Research On Mesoscale Image Reconstruction And Mechanical Properties Of Bone Tissue Based On Medical Images

Histological studies have proved that bone microstructure is an important determinant of bone strength and fracture risk.Clinical CT images cannot be refined because of their low resolution,and are not suitable for clinical screening,diagnosis and treatment of bone microstructure related diseases.Micro-CT has a higher spatial resolution and allows quantitative characterization of bone microstructure,which has been widely used in clinical medicine.However,the increase in CT scan resolution is accompanied by a decrease in scan size and an increase in radiation dose,and in vitro scanning is required,making higher-resolution CT scans unsuitable for clinical diagnosis and treatment.Therefore,if you can obtain high-resolution images that meet the requirements of fine structure at a lower radiation dose and a larger scan size,this will be a good help for improving the accuracy and efficiency of related clinical applications and research.This is an exploration and expansion of the research on the biomechanical performance of bone microstructure.Therefore,this paper focuses on the super-resolution reconstruction of medical images at mesoscale.This article first studies the mechanical properties of bone microstructures and their relationship with geometric features based on Micro-CT images.The mechanical properties of the bone microstructure reflect the comprehensive mechanical properties of the bone under the material properties and structural properties.Based on previous studies,this paper used finite element simulation technology to perform load testing,and used traditional machine learning methods to test the results Through data analysis,it was found that there is a good mapping relationship between the geometric features of bone microstructures and their mechanical properties,and the ability to characterize geometric features is more prominent.Second is the research on super-resolution reconstruction of homologous imaging equipment.This part of the content takes Micro-CT images as the research object,adopts deep learning methods,and designs the AESR model of the encoder structure in a targeted manner,and uses phased up-sampling and multi-weight Loss fusion to train the neural network.In terms of standard evaluation indicators PSNR and SSIM,it has achieved better results than existing models.In addition,this paper also uses the geometric characteristics of bone microstructures as evaluation indicators to evaluate the effect of super-resolution reconstruction under actual application scenarios.The results show that the reconstruction effect is consistent with the real results in most of the indicators,which largely proves the feasibility and effectiveness of the solution for super-resolution reconstruction under homologous imaging equipment.Finally,this paper also studies the super-resolution reconstruction of images under heterogeneous imaging equipment.Among them,low-resolution images come from ordinary spiral CT scanning equipment,and high-resolution images come from MicroCT scanning equipment.In view of the differences in data sources,this paper first uses the classic registration method for data registration,and then uses a deep learning network structure based on 3DCNN to provide more spatial information for superresolution reconstruction.Through the evaluation of standard evaluation indexes and geometric characteristics of bone microstructure,it is found that although the model constructed in this paper has achieved a certain reconstruction effect,it is still different from the real image.This shows that for the super-resolution reconstruction under heterogeneous imaging equipment,the research ideas of this article are worth learning,but there are still deficiencies,especially the registration of heterogeneous data,there is still great research significance and research space.