Coded aperture design by uniform sensing in compressive X-ray tomosynthesis systems
Date
2016
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Publisher
University of Delaware
Abstract
X-ray tomosynthesis systems are essential in medical diagnosis. They are currently used in mammography, lung nodule detection among other medical applications. As with computed tomography systems, radiation dose is of great concern and new approaches have been recently proposed to address this issue. Coded aperture compressive x-ray tomosynthesis, for example, places a coded aperture in front of an x-ray source in order to obtain patterned projections of a three-dimensional object on a two dimensional detector plane. By using different coded apertures in a multiple source system, multiplexed projections can be obtained instead of sequential projections as in conventional tomosynthesis systems, thus using less measurements which leads to the reduction of radiation. Compressed sensing (CS) reconstruction algorithms are then used to recover the three-dimensional data cube. Random structures for the coded apertures have been proposed before, however they operate poorly in exploiting the inherent projection patterns proper of the system. This thesis proposes an algorithm for the design and optimization of the coded apertures in a multiple source compressive x-ray tomosynthesis system. A uniform energy criteria on the voxels and detector elements is used so that the object is uniformly sensed and the elements of the detector plane uniformly sense the information. Both simulation and experimental results for optimized coded apertures are shown and their performance is compared to the use of random coded apertures.