We have designed and built a stationary digital breast tomosynthesis (DBT) system containing a carbon nanotube
based field emission x-ray source array to examine the possibility of obtaining a reduced scan time and improved
image quality compared to conventional DBT systems. There are 25 individually addressable x-ray sources in our
linear source array that are evenly angularly spaced to cover an angle of 48°. The sources are turned on sequentially
during imaging and there is no motion of either the source or the detector. We present here an iterative reconstruction
method based on a modified Ordered-Subset Convex (MOSC) algorithm that was employed for the reconstruction of
images from the new DBT system. Using this algorithm based on a maximum-likelihood model, we reconstruct on
non-cubic voxels for increased computational efficiency resulting in high in-plane resolution in the images. We have
applied the reconstruction technique on simulated and phantom data from the system. Even without the use of the
subsets, the reconstruction of an experimental 9-beam system with 960×768 pixels took less than 6 minutes (10
iterations). The projection images of a simulated mammography accreditation phantom were reconstructed using
MOSC and a Simultaneous Algebraic Reconstruction technique (SART) and the results from the comparison between
the two algorithms allow us to conclude that the MOSC is capable of delivering excellent image quality when used in
tomosynthesis image reconstruction.
We perform simulation studies of proposed square and hexagonal geometries of a multi-source X-ray micro-computed
tomography (CT) system. The system uses linear arrays of the carbon nano-tube (CNT)-based X-ray sources which are
individually addressable. In the square geometry, two linear source arrays and two area detectors form a square; whereas
in the hexagonal geometry, three linear source arrays and three area detectors form a hexagon. The tomographic angular
sampling for both geometries requires no motion of the sources or subject. Based on the sinogram maps, the hexagonal
geometry has improved angular coverage than the square geometry. The ordered-subset convex iterative algorithm is
implemented in both geometries for reconstructions from cone-beam projection data. The reconstructed images from
both geometries are generally consistent with the phantom, although some streaking artifacts due to the limited-angle
nature of the geometries are observed. The two geometries show similar performance in resolution-noise tradeoff. The
gap-free hexagonal geometry produces lower mean squared error in the reconstructed images; when gaps between the
source arrays and detectors are introduced, the angular coverage of the hexagonal geometry degrades faster and becomes
worse than the square geometry. The impact of gaps on the imaging properties must be studied further.
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