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High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly

Deepthi Rajashekar*, Matthias Wilms, M. Ethan MacDonald, Jan Ehrhardt, Pauline Mouches, Richard Frayne, Michael D. Hill, Nils D. Forkert

*Korrespondierende/r Autor/-in für diese Arbeit

Abstract

Normative brain atlases are a standard tool for neuroscience research and are, for example, used for spatial normalization of image datasets prior to voxel-based analyses of brain morphology and function. Although many different atlases are publicly available, they are usually biased with respect to an imaging modality and the age distribution. Both effects are well known to negatively impact the accuracy and reliability of the spatial normalization process using non-linear image registration methods. An important and very active neuroscience area that lacks appropriate atlases is lesion-related research in elderly populations (e.g. stroke, multiple sclerosis) for which FLAIR MRI and non-contrast CT are often the clinical imaging modalities of choice. To overcome the lack of atlases for these tasks and modalities, this paper presents high-resolution, age-specific FLAIR and non-contrast CT atlases of the elderly generated using clinical images.

OriginalspracheEnglisch
Aufsatznummer56
ZeitschriftScientific Data
Jahrgang7
Ausgabenummer1
ISSN2052-4463
DOIs
PublikationsstatusVeröffentlicht - 01.12.2020

Fördermittel

This work was supported by the Hotchkiss Brain Institute, Heart and Stroke Foundation of Canada Grant in aid (G-17-0018368), the Canada Research Chairs (CRC) Program, and the Canadian Open Neuroscience Platform (CONP). The FLAIR data was provided by the Calgary Normative Study, which was partially funded by the Canadian Institutes for Health Research (FDN-333931). We acknowledge the support of Cheryl R. McCreary, PhD, Linda B. Andersen, PhD, and Marinna Salluzzi, PhD, acquiring and managing this dataset. The NCCT data was provided by the ESCAPE trial sponsored by the University of Calgary. Funding for ESCAPE was provided by Covidien through an unrestricted grant.

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

  1. SDG 3 – Gesundheit und Wohlergehen
    SDG 3 – Gesundheit und Wohlergehen
  2. SDG 9 – Industrie, Innovation und Infrastruktur
    SDG 9 – Industrie, Innovation und Infrastruktur

Coronavirus-Bezug

  • Forschung zu SARS-CoV-2 / COVID-19

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