Fine-Tuning a Denoiser for Low-Dose CT Image Reconstruction
Year: 2026
Type: conference-paper
Abstract: A commonly employed imaging modality that relies on ionizing radiation is Computed Tomography (CT). While lowering the radiation dose is beneficial for patient health, it can result in reduced image quality. Therefore, improving low-dose CT (LDCT) reconstruction is a significant area of research. The LoDoPaB-CT benchmark evaluates LDCT reconstruction methods, where many top methods use UNet-type architectures. We explore a two-stage approach for LDCT reconstruction: the first stage employs traditional filtered backprojection (FBP), while the second stage performs CT image enhancement. Our training strategy involves pretraining a neural network to denoise natural grayscale images, which are corrupted by Gaussian noise, followed by fine-tuning the network for CT image enhancement using LDCT and normal-dose CT (NDCT) pairs. Experiments on various small subsets of the LoDoPaB-CT dataset demonstrate the effectiveness of our method, showing that less task-specific data are required for training.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7170094067