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

@article{Chan2022AutomatedQC,
  title={Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging},
  author={Emily Chan and Ciara O’Hanlon and Carlota Asegurado Marquez and Marwenie F. Petalcorin and Jorge Mariscal Harana and Haotian Gu and Raymond J. Kim and Robert M. Judd and Philip J. Chowienczyk and Julia Anne Schnabel and Reza Razavi and Andrew P. King and Bram Ruijsink and Esther Puyol-Ant{\'o}n},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.14212}
}
. 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… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 19 REFERENCES

Learning-Based Quality Control for Cardiac MR Images

A fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks, using a hybrid decision forest method to extract landmarks and probabilistic segmentation maps from both long- and short- axis images as a basis to perform the quality checks.

Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

Automated Quality Assessment of Cardiac MR Images Using Convolutional Neural Networks

Inspired by the success of deep learning methods, Convolutional Neural Networks are trained to construct a set of discriminative features for automatic detection of missing slices in Cardiac Magnetic Resonance Imaging scans, which is currently performed by tedious visual assessment.

Deep Learning for Classification and Selection of Cine CMR Images to Achieve Fully Automated Quality-Controlled CMR Analysis From Scanner to Report

This framework for automated detection and quality-controlled selection of standard cine sequences images from clinical CMR exams, prior to analysis of cardiac function, is developed and could be used at the beginning of a pipeline for automated cine CMR analysis.

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentsation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation.

Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies.

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

This work proposes a novel deep generative model-based framework for quality control of cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.

A new vessel segmentation algorithm for robust blood flow quantification from two‐dimensional phase‐contrast magnetic resonance images

The proposed semi‐automatic vessel segmentation algorithm with shape constraints based on manual vessel delineations can be used for efficient analysis of flow and shunt volumes in the aorta and pulmonary artery.