Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

NCT06372756 | Recruiting

• 1 other identifier: 102122
• Study Type: observational
• Target: 1,200
• Locations: 1 country
• Sites: 1

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.

Conditions

Deep Learning

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• The signal-to-noise ratio calculated from image CT values and noise

  2026.1

Study Arms (2)

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

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

Interventions

Deep learning image reconstruction DIAGNOSTIC_TEST

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; Standard dose group

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Healthy or diseased adults undergoing CT vascular imaging

You may qualify if:

• 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.

You may not qualify if:

• Age \<18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.

Sponsors & Collaborators

• Hao Tang: lead

Study Sites (1)

Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology

Wuhan, Hubei, 430000, China

RECRUITING

Study Officials

• Hao Tang, Doctor

  Tongji Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Youfa M Tang, Doctor

CONTACT

8613554101223; 1525573397@qq.com

Tan, Doctor

CONTACT

86 159 2631 4149; 1655118783@qq.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: associate chief physician

Study Record Dates

• First Submitted: April 11, 2024
• First Posted: April 18, 2024
• Study Start: June 1, 2023
• Primary Completion: December 1, 2025
• Study Completion: March 1, 2026
• Last Updated: April 18, 2024

Record last verified: 2024-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)