Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale
Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale
Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria.
Cardiac functional indexes, Cardiac morphological analysis, Cardiac MR, Deep learning, Fully automatic analysis, Population imaging, Statistical shape models, UK Biobank
Xia, Yan
e6c0b611-427b-4871-86f1-406efee13bb5
Chen, Xiang
2bddccea-9285-45d3-ba1f-046aa5c32d09
Ravikumar, Nishant
e31cd3f7-b112-4078-b5fc-0cfc005a061a
Attar, Rahman
f5efd538-042a-4647-9d46-1370d3049b72
Kelly, Christopher
e51bbaef-d8d4-4273-a784-76f4ef47ad25
Aung, Nay
709b152d-e704-4fdc-b066-7eafaa643a0b
Neubauer, Stefan
c8a34156-a4ed-4dfe-97cb-4f47627d927d
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Frangi, Alejandro F.
35127be7-3586-4fff-a4a2-bb79cc228141
1 August 2022
Xia, Yan
e6c0b611-427b-4871-86f1-406efee13bb5
Chen, Xiang
2bddccea-9285-45d3-ba1f-046aa5c32d09
Ravikumar, Nishant
e31cd3f7-b112-4078-b5fc-0cfc005a061a
Attar, Rahman
f5efd538-042a-4647-9d46-1370d3049b72
Kelly, Christopher
e51bbaef-d8d4-4273-a784-76f4ef47ad25
Aung, Nay
709b152d-e704-4fdc-b066-7eafaa643a0b
Neubauer, Stefan
c8a34156-a4ed-4dfe-97cb-4f47627d927d
Petersen, Steffen E.
04f2ce88-790d-48dc-baac-cbe0946dd928
Frangi, Alejandro F.
35127be7-3586-4fff-a4a2-bb79cc228141
Xia, Yan, Chen, Xiang, Ravikumar, Nishant, Attar, Rahman and Frangi, Alejandro F.
,
et al.
(2022)
Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale.
Medical Image Analysis, 80 (8), [102498].
(doi:10.1016/j.media.2022.102498).
Abstract
Accurate 3D modelling of cardiac chambers is essential for clinical assessment of cardiac volume and function, including structural, and motion analysis. Furthermore, to study the correlation between cardiac morphology and other patient information within a large population, it is necessary to automatically generate cardiac mesh models of each subject within the population. In this study, we introduce MCSI-Net (Multi-Cue Shape Inference Network), where we embed a statistical shape model inside a convolutional neural network and leverage both phenotypic and demographic information from the cohort to infer subject-specific reconstructions of all four cardiac chambers in 3D. In this way, we leverage the ability of the network to learn the appearance of cardiac chambers in cine cardiac magnetic resonance (CMR) images, and generate plausible 3D cardiac shapes, by constraining the prediction using a shape prior, in the form of the statistical modes of shape variation learned a priori from a subset of the population. This, in turn, enables the network to generalise to samples across the entire population. To the best of our knowledge, this is the first work that uses such an approach for patient-specific cardiac shape generation. MCSI-Net is capable of producing accurate 3D shapes using just a fraction (about 23% to 46%) of the available image data, which is of significant importance to the community as it supports the acceleration of CMR scan acquisitions. Cardiac MR images from the UK Biobank were used to train and validate the proposed method. We also present the results from analysing 40,000 subjects of the UK Biobank at 50 time-frames, totalling two million image volumes. Our model can generate more globally consistent heart shape than that of manual annotations in the presence of inter-slice motion and shows strong agreement with the reference ranges for cardiac structure and function across cardiac ventricles and atria.
Text
four-chamber_CMR_quantification_of_the_UK biobank
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More information
Accepted/In Press date: 24 May 2022
Published date: 1 August 2022
Additional Information:
Funding Information:
This research has been conducted using the UK Biobank Resource under Application 11350. The authors are grateful to all UK Biobank participants and staff. AFF acknowledges support from the Royal Academy of Engineering Chair in Emerging Technologies Scheme (CiET1819/19), EPSRC-funded Grow MedTech CardioX (POC041), and the MedIAN Network (EP/N026993/1) funded by the Engineering and Physical Sciences Research Council (EPSRC).
Publisher Copyright:
© 2022 The Authors
Keywords:
Cardiac functional indexes, Cardiac morphological analysis, Cardiac MR, Deep learning, Fully automatic analysis, Population imaging, Statistical shape models, UK Biobank
Identifiers
Local EPrints ID: 478339
URI: http://eprints.soton.ac.uk/id/eprint/478339
ISSN: 1361-8415
PURE UUID: 53506305-bf96-48b2-b75c-1250bdfb0c5c
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Date deposited: 28 Jun 2023 16:57
Last modified: 17 Mar 2024 13:18
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Contributors
Author:
Yan Xia
Author:
Xiang Chen
Author:
Nishant Ravikumar
Author:
Rahman Attar
Author:
Christopher Kelly
Author:
Nay Aung
Author:
Stefan Neubauer
Author:
Steffen E. Petersen
Author:
Alejandro F. Frangi
Corporate Author: et al.
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