Name
Title | ||
|---|---|---|
A Clinical Application of Ensemble ICA to the Quantification of Myocardial Blood Flow in Dynamic H152 O H^{{15}}_{2} O PET |
Abstract | ||
|---|---|---|
Ensemble independent component analysis (ICA) is a Bayesian multivariate data analysis method which allows various prior distributions for parameters and latent variables, leading to flexible data fitting. In this paper we apply ensemble ICA with a rectified Gaussian prior to dynamic (H15O)-O-2 positron emission tomography ( PET) image data, emphasizing its clinical usefulness by showing that major cardiac components are successfully extracted in an unsupervised manner and myocardial blood flow can be estimated in 15 among 20 patients. Detailed experiments and results are illustrated. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s11265-007-0080-7 | JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY |
Keywords | DocType | Volume |
Bayesian learning,independent component analysis (ICA),myocardial blood flow quantification,positron emission tomography (PET) | Journal | 49 |
Issue | ISSN | Citations |
2 | 0922-5773 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Byeong Il Lee | 1 | 1 | 0.75 |
| Jae Sung Lee | 2 | 0 | 1.35 |
| Dongsoo Lee | 3 | 233 | 30.63 |
| Won Jun Kang | 4 | 0 | 1.01 |
| Jong Jin Lee | 5 | 0 | 0.68 |
| Seungjin Choi | 6 | 1444 | 133.30 |