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. Author manuscript; available in PMC: 2026 Aug 9.
Published in final edited form as: Magn Reson Imaging. 2025 Aug 9;123:110489. doi: 10.1016/j.mri.2025.110489

Relationships between Diffusion Tensor Parameters and Measures of Skeletal Muscle Fiber Size and Strength After Anterior Cruciate Ligament Injury

Meredith K Owen a, Peter A Hardy b,c, Bruce M Damon d,e, Thorsten Feiweier f, McKenzie S White a, Nicholas T Thomas g, Christopher S Fry g, Brian Noehren a
PMCID: PMC12700111  NIHMSID: NIHMS2107693  PMID: 40789562

Abstract

Alterations in skeletal muscle morphology after an anterior cruciate ligament (ACL) tear are a major contributing factor to protracted quadriceps muscle weakness limiting person’s return to function. Microstructural changes in skeletal muscle are difficult to assess noninvasively, but understanding these changes is vital to provide clinicians with additional information to assess injury and guide recovery. The purpose of this study was to evaluate the potential of magnetic resonance diffusion tensor imaging to provide noninvasive metrics of quadriceps muscle morphology and strength. Following a primary ACL tear and prior to surgical reconstruction, 44 individuals underwent bilateral isometric knee extension testing, vastus lateralis muscle biopsies, and diffusion tensor magnetic resonance of the thighs. Significant between limb differences were identified for quadriceps strength, fractional anisotropy, mean diffusivity, radial diffusivity, pooled fiber cross-sectional area, and pooled fiber minimum feret diameter. Fractional anisotropy and radial diffusivity were significantly associated with isometric knee extension peak torque for both limbs, after adjusting for mass. Fractional anisotropy was also significantly associated pooled cross-sectional area in the ACL-deficient limb only, after adjusting for mass. Our findings suggest that fractional anisotropy, mean diffusivity, and radial diffusivity can differentiate between the vastus lateralis of an ACL-injured limb and the healthy limb. Additionally, fractional anisotropy exhibited relationships with both measures of vastus lateral muscle fiber size and quadriceps strength for the ACL-deficient limb. These finding suggest that measures of diffusivity may be used cross-sectionally to infer between limb differences after an ACL-injury and that FA may be used to infer knee extensor strength.

Keywords: immunohistochemistry, diffusion tensor imaging, vastus lateralis, muscle strength, fiber size

1. INTRODUCTION

Traumatic knee injuries, such as an anterior cruciate ligament (ACL) tear, result in significant impairments in quadriceps muscle strength and detrimental alterations in muscle morphology that can persist despite surgery and extensive rehabilitation. Even with post-ACL injury rehabilitation focusing heavily on quadriceps strengthening, patients still struggle to achieve symmetrical strength between the injured and non-injured limbs, which ultimately limits return to previous levels of physical function. One potential contributing factor is a lack of specificity in the rehabilitation to adequately address underlying adaptations in fiber morphology that present after injury and have a direct effect on the ability of a muscle to regain strength [1–3]. Understanding the structural changes at the muscle fiber level is necessary to better understand the effects of the injury and to develop specific targeted therapies to improve outcomes.

Specifically, following an ACL tear, histological analysis of the quadriceps muscle tissue showed decreases in muscle fiber cross-sectional area, specifically for type II fibers [4]. Underlying causes for decreased fiber size are multifaceted but overall propagate to decreases in muscle strength and reduced physical function. While these insights from direct sampling of the muscle tissues are powerful, the use of muscle biopsies is not practical for routine clinical care or large scale clinical studies, necessitating other non-invasive approaches, such as the use of diffusion tensor imaging (DTI). Previous work in other muscles has sought to characterize changes in the diffusion tensor due to skeletal muscle morphology. For example, passive shortening and lengthening of the calf muscle have been linked to acute changes in mean diffusivity (MD), as well as λ2 and λ3, suggesting fiber diameter may be the driving factor in diffusion changes [5–7]. In addition, Berry et al. utilized modeling methods to initially explore the relationship between simulated skeletal muscle tissue microstructure and the diffusion tensor, which suggested that fiber size is the strongest predictor of diffusivity measures (mean diffusivity, λ1, λ2, and FA) [8, 9]. In a small male-only cohort, higher proportions of type 1 fibers, which have a smaller diameter, were found to be associated with higher FA in the calf muscle [10]. These in silico findings and the initial direct relationships found in a small, homogenous sample provide a useful foundation to justify further exploration of the relationship between measures of diffusivity and skeletal muscle fiber characteristics more directly.

Therefore, the purpose of this work was to determine, in a clinical cohort, if measures of diffusion differ between limbs after an ACL tear and then to relate these diffusivity metrics to histological measures of muscle morphology and clinical assessments of muscle strength. We hypothesized that diffusion parameters, such as FA, mean diffusivity, and radial diffusivity would be statistically different between the vastus lateralis of the ACL-deficient limb and the contralateral uninjured limb, with higher FA values and lower mean and radial diffusivity of the ACL-deficient . Further, we hypothesized that FA and radial diffusivity would be significantly associated with both clinical measures of strength (isometric peak knee extensor torque) and histological measures of muscle size (pooled fiber cross-sectional area and minimum feret diameter).

2. MATERIALS AND METHODS

Following approval by the university Institutional Review Board (IRB Number: 42791), participants with a recent ACL tear were recruited to complete bilateral magnetic resonance imaging, muscle biopsy, and isometric strength testing. This study included participants from a completed clinical trial (NCT03364647). Detailed information on inclusion/exclusion criteria is provided in the trial papers [11, 12] and is summarized here. Individuals were excluded if they met one or more of the following criteria: age younger than 15 years or older than 40 years, history of previous ACL tear, history of previous surgeries or conditions that might affect gait, or body mass index greater than 35. Additionally, participants were excluded if they were unable to undergo a muscle biopsy or if movement artifact was present in their images. All participants completed informed consent or participant assent and parental consent prior to completing any study related activities.

All reported data were collected within 10 weeks of initial injury and prior to reconstruction surgery and randomization into a treatment group for the clinical trial. ACL tears were diagnosed by an orthopaedic surgeon and time of injury was determined by self-report from the participant. Participants were weight bearing and had full range of motion at the time of testing. MRI and strength testing were performed prior to muscle biopsy. All testing activities were performed within a week of each other, and in most cases on the same day.

2.1. Imaging

Participants underwent magnetic resonance imaging of both thighs on a 3T clinical scanner (MAGNETOM Prisma, Siemens Healthineers AG, Forchheim, Germany). Individuals were positioned supine, feet-first, and with the limb to be scanned centered on the table. A flexible, 18-element, phased array body coil was placed securely around the thigh. The ACL-deficient limb was scanned first followed by the contralateral limb.

Diffusion tensor imaging was performed similar to previously published methods [4, 13]. Briefly, 21 axial slices with 6 mm slice thickness and no interslice gap were acquired using a research echoplanar imaging (EPI) sequence with stimulated-echo diffusion preparation (STEAM), which has a longer diffusion encoding time and is favorable for muscle imaging [14]. EPI phase encoding direction was posterior to anterior. Specific sequence parameters included a repetition time/echo time of 7600/40 ms, mixing time of 173.0 ms, gradient separation (Δ) of 185 ms, and gradient duration (δ) of 5.4 ms. A total of 24 gradient directions at a high diffusion weighting factor (b) of 500 s/mm2, with three excitations for signal averaging, were captured. Nine repetitions of a low diffusion weighting factor of 45 s/mm2 were captured. The field of view was set to 192 × 192 mm2 with an acquisition matrix of 96 × 96, producing image voxels with dimension of 2 × 2 × 6 mm3. The diffusion tensor was computed similar to methods in Noehren et al. [13]. The tensor was calculated using the full b-matrix for each voxel above a predefined SNR threshold (MATLAB; The MathWorks Inc., Natick, MA). The vastus lateralis was manually segmented on the b=0 images (Medical Image Processing, Analysis, and Visualization, NIH, Bethesda, MA), and average values for the three orthogonal eigenvalues (λ1, λ2, and λ3) and fractional anisotropy (FA) were determined for the segmented region only. Mean diffusivity (MD) was calculated as the average of the three eigenvalues. Radial diffusivity (RD) was calculated as the average of λ2 and λ3.

2.2. Vastus lateralis muscle biopsies

Percutaneous muscle biopsies from the vastus lateralis were performed using a Bergström 5 mm muscle biopsy needle with suction [15]. Biopsy samples were taken from a standardized location at the lateral portion of the vastus lateralis, with the biopsy site between 150 and 250 mm from the mid patella. Approximately 50 mg was mounted in tragacanth gum on cork and flash frozen in liquid nitrogen-cooled 2-methylbutane. Samples were stored at −80°C until processing.

2.2.1. Immunohistochemistry

Frozen human quadriceps tissue was sectioned (7μm thickness) on a cryostat and air dried for one hour. Unfixed slides were incubated overnight at room temperature with a rabbit polyclonal anti-laminin (1:100, #L9393, Sigma, St. Louis, MO). The following day, slides were incubated with a secondary antibody, goat anti-rabbit AF350 (1:500, #A11046, laminin), from Thermo Fisher (Thermo Fisher Scientific, Waltham, MA). After being post-fixed in methanol for 5 min, slides were mounted with fluorescent mounting media (Vectashield, #H-1000; Vector Laboratories, Burlingame, CA) and then cover slipped, as previously described [16].

2.2.2. Fiber Size Measurements

Images were captured with a 100x objective magnification at room temperature using a Zeiss upright microscope (AxioImager M2; Zeiss, Oberkochen, Germany). CSA (μm2) and MFD (μm) were directly assessed using MyoVision automated software [17] for each fiber and pooled to generate a mean CSA and MFD (pooled CSA/pooled MFD). All image analysis was performed in an automated and blinded manner to sex and injury status.

2.3. Knee Extension Strength Testing

Quadriceps strength was estimated through maximum voluntary isometric knee extension (Biodex System 4 isokinetic dynamometer, Biodex Medical Systems, Inc.). Participants were seated with the tested limb at 90 degrees of knee flexion and hips at 85 degrees of flexion. The knee joint center was aligned with the axis of rotation of the dynamometer arm. The distal end of the arm was secured approximately 3 cm above the malleoli. Chest straps, a waist strap, and a thigh strap were used to minimize additional participant movement during the isometric contraction. Participants were allowed one practice trial to familiarize with the testing protocol prior to four maximum effort trials. For maximum effort trials, participants were instructed to kick as hard and as fast as possible. A rest period of 30 seconds was allowed between each trial. Verbal and visual encouragement were provided to facilitate maximum effort. Data were sampled at 100 Hz and filtered using a fourth-order, lowpass Butterworth zero-lag digital filter at 6 Hz. Peak torque was defined as the maximum torque value during the 5-second kick and overall highest peak torque of the four trials was isolated for analysis. Testing was performed on both limbs, starting with the contralateral limb.

2.4. Statistical Analysis

All outcome variables were compared between limbs using paired t-tests with Cohen’s d effect sizes and mean differences with associated 95% confidence intervals. For each limb, associations between DTI parameters (FA, MD, and RD) and fiber CSA, and MFD, and quadriceps peak torque were determined with multiple linear regressions adjusting for body mass. Additionally, associations between fiber CSA and MFD and quadriceps peak torque were determined with multiple linear regressions adjusting for body mass. Overall model R2, adjusted R2, and p-values for the planned comparisons are reported. Throughout all analyses, methods assumptions were checked either through sample size requirements to use asymptotic methods (for t-tests) or through residual plot distributions (for regression models). Log transformations were applied to variables with skewed data prior to regression analyses. All analyses were performed using SPSS (version 29.0.2.0, SPSS, Inc., IBM) and p-values less than or equal to .05 were considered significant.

3. RESULTS

44 participants (20 female; 21 ± 6 years; 1.7 ± 0.1 m; 75.4 ± 16.1 kg; 22 ± 17 days post-injury) were enrolled and completed all testing activities. Table 1 includes means and effect sizes for between limb differences for all variables of interest in the study. Isometric quadriceps peak torque was significantly lower for the ACL-deficient limb. All diffusion measures were significantly different between limbs with higher FA and lower MD and RD for the ACL-deficient limb. Additionally, both pooled fiber CSA and fiber minimum feret diameter were significantly smaller for the ACL-deficient limb.

Table 1.

Between-limb comparisons for all variables of interest. Values are reported as mean (standard deviation).

ACL-D CONTR Effect Sizea p-value
Strength
   ISOM (Nm) 157.6 (63.8) 210.8 (65.9) −1.14 [−1.52, −0.76] <.001
Diffusion Parameters
   FA 0.33 (0.03) 0.30 (0.03) 0.93 [0.75, 1.28] <.001
   MD (x10−3 m2/s) 1.24 (0.05) 1.26 (0.04) −0.62 [−0.94, −0.30] <.001
   RD (x10−3 m2/s) 0.99 (0.05) 1.03 (0.05) −0.81 [−1.15, −0.50] <.001
Fiber Geometry
Pooled CSA (μm2) 4143.5 (937.3) 4811.6 (980.3) −0.79 [−1.12, −0.44] <.001
Pooled MFD (μm) 58.1 (7.1) 61.6 (7.1) −0.65 [−0.97, −0.32] <.001
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ISOM = Isometric Peak Torque; FA = Fractional Anisotropy; MD = Mean Diffusivity; RD = Radial Diffusivity; CSA = cross-sectional area; MFD = minimum feret diameter

a

Cohen’s d, 95% confidence interval shown in brackets

Bolded p-values indicate statistical significance

Table 2 lists the results of planned multiple linear regressions. After adjusting for body mass, fractional anisotropy was negatively correlated with isometric peak torque for both the ACL-deficient limb and the contralateral limb. Radial diffusivity was positively correlated with isometric peak torque for both limbs. FA was also negatively correlated with fiber CSA, but only for the ACL-deficient limb. No other associations were significant. Scatterplots for statistically significant relationships are shown in Figures 1 and 2. Representative histological laminin stains for fiber CSA and representative images for non-diffusion weighted images, MD maps, and FA maps are included for the ACL-deficient limb of a participant with high FA and a participant with low FA (Figure 3). Data points for the two representative participants are indicated on the scatterplots by a shaded triangle (low FA, large CSA, high strength) and shaded star (high FA, small CSA, low strength). Scatterplots for all planned comparisons are provided in the supplemental digital content.

Table 2.

Relationships between strength and fiber metrics and diffusion tensor parameters.

ISOM Pooled Fiber CSA Pooled MFD
ACL-D CONTR ACL-D CONTR ACL-D CONTR
FA R2 .437# .664 .285 .205 .175 .065
Adjusted R2 .409# .648 .250 .167 .135 .019
p-value <.001 # <.001 .019 .280 .011 .166
MD R2 .163 .528 .188 .199 .037 .061
Adjusted R2 .122 .505 .148 .160 −.010 .015
p-value .193 .307 .570 .357 .655 .187
RD R2 .300 .587 .189 .182 .046 .021
Adjusted R2 .266 .567 .150 .142 .000 −.027
p-value .003 .011 .535 .966 .441 .822
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ISOM = Isometric Peak Torque; ACL-D = ACL-deficient limb; CSA = cross-sectional area; MFD = minimum feret diameter; FA = Fractional Anisotropy; MD = mean diffusivity;

RD = radial diffusivity

Bolded p-values indicate statistical significance

#

FA vs. ln(ISOM) for the ACL-D

Fig. 1.

Fig. 1.

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Scatterplots for planned comparisons between diffusion parameters with statistical significance, FA (panels A and B) and RD (panels C and D) versus isometric peak torque for both limbs. Data points for two representative participants (with images for the ACL-deficient limb shown in figure 3) are indicated on the scatterplots by a shaded triangle (low FA, large CSA, high strength) and shaded star (high FA, small CSA, low strength).

Fig. 2.

Fig. 2.

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Scatterplots for FA versus pooled fiber CSA (A) and MFD (B) for the ACL-deficient limb. Data points for two representative participants (with images shown in figures 3 and 4) are indicated on the scatterplots by a shaded triangle (low FA, large CSA, high strength) and shaded star (high FA, small CSA, low strength).

Fig. 3.

Fig. 3.

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Representative laminin stains for fiber CSA, and B0, MD, and FA images for the ACL-deficient limb for two participants, one with a low FA value and large fiber CSA (shown in the top row) and one with a high FA value and small fiber CSA (shown in the bottom row). The two participants shown in the figure above are indicated by shaded triangle and star data points in figures 1 and 2.

4. DISCUSSION

The purpose of this study was to assess the ability of the diffusion tensor to provide insights on skeletal muscle fiber size and muscle function following an ACL injury. We found significant differences in FA, MD, and RD between the injured (ACL-deficient) and contralateral limbs soon after an ACL injury. These differences were paralleled by significant differences in muscle fiber size (CSA and MFD) and isometric peak torque, which are similar to what we have seen in prior work [18, 19]. However, only FA and fiber size (CSA and MFD) of the ACL-deficient limb were significantly associated. All other comparisons between FA, MD, and RD and muscle fiber size were non-significant for both limbs, which contrasts with our initial hypotheses. Collectively, our results indicate that diffusivity measures can discriminate between injured muscle and healthy muscle and that FA is associated with muscle strength. The data, however, did not identify an overall association between FA, MD, and RD and histological measures of skeletal muscle fiber size.

The direct relationship between skeletal muscle fiber morphology, including fiber cross-sectional area, and DTI parameters has been preliminarily explored in prior work. For example, work with simulated muscle tissue has indicated that fiber size is the strongest predictor of MD, FA, and λ2 and λ3 [8] . In addition, work in denervation and disuse models of skeletal muscle atrophy have also shown greater restriction of water diffusion due to decreases in fiber size [9, 20, 21]. These previous findings seem to only partially translate to our findings, as we found that only FA and fiber cross sectional area of the ACL-deficient limb were significantly associated. This relationship was not significant in the non-injured limb. One potential explanation could be in the spread of the data for the non-injured limb. Because the individuals in this clinical cohort were all higher level athletes before injury, the healthy muscle is likely very similar across participants. For FA, MD, and RD the variance was lower for the non-injured limb data sets compared to the injured limb perhaps affecting our ability to discover any associations with muscle size or strength. Future work in a more heterogenous sample might shed more light on whether these relationships do exist. The lack of associations found in our clinical cohort may also be due to other pathological changes to skeletal muscle not accounted for in the measurement and more complex skeletal muscle variations which are not present for simulated work. It should also be noted that previous studies have suggested multiple diffusion times or more complex diffusion models may be needed to capture all aspects of muscle microstructure [8, 22, 23] rather than the single diffusion time used in this study. The random permeable barrier model [24, 25] is an additional approach to assess tissue microstructure. Still, studies in those with Becker muscular dystrophy have found no relationship between random permeable barrier model predicted fiber size and histologically measured fiber size [26]. Overall, the scanning techniques used, and patient population studied for this work, did not indicate a comprehensive relationship between measures of diffusivity such as MD, RD, and FA and histological measures of muscle fiber size, with significant associations limited to just the ACL-deficient limb.

Muscle atrophy after an ACL injury is highly pervasive and has a negative effect on overall quadriceps strength [27, 28]. Isometric knee extension peak torque is used clinically to assess quadriceps strength and is often used to make rehabilitation and/or return to sport decisions. We found significant associations between isometric knee extension peak torque and fractional anisotropy (negatively) and radial diffusivity (positively) for both limbs. While no prior work exists in injured populations, there is some work in healthy populations examining the relationship between the diffusion tensor and strength of the quadriceps. Carpenter et al. found a moderate correlation between peak twitch force (i.e. highest involuntary peak force after an electrical stimulation) of the quadriceps and mean and radial diffusivity, but no relationships between peak twitch force and FA [29]. In other muscles, previous groups have found the ratio of lumbar extension to flexion strength [30] and maximal soleus power [31] to be positively associated with both MD and RD. However, for the relationships between measures of strength and FA in these other muscle groups the evidence is conflicting, with Scheel et al. finding a negative association between maximal muscle power of the calf and FA [31] and Klupp et al. finding no association between lumbar flexion/extension ratio and FA [30]. These differences might be in part to both the different metrics used to classify strength/power and the difference in function and structure of the muscle groups.

Much of the prior work has hypothesized that the relationship between muscle strength and diffusion measures may be due to larger muscle fibers leading to greater strength [8, 31]. This hypothesis seems to hold true for the ACL-deficient limb in our cohort, but not in the contralateral limb. However, the strong associations between FA/RD and strength, combined with the lack of associations between FA/RD and fiber size of the non-injured limb, suggest that FA and RD may reflect other aspects of skeletal muscle composition that contribute to overall gross muscle strength beyond fiber size alone. While fiber size is often used as a predictor of strength and vice versa, many other factors contribute to overall strength in both healthy and injured muscle, including neural factors, collagen content and organization, fatty infiltration, and protein synthesis, among others [32, 33]. Causes for muscle atrophy after ACL injury, specifically, are multifaceted and include changes to the underlying skeletal muscle architecture and physiology like increased fibrosis and changes in protein synthesis [1–4, 28, 34]. While these changes seem to limit prediction of muscle fiber size from the diffusion tensor for muscle of an un-injured limb, diffusivity measures appear to be influenced by overall muscle architecture, which holistically affects muscle function. Additionally, FA does appear to be sensitive to detect meaningful changes in strength, with similar relationships for both the injured and healthy limb. While the cost and time for MRI as compared to standard strength testing may be prohibitive for serial testing, the utility of DTI may be in providing a proxy for strength when it is impossible to measure, such as in the cases of certain meniscal injuries that accompany ACL tears (e.g. bucket-handle tears). Further validation of the technique could also expand its utility to assessment of other injuries where individuals are not able to maximally contract their muscle.

4.1. Limitations

There are several limitations of the current study which must be considered with the results. Measurement scale differed across outcomes; muscle tissue samples were taken from a single site at the middle portion of the vastus lateralis, estimates of the diffusion tensor were averaged from 21 slices centered at the location of the biopsy, and isometric strength could only be isolated to the quadriceps muscles in general. However, it is not expected that measurements of CSA would vary longitudinally along the limbs in the population included in this study [35] and the vastus lateralis is one of the primary force generating muscles of the quadriceps; thus, it is expected that changes to the vastus lateralis will propagate to overall quadriceps strength. Previous work has also indicated that DTI parameters do not vary across the individual muscles of the quadriceps, indicating that similar relationships between strength and DTI parameters may also be present for these other quadriceps muscles [29]. Additionally, the stimulated-echo sequence utilized in this study used only a single diffusion time. The sequence parameters were selected to maximize diffusion directions while maintaining a relatively short scan time (~10 minutes) and a high signal-to-noise ratio while still facilitating clinical integration. The diffusion time (Δ) used in this study was 185 ms, exceeding the minimum time of 170 ms suggested by Berry et al., and was optimized for measuring diffusion within skeletal muscle [22]. Similar times have been used to study skeletal muscle previously [36].

4.2. Conclusion

Finding noninvasive methods, such as DTI, to infer direct measures of skeletal muscle size and composition is of great interest to both clinical and research communities. Understanding the status of skeletal muscle after injury provides clinicians with more information to develop appropriate treatments in circumstances when assessing strength is impractical. The ACL-injured cohort in this study provided a good genetic control between limbs to initially investigate the relationships in both healthy and injured muscle. We found significant between limb differences for both direct measures of muscle size and measures of diffusivity. We also found relationships between isometric knee extension peak torque and fractional anisotropy regardless of injury status, but a lack of significant relationships with measures of fiber size for the uninjured contralateral limb. These finding suggest that measures of diffusivity may be used cross-sectionally to infer between limb difference and FA may be used to infer strength. Future studies should seek to further elucidate the complex relationships between diffusivity measured noninvasively with direct measures of fiber size obtained from muscle tissue. Perhaps, more complex diffusion models can be used to determine the specific nuances and then can be related back to clinically viable sequences, such as the stimulated echo sequence used in this study.

Supplementary Material

1

HIGHLIGHTS.

  • DTI can be used to distinguish muscle between limbs after ACL injury

  • DTI can provide information on knee extensor strength for healthy and injured limbs

  • Lack of association between DTI and fiber size indicates limits with clinical scans

Grant Support

This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases through grant R01AR071398 (BN) and R01AR073831 (BMD) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development through grant F32HD112067 (MKO). The images were obtained on a MR scanner which was partially funded through NIH grant 1S10OD023573.

Footnotes

Data Statement: Data will be made available upon reasonable request to the corresponding author.

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REFERENCES

  • 1.Fry CS, et al. , ACL injury reduces satellite cell abundance and promotes fibrogenic cell expansion within skeletal muscle. J Orthop Res, 2017. 35(9): p. 1876–1885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Keeble AR, et al. , Depressed Protein Synthesis and Anabolic Signaling Potentiate ACL Tear-Resultant Quadriceps Atrophy. American Journal of Sports Medicine, 2023. 51(1): p. 81–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Peck BD, et al. , Anterior Cruciate Ligament Tear Promotes Skeletal Muscle Myostatin Expression, Fibrogenic Cell Expansion, and a Decline in Muscle Quality. Am J Sports Med, 2019. 47(6): p. 1385–1395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Noehren B, et al. , Cellular and Morphological Alterations in the Vastus Lateralis Muscle as the Result of ACL Injury and Reconstruction. J Bone Joint Surg Am, 2016. 98(18): p. 1541–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Schwenzer NF, et al. , Diffusion tensor imaging of the human calf muscle: distinct changes in fractional anisotropy and mean diffusion due to passive muscle shortening and stretching. NMR Biomed, 2009. 22(10): p. 1047–53. [DOI] [PubMed] [Google Scholar]
  • 6.Bolsterlee B, et al. , How does passive lengthening change the architecture of the human medial gastrocnemius muscle? J Appl Physiol (1985), 2017. 122(4): p. 727–738. [DOI] [PubMed] [Google Scholar]
  • 7.Sinha U, et al. , Human soleus muscle architecture at different ankle joint angles from magnetic resonance diffusion tensor imaging. J Appl Physiol (1985), 2011. 110(3): p. 807–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Berry DB, et al. , Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle. Magn Reson Med, 2018. 80(1): p. 317–329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Berry DB, et al. , * A 3D Tissue-Printing Approach for Validation of Diffusion Tensor Imaging in Skeletal Muscle. Tissue Eng Part A, 2017. 23(17–18): p. 980–988. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Scheel M, et al. , Fiber type characterization in skeletal muscle by diffusion tensor imaging. NMR Biomed, 2013. 26(10): p. 1220–4. [DOI] [PubMed] [Google Scholar]
  • 11.Erickson LN, et al. , Effect of Blood Flow Restriction Training on Quadriceps Muscle Strength, Morphology, Physiology, and Knee Biomechanics Before and After Anterior Cruciate Ligament Reconstruction: Protocol for a Randomized Clinical Trial. Phys Ther, 2019. 99(8): p. 1010–1019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Erickson LN, et al. , The Efficacy of Blood Flow Restriction Training to Improve Quadriceps Muscle Function after ACL Reconstruction. Med Sci Sports Exerc, 2024. 57(2): p. 227–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Noehren B, et al. , Comparison of twice refocused spin echo versus stimulated echo diffusion tensor imaging for tracking muscle fibers. J Magn Reson Imaging, 2015. 41(3): p. 624–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Marth AA, et al. , Stimulated echo acquisition mode (STEAM) diffusion tensor imaging with different diffusion encoding times in the supraspinatus muscle: Test-retest reliability and comparison to spin echo diffusion tensor imaging. NMR Biomed, 2025. 38(1): p. e5279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Shanely RA, et al. , Human skeletal muscle biopsy procedures using the modified Bergstrom technique. J Vis Exp, 2014(91): p. 51812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Brightwell CR, et al. , GDF8 inhibition enhances musculoskeletal recovery and mitigates posttraumatic osteoarthritis following joint injury. Science Advances, 2023. 9(48): p. eadi9134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Wen Y, et al. , MyoVision: software for automated high-content analysis of skeletal muscle immunohistochemistry. J Appl Physiol (1985), 2018. 124(1): p. 40–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Owen MK, et al. , Sex Differences in Quadriceps Atrophy After Anterior Cruciate Ligament Tear. Sports Health, 2024. 17(2): p. 236–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Graham MC, et al. , Muscle Fiber Cross-Sectional Area is Associated with Quadriceps Strength and Rate of Torque Development After ACL Injury. J Strength Cond Res, 2024. 38(6): p. e273–e279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Zhang J, et al. , Magnetic resonance imaging of mouse skeletal muscle to measure denervation atrophy. Exp Neurol, 2008. 212(2): p. 448–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Malis V, et al. , Diffusion tensor imaging and diffusion modeling: Application to monitoring changes in the medial gastrocnemius in disuse atrophy induced by unilateral limb suspension. J Magn Reson Imaging, 2019. 49(6): p. 1655–1664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Berry DB, et al. , Varying diffusion time to discriminate between simulated skeletal muscle injury models using stimulated echo diffusion tensor imaging. Magn Reson Med, 2021. 85(5): p. 2524–2536. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Karampinos DC, et al. , Myofiber ellipticity as an explanation for transverse asymmetry of skeletal muscle diffusion MRI in vivo signal. Ann Biomed Eng, 2009. 37(12): p. 2532–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Sigmund EE, et al. , Time-dependent diffusion in skeletal muscle with the random permeable barrier model (RPBM): application to normal controls and chronic exertional compartment syndrome patients. NMR in Biomedicine, 2014. 27(5): p. 519–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Novikov DS, et al. , Revealing mesoscopic structural universality with diffusion. Proc Natl Acad Sci U S A, 2014. 111(14): p. 5088–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Cameron D, et al. , Diffusion-tensor magnetic resonance imaging captures increased skeletal muscle fibre diameters in Becker muscular dystrophy. J Cachexia Sarcopenia Muscle, 2023. 14(3): p. 1546–1557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Kuenze CM, Blemker SS, and Hart JM, Quadriceps function relates to muscle size following ACL reconstruction. J Orthop Res, 2016. 34(9): p. 1656–62. [DOI] [PubMed] [Google Scholar]
  • 28.Lepley LK, et al. , Muscle Atrophy After ACL Injury: Implications for Clinical Practice. Sports Health, 2020: p. 1941738120944256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Carpenter RS, et al. , Association of vastus lateralis diffusion properties with in vivo quadriceps contractile function in premenopausal women. Scand J Med Sci Sports, 2023. 33(3): p. 213–223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Klupp E, et al. , Paraspinal Muscle DTI Metrics Predict Muscle Strength. J Magn Reson Imaging, 2019. 50(3): p. 816–823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Scheel M, et al. , Diffusion tensor imaging of skeletal muscle--correlation of fractional anisotropy to muscle power. Rofo, 2013. 185(9): p. 857–61. [DOI] [PubMed] [Google Scholar]
  • 32.Naimo MA, et al. , Skeletal Muscle Quality: A Biomarker for Assessing Physical Performance Capabilities in Young Populations. Front Physiol, 2021. 12: p. 706699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.McGregor RA, Cameron-Smith D, and Poppitt SS, It is not just muscle mass: a reveiw of muscle quality, composition and metabolism during ageing as determinants of muscle function and mobility in later life. Longevity & Healthspan, 2014. 3(9). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Noehren B, et al. , T1rho imaging as a non-invasive assessment of collagen remodelling and organization in human skeletal muscle after ligamentous injury. J Physiol, 2021. 599(23): p. 5229–5242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Horwath O, et al. , Variablity in vastus lateralis fiber type distribution, fiber size and myonuclear content along and the between legs. J Appl Physiol, 2021. 131(1): p. 158–173. [DOI] [PubMed] [Google Scholar]
  • 36.McDowell AR, et al. , Clinically feasible diffusion MRI in muscle: Time dependence and initial findings in Duchenne muscular dystrophy. Magn Reson Med, 2021. 86: p. 3192–3200. [DOI] [PubMed] [Google Scholar]

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