Billings Nicola, Appleby Ryan, Komeili Amin, Poirier Valerie, Pinard Christopher, Ukwatta Eranga
Department of Engineering, College of Engineering and Physical Sciences, University of Guelph, Guelph, ON, Canada.
Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
Am J Vet Res. 2025 Mar 17;86(6). doi: 10.2460/ajvr.24.08.0248. Print 2025 Jun 1.
The purpose of this research was to examine the feasibility of utilizing generative adversarial networks (GANs) to generate accurate pseudo-CT images for dogs.
This study used head standard CT images and T1-weighted transverse with contrast 3-D fast spoiled gradient echo head MRI images from 45 nonbrachycephalic dogs that received treatment between 2014 and 2023. Two conditional GANs (CGANs), one with a U-Net generator and a PatchGAN discriminator and another with a residual neural network (ResNet) U-Net generator and ResNet discriminator were used to generate the pseudo-CT images.
The CGAN with a ResNet U-Net generator and ResNet discriminator had an average mean absolute error of 109.5 ± 153.7 HU, average peak signal-to-noise ratio of 21.2 ± 4.31 dB, normalized mutual information of 0.89 ± 0.05, and dice similarity coefficient of 0.91 ± 0.12. The dice similarity coefficient for the bone was 0.71 ± 0.17. Qualitative results indicated that the most common ranking was "slightly similar" for both models. The CGAN with a ResNet U-Net generator and ResNet discriminator produced more accurate pseudo-CT images than the CGAN with a U-Net generator and PatchGAN discriminator.
The study concludes that CGAN can generate relatively accurate pseudo-CT images but suggests exploring alternative GAN extensions.
Implementing generative learning into veterinary radiation therapy planning demonstrates the potential to reduce imaging costs and time.
