COUNT-WEIGHTED LEAST SQUARES PARAMETER ESTIMATION FOR A PHOTON-COUNTING DETECTOR

摘要

A method and apparatus for estimating a parameter vector including a plurality of parameters of a detector response model of a photon-counting detector. The method includes calculating a modeled spectrum based on an input spectrum and an initial value of the plurality of parameters. For each detector, a difference between the normalized photon count of the measured spectrum and the normalized modeled spectrum is calculated. A root mean square error (RMSE) between the measured and modeled spectra is obtained by squaring the normalized difference and weighting the normalized difference by a weighting factor. The parameter vector is updated until an optimum RMSE value is achieved. Upon determining optimal values of the parameter vector, measured data that is obtained via a patient scan is corrected based on the optimal parameter vector.

主权项

1. A method for determining an optimal parameter vector, the parameter vector including a plurality of parameters of a detector response model of a photon-counting detector, the method comprising:
determining a normalized measured photon count for each energy bin of the photon-counting detector; setting an initial incident photon spectrum and an initial value for each parameter of the plurality of parameters; calculating, using the detector response model, a normalized modeled photon count for each energy bin of the photon-counting detector, based on the incident photon spectrum and the plurality of parameters; computing, for each energy bin, a square of a difference between the normalized measured photon count and the normalized modeled photon count; weighting the computed square of each energy bin by a weighting factor to generate weighted squares; summing the weighted squares of each energy bin and computing a root-mean-square error for the photon-counting detector; updating at least one of the parameter of the parameter vector; and repeating the calculating, computing, weighting, summing, and updating steps until a stopping criteria is met, so as to determine the optimal parameter vector.