NCT06372756

Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,200 (estimated)
• Sponsor: Hao Tang · Academic / Other
• Sex: All
• Age: 18 Years – 90 Years
• Healthy volunteers: Accepted

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.

Conditions

• Deep Learning

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	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.

Timeline

• Start date: 2023-06-01
• Primary completion: 2025-12-01
• Completion: 2026-03-01
• First posted: 2024-04-18
• Last updated: 2024-04-18

Locations

1 site across 1 country: China