My last post showed that detection performance is determined by the signal to noise ratio (SNR). I derived a formula for the SNR with multispectral measurements, which depends on the covariance of the A-space data. This post shows how to compute the A-space covariance from the x-ray data noise and the effective attenuation coefficient matrix. In general, this depends on the type of estimator used so instead I will use the Cramèr-Rao lower bound (CRLB), which is the minimum covariance for any unbiased estimator. This gives a general result independent of the specific estimator implementation. I will show that the constant covariance CRLB is sufficiently accurate for our purposes. These results will allow us to compute signal to noise ratios for limited energy resolution measurements that are directly comparable to the Tapiovaara-Wagner[Tapiovaara1985] optimal SNR with complete energy information.

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In my last posts I discussed the background for applying statistical detection theory to x-ray imaging. In this post, I will show how to incorporate the A-space description into the model. This will lead me to discuss the effect of the basis set functions on the approximation or representation error of the attenuation coefficients of body materials. I will show that there are optimal functions that minimize the error but that other basis functions, such as the attenuation coefficient functions of different materials, do not lead to substantially larger errors. Once the A-space description is in the model, we can derive a signal to noise ratio that is directly comparable to the Tapiovaara-Wagner SNR[Tapiovaara1985] and we can compare the SNR with limited energy resolution to the ideal with complete spectral information.

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In my previous post, I showed that multivariate normal is a good model for x-ray measurements and in my last post I described the general properties of this distribution. In this post, I will discuss statistical detection theory with the normal model. I will show that the performance is characterized by a suitably defined signal to noise ratio. This will enable me to close the loop to the main topic of this series of posts, which is to explain the results in my paper, “Near optimal energy selective x-ray imaging system performance with simple detectors[Alvarez2010]”, which is available for free download here.

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In my last post, I showed that the multivariate normal, abbreviated multinormal, is a good model for the noise w in a linearized x-ray system model. In this post, I will discuss some of the properties of the multinormal distribution. I will show a rationale for its expression using vectors and matrices. This will lead me to discuss matrix calculus. I will describe diagonalizing and whitening transformations and derive the moment generating functions of the uninormal and multinormal to show that linear combinations of multinormals are also multinormal. This post will provide math background for my discussions of detection and maximum likelihood estimation with the linearized x-ray model.

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