In-silico CT simulations of deep learning generated heterogeneous phantoms.

Authors

Salinas CS,Magudia K,Sangal A,Ren L,Segars PW

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.

Topics

Journal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.