2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-1.6
Paper Title LOW-DOSE CT DENOISING USING A STRUCTURE-PRESERVING KERNEL PREDICTION NETWORK
Authors Lu Xu, The Chinese University of Hong Kong, Hong Kong SAR of China; Yuwei Zhang, Ying Liu, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, China; Daoye Wang, ETH Zurich, Switzerland; Mu Zhou, SenseBrain Technology Limited LLC, United States; Jimmy Ren, SenseTime Research; Qing Yuan Research Institute, Shanghai Jiao Tong University, Hong Kong SAR of China; Jingwei Wei, Institute of Automation, Chinese Academy of Sciences, China; Zhaoxiang Ye, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, China
SessionTEC-1: Restoration and Enhancement 1
LocationArea G
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.