Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization

• URL: http://arxiv.org/abs/2605.25348v1
• Date: Mon, 25 May 2026 02:04:29 GMT
• Title: Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization
• Abstract summary: This work investigates whether graph-based regularization can provide meaningful noise reduction under strict resource constraints.<n>We propose Deep Graph Laplacian Regularization (Deep GLR), integrating graph regularization into a Proximal Forward-Backward Splitting optimization framework.<n> evaluated on the LoDoPaB-CT benchmark, Deep GLR achieves 30.70 dB PSNR, representing a 6.33 dB improvement over filtered backprojection.
• Score: 3.9023730200298203
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Low-dose computed tomography (LDCT) reconstruction faces a critical tradeoff between reconstruction quality and resource requirements. While recent deep learning methods achieve state-of-the-art performance, they typically rely on over 500,000 parameters trained on large-scale datasets exceeding 35,000 scans. This work investigates whether graph-based regularization can provide meaningful noise reduction under strict resource constraints. We propose Deep Graph Laplacian Regularization (Deep GLR), integrating quadratic graph regularization into a Proximal Forward-Backward Splitting optimization framework with three lightweight CNN modules. Evaluated on the LoDoPaB-CT benchmark, Deep GLR achieves 30.70 dB PSNR, representing a 6.33 dB improvement over filtered backprojection, while using only 91,848 parameters trained on 1000 samples (2.8\% of standard training set). Compared to benchmark methods, this represents 5.8 times better parameter efficiency and 30 times better data efficiency per dB improvement. The learned graph bandwidth parameter ($ε$=1.25) converges to interpretable values, suggesting the method captures meaningful image priors rather than overfitting. While a 13 dB gap remains versus state-of-the-art methods, results demonstrate that graph-based regularization provides a favorable efficiency-quality tradeoff for resource-constrained medical imaging scenarios.

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