In-silico CT simulations of deep learning generated heterogeneous phantoms

Current virtual imaging phantoms primarily emphasize geometric accuracy of anatomical structures. However, to enhance realism, it is also important to incorporate intra-organ detail. Because biological tissues are heterogeneous in composition, virtual phantoms should reflect this by including realistic intra-organ texture and material variation. We propose training two 3D Double U-Net conditional generative adversarial networks (3D DUC-GAN) to generate sixteen unique textures that encompass organs found within the torso. The model was trained on 378 CT image-segmentation pairs taken from a publicly available dataset with 18 additional pairs reserved for testing. Textured phantoms were generated and imaged using DukeSim, a virtual CT simulation platform. Results showed that the deep learning model was able to synthesize realistic heterogeneous phantoms from a set of homogeneous phantoms. These phantoms were compared with original CT scans and had a mean absolute difference of 46.15 ± 1.06 HU. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were 0.86 ± 0.004 and 28.62 ± 0.14, respectively. The maximum mean discrepancy between the generated and actual distribution was 0.0016. These metrics marked an improvement of 27%, 5.9%, 6.2%, and 28% respectively, compared to current homogeneous texture methods. The generated phantoms that underwent a virtual CT scan had a closer visual resemblance to the true CT scan compared to the previous method. The resulting heterogeneous phantoms offer a significant step toward more realistic in silico trials, enabling enhanced simulation of imaging procedures with greater fidelity to true anatomical variation.