Simulating scanner-and algorithm-specific 3D CT noise texture using physics-informed 2D and 2.5D generative neural network models.

Low-dose CT simulation is needed to assess reconstruction/denoising techniques and optimize dose. Projection-domain noise-insertion methods require manufacturers' proprietary tools. Image-domain noise-insertion methods face various challenges that affect generalizability, and few have been systematically validated for 3D noise synthesis. To improve generalizability, we presented a physics-informed model-based generative neural network for simulating scanner- and algorithm-specific low-dose CT exams (PALETTE). PALETTE included a noise-prior-generation process, a Noise2Noisier sub-network, and a noise-texture-synthesis sub-network. Custom regularization terms were developed to enforce 3D noise texture quality. Using PALETTE, one 2D and two 2.5D models (denoted as 2.5D N-N and N-1) were developed to conduct 2D and effective 3D noise modeling, respectively (input/output images: 2D - 1/1, 2.5D N-N - 3/3, 2.5D N-1 - 5/1). These models were trained and tested with an open-access abdominal CT dataset, including 20 testing cases reconstructed with two kernels and various field-of-view. In visual inspection, the 2D and 2.5D N-N models generated realistic local and global noise texture, while 2.5D N-1 showed more perceptual difference using the sharper kernel and coronal reformat. In quantitative evaluation, local noise level was compared using mean-absolute-percent-difference (MAPD), and global spectral similarity was assessed using spectral correlation mapper (SCM) and spectral angle mapper (SAM). The 2D model provided equivalent or relatively better performance than 2.5D models, showing well-matched local noise levels and high spectral similarity compared to the reference (sharper/smoother kernels): MAPD - 2D 1.5%/5.6% (p>0.05), 2.5D N-N 8.5%/7.9% (p<0.05), 2.5D N-1 12.3%/10.9% (p<0.05); mean SCM - 2D 0.97/0.97, 2.5D N-N 0.96/0.97, 2.5D N-1 0.85/0.97; mean SAM - 2D 0.12/0.12, 2.5D N-N 0.14/0.12, 2.5D N-1 0.37/0.12. With tripled model width, the 2.5D N-N outperformed N-1. This indicated 2.5D models need more learning capacity to further enhance 3D noise modeling. Using physics-based prior information, PALETTE can provide high-quality low-dose CT simulation to resemble scanner- and algorithm-specific 3D noise characteristics.