Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

Recruiting

• Conditions: Deep Learning
• Interventions: Diagnostic Test: Deep learning image reconstruction

• 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.

Study Timeline

• Study Start: 2023-06-01
• Status Verified: 2024-04
• Study First Submitted: 2024-04-11
• Study First Submitted QC: 2024-04-16
• Last Update Submitted: 2024-04-16
• Study First Posted: 2024-04-18
• Last Update Posted: 2024-04-18
• Primary Completion: 2025-12
• Study Completion: 2026-03

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1200

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Standard dose group	Deep learning image reconstruction	Raw data from 400 patients with conventional dose head and neck CTA, coronary CTA, and abdominal CTA were included. Filtered back-projection, iteration, and deep learning reconstruction were performed. To evaluate the impact of deep learning reconstruction on image quality and diagnostic performance in patients with conventional dose CTA.
Double low dose group	Deep learning image reconstruction	Raw data from 800 patients with low tube voltage and contrast medium head and neck CTA, coronary CTA, and abdominal CTA were included. Filtered back-projection, iteration, and deep learning reconstruction were performed. To evaluate the impact of deep learning reconstruction on image quality and diagnostic performance in patients with double-low-dose CTA.

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.

Trial Locations

Locations (1)

Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology; 🇨🇳 Wuhan, Hubei, China