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Learning-based quality control for cardiac MR images

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Title: Learning-based quality control for cardiac MR images
Authors: Tarroni, G
Oktay, O
Bai, W
Schuh, A
Suzuki, H
Passerat-Palmbach, J
De Marvao, A
O'Regan, D
Cook, S
Glocker, B
Matthews, P
Rueckert, D
Item Type: Journal Article
Abstract: The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operatordependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
Issue Date: 1-May-2019
Date of Acceptance: 17-Oct-2018
URI: http://hdl.handle.net/10044/1/65595
DOI: https://doi.org/10.1109/TMI.2018.2878509
ISSN: 0278-0062
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 1127
End Page: 1138
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 38
Issue: 5
Copyright Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
British Heart Foundation
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: EP/P001009/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Image quality assessment
magnetic resonance imaging
motion compensation and analysis
Nuclear Medicine & Medical Imaging
08 Information and Computing Sciences
09 Engineering
Publication Status: Published
Online Publication Date: 2018-11-01
Appears in Collections:Computing
Department of Brain Sciences
Faculty of Engineering