6 research outputs found
High resolution image reconstruction with constrained, total-variation minimization
This work is concerned with applying iterative image reconstruction, based on
constrained total-variation minimization, to low-intensity X-ray CT systems
that have a high sampling rate. Such systems pose a challenge for iterative
image reconstruction, because a very fine image grid is needed to realize the
resolution inherent in such scanners. These image arrays lead to
under-determined imaging models whose inversion is unstable and can result in
undesirable artifacts and noise patterns. There are many possibilities to
stabilize the imaging model, and this work proposes a method which may have an
advantage in terms of algorithm efficiency. The proposed method introduces
additional constraints in the optimization problem; these constraints set to
zero high spatial frequency components which are beyond the sensing capability
of the detector. The method is demonstrated with an actual CT data set and
compared with another method based on projection up-sampling.Comment: This manuscript appears in the proceedings of the 2010 IEEE medical
imaging conferenc
A constrained, total-variation minimization algorithm for low-intensity X-ray CT
Purpose: We develop an iterative image-reconstruction algorithm for
application to low-intensity computed tomography (CT) projection data, which is
based on constrained, total-variation (TV) minimization. The algorithm design
focuses on recovering structure on length scales comparable to a detector-bin
width.
Method: Recovering the resolution on the scale of a detector bin, requires
that pixel size be much smaller than the bin width. The resulting image array
contains many more pixels than data, and this undersampling is overcome with a
combination of Fourier upsampling of each projection and the use of
constrained, TV-minimization, as suggested by compressive sensing. The
presented pseudo-code for solving constrained, TV-minimization is designed to
yield an accurate solution to this optimization problem within 100 iterations.
Results: The proposed image-reconstruction algorithm is applied to a
low-intensity scan of a rabbit with a thin wire, to test resolution. The
proposed algorithm is compared with filtered back-projection (FBP).
Conclusion: The algorithm may have some advantage over FBP in that the
resulting noise-level is lowered at equivalent contrast levels of the wire.Comment: This article has been submitted to "Medical Physics" on 9/13/201
Optimization of a dual-energy contrast-enhanced technique for a photon-counting digital breast tomosynthesis system: II. An experimental validation
Optimization of a dual-energy contrast-enhanced technique for a photon-counting digital breast tomosynthesis system: I. A theoretical model
Purpose: Dual-energy (DE) iodine contrast-enhanced x-ray imaging of the breast has been shown to identify cancers that would otherwise be mammographically occult. In this article, theoretical modeling was performed to obtain optimally enhanced iodine images for a photon-counting digital breast tomosynthesis (DBT) system using a DE acquisition technique