Automatic detection of aortic valve pathology using 3-chamber cine MRI

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic valve often manifest by the dephasing of blood signal in gated balanced steady-state free precession (bSSFP) scans (cine MRI) [1]. This suggests cine MRI may be used to identify patients with blood flow abnormalities who would benefit from additional detailed PC-MRI scans. Nevertheless, there are currently no tools to automatically identify abnormalities in blood flow through the aortic root in cine MRI which can be used to improve diagnosis and optimise clinical CMR protocols. Aim To determine whether a classification neural network (NN) can automatically detect aortic valve pathology from 3-chamber cine MRI, without the need for dedicated additional imaging or manual annotation. Data We train and test our approach on a retrospective clinical dataset from three UK hospitals, using single-slice bSSFP 3-chamber cine MRI (cardiac frames: 31 ± 15, spatial resolution: 1.17–1.56 x 1.17–1.56 x 8 mm3) from n = 577 patients. Using information from clinical assessments, each image was labelled as: aortic regurgitation (AR) (13%), aortic stenosis (AS) (11%), mixed valve disease (MVD) (9%), or showing no relevant pathology (67%). 322 randomly selected images were used for training, 81 for validation and 174 for testing. Methods We trained a 3D classification NN (DenseNet201 model [2] with 4 dense blocks which include 3D (i.e., spatiotemporal) convolutional layers; Adam optimisation and a cross-entropy loss function) to classify the time-resolved cine MR images. We used binary labels: "pathology" (corresponding to AR, AS or MVD) or "no pathology". As a pre-processing step, we automatically identified the heart in all cine MRI frames using an in-house algorithm that detected motion in the image across the cardiac cycle, and cropped the images to remove non-cardiac anatomical structures. After the classification, we used gradient-weighted class activation mapping (Grad-CAM) [3] to gain confidence that the classification was most dependent on voxels close to the aortic root. Fig 1 shows an overview of the image processing pipeline. Results Our classification model accurately predicts aortic valve pathology using 3-chamber cine MRI: Fig 2 shows the confusion matrix and the evaluation metrics of the study. The Grad-CAM heatmaps showed the aortic region contributed the most to the classification, as seen in Fig 1. Conclusion Convolutional NNs can identify aortic valve pathology automatically from 3-chamber cine MRI, without the need for manual annotations. This can be used to improve diagnosis and optimise clinical CMR protocols, namely as a triage tool to identify the patients most likely to benefit from additional PC-MRI sequences.