Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging

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 0.958 and 0.914 were obtained for view classification and QC, respectively. For segmentation, Dice scores were $>$0.969 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 986 cases, indicating the potential for the use of this pipeline in a clinical setting.

Large-scale, multi-centre, multi-disease validation of an AI clinical tool for cine CMR analysis

INTRODUCTION: Artificial intelligence (AI) has the potential to facilitate the automation of CMR analysis for biomarker extraction. However, most AI algorithms are trained on a specific input domain (e.g., single scanner vendor or hospital-tailored imaging protocol) and lack the robustness to perform optimally when applied to CMR data from other input domains. METHODS: Our proposed framework consists of an AI-based algorithm for biventricular segmentation of short-axis images, followed by a post-analysis quality control to detect erroneous results. The segmentation algorithm was trained on a large dataset of clinical CMR scans from two NHS hospitals (n=2793) and validated on additional cases from this dataset (n=441) and on five external datasets (n=6808). The validation data included CMR scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. RESULTS: Our method yielded median Dice scores over 87%, translating into median absolute errors in cardiac biomarkers within the range of inter-observer variability: <8.4mL (left ventricle), <9.2mL (right ventricle), <13.3g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good agreement. CONCLUSIONS: We show that our proposed tool, which combines a state-of-the-art AI algorithm trained on a large-scale multi-domain CMR dataset with a post-analysis quality control, allows us to robustly deal with routine clinical data from multiple centres, vendors, and cardiac diseases. This is a fundamental step for the clinical translation of AI algorithms. Moreover, our method yields a range of additional biomarkers of cardiac function (filling and ejection rates, regional wall motion, and strain) at no extra computational cost.