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Chan, E.* ; O’Hanlon, C.* ; Marquez, C.A.* ; Petalcorin, M.* ; Mariscal-Harana, J.* ; Gu, H.* ; Kim, R.J.* ; Judd, R.M.* ; Chowienczyk, P.J.* ; Schnabel, J.A. ; Razavi, R.* ; King, A.P.* ; Ruijsink, B.* ; Puyol-Antón, E.*

Automated quality controlled analysis of 2D phase contrast cardiovascular magnetic resonance imaging.

13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 202 13593 LNCS, 101-111 (2023)
DOI
Flow analysis carried out using phase contrast cardiac magnetic resonance imaging (PC-CMR) enables the quantification of important parameters that are used in the assessment of cardiovascular function. An essential part of this analysis is the identification of the correct CMR views and quality control (QC) to detect artefacts that could affect the flow quantification. We propose a novel deep learning based framework for the fully-automated analysis of flow from full CMR scans that first carries out these view selection and QC steps using two sequential convolutional neural networks, followed by automatic aorta and pulmonary artery segmentation to enable the quantification of key flow parameters. Accuracy values of 0.998 and 0.828 were obtained for view classification and QC, respectively. For segmentation, Dice scores were >0.964 and the Bland-Altman plots indicated excellent agreement between manual and automatic peak flow values. In addition, we tested our pipeline on an external validation data set, with results indicating good robustness of the pipeline. This work was carried out using multivendor clinical data consisting of 699 cases, indicating the potential for the use of this pipeline in a clinical setting.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cardiac Function ; Cardiac Magnetic Resonance ; Deep Learning ; Multi-vendor ; Quality Control ; View-selection
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 202
Quellenangaben Band: 13593 LNCS, Heft: , Seiten: 101-111 Artikelnummer: , Supplement: ,
Verlag Springer International Publishing Ag
Verlagsort Gewerbestrasse 11, Cham, Ch-6330, Switzerland
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen Department of Health National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre
National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative award
Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King's College London
UKRI London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare
DOI 10.1007/978-3-031-23443-9_10
WOS ID 000967781000010
Scopus ID 85147989055
Erfassungsdatum 2023-11-28
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