Image Registration Using Tensor Grids for Lung Ventilation Studies

Heike Ruppertshofen; Sven Kabus; Bernd Fischer

doi:10.1007/978-3-540-93860-6_24

Image Registration Using Tensor Grids for Lung Ventilation Studies

Heike Ruppertshofen, Sven Kabus, Bernd Fischer

Institute of Mathematics

Abstract

In non-parametric image registration it is often not possible to work with the original resolution of the images due to high processing times and lack of memory. However, for some medical applications the information contained in the original resolution is crucial in certain regions of the image while being negligible in others. To adapt to this problem we will present an approach using tensor grids, which provide a sparser image representation and thereby allow the use of the highest image resolution locally. Applying the presented scheme to a lung ventilation estimation shows that one may considerably save on time and memory while preserving the registration quality in the regions of interest.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2009
Editors	Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels, Thomas Tolxdorff
Number of pages	5
Place of Publication	Berlin, Heidelberg
Publisher	Springer Berlin Heidelberg
Publication date	01.03.2009
Pages	117-121
ISBN (Print)	978-3-540-93859-0
ISBN (Electronic)	978-3-540-93860-6
DOIs	https://doi.org/10.1007/978-3-540-93860-6_24
Publication status	Published - 01.03.2009
Event	Workshops Bildverarbeitung fur die Medizin 2009 - Heidelberg, Germany Duration: 22.03.2009 → 25.03.2009 Conference number: 97568

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10.1007/978-3-540-93860-6_24

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title = "Image Registration Using Tensor Grids for Lung Ventilation Studies",

abstract = "In non-parametric image registration it is often not possible to work with the original resolution of the images due to high processing times and lack of memory. However, for some medical applications the information contained in the original resolution is crucial in certain regions of the image while being negligible in others. To adapt to this problem we will present an approach using tensor grids, which provide a sparser image representation and thereby allow the use of the highest image resolution locally. Applying the presented scheme to a lung ventilation estimation shows that one may considerably save on time and memory while preserving the registration quality in the regions of interest.",

author = "Heike Ruppertshofen and Sven Kabus and Bernd Fischer",

year = "2009",

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doi = "10.1007/978-3-540-93860-6_24",

language = "English",

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series = "Informatik aktuell",

publisher = "Springer Berlin Heidelberg",

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editor = "Hans-Peter Meinzer and Deserno, {Thomas Martin} and Heinz Handels and Thomas Tolxdorff",

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2009",

note = "Workshops Bildverarbeitung fur die Medizin 2009 , BVM 2009 ; Conference date: 22-03-2009 Through 25-03-2009",

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Ruppertshofen, H, Kabus, S & Fischer, B 2009, Image Registration Using Tensor Grids for Lung Ventilation Studies. in H-P Meinzer, TM Deserno, H Handels & T Tolxdorff (eds), Bildverarbeitung für die Medizin 2009. Informatik aktuell, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 117-121, Workshops Bildverarbeitung fur die Medizin 2009 , Heidelberg, Baden-Württemberg, Germany, 22.03.09. https://doi.org/10.1007/978-3-540-93860-6_24

Image Registration Using Tensor Grids for Lung Ventilation Studies. / Ruppertshofen, Heike; Kabus, Sven; Fischer, Bernd.
Bildverarbeitung für die Medizin 2009. ed. / Hans-Peter Meinzer; Thomas Martin Deserno; Heinz Handels; Thomas Tolxdorff. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. p. 117-121 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AU - Fischer, Bernd

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N2 - In non-parametric image registration it is often not possible to work with the original resolution of the images due to high processing times and lack of memory. However, for some medical applications the information contained in the original resolution is crucial in certain regions of the image while being negligible in others. To adapt to this problem we will present an approach using tensor grids, which provide a sparser image representation and thereby allow the use of the highest image resolution locally. Applying the presented scheme to a lung ventilation estimation shows that one may considerably save on time and memory while preserving the registration quality in the regions of interest.

AB - In non-parametric image registration it is often not possible to work with the original resolution of the images due to high processing times and lack of memory. However, for some medical applications the information contained in the original resolution is crucial in certain regions of the image while being negligible in others. To adapt to this problem we will present an approach using tensor grids, which provide a sparser image representation and thereby allow the use of the highest image resolution locally. Applying the presented scheme to a lung ventilation estimation shows that one may considerably save on time and memory while preserving the registration quality in the regions of interest.

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BT - Bildverarbeitung für die Medizin 2009

A2 - Meinzer, Hans-Peter

A2 - Deserno, Thomas Martin

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CY - Berlin, Heidelberg

T2 - Workshops Bildverarbeitung fur die Medizin 2009

Y2 - 22 March 2009 through 25 March 2009

ER -

Image Registration Using Tensor Grids for Lung Ventilation Studies

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