Validation of shear wave elastography in skeletal muscle
Introduction
Normal skeletal muscle stiffness results from active tension produced by muscle contraction and passive tension produced largely by connective tissue (Hill, 1938, Huxley, 1957). While the manifestations of deficits in the active component are readily diagnosed and unmistakably detrimental to daily function (NIMSD Consortium, 1996, Fried et al., 2001), analogous alterations in the passive component are less understood. This apparent lack of information should not imply that passive skeletal muscle stiffness does not play a key role in skeletal muscle growth, metabolism, or function—as it is integral for all three (Jaspers et al., 1999, Kjaer, 2004, Tatsumi et al., 2006, Werle, 2008). Numerous in vitro studies have identified the connective tissue network of collagen within the extracellular matrix (ECM) as a key contributor to passive stiffness in a variety of skeletal muscles (Bensamoun et al., 2006, Borg and Caulfield, 1980, Brown et al., 2012, Rowe et al., 2010). It is becoming increasingly apparent that the ECM is vital for mechanotransduction, as well as muscle growth and adaptation (Jaspers et al., 1999, Kjaer, 2004, Tatsumi et al., 2006, Werle, 2008). Later research indicates that passive stiffness may also play a role in muscle performance and adaptation to exercise (Fouré et al., 2011). Increased collagen content and stiffness are seen in numerous musculoskeletal pathologies, including spasticity (Brown et al., 1999, Damiano et al., 2001, Gracies, 2005, Vaz et al., 2006), as well as typical aging (Alnaqeeb et al., 1984, Botelho et al., 1954, Larsson et al., 1979, Tomonaga, 1977). It is clear that muscle stiffness is closely related to joint constraint—increased muscle stiffness is associated with poor range of motion, while reductions in muscle stiffness may predispose to joint subluxation (Johns and Wright, 1962, Vandervoort, 1999). Unfortunately, in vivo passive stiffness measures historically focused primarily on either qualitative measures (Park and Kwon, 2012) or the stiffness of entire joints and muscle groups (Sinkjaer and Magnussen, 1994), thus limiting the advancement of our clinical and scientific understanding. Reliable, noninvasive, quantitative techniques for measuring and monitoring skeletal muscle stiffness are necessary not only to advance our understanding of the mechanism and effects of altered skeletal muscle stiffness but also to improve diagnosis and treatment following injury.
A number of techniques are currently available for monitoring muscle stiffness in vivo. Myotonometry is quick and inexpensive, but tends to be superficial or merely qualitative (Bizzini and Mannion, 2003, Park and Kwon, 2012). Stretch-release techniques elegantly distinguish the stretch reflex stiffness from intrinsic joint stiffness (Sinkjaer et al., 1988), but are unable to quantify the mechanical properties of individual skeletal muscles. Similarly, range of motion measures can quantify resistance to movement, but also evaluates the properties of entire joints and are unable to target individual muscles for assessment (Rabita et al., 2005). Magnetic resonance elastography shows great promise for quantifying the stiffness of whole muscles and muscle groups across a range of ages and contraction levels (Debernard et al., 2011a, Debernard et al., 2011b), but is costly and lacks real-time application (Jenkyn et al., 2003). Ultrasound shows great utility in evaluating underlying architecture of more superficial muscles (Rutherford and Jones, 1992), and a variety of techniques have been used to evaluate muscle stiffness (Park and Kwon, 2012), even in concert with magnetic resonance elastography (Debernard et al., 2013), but, until recently, ultrasound has been purely qualitative. As skeletal muscle is a very dynamic tissue, an ideal technique would be capable of real-time measurement to quantify skeletal muscle stiffness throughout its functional range. Such a technique should be sensitive to small changes in stiffness and capable of determining material properties in light of the functionally relevant and often complex skeletal muscle architecture.
Quantitative ultrasound elastography is beginning to emerge as a promising diagnostic tool for evaluating the mechanical properties of skeletal muscle. Unlike earlier qualitative ultrasound elastography techniques, shear wave elastography (SWE) is an ultrasound-based technique that can characterize tissue mechanical properties based on the propagation of remotely induced shear waves (Bercoff et al., 2004, Chen et al., 2009, Palmeri et al., 2008, Sarvazyan et al., 1998). Shear modulus can be readily calculated from the measured shear wave propagation velocity and tissue density (Yamakoshi et al., 1990). A variety of ultrasound-based elastography techniques have been compared with magnetic resonance elastography, with good agreement in a variety of tissues and phantoms (Bensamoun et al., 2008, Dutt et al., 2000, Oudry et al., 2009). Several investigators have begun to apply similar ultrasound techniques and it is clear that increased skeletal muscle force production is associated with increased stiffness (Gennisson et al., 2005, Gennisson et al., 2010, Shinohara et al., 2010, Zhao et al., 2009). While these experiments are of great value, up until now no study has compared ultrasound elastography results with traditional materials testing for skeletal muscle. To ensure investigators are obtaining robust and meaningful results, baseline reliability and validity information must be obtained for the application of such novel techniques to anisotropic and inhomogeneous skeletal muscle tissue. Gennisson et al. (2003) found that shear waves propagate much more readily along beef muscle fibers longitudinally, as compared to perpendicularly or any interval of rotation therein. Later investigations support these initial findings, as parallel transducer orientations obtained the most reliable measures of muscle elasticity (Gennisson et al., 2010). Despite the recent advances applying ultrasound elasticity imaging to skeletal muscle, these techniques are yet to be validated taking into account the mobility and dynamic properties of skeletal muscle. The purpose of this study was to validate SWE throughout the functional range of motion of skeletal muscle. We hypothesized that combining traditional materials testing techniques with SWE measurements will show increased stiffness measures with increasing tensile load, and will correlate well with each other throughout the tensile range when the ultrasound transducer is oriented parallel to the muscle fibers.
Section snippets
Specimen preparation
We obtained four right brachialis whole-muscle samples immediately post-mortem from 6- to 9-month old female swine. All animal care was in accordance with the Mayo Clinic Institutional Animal Care and Use Committee guidelines. We completed all muscle testing within 5 h of sacrifice, so rigor mortis was not expected to play a significant role in the mechanical testing (Van Ee et al., 2000). At the time of sacrifice, all animals were euthanized with injections of telazol (2.5 cm3, 100 mg/ml),
Results
All four brachialis specimens displayed similar trends throughout tensile testing and were thus grouped together for subsequent analysis. The descriptive statistics for the four swine brachialis specimens are included in Table 1. Young׳s moduli increased with increasing displacement throughout the tensile test for all specimens, as demonstrated by the representative set of elastic moduli–displacement curves are included in Fig. 4. All other specimens displayed similar behavior. Shear moduli
Discussion
As hypothesized, both parallel SWE and MTS showed increased stiffness measures with increasing tensile load. These techniques also correlated well with each other throughout the tensile range, as indicated by the GEE parameter estimate for shear modulus obtained with parallel transducer orientation. The notable discrepancy between shear modulus (via SWE) and Young's modulus (via MTS) for 45° and perpendicular ultrasound transducer orientations was well-anticipated, as it is understood that
Conflict of interest statement
The authors do not have any financial or personal relationships to disclose that could have inappropriately biased this work.
Acknowledgments
The authors thank Lawrence Berglund for technical assistance. This work was supported in part by NIH grant number TR000136. SFE was supported by the National Institute of General Medical Sciences (T32 GM 65841).
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