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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-05-01

Study Completion Date

2021-12-30

Brief Summary

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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.

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Detailed Description

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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

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Osteoporosis

Study Design

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Observational Model Type

CASE_CROSSOVER

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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MSCT image

Scanning of the vertebral body using MSCT

Radiological scanning

Intervention Type DIAGNOSTIC_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 learning

Intervention Type OTHER

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 testing

Intervention Type PROCEDURE

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.

micro-CT image

Scanning of the vertebral body using micro-CT

Radiological scanning

Intervention Type DIAGNOSTIC_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.

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

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.

Intervention Type OTHER

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.

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

* 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) .