Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

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

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.

Study Overview

• Status: Recruiting
• Conditions: Deep Learning
• Intervention / Treatment: Diagnostic test: Deep learning image reconstruction

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 Type: Observational
• Enrollment (Estimated): 1200

Contacts and Locations

• Study Contact: Name: Youfa M Tang, Doctor; Phone Number: 8613554101223; Email: 1525573397@qq.com
• Study Contact Backup: Name: Tan, Doctor; Phone Number: 86 159 2631 4149; Email: 1655118783@qq.com

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430000
      • Recruiting
      • Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology
      • Contact: Youfa M Tang; Phone Number: +8613554101223; Email: 1525573397@qq.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Healthy or diseased adults undergoing CT vascular imaging

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Standard dose group; 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.	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.
Double low dose group; 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.	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The specificity and sensitivity calculated through the optimal cutoff value of the receiver operating characteristic curve.	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.	2026.1

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The signal-to-noise ratio calculated from image CT values and noise	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.	2026.1

Collaborators and Investigators

• Sponsor: Hao Tang
• Investigators: Principal Investigator: Hao Tang, Doctor, Tongji Hospital

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2023
• Primary Completion (Estimated): December 1, 2025
• Study Completion (Estimated): March 1, 2026

Study Registration Dates

• First Submitted: April 11, 2024
• First Submitted That Met QC Criteria: April 16, 2024
• First Posted (Actual): April 18, 2024

Study Record Updates

• Last Update Posted (Actual): April 18, 2024
• Last Update Submitted That Met QC Criteria: April 16, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: 102122

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: To protect the participant privacy, the relevant data is not shared until the participants' consent

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No