Author Description

Biomechanical analysis has emerged as a powerful tool in clinical diagnosis across various diseases, for example, cardiovascular diseases, cancer, and bone diseases. Diseased soft tissues, such as plaques and tumours, exhibit abnormal stiffness and elasticity, reflecting changes in deformation and stress/strain characteristics. Consequently, biomechanical characteristics serve as crucial diagnostic evidence in clinical practice. With advancements in medical imaging modalities and computational algorithms, elastography imaging like ultrasound/MR elastography and phase-contrast imaging, imaging-based motion tracking and deformation estimation algorithms like digital imaging correlation (DIC) and optical flow, alongside computational simulation models like finite element analysis (FEA), offer promising avenues for non-invasive and touchless exploration of biomechanical characteristics. Despite notable achievements, there persists a pressing need for further exploration and refinement of these technologies to enhance their performance, improve accuracy, and validate their clinical applicability. This Research Topic is dedicated to exploring the forefront of computational models and imaging analysis algorithms in biomechanical analysis. Our objective is to showcase cutting-edge research that encompasses non-invasive and contactless technologies, methods and algorithms for revealing biomechanical characteristics, alongside the application and validation of clinical data. Authors are encouraged to submit a variety of article types, including Research, Case Reports, Perspective pieces, Systematic or Mini-Reviews, and Meta-Analyses on the following sub-topics: • Elastography imaging technologies and the relevant clinical applications. • Full-field displacement estimation algorithms, for example, digital image/volume correlation (DIC/DVC) and optical flow. • Feature tracking algorithms and prog