Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields

Abstract Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises...

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Autores principales:	Hadi Wiputra, Wei Xuan Chan, Yoke Yin Foo, Sheldon Ho, Choon Hwai Yap
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/97e31f61866541b2b96f01533eefc270
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spelling	oai:doaj.org-article:97e31f61866541b2b96f01533eefc2702021-12-02T15:09:40ZCardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields10.1038/s41598-020-75525-42045-2322https://doaj.org/article/97e31f61866541b2b96f01533eefc2702020-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-75525-4https://doaj.org/toc/2045-2322Abstract Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises a spatio-temporal model of motion, b-splines of Fourier, to fit to displacement fields from pairwise image registration. In the process, it enforces spatial and temporal smoothness and consistency, cyclic nature of cardiac motion, and better adherence to the stroke volume of the heart. Flexibility is further given for inclusion of any set of registration displacement fields. The approach gave high accuracy. When applied to human adult Ultrasound data from a Cardiac Motion Analysis Challenge (CMAC), the proposed method is found to have 10% lower tracking error over CMAC participants. Satisfactory cardiac motion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick embryonic heart ultrasound images, and zebrafish embryonic microscope images, with the average Dice coefficient between estimation motion and manual segmentation at 0.82–0.87. The approach of performing regularisation as an add-on layer after the completion of image registration is thus a viable option for cardiac motion estimation that can still have good accuracy. Since motion estimation algorithms are complex, dividing up regularisation and registration can simplify the process and provide flexibility. Further, owing to a large variety of existing registration algorithms, such an approach that is usable on any algorithm may be useful.Hadi WiputraWei Xuan ChanYoke Yin FooSheldon HoChoon Hwai YapNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hadi Wiputra Wei Xuan Chan Yoke Yin Foo Sheldon Ho Choon Hwai Yap Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields
description	Abstract Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises a spatio-temporal model of motion, b-splines of Fourier, to fit to displacement fields from pairwise image registration. In the process, it enforces spatial and temporal smoothness and consistency, cyclic nature of cardiac motion, and better adherence to the stroke volume of the heart. Flexibility is further given for inclusion of any set of registration displacement fields. The approach gave high accuracy. When applied to human adult Ultrasound data from a Cardiac Motion Analysis Challenge (CMAC), the proposed method is found to have 10% lower tracking error over CMAC participants. Satisfactory cardiac motion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick embryonic heart ultrasound images, and zebrafish embryonic microscope images, with the average Dice coefficient between estimation motion and manual segmentation at 0.82–0.87. The approach of performing regularisation as an add-on layer after the completion of image registration is thus a viable option for cardiac motion estimation that can still have good accuracy. Since motion estimation algorithms are complex, dividing up regularisation and registration can simplify the process and provide flexibility. Further, owing to a large variety of existing registration algorithms, such an approach that is usable on any algorithm may be useful.
format	article
author	Hadi Wiputra Wei Xuan Chan Yoke Yin Foo Sheldon Ho Choon Hwai Yap
author_facet	Hadi Wiputra Wei Xuan Chan Yoke Yin Foo Sheldon Ho Choon Hwai Yap
author_sort	Hadi Wiputra
title	Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields
title_short	Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields
title_full	Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields
title_fullStr	Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields
title_full_unstemmed	Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields
title_sort	cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/97e31f61866541b2b96f01533eefc270
work_keys_str_mv	AT hadiwiputra cardiacmotionestimationfrommedicalimagesaregularisationframeworkappliedonpairwiseimageregistrationdisplacementfields AT weixuanchan cardiacmotionestimationfrommedicalimagesaregularisationframeworkappliedonpairwiseimageregistrationdisplacementfields AT yokeyinfoo cardiacmotionestimationfrommedicalimagesaregularisationframeworkappliedonpairwiseimageregistrationdisplacementfields AT sheldonho cardiacmotionestimationfrommedicalimagesaregularisationframeworkappliedonpairwiseimageregistrationdisplacementfields AT choonhwaiyap cardiacmotionestimationfrommedicalimagesaregularisationframeworkappliedonpairwiseimageregistrationdisplacementfields
_version_	1718387767677812736

Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields

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