Salinas C S, Magudia K, Sangal A, Ren L, Segars W P
Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Duke University, United States of America.
Department of Radiology, Duke University Medical Center, United States of America.
Biomed Phys Eng Express. 2025 Jul 10;11(4). doi: 10.1088/2057-1976/ade9c9.
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.
当前的虚拟成像体模主要强调解剖结构的几何准确性。然而，为了提高真实感，纳入器官内部细节也很重要。由于生物组织的成分是异质的，虚拟体模应通过包含逼真的器官内部纹理和材料变化来反映这一点。我们建议训练两个3D双U-Net条件生成对抗网络（3D DUC-GAN），以生成16种独特的纹理，这些纹理涵盖了躯干内的器官。该模型在从一个公开可用数据集中获取的378对CT图像分割对上进行训练，另外保留18对用于测试。使用虚拟CT模拟平台DukeSim生成并成像有纹理的体模。结果表明，深度学习模型能够从一组均匀体模中合成逼真的异质体模。将这些体模与原始CT扫描进行比较，平均绝对差为46.15±1.06 HU。结构相似性指数（SSIM）和峰值信噪比（PSNR）分别为0.86±0.004和28.62±0.14。生成的分布与实际分布之间的最大平均差异为0.0016。与当前的均匀纹理方法相比，这些指标分别提高了27%、5.9%、6.2%和28%。与之前的方法相比，经过虚拟CT扫描生成的体模在视觉上与真实CT扫描更相似。由此产生的异质体模朝着更逼真的计算机模拟试验迈出了重要一步，能够以更高的保真度增强对成像程序的模拟，以反映真实的解剖变异。
