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

Sponsor

Qianfoshan Hospital (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05550012

Collaborator

(none)

100

Enrollment

Location

Arms

Anticipated Duration (Months)

14.4

Patients Per Site Per Month

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Deep Learning	Other: low-dose CT	N/A

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 Design

Study Type:

Interventional

Anticipated Enrollment :

100 participants

Allocation:

Randomized

Intervention Model:

Parallel Assignment

Masking:

None (Open Label)

Primary Purpose:

Other

Official Title:

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

Anticipated Study Start Date :

Sep 30, 2022

Anticipated Primary Completion Date :

Mar 30, 2023

Anticipated Study Completion Date :

Apr 30, 2023

Arms and Interventions

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.

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.

Outcome Measures

Primary Outcome Measures

signal-to-noise ratio (SNR) [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
contrast to noise ratio (CNR) [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
diagnostic confidence [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

Eligibility Criteria

Criteria

Ages Eligible for Study:

N/A and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

those scheduled for contrast-enhanced liver CT

Exclusion Criteria:

images affected by artifacts (motion or implants)

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Qianfoshan Hospital (The First Affiliated Hospital of Shandong First Medical University)	Jinan	Shandong	China

Sponsors and Collaborators

Qianfoshan Hospital

Investigators

Study Director: Qingshi Zeng, Qianfoshan Hospital

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Qingshi Zeng, Clinical Professor, Qianfoshan Hospital

ClinicalTrials.gov Identifier:

NCT05550012

Other Study ID Numbers:

LD-SH-2022

First Posted:

Sep 22, 2022

Last Update Posted:

Sep 22, 2022

Last Verified:

Sep 1, 2022

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Study Results

No Results Posted as of Sep 22, 2022