Published on: March 18, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2403.11672

Summary

• What is new: Introduction of WIA-LD2ND for LDCT denoising using only NDCT data.
• Why this is important: Reduced signal-to-noise ratio in low-dose CT images leading to degraded quality.
• What the research proposes: A novel self-supervised CT image denoising method, WIA-LD2ND, using NDCT data.
• Results: Outperforms existing state-of-the-art methods in LDCT denoising.

Technical Details

Technological frameworks used: nan

Models used: WIA-LD2ND, consisting of Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)

Data used: Two public LDCT denoising datasets

Potential Impact

Healthcare technology, medical imaging companies, and potentially companies focused on computational efficiency in imaging.

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