Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images
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 use...
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Nature Portfolio
2021
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oai:doaj.org-article:679cf02eda8147b9be675fd9ac1dfbf82021-12-02T13:20:14ZHyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images10.1038/s41598-021-84935-x2045-2322https://doaj.org/article/679cf02eda8147b9be675fd9ac1dfbf82021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84935-xhttps://doaj.org/toc/2045-2322Abstract 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 ( $$p_1=0.0065$$ p 1 = 0.0065 , $$p_2=0.0011$$ p 2 = 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 $$8.51\pm 0.8\%$$ 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.Orod RazeghiMattias HeinrichThomas E. FastlCesare CorradoRashed KarimAdelaide De VecchiTom BanksPatrick DonnellyJonathan M. BeharJustin GouldRonak RajaniChristopher A. RinaldiSteven NiedererNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) |
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DOAJ |
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Medicine R Science Q |
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Medicine R Science Q Orod Razeghi Mattias Heinrich Thomas E. Fastl Cesare Corrado Rashed Karim Adelaide De Vecchi Tom Banks Patrick Donnelly Jonathan M. Behar Justin Gould Ronak Rajani Christopher A. Rinaldi Steven Niederer Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images |
| description |
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 ( $$p_1=0.0065$$ p 1 = 0.0065 , $$p_2=0.0011$$ p 2 = 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 $$8.51\pm 0.8\%$$ 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. |
| format |
article |
| author |
Orod Razeghi Mattias Heinrich Thomas E. Fastl Cesare Corrado Rashed Karim Adelaide De Vecchi Tom Banks Patrick Donnelly Jonathan M. Behar Justin Gould Ronak Rajani Christopher A. Rinaldi Steven Niederer |
| author_facet |
Orod Razeghi Mattias Heinrich Thomas E. Fastl Cesare Corrado Rashed Karim Adelaide De Vecchi Tom Banks Patrick Donnelly Jonathan M. Behar Justin Gould Ronak Rajani Christopher A. Rinaldi Steven Niederer |
| author_sort |
Orod Razeghi |
| title |
Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images |
| title_short |
Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images |
| title_full |
Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images |
| title_fullStr |
Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images |
| title_full_unstemmed |
Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images |
| title_sort |
hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/679cf02eda8147b9be675fd9ac1dfbf8 |
| work_keys_str_mv |
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