Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
- The raw data from patients who underwent head and neck CTA, coronary CTA, and abdominal CTA in both standard dose and double low-dose groups were included.
- Techniques such as filtered back projection, iterative reconstruction, and deep learning reconstruction were performed.
- The feasibility of deep learning reconstruction in double low-dose CTA was evaluated based on image quality and diagnostic performance.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Youfa M Tang, Doctor
- Phone Number: 8613554101223
- Email: 1525573397@qq.com
Study Contact Backup
- Name: Tan, Doctor
- Phone Number: 86 159 2631 4149
- Email: 1655118783@qq.com
Study Locations
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Hubei
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Wuhan, Hubei, China, 430000
- Recruiting
- Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology
-
Contact:
- Youfa M Tang
- Phone Number: +8613554101223
- Email: 1525573397@qq.com
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients with head and neck CTA, coronary artery CTA, and abdominal CTA due to stroke, coronary heart disease and abdominal inflammatory disease, and abdominal tumors.
Exclusion Criteria:
- Age <18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Standard dose group
Raw data from 400 patients with conventional dose head and neck CTA, coronary CTA, and abdominal CTA were included.
Filtered back-projection, iteration, and deep learning reconstruction were performed.
To evaluate the impact of deep learning reconstruction on image quality and diagnostic performance in patients with conventional dose CTA.
|
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.
|
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Double low dose group
Raw data from 800 patients with low tube voltage and contrast medium head and neck CTA, coronary CTA, and abdominal CTA were included.
Filtered back-projection, iteration, and deep learning reconstruction were performed.
To evaluate the impact of deep learning reconstruction on image quality and diagnostic performance in patients with double-low-dose CTA.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The specificity and sensitivity calculated through the optimal cutoff value of the receiver operating characteristic curve.
Time Frame: 2026.1
|
The specificity and sensitivity were calculated separately for the standard dose group and the double low-dose group using the optimal cutoff value from the receiver operating characteristic curve, for the purpose of comparing diagnostic accuracy between the two groups.
|
2026.1
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The signal-to-noise ratio calculated from image CT values and noise
Time Frame: 2026.1
|
The signal-to-noise ratio was calculated separately for the standard dose group and the double low-dose group using image CT values and noise, to assess the image quality between the two groups.
|
2026.1
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Hao Tang, Doctor, Tongji Hospital
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- 102122
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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