@inbook{749bb92696be46e18de40b7038095c22,
title = "Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging",
abstract = "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.",
keywords = "Cardiac function, Cardiac magnetic resonance, Deep learning, Multi-vendor, Quality control, View-selection",
author = "Emily Chan and Ciaran O{\textquoteright}Hanlon and Marquez, {Carlota Asegurado} and Marwenie Petalcorin and Jorge Mariscal-Harana and Haotian Gu and Kim, {Raymond J.} and Judd, {Robert M.} and Phil Chowienczyk and Schnabel, {Julia A.} and Reza Razavi and King, {Andrew P.} and Bram Ruijsink and Esther Puyol-Ant{\'o}n",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 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 ; Conference date: 18-09-2022 Through 18-09-2022",
year = "2022",
doi = "10.1007/978-3-031-23443-9_10",
language = "English",
isbn = "9783031234422",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "101--111",
editor = "Oscar Camara and Esther Puyol-Ant{\'o}n and Avan Suinesiaputra and Alistair Young and Chen Qin and Maxime Sermesant and Shuo Wang",
booktitle = "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",
address = "Germany",
}