用于配对与非配对低剂量肺部计算机断层扫描重建核和谐化的多路径cycleGAN
Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels
AI总结：
提出一种基于共享潜在空间的多路径cycleGAN模型，通过混合配对与非配对低剂量CT数据进行核和谐化，有效减少肺气肿量化偏差并保持解剖保真度。
摘要：
计算机断层扫描（CT）中的重建核会影响空间分辨率和噪声特性，在肺气肿量化等定量成像测量中引入系统性变异。因此，选择合适的核对于一致的定量分析至关重要。我们提出了一种用于CT核和谐化的多路径cycleGAN模型，该模型在来自低剂量肺癌筛查队列的配对和非配对混合数据上进行训练。该模型采用具有共享潜在空间的特定域编码器和解码器，并使用为每个域量身定制的判别器。我们使用National Lung Screening Trial (NLST) 数据集中七种代表性核的各100次扫描，在42种核组合上训练该模型。为评估性能，将每种核的240次扫描和谐化为参考软核，并在和谐化前后对肺气肿进行量化。采用一般线性模型评估年龄、性别、吸烟状况和核对肺气肿的影响。我们还评估了从软核到参考硬核的和谐化。为评估解剖一致性，我们比较了TotalSegmentator在和谐化图像与原始图像之间生成的肺血管、肌肉和皮下脂肪组织的分割结果。我们的模型与传统cycleGAN和可切换cycleGAN进行了基准测试。对于配对核，我们的方法减少了肺气肿评分的偏差，如Bland-Altman图所示（p<0.05）。对于非配对核，和谐化消除了肺气肿的混杂差异（p>0.05）。高Dice分数证实了对肌肉和脂肪解剖结构的保留，同时肺血管重叠度保持合理。总体而言，我们具有共享潜在空间的多路径cycleGAN实现了跨配对与非配对CT核的稳健和谐化，改善了肺气肿量化并保持了解剖保真度。
英文摘要：
Reconstruction kernels in computed tomography (CT) affect spatial resolution and noise characteristics, introducing systematic variability in quantitative imaging measurements such as emphysema quantification. Choosing an appropriate kernel is therefore essential for consistent quantitative analysis. We propose a multipath cycleGAN model for CT kernel harmonization, trained on a mixture of paired and unpaired data from a low-dose lung cancer screening cohort. The model features domain-specific encoders and decoders with a shared latent space and uses discriminators tailored for each domain.We train the model on 42 kernel combinations using 100 scans each from seven representative kernels in the National Lung Screening Trial (NLST) dataset. To evaluate performance, 240 scans from each kernel are harmonized to a reference soft kernel, and emphysema is quantified before and after harmonization. A general linear model assesses the impact of age, sex, smoking status, and kernel on emphysema. We also evaluate harmonization from soft kernels to a reference hard kernel. To assess anatomical consistency, we compare segmentations of lung vessels, muscle, and subcutaneous adipose tissue generated by TotalSegmentator between harmonized and original images. Our model is benchmarked against traditional and switchable cycleGANs. For paired kernels, our approach reduces bias in emphysema scores, as seen in Bland-Altman plots (p<0.05). For unpaired kernels, harmonization eliminates confounding differences in emphysema (p>0.05). High Dice scores confirm preservation of muscle and fat anatomy, while lung vessel overlap remains reasonable. Overall, our shared latent space multipath cycleGAN enables robust harmonization across paired and unpaired CT kernels, improving emphysema quantification and preserving anatomical fidelity.
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