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

TL;DR

This paper introduces a deep learning framework for fully automated analysis of 2D phase contrast CMR images, including view selection, quality control, and vessel segmentation, achieving high accuracy and robustness on large clinical data.

Contribution

It presents a novel deep learning pipeline that automates view classification, quality control, and vessel segmentation in CMR flow analysis, improving efficiency and reliability.

Findings

View classification accuracy of 95.8%

Quality control accuracy of 91.4%

High segmentation Dice scores (>0.969)

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 0.958 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.