Low-dose CT imaging performance of U-Net for varied spatial resolutions

Abstract

For the U-Net based low dose CT (LDCT) imaging, there remains an interesting question: can the LDCT imaging neural network trained at one image resolution be transferred and applied directly onto another LDCT imaging application of different image resolution, provided that both the noise level and the structural content are similar? To answer this question, numerical simulations are performed with high-resolution (HR) and low-resolution (LR) LDCT images having comparable noise levels. Results demonstrated that the U-Net trained with LR CT images can be used to effectively reduce the noise on HR CT images, and vice versa. However, additional artifacts may be generated when transferring the same U-Net to a different LDCT imaging task with varied image spatial resolution due to the noise induced 2D features. For example, noticeable bright spots were generated at the edges of the FOV when the HR CT image is denoised by the LR CT image trained U-Net. In conclusion, this study suggests that it is necessary to retrain the U-Net for a dedicated LDCT imaging application.