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Learning deformation and structure simultaneously: In situ endograft deformation analysis

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Title: Learning deformation and structure simultaneously: In situ endograft deformation analysis
Authors: Langs, Georg1,2 langs@csail.mit.edu, Paragios, Nikos3,4 nikos.paragios@ecp.fr, Desgranges, Pascal5 pascal.desgranges@hmn.aphp.fr, Rahmouni, Alain5 alain.rahmouni@hmn.ap-hop-paris.fr, Kobeiter, Hicham5,6 hicham.kobeiter@hmn.aphp.fr
Source: Medical Image Analysis. Feb2011, Vol. 15 Issue 1, p12-21. 10p.
Subjects: Diagnostic imaging, Surgical & topographical anatomy, Heart beat, Algorithms, Tomography, Aorta, Organ rupture
Abstract: Abstract: The learning of the shape and appearance behavior of complex anatomical structures is of growing importance in the successful use of medical imaging data. We propose a method to simultaneously learn a model of shape variation and the behavioral structure of objects in volumetric data sets. The algorithm performs a group-wise registration of a set of examples, and accounts for the heterogeneous deformation or variability properties of the data. We use the method for the in situ analysis of endograft deformation in the thoracic aorta during the cardiac cycle. The method is based on an emerging model of the shape variation, which is learned autonomously from a gated computed tomography sequence. It automatically adapts to the highly non-uniform elasticity properties of the structure during learning. The resulting deformation model is used for the measurement of global and local characteristics of the endograft movement. The method allows for the in situ localization of the stent during the cardiac cycle, and the measurement of its deformation. Furthermore, it makes the comparison of different endograft designs possible, and can serve as a basis for fitting a physical model of the endograft- and vessel surface to individual patients. The latter is essential for long-term risk assessment of the impact of endografts in highly mobile areas. We evaluate the approach on 10 data sets from patients that underwent endograft placement after traumatic ruptures of the thoracic aorta. [ABSTRACT FROM AUTHOR]
Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 55214219
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="AR" term="%22Langs%2C+Georg%22">Langs, Georg</searchLink><relatesTo>1,2</relatesTo><i> langs@csail.mit.edu</i><br /><searchLink fieldCode="AR" term="%22Paragios%2C+Nikos%22">Paragios, Nikos</searchLink><relatesTo>3,4</relatesTo><i> nikos.paragios@ecp.fr</i><br /><searchLink fieldCode="AR" term="%22Desgranges%2C+Pascal%22">Desgranges, Pascal</searchLink><relatesTo>5</relatesTo><i> pascal.desgranges@hmn.aphp.fr</i><br /><searchLink fieldCode="AR" term="%22Rahmouni%2C+Alain%22">Rahmouni, Alain</searchLink><relatesTo>5</relatesTo><i> alain.rahmouni@hmn.ap-hop-paris.fr</i><br /><searchLink fieldCode="AR" term="%22Kobeiter%2C+Hicham%22">Kobeiter, Hicham</searchLink><relatesTo>5,6</relatesTo><i> hicham.kobeiter@hmn.aphp.fr</i>
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  Data: <searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Surgical+%26+topographical+anatomy%22">Surgical & topographical anatomy</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+beat%22">Heart beat</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Aorta%22">Aorta</searchLink><br /><searchLink fieldCode="DE" term="%22Organ+rupture%22">Organ rupture</searchLink>
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  Data: Abstract: The learning of the shape and appearance behavior of complex anatomical structures is of growing importance in the successful use of medical imaging data. We propose a method to simultaneously learn a model of shape variation and the behavioral structure of objects in volumetric data sets. The algorithm performs a group-wise registration of a set of examples, and accounts for the heterogeneous deformation or variability properties of the data. We use the method for the in situ analysis of endograft deformation in the thoracic aorta during the cardiac cycle. The method is based on an emerging model of the shape variation, which is learned autonomously from a gated computed tomography sequence. It automatically adapts to the highly non-uniform elasticity properties of the structure during learning. The resulting deformation model is used for the measurement of global and local characteristics of the endograft movement. The method allows for the in situ localization of the stent during the cardiac cycle, and the measurement of its deformation. Furthermore, it makes the comparison of different endograft designs possible, and can serve as a basis for fitting a physical model of the endograft- and vessel surface to individual patients. The latter is essential for long-term risk assessment of the impact of endografts in highly mobile areas. We evaluate the approach on 10 data sets from patients that underwent endograft placement after traumatic ruptures of the thoracic aorta. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Organ rupture
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      – TitleFull: Learning deformation and structure simultaneously: In situ endograft deformation analysis
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              Text: Feb2011
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