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OCHS, M.F.; STOYANOVA, R.S.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.
Brookhaven National Lab., Upton, NY (United States). Funding organisation: USDOE Office of Energy Research (ER) (United States)1999
Brookhaven National Lab., Upton, NY (United States). Funding organisation: USDOE Office of Energy Research (ER) (United States)1999
AbstractAbstract
[en] 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 T1 recovery curves and editing of tissue type in resulting images
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22 May 1999; 1 p; International Society of Magnetic Resonance Medicine meeting; Philadelphia, PA (United States); 22-28 May 1999; KP--140103; AC02-98CH10886; Also available from OSTI as DE00760986; PURL: https://www.osti.gov/servlets/purl/760986-osuzqq/native/
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