A phantom study on component segregation for MR images using ICA.
OBJECTIVE
A phantom set was devised to evaluate capability of independent component analysis (ICA) as an image filter for magnetic resonance (MR) images to segregate components.


METHODS
Four components (free water [FW], olive oil [OL], 2% and 4% agar gels [2A and 4A, respectively]) were arranged in a phantom set. Seven MR images were obtained with different echo time and repetition time. ICA was performed on 23 combinations of four components. A segregation rate higher than 70% was defined as effective.


RESULTS
Four-component segregation was obtained in 5 of 23 combinations. The best result showed a mean of 87% across the four components. For each component, there were 20 of 23 for FW, 22 for OL, 9 for 2A, and 16 for 4A.


CONCLUSIONS
The results demonstrated ICA works as an image filter and provides new contrast images that unambiguously segregate components in MR images. For practical application, the best performance should be obtained when T(1)W, T(2)W, and proton density images are included in the dataset for ICA.
Functional Brain Imaging Lab, Department of Gerontechnology, National Institute for Geriatrics and Gerontology, Morioka-chou Gengo 36-3, Ohbu, Aichi, Japan.
Makoto
M
Miyakoshi
Moyoko
M
Tomiyasu
Epifanio
E
Bagarinao
Shumei
S
Murakami
Toshiharu
T
Nakai
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity