Noise Controlled CT Super-Resolution with Conditional Diffusion Model
This work addresses noise control in CT super-resolution for medical imaging, but it appears incremental as it builds on existing diffusion models with hybrid data.
The paper tackled the problem of noise amplification in CT image super-resolution by proposing a conditional diffusion model trained on hybrid datasets, and experimental results validated its effectiveness for practical CT imaging applications.
Improving the spatial resolution of CT images is a meaningful yet challenging task, often accompanied by the issue of noise amplification. This article introduces an innovative framework for noise-controlled CT super-resolution utilizing the conditional diffusion model. The model is trained on hybrid datasets, combining noise-matched simulation data with segmented details from real data. Experimental results with real CT images validate the effectiveness of our proposed framework, showing its potential for practical applications in CT imaging.