  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 (StructKPN) 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.
