Weight preserving image registration for monitoring disease progression in lung CT.

Vladlena Gorbunova; Pechin Chien Pau Lo; Ashraf Haseem; Asger Dirksen; Mads Nielsen; Marleen de Bruijne

doi:10.1007/978-3-540-85990-1_104

Weight preserving image registration for monitoring disease progression in lung CT.

Vladlena Gorbunova, Pechin Chien Pau Lo, Ashraf Haseem, Asger Dirksen, Mads Nielsen, Marleen de Bruijne

Datalogisk Institut

34 Citationer (Scopus)

Abstract

We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

Originalsprog	Engelsk
Titel	Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008 : 11th International Conference, New York, NY, USA, September 6-10, 2008, proceedings, Part II
Redaktører	D. Metaxas, L. Axel, G. Fichtinger, G. Szekely
Antal sider	8
Forlag	Springer
Publikationsdato	2008
Sider	863-871
ISBN (Trykt)	978-3-540-85989-5
DOI	https://doi.org/10.1007/978-3-540-85990-1_104
Status	Udgivet - 2008
Begivenhed	International Conference on Medical Image Computing and Computer-Assisted Intervention - New York, NY, USA Varighed: 6 sep. 2008 → 10 sep. 2008 Konferencens nummer: 11

Konference

Konference	International Conference on Medical Image Computing and Computer-Assisted Intervention
Nummer	11
Land/Område	USA
By	New York, NY
Periode	06/09/2008 → 10/09/2008

Navn	Lecture notes in computer science
Nummer	5242
ISSN	0302-9743

Adgang til dokumentet

10.1007/978-3-540-85990-1_104

Citationsformater

Gorbunova, V., Lo, P. C. P., Haseem, A., Dirksen, A., Nielsen, M., & de Bruijne, M. (2008). Weight preserving image registration for monitoring disease progression in lung CT. I D. Metaxas, L. Axel, G. Fichtinger, & G. Szekely (red.), Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, proceedings, Part II (s. 863-871). Springer. Lecture notes in computer science Nr. 5242 https://doi.org/10.1007/978-3-540-85990-1_104

Weight preserving image registration for monitoring disease progression in lung CT. / Gorbunova, Vladlena; Lo, Pechin Chien Pau; Haseem, Ashraf et al.

Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, proceedings, Part II. red. / D. Metaxas; L. Axel; G. Fichtinger; G. Szekely. Springer, 2008. s. 863-871 (Lecture notes in computer science; Nr. 5242).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Gorbunova, V, Lo, PCP, Haseem, A, Dirksen, A, Nielsen, M & de Bruijne, M 2008, Weight preserving image registration for monitoring disease progression in lung CT. i D Metaxas, L Axel, G Fichtinger & G Szekely (red), Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, proceedings, Part II. Springer, Lecture notes in computer science, nr. 5242, s. 863-871, International Conference on Medical Image Computing and Computer-Assisted Intervention, New York, NY, USA, 06/09/2008. https://doi.org/10.1007/978-3-540-85990-1_104

Gorbunova V, Lo PCP, Haseem A, Dirksen A, Nielsen M , de Bruijne M. Weight preserving image registration for monitoring disease progression in lung CT. I Metaxas D, Axel L, Fichtinger G, Szekely G, red., Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, proceedings, Part II. Springer. 2008. s. 863-871. (Lecture notes in computer science; Nr. 5242). doi: 10.1007/978-3-540-85990-1_104

Gorbunova, Vladlena ; Lo, Pechin Chien Pau ; Haseem, Ashraf et al. / Weight preserving image registration for monitoring disease progression in lung CT. Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, proceedings, Part II. red. / D. Metaxas ; L. Axel ; G. Fichtinger ; G. Szekely. Springer, 2008. s. 863-871 (Lecture notes in computer science; Nr. 5242).

@inproceedings{43b477f0c85c11dd9473000ea68e967b,

title = "Weight preserving image registration for monitoring disease progression in lung CT.",

abstract = "We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.",

author = "Vladlena Gorbunova and Lo, {Pechin Chien Pau} and Ashraf Haseem and Asger Dirksen and Mads Nielsen and {de Bruijne}, Marleen",

year = "2008",

doi = "10.1007/978-3-540-85990-1_104",

language = "English",

isbn = "978-3-540-85989-5",

series = "Lecture notes in computer science",

publisher = "Springer",

number = "5242",

pages = "863--871",

editor = "D. Metaxas and L. Axel and G. Fichtinger and G. Szekely",

booktitle = "Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008",

note = "International Conference on Medical Image Computing and Computer-Assisted Intervention ; Conference date: 06-09-2008 Through 10-09-2008",

}

TY - GEN

T1 - Weight preserving image registration for monitoring disease progression in lung CT.

AU - Gorbunova, Vladlena

AU - Lo, Pechin Chien Pau

AU - Haseem, Ashraf

AU - Dirksen, Asger

AU - Nielsen, Mads

AU - de Bruijne, Marleen

N1 - Conference code: 11

PY - 2008

Y1 - 2008

N2 - We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

AB - We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

U2 - 10.1007/978-3-540-85990-1_104

DO - 10.1007/978-3-540-85990-1_104

M3 - Article in proceedings

SN - 978-3-540-85989-5

T3 - Lecture notes in computer science

SP - 863

EP - 871

BT - Mecical Image Computing and Computer-Assisted Intervention - MICCAI 2008

A2 - Metaxas, D.

A2 - Axel, L.

A2 - Fichtinger, G.

A2 - Szekely, G.

PB - Springer

T2 - International Conference on Medical Image Computing and Computer-Assisted Intervention

Y2 - 6 September 2008 through 10 September 2008

ER -