arXiv AI
Jul 31

PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

arXiv:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.

By Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski
Hugging Face Trending Papers
6d ago

Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching

Noise2Match is a self‑supervised method for low‑dose CT denoising that uses an adaptive inter‑slice matching network to model and correct the label transition between adjacent slices. By ensuring the equal‑mean condition of the Noise2Noise framework, it theoretically matches supervised training with paired noisy‑clean data. The approach also extends effectively to low‑dose cone‑beam CT, outperforming existing image‑domain self‑supervised methods.

arXiv Machine Learning
Sep 16

A deep dictionary network-based foundation model for ultra-low-dose CT denoising

The paper introduces a deep dictionary network (DDN) foundation model designed for ultra‑low‑dose CT (ULDCT) denoising across multiple organs. By cascading convolutional sparse coding layers with iterative soft‑thresholding, the architecture offers inherent interpretability, while dynamic dictionary and threshold modules enhance representation. The model is pre‑trained on over one million normal‑dose CT images and fine‑tuned on multi‑organ ULDCT datasets, achieving state‑of‑the‑art performance that consistently outperforms existing methods.

By Baoshun Shi, Shuangyi Yang, Ke Jiang, Bin Zhu, Zhanli Hu, Huazhu Fu