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Research Papers

Wireless Implantable Sensor for Noninvasive, Longitudinal Quantification of Axial Strain Across Rodent Long Bone Defects

[+] Author and Article Information
Brett S. Klosterhoff, Robert E. Guldberg

George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332;
Parker H. Petit Institute for
Bioengineering and Bioscience,
Georgia Institute of Technology,
Atlanta, GA 30332

Keat Ghee Ong

Department of Biomedical Engineering,
Michigan Technological University,
Houghton, MI 49931

Laxminarayanan Krishnan, Kevin M. Hetzendorfer

Parker H. Petit Institute for
Bioengineering and Bioscience,
Georgia Institute of Technology,
Atlanta, GA 30332

Young-Hui Chang

School of Biological Sciences,
Georgia Institute of Technology,
Atlanta, GA 30332

Mark G. Allen

School of Electrical and
Computer Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332;
Department of Electrical and
Systems Engineering,
University of Pennsylvania,
Philadelphia, PA 19104

Nick J. Willett

Parker H. Petit Institute for
Bioengineering and Bioscience,
Georgia Institute of Technology,
Atlanta, GA 30332;
Department of Orthopaedics,
Emory University,
Atlanta, GA 30303;
Atlanta Veteran’s Affairs Medical Center,
Department of Orthopaedics,
Decatur, GA 30033;
Wallace H. Coulter Department
of Biomedical Engineering,
Georgia Institute of Technology
and Emory University,
Atlanta, GA 30332

Manuscript received May 12, 2017; final manuscript received September 15, 2017; published online October 3, 2017. Editor: Victor H. Barocas.

J Biomech Eng 139(11), 111004 (Oct 03, 2017) (8 pages) Paper No: BIO-17-1208; doi: 10.1115/1.4037937 History: Received May 12, 2017; Revised September 15, 2017

Bone development, maintenance, and regeneration are remarkably sensitive to mechanical cues. Consequently, mechanical stimulation has long been sought as a putative target to promote endogenous healing after fracture. Given the transient nature of bone repair, tissue-level mechanical cues evolve rapidly over time after injury and are challenging to measure noninvasively. The objective of this work was to develop and characterize an implantable strain sensor for noninvasive monitoring of axial strain across a rodent femoral defect during functional activity. Herein, we present the design, characterization, and in vivo demonstration of the device’s capabilities for quantitatively interrogating physiological dynamic strains during bone regeneration. Ex vivo experimental characterization of the device showed that it possessed promising sensitivity, signal resolution, and electromechanical stability for in vivo applications. The digital telemetry minimized power consumption, enabling extended intermittent data collection. Devices were implanted in a rat 6 mm femoral segmental defect model, and after three days, data were acquired wirelessly during ambulation and synchronized to corresponding radiographic videos, validating the ability of the sensor to noninvasively measure strain in real-time. Together, these data indicate the sensor is a promising technology to quantify tissue mechanics in a specimen specific manner, facilitating more detailed investigations into the role of the mechanical environment in dynamic bone healing and remodeling processes.

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References

USBJI, 2014, “ The Burden of Musculoskeletal Diseases in the United States (BMUS),” United States Bone and Joint Initiative, Rosemont, IL, accessed Sept. 25, 2017, http://www.boneandjointburden.org
Zura, R. , Xiong, Z. , Einhorn, T. , Watson, J. T. , Ostrum, R. F. , Prayson, M. J. , Della Rocca, G. J. , Mehta, S. , McKinley, T. , Wang, Z. , and Steen, R. G. , 2016, “ Epidemiology of Fracture Nonunion in 18 Human Bones,” JAMA Surg., 151(11), p. e162775. [CrossRef] [PubMed]
Tarchala, M. , Harvey, E. J. , and Barralet, J. , 2016, “ Biomaterial-Stabilized Soft Tissue Healing for Healing of Critical-Sized Bone Defects: The Masquelet Technique,” Adv. Healthcare Mater., 5(6), pp. 630–640. [CrossRef]
Ilizarov, G. A. , 1989, “ The Tension-Stress Effect on the Genesis and Growth of Tissues—Part I: The Influence of Stability of Fixation and Soft-Tissue Preservation,” Clin. Orthop. Relat. Res., 238, pp. 249–281.
Frost, H. M. , 2001, “ From Wolff’s Law to the Utah Paradigm: Insights About Bone Physiology and Its Clinical Applications,” Anat. Rec., 262(4), pp. 398–419. [CrossRef] [PubMed]
Rot, C. , Stern, T. , Blecher, R. , Friesem, B. , and Zelzer, E. , 2014, “ A Mechanical Jack-Like Mechanism Drives Spontaneous Fracture Healing in Neonatal Mice,” Dev. Cell, 31(2), pp. 159–170. [CrossRef] [PubMed]
Gerstenfeld, L. C. , Cullinane, D. M. , Barnes, G. L. , Graves, D. T. , and Einhorn, T. A. , 2003, “ Fracture Healing as a Post-Natal Developmental Process: Molecular, Spatial, and Temporal Aspects of Its Regulation,” J. Cell. Biochem., 88(5), pp. 873–884. [CrossRef] [PubMed]
Perren, S. M. , 1979, “ Physical and Biological Aspects of Fracture Healing With Special Reference to Internal Fixation,” Clin. Orthop. Relat. Res., 138, pp. 175–196.
Goodship, A. E. , and Kenwright, J. , 1985, “ The Influence of Induced Micromovement Upon the Healing of Experimental Tibial Fractures,” J. Bone Joint Surg. Br., 67(4), pp. 650–655. [CrossRef] [PubMed]
Claes, L. E. , Claes, L. E. , Heigele, C. A. , Neidlinger-Wilke, C. , Neidlinger-Wilke, C. , Kaspar, D. , Kaspar, D. , Seidl, W. , Seidl, W. , Margevicius, K. J. , Margevicius, K. J. , Augat, P. , and Augat, P. , 1998, “ Effects of Mechanical Factors on the Fracture Healing Process,” Clin. Orthop. Relat. Res., 355S, pp. S132–S147. [CrossRef]
Claes, L. E. , and Heigele, C. A. , 1999, “ Magnitudes of Local Stress and Strain Along Bony Surfaces Predict the Course and Type of Fracture Healing,” J. Biomech., 32(3), pp. 255–266. [CrossRef] [PubMed]
Boerckel, J. D. , Kolambkar, Y. M. , Stevens, H. Y. , Lin, A. S. P. , Dupont, K. M. , and Guldberg, R. E. , 2012, “ Effects of In Vivo Mechanical Loading on Large Bone Defect Regeneration,” J. Orthop. Res., 30(7), pp. 1067–1075. [CrossRef] [PubMed]
Boerckel, J. D. , Uhrig, B. A. , Willett, N. J. , Huebsch, N. , and Guldberg, R. E. , 2011, “ Mechanical Regulation of Vascular Growth and Tissue Regeneration In Vivo,” Proc. Natl. Acad. Sci., 108(37), pp. E674–E680. [CrossRef]
Morgan, E. F. , Salisbury Palomares, K. T. , Gleason, R. E. , Bellin, D. L. , Chien, K. B. , Unnikrishnan, G. U. , and Leong, P. L. , 2010, “ Correlations Between Local Strains and Tissue Phenotypes in an Experimental Model of Skeletal Healing,” J. Biomech., 43(12), pp. 2418–24. [CrossRef] [PubMed]
Miller, G. J. , Gerstenfeld, L. C. , and Morgan, E. F. , 2015, “ Mechanical Microenvironments and Protein Expression Associated With Formation of Different Skeletal Tissues During Bone Healing,” Biomech. Model. Mechanobiol., 14(6), pp. 1239–1253.
Betts, D. C. , and Müller, R. , 2014, “ Mechanical Regulation of Bone Regeneration: Theories, Models, and Experiments,” Front. Endocrinol., 5, p. 211 (Lausanne). [CrossRef]
Histing, T. , Garcia, P. , Holstein, J. H. , Klein, M. , Matthys, R. , Nuetzi, R. , Steck, R. , Laschke, M. W. , Wehner, T. , Bindl, R. , Recknagel, S. , Stuermer, E. K. , Vollmar, B. , Wildemann, B. , Lienau, J. , Willie, B. , Peters, A. , Ignatius, A. , Pohlemann, T. , Claes, L. , and Menger, M. D. , 2011, “ Small Animal Bone Healing Models: Standards, Tips, and Pitfalls Results of a Consensus Meeting,” Bone, 49(4), pp. 591–599. [CrossRef] [PubMed]
Horner, E. A. , Kirkham, J. , Wood, D. , Curran, S. , Smith, M. , Thomson, B. , and Yang, X. B. , 2010, “ Long Bone Defect Models for Tissue Engineering Applications: Criteria for Choice,” Tissue Eng. Part B. Rev., 16(2), pp. 263–271. [CrossRef] [PubMed]
Klosterhoff, B. S. , Nagaraja, S. , Dedania, J. J. , Guldberg, R. E. , and Willett, N. J. , 2017, “ Material and Mechanobiological Considerations for Bone Regeneration,” Materials and Devices for Bone Disorders, S. Bose , and A. Bandyopadhyay , eds., Academic Press, Cambridge, MA, pp. 197–264. [CrossRef]
Wulsten, D. , Glatt, V. , Ellinghaus, A. , Schmidt-Bleek, K. , Petersen, A. , Schell, H. , Lienau, J. , Sebald, W. , Plöger, F. , Seemann, P. , and Duda, G. N. , 2011, “ Time Kinetics of Bone Defect Healing in Response to BMP-2 and GDF-5 Characterised by In Vivo Biomechanics,” Eur. Cell. Mater., 21, pp. 177–192. [CrossRef] [PubMed]
Schwarz, C. , Wulsten, D. , Ellinghaus, A. , Lienau, J. , Willie, B. M. , and Duda, G. N. , 2013, “ Mechanical Load Modulates the Stimulatory Effect of BMP2 in a Rat Nonunion Model,” Tissue Eng. Part A, 19(1–2), pp. 247–254. [CrossRef] [PubMed]
Gibney, E. , 2015, “ The Inside Story on Wearable Electronics,” Nature, 528(7580), pp. 26–28. [CrossRef] [PubMed]
McGilvray, K. C. , Unal, E. , Troyer, K. L. , Santoni, B. G. , Palmer, R. H. , Easley, J. T. , Demir, H. V. , and Puttlitz, C. M. , 2015, “ Implantable Microelectromechanical Sensors for Diagnostic Monitoring and Post-Surgical Prediction of Bone Fracture Healing,” J. Orthop. Res., 33(10), pp. 1439–46. [CrossRef] [PubMed]
Wise, K. D. , 2007, “ Integrated Sensors, MEMS, and Microsystems: Reflections on a Fantastic Voyage,” Sens. Actuators, A, 136(1), pp. 39–50. [CrossRef]
Wachs, R. A. , Ellstein, D. , Drazan, J. , Healey, C. P. , Uhl, R. L. , Connor, K. A. , and Ledet, E. H. , 2013, “ Elementary Implantable Force Sensor: For Smart Orthopaedic Implants,” Adv. Biosens. Bioelectron., 2(4), p. 12477.
Klosterhoff, B. S. , Tsang, M. , She, D. , Ong, K. G. , Allen, M. G. , Willett, N. J. , and Guldberg, R. E. , 2017, “ Implantable Sensors for Regenerative Medicine,” ASME J. Biomech. Eng., 139(2), p. 021009. [CrossRef]
Oest, M. E. , Dupont, K. M. , Kong, H.-J. , Mooney, D. J. , and Guldberg, R. E. , 2007, “ Quantitative Assessment of Scaffold and Growth Factor-Mediated Repair of Critically Sized Bone Defects,” J. Orthop. Res., 25(7), pp. 941–950. [CrossRef] [PubMed]
Kolambkar, Y. M. , Dupont, K. M. , Boerckel, J. D. , Huebsch, N. , Mooney, D. J. , Hutmacher, D. W. , and Guldberg, R. E. , 2011, “ An Alginate-Based Hybrid System for Growth Factor Delivery in the Functional Repair of Large Bone Defects,” Biomaterials, 32(1), pp. 65–74. [CrossRef] [PubMed]
Wehner, T. , Wolfram, U. , Henzler, T. , Niemeyer, F. , Claes, L. , and Simon, U. , 2010, “ Internal Forces and Moments in the Femur of the Rat During Gait,” J. Biomech., 43(13), pp. 2473–2479. [CrossRef] [PubMed]
Luo, M. , Martinez, A. W. , Song, C. , Herrault, F. , and Allen, M. G. , 2014, “ A Microfabricated Wireless RF Pressure Sensor Made Completely of Biodegradable Materials,” J. Microelectromech. Syst., 23(1), pp. 4–13. [CrossRef]
Bauman, J. M. , and Chang, Y.-H. , 2010, “ High-Speed X-ray Video Demonstrates Significant Skin Movement Errors With Standard Optical Kinematics During Rat Locomotion,” J. Neurosci. Methods, 186(1), pp. 18–24. [CrossRef] [PubMed]
Epari, D. R. , Duda, G. N. , and Thompson, M. S. , 2010, “ Mechanobiology of Bone Healing and Regeneration: In Vivo Models,” Proc. Inst. Mech. Eng., Part H, 224(12), pp. 1543–1553. [CrossRef]
Claes, L. E. , and Cunningham, J. L. , 2009, “ Monitoring the Mechanical Properties of Healing Bone,” Clin. Orthop. Relat. Res., 467(8), pp. 1964–71. [CrossRef] [PubMed]

Figures

Grahic Jump Location
Fig. 1

(a) Exploded view schematic of instrumented internal femoral fixation plate with sensor adhered in recessed pocket on plate and lead wires routed through machined slot to the transceiver pack mounted in the abdominal cavity. Note: plate and transceiver schematics are not to same scale. (b) Photograph of instrumented plate before surgical implantation.

Grahic Jump Location
Fig. 2

(a) Experimental set-up for eccentric cyclical compression testing of instrumented fixation plates. (b) Elastic moduli of surrogate defect materials, which are placed in the gap between the loading blocks to simulate the progression of mechanical properties during bone defect repair. (c) Example output during a cyclic test, where local strain in the sensor region as measured by laser extensometer is plotted alongside the corresponding voltage signal from the sensor.

Grahic Jump Location
Fig. 3

(a) Sensor output plots for a range of physiological load magnitudes. The color of the dot represents a different sensor and the color of the line represents a different defect surrogate material. Cyclical tests were repeated in triplicate for each loading case, and error bars depicting standard deviation are included on all data points. (b) Normalizing the voltage output of the sensor by applied force demonstrates the sensor is able to discern changes in the stiffness of the defect region, and therefore appears promising to detect progression of bone repair under physiological load conditions, ***p < 0.001, Analysis of Variance, all comparisons.

Grahic Jump Location
Fig. 4

Regression of SNR of sensor output versus the local axial strain of the sensor region as measured by laser extensometer. Employing a limit of detection cut-off criteria of 20 dB (corresponding to a signal amplitude-to-noise ratio of 10–1) indicates the sensor can reliably detect plate strains as low as 300 με, suggesting the sensor possessed sufficient resolution to obtain measurements until and potentially after robust bridging of the bone defect occurred.

Grahic Jump Location
Fig. 5

(a) Fatigue testing of the devices (n = 3) for 10,000 cycles at maximum anticipated physiological strain. The normalized raw outputs for each device is shown, and their respective regression lines are depicted by the bright dotted lines. Throughout 10,000 cycles the outputs were stable within ±4 percent and the resultant slope was not significantly different than zero (p = 0.877). (b) Instrumented fixation plates were submerged in saline maintained at body temperature for 4 weeks and sensitivity was evaluated by mechanical testing at weekly intervals. The output was stable within 7% throughout the test and sensitivity plots for all time-points remained highly linear (r2 range = 0.9938–0.9988).

Grahic Jump Location
Fig. 6

(a) Representative in vivo strain versus time measurement recorded wirelessly during ambulation on a treadmill three days after the creation of a 6 mm segmental femoral defect. Actual data points are depicted as circles with a spline curve-fit illustrated by the black line. (b) During data acquisition, high-speed radiographic videos were acquired by two X-ray cameras mounted at different angles. The videos were synchronized with the recorded sensor output to validate the ability of the sensor to noninvasively quantify functional strains in real-time (see Supplemental Videos 1 and 2 which are available under “Supplemental Data” tab for this paper on the ASME Digital Collection). Radiopaque objects including the stainless steel components which anchor the femoral fixation plate, the abdominally implanted transceiver circuit pack, and the incision wound clips are labeled.

Grahic Jump Location
Fig. 7

Strain amplitude distributions during ambulation. The median and 95th percentile strain amplitude were computed to be 1929 με and 5543 με, and 1889 με and 6041 με for each implant, respectively.

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