APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES
Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content.
- Research Organization:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research (ER) (US)
- DOE Contract Number:
- AC02-98CH10886
- OSTI ID:
- 760985
- Report Number(s):
- BNL-66562; KP140103; R&D Project: CO15; KP140103; TRN: US0005240
- Resource Relation:
- Conference: PROCEEDINGS OF THE INTERNATIONAL SOCIETY OF MAGN. RESON. MED., PHILDELPHIA, PA (US), 05/22/1999--05/28/1999; Other Information: PBD: 22 May 1999
- Country of Publication:
- United States
- Language:
- English
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