Local Mechanical Characterization of Cardiovascular Tissues: Methods, Challenges, and Pathways to Clinical Use

Dong QiuDar Weiss^*

• University of Denver Department of Mechanical Engineering, Denver, United States

Cardiovascular tissues exhibit complex mechanical behaviors that are nonlinear, anisotropic, and spatially heterogeneous. These local and regional variations play a critical role in disease initiation, progression, and treatment outcomes, yet conventional approaches often rely on specimen-averaged properties that overlook this heterogeneity. This review highlights recent advances in local mechanical characterization, spanning experimental methods, imaging-based assessments, and computational strategies. Traditional mechanical tests, such as uniaxial, biaxial, and indentation methods, remain foundational but assume uniform material properties. Surface-based techniques, particularly digital image correlation, now enable high-resolution full-field strain mapping in vitro and even intraoperatively, while volumetric approaches—including ultrasound, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Optical Coherence Tomography (OCT)—extend characterization to through-thickness and into in vivo settings. Digital volume correlation (DVC) further enhances these modalities by extracting three-dimensional internal displacement fields, though its use in cardiovascular tissues is still emerging. To translate these data into clinically relevant metrics, inverse methods such as the Virtual Fields Method (VFM) and inverse finite element analysis (iFEA) are used to estimate region-specific constitutive parameters. Emerging machine learning and physics-informed frameworks further accelerate model selection, parameter identification, and uncertainty quantification. Despite significant progress, major challenges remain in image quality in dynamic in vivo environments, uncertain boundary conditions, computational costs, and the lack of standardized protocols. Future progress will rely on integrating multimodal imaging, robust inverse modeling, and physics-informed machine learning into reproducible pipelines capable of generating patient-specific mechanical maps. Ultimately, local characterization holds the potential to transform risk prediction, medical device optimization, and personalized treatment planning in cardiovascular medicine.