Local tissue-weight-based nonrigid registration of lung images with application to regional ventilation

Youbing Yin; Eric A. Hoffman; Ching-Long Lin

doi:10.1117/12.811715

27 February 2009 Local tissue-weight-based nonrigid registration of lung images with application to regional ventilation

Youbing Yin, Eric A. Hoffman, Ching-Long Lin

Author Affiliations +

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 72620C (2009) https://doi.org/10.1117/12.811715
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In this paper, a new nonrigid image registration method is presented to align two volumetric lung CT datasets with an application to estimate regional ventilation. Instead of the sum of squared intensity difference (SSD), we introduce the sum of squared tissue volume difference (SSTVD) as the similarity criterion to take into account the variation of intensity due to respiration. This new criterion aims to minimize the local difference of tissue volume inside the lungs between two images scanned in the same session or over short periods of time, thus preserving the tissue weight of the lungs. Our approach is tested using a pair of volumetric lung datasets acquired at 15% and 85% of vital capacity (VC) in a single scanning session. The results show that the new SSTVD predicts a smaller registration error and also yields a better alignment of structures within the lungs than the normal SSD similarity measure. In addition, the regional ventilation derived from the new method exhibits a much more improved physiological pattern than that of SSD.

Citation Download Citation

Youbing Yin, Eric A. Hoffman, and Ching-Long Lin "Local tissue-weight-based nonrigid registration of lung images with application to regional ventilation", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 72620C (27 February 2009); https://doi.org/10.1117/12.811715

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