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

34 Citations (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.
Original languageEnglish
Title of host publicationMecical Image Computing and Computer-Assisted Intervention - MICCAI 2008 : 11th International Conference, New York, NY, USA, September 6-10, 2008, proceedings, Part II
EditorsD. Metaxas, L. Axel, G. Fichtinger, G. Szekely
Number of pages8
PublisherSpringer
Publication date2008
Pages863-871
ISBN (Print)978-3-540-85989-5
DOIs
Publication statusPublished - 2008
EventInternational Conference on Medical Image Computing and Computer-Assisted Intervention - New York, NY, United States
Duration: 6 Sept 200810 Sept 2008
Conference number: 11

Conference

ConferenceInternational Conference on Medical Image Computing and Computer-Assisted Intervention
Number11
Country/TerritoryUnited States
CityNew York, NY
Period06/09/200810/09/2008
SeriesLecture notes in computer science
Number5242
ISSN0302-9743

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