Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels

2025-05-28

AI Summary

CT reconstruction kernel differences introduce systematic bias in pulmonary quantitative analysis—e.g., emphysema scoring—particularly under low-dose scanning, compromising clinical comparability across scanners and protocols. To address this, we propose a multi-path cycleGAN framework with a shared latent space, the first to jointly model both paired and unpaired cross-kernel CT data translation. Our method incorporates kernel-specific encoders/decoders, a multi-discriminator architecture, and anatomical fidelity constraints guided by TotalSegmentator segmentation. Experiments demonstrate statistically significant reduction in emphysema score variability (p < 0.05) and elimination of confounding inter-kernel differences in unpaired settings (p > 0.05). Muscle and fat segmentation achieves Dice coefficients > 0.9; vascular structure overlap remains anatomically plausible. The approach outperforms conventional and switchable cycleGAN baselines in both quantitative accuracy and anatomical consistency.

Technology Category

• Machine Learning: Kernel Methods
• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Harmonizing CT reconstruction kernels to reduce variability in quantitative imaging.
• Improving emphysema quantification consistency across different kernel types.
• Preserving anatomical fidelity while harmonizing paired and unpaired CT data.

Innovation

• Multipath cycleGAN for CT kernel harmonization
• Shared latent space with domain-specific encoders
• Handles both paired and unpaired data effectively