Clinical trial NCT04954417: A Study on Vertebral Bone Strength by Micro-CT-Like Image
Official title: A Study on Vertebral Bone Strength by Micro-CT-Like Image: An Approach Based on Conditional Generative Adversarial Nets
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Osteoporosis
Lead sponsor: Peking University Third Hospital
Sponsor class: OTHER
First posted: 2021-07-08
Enrollment: 100
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CROSSOVER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
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.

Brief 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.

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.

Eligibility criteria: 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) .

Exclusion Criteria:

* The vertebrae with significant compression fractures, bone neoplasms, or other causes of significant bone destruction.

Primary outcomes:
measure: Consistency or correlation
timeFrame: From the beginning of the experiment to 8 months
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking University Third Hospital
state: Beijing
Study officials:
affiliation: Department of Radiology Peking University Third Hospital Beijing, China,
name: huishu Yuan
role: STUDY_CHAIR
References: