  Objectives: Computerized phantoms play an essential role in various
applications of medical imaging research. Although the existing computerized
phantoms can model anatomical variations through organ and phantom scaling,
this does not provide a way to fully reproduce anatomical variations seen in
humans. However, having a population of phantoms that models the variations in
patient anatomy and, in nuclear medicine, uptake realization is essential for
comprehensive validation and training. In this work, we present a novel image
registration method for creating highly anatomically detailed anthropomorphic
phantoms from a single digital phantom. Methods: We propose a
deep-learning-based registration algorithm to predict deformation parameters
for warping an XCAT phantom to a patient CT scan. This proposed algorithm
optimizes a novel SSIM-based objective function for a given image pair
independently of the training data and thus is truly and fully unsupervised. We
evaluate the proposed method on a publicly available low-dose CT dataset from
TCIA. Results: The performance of the proposed model was compared with that of
several state-of-the-art methods, and outperformed them by more than 8%,
measured by the SSIM and less than 30%, by the MSE. Conclusion: A
deep-learning-based unsupervised registration method was developed to create
anthropomorphic phantoms while providing "gold-standard" anatomies that can be
used as the basis for modeling organ properties. Significance: Experimental
results demonstrate the effectiveness of the proposed method. The resulting
anthropomorphic phantom is highly realistic. Combined with realistic
simulations of the image formation process, the generated phantoms could serve
in many applications of medical imaging research.
