Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909483132780544 |
|---|---|
| author | Krishnan, Aravind R. Liu, Yihao Xu, Kaiwen Kim, Michael E. Remedios, Lucas W. Rudravaram, Gaurav Saunders, Adam M. Richmond, Bradley W. Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Zuo, Lianrui |
| author_facet | Krishnan, Aravind R. Liu, Yihao Xu, Kaiwen Kim, Michael E. Remedios, Lucas W. Rudravaram, Gaurav Saunders, Adam M. Richmond, Bradley W. Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Zuo, Lianrui |
| contents | Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue motion in COPD patients. Deformable image registration of these scans assesses regional lung volumetric changes. However, variations in reconstruction kernels between paired scans introduce errors in quantitative analysis. This work proposes a two-stage pipeline to harmonize reconstruction kernels and perform deformable image registration using data acquired from the COPDGene study. We use a cycle generative adversarial network (GAN) to harmonize inspiratory scans reconstructed with a hard kernel (BONE) to match expiratory scans reconstructed with a soft kernel (STANDARD). We then deformably register the expiratory scans to inspiratory scans. We validate harmonization by measuring emphysema using a publicly available segmentation algorithm before and after harmonization. Results show harmonization significantly reduces emphysema measurement inconsistencies, decreasing median emphysema scores from 10.479% to 3.039%, with a reference median score of 1.305% from the STANDARD kernel as the target. Registration accuracy is evaluated via Dice overlap between emphysema regions on inspiratory, expiratory, and deformed images. The Dice coefficient between inspiratory emphysema masks and deformably registered emphysema masks increases significantly across registration stages (p<0.001). Additionally, we demonstrate that deformable registration is robust to kernel variations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD Krishnan, Aravind R. Liu, Yihao Xu, Kaiwen Kim, Michael E. Remedios, Lucas W. Rudravaram, Gaurav Saunders, Adam M. Richmond, Bradley W. Sandler, Kim L. Maldonado, Fabien Landman, Bennett A. Zuo, Lianrui Image and Video Processing Computer Vision and Pattern Recognition Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue motion in COPD patients. Deformable image registration of these scans assesses regional lung volumetric changes. However, variations in reconstruction kernels between paired scans introduce errors in quantitative analysis. This work proposes a two-stage pipeline to harmonize reconstruction kernels and perform deformable image registration using data acquired from the COPDGene study. We use a cycle generative adversarial network (GAN) to harmonize inspiratory scans reconstructed with a hard kernel (BONE) to match expiratory scans reconstructed with a soft kernel (STANDARD). We then deformably register the expiratory scans to inspiratory scans. We validate harmonization by measuring emphysema using a publicly available segmentation algorithm before and after harmonization. Results show harmonization significantly reduces emphysema measurement inconsistencies, decreasing median emphysema scores from 10.479% to 3.039%, with a reference median score of 1.305% from the STANDARD kernel as the target. Registration accuracy is evaluated via Dice overlap between emphysema regions on inspiratory, expiratory, and deformed images. The Dice coefficient between inspiratory emphysema masks and deformably registered emphysema masks increases significantly across registration stages (p<0.001). Additionally, we demonstrate that deformable registration is robust to kernel variations. |
| title | Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.05119 |