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Quantification from cine cardiac MR

Cardiologists routinely use cine cardiac MR scans to assess the health of the heart. Traditionally, deriving the functional metrics they need has been a laborious process, involving manually tracing round cardiac structures in multiple MR images. We have shown how AI-based segmentation tools can be used to automate this process, and furthermore implement automated quality control checks to ensure that the estimated metrics can be trusted by clinicians. Our metrics go beyond the commonly used metric of left ventricular ejection fraction, to quantify both diastolic and systolic function as well as the function of other cardiac chambers. We are currently working with Perspectum to translate this technology into their COVERSCAN product for multi-organ health assessment.


B. Ruijsink, E. Puyol-Antón, I. Oksuz, M. Sinclair, W. Bai, J. A. Schnabel, R. Razavi, A. P. King, "Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function", JACC: Cardiovascular Imaging, 2020. (open access paper)


B. Ruijsink, E. Puyol-Antón, Y. Li, W. Bai, E. Kerfoot, R. Razavi, A. P. King, "Quality-aware Semi-supervised Learning for CMR Segmentation", Proceedings MICCAI STACOM, 2020. (Arxiv paper)

J. Mariscal-Harana, V. Vergani, C. Asher, R. Razavi, A. P. King, B. Ruijsink, E. Puyol-Antón, "Large-scale, Multi-vendor, Multi-protocol, Quality-controlled Analysis of Clinical Cine CMR Using Artificial Intelligence", European Heart Journal - Cardiovascular Imaging, Volume 22, Issue Supplement_2, 2021. (abstract)


Overview of framework for quality-controlled framework for cardiac MR image quantification


Examples of manual and automatic segmentations of cardiac MR ShMOLLI T1 images

Tissue quantification from cardiac MR T1 imaging

We also developed a similar pipeline for automated quantification of ShMOLLI T1 data. This pipeline also featured robust quality control steps, this time based upon segmentation uncertainty. Disease-specific reference ranges from the UK Biobank dataset were published for the first time.


E. Puyol-Antón, B. Ruijsink, C. F. Baumgartner, P.-G. Masci, M. Sinclair, E. Konukoglu, R. Razavi, A. P. King, "Automated Quantification of Myocardial Tissue Characteristics From Native T1 Mapping Using Neural Networks With Uncertainty-based Quality-control", Journal of Cardiovascular Magnetic Resonance, 22:60, 2020. (open access paper)

Functional quantification from echocardiography

Although cardiac MR is considered to be the "gold standard" imaging modality for assessment of most aspects of cardiac function, by far the most commonly used modality is cardiac ultrasound, or echocardiography. Most work on AI-based functional assessment from echocardiography has focused on ventricular volumes at just the end-diastole and end-systole phases, which can then be used to calculate the ejection fraction. We have shown how AI can be used to estimate a wider range of functional biomarkers and compared the results to those estimated from cardiac MR.


E. Puyol-Antón, B. Ruijsink, B. S. Sidhu, J. Gould, B. Porter, M. K. Elliott, V. Mehta, H. Gu, C. A. Rinaldi, M. Cowie, P. Chowienczyk, R. Razavi, A. P. King, "AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography", Proceedings MICCAI ASMUS, 2022. (Arxiv paper)


Example segmentations on the EchoNet Dynamic dataset


Example segmentations on Guy's and St Thomas' Foundation Trust clinical data

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