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Muscle geometry affects accuracy of forearm volume determination by magnetic resonance imaging (MRI)

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Abstract

Upper extremity musculoskeletal modeling is becoming increasingly sophisticated, creating a growing need for subject-specific muscle size parameters. One method for determining subject-specific muscle volume is magnetic resonance imaging (MRI). The purpose of this study was to determine the validity of MRI-derived muscle volumes in the human forearm across a variety of muscle sizes and shapes. Seventeen cadaveric forearms were scanned using a fast-spoiled gradient echo pulse sequence with high isotropic spatial resolution (1 mm3 voxels) on a 3T MR system. Pronator teres (PT), extensor carpi radialis brevis (ECRB), extensor pollicis longus (EPL), flexor carpi ulnaris (FCU), and brachioradialis (BR) muscles were manually segmented allowing volume to be calculated. Forearms were then dissected, muscles isolated, and muscle masses obtained, which allowed computation of muscle volume. Intraclass correlation coefficients (ICC2,1) and absolute volume differences were used to compare measurement methods. There was excellent agreement between the anatomical and MRI-derived muscle volumes (ICC=0.97, relative error=12.8%) when all 43 muscles were considered together. When individual muscles were considered, there was excellent agreement between measurement methods for PT (ICC=0.97, relative error=8.4%), ECRB (ICC=0.93, relative error=7.7%), and FCU (ICC=0.91, relative error=9.8%), and fair agreement for EPL (ICC=0.68, relative error=21.6%) and BR (ICC=0.93, relative error=17.2%). Thus, while MRI-based measurements of muscle volume produce relatively small errors in some muscles, muscles with high surface area-to-volume ratios may predispose them to segmentation error, and, therefore, the accuracy of these measurements may be unacceptable.

Introduction

The ability to quantify skeletal muscle dimensions accurately in vivo is becoming increasingly important. Muscle size (i.e., mass or volume) improves the specificity and predictive power of biomechanical models of the musculoskeletal system which often rely on this parameter to estimate mechanical force (Holzbaur et al., 2005). It may also be useful to determine the efficacy of strength training (Harridge et al., 1999), adaptation to space flight (LeBlanc et al., 2000), and response to aging (Overend et al., 1992).

One injury that is particularly devastating to patient function is spinal cord injury. Depending on the level and severity of the injury, sensory and motor innervation to a muscle may be completely lost, resulting in a non-functional muscle. However, motor loss may be partially restored by transferring the distal tendon of a healthy, functional muscle to the distal tendon of a non-functional muscle. The net effect is to “power” the non-functional tendon with a healthy muscle to restore mobility. This intervention is commonly used after spinal cord injury where some muscles remain functional, while others are impaired or non-functional (Riordan, 1983). Pronator teres (PT) is a commonly used donor muscle in these tendon transfer surgeries as its relatively high spinal innervation leaves it functional after C6 spinal cord injury. It can be used to restore wrist extension by transfer to extensor carpi radialis brevis (ECRB) and thumb extension to extensor pollicis longus (EPL) (Riordan, 1983). Flexor carpi ulnaris (FCU) is a candidate for surgical restoration of digital extension following high radial nerve palsy (Zachary, 1946), and brachioradialis (BR) can be used to restore thumb flexion in patients with tetrapelegia (Hentz et al., 1983). Literature regarding optimizing the outcome of tendon-transfer surgeries has focused on pre-operative decisions to match muscle function of transferred muscles (Brand et al., 1981) and intraoperative techniques to reattach muscles at their optimal length (Fridén and Lieber, 2002). However, there is little literature documenting muscle function post-operatively and the ability to serially measure muscle size would provide valuable insights into muscle function in the weeks and months that follow surgery.

Techniques currently used to measure muscle size in vivo are not specific to single muscles and their accuracy depends on a number of uncontrollable variables. For example, the accuracy of external anthropometric measures (Jones and Pearson, 1969) varies depending on the subject, the geometry of the limb of interest, and the amount of subcutaneous fat (Rice et al., 1990). The resolution of bioelectric impedance analysis (Brown et al., 1988) and dual-energy X-ray absorbitiometry (DXA; Shih et al., 2000) only provides an estimate of total limb muscle mass and is not single muscle specific.

A number of noninvasive techniques offer the potential for measuring a subjects’ musculoskeletal dimensions. Although several imaging modalities have been utilized for this purpose (i.e., computed tomography (CT) and ultrasound (US)), magnetic resonance imaging (MRI) offers distinct advantages over these modalities. MRI images provide high contrast of muscle, fat, and connective tissue, allowing delineation of muscle borders. MRI does not expose subjects to ionizing radiation and thus may be advantageous to CT in longitudinal studies where subjects require multiple scans. Furthermore, MRI provides a large field of view (FOV) relative to US, which enables visualization of whole muscles and limbs. However, the accuracy of measuring muscle size (volume) with MRI has not been well established. Previous attempts to validate MRI-based muscle volume measurements relied on phantom calibrations (Tracy et al., 2003) or a wide range of muscle sizes (Fukunaga et al., 2001; Scott et al., 1993) which do not establish the accuracy of serial volumetric measurements under realistic conditions. The most rigorous validation (Tingart et al., 2003) suggested very accurate MR-based volume measurements (errors ∼4%) in rotator cuff muscles, yet the muscles examined in this study have well-defined bony compartments, and those that did not (infraspinatus and teres minor) were combined into a single volume measurement. This oversimplified approach minimizes muscle identification errors. Additionally, high-field strength MR systems, which promise better signal-to-noise ratios (SNR) and higher spatial resolution, but may also have larger spatial distortions, which have not been studied.

To establish the accuracy of measuring muscle volumes in vivo, we characterized the hardware and muscle-specific errors associated with measuring muscle volumes in the forearm using a commercially available high-field strength MRI system. These experiments are unique in that they establish fixed and modifiable sources of measurement error in perhaps the most complex extremity system (wrist and hand) examined to date.

Section snippets

Methods

Forearm specimens (distal third of the humerus to the carpals) were obtained from 17 fixed cadavers (82±8 years; PT, ECRB, and EPL: n=10, FCU: n=7, BR: n=6). Prior to imaging, it was determined that a 35 cm imaging FOV would allow the region between the distal carpal row and the proximal humeral epicondyles to be visualized in all specimens. To characterize the spatial distortions produced by magnetic field inhomogeneities within this FOV, a 48 cm long, 4.3 cm diameter water-filled plastic pipe

Results and discussion

Phantom testing determined that a 35 cm FOV yields volume errors as high as 21% at the ends of the FOV (Fig. 2). However, forearm muscles may not all be subjected to this same degree of error because, unlike the phantom vials, muscles spanned a range of horizontal distances within the FOV. Therefore, although these data were useful to determine hardware-associated errors, they likely overestimated error that would be observed in vivo.

There was good agreement between MRI and dissection-based

Summary

These data provide the first quantitative evidence that high-resolution MRI can accurately quantify forearm muscle volume. The volume errors observed in this study were the result of both spatial distortions incurred by acquiring images with a relatively large FOV and manual segmentation errors. In the latter case, muscle shape likely influenced the magnitude of volumetric errors. Modifiable measurement errors, such as FOV size can be minimized simply by matching the FOV dimension to the region

Conflict of interest

The authors confirm that the publication of this paper involves no conflict of interest of any kind.

Acknowledgments

The authors gratefully acknowledge the Body Donation Program and the Anatomical Services Department at the University of California, San Diego. Support for this study was provided by the National Institutes of Health (Grants HD048501 and HD050837).

References (18)

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