In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Tarroni:2020,
author = {Tarroni, G and Bai, W and Oktay, O and Schuh, A and Suzuki, H and Glocker, B and Matthews, P and Rueckert, D},
journal = {Scientific Reports},
title = {Large-scale quality control of cardiac imaging in population studies: application to UK Biobank},
url = {http://hdl.handle.net/10044/1/76571},
volume = {10},
year = {2020}
}
TY - JOUR
AB - In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment isunfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) imagesto the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics(heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factorsincluding acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage(i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of thestacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slicemotion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastoliccardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involvedin UKBB CMR acquisition and for the ones who use the dataset for research purposes.
AU - Tarroni,G
AU - Bai,W
AU - Oktay,O
AU - Schuh,A
AU - Suzuki,H
AU - Glocker,B
AU - Matthews,P
AU - Rueckert,D
PY - 2020///
SN - 2045-2322
TI - Large-scale quality control of cardiac imaging in population studies: application to UK Biobank
T2 - Scientific Reports
UR - http://hdl.handle.net/10044/1/76571
VL - 10
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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