Low-Dose CT via Deep Neural Network

2016/09/27 by Hu Chen, Yi Zhang, Chen, Hu +11
Engineering · Medicine · #Advanced X-ray and CT Imaging #FOS: Physical sciences #Medical Imaging Techniques and Applications #Medical Physics (physics.med-ph) #Radiation Dose and Imaging

paper · pdf · doi:10.48550/arxiv.1609.08508

openalex publication_date 2016/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imaging quality. In this paper, we propose a noise reduction method for low-dose CT via deep learning without accessing the original projection data. An architecture of deep convolutional neural network was considered to map the low-dose CT images into its corresponding normal-dose CT images patch by patch. Qualitative and quantitative evaluations demonstrate a state-the-art performance of the proposed method.

Citations

• Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior
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• Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
• rm K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
• A Perspective on Deep Imaging

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