Data Fusion Approach for Constructing Unsupervised Augmented Voxel-Based Statistical Anthropomorphic Phantoms

Approaches to constructing statistical phantoms (SPs) for the needs of data fusion with the real data have attracted a lot of attention in the recent years. SPs assist with radiation dosimetry and provide metrics for estimating image quality in the medical X-ray systems. Unlike existing SPs, we propose to consider both material electron densities and anatomical size variations models to portray realistic variations in radiologic data. We introduce a new adaptive unsupervised fusion approach for data augmentation, capable of generating a variety of medically adequate images. The model is based on a combination of continuous Poisson modelling of voxel values, Monte Carlo rejection sampling scheme, and a landmark-based warping. Unlike mere average of HU values of each organ (typical in the other state-of-art SPs), our augmented voxels depict intensity fluctuations, effectively mimicking a distribution of electron densities within each organ. In the experimental section, we evaluate the proposed method and demonstrate its superiority compared to the existing methods. This phantom generation could be instrumental for assessing dose uncertainty, unsupervised refinement of image reconstruction, image classification, and semantic segmentation tasks carried out by machine learning algorithms in the scenarios of limited available data.