A super-voxel-based method for generating surrogate lung ventilation images from CT

Zhi Chen; Yu-Hua Huang; Feng-Ming Kong; Wai Yin Ho; Ge Ren; Jing Cai

doi:10.3389/fphys.2023.1085158

A super-voxel-based method for generating surrogate lung ventilation images from CT

Front Physiol. 2023 Apr 26:14:1085158. doi: 10.3389/fphys.2023.1085158. eCollection 2023.

Authors

Zhi Chen¹, Yu-Hua Huang¹, Feng-Ming Kong^{2

3}, Wai Yin Ho⁴, Ge Ren¹, Jing Cai¹

Affiliations

¹ Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
² Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China.
³ Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China.
⁴ Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, China.

Abstract

Purpose: This study aimed to develop and evaluate ${C T V I}_{S V D}$ , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D _mean) and mean ventilation values (Vent _mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D _mean values to yield ${C T V I}_{S V D}$ . For the performance evaluation, the voxel- and region-wise differences between ${C T V I}_{S V D}$ and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, ${C T V I}_{H U}$ and ${C T V I}_{J a c}$ , and compared with the SPECT images. Results: The correlation between the D _mean and Vent _mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the ${C T V I}_{S V D}$ method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the ${C T V I}_{H U}$ (0.33 ± 0.14, p < 0.05) and ${C T V I}_{J a c}$ (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for ${C T V I}_{S V D}$ (0.63 ± 0.07) was significantly higher than the corresponding values for the ${C T V I}_{H U}$ (0.43 ± 0.08, p < 0.05) and ${C T V I}_{J a c}$ (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between ${C T V I}_{S V D}$ and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

Keywords: 4DCT; lung cancer; radiotherapy; super-voxel; ventilation.

Grants and funding

This work was partly supported by the General Research Fund (GRF 15103520) from The University Grants Committee, and Health and Medical Research Fund (HMRF 07183266, HMRF 09200576) from The Health Bureau, The Government of the Hong Kong Special Administrative Regions.