Skeletal muscles serve
critical mechanical and physiological roles in the body. The mechanical
functions include generating force and actuating movement by shortening or
lengthening under load. It transpires that a muscle’s abilities to generate force
or undergo large and/or rapid length excursions are influenced by the internal
arrangement of muscle fibers with respect to the muscle’s mechanical line of
action. This property is commonly known as muscle architecture. The purpose of
this contribution is to describe the use of diffusion-tensor (DT-) MRI muscle
fiber tracking for studying these structural properties. I will discuss the
importance of muscle structure to muscle function, how muscle architecture is
typically assessed, and describe DT-MRI and its application to muscle. I will provide
examples of how DT-MRI data have been used to provide new insights into how
muscle function, and lastly, highlight important future research directions.
Two important properties of water diffusion in
skeletal muscle allow DT-MRI tractography to be performed. First, D for water in skeletal muscle fibers is
lower than D in free water (1).
Also, water diffuses about 40% more rapidly along skeletal muscle fiber’s long
axis than perpendicular to this axis (2).
The reduction in D may result from
the finite permeability of the cell membrane to water, an action of
intracellular solid phase proteins as physical barriers to water displacement,
and/or the transient binding of water to macromolecules and ions with smaller
diffusion coefficients than that for free water (1,3-5).
Because sarcomeres are ~2-3 mm long but
have an inter-filament spacing of ~30 nm, and because muscle fibers have a
highly elongated overall geometry, there is a greater spatial frequency of
diffusion-hindering structures in the transverse direction than in the
longitudinal direction. This property contributes to diffusion anisotropy in
muscle (3,4).
Indeed, recent work by Scheel et al. suggests that the higher FA and
lower 2nd and 3rd tensor eigenvalues correspond to
smaller fiber diameters (6). Studies in both skeletal (7,8)
and cardiac (9,10)
muscle have reported the correspondence of the 1st tensor eigenvector
to the histology-determined muscle fiber direction. It is this correspondence
that allows DT-MRI fiber tracking to be used to represent skeletal muscle
architecture (Figure 1).
Muscle fiber
tracking has been used to assess the structure of a variety of small animal
muscles and human muscles. In vivo
small animal studies have been performed in the rat gastrocnemius (11)
and the mouse hindlimb (12).
The studies in the rat gastrocnemius provided validation of the pennation angle
measurements; Heemskerk et al.
calculated the PCSA as the dot product of a measured anatomical cross-sectional
area and the fiber direction (12).
DT-MRI-based fiber tracking also has been performed in a variety of human
muscles, including the plantarflexor (13,14,15,16)
and tibialis anterior (17,18,19)
muscles of the leg, the quadriceps muscles of the thigh (20,21,22,23),
the forearm muscles (24),
and the muscles of the female pelvic floor (25).
Clinically relevant applications of fiber tracking have included examining structural
changes in the genioglossus muscle due to oral appliance use (26),
studying the effects of chronic lateral patella dislocation (22),
and examining age-associated changes in muscle architecture (27).
DT-MRI fiber
tracking can provide new insights into muscle structure. For example, in the
tibialis anterior muscle, Lansdown et al.
observed larger values of the pennation angle theta in the superior portion of the muscle than in the inferior portion
(17).
Although a 3-D US study had observed such heterogeneity (28),
this finding had not been previously reported in the 2-D US literature (33,29).
This heterogeneity was shown to be related to changes in the aponeurosis’s
orientation within the axial plane (17).
Heterogeneity in fiber track length (L)
has also been reported using DT-MTI tracking methods (19,30),
and preliminary reports exist to indicate that theta and curvature decrease and
L increase upon muscle elongation (30,31).
Schwenzer et al. (32)
and Sinha et al. (15)
have reported changes in the orientation of the principal eigenvector due to
muscle lengthening and that presumably changes in theta. These data indicate
that DT-MRI and DT-MRI-based fiber tracking can provide new insights into
muscle architecture not obtainable via 2-D imaging methods that do not use a
fixed frame of reference. Also, when proper positioning procedures are used,
DT-MRI is not subject to experimental errors such as the muscle deformation
caused by US probe placement (33).
There have been a few studies in which DT-MRI
fiber tracking, in conjunction with other methods, has been used to predict and
investigate the active mechanics of muscle.
Muscle deformation during contraction can be characterized by a strain
tensor. Felton et al. used DT-MRI to
assess the tongue’s architecture and combined this with phase contrast MRI to
measure strain rates in the tongue during swallowing. This enabled them to relate
the architecture of the intrinsic and extrinsic muscles of the tongue with the
manner in which the tongue deformed during swallowing (34).
Englund et al. used spatial tagging MRI
to estimate the 3-D strain tensor associated with isometric contraction of the
tibialis anterior (35).
Finally, Levin et al. have used DT-MRI
data to generate musculoskeletal models (36).
These initial efforts illustrate the potential for combining functional MRI or
other computational methods and DT-MRI-based muscle architectural information to
obtain new insights into muscle structure-function relationships.
It has been shown that DT-MRI has provided new
insights into muscle structure not available through other methods; and that the
wide range of other measurement capabilities that is available through
physiological MRI methods affords considerable possibilities for using DT-MRI
to understand the integration of structure and function of skeletal muscle. Several
important future research directions that are needed to realize fully the
potential for DT-MRI skeletal muscle fiber tracking: (a) Widespread dissemination of
muscle-specific DT-MRI software tools; (b) Determination and validation
of the best approaches for acquiring, generating, and analyzing DT-MRI fiber
tracking data under conditions of fat infiltration; (c) Determination
and validation of the best approaches for using DT-MRI fiber tracking to
quantify muscle architecture in the active state; and (d) Development
of approaches for synthesizing DT-MRI fiber tracking and other types of
functional imaging data. This existing strengths of DT-MRI fiber tracking,
coupled with continued advancements of this kind, will favor the continued use
of DT-MRI to generate new knowledge concerning the relationships among muscle
structure and function, in health and disease.
References
1. Finch
ED, Harmon JF, Muller BH. Pulsed NMR measurements of the diffusion constant of
water in muscle. Arch Biochem Biophys 1971;147(1):299-310.
2. Cleveland
GG, Chang DC, Hazlewood CF, Rorschach HE. Nuclear magnetic resonance
measurement of skeletal muscle: anisotropy of the diffusion coefficient of the
intracellular water. Biophys J 1976;16(9):1043-1053.
3. Rorschach
HE, Chang DC, Hazlewood CF, Nichols BL. The diffusion of water in striated
muscle. Ann N Y Acad Sci 1973;204:445-452.
4. Chang
DC, Rorschach HE, Nichols BL, Hazlewood CF. Implications of diffusion
coefficient measurements for the structure of cellular water. Ann N Y Acad Sci
1973;204:434-443.
5. Clark
ME, Burnell EE, Chapman NR, Hinke JA. Water in barnacle muscle. IV. Factors
contributing to reduced self-diffusion. Biophys J 1982;39(3):289-299.
6. Scheel
M, von Roth P, Winkler T, Arampatzis A, Prokscha T, Hamm B, Diederichs G. Fiber
type characterization in skeletal muscle by diffusion tensor imaging. NMR in
Biomedicine 2013;26(10):1220-1224.
7. Van
Donkelaar CC, Kretzers LJ, Bovendeerd PH, Lataster LM, Nicolay K, Janssen JD,
Drost MR. Diffusion tensor imaging in biomechanical studies of skeletal muscle
function. J Anat 1999;194 ( Pt 1):79-88.
8. Napadow
VJ, Chen Q, Mai V, So PT, Gilbert RJ. Quantitative analysis of
three-dimensional-resolved fiber architecture in heterogeneous skeletal muscle
tissue using nmr and optical imaging methods. Biophys J 2001;80(6):2968-2975.
9. Hsu
EW, Muzikant AL, Matulevicius SA, Penland RC, Henriquez CS. Magnetic resonance
myocardial fiber-orientation mapping with direct histological correlation. Am J
Physiol 1998;274(5 Pt 2):H1627-1634.
10. Scollan
DF, Holmes A, Winslow R, Forder J. Histological validation of myocardial
microstructure obtained from diffusion tensor magnetic resonance imaging. Am J
Physiol 1998;275(6 Pt 2):H2308-2318.
11. Damon
BM, Ding Z, Anderson AW, Freyer AS, Gore JC. Validation of diffusion tensor
MRI-based muscle fiber tracking. Magn Reson Med 2002;48(1):97-104.
12. Heemskerk
AM, Strijkers GJ, Vilanova A, Drost MR, Nicolay K. Determination of mouse
skeletal muscle architecture using three-dimensional diffusion tensor imaging.
Magn Reson Med 2005;53(6):1333-1340.
13. Sinha
S, Sinha U, Edgerton VR. In vivo diffusion tensor imaging of the human calf
muscle. J Magn Reson Imaging 2006;24(1):182-190.
14. Sinha
S, Sinha U. Reproducibility analysis of diffusion tensor indices and fiber
architecture of human calf muscles in vivo at 1.5 Tesla in neutral and
plantarflexed ankle positions at rest. J Magn Reson Imaging 2011;34(1):107-119.
15. Sinha
U, Sinha S, Hodgson JA, Edgerton RV. Human soleus muscle architecture at
different ankle joint angles from magnetic resonance diffusion tensor imaging.
J Appl Physiol 2011;110(3):807-819.
16. Okamoto
Y, Kunimatsu A, Kono T, Kujiraoka Y, Sonobe J, Minami M. Gender differences in
MR muscle tractography. Magn Reson Med Sci 2010;9(3):111-118.
17. Lansdown
DA, Ding Z, Wadington M, Hornberger JL, Damon BM. Quantitative diffusion tensor
MRI-based fiber tracking of human skeletal muscle. J Appl Physiol
2007;103(2):673-681.
18. Heemskerk
AM, Sinha TK, Wilson KJ, Ding Z, Damon BM. Quantitative assessment of DTI-based
muscle fiber tracking and optimal tracking parameters. Magn Reson Med
2009;61(2):467-472.
19. Heemskerk
AM, Sinha TK, Wilson KJ, Ding Z, Damon BM. Repeatability of DTI-based skeletal
muscle fiber tracking. NMR Biomed 2010;23(3):294-303.
20. Noehren
B, Andersen A, Feiweier T, Damon B, Hardy P. Comparison of twice refocused spin
echo versus stimulated echo diffusion tensor imaging for tracking muscle
fibers. Journal of Magnetic Resonance Imaging 2015;41(3):624-632.
21. Budzik
JF, Le Thuc V, Demondion X, Morel M, Chechin D, Cotten A. In vivo MR
tractography of thigh muscles using diffusion imaging: initial results. Eur
Radiol 2007;17(12):3079-3085.
22. Kan
JH, Heemskerk AM, Ding Z, Gregory A, Mencio G, Spindler K, Damon BM. DTI-based
muscle fiber tracking of the quadriceps mechanism in lateral patellar
dislocation. J Magn Reson Imaging 2009;29(3):663-670.
23. Kermarrec
E, Budzik JF, Khalil C, Le Thuc V, Hancart-Destee C, Cotten A. In vivo
diffusion tensor imaging and tractography of human thigh muscles in healthy
subjects. AJR Am J Roentgenol 2010;195(5):W352-356.
24. Levin
DI, Gilles B, Madler B, Pai DK. Extracting skeletal muscle fiber fields from
noisy diffusion tensor data. Med Image Anal 2011;15(3):340-353.
25. Zijta
F, Froeling M, van der Paardt M, Lakeman M, Bipat S, Montauban van Swijndregt
A, Strijkers G, Nederveen A, Stoker J. Feasibility of diffusion tensor imaging
(DTI) with fibre tractography of the normal female pelvic floor. Eur Radiol
2011;21(6):1243-1249.
26. Shinagawa
H, Murano EZ, Zhuo J, Landman B, Gullapalli RP, Prince JL, Stone M. Effect of
oral appliances on genioglossus muscle tonicity seen with diffusion tensor
imaging: a pilot study. Oral Surg Oral Med Oral Pathol Oral Radiol Endod
2009;107(3):e57-63.
27. Sinha
U, Csapo R, Malis V, Xue Y, Sinha S. Age-related differences in diffusion
tensor indices and fiber architecture in the medial and lateral gastrocnemius.
Journal of Magnetic Resonance Imaging 2015;41(4):941-953.
28. Hiblar
T, Bolson EL, Hubka M, Sheehan FH, Kushmerick MJ. Three dimensional ultrasound
analysis of fascicle orientation in human tibialis anterior muscle enables
analysis of macroscopic torque at the cellular level. In: Sugi H, editor.
Molecular and Cellular Aspects of Muscle Contraction. Volume 538, Advances in
Experimental Medicine and Biology. New York, NY: Springer; 2003. p 635-645.
29. Maganaris
CN, Baltzopoulos V, Ball D, Sargeant AJ. In vivo specific tension of human
skeletal muscle. J Appl Physiol 2001;90(3):865-872.
30. Heemskerk
AM, Damon BM. DTI-based fiber tracking reveals a multifaceted alteration of
pennation angle and fiber tract length upon muscle lengthening. 2009; ISMRM Honolulu,
HI.
31. Heemskerk
AM, Ding Z, Sinha TK, Wilson KJ, Damon BM. In vivo muscle fiber curvature measurements
using DT-MRI. ISMRM 2011; Montreal, QC, Canada.
32. Schwenzer
NF, Steidle G, Martirosian P, Schraml C, Springer F, Claussen CD, Schick F.
Diffusion tensor imaging of the human calf muscle: distinct changes in
fractional anisotropy and mean diffusion due to passive muscle shortening and
stretching. NMR in Biomedicine 2009;22(10):1047-1053.
33. Bolsterlee
B, Veeger HEJ, van der Helm FCT, Gandevia SC, Herbert RD. Comparison of
measurements of medial gastrocnemius architectural parameters from ultrasound
and diffusion tensor images. Journal of Biomechanics;48(6):1133-1140.
34. Felton
SM, Gaige TA, Benner T, Wang R, Reese TG, Wedeen VJ, Gilbert RJ. Associating
the mesoscale fiber organization of the tongue with local strain rate during
swallowing. J Biomech 2008;41(8):1782-1789.
35. Englund
EK, Elder CP, Xu Q, Ding Z, Damon BM. Combined diffusion and strain tensor MRI
reveals a heterogeneous, planar pattern of strain development during isometric
muscle contraction. American Journal of Physiology - Regulatory, Integrative
and Comparative Physiology 2011;300(5):R1079-R1090.
36. Levin
DIW, Gilles B, Mädler B, Pai DK. A fiber tracking method for building patient
specific dynamic musculoskeletal models from diffusion tensor data. MICCAI
Workshop on Computational Diffusion MRI. New York, NY; 2008. p 62-71.