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Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
Sponsor: Hao Tang
Summary
The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA.
Official title: Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging
Key Details
Gender
All
Age Range
18 Years - 90 Years
Study Type
OBSERVATIONAL
Enrollment
1200
Start Date
2023-06-01
Completion Date
2026-03
Last Updated
2024-04-18
Healthy Volunteers
Yes
Conditions
Interventions
Deep learning image reconstruction
Deep learning image reconstruction (DLIR) is a newly developed artificial intelligence noise reduction algorithm in recent years. It trains massive high-quality FBP data sets to learn to distinguish noise and signal, so as to selectively reduce noise and reconstruct high-quality images with low-quality image data.
Locations (1)
Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology
Wuhan, Hubei, China