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New self-supervised methods improve low-dose CT denoising for medical imaging

By PulseAugur Editorial · [2 sources] ·

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

IMPACT Advances in self-supervised denoising could enable safer medical imaging by reducing radiation dose without sacrificing image quality.

RANK_REASON Two arXiv papers detail novel self-supervised learning methods for low-dose CT denoising.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New self-supervised methods improve low-dose CT denoising for medical imaging

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yichao Liu, Zongru Shao, Yueyang Teng, Junwen Guo ·

    Progressive $\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising

    arXiv:2601.14180v3 Announce Type: replace Abstract: Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to collect. How…

  2. arXiv cs.CV TIER_1 English(EN) · Johannes B. Thalhammer, Lorenzo D'Amico, Lucy Costello, Sebastian Peterhansl, Daniel Frey, Tina Dorosti, Florian Schaff, Jannis Ahlers, Ronan Smith, Marcus Kitchen, Franz Pfeiffer, Martin Donnelley, Daniela Pfeiffer, Kaye S. Morgan ·

    Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT

    arXiv:2605.01075v1 Announce Type: new Abstract: Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However, safe translation to the clinic will require a substantial radiation dose reduct…

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