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A Study on Vertebral Bone Strength by Micro-CT-Like Image

NCT04954417 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2021-07-08

No results posted yet for this study

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.

Conditions

Interventions

DIAGNOSTIC_TEST

Radiological scanning

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.

OTHER

Deep learning

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.

PROCEDURE

Bio-mechanical testing

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.

Sponsors & Collaborators

  • Peking University Third Hospital

    lead OTHER

Principal Investigators

  • huishu Yuan · Department of Radiology Peking University Third Hospital Beijing, China,

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-05-01
Primary Completion
2021-12-30
Completion
2021-12-30

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04954417 on ClinicalTrials.gov