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A New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Low-dose Liver CT

NCT05550012 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 100

Last updated 2022-09-22

No results posted yet for this study

Summary

CT-enhanced scans are routine imaging modality for the diagnosis and follow-up of liver disease. However, this means that patients will receive more radiation dose. Therefore, it is necessary to reduce the radiation dose received by patients as much as possible. Deep learning-based reconstruction algorithms have been introduced to improve image quality recently. For many years, researchers attempt to maintain image quality using an advanced method while reducing radiation dose. Recently, a new deep-learning based iterative reconstruction algorithm, namely artificial intelligence iterative reconstruction (AIIR, United Imaging Healthcare, Shanghai, China) has been introduced. In this study, we evaluate the image and diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT.

Conditions

  • Deep Learning

Interventions

OTHER

low-dose CT

those patients undergo low-dose liver CT in portal vein and delayed phase.

Sponsors & Collaborators

  • Qianfoshan Hospital

    lead OTHER

Principal Investigators

  • Qingshi Zeng · Qianfoshan Hospital

Study Design

Allocation
RANDOMIZED
Purpose
OTHER
Masking
NONE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-09-30
Primary Completion
2023-03-30
Completion
2023-04-30

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 NCT05550012 on ClinicalTrials.gov