Deep Learning
Conditions
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.
Interventions
DIAGNOSTIC_TESTDeep 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.
Sponsors
Hao Tang
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
Yes
Inclusion criteria
* 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 criteria
* Age \<18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Contacts
Primary ContactYoufa M Tang, Doctor
Backup ContactTan, Doctor
Outcome results
None listed