Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging - Chan, Emily; O’Hanlon, Ciaran; Marquez, Carlota Asegurado; Petalcorin, Marwenie; Mariscal-Harana, Jorge; Gu, Haotian; Kim, Raymond J.; Judd, Robert M.; Chowienczyk, Phil; Schnabel, Julia A.; Razavi, Reza; King, Andrew P.; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
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.
CRIS-Team Scopus Importer:2023-03-08
Keywords: Cardiac function; Cardiac magnetic resonance; Deep learning; Multi-vendor; Quality control; View-selection