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Title: Learned Alternating Minimization Algorithm for Dual-Domain Sparse-View CT Reconstruction
We propose a novel Learned Alternating Minimization Algorithm (LAMA) for dual-domain sparse-view CT image reconstruction. LAMA is naturally induced by a variational model for CT reconstruction with learnable nonsmooth nonconvex regularizers, which are parameterized as composite functions of deep networks in both image and sinogram domains. To minimize the objective of the model, we incorporate the smoothing technique and residual learning architecture into the design of LAMA. We show that LAMA substantially reduces network complexity, improves memory efficiency and reconstruction accuracy, and is provably convergent for reliable reconstructions. Extensive numerical experiments demonstrate that LAMA outperforms existing methods by a wide margin on multiple benchmark CT datasets.  more » « less
Award ID(s):
2152961
NSF-PAR ID:
10484457
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Springer, Cham.
Date Published:
Journal Name:
Lecture Notes in Computer Science, vol 14229
ISSN:
1611-3349
Format(s):
Medium: X
Location:
Medical Image Computing and Computer Assisted Intervention (MICCA2023), Vancouver/CANADA
Sponsoring Org:
National Science Foundation
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