Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank

• Published in: Scientific reports Vol. 10; no. 1; p. 2408
• Main Authors: Tarroni, Giacomo, Bai, Wenjia, Oktay, Ozan, Schuh, Andreas, Suzuki, Hideaki, Glocker, Ben, Matthews, Paul M., Rueckert, Daniel
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.02.2020; Nature Publishing Group
• Subjects: 59/57; 639/705/117; 692/4019/2776; Aged; Biobanks; Biological Specimen Banks; Cardiac Imaging Techniques - methods; Female; Heart; Heart - diagnostic imaging; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Male; Middle Aged; multidisciplinary; Phenotypes; Population studies; Population-based studies; Quality Control; Science; Science (multidisciplinary); United Kingdom; United Kingdom; United Kingdom > UK
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-58212-2

In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to 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 factors including 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 the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes.