OsteoporosisNCT04954417
A Study on Vertebral Bone Strength by Micro-CT-Like Image
An observational study of Osteoporosis, run by Peking University Third Hospital at 1 site in China.
Trial summary
In this study, we use conditional generation adversarial network to enhance the resolution of MSCT images and obtain micro-CT-like images. Based on this, we measure the bone structure indexes of micro-CT-like images and analyzed the correlation between bone structure and bio-mechanical indexes. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Osteoporosis
- What is being tested?
- Radiological scanning, Deep learning and Bio-mechanical testing
- Type of treatment
- Surgery or procedure
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 1 May 2021to 30 Dec 2021
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The specimens for this experiment were obtained from formalin-fixed human cadavers.The donors had dedicated their body for educational and research purposes to the local Institute of Anatomy prior to death, in compliance with local institutional and legislative requirements.
Who may be able to join
- We will collect human formalin-fixed vertebral specimens provided by the Department of Anatomy, Peking University School of Medicine. The expected number of enrolled specimens is approximately 100 vertebrae (between T6 and L5) .
Who cannot join
- The vertebrae with significant compression fractures, bone neoplasms, or other causes of significant bone destruction.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking University Third Hospital
Beijing, Beijing Municipality, 010
Huishu Yuan, Dr, huishuy@bjmu.edu.cn
About this study
Official title: A Study on Vertebral Bone Strength by Micro-CT-Like Image: An Approach Based on Conditional Generative Adversarial Nets
In this study, we use conditional generation adversarial network to enhance the resolution of MSCT images and obtain micro-CT-like images. Based on this, we measure the bone structure indexes of micro-CT-like images and analyzed the correlation between bone structure and bio-mechanical indexes.
Read the detailed description
In this study, we expect to collect 10 sets of vertebrae (between T6 and L5) from 10 formalin-fixed human cadavers. The study protocol was reviewed and approved by the local Institutional Review Boards.The collected specimens subject to normalized Micro-CT and MSCT image scanning, image reconstruction using standard algorithms, and bone structure observation using bone algorithms to obtain high-quality, standardized axial images. We will use a conditional generation adversarial network-based image mapping technique to train the mapping model between MSCT images and Micro-CT images, find the correspondence between the image information contained in each of MSCT and Micro-CT, and finally obtain high-resolution micro-CT-like images. After obtaining the Micro-CT-like images of the samples, we will segment and annotate the images (including different structures such as vertebral body, bone cortex) and train the Teacher-Student \& U-Net++ deep learning architecture to achieve accurate segmentation of vertebral body regions, and obtain cancellous bone regions of interest. Based on this, we will analyze the bone structure characteristics of each spatial region in the images, including the thickness, spacing, orientation and distribution pattern of bone trabeculae and other indicators. Ultimately, we will cut a standardized cubic sample from the vertebral cancellous bone , obtain the bio-mechanical performance index of this cancellous bone sample by mechanical experiments, and analyze the correlation between bone structure of Micro-CT-like images and bio-mechanical index.
- Conditions
- Osteoporosis
- Being tested
- Radiological scanning, Deep learning, Bio-mechanical testing
- Medical subject headings
- Osteoporosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Radiological scanningDiagnostic test
The collected specimens subject to normalized Micro-CT and MSCT image acquisition, image reconstruction using standard algorithms, and bone structure observation using bone algorithms to obtain high-quality, standardized axial images.
Deep learningOther
We will use a conditional generation adversarial network-based image mapping technique to train the mapping model between MSCT images and Micro-CT images, find the correspondence between the image information contained in each of MSCT and Micro-CT, and finally obtain high-resolution micro-CT-like images.
Bio-mechanical testingProcedure or surgery
we will cut a standardized cubic sample from the vertebral cancellous bone , obtain the bio-mechanical performance index of this cancellous bone sample by mechanical experiments, and analyze the correlation between bone structure of Micro-CT-like images and bio-mechanical index.
| Study group | Role | What they receive |
|---|---|---|
| MSCT image | Scanning of the vertebral body using MSCT Receives: Diagnostic Test: Radiological scanning; Other: Deep learning; Procedure: Bio-mechanical testing | |
| micro-CT image | Scanning of the vertebral body using micro-CT Receives: Diagnostic Test: Radiological scanning |
What is being measured
The primary outcome is the main question the study is designed to answer.
Consistency or correlation
Analyze the consistency or correlation between micro-CT and micro-CT-like bone structure indexes; analyze the correlation between micro-CT-like bone structure indexes and bio-mechanical indexes
Measured: From the beginning of the experiment to 8 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2021
- Study start1 May 2021
- Primary completion30 Dec 2021 (estimated)
- Study completion30 Dec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OsteoporosisStudies 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
- Case crossover
- Time perspective
- Cross-sectional
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- huishu Yuan, Study chair, Department of Radiology Peking University Third Hospital Beijing, China,
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04954417
- Sponsor's protocol ID
- M2021179
- Status last verified
- Jun 2021
- First submitted
- 29 Jun 2021
- Last updated
- 8 Jul 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study on Vertebral Bone Strength by Micro-CT-Like Image: An Approach Based on Conditional Generative Adversarial Nets
- ClinicalTrials.gov ID
- NCT04954417
- Acronym / sponsor ID
- M2021179
- Conditions
- Osteoporosis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 1 May 2021 (actual)
- Primary completion
- 30 Dec 2021 (estimated)
- Study completion
- 30 Dec 2021 (estimated)
- First posted
- 8 Jul 2021 (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.
In this study, we use conditional generation adversarial network to enhance the resolution of MSCT images and obtain micro-CT-like images. Based on this, we measure the bone structure indexes of micro-CT-like images and analyzed the correlation between bone structure and bio-mechanical indexes.
Detailed description
In this study, we expect to collect 10 sets of vertebrae (between T6 and L5) from 10 formalin-fixed human cadavers. The study protocol was reviewed and approved by the local Institutional Review Boards.The collected specimens subject to normalized Micro-CT and MSCT image scanning, image reconstruction using standard algorithms, and bone structure observation using bone algorithms to obtain high-quality, standardized axial images. We will use a conditional generation adversarial network-based image mapping technique to train the mapping model between MSCT images and Micro-CT images, find the correspondence between the image information contained in each of MSCT and Micro-CT, and finally obtain high-resolution micro-CT-like images. After obtaining the Micro-CT-like images of the samples, we will segment and annotate the images (including different structures such as vertebral body, bone cortex) and train the Teacher-Student \& U-Net++ deep learning architecture to achieve accurate segmentation of vertebral body regions, and obtain cancellous bone regions of interest. Based on this, we will analyze the bone structure characteristics of each spatial region in the images, including the thickness, spacing, orientation and distribution pattern of bone trabeculae and other indicators. Ultimately, we will cut a standardized cubic sample from the vertebral cancellous bone , obtain the bio-mechanical performance index of this cancellous bone sample by mechanical experiments, and analyze the correlation between bone structure of Micro-CT-like images and bio-mechanical index.
Study design
- Observational model
- Case crossover
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| MSCT image | – | Diagnostic Test: Radiological scanning, Other: Deep learning, Procedure: Bio-mechanical testingScanning of the vertebral body using MSCT |
| micro-CT image | – | Diagnostic Test: Radiological scanningScanning of the vertebral body using micro-CT |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The specimens for this experiment were obtained from formalin-fixed human cadavers.The donors had dedicated their body for educational and research purposes to the local Institute of Anatomy prior to death, in compliance with local institutional and legislative requirements.
Key inclusion criteria (1 listed)
- We will collect human formalin-fixed vertebral specimens provided by the Department of Anatomy, Peking University School of Medicine. The expected number of enrolled specimens is approximately 100 vertebrae (between T6 and L5) .
Key exclusion criteria (1 listed)
- The vertebrae with significant compression fractures, bone neoplasms, or other causes of significant bone destruction.
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 |
|---|---|---|
| Radiological scanningThe collected specimens subject to normalized Micro-CT and MSCT image acquisition, image reconstruction using standard algorithms, and bone structure observation using bone algorithms to obtain high-quality, standardized axial images. | Diagnostic test | MSCT image, micro-CT image |
| Deep learningWe will use a conditional generation adversarial network-based image mapping technique to train the mapping model between MSCT images and Micro-CT images, find the correspondence between the image information contained in each of MSCT and Micro-CT, and finally obtain high-resolution micro-CT-like images. | Other | MSCT image |
| Bio-mechanical testingwe will cut a standardized cubic sample from the vertebral cancellous bone , obtain the bio-mechanical performance index of this cancellous bone sample by mechanical experiments, and analyze the correlation between bone structure of Micro-CT-like images and bio-mechanical index. | Procedure or surgery | MSCT image |
Primary outcome
Consistency or correlation
Time frame: From the beginning of the experiment to 8 months
Analyze the consistency or correlation between micro-CT and micro-CT-like bone structure indexes; analyze the correlation between micro-CT-like bone structure indexes and bio-mechanical indexes
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jul 2021.
- Individual participant dataSharing planned: No
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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.
huishu Yuan study chair
Department of Radiology Peking University Third Hospital Beijing, China,
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 Osteoporosis, run by Peking University Third Hospital 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 8 Jul 2021. 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 Dan Jin (+8613161525366, jindan1418@bjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Consistency or correlation (time frame: From the beginning of the experiment to 8 months). 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 8 Jul 2021.