ArticleMedical physics2024
Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.
Article in Medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 6 citations in OpenAlex.
- Impact of reconstruction kernel variability on segmentation consistency in low-dose thoracic CT.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation.PloS one · 2026Article
- Multidimensional modeling and stratification of nutritional risk in non-small cell lung cancer based on artificial intelligence-derived CT body composition phenotypes.Frontiers in nutrition · 2026Article
- Application of multi-scale dynamic enhancement based on deep neural network and CT urinary tract secretory phase image fusion in the diagnosis of urinary system diseases.BMC medical imaging · 2025Article
- Evaluation of unified harmonization of CT images across multiple tasks: A step towards AI generalizability.Medical physics · 2025Article
- Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels.Medical physics · 2025Article
- A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction.Physics in medicine and biology · 2025Article
- Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Differential Radiomics-Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort.Cancer medicine · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT-based assessment as kernel differences can lead to substantial shifts in measurements unrelated to underlying anatomical structures. PURPOSE: In this study, we investigate kernel harmonization in a multi-vendor low-dose CT lung cancer screening cohort and evaluate our approach's validity in quantitative CT-based assessments.
methodsUsing the National Lung Screening Trial, we identified CT scan pairs of the same sessions with one reconstructed from a soft tissue kernel and one from a hard kernel. In total, 1000 pairs of five different paired kernel types (200 each) were identified. We adopt the pix2pix architecture to train models for kernel conversion. Each model was trained on 100 pairs and evaluated on 100 withheld pairs. A total of 10 models were implemented. We evaluated the efficacy of kernel conversion based on image similarity metrics including root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) as well as the capability of the models to reduce measurement shifts in quantitative emphysema and body composition measurements. Additionally, we study the reproducibility of standard radiomic features for all kernel pairs before and after harmonization.
resultsOur approach effectively converts CT images from one kernel to another in all paired kernel types, as indicated by the reduction in RMSE (p < 0.05) and an increase in the PSNR (p < 0.05) and SSIM (p < 0.05) for both directions of conversion for all pair types. In addition, there is an increase in the agreement for percent emphysema, skeletal muscle area, and subcutaneous adipose tissue (SAT) area for both directions of conversion. Furthermore, radiomic features were reproducible when compared with the ground truth features.
conclusionsKernel conversion using deep learning reduces measurement variation in percent emphysema, muscle area, and SAT area.
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