Name
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 5.0mm mean of error for right diaphragm motion and 3.8mm mean of error for left diaphragm motion. Model analysis using GND-PCA provides about 1mm margin of error and is able to reconstruct the diaphragm model by fewer samples. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1155/2013/482941 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Keywords | Field | DocType |
movement,magnetic resonance imaging,diaphragm,respiratory mechanics,computer simulation,young adult,computational biology,principal component analysis | Computer vision,Diaphragm (structural system),Normalization (statistics),Respiratory motion,Computer science,Artificial intelligence,Statistical model,Margin of error,Principal component analysis | Journal |
Volume | ISSN | Citations |
2013 | 1748-670X | 0 |
PageRank | References | Authors |
0.34 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Windra Swastika | 1 | 0 | 0.68 |
| Yoshitada Masuda | 2 | 1 | 1.07 |
| Rui Xu | 3 | 23 | 4.30 |
| Shoji Kido | 4 | 53 | 16.61 |
| Yen-Wei Chen | 5 | 720 | 155.73 |
| Hideaki Haneishi | 6 | 156 | 39.40 |