Enhancing Low-Count PET Image Quality via Unsupervised Deep Learning using Novel Cost Function – A PET/MRI study

Motivation: PET/MRI enables MR-assisted PET denoising for low-count PET. Recent work has demonstrated the potential of unsupervised deep learning (uDL) denoising method with the advantage of not requiring large amount of training data. We believe the performance of uDL denoising with MR guidance can be further improved. Goal(s): To demonstrate the efficacy of a novel cost function in enhancing the quality of denoised low-count PET images using uDL techniques. Approach: We utilized dNet with MRI as input and low-count PET as target, along with a novel cost function consisting of Bowsher prior and mean square error. Results: Our method outperformed all other denoising methods. Impact: The impact of the study is the potential for significant improvements in low-count PET image quality through advanced denoising techniques, which could enhance diagnostic accuracy while reducing radiation exposure for patients.