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This paper introduces PLOT-CT, a novel framework for low-dose CT reconstruction that operates in the pre-log domain using Voronoi decomposition to address noise amplification from logarithmic transformation. The method decomposes pre-log sinograms into distinct components embedded in separate latent spaces, enhancing feature learning and noise mitigation. Experiments demonstrate that PLOT-CT achieves state-of-the-art performance, with a 2.36dB PSNR improvement over existing methods at the 1e4 incident photon level.
Achieve state-of-the-art low-dose CT reconstruction by operating directly on pre-log measurements, sidestepping noise amplification from log transformations.
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.