Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network [article]

Lu Xu, Yuwei Zhang, Ying Liu, Daoye Wang, Mu Zhou, Jimmy Ren, Jingwei Wei, Zhaoxiang Ye
2021 arXiv   pre-print
Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health. Despite recent advances, CNN-based approaches typically apply filters in a spatially invariant way and adopt similar pixel-level losses, which treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises. To address this issue, we propose a Structure-preserving Kernel Prediction Network
more » ... PN) that combines the kernel prediction network with a structure-aware loss function that utilizes the pixel gradient statistics and guides the model towards spatially-variant filters that enhance noise removal, prevent over-smoothing and preserve detailed structures for different regions in CT imaging. Extensive experiments demonstrated that our approach achieved superior performance on both synthetic and non-synthetic datasets, and better preserves structures that are highly desired in clinical screening and low-dose protocol optimization.
arXiv:2105.14758v3 fatcat:nlciihoyszealfxmhnmlan3qim