Researchers have developed new self-supervised learning methods for denoising low-dose CT scans, a crucial step for reducing radiation exposure in medical imaging. One approach, Progressive $\mathcal{J}$-Invariant Learning, uses a step-wise mechanism and noise injection to improve denoising efficiency and performance, outperforming existing self-supervised methods on a Mayo LDCT dataset. Another method, Neighbor2Inverse, adapts the Neighbor2Neighbor principle for phase-contrast CT, creating denoising networks from subsampled projections to preserve structural details while suppressing noise, showing promise for both specialized and clinical CT applications. AI
影响 Advances in self-supervised denoising could enable safer medical imaging by reducing radiation dose without sacrificing image quality.
排序理由 Two arXiv papers detail novel self-supervised learning methods for low-dose CT denoising.
- arXiv
- CT
- Johannes Thalhammer
- Mayo LDCT dataset
- Neighbor2Inverse
- PBI-CT
- Progressive $\mathcal{J}$-Invariant Learning
- Yichao Liu
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