Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

H

Hao Tang

Status

Enrolling

Conditions

Deep Learning

Treatments

Diagnostic Test: Deep learning image reconstruction

Study type

Observational

Funder types

Other

Identifiers

NCT06372756
102122

Details and patient eligibility

About

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.

Full description

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. Techniques such as filtered back projection, iterative reconstruction, and deep learning reconstruction were performed. The feasibility of deep learning reconstruction in double low-dose CTA was evaluated based on image quality and diagnostic performance.

Enrollment

1,200 estimated patients

Sex

All

Ages

18 to 90 years old

Volunteers

Accepts Healthy Volunteers

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.

Trial design

1,200 participants in 2 patient groups

Standard dose group
Description:
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.
Treatment:
Diagnostic Test: Deep learning image reconstruction
Double low dose group
Description:
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.
Treatment:
Diagnostic Test: Deep learning image reconstruction

Trial contacts and locations

1

Loading...

Central trial contact

Youfa M Tang, Doctor; Tan, Doctor

Data sourced from clinicaltrials.gov

Clinical trials

Find clinical trialsTrials by location
© Copyright 2024 Veeva Systems