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

09/28/2022
by   Emily Chan, et al.
28

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.

READ FULL TEXT
research
03/21/2023

Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry

Purpose: To develop and evaluate an end-to-end deep learning pipeline fo...
research
08/25/2023

Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI...
research
03/04/2023

Detection of the Arterial Input Function Using DSC-MRI Data

Accurate detection of arterial input function is a crucial step in obtai...
research
08/31/2023

Echocardiographic View Classification with Integrated Out-of-Distribution Detection for Enhanced Automatic Echocardiographic Analysis

In the rapidly evolving field of automatic echocardiographic analysis an...
research
12/06/2021

Quality control for more reliable integration of deep learning-based image segmentation into medical workflows

Machine learning algorithms underpin modern diagnostic-aiding software, ...
research
06/03/2022

Detection of Fibrosis in Cine Magnetic Resonance Images Using Artificial Intelligence Techniques

Background: Artificial intelligence techniques have demonstrated great p...

Please sign up or login with your details

Forgot password? Click here to reset