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This review synthesizes the biomechanical underpinnings of neck tissue mechanics, their alteration in various pathologies, and the application of ultrasound elastography (USE) and magnetic resonance elastography (MRE) for in vivo measurement. While USE shows diagnostic utility for thyroid nodules and lymph nodes, neck MRE is still in its nascent stages. The authors highlight the significant influence of acquisition parameters on reported stiffness values and advocate for incorporating advanced rheological properties like anisotropy and viscosity for improved clinical assessment and computational modeling.
Neck tissue mechanical properties, crucial for diagnosing disorders from pain to malignancy, are now quantitatively measurable via USE and MRE, revealing significant parameter dependencies and promising richer diagnostic insights beyond simple stiffness.
Many neck disorders, from chronic neck pain and headache to thyroid and nodal malignancy and radiation-induced fibrosis, both arise from and alter the mechanical properties of neck tissues, yet clinical assessment still relies on morphology, palpation and symptoms. Ultrasound and magnetic resonance elastography (MRE) now allow these properties to be measured in vivo, but the neck literature differs widely in methodology and remains dispersed across modalities and tissues. This review brings together the biomechanical basis of neck tissue behavior, the clinical conditions that alter it, and the principles and applications of ultrasound elastography and MRE in the neck. We show that ultrasound elastography has demonstrated diagnostic value for thyroid nodules and cervical lymph nodes, while neck MRE has been shown to be feasible but remains at an early stage. Across both modalities, reported values depend strongly on fiber orientation, posture, muscle activation and acquisition conditions. We therefore propose reporting recommendations and discuss how measurements beyond stiffness, including anisotropy, viscosity and nonlinearity, could improve disease management and inform computational models of the neck.