Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images
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Author(s)
Type
Journal Article
Abstract
Recent dose reduction techniques have made retrospective computed tomography (CT) scans more
applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. However, hyperparameters of generic image intensity-based registration techniques, which are used for tracking motion, have not been systematically optimised for this modality. There is limited work on their validation for measuring regional strains from retrospective gated CCT images and open-source software for motion analysis is not widely available. We calculated strain using our open-source platform by applying an image registration warping field to a triangulated mesh of the left ventricular endocardium. We optimised hyperparameters of two registration methods to track the wall motion. Both methods required a single semi-automated segmentation of the left ventricle cavity at end-diastolic phase. The motion was characterised by the circumferential and longitudinal strains, as well as local area change throughout the cardiac cycle from a dataset of 24 patients. The derived motion was validated against manually annotated anatomical landmarks and the calculation of strains were verified using idealised problems. Optimising hyperparameters of registration
methods allowed tracking of anatomical measurements with a mean error of 6.63% across frames, landmarks, and patients, comparable to an intra-observer error of 7.98%. Both registration methods differentiated between normal and dyssynchronous contraction patterns based on circumferential strain (p1 = 0.0065, p2 = 0.0011). To test whether a typical 10 temporal frames sampling of retrospective gated CCT datasets affects measuring cardiac mechanics, we compared motion tracking results from 10 and 20 frames datasets and found a maximum error of 8.51 ± 0.8%. Our findings show that intensity-based registration techniques with optimal hyperparameters are able to accurately
measure regional strains from CCT in a very short amount of time. Furthermore, sufficient sensitivity
can be achieved to identify heart failure patients and left ventricle mechanics can be quantified with
10 reconstructed temporal frames. Our open-source platform will support increased use of CCT for
quantifying cardiac mechanics,
applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. However, hyperparameters of generic image intensity-based registration techniques, which are used for tracking motion, have not been systematically optimised for this modality. There is limited work on their validation for measuring regional strains from retrospective gated CCT images and open-source software for motion analysis is not widely available. We calculated strain using our open-source platform by applying an image registration warping field to a triangulated mesh of the left ventricular endocardium. We optimised hyperparameters of two registration methods to track the wall motion. Both methods required a single semi-automated segmentation of the left ventricle cavity at end-diastolic phase. The motion was characterised by the circumferential and longitudinal strains, as well as local area change throughout the cardiac cycle from a dataset of 24 patients. The derived motion was validated against manually annotated anatomical landmarks and the calculation of strains were verified using idealised problems. Optimising hyperparameters of registration
methods allowed tracking of anatomical measurements with a mean error of 6.63% across frames, landmarks, and patients, comparable to an intra-observer error of 7.98%. Both registration methods differentiated between normal and dyssynchronous contraction patterns based on circumferential strain (p1 = 0.0065, p2 = 0.0011). To test whether a typical 10 temporal frames sampling of retrospective gated CCT datasets affects measuring cardiac mechanics, we compared motion tracking results from 10 and 20 frames datasets and found a maximum error of 8.51 ± 0.8%. Our findings show that intensity-based registration techniques with optimal hyperparameters are able to accurately
measure regional strains from CCT in a very short amount of time. Furthermore, sufficient sensitivity
can be achieved to identify heart failure patients and left ventricle mechanics can be quantified with
10 reconstructed temporal frames. Our open-source platform will support increased use of CCT for
quantifying cardiac mechanics,
Date Issued
2021-03-11
Date Acceptance
2021-01-04
Citation
Scientific Reports, 2021, 11
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Volume
11
Issue
1
Copyright Statement
© The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/33707527
PII: 10.1038/s41598-021-84935-x
Publication Status
Published
Coverage Spatial
England
Article Number
5718
Date Publish Online
2021-03-11