APPLICATION OF PRINCIPAL COMPONENT ANALYSIS AND BAYESIAN DECOMPOSITION TO RELAXOGRAPHIC IMAGING
Conference
·
OSTI ID:760986
Recent developments in high field imaging have made possible the acquisition of high quality, low noise relaxographic data in reasonable imaging times. The datasets comprise a huge amount of information (>>1 million points) which makes rigorous analysis daunting. Here, the authors present results demonstrating that Principal Component Analysis (PCA) and Bayesian Decomposition (BD) provide powerful methods for relaxographic analysis of T{sub 1} recovery curves and editing of tissue type in resulting images.
- 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:
- 760986
- Report Number(s):
- BNL-66561; KP140103; R&D Project: CO15; KP140103; TRN: US0005241
- Resource Relation:
- Conference: PROCEEDINGS OF THE INTERNATIONAL SOCIETY OF MAGN. RESON. MED., PHILADELPHIA, 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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