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

September 18, 2022 updated by: Qingshi Zeng, Qianfoshan Hospital

Evaluation of a New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Different Enhancement Phases of Low-dose Liver CT

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

In our hospital, patients with abdominal pelvic cancer undergo follow-up low-dose CT for the evaluation of treatment plan after clinical treatment or disease progress. The raw-data of low-dose CT were collected retrospectively and reconstructed using KARL and AIIR algorithm. 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.

Study Type

Interventional

Enrollment (Anticipated)

100

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Shandong
      • Jinan, Shandong, China
        • Qianfoshan Hospital (The First Affiliated Hospital of Shandong First Medical University)
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • those scheduled for contrast-enhanced liver CT

Exclusion Criteria:

  • images affected by artifacts (motion or implants)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Other
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: standard-dose CT
those patients undergo standard-dose liver CT in portal vein and delayed phase
Experimental: low-dose CT
those patients undergo low-dose liver CT in portal vein and delayed phase
Other: low-dose CT
those patients undergo low-dose liver CT in portal vein and delayed phase.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
signal-to-noise ratio (SNR)
Time Frame: 6 months
Evaluate the image 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
6 months
contrast to noise ratio (CNR)
Time Frame: 6 months
Evaluate the image 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
6 months
diagnostic confidence
Time Frame: 6 months
Evaluate the 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
6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Qianfoshan Hospital

Investigators

  • Study Director: Qingshi Zeng, Qianfoshan Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

September 30, 2022

Primary Completion (Anticipated)

March 30, 2023

Study Completion (Anticipated)

April 30, 2023

Study Registration Dates

First Submitted

September 14, 2022

First Submitted That Met QC Criteria

September 18, 2022

First Posted (Actual)

September 22, 2022

Study Record Updates

Last Update Posted (Actual)

September 22, 2022

Last Update Submitted That Met QC Criteria

September 18, 2022

Last Verified

September 1, 2022

More Information

Terms related to this study

Other Study ID Numbers

  • LD-SH-2022

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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