A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Ines MacHado, Esther Puyol-Anton, Kerstin Hammernik, Gastao Cruz, Devran Ugurlu, Ihsane Olakorede, Ilkay Oksuz, Bram Ruijsink, Miguel Castelo-Branco, Alistair Young, Claudia Prieto, Julia Schnabel, Andrew King

Research output: Contribution to journalArticlepeer-review


Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this article, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework produces high quality reconstructions and segmentations, leading to undersampling factors that are optimised on a scan-by-scan basis. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform simulations of radial k-space acquisitions using in-vivo cine CMR data from 270 subjects from the UK Biobank (with synthetic phase) and in-vivo cine CMR data from 16 healthy subjects (with real phase). The results demonstrate that the optimal undersampling factor varies for different subjects by approximately 1 to 2 seconds per slice. We show that our method can produce quality-controlled images in a mean scan time reduced from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to lie within 5% mean absolute difference.

Original languageEnglish
Pages (from-to)855-865
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number3
Publication statusPublished - 1 Mar 2024

Bibliographical note

Publisher Copyright:
© 1964-2012 IEEE.


This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) Programme SmartHeart under Grant EP/P001009/1, in part by the Wellcome/EPSRC Centre for Medical Engineering under Grant WT 203148/Z/16/Z, in part by the National Institute for Health Research (NIHR) Biomedical Research Centre and Cardiovascular MedTech Co-operative based at Guy s and St Thomas NHS Foundation Trust and King s College London, and in part by Health Data Research UK, an initiative funded by U.K. Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities.

FundersFunder number
St Thomas NHS Foundation Trust
UK Research and Innovation
Engineering and Physical Sciences Research CouncilEP/P001009/1
National Institute for Health and Care Research
Department of Health and Social Care
King's College London
Wellcome EPSRC Centre for Medical EngineeringWT 203148/Z/16/Z
leading medical research charities


    • Cardiac MRI
    • UK BioBank
    • deep learning
    • fast reconstruction
    • quality assessment
    • segmentation


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