Abstract Purpose

Recently, there has been an increasing interest in the role of deep fascia mobility in musculoskeletal dynamics and chronic pain mechanisms; however, no strategies have been presented so far to study in vivo fascial motion in 3D. This paper presents a semiautomatic method, based on ultrasound (US) imaging, enabling a 3D evaluation of fascia mobility. Methods

The proposed approach relies on the acquisition of 3D US datasets at rest and during a voluntary muscular contraction and consists of two phases: 3D US dataset analysis and generation of a displacement vector field using a block matching technique (Phase 1) and validation and filtering of the resulting displacement vector field for outliers removal (Phase 2). The accuracy and effectiveness of the proposed method were preliminarily tested on different 3D US datasets, undergoing either simulated (procedural) or real (muscular contraction) deformations. Results

As for the simulated deformation, estimated displacement vectors resulting from Phase 1 presented a mean magnitude percentage error of 8.05 % and a mean angular error of 4.78∘" role="presentation" style="box-sizing: inherit; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">4.78∘4.78∘ which, after Phase 2, were reduced by 69.44 and by 83.05 %, respectively. Tests on real deformations further validated the effectiveness of Phase 2 in the removal of outliers from the displacement vector field. Conclusions

Obtained results preliminarily demonstrate the viability of the proposed algorithm for the analysis of fascia mobility. Such analysis can enable clinicians to better understand the fascia role in musculoskeletal dynamics and disorder. Further experiments are needed to optimize the method in consideration of the anatomical region to be studied.