Deep LearningNCT06372756
Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
An observational study of Deep Learning, run by Hao Tang at 1 site in China.
Trial summary
The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA. Read more about this study
- Who is it for?
- Ages 18 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Deep Learning
- What is being tested?
- Deep learning image reconstruction
- How many people
- 1,200 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2023to Mar 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Healthy or diseased adults undergoing CT vascular imaging
Who may be able to join
- 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.
Who cannot join
- Age <18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology
Wuhan, Hubei, 430000
Youfa M Tang, +8613554101223, 1525573397@qq.com
About this study
Official title: Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging
The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA.
Read the 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.
- Conditions
- Deep Learning
- Being tested
- Deep learning image reconstruction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning image reconstructionDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Deep learning image reconstruction | |
| 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. Receives: Diagnostic Test: Deep learning image reconstruction |
What is being measured
The primary outcome is the main question the study is designed to answer.
The specificity and sensitivity calculated through the optimal cutoff value of the receiver operating characteristic curve.
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.
Measured: 2026.1
Secondary outcomes (1)
The signal-to-noise ratio calculated from image CT values and noise
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.
Measured: 2026.1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Apr 2024
- Study start1 Jun 2023
- Primary completionDec 2025 (estimated)
- Study completionMar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Deep LearningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hao Tang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Hao Tang, Tongji Hospital
- Investigators
- Hao Tang, Doctor, Principal investigator, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06372756
- Sponsor's protocol ID
- 102122
- Status last verified
- Apr 2024
- First submitted
- 11 Apr 2024
- Last updated
- 18 Apr 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: To protect the participant privacy, the relevant data is not shared until the participants' consent
Study at a glance
From the registry record, last updated by the sponsor on 18 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging
- ClinicalTrials.gov ID
- NCT06372756
- Acronym / sponsor ID
- 102122
- Conditions
- Deep Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,200 participants (estimated)
- Lead sponsor
- Hao TangUniversity, hospital or other
- Study start
- 1 Jun 2023 (actual)
- Primary completion
- Dec 2025 (estimated)
- Study completion
- Mar 2026 (estimated)
- First posted
- 18 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Standard dose group | – | Diagnostic Test: Deep learning image reconstructionRaw 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. |
| Double low dose group | – | Diagnostic Test: Deep learning image reconstructionRaw 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. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Healthy or diseased adults undergoing CT vascular imaging
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- Age <18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Deep learning image reconstructionDeep 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. | Diagnostic test | Double low dose group, Standard dose group |
Primary outcome
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.
1 secondary outcome
The signal-to-noise ratio calculated from image CT values and noise
Time frame: 2026.1
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Apr 2024.
- Individual participant dataSharing planned: No. To protect the participant privacy, the relevant data is not shared until the participants' consent
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Hao Tang, Doctor principal investigator
Tongji Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Learning, run by Hao Tang at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 18 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Youfa M Tang, Doctor (8613554101223, 1525573397@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The specificity and sensitivity calculated through the optimal cutoff value of the receiver operating characteristic curve. (time frame: 2026.1). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Apr 2024.