通过预对数域的Voronoi分解，提升低剂量CT重建的清晰度和精度。
PLOT-CT: Pre-log Voronoi Decomposition Assisted Generation for Low-dose CT Reconstruction
- 用Voronoi分解将预对数投影数据拆分为不同成分，分入独立隐空间。
- 在1万入射光子水平下，比传统方法提升2.36dB的PSNR。
- 适合需要高保真低剂量CT图像的医学影像研究者使用。
低剂量计算机断层扫描（LDCT）重建因辐射减少导致噪声严重、数据保真度下降而面临根本性挑战。现有方法多在图像域或后对数投影域操作，未能充分利用预对数测量中的丰富结构信息，且极易受噪声影响。对数变换会显著放大这些数据中的噪声，对重建精度提出极高要求。为此，我们提出PLOT-CT，一种基于预对数域Voronoi分解的生成式重建框架。该方法首先对预对数投影图进行Voronoi分解，将数据解耦为不同潜在成分，并分别嵌入独立隐空间。这种显式分解显著增强模型学习判别特征的能力，有效抑制噪声并保留预对数域固有信息，从而直接提升重建精度。大量实验表明，PLOT-CT在预对数域1×10⁴入射光子水平下，相较传统方法实现2.36dB的PSNR提升，达到当前最优性能。
原文摘要 · Abstract (English)
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.
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