QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction

• URL: http://arxiv.org/abs/2402.17951v3
• Date: Thu, 28 Mar 2024 21:29:56 GMT
• Title: QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction
• Authors: Ishak Ayad, Nicolas Larue, Maï K. Nguyen,
• Abstract summary: We introduce QN-Mixer, an algorithm based on the quasi-Newton approach. Incept-Mixer is an efficient neural architecture that serves as a non-local regularization term. Our approach intelligently downsamples information, significantly reducing computational requirements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inverse problems span across diverse fields. In medical contexts, computed tomography (CT) plays a crucial role in reconstructing a patient's internal structure, presenting challenges due to artifacts caused by inherently ill-posed inverse problems. Previous research advanced image quality via post-processing and deep unrolling algorithms but faces challenges, such as extended convergence times with ultra-sparse data. Despite enhancements, resulting images often show significant artifacts, limiting their effectiveness for real-world diagnostic applications. We aim to explore deep second-order unrolling algorithms for solving imaging inverse problems, emphasizing their faster convergence and lower time complexity compared to common first-order methods like gradient descent. In this paper, we introduce QN-Mixer, an algorithm based on the quasi-Newton approach. We use learned parameters through the BFGS algorithm and introduce Incept-Mixer, an efficient neural architecture that serves as a non-local regularization term, capturing long-range dependencies within images. To address the computational demands typically associated with quasi-Newton algorithms that require full Hessian matrix computations, we present a memory-efficient alternative. Our approach intelligently downsamples gradient information, significantly reducing computational requirements while maintaining performance. The approach is validated through experiments on the sparse-view CT problem, involving various datasets and scanning protocols, and is compared with post-processing and deep unrolling state-of-the-art approaches. Our method outperforms existing approaches and achieves state-of-the-art performance in terms of SSIM and PSNR, all while reducing the number of unrolling iterations required.

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