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## Scholarly Works (30 results)

Region of interest (ROI) quantitation is an important task in emission tomography (e.g., positron emission tomography and single photon emission computed tomography). It is essential for exploring clinical factors such as tumor activity, growth rate, and the efficacy of therapeutic interventions. Bayesian methods based on the maximum a posteriori principle (or called penalized maximum likelihood methods) have been developed for emission image reconstructions to deal with the low signal to noise ratio of the emission data. Similar to the filter cut-off frequency in the filtered backprojection method, the smoothing parameter of the image prior in Bayesian reconstruction controls the resolution and noise trade-off and hence affects ROI quantitation. In this paper we present an approach for choosing the optimum smoothing parameter in Bayesian reconstruction for ROI quantitation. Bayesian reconstructions are difficult to analyze because the resolution and noise properties are nonlinear and object-dependent. Building on the recent progress on deriving the approximate expressions for the local impulse response function and the covariance matrix, we derived simplied theoretical expressions for the bias, the variance, and the ensemble mean squared error (EMSE) of the ROI quantitation. One problem in evaluating ROI quantitation is that the truth is often required for calculating the bias. This is overcome by using ensemble distribution of the activity inside the ROI and computing the average EMSE. The resulting expressions allow fast evaluation of the image quality for different smoothing parameters. The optimum smoothing parameter of the image prior can then be selected to minimize the EMSE.

Traditionally, the figures of merit used in designing a PET scanner are spatial resolution, noise equivalent count rate, noise equivalent sensitivity, etc. These measures, however, do not directly reflect the lesion detectability using the PET scanner. Here we propose to optimize PET scanner design directlyfor lesion detection. The signal-to-noise ratio (SNR) of lesion detection can be easily computed using the theoretical expressions that we have previously derived. Because no time consuming Monte Carlo simulation is needed, the theoretical expressions allow evaluation of a large range of parameters. The PET system parameters can then be chosen to achieve the maximum SNR for lesion detection. The simulation study shown in this paper was focused a single ring PET scanner without depth of interaction measurement. Randoms and scatters were also ignored.

We explore the causes of performance limitation in positron emission mammography cameras. We compare two basic camera geometries containing the same volume of 511 keV photon detectors, one with a parallel plane geometry and another with a rectangular geometry. We find that both geometries have similar performance for the phantom imaged (in Monte Carlo simulation), even though the solid angle coverage of the rectangular camera is about 50 percent higher than the parallel plane camera. The reconstruction algorithm used significantly affects the resulting image; iterative methods significantly outperform the commonly used focal plane tomography. Finally, the characteristics of the tumor itself, specifically the absolute amount of radiotracer taken up by the tumor, will significantly affect the imaging performance.