Name
Title | ||
|---|---|---|
MAP-based kinetic analysis for voxel-by-voxel compartment model estimation: detailed imaging of the cerebral glucose metabolism using FDG. |
Abstract | ||
|---|---|---|
We propose a novel algorithm for voxel-by-voxel compartment model analysis based on a maximum a posteriori (MAP) algorithm. Voxel-by-voxel compartment model analysis can derive functional images of living tissues, but it suffers from high noise statistics in voxel-based PET data and extended calculation times. We initially set up a feature space of the target radiopharmaceutical composed of a measured plasma time activity curve and a set of compartment model parameters, and measured the noise distribution of the PET data. The dynamic PET data were projected onto the feature space, and then clustered using the Mahalanobis distance. Our method was validated using simulation studies, and compared with ROI-based ordinary kinetic analysis for FDG. The parametric images exhibited an acceptable linear relation with the simulations and the ROI-based results, and the calculation time took about 10 min. We therefore concluded that our proposed MAP-based algorithm is practical. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.neuroimage.2005.08.046 | NeuroImage |
Keywords | Field | DocType |
MAP,PET,Parametric image,FDG,Kinetic analysis | Voxel,Parametric Image,Cognitive psychology,Mahalanobis distance,Artificial intelligence,Time Activity Curve,Computer vision,Feature vector,Pattern recognition,Compartment (ship),Parametric statistics,Maximum a posteriori estimation,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 4 | 1053-8119 |
Citations | PageRank | References |
2 | 0.45 | 5 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuichi Kimura | 1 | 51 | 14.32 |
| Mika Naganawa | 2 | 41 | 8.49 |
| Jun Yamaguchi | 3 | 2 | 0.79 |
| Yuki Takabayashi | 4 | 2 | 0.79 |
| A Uchiyama | 5 | 28 | 5.88 |
| Keiichi Oda | 6 | 40 | 7.93 |
| K Ishii | 7 | 30 | 5.05 |
| Kiichi Ishiwata | 8 | 35 | 5.49 |