Efficient, Non-Iterative Estimator for Imaging Contrast Agents With Spectral X-Ray Detectors PROJECT TITLE :Efficient, Non-Iterative Estimator for Imaging Contrast Agents With Spectral X-Ray DetectorsABSTRACT:An estimator to image contrast agents and body materials with x-ray spectral measurements is described. The estimator is usable with the three or more basis functions that are needed to represent the attenuation coefficient of high atomic range materials. The estimator variance is equal to the Crame0zero;r-Rao lower certain (CRLB) and it's unbiased. Its parameters are computed from measurements of a calibration phantom with the clinical x-ray system and it's non-iterative. The estimator is compared with an iterative most likelihood estimator. The estimator 1st computes a linearized most likelihood estimate of the road integrals of the premise set coefficients. Corrections for errors in the initial estimates are computed by interpolation with calibration phantom information. The ultimate estimate is that the initial estimate and the correction. The performance of the estimator is measured employing a Monte Carlo simulation. Random photon counting with pulse height analysis data are generated. The mean squared errors of the estimates are compared to the CRLB. The random data are also processed with an iterative maximum likelihood estimator. Previous implementations of iterative estimators required advanced physics instruments not usually accessible in clinical institutions. The estimator mean squared error is basically equal to the CRLB. The estimator outputs are close to those of the iterative estimator but the computation time is approximately a hundred and eighty times shorter. The estimator is efficient and has benefits over alternate approaches such as iterative estimators. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Dynamical Equivalent Circuit for 1-D Periodic Compound Gratings Mining High Utility Patterns in One Phase without Generating Candidates