In-silico CT simulations of deep learning generated heterogeneous phantoms.
Authors
Affiliations (4)
Affiliations (4)
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories,, Duke University, Suite 302 Hock Plaza, 2424 Erwin Road, Durham, NC 27705, USA, Durham, North Carolina, 27708-0187, UNITED STATES.
- Department of Radiology, Duke University Medical Center, Department of Radiology, 3808 DUMC, Durham, NC 27710, Durham, North Carolina, 27710-1000, UNITED STATES.
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201, United States, Baltimore, Maryland, 21201, UNITED STATES.
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Duke University, Suite 302 Hock Plaza, 2424 Erwin Road, Durham, NC 27705, USA, Durham, North Carolina, 27708-0187, UNITED STATES.
Abstract
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.