PLOT-CT: Pre-log Voronoi Decomposition Assisted Generation for Low-dose CT Reconstruction
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Abstract (arXiv)
Low-dose computed tomography (LDCT) reconstruction is fundamentally challenged by severe noise and compromised data fidelity under reduced radiation exposure. Most existing methods operate either in the image or post-log projection domain, which fails to fully exploit the rich structural information in pre-log measurements while being highly susceptible to noise. The requisite logarithmic transformation critically amplifies noise within these data, imposing exceptional demands on reconstruction precision. To overcome these challenges, we propose PLOT-CT, a novel framework for Pre-Log vOronoi decomposiTion-assisted CT generation. Our method begins by applying Voronoi decomposition to pre-log sinograms, disentangling the data into distinct underlying components, which are embedded in separate latent spaces. This explicit decomposition significantly enhances the model's capacity to learn discriminative features, directly improving reconstruction accuracy by mitigating noise and preserving information inherent in the pre-log domain. Extensive experiments demonstrate that PLOT-CT achieves state-of-the-art performance, attaining a 2.36dB PSNR improvement over traditional methods at the 1e4 incident photon level in the pre-log domain.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 12 Feb 2026.
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References · 11
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
- 2016A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstructioncited by 87
- 2018Denoising Prior Driven Deep Neural Network for Image Restorationcited by 80
- 2018Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoisingcited by 22
- 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstructioncited by 11
- 2019Low-Dose CT with Deep Learning Regularization via Proximal Forward Backward Splittingcited by 9
- 2023Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignmentcited by 4
- 2024WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusioncited by 2
- 2024Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoiserscited by 1
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