Adaptive X-ray Computed Tomography

Abstract

An adaptive pre-clinical x-ray computed tomography system, named "FaCT" was designed, built, and tested at the University of Arizona's Center for Gamma-Ray Imaging (CGRI). The FaCT system possesses the unique ability to change its magnification and dynamically mask the x-ray beam profile. Using these two abilities, the FaCT system can adapt its configuration to the object being imaged, and the task being performed, while achieving a reduction in the radiation dose applied for imaging.Development of the system included the design of all mechanical components, motion systems, and safety systems. It also included system integration of all electronics, motors, and communication channels. Control software was developed for the system and several high-performance reconstruction algorithms were implemented on graphics processing units for reconstructing tomographic data sets acquired by the system. A new geometrical calibration method was developed for calibrating the system that makes use of the full image data gathered by the system and does not rely on markers.An adaptive imaging procedure consisting of a preliminary scout scan, human guidance, and a diagnostic quality scan was developed for imaging small volumes of interest in the interior of an object at substantially reduced dose. The adaptive imaging procedure makes use of FaCT's adjustable magnification, beam-masking capability, and high-performance reconstruction software to achieve high-quality reconstruction of a volume of interest with less dose than would be required by a traditional x-ray computed tomography system without adaptive capabilities.To address ongoing research into mathematical rules for adapting an imaging system, such as FaCT, to better perform a given estimation task, a method of quantifying a system's ability to estimate a parameter of interest in the presence of nuisance parameters based on the Fisher Information was proposed. The method requires a statistical model of object variability. Possible strategies for increasing the performance of an estimation task, given an adaptive system, were suggested.