Diabetes mellitus can lead to severe complications of the lower extremity, including ulceration and subsequent amputation. Nearly two thirds of all non-traumatic amputations in the U.S. occur in persons with diabetes. Developing improved treatment options requires a better fundamental understanding of diabetic disease pathomechanics. Despite growing agreement that aberrant pressures and shear stresses are linked to ulceration, the specific causative mechanism of ulceration remains poorly understood. The changes in the biomechanics of the foot due to diabetes have been investigated from a material perspective (e.g., isolated plantar fat) and a full body motion analysis perspective (e.g., joint range of motion). From a structural perspective—in which tissues of different material properties and different geometries interact to create a bulk response—most studies have been two dimensional and limited in the number of locations considered. Given the complex structure and functional motion of the foot, it should be investigated in three dimensions across the entire plantar surface. However, many techniques that afford volumetric inquiry have other concerns: dissection is disruptive, finite element models rely on assumptions and simplifications, computed tomography has poor soft tissue resolution, and MRI is computationally and fiscally expensive. Ultrasound has good soft tissue resolution, but most commercial systems create planar images or are limited to small, angularly swept volumes. Ultrasound can also be difficult to read for the naïve user. In order to overcome these barriers, we propose these specific aims: 1) Develop a mechanical system and the necessary software to generate a three-dimensional scan of the entire plantar soft tissue, using B-mode ultrasound for structural information and shear wave elastography for tissue properties. 2) Collect plantar soft tissue scans for 7 diabetic non-neuropathic subjects and 7 non-diabetic subjects. 3) Analyze these scans using segmentation and strain information calculated with digital volume correlation as well as an interpretable classification neural network. The successful completion of this pilot study will demonstrate the utility of the proposed methods and generate the necessary data for a power analysis to support an R01 grant application.