Computational investigation of the Laplace law in compression therapy.
This study aims to use computational methods for elucidating the effect of limb shape on subgarment and subcutaneous pressures, stresses and strains. A framework was built that generates computational models from 3D arm scans using a depth sensing camera. Finite Element Analysis (FEA) was performed on the scans taken from 23 lymphoedema patients. Subgarment pressures were calculated based on local curvature for each patient and showed a large variability of pressure across each arm. Across the cohort an average maximum subgarment pressure of 5100 Pa was found as opposed to an intended garment pressure of 2500 Pa. Subcutaneous results show that stresses/strains in the adipose tissues more closely follow the subgarment pressures than in the stiffer skin tissues. Another novel finding was that a negative axial gradient in subgarment pressure (from wrist to elbow) consistently led to positive axial gradients for the Von Mises stresses in the adipose tissues; a phenomenon caused by a combination of arm shape and the stiffness ratio between skin and adipose tissues. In conclusion, this work fills a knowledge gap in compression therapy in clinical practice and can inform garment design or lead to optimal treatment strategies.