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

• URL: http://arxiv.org/abs/2105.14758v1
• Date: Mon, 31 May 2021 07:42:21 GMT
• Title: Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network
• Authors: Lu Xu, Yuwei Zhang, Ying Liu, Daoye Wang, Mu Zhou, Jimmy Ren, Zhaoxiang Ye
• Abstract summary: CNN-based approaches treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises. We propose a Structure-preserving Kernel Prediction Network (StructKPN) that combines the kernel prediction network with a structure-aware loss function. 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.
• Score: 10.09577595969254
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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