Towards Optimising MRI Characterisation of Tissue (TOMCAT) Dataset including all Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) data

Data in Brief

Ashley York¹  Thomas B Shaw²  Steffen Bollmann²  Markus Barth³ 

[1] Corresponding authors.;Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia;School of Psychology, The University of Queensland, Brisbane, Australia;

关键词: Hippocampus;    Longitudinal studies;    Segmentation;    Magnetic resonance imaging;    Image processing;    Computer-assisted;   

DOI  :  

来源: DOAJ

【 摘 要 】

Seven healthy participants were scanned using a Siemens Magnetom 7 Tesla (T) whole-body research MRI scanner (Siemens Healthcare, Erlangen, Germany). The first scan session was acquired in 2016 (time point one), the second and third session in 2019 (time point two and three, respectively) with the third session acquired 45 min following the second as a scan-rescan condition. The following scans were acquired for all time points: structural T1 weighted (T1w) MP2RAGE, high in-plane resolution Turbo-Spin Echo (TSE) dedicated for hippocampus subfield segmentation. The data were used in three projects to date, for more insight see: 1) Non-linear realignment for Turbo-Spin Echo retrospective motion correction and hippocampus segmentation improvement [1] 2) Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI [2]. 3) The challenge of bias-free coil combination for quantitative susceptibility mapping at ultra-high field [3]. Data were converted from DICOM to nifti format following the Brain Imaging Data Structure (BIDS) [4]. Data were analysed for the accompanying manuscript “Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI” including test-retest reliability and longitudinal Bayesian Linear Mixed Effects (LME) modelling.

【 授权许可】

Unknown