Mar 17 2016

Estimator for contrast agents-3 Monte Carlo simulation

Tag: Implementation,Physics,softwareadmin @ 10:51 am
In this post I continue the discussion of the paper[2], “Efficient, non-iterative estimator for imaging contrast agents with spectral x-ray detectors,” which is available for free download here. The paper extends the previous A-table estimator[1], see this post, to three or more dimension basis sets so it can be used with high atomic number contrast agents. Here I describe the Matlab code to reproduce the figures that summarize the Monte Carlo simulation of the estimators’ performance. The Monte Carlo simulation verifies that the new estimator achieves the Cramèr-Rao lower bound (CRLB) and compares it to an iterative estimator. The simulation code is included with the package for this post.

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Mar 07 2016

Estimator for contrast agents-2 The iterative estimator

Tag: Implementation,Math,softwareadmin @ 10:56 am
As its name implies, the maximum likelihood estimate is the value of the dependent variable that maximizes the likelihood given the measured data. One way to implement it is to use an iterative algorithm, which I discussed here. In this post, I give a detailed a description of the code for an iterative estimator. The implementation is different than the one used in the previous post and is included as AsolveIterFromSpectrum.m in the code package .

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Mar 03 2016

Estimator for contrast agents 1

Tag: Math,Noise,softwareadmin @ 11:22 am
The next series of posts discuss my recently published paper, “Efficient, non-iterative estimator for imaging contrast agents with spectral x-ray detectors,” available for free download here. The paper extends the previous A-table estimator, see this post, to three or more dimension basis sets so it can be used with high atomic number contrast agents. It also compares the A-table estimator to an iterative estimator.
This post describes the software to implement the new estimator. The next posts describe the code for an iterative estimator, compare the performance of the new estimator to the iterative estimator and the CRLB, compare the new estimator with a neural network estimator, and finally discuss an alternate implementation using a neural network as the interpolator.

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