# Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization

Source: [Parameter-Efficient CT Reconstruction via Deep Graph Laplacian Regularization](<https://www.sota2.com/research/paper/6a152964dbd7150506f7e321>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-27T05:00:13.467Z

Authors: Veera Varuni Radhakrishnan, Chinthaka Dinesh, Qurat-ul-Ain Azim

Published: 2026-05-25T02:04:29.000Z

## 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 ($\\epsilon$=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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [CT Reconstruction on LoDoPaB-CT (val)](<https://www.sota2.com/research/sota/ct-reconstruction-on-lodopab-ct-val.md>): CT Reconstruction; LoDoPaB-CT (val); PSNR (dB): 30.7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.25348>)

[PDF](<https://arxiv.org/pdf/2605.25348.pdf>)