Published March 1, 2017
| Version 10006930
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Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations
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Description
The reconstruction from sparse-view projections is one
of important problems in computed tomography (CT) limited by
the availability or feasibility of obtaining of a large number of
projections. Traditionally, convex regularizers have been exploited
to improve the reconstruction quality in sparse-view CT, and the
convex constraint in those problems leads to an easy optimization
process. However, convex regularizers often result in a biased
approximation and inaccurate reconstruction in CT problems. Here,
we present a nonconvex, Lipschitz continuous and non-smooth
regularization model. The CT reconstruction is formulated as a
nonconvex constrained L1 − L2 minimization problem and solved
through a difference of convex algorithm and alternating direction
of multiplier method which generates a better result than L0 or L1
regularizers in the CT reconstruction. We compare our method with
previously reported high performance methods which use convex
regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV)
on the test phantom images. The results show that there are benefits in
using the nonconvex regularizer in the sparse-view CT reconstruction.
of important problems in computed tomography (CT) limited by
the availability or feasibility of obtaining of a large number of
projections. Traditionally, convex regularizers have been exploited
to improve the reconstruction quality in sparse-view CT, and the
convex constraint in those problems leads to an easy optimization
process. However, convex regularizers often result in a biased
approximation and inaccurate reconstruction in CT problems. Here,
we present a nonconvex, Lipschitz continuous and non-smooth
regularization model. The CT reconstruction is formulated as a
nonconvex constrained L1 − L2 minimization problem and solved
through a difference of convex algorithm and alternating direction
of multiplier method which generates a better result than L0 or L1
regularizers in the CT reconstruction. We compare our method with
previously reported high performance methods which use convex
regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV)
on the test phantom images. The results show that there are benefits in
using the nonconvex regularizer in the sparse-view CT reconstruction.
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References
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