Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.

政策移植、政策扩散与政策创新的比较研究

政策移植、政策扩散与政策创新作为政策研究领域的重要研究方向,在理论发展与实践应用中都存在着一定程度的交叉性现象。因此,专门针对概念界定与关系梳理的比较研究对于理清上述三种政策领域的不同之处以及相互关联性具有十分重要的理论价值,也为相关领域的后续研究奠定理论基础。通过文献综述,将国内外主要代表学者的概念界定进行归纳总结;进而以研究重点、研究方法、研究范围等为视角两两对比,从时间和内容两个维度出发,将政策移植、政策扩散与政策创新置于同一政策过程中分析比较其发展特点,试图形成一个比较清晰、完整的研究框架,完成政策移植、政策扩散与政策创新在基本概念上的基础研究工作。

Optical coherence elastography of 3D bilayer soft solids using full-field and partial displacement measurements

Characterizing nonhomogeneous elastic property distribution of soft tissues plays a crucial role in disease diagnosis and treatment.In this paper,we will apply the optical coherence elastography to reconstruct the shear modulus elastic property distribution of a bilayer solid.In the computational aspect,we adopt a well-established inverse technique that solves for every nodal shear modulus in the problem domain(NO method).Additionally,we also propose a novel inverse method that assumes the shear moduli merely vary along the thickness of the bilayer solid(TO method).The inversion tests using simulated data demonstrate that TO method performs better in reconstructing the shear modulus distribution.Further,we utilize the experimental data obtained from the optical coherence tomography to reconstruct the shear modulus distribution of a bilayer phantom.We observe that the quality of the reconstructed shear modulus distribution obtained by the partial displacement measurement is better than that obtained by the full-field displacement measurement.Particularly,merely using the displacement component along the loading direction significantly improves the reconstructed results.This work is of great significance in applying optical coherence elastography(OCE)to characterize the elastic property distribution of layered soft tissues such as skins and corneas.