LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT

• URL: http://arxiv.org/abs/2410.21111v1
• Date: Mon, 28 Oct 2024 15:13:04 GMT
• Title: LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT
• Authors: Chi Ding, Qingchao Zhang, Ge Wang, Xiaojing Ye, Yunmei Chen,
• Abstract summary: We develop a Learned Alternating Minimization Algorithm (LAMA) to solve problems via two-block optimization. LAMA is naturally induced as a variational model with learnable regularizers in both data and image domains. It is demonstrated that LAMA reduces network complexity, memory efficiency, and reconstruction accuracy, stability, and interpretability.
• Score: 4.573246328161056
• License:
• Abstract: Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block optimization by synergizing data-driven and classical techniques with proven convergence. LAMA is naturally induced by a variational model with learnable regularizers in both data and image domains, parameterized as composite functions of neural networks trained with domain-specific data. We allow these regularizers to be nonconvex and nonsmooth to extract features from data effectively. We minimize the overall objective function using Nesterov's smoothing technique and residual learning architecture. It is demonstrated that LAMA reduces network complexity, improves memory efficiency, and enhances reconstruction accuracy, stability, and interpretability. Extensive experiments show that LAMA significantly outperforms state-of-the-art methods on popular benchmark datasets for Computed Tomography.

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