Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging
Hao Tang
1,200 participants
Jun 1, 2023
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria1
- Patients with head and neck CTA, coronary artery CTA, and abdominal CTA due to stroke, coronary heart disease and abdominal inflammatory disease, and abdominal tumors.
Exclusion Criteria1
- Age <18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.
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Interventions
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)
View Full Details on ClinicalTrials.gov
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NCT06372756