Figure 3: Average skin (left), fat (middle), and muscle (right) thicknesses for the central locations of the arm (top row) and leg (bottom row). Horizontal axis key: First letter – L: lower, U: upper, Second Letter – A: anterior, L: lateral, M: medial, P: posterior. Error bars represent standard deviation.

IN VIVO SKIN, FAT, AND MUSCLE TISSUE THICKNESSES IN HUMAN UPPER AND LOWER EXTREMITIES

1,2 Erica E. Morrill, 1Tammy M. Owings, 1,2Tyler Schimmoeller, 1Robb Colbrunn, 1Tara Bonner, 1,2Benjamin Landis, 1,2Ahmet Erdemir, PhD

1 Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, USA

2 Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

email: morrile2@ccf.org

INTRODUCTION

Musculoskeletal extremity injuries are prevalent in both the military and civilian communities. In fact, they are responsible for about 50% of combat injuries sustained during warfare [1,2]. War surgeons must be prepared for these types of injuries, which require special experience, outside of normal trauma experience [3]. Finite element models of the extremities can be built using the appropriate tissue properties and geometry, which can then aid in the training and certification of field surgeons through computational surgical simulations. However, anatomically relevant finite element modeling requires accurate experimental quantification of the materials of interest.

Musculoskeletal extremities exhibit a multi-layer tissue structure that is composed of skin, fat, and muscle tissue. Soft tissue structures of extremities can be viewed using MRI and CT imaging [4], however these imaging modalities are costly and not readily accessible. In addition, there is a lack of data describing limb soft tissue variations across diverse populations. The goal of this study is to provide a freely accessible database of skin, fat, and muscle tissue thicknesses for in vivo human musculoskeletal extremities.

METHODS

A custom load-transducer instrumented ultrasound device was used to image skin, fat, and muscle layers in the upper and lower extremities (Acuson S3000, Siemens, USA) (Figure 1). The ultrasound probe (14L5 or 9L4) was attached to a 6-axis load transducer (Nano25, ATI Industrial Automation, USA), to ensure that the bulk tissue was at its relaxed state (force magnitude < 2 N).

Figure 1: Assembled ultrasound device.

The extremities were split into 4 segments (upper/lower arm/leg). Each segment was imaged in 4 anatomical positions (posterior, anterior, medial, and lateral) at 3 locations (proximal, central, and distal), giving a total of 48 collection sites. For the lower leg segment, the posterior depth was imaged to the tibia, while all other locations were imaged to the fibula. Similarly, for the lower arm segment, the medial depth was imaged to the ulna, while all other locations were imaged to the fibula.

Figure 2: Sample ultrasound image of the central anterior upper arm. Red dots show the tissue boundaries.

Skin, fat, and muscle tissue thicknesses were measured from the B-mode ultrasound images using a custom graphical user interface developed in Python. The user drags dots to each tissue boundary (transducer/skin, skin/fat, fat/muscle, and muscle/bone) (Figure 2). The tissue thicknesses and forces are extracted and recorded for the frame corresponding to the minimum force.

RESULTS AND DISCUSSION

A total of 32 subjects were tested (15 female, 17 male). The average age, height, mass, and BMI were XX ± XX yrs, 170 ± 9 cm, 74.5 ± 16.5 kg, and 25.8 ± 5.3 kg/m2, respectively. Activity level, ethnicity and race were also noted for each subject.

Average thicknesses of skin, fat and muscle tissues for the central location are shown in Figure 3. The remaining data is publicly accessible via the project database web page [5].

The changes in soft tissue distribution along the arm and leg segments can be used to build finite element models and simulations for a specific demographic of people (i.e. military fit, sedentary activity level, male/female, etc).

CONCLUSIONS

This study has established a tissue thickness database using ultrasound imaging, which is readily accessible and inexpensive. Future work will include development and validation of computational models and surgical simulations built using this geometry [6].

REFERENCES

[1] Belmont, PJ, et al. J Trauma Acute Care Surg, 73(1), 3-12, 2012. [2] Belmont, PJ, et al. J Trauma, 68(1), 204-210, 2010. [3] Hamdan, T.A. J Am Acad Orthop Surg, 14(10), S32-S36, 2006.[4] Carbone, V., et al. J Biomech, 48(5), 734-741, 2015. [5] OperationMULTiSBeta@https://multisbeta.stanford .edu/. [6] https://simtk.org/ projects/multis.

ACKNOWLEDGMENTS

This study has been supported by USAMRMC (W81XWH-15-1-0232). The views, opinions and/or findings contained in this document are those of the authors and do not necessarily reflect the views of the funding agency.

41st Annual Meeting of the American Society of Biomechanics, Boulder, CO, USA, August 8th – 11th, 2017

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