Image-based methods to characterize morphological, biomechanical and poroelastic parameters of blood cells and skin tissue

Biomedical imaging is a cornerstone of investigations into the state of health/disease in individuals and the mechanics of cells and tissue. In this thesis, we explore different imaging technologies to extract biomechanical properties of blood cells and skin tissue for detecting diseases and optimizing interactions with biomedical devices. Firstly, utilizing high-speed imaging, motion tracking and force measurements, we explore the mechanics of needle insertion into transparent soft solids. A double-insertion technique is applied to determine different force components (deformation, friction, spreading and cutting forces), and estimate toughness for different insertion velocities. The toughness increases with needle velocity (50 N/m to 800 N/m for velocities ≤ 2.5 m/s), and is within the range of toughness for biological and synthetic materials measured by others. The estimates of forces and toughness, and the methods to determine the same, can be used to optimize needle insertions. Secondly, utilizing optical coherence tomography and digital image correlation, we visualize fluid injections in skin tissue and quantify skin tissue deformation. The poroelastic behavior of skin tissue is modelled using a semi-empirical exponential relationship between permeability and strain, and parameters for the permeability-strain model are extracted for skin tissue. The fluid flow estimated by the permeability model with optimized macroscopic parameters matches closely the recorded flow rate, thus validating our semi-empirical model. The semi-empirical model and the model parameters can be used to optimize injection of fluid, such as drugs and vaccines, into skin. Lastly, utilizing low-cost automated microscopy and image analysis techniques, including neural-network based segmentation and machine learning classification, we classify individuals with/without sickle cell disease and β-thalassemia with an overall area under receiver operating characteristic curve, sensitivity and specificity of 0.940, 84.6%, and 92.3%, respectively. The image-based detection technique and 5 other low-cost point-of-care techniques are evaluated against the gold-standard test for 138 participants in Nepal and Canada, to determine the feasibility of using such low-cost point-of-care tests to screen for sickle cell disease β-thalassemia in low-resource and rural/remote settings. Most of the low-cost techniques are suitable for detecting severe disease cases, but only two are suitable for detecting β-thalassemia trait.