Original Research

In Vivo Diffusion Tensor Imaging of the Human CalfMuscle

Shantanu Sinha, PhD,1 Usha Sinha, PhD,2 and V. Reggie Edgerton, PhD3,4

Purpose: To demonstrate the feasibility of in vivo calf mus-cle fiber tracking in human subjects.

Materials and Methods: An EPI-based diffusion tensorimaging (DTI) sequence with six-direction diffusion gradi-ent sensitization was implemented, and DT images wereacquired at 3 Tesla on five subjects using an extremity coil.The mean diffusivity, fractional anisotropy (FA), and fiberangle (with respect to the magnet z-axis) were measured indifferent muscles, and fibers were tracked from severalregions of interest (ROIs).

Results: The fiber orientations in the current DTI studiesagree well with those determined in previous spectroscopicstudies. The orientation angles ranged from 13.4° in thelateral gastrocnemius to 48.5° in the medial soleus. Thediffusion ellipsoid in muscle tissue is anisotropic and ap-proximates a prolate model, as shown by color maps of theanisotropy. Fibers were tracked from the different muscleregions, and the unipennate and bipennate structure ofmuscle fibers was visualized.

Conclusion: The study clearly shows that in vivo fibertracking of muscle fibers is feasible and could potentially beapplied to study muscle structure function relationships.

Key Words: muscle diffusion tensor imaging; diffusion an-isotropy; fiber orientation; muscle fiber tracts; 3T in vivomuscle DTIJ. Magn. Reson. Imaging 2006;24:182–190.© 2006 Wiley-Liss, Inc.

MRI PROVIDES a unique means of noninvasively mea-suring the random Brownian motion of water moleculesbased on diffusion-weighted imaging (DWI). Further-

more, by designing the pulse sequence to be sensitive tothe directionality of diffusion (diffusion tensor imaging(DTI)), one can monitor the microstructure of tissue (1).The diffusion directionality is expressed by an anisot-ropy index (e.g., fractional anisotropy (FA) and latticeindex) and reflects microstructural organization (1,2).DTI has been used extensively to study the microstruc-tural architecture of the normal and diseased brain(2–4) and has been applied relatively less frequently toother organs, such as the abdomen (5), kidney (6), heart(7), skeletal muscle (8–11), and lingual muscles (12).

DTI studies of muscle have been performed in vitro onstriated muscle, and fiber directions obtained from DTIhave been shown to conform to fascicle striation pat-terns that are visible on high-resolution MRI, and withfiber directions in an actual longitudinal section (ALS)through the same muscle (8). In vitro lingual musclefiber orientation has been mapped by DTI and com-pared well with determinations made by optical imaging(two-photon excitation microscopy) methods (12). High-resolution in vivo DTI of rat skeletal muscle was per-formed by Damon et al (9), who were able to determinethe pennation angle in the lateral gastrocnemius. Thepennation angles were confirmed by direct anatomicalinspection and established the validity of fiber trackingfrom DTI data as a tool for in vivo structural analysis ofsmall-animal skeletal muscle.

The studies summarized above clearly indicate thatDTI is a viable noninvasive tool for assessing musclearchitecture. However, most of those studies were per-formed in vitro, where gross motion is not an issue andhigh-SNR sequences with longer acquisition times canbe employed (8,12). The in vivo animal study (9) wasperformed at high field (4.7 T) with anesthetized rats,which again allowed the use of high SNR and slowersequences (spin-echo sequences and TEs of 23 msec).The extension to in vivo muscle DTI in humans requiresthe use of fast sequences (preferably single-shot) toreduce the deleterious effects of gross motion. Bauerand Reiser (10) performed musculoskeletal diffusion-weighted imaging (DWI) with steady-state free preces-sion (SSFP) sequences. However, this sequence doesnot permit the precise determination of the DT, and isthus limited to qualitative comparisons. More recently,Galban et al (11) used an echo-planar DW sequence todetermine the DT in a single slice thorough the calfmuscle at 1.5T (11). In this paper we extend the work of

1Department of Radiology, UCSD School of Medicine, University ofCalifornia–San Diego, San Diego, California, USA.2Department of Radiological Sciences, David Geffen School of Medicine,University of California–Los Angeles, Los Angeles, California, USA.3Brain Research Institute, David Geffen School of Medicine, Universityof California–Los Angeles, Los Angeles, California, USA.4Department of Physiological Sciences, David Geffen School of Medi-cine, University of California–Los Angeles, Los Angeles, California, USA.Presented at the 10th Annual Meeting of ISMRM, Honolulu, HI, USA,2002.Address reprint requests to: S.S., Department of Radiology, UCSDSchool of Medicine, 410 Dickinson Street, San Diego, CA 92103-8756.E-mail: shsinha@ucsd.edu.Received April 19, 2005; Accepted March 20, 2006.DOI 10.1002/jmri.20593Published online 25 May 2006 in Wiley InterScience (www.interscience.wiley.com).

JOURNAL OF MAGNETIC RESONANCE IMAGING 24:182–190 (2006)

© 2006 Wiley-Liss, Inc. 182

Galban et al (11) to present preliminary results ob-tained at 3T and highlight the potential for both extract-ing the DT and tracking muscle fiber.

MATERIALS AND METHODS

Image Acquisition

Studies were performed on five healthy subjects (fourmales and one female, mean age � 32 years). DW im-ages in the axial view were acquired from the mid-calfregion on a 3T system (Magnetom Trio; Siemens Medi-cal Systems, Erlangen, Germany) with the subject’s legin a relaxed state, using a transmit/receive circularlypolarized extremity coil. High-resolution images thatmatched the anatomical location of the DW images werealso acquired using T2-weighted fast spin echo (FSE)acquisition. The subjects were in a supine state withthe long axis of the leg placed parallel to the magneticfield. Care was taken to position the subjects with thelong axis of the leg placed parallel to the magnetic field,with the center of the coil approximately 10 cm belowthe tibial head. The coil was fixed to the magnet table,always at the same position, to ensure similar parallelpositioning for all of the subjects. The DTI acquisitionconsisted of one baseline EPI and six DW images (b-factor � 600 s/mm2) along the following gradient direc-tions: G1 � 1/�2(1,0,1)T, G2 � 1/�2 (–1,0,1)T, G3 �1/�2 (0,1,1)T, G4 � 1/�2 (0,1,–1)T, G5 � 1/�2(1,1,0)T, and G6 � 1/�2 (–1,1,0)T. The image acquisi-tion parameters for the diffusion images were as fol-lows: TE/TR/FOV/matrix � 69 msec/3300 msec/200 � 165/128 � 128. A total of 25 slices were acquiredcontiguously, and 16 repeats of the acquisition weremagnitude averaged (acquisition time � 6 minutes). Ab-value of 600 s/mm2 (in contrast to the 1000 s/mm2

used in brain diffusion imaging) was employed, sincehigher b-values resulted in a very low SNR for the DWimages. The choice of the lower b-value also allowed aminimum TE time of 69 msec. Further reductions in thetotal readout time per slice and TE were achieved byusing a rectangular field of view (FOV � 0.825) as wella 5/8 Fourier acquisition along the phase-encode di-rection. The combination of rectangular FOV and par-tial Fourier acquisition reduced the total number ofphase-encode lines to 74, which also helped reducegeometric distortions from susceptibility differences.Furthermore, eddy-current distortions in the DW im-ages were reduced by a dual 180° pulse design thatcompensates for eddy-current effects (13). The in-planeimage resolution was 1.6 mm � 1.6 mm with a slicethickness of 5 mm. A frequency-selective fat-saturationpulse was used to suppress the fat signal. The FOValong the long axis of the muscles was 125 mm (25slices � 5 mm). This limited coverage ensured B1 ho-mogeneity and gradient linearity over the imaged re-gion.

Image Analysis

The DW images were analyzed offline to generate mapsof the DT using software written in-house in IDL (Re-search Systems, Boulder, CO, USA). The effective DTcan be derived from:

ln� S�b�

S�b � 0�� � � �i�1

3 �j�1

3

bijDij (1)

where S(b) is the signal intensity in the presence ofthe diffusion gradients and S(b � 0) is the baseline(without any diffusion gradients) image. The DW imagesand their corresponding b-matrix (bij) are used to esti-mate Dij from multivariate linear regression. The DTcalculated at each pixel was then diagonalized to yieldthe eigenvalues (�1, �2, �3) and the corresponding eig-envectors. The FA was calculated from the eigenvaluesas (1):

FA � �32

���1 � �av�2 � ��2 � �av�

2 � ��3 � �av�2

��12 � �2

2 � �32 (2)

where �i are the eigenvalues, and �av, the mean appar-ent diffusion coefficient (ADC), is the average value (�1

��2 ��3 /3). FA is a robust intravoxel measure thatyields values between 0 (perfectly isotropic diffusion, asin a perfect sphere) and 1 (perfectly anisotropic, as inthe hypothetical case of a long cylinder of minimal di-ameter). FA represents the magnitude of the anisotropiccomponent of the tensor as a percentage of the magni-tude of the total DT. In addition to the FA maps, theanisotropy was also visualized as a color map that re-flected the relative magnitude of the eigenvalues. Theanisotropy color map was generated using the followingscheme: the principle eigenvalue was set to 255 andmapped to the red channel. The other two eigenvalueswere scaled by a factor (255/principle eigenvalue) andassigned to green (secondary eigenvalue) and blue (ter-tiary eigenvalue). This mapping enables visualization ofthe type of anisotropy at each pixel. In this scheme,isotropic pixels (�1 � �2 � �3) will appear white to palered, prolate anisotropic pixels (�1 �� �2 �3) will appearshades of orange, oblate anisotropic pixels (�1 � �2 ���3) will appear yellow, and pixels with anisotropy ((�1 ���2 �� �3) will appear predominantly red. In order tovisualize the anisotropy in pixels with smaller diffusiontrace values, the values were not scaled to the largesttrace value in the image. Isotropic pixels should onlyappear white; however, noise in the acquired imagespropagates into the tensor calculation and in fact af-fects isotropic/low anisotropy structures more thanhigh anisotropy regions, introducing spurious anisot-ropy (14). Thus noise often distorts the color map ofisotropic regions, leading to very pale red shades in-stead of white.

Fiber direction was visualized as a color map, whichwas generated based on the eigenvector correspondingto the principle eigenvalue. The projection of this eigen-vector on the right–left (RL) axis was assigned to the redchannel, the projection on the anterior–posterior (AP)axes was assigned to the green channel, and the pro-jection on the superior–inferior (SI) axes was assignedto the blue channel. In addition to the eigenvector of theprinciple eigenvalue, color maps of the eigenvectorscorresponding to the secondary and tertiary eigenval-ues were also generated using the same color-mappingscheme as for the principle eigenvector.

Diffusion Tensor Imaging of Muscle 183

Regions of interest (ROIs) were first identified in thehigh-resolution T2-weighted FSE images and includedthe following muscles: tibialis anterior (TA), soleus me-dial head (SM), soleus lateral head (SL), gastrocs lateralhead (GL), and gastrocs medial head (GM). The ROIswere then transferred from the high-resolution anatom-ical images to the lower-resolution DT images. The fol-lowing parameters were extracted for each ROI: eigen-values (�1, �2, �3), mean diffusivity (�av), FA, and theangle made by the muscle fiber (specified by the eigen-vector corresponding to the primary eigenvalue) withthe SI axis of subject (z-axis in magnet frame). The ROIlocations and the imaging slice were chosen so thatcomparisons between the current DTI data and thosefrom earlier studies using DTI and spectroscopy wouldbe meaningful (11,15).

Fiber tracking was performed with the use of thesoftware dTV for MR-DTI analysis developed by the Im-age Computing and Analysis Laboratory of the Depart-ment of Radiology, University of Tokyo Hospital, Japan(available at http://www.ut-radiology.umin.jp/people/masutani/dTV.htm). The seed points for fiber trackingwere identified within manually delineated ROIs at oneanatomical location. The criterion used to specify ter-mination conditions for fiber tracking was set at FA 0.18 for all muscles except the soleus, where the termi-nation criterion was modified to accommodate thelower FA (0.12). It is to be noted that fiber tracking

proceeds in both directions from the seed voxels of agiven ROI. This is because the eigenvector directionsare indeterminate along the positive and negative direc-tions.

RESULTS

Figure 1 shows the contours overlaid from the baseline(b � 0) image on the matching slice in the DW image set.Since the contours match to within the pixel resolution,postprocessing for eddy-current-induced geometricdistortions was not required. In addition, we evaluatedthe susceptibility-induced distortions common to allecho-planar acquisitions (baseline and DW) by overlay-ing the contour from the baseline echo-planar imageonto the T2-weighted FSE image (Fig. 1). The suscepti-bility-induced distortions were within 1–2 pixels.

Figure 2 shows images of the diffusion eigenvalues,mean diffusion coefficient, orientation angle of themuscle with respect to the z-axis (roughly coincidingwith the SI or long axis of the leg), and FA at oneanatomic level for one subject. Table 1 lists the values ofthe same parameters for five muscles, averaged over allfive subjects. The values shown here were measuredwith ROIs (3 � 3 pixels) placed (manual placement) ineach muscle. These ROIs are shown as small graysquares in the top left image of Fig. 2 and are annotatedin the legend. Small ROIs of 3 � 3 pixels were used in

Figure 1. Typical images froma diffusion data set: (a) T2-weighted FSE, (b) T2-weighted(b � 0) echo planar, and (c–h)DW (b � 600 s/mm2) sensi-tized along six directions. Thecontour was manually tracedon the b � 0 image (b) andoverlaid on the others.

Figure 2. The principal eigenvalue images at one axial level: (a) �1, (b) �2, (c) �3, (d) �av, (e) FA, and (f) the fiber angle with thez-axis (magnet frame of reference). The reduced FA of the soleus can be seen as a hypointense region in e. The fiber angle map(f) shows that fibers are parallel to the z-axis (hypointense regions reflect small angles with respect to the z-axis) in contrast tothe soleus, which is hyperintense. The ROIs used to analyze diffusion in the different muscles are shown in image a with thefollowing legend for the annotations: 1) tibalis anterior, 2) m. gastrocnemius (medial head), 3) m. gastrocnemius (lateral head),4) m. soleus (medial part), and 5) m. soleus (lateral part).

184 Sinha et al.

order to avoid regions of fascia, nerves, fat, and bloodvessels. Student’s one-tailed paired t-test showed thatthe difference between the three eigenvalues was signif-icant for all five muscles analyzed (P 0.001). Themagnitudes of the eigenvalues and the statistically sig-nificant differences confirm that the diffusion ellipsoidis prolate (�1 �� �2 � �3): the average difference of themeans is 0.8 for �1 and �2, and 0.2 for �2 and �3. Thisprolate model of diffusion is similar to the type of diffu-sion anisotropy found in the kidney medulla (6). Themagnitude of the eigenvalues in the ROI measurementsconfirms the color anisotropy map (Fig. 3). Table 2 sum-marizes the analysis of the muscle groups using Stu-dent’s two-tailed paired t-test for the eigenvalues, aver-age diffusion, and FA (significance level of P 0.05).

The color maps of the three eigenvectors (primary,secondary, and tertiary) are shown along with the colormap of the anisotropy (Fig. 3). The predominantly bluecolor (corresponding to SI) of the principle eigenvectordirection of the GM, GL, and TA muscles conforms with

the fiber orientation angles listed in Table 1. The signif-icant green and red colors in the SM and SL muscleshow that in this region the fibers are oriented in the LRand AP directions. The color map of the diffusion an-isotropy (Fig. 3d) confirms the lower FA value in thesoleus (Table 1). Figure 4 shows the principle eigenvec-tor (corresponding to the maximum eigenvalue) alsoprojected onto three orthogonal two-dimensionalplanes, and confirms the color map image of Fig. 3 (topmiddle image).

Fibers were tracked from ROIs located in the medialand lateral soleus, medial and lateral gastrocs, andtibialis anterior (Figs. 5–8). In Figs. 5–7 small ROIswithin the different muscle regions are defined (as op-posed to selecting the entire muscle) in order to visual-ize fiber tracts more clearly. The fiber-tracking softwareinterpolates the acquired axial images to isotropic res-olution (1.6 mm � 1.6 mm � 1.6 mm) and prior toreslicing along the coronal and sagittal orientations(Figs. 5b and c, 7b and c, and 8b). The software also

Table 1DTI Values for the Muscle Groups

TA GM GL SM SL

�1a 2.06 � 0.13 2.13 � 0.05 2.29 � 0.10 1.93 � 0.11 1.95 � 0.2�2a 1.42 � 0.13 1.45 � 0.03 1.52 � 0.16 1.48 � 0.18 1.58 � 0.3�3a 1.21 � 0.03 1.19 � 0.08 1.19 � 0.07 1.19 � 0.12 1.40 � 0.2�ava 1.56 � 0.09 1.59 � 0.04 1.67 � 0.10 1.53 � 0.08 1.65 � 0.2FAb 0.28 � 0.01 0.30 � 0.03 0.33 � 0.03 0.25 � 0.04 0.18 � 0.04Anglec 14.78 � 6.3 24.86 � 4.6 13.42 � 3.1 48.56 � 17.1 35.63 � 22

*All values reported here are the average of the five subjects.a�1, �2, �3, �av are in units of (�10–3 mm2 second).bFA dimensionless anisotropy index.cRefers to the angle (in degrees) made by the principle eigenvector (corresponding to �1) with the SI axis of subject.TA � tibialis anterior, GM � gastrocnemius (medial head), GL � gastrocnemius (lateral head), SM � soleus (medial head), SL � soleus(lateral head).

Figure 3. (a) T2-weighted FSE image, (b) color maps of the projection of the principle eigenvector, (c) secondary eigenvector, (d)tertiary eigenvector, and (e) anisotropy. Color legends for the projections: red channel, RL axis; green channel, AP axis; bluechannel, SI axis. The predominant color in the TA, GM, and GL is blue in the principle eigenvector projection, confirming the SIdirection, whereas green and red colors in the soleus show that the fiber direction is in-plane for this muscle. The color codingfor the anisotropy map is shown below the images. The color bar represents different combinations of �1, �2, and �3, with eachcolor separated by a white block. The values of �1, �2 , �3 are from left to right (not including the white blocks): 1) (255:225:208),2) (255:212:127), 3) (255:207, 62), 4) (255, 174, 83), 5) (255, 172, 123), 6) 255:120:82), 7) (255, 61,61), and 8) (255,0,0). Colorblocks 2 and 3 correspond to oblate diffusion ellipsoids, and color blocks 5–7 correspond to prolate diffusion ellipsoids.

Diffusion Tensor Imaging of Muscle 185

allows identification of the seed points on any orienta-tion (Fig. 5: seed points identified in four ROIs in thecoronal orientation; Figs. 6–8: seed points identified inthe axial orientation). The fiber tracts were color-codedaccording to the fiber direction (Fig. 5) or the FA value(Figs. 6–8). The results of fiber tracking shown in Figs.5–8 highlight the method’s ability to visualize unipen-nate and bipennate architecture and to track fibersfrom aponeurosis to aponeurosis. The potential to mea-sure fiber lengths and pennation angles is indicated;however, a quantitative analysis was not within thescope of this study.

DISCUSSION

DTI of calf muscle presents several challenges whencompared to similar studies in the brain. The signal-to-noise ratio (SNR) is a limiting factor in muscle imagingbecause muscle has low T2 compared to brain tissue(16). In the current study the diffusion sequence wasoptimized by a combination of rectangular FOVs, par-tial Fourier acquisition, and a relatively low b-value of600 s/mm2 to decrease the TE. Partial Fourier allowsshorter readout times, which translates to a reductionin blurring and susceptibility-based artifacts comparedto a full k-space acquisition (17,18). Further, the SNRdecrease from partial Fourier acquisition (factor of�5/8) was compensated for by the gain due to the TEdecrease (factor of exp(TEfull – TEpartial)/T2), giving a netSNR increase by a factor of 1.55 compared to a fullk-space acquisition. Here TEfull and TEpartial (the TEs offull and partial k-space acquisitions) were 90 msec and

69 msec, respectively, with T2 of muscle at 31 msec at3T. A lower b-value (compared to the routinely used1000 s/mm2 in brain DTI) was chosen because themean ADC of muscle is greater than that of brain (mus-cle ADC: 1.55 � 10–3 s/mm2; brain ADC: 0.89 � 10–3

s/mm2). Further improvements in SNR were realized byincreasing the number of averages to 16 and imaging athigh fields (3T).

An additional point is the choice of the b-value for thebaseline images, which was close to 0 s/mm2 in thecurrent study. A more optimal choice for the low b-value would be 100 s/mm2 to reduce the contributionsfrom perfusion. However, it should be kept in mind thatthe perfusion fraction of muscle tissue at rest is around2%, and thus it contributes very little to the baselinesignal intensity and/or attenuation (19). It should benoted that biexponential behavior arising from theabove-mentioned perfusion effects is distinct from thebiexponential effects seen in the brain at very highb-values of 5000 s/mm2 (20). Earlier work using mul-tiple b-values to 1000 s/mm2 failed to detect any biex-ponential decay in muscle in the b-value range of 100–1000 s/mm2 (21).

Another challenge common to all EPI-based tech-niques is the spatial distortion that arises from mag-netic field inhomogeneities. We confirmed the absenceof significant spatial distortions by overlaying the con-tours from the b � 0 baseline image on the DW images(Fig. 1c–h). It should be noted that spatial distortionscommon to baseline and DW echo-planar images arenot addressed by the dual 180° pulse design used in thecurrent study, which reduces only the spatial distortionartifacts from eddy currents. However, since this dis-tortion is common to all voxels, the diffusion indicescan still be calculated accurately on a voxel-by-voxelbasis (Fig. 1).

Another source of artifacts in echo-planar images ischemical shift differences (e.g., between fat and watervoxels). Although fat suppression was used in the cur-rent study, the fat was incompletely suppressed insome regions (e.g., in the marrow of the tibia; Fig 1). Theunsuppressed fat signal is mismapped to a differentlocation along the phase-encode direction, and the ar-tifact appears more pronounced with diffusion weight-ing because fat has a low diffusion coefficient and itssignal intensity is not as attenuated as that in adjacentmuscle tissue (Fig. 1). Care was taken to place the ROIsused in the analysis of the different muscle groupsaway from regions of chemical shift artifacts (e.g., theposterior tibialis). Further work incorporating spectral-spatial, slice-selective, fat-saturation pulses that are

Figure 4. Projection of the eig-envector corresponding to theprincipal eigenvalue on the ZYplane (left panel), ZX plane (mid-dle panel), and YZ plane (rightpanel). For each plane the y-axisof the plot is specified first, fol-lowed by the x-axis. The slice isthe same as shown in Fig. 3.

Table 2Statistical Analysis of DTI Values in Pairs of Muscle Groups†

Musclegroups

�1 �2 �3 �av FA

TA, GLTA, GM * * * *TA, SLTA, SM *GL, GM * *GL, SL * *GL, SMGM, SL * * *GM, SM *SL, SM *

Statistical significance was set at (*P 0.05) between differentmuscle groups for all the DTI values.TA � tibialis anterior, GM � gastrocnemius (medial head), GL �gastrocnemius (lateral head), SM � soleus ((medial head), SL �soleus (lateral head).

186 Sinha et al.

less sensitive to magnetic field inhomogeneities is re-quired to obtain more efficient fat suppression.

Galban et al (11) recently reported the DT indices ofcalf muscles in six subjects at 1.5 T. Their results andthose of the current study are in fair agreement, i.e., thesmallest mean diffusivity is found in the TA muscle,and the highest mean diffusivity occurs in the SL mus-cle (Table 1). On the other hand, in both studies the

lowest FA value was found in the soleus muscle. Apotential reason for the low FA value in the SL musclemay arise from voxels in the ROI that include musclefibers from the posterior and anterior soleus. In theposterior soleus the fibers have a unipennate structureand are directed from anteriosuperior to posteroinfe-rior, whereas the anterior soleus shows a bipennatestructure that runs superomedially and superolaterally

Figure 5. (a) Acquired axial, (b) refor-matted coronal, and (c) reformatted sag-ittal images; (d) three-dimensional coro-nal view with arrow showing theaponeurosis; and (e) three-dimensionalsagittal views with overlaid fibers. Fiberswere tracked from elliptical ROIs placedat four locations along the aponeurosison the coronal image (ROIs shown inblue in image b). The ROIs includedmuscle fibers in the soleus and gastrocsregions. Fibers were tracked from theseROIs and displayed with direction-de-pendent color coding (red: LR; green: AP;blue: SI). The cross-hair identifies thesame spatial location on all frames.Numbered regions in image a: 1) tibia, 2)soleus, 3) medial gastrocs, 4) aponeuro-sis, 5) lateral gastrocs, 6) fibula, and 7)tibialis anterior.

Figure 6. Seed points identified in the medialgastrocs (blue ROI in the top left inset). Fiberstracked through this ROI are shown in thesagittal three-dimensional view. The origin ofthe muscle fibers at one aponeurosis and thetermination of the fibers at another aponeuro-sis are clearly visualized. The bottom left insetshows the same sagittal view without the over-laid fibers for a clearer depiction of the originand termination aponeurosis. The color of thefibers corresponds to the FA (red shades:higher FA). Numbered regions: 1) medial gas-trocs, and 2 and 3) aponeurosis.

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(22). It is possible that voxels that contain mixtures ofthese fibers will show a net reduction in FA. Damon etal (9) found a positional dependence of the DTI param-eters as a function of the distance from the knee in ratskeletal muscle. Examination of the mid-calf region inhuman subjects did not show a positional variation ofFA. However, the main region of variation of FA andmean diffusivity in the rat skeletal muscle is close to theknee, and this region was not imaged in the currentstudy.

The results of our statistical analysis of diffusion in-dices across the muscle groups are in contrast to thosereported by Galban et al (11), in that in the currentstudy �1 showed the largest number of pairs of statis-tically different muscles, and �3 had the smallest num-ber (Table 2). Galban et al (11) showed a reverse trend inthe muscle groups. The reasons for the discrepancy arenot quite clear. Further work at both 1.5T and 3T withimproved imaging sequences, different b-values anddiffusion times, and a larger number of subjects is re-quired to investigate the source of the observed differ-ences. Galban et al (11) attempted to explain the diffu-sion eigenvalues and variation across muscle groupsbased on histological/microscopic studies of musclearchitecture (23–25). Variations of �1 in different mus-cles were attributed to the presence of structural bar-riers whose dimensions approximately matched the av-

erage distance diffused during the typical diffusiontimes used in spin-echo echo-planar diffusion se-quences (11). The second eigenvalue, �2, was postu-lated to be sensitive to cross-sectional orientation of thefibers. Variations of �3 in the muscle groups were at-tributed to variations in the fiber physiological cross-sectional area (PCSA).

Fiber orientation has been measured by spectro-scopic methods and is based on the fact that someresonances show orientation-dependent dipolar split-ting caused by incomplete motional averaging (15). Fi-ber orientation is defined with respect to the z-axis ofthe magnet (corresponding to the subject’s SI axis). Thespectroscopy-derived orientations for the differentmuscles agree approximately with those determinedfrom the DT in this study. The largest deviation from aparallel orientation is found in the soleus muscle (lat-eral and medial heads), by both methods. Interestingly,compared to the other muscle regions, the angular ori-entation of the soleus lateral had a wider spread in boththe present study and Ref. 15 (Table 1). This large rangemay reflect either localization differences or real inter-individual differences. The spectroscopic study waschosen as the reference because it is the only in vivostudy to date that has determined fiber orientations incalf muscle. Ultrasonography (US) measures the pen-nation angles that are defined with respect to an apo-

Figure 7. (a) Acquired axial, (b) reformatted coronal, and (c) reformatted saggital images; (d) magnified view of the axial imagein part a with the manually delineated region shown in blue; (e) three-dimensional coronal view with overlaid fibers; and (f)three-dimensional sagittal view with overlaid fibers. The ROI is placed across the aponeurosis in the TA muscle, and thebipennate structure of the fibers is seen in the three-dimensional coronal view (top right). The color of the fibers corresponds tothe FA (red shades: higher FA). Numbered regions: 1) tibialis anterior and 2) aponeurosis.

188 Sinha et al.

neurosis. It should be noted that small differences infiber orientation may have arisen from differences inpatient positioning, even though we took care to ensurereliability in positioning. An internal reference, such asthe tibial shaft axis or aponeurosis, can be used toprovide more robust measures that are independent ofpatient positioning.

Figure 5d shows that the fibers of the soleus musclehave a complicated architecture and change directionsalong the SI direction. US and morphologic measure-ments of the soleus indicate that it is compartmental-ized into a unipennate posterior group and a bipennateanterior group (22,26). Furthermore, three-dimen-sional structure and velocity maps from MRI haveshown that the anterior soleus fibers are arranged ra-dially around the median septum (27). The fiber ar-rangement is also radial in the superior part of theposterior soleus (27). This provides a qualitative valida-tion of the fibers tracked from the current DTI study,since in the inferior locations of the posterior soleus aunipennate architecture with an SI direction of the fi-bers followed by a radial distribution pattern of fibers(LR) tracked from the ROIs placed more superiorly(Fig. 5d).

Figure 6d shows that the medial gastrocs is unipen-nate and the fibers are approximately parallel along theentire length of the aponeurosis. The current DTI mea-surements confirm earlier studies in which direct ana-tomical examination on cadaver muscles and US mea-surements of the medial gastrocnemius showed thismuscle group to be unipennate (28,29). The angle ofpennation (measured manually by using interactivetools available from an image viewer) roughly corre-sponds to 36°, which agrees with US measurements of41 � 5° for the pennation angle of the medial gastrocsmuscle (29).

The fiber tracks of the TA muscle in the coronal viewshow the bipennate insertion of the muscle fascicles onthe distal aponeurosis and unipennate arrangement atthe more proximal location (Figs. 7e and 8, respec-tively). This conforms to the known fiber organization ofthe TA muscle (from US studies), i.e., unipennate on theproximal tendon plate and bipennate on the distal, in-ternal tendon plate (30–32). A larger ROI was chosenfor the fibers tracked in Fig. 8, which included both theTA and the extensor hallucis longus. The unipennatefibers of this muscle group are also included in thefibers shown in Fig. 8.

B-mode US is a noninvasive imaging modality thathas been widely used to image muscle architecture(26,29,30). It is fast and allows real-time measurementsof fiber lengths and pennation angles to be made evenduring isometric contraction (29,30). However, it is es-sentially a two-dimensional technique, and the fibersare in reality three-dimensional structures. Further,the US image window is usually not large enough tovisualize the whole length of a skeletal muscle. MR-DTI-based measurements, on the other hand, are time-con-suming but can track fibers in three dimensions andover a large FOV.

It should be noted that the focus of the current studydoes not include measurements of pennation angles,which would require more sophisticated tracking of theaponeurosis as well as bundling of fibers with similarcharacteristics (9). Similarly, determination of fiberlengths requires the ability to track fibers reliably fromaponeurosis to aponeurosis (this is shown only quali-tatively in this paper). The noise in the DT images oftenleads to fibers terminating before they reach the apo-neurosis, which leads to potential errors in fiber lengthmeasurements. Therefore, we did not make such esti-mates in this study. Further research to improve the

Figure 8. (a) Acquired axialimage, (b) reformatted coronalimage, (c) axial view with ROIdefined for seed pixels, and (d)three-dimensional coronal viewwith overlaid fibers. Seed pointsare identified in the TA and theextensor hallucis longus (thislocation is superior to thatshown in Fig. 7). The unipen-nate structure of both the TA atthis location and the extensorhallucis longus muscles isclearly visualized in the coro-nal view. The color of the fiberscorresponds to the FA (redshades: higher FA).

Diffusion Tensor Imaging of Muscle 189

image quality of muscle DW images will enable accuratemeasurement of these indices of muscle architecture.The technique can be extended to monitor muscle fiberchanges under exercise conditions. It should be notedthat the current imaging protocol takes six minutes for16 averages; however, each DTI data set takes less thanhalf a minute to acquire, so it should be possible toacquire one to two averages under stress conditions.The imaging can be repeated under the same stressconditions to gain the required SNR for the images.

In conclusion, this study shows that in vivo muscleDTI including the measurement of mean diffusivity, FA,and fiber orientation is feasible. These indices of micro-structure, along with the ability to track fibers andvisualize fiber sheet direction, provide an unprece-dented opportunity to noninvasively study musclestructure–function relationships under rest and exer-cise conditions.

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