Optimal data acquisition in tomography.

In tomography, three-dimensional images of a medium are reconstructed from a set of two-dimensional projections. Each projection is the result of a measurement made by the scanner via radiating some form of energy and collecting the scattered field after interacting with the medium. The information content of these measurements is not equal, and one projection can be more informative than others. By choosing the most informative measurement at every step of scanning, an optimal tomography system can maximize the speed of data acquisition and temporal resolution of acquired images, reducing the operation cost and exposure to possible harmful radiations. The aim of this paper is to introduce mathematical algorithms that can be used to design measurements with optimal information content when imaging static or dynamically evolving objects.

Optimal Scanning Protocol for Optical Tomography.

Tomography is a two step process in which the sample under test is first scanned by the hardware of the system to acquire data and then the operating software reconstruct images from the gathered information. The main objective of this work is to optimize the scanning process to acquire maximum amount of information in each measurement when the system is scanning the sample. By exploiting our prior information about the sample and using estimation theory, we developed a systematic approach to implement the optimal scanning protocol. Results of this study provide strong evidence that the developed algorithms can speed up data acquisition. Also it is shown that the proposed method can reduce the impact of noise as well as improving the reconstruction error while performing less number of measurements.Clinical relevance- The proposed method can enhance data acquisition time, exposure dosage and cost of operation in medical applications of tomography.

Optimization of data acquisition operation in optical tomography based on estimation theory.

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.