The data acquisition process is occasionally the most time consuming and costly operation in tomography. Currently, raster scanning is still the common practice in making sequential measurements in most tomography scanners. Raster scanning is known to be slow and such scanners usually cannot catch up with the speed of changes when imaging dynamically evolving objects. In this research, we studied the possibility of using estimation theory and our prior knowledge about the sample under test to reduce the number of measurements required to achieve a given image quality. This systematic approach for optimization of the data acquisition process also provides a vision toward improving the geometry of the scanner and reducing the effect of noise, including the common state-dependent noise of detectors. The theory is developed in the article and simulations are provided to better display discussed concepts.
In this article we introduce a systematic approach for optimal scanning of dynamically evolving objects, including cases where the dynamics is unknown. The method is specifically designed to optimize each measurement and engineer illumination patterns with the goal of reducing the uncertainty left in our estimation of the sample. Concurrently, the algorithm uses system identification techniques to develop a mathematical model for the dynamics under test based on the acquired data and it uses the model to predict changes in the distribution and optimize upcoming measurements. The theory is developed and simulations are provided to better display discussed concepts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.