Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

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