Text controllable PET denoising
This addresses noise issues in PET imaging for medical diagnostics, though it appears incremental as it adapts existing methods like CLIP and U-Net.
The study tackled the problem of noise in PET images by proposing a text-guided denoising method that enhances images across various count levels, resulting in significant improvements in qualitative and quantitative assessments.
Positron Emission Tomography (PET) imaging is a vital tool in medical diagnostics, offering detailed insights into molecular processes within the human body. However, PET images often suffer from complicated noise, which can obscure critical diagnostic information. The quality of the PET image is impacted by various factors including scanner hardware, image reconstruction, tracer properties, dose/count level, and acquisition time. In this study, we propose a novel text-guided denoising method capable of enhancing PET images across a wide range of count levels within a single model. The model utilized the features from a pretrained CLIP model with a U-Net based denoising model. Experimental results demonstrate that the proposed model leads significant improvements in both qualitative and quantitative assessments. The flexibility of the model shows the potential for helping more complicated denoising demands or reducing the acquisition time.