Pre-computed backprojection based penalized-likelihood (PPL) reconstruction with an edge-preserved regularizer for stationary Digital Breast Tomosynthesis
2014
Stationary Digital Breast Tomosynthesis (sDBT) is a carbon nanotube based breast imaging device with fast
data acquisition and decent projection resolution to provide three dimensional (3-D) volume information. To-
mosynthesis 3-D image reconstruction is faced with the challenges of the cone beam geometry and the incomplete
and nonsymmetric sampling due to the sparse views and limited view angle. Among all available reconstruction
methods, statistical iterative method exhibits particular promising since it relies on an accurate physical and
statistical model with prior knowledge. In this paper, we present the application of an edge-preserved regularizer
to our previously proposed precomputed backprojection based penalized-likelihood (PPL) reconstruction. By
using the edge-preserved regularizer, our experiments show that through tuning several parameters, resolution
can be retained while noise is reduced significantly. Compared to other conventional noise reduction techniques
in image reconstruction, less resolution is lost in order to gain certain noise reduction, which may benefit the
research of low dose tomosynthesis.
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