Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
- Conditions
- Deep Learning
- Registration Number
- NCT06372756
- Lead Sponsor
- Hao Tang
- Brief 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.
- Detailed Description
1. The raw data from patients who underwent head and neck CTA, coronary CTA, and abdominal CTA in both standard dose and double low-dose groups were included.
2. Techniques such as filtered back projection, iterative reconstruction, and deep learning reconstruction were performed.
3. The feasibility of deep learning reconstruction in double low-dose CTA was evaluated based on image quality and diagnostic performance.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 1200
- 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.
- Age <18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The specificity and sensitivity calculated through the optimal cutoff value of the receiver operating characteristic curve. 2026.1 The specificity and sensitivity were calculated separately for the standard dose group and the double low-dose group using the optimal cutoff value from the receiver operating characteristic curve, for the purpose of comparing diagnostic accuracy between the two groups.
- Secondary Outcome Measures
Name Time Method The signal-to-noise ratio calculated from image CT values and noise 2026.1 The signal-to-noise ratio was calculated separately for the standard dose group and the double low-dose group using image CT values and noise, to assess the image quality between the two groups.
Related Research Topics
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Trial Locations
- Locations (1)
Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology
🇨🇳Wuhan, Hubei, China
Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology🇨🇳Wuhan, Hubei, ChinaYoufa M TangContact+86135541012231525573397@qq.com