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ECR 2025

Foundation Model-based Unsupervised CT Kernel Conversion for Standardizing Emphysema Quantification

  • Mar. 2025

Purpose or Learning Objective

Emphysema quantification is crucial for evaluation and management of chronic obstructive pulmonary disease (COPD). Typically, emphysema is identified in computed tomography (CT) images reconstructed with smooth kernels. However, CT reconstruction kernels vary, and raw data are often deleted after reconstruction, making it hard to adjust the kernel retrospectively. Therefore, this study aims to develop and validate a method for kernel conversion to standardize emphysema quantification using a foundational deep learning model.

 

Methods or Background

Paired CT images from nine cases reconstructed with different kernels were used. Automated lung segmentation was performed using TotalSegmentator, a foundational deep learning model. An unsupervised kernel conversion method was then applied to transform the images to a pre-defined kernel. The kernel conversion was evaluated by comparing the emphysema score (ES), defined as the ratio of regions with HU below -950 within the lung area, before and after the conversion.

 

Results or Findings

Before kernel conversion, the mean ES difference between images reconstructed with smoother kernels (ex: B30f and STANDARD) and those with sharper kernels (ex: B60f and LUNG) was 11.00±6.85%. After conversion to the target smooth kernel, the mean ES difference was reduced to 2.30±2.65%. Although the sample size was small, this reduction was statistically significant based on a paired t-test (p=0.011).

 

Conclusion

The foundational model enables the conversion of CT images reconstructed with different kernels to a target smooth kernel, allowing for standardized emphysema quantification without the need for additional datasets for model development. This result suggests that the approach can be easily used by anyone with the appropriate software.

 

Limitations

For more rigorous validation, it is necessary to not only compare the difference of ES before and after kernel conversion but also comparative evaluation on ground-truth emphysema masks.

 

Funding for this study

Not applicable.

Link

https://connect.myesr.org/?esrc_course=artificial-intelligence-in-chest-imaging

Author

Doohyun Park, Jung-Hyun Kang, Jonghun Jeong

Tag in

#medical_image

#VUNO Med®-LungCT AI™

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