Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.

Purpose: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems.

Monte Carlo-based assessment of the trade-off between spatial resolution, field-of-view and scattered radiation in the variable resolution X-ray CT scanner.

Objective: The purpose of this work is to evaluate the impact of optimization of magnification on performance parameters of the variable resolution X-ray (VRX) CT scanner.

Methods: A realistic model based on an actual VRX CT scanner was implemented in the GATE Monte Carlo simulation platform. To evaluate the influence of system magnification, spatial resolution, field-of-view (FOV) and scatter-to-primary ratio of the scanner were estimated for both fixed and optimum object magnification at each detector rotation angle.

Novel detector design for reducing intercell x-ray cross-talk in the variable resolution x-ray CT scanner: a Monte Carlo study.

Purpose: The variable resolution x-ray (VRX) CT scanner provides substantial improvement in the spatial resolution by matching the scanner's field of view (FOV) to the size of the object being imaged. Intercell x-ray cross-talk is one of the most important factors limiting the spatial resolution of the VRX detector. In this work, a new cell arrangement in the VRX detector is suggested to decrease the intercell x-ray cross-talk.