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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Deep Learning

Intervention / Treatment
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* Other: low-dose CT


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: those scheduled for contrast-enhanced liver CT Exclusion Criteria: images affected by artifacts (motion or implants)
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

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


Arms and Interventions

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

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| signal-to-noise ratio (SNR) | 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) | 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 | 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 |