**Publications**(42)

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In this paper a graphical analysis of Estimation and Detection Information Trade-off (EDIT) will be explored. EDIT produces curves which allow for a decision to be made for system optimization based on design constraints and costs associated with estimation and detection. EDIT analyzes the system in the estimation information and detection information space where the user is free to pick their own method of calculating these measures. The user of EDIT can choose any desired figure of merit for detection information and estimation information then the EDIT curves will provide the collection of optimal outcomes.

The paper will ﬁrst look at two methods of creating EDIT curves. These curves can be calculated using a wide variety of systems and ﬁnding the optimal system by maximizing a ﬁgure of merit. EDIT could also be found as an upper bound of the information from a collection of system. These two methods allow for the user to choose a method of calculation which best ﬁts the constraints of their actual system.

Applying the J-optimal channelized quadratic observer to SPECT myocardial perfusion defect detection

*L x M*channel matrix and in prior work we introduced an iterative gradient-based method for calculating the channel matrix. The dimensionality reduction from M measurements to L channels yields better estimates of these sample statistics from smaller sample sizes, and since the channelized covariance matrix is

*L x L*instead of

*M x M*, the matrix inverse is easier to compute. The novelty of our approach is the use of Jeffrey’s divergence (J) as the figure of merit (FOM) for optimizing the channel matrix. We previously showed that the J-optimal channels are also the optimum channels for the AUC and the Bhattacharyya distance when the channel outputs are Gaussian distributed with equal means. This work evaluates the use of J as a surrogate FOM (SFOM) for AUC when these statistical conditions are not satisfied.

_{2}:Eu . Poisson light is expected to yield zero temporal correlations, while super-Poisson light is expected to yield positive, and sub-Poisson light is expected to yield negative temporal correlation. Scintillation light in SrI

_{2}:Eu was found to be negatively correlated. Therefore, we conclude that the scintillation light in SrI

_{2}:Eu is sub-Poisson.

^{3}R. The FastSPECT II system consisted of two rings of eight scintillation cameras each. The resulting dimensions of H were 68921 voxels by 97344 detector pixels. The M

^{3}R system is a four camera system that was reconfigured to measure image space using a single scintillation camera. The resulting dimensions of H were 50864 voxels by 6241 detector pixels. In this paper we present results of the SVD of each system and discuss calculation of the measurement and null space for each system.

*D*Laguerre-Gauss and difference-of-Gaussian channels to calculate area under the receiver-operating characteristic curve (AUC). Previous work presented at this meeting described a unique, small-animal SPECT system (M

^{3}R) capable of operating under a myriad of hardware configurations and ideally suited for image quality studies. Measured system matrices were collected for several hardware configurations of M

^{3}R. The data used to implement these two methods was then generated by taking simulated objects through the measured system matrices. The results of these two methods comprise a combination of qualitative and quantitative analysis that is well-suited for hardware assessment.

*a posteriori*estimators and maximum likelihood estimators. Multiple signals may be accomodated in this framework by making the number of signals one of the parameters in the set to be estimated.

Evaluating estimation techniques in medical imaging without a gold standard: experimental validation

_{a}, using a well-known formula involving an error function. The ROC curve can also be determined by psychophysical studies for humans performing the same task, and again figures of merit such as AUC and d

_{z}can be derived. Since the likelihood ratio is optimal, however, the d

_{a}values for the human must necessarily be less than those for the ideal observer, and the square of the ratio of d

_{a}(human)/d

_{a}(ideal) is frequently taken as a measure of the perceptual efficiency of the human. The applicability of this efficiency measure is limited, however, since there are very few problems for which we can actually compute d

_{a}or AUC for the ideal observer. In this paper we examine some basic mathematical properties of the likelihood ratio and its logarithm. We demonstrate that there are strong constraints on the form of the probability density functions for these test statistics. In fact, if one knows, say, the density on the logarithm of the likelihood ratio under the null hypothesis, the densities of both the likelihood and the log-likelihood under both hypotheses are specified in terms of a likelihood-generating function. From this single function one can obtain all moments of both the likelihood and the log-likelihood under both hypotheses. Moreover, a AUC is expressed to an excellent approximation by a single point on the function. We illustrate these mathematical properties by considering the problem of signal detection with uncertain signal location.

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