Development of a Voxel-Based RadiomicsCalculation Platform for Medical Image Analysis

Purpose: To develop a novel voxel-based radiomics extraction technique, and to investigate the potential association between spatially-encoded radiomics features of the lungs and pulmonary function.

Methods: We developed a voxel-based radiomics feature extraction platform to generate radiomics filtered images. Specifically, for each voxel in the image, 62 radiomics features were calculated in a rotationally-invariant 3D neighbourhood to capture spatially-encoded information. In general, such an approach results in an image tensor object, i.e., each voxel in the original image is represented by a 62-dimensional radiomics feature vector. Two digital phantoms are then designed to validate the technique's ability to quantify regional image information. To test the technique as a potential pulmonary biomarker, we generated radiomics filtered images for 25 lung CT image and are subsequently evaluated against corresponding Galligas PET images, as the ground truth for pulmonary function, using voxel-wise Spearman correlation (r). The Canonical Correlation Analysis (CCA)-based feature fusion method is also implemented to enhance such a correlation. Finally, the Spearman distributions were compared with 37 individual CT ventilation image (CTVI) algorithms to assess the overall performance relative to conventional CT-based techniques.

Results: Several radiomics filtered images were identified to be correlated with Galligas PET lung imaging. The most robust association was found to be the Run Length Encoding feature, Run-Length Non-uniformity (0.21

Conclusions: This preliminary study indicates that spatially-encoded lung texture and lung density are potentially associated with pulmonary function as measured via Galligas PET ventilation images. Collectively, low density, heterogeneous coarse lung texture was often associated with lower Galligas radiotracer amounts.