gnd-pca-based statistical modeling of diaphragm motion extracted from 4d mri

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ID: 259365
2013
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Abstract
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.
Reference Key
swastika2013computationalgnd-pca-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Windra Swastika;Yoshitada Masuda;Rui Xu;Shoji Kido;Yen-Wei Chen;Hideaki Haneishi
Journal advanced functional materials
Year 2013
DOI
10.1155/2013/482941
URL
http://dx.doi.org/10.1155/2013/482941
Keywords
medical informatics computer applications to medicine

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