Analysis of point-to-point lung motion with full inspiration and expiration CT data using non-linear optimization method: optimal geometric assumption model for the effective registration algorithm

Namkug Kim; Joon Beom Seo; Jeong Nam Heo; Suk-Ho Kang

doi:10.1117/12.709307

26 March 2007 Analysis of point-to-point lung motion with full inspiration and expiration CT data using non-linear optimization method: optimal geometric assumption model for the effective registration algorithm

Namkug Kim, Joon Beom Seo, Jeong Nam Heo, Suk-Ho Kang

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65121T (2007) https://doi.org/10.1117/12.709307
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

The study was conducted to develop a simple model for more robust lung registration of volumetric CT data, which is essential for various clinical lung analysis applications, including the lung nodule matching in follow up CT studies, semi-quantitative assessment of lung perfusion, and etc. The purpose of this study is to find the most effective reference point and geometric model based on the lung motion analysis from the CT data sets obtained in full inspiration (In.) and expiration (Ex.). Ten pairs of CT data sets in normal subjects obtained in full In. and Ex. were used in this study. Two radiologists were requested to draw 20 points representing the subpleural point of the central axis in each segment. The apex, hilar point, and center of inertia (COI) of each unilateral lung were proposed as the reference point. To evaluate optimal expansion point, non-linear optimization without constraints was employed. The objective function is sum of distances from the line, consist of the corresponding points between In. and Ex. to the optimal point x. By using the nonlinear optimization, the optimal points was evaluated and compared between reference points. The average distance between the optimal point and each line segment revealed that the balloon model was more suitable to explain the lung expansion model. This lung motion analysis based on vector analysis and non-linear optimization shows that balloon model centered on the center of inertia of lung is most effective geometric model to explain lung expansion by breathing.

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Namkug Kim, Joon Beom Seo, Jeong Nam Heo, and Suk-Ho Kang "Analysis of point-to-point lung motion with full inspiration and expiration CT data using non-linear optimization method: optimal geometric assumption model for the effective registration algorithm", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65121T (26 March 2007); https://doi.org/10.1117/12.709307

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KEYWORDS

Lung

Motion models

Computed tomography

Data modeling

Motion analysis

Curtains

Optimization (mathematics)

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