimates of the true WMHLs, T1w-Only segmentations can still provide estimates that hold strong correlations with the actual WMHLs, age, and performance on various cognitive/clinical scales, giving added value to datasets where T2w/PD or FLAIR are not available.

Original languageEnglish (US)
JournalHuman Brain Mapping
DOIs
StateAccepted/In press - Jan 1 2017

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Protons
Magnetic Resonance Spectroscopy
White Matter
Datasets
Alzheimer Disease
Economics
Population

Keywords

  • Azheimer's disease
  • Cerebrovascular disease
  • Cognitive impairment
  • Small vessel disease
  • T1w hypointensities
  • White matter integrity

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

Cite this

Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging. / Dadar, Mahsa; Maranzano, Josefina; Ducharme, Simon; Carmichael, Owen T.; DeCarli, Charles; Collins, D. Louis.

In: Human Brain Mapping, 01.01.2017.

Research output: Contribution to journalArticle

Dadar, Mahsa ; Maranzano, Josefina ; Ducharme, Simon ; Carmichael, Owen T. ; DeCarli, Charles ; Collins, D. Louis. / Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging. In: Human Brain Mapping. 2017.
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AU - Maranzano, Josefina

AU - Ducharme, Simon

AU - Carmichael, Owen T.

AU - DeCarli, Charles

AU - Collins, D. Louis

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N2 - Introduction: Fluid-attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints. Methods: In this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information. Results: The WMHLs obtained from T1w-Only segmentations correlated with the manual WMHLs (ADNI1: r=.743, p<.001, ADNI2/GO: r=.904, p<.001), segmentations obtained from T1w+T2w+PD for ADNI1 (r=.888, p<.001) and T1w+FLAIR for ADNI2/GO (r=.969, p<.001), age (ADNI1: r=.391, p<.001, ADNI2/GO: r=.466, p<.001), and ADAS13 (ADNI1: r=.227, p<.001, ADNI2/GO: r=.190, p<0.001), and NPI (ADNI1: r=.290, p<.001, ADNI2/GO: r=0.144, p<.001), controlling for age. Conclusion: Our results suggest that while T2w/PD and FLAIR provide more accurate estimates of the true WMHLs, T1w-Only segmentations can still provide estimates that hold strong correlations with the actual WMHLs, age, and performance on various cognitive/clinical scales, giving added value to datasets where T2w/PD or FLAIR are not available.

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