TY - GEN
T1 - Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging
AU - Chan, Emily
AU - O’Hanlon, Ciaran
AU - Marquez, Carlota Asegurado
AU - Petalcorin, Marwenie
AU - Mariscal-Harana, Jorge
AU - Gu, Haotian
AU - Kim, Raymond J.
AU - Judd, Robert M.
AU - Chowienczyk, Phil
AU - Schnabel, Julia A.
AU - Razavi, Reza
AU - King, Andrew P.
AU - Ruijsink, Bram
AU - Puyol-Antón, Esther
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Cardiac function
KW - Cardiac magnetic resonance
KW - Deep learning
KW - Multi-vendor
KW - Quality control
KW - View-selection
UR - http://www.scopus.com/inward/record.url?scp=85147989055&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-23443-9_10
DO - 10.1007/978-3-031-23443-9_10
M3 - Conference contribution
AN - SCOPUS:85147989055
SN - 9783031234422
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 101
EP - 111
BT - Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers
A2 - Camara, Oscar
A2 - Puyol-Antón, Esther
A2 - Suinesiaputra, Avan
A2 - Young, Alistair
A2 - Qin, Chen
A2 - Sermesant, Maxime
A2 - Wang, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 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 2022
Y2 - 18 September 2022 through 18 September 2022
ER -