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.

翻译：低剂量计算机断层扫描（LDCT）重建面临的核心挑战在于辐射剂量降低导致的严重噪声和数据保真度下降。现有方法大多在图像域或后对数投影域进行处理，未能充分利用预对数测量中的丰富结构信息，且对噪声高度敏感。必需的对数变换会显著放大数据中的噪声，对重建精度提出了极高要求。为克服这些挑战，本文提出PLOT-CT——一种基于预对数Voronoi分解的CT生成新框架。该方法首先对预对数正弦图进行Voronoi分解，将数据解耦为不同的基础分量并嵌入独立隐空间。这种显式分解显著增强了模型学习判别性特征的能力，通过抑制噪声并保留预对数域的固有信息，直接提升了重建精度。大量实验表明，PLOT-CT在预对数域1e4入射光子水平下，相比传统方法实现了2.36dB的PSNR提升，达到了最先进的性能水平。