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https://hdl.handle.net/10316/114131
Title: | A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis | Authors: | Machado, Inês Puyol-Anton, Esther Hammernik, Kerstin Cruz, Gastao Ugurlu, Devran Olakorede, Ihsane Oksuz, Ilkay Ruijsink, Bram Castelo-Branco, Miguel Young, Alistair Prieto, Claudia Schnabel, Julia King, Andrew |
Keywords: | Cardiac MRI; deep learning; fast reconstruction; quality assessment; segmentation; UK BioBank | Issue Date: | Mar-2024 | Publisher: | IEEE | Project: | 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. | Serial title, monograph or event: | IEEE Transactions on Biomedical Engineering | Volume: | 71 | Issue: | 3 | Abstract: | 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. | URI: | https://hdl.handle.net/10316/114131 | ISSN: | 0018-9294 1558-2531 |
DOI: | 10.1109/TBME.2023.3321431 | Rights: | openAccess |
Appears in Collections: | I&D ICNAS - Artigos em Revistas Internacionais I&D CIBIT - Artigos em Revistas Internacionais |
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