GND-PCA-Based Statistical Modeling of Diaphragm Motion Extracted from 4D MRI

• Published in: Computational and mathematical methods in medicine 2013-01, Vol.2013 (2013), p.1-9
• Main Authors: Swastika, Windra, Masuda, Yoshitada, Xu, Rui, Kido, Shoji, Chen, Yen-Wei, Haneishi, Hideaki
• Format: Article
• Language: English
• Subjects: Adult; Computational Biology; Computer Simulation; Diaphragm - physiology; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - statistics & numerical data; Imaging, Three-Dimensional - methods; Imaging, Three-Dimensional - statistics & numerical data; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - statistics & numerical data; Middle Aged; Models, Biological; Models, Statistical; Movement - physiology; Principal Component Analysis; Respiratory Mechanics; Young Adult
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2013/482941

We analyzed a statistical model of diaphragm motion using regular principal component analysis (PCA) and generalized N-dimensional PCA (GND-PCA). First, we generate 4D MRI of respiratory motion from 2D MRI using an intersection profile method. We then extract semiautomatically the diaphragm boundary from the 4D-MRI to get subject-specific diaphragm motion. In order to build a general statistical model of diaphragm motion, we normalize the diaphragm motion in time and spatial domains and evaluate the diaphragm motion model of 10 healthy subjects by applying regular PCA and GND-PCA. We also validate the results using the leave-one-out method. The results show that the first three principal components of regular PCA contain more than 98% of the total variation of diaphragm motion. However, validation using leave-one-out method gives up to 5.0 mm mean of error for right diaphragm motion and 3.8 mm mean of error for left diaphragm motion. Model analysis using GND-PCA provides about 1 mm margin of error and is able to reconstruct the diaphragm model by fewer samples.