(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose: Ventilation imaging modalities such as radioactive aerosols PET and SPECT, and hyperpolarized gas MRI, are not widely available at many institutions. In contrast, 4DCT images are part of standard treatment planning for lung malignances and contain characteristics that reflect changes in air content of the lungs due to ventilation. The purpose of this work was to develop a voxel-based delta radiomic feature extraction process using 4DCT images to quantify lung ventilation.
Methods: Twenty-five patients from the VAMPIRE dataset were used in this study with 4DCT/Galligas 4DPET images. For each patient, end-of-exhalation (EOE) and end-of-inhalation (EOI) phase CT images were both registered to the average phase CT using a contour-based deformable image registration algorithm. Next, 62 radiomic features were extracted spatially throughout the lungs using a sliding-window technique. The resulting tensor images were extracted to create 62 delta radiomic feature maps. Delta feature maps were compared with corresponding Galligas PET images by calculating spearman correlation, mutual information. Delta feature distributions in clinical defect and non-defect lung regions were compared. The effect of sliding window kernel size was characterized to investigate its impact on correlation with Galligas PET.
Results: The best agreement between delta feature maps and Galligas PET images using a 5x5x5cm3 kernel was obtained by first-order energy, which demonstrates a mean spearman correlation of r(s)=0.45±0.16. Other highly correlated filtered images were of features designed to capture high gray level intensities. Correlations with Galligas PET were found to increase and then saturate with increasing kernel size.
Conclusion: We have developed a promising method to quantify lung ventilation using voxel-based delta radiomics extracted from thoracic 4DCT. The results were comparable with a HU-based CT ventilation imaging (CTVI) method. Voxel-based radiomics is a potentially useful technique that can be used to generate synthetic ventilation images from standard-of-care image data.
Keywords
Ventilation/perfusion, Feature Extraction, Quantitative Imaging
Taxonomy
IM/TH- Image Analysis Skills (broad expertise across imaging modalities): Imaging biomarkers
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