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Deep Learning Reconstruction Algorithms in Dual Low-dose CTA

NCT06372756 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1200

Last updated 2024-04-18

No results posted yet for this study

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

Interventions

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.

Sponsors & Collaborators

  • Hao Tang

    lead OTHER

Principal Investigators

  • Hao Tang, Doctor · Tongji Hospital

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-06-01
Primary Completion
2025-12-31
Completion
2026-03-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06372756 on ClinicalTrials.gov