Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

Overview

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.

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.

Eligibility

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.