Dr. Dawei Song Profile

Dr. Dawei Song

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Proceedings Article | 4 March 2019 Presentation

Quantitative micro-elastography for cell mechanobiology (Conference Presentation)

Matt Hepburn, Philip Wijesinghe, Luke Major, Lixin Chin, Nicholas Hugenberg, Dawei Song, Assad Oberai, Yu Suk Choi, Brendan Kennedy

Proceedings Volume 10867, 1086728 (2019) https://doi.org/10.1117/12.2511486

KEYWORDS: Tissue optics, Image compression, 3D image processing, Coherence (optics), Elastography, Natural surfaces, Optical coherence tomography, Visualization, Stem cells

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Variations in the mechanical properties of the extracellular environment can alter important aspects of cell function such as proliferation, migration, differentiation and survival. However, many of the techniques available to study these effects lack the ability to characterise cell-to-cell and cell-to-environment interactions on the microscopic scale in three dimensions (3D). Quantitative micro-elastography (QME) is an extension of compression optical coherence elastography that utilizes a compliant layer with known mechanical properties to estimate the axial stress at the tissue surface, which combined with axial strain, is used to map the 3D microscale elasticity of tissue into an image. Despite being based on OCT, limitations in post-processing techniques used to determine axial strain prevented QME to quantify the elasticity of individual cells. In this study we extend the capability of QME to present, to the best of our knowledge, the first images of the elasticity of cells and their environment in 3D over millimeter field-of-views. We improve the accuracy and resolution of QME by incorporating an efficient, iterative solution to the inverse elasticity problem using adjoint elasticity equations to enable QME to visualize individual cells for the first time. We present images of human stem cells embedded in soft gelatin methacryloyl (GelMa) hydrogels and demonstrate these cells elevate the stiffness of the GelMa from 3-kPa to approximately 25-kPa. Our QME system is developed using commercially available components that can be readily made available to biologists, highlighting the potential for QME to emerge as an important tool in the field of mechanobiology.

Proceedings Article | 4 March 2019 Presentation

Linear and nonlinear optical coherence elastography in three dimensions (Conference Presentation)

Assad Oberai, Nicholas Hugenberg, Matt Hepburn, Philip Wijesinghe, Dawei Song, Brendan Kennedy

Proceedings Volume 10880, 1088002 (2019) https://doi.org/10.1117/12.2509574

KEYWORDS: Elastography, Coherence (optics), Tissues, Phase measurement, Optical coherence tomography, Inverse problems, In vivo imaging

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In a typical experiment in compression elastography a sample is compressed to an overall strain of about 1-5%, and then perturbed with a much smaller strain in the range of 0.05%-0.1%. The displacement field corresponding to this perturbative excitation is measured using phase-sensitive OCT. This three-dimensional perturbative displacement data carries within it a wealth of information regarding the volumetric distribution of linear elastic properties of tissue. In this talk we will describe a class of iterative algorithms that use this data input and generate volumetric maps of linear elastic properties of biological specimens. The main idea behind these algorithms is to pose this inverse problem as a constrained minimization problem and use adjoint equations, spatially adaptive resolution and domain decomposition techniques to solve this problem. We will also consider the case when the overall compression and the perturbative excitation steps are repeated several times while increasing the overall strain. For example, a sequence wherein the overall strain varies as 2, 4, 6, 8, and 10%, and each increment is followed by a small perturbative excitation. The measured displacement field corresponding to this small excitation is sensitive to the nonlinear elastic properties of the specimen, which determine how its elastic modulus varies with increasing strain. We will extend the algorithms designed to infer the linear elastic properties of biological specimens to infer these non-linear elastic properties. We will demonstrate our ability to infer linear and nonlinear elastic properties on tissue-phantom, and ex-vivo and in-vivo tissue samples.

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