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Quantitative multi-parametric MRI reveals micro-structural changes in upper-leg muscles after running a marathon

Melissa T. Hooijmans¹, Jithsa R.C. Monte², Martijn Froeling³, Sandra van den Berg-Faay², Adrianus J. Bakermans², Vincent L. Aengevaeren⁴, Mario Maas², Thijs M.H. Eijsvogels⁴, Aart J. Nederveen², and Gustav J. Strijkers¹

¹Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands, ²Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands, ³Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, ⁴Radboud Institute for Health Sciences, Department of Physiology, Radboud University Medical Center, Nijmegen, Netherlands

Synopsis

Quantitative MR techniques have shown promise for detection of muscle micro trauma. Muscle injury and recovery involve many pathophysiological processes including, inflammation, regeneration and fibrosis, therefore multi-parametric approaches are critically needed. This study used a multi-parametric quantitative approach to assess micro structural changes in the upper leg muscles after running a marathon on an individual muscle basis as well as on a localized level. Our results indicate that diffusion indices are highly sensitive to detect micro-structural changes on a localized and whole volume basis and that this approach could prove valuable for improved outcome prediction and risk-assessment of sports-related-injuries.

Introduction

Muscle injuries are the most prevalent injuries in recreational and elite sports. Currently, conventional MRI (T2-weighted) assists in diagnosis of muscle injury but fails to detect micro trauma and to predict recovery¹. Quantitative MR techniques, including DTI-MRI and qT2, have shown promise for detection of micro trauma and to monitor recovery^2,3. The first aim of this study was to assess micro-structural changes in the upper leg muscles after running a marathon using a multi-parametric quantitative MR approach. Furthermore, the muscle’s vulnerability to injury depends on specific muscle function and is not homogeneous within individual muscles^4-6. Yet, muscle injury is frequently assessed using whole muscle quantitative MRI indices, which may result in an incomplete picture of the actual damage. Here, we assessed changes in quantitative MRI indices on an individual muscle basis as well as on a localized level.

Methods

MR datasets were acquired in both upper legs of 11 marathon runners (51 [50-56] years) at three time points: 1 week prior to the marathon (baseline), 24-48 hrs. post marathon (post-marathon) and 2-weeks post marathon (follow-up)(Figure 1A) on a 3T Philips Ingenia system with 16-channel anterior and 12-channel posterior receive coil. Subjects were placed in feet first supine position. The data were acquired in three stacks (Figure 1B) with 30mm overlap covering 498mm with a Field-of-View of 480x276mm². The scanning protocol for each stack included a multi-echo spin-echo (MSE) sequence, a spin-echo EPI diffusion weighted sequence, a Dixon sequence and T2-weighted SPIR sequence (Table 1).

Data-analyses

All data analyses were performed using DTITools for Mathematica (github.com/mfroeling/DTITools). Diffusion data were de-noised, corrected for motion and eddy currents, after which the tensor and perfusion factors were estimated using an IVIM based iWLLS algorithm⁷. T2-mapping data were fitted using an extended phase graph (EPG) approach⁸, and fat fraction maps were reconstructed using IDEAL decomposition⁹ with eight reference fat peaks and a single T2* decay¹⁰. The T2-weighted SPIR images were graded by an experienced radiologist using a standardized grading system¹¹. Eight muscles in both upper legs, i.e., Biceps Femoris Short Head (BFS), Biceps Femoris Long Head (BFL), Semitendinosus (ST), Semimembranosus (SM), Vastus Medialis (VM), Vastus Lateralis (VL), Vastus Intermedius (VI), and Rectus Femoris (RF) muscle, were manually segmented based on the Out-of-Phase DIXON images (ITK-snap, Figure 1B). The VM and BFL muscles were divided in 5 equal segments for the localized assessment of muscle damage. Muscles with insufficient SNR-levels or graded for overt injury were excluded from further analysis. Differences between time points were assessed with a Multi-Level Linear-Mixed-Model. Comparison between whole muscle and localized measurements of Mean Diffusivity (MD) and qT2 values was done using a MANCOVA.

Results

Multi-parametric images of a representative subject are shown in figure 2. An overall time-effect was found for diffusion indices, MD(p<0.001), λ2(p=0.001) and λ3(p<0.001), increasing post-marathon and returning to baseline values at follow-up (Figure 3). The BFL, ST and all quadriceps muscles significantly contributed to this effect. FA, λ1, perfusion fractions and %fat did not change following the marathon while qT2 values were still lower after two weeks recovery (p=0.002). Localized assessment of MD (Δ8.5 [1.7-14.7] %) and qT2 (Δ1.5 [-1.3-1.8]%) showed more pronounced effects than whole muscle assessment (MD Δ1.5%; qT2 Δ0.4%), with a significant time-effect for MD in the VM (5/5 segments; p<0.003) and BFL muscle (2/5 segments; P<0.001) and for qT2 in the BFL muscle (1/5 segments; p<0.003) (Figure 4) compared to baseline.

Discussion

Diffusion indices, MD, λ2 and λ3 were elevated in the majority of upper legs muscles post-marathon and returned to baseline values after two weeks recovery. The other diffusion parameters showed a similar but less pronounced pattern whereas muscle perfusion fractions and water T2 values showed a more heterogeneous pattern. Importantly, absence of clear T2 or perfusion effects suggest that diffusion parameters are more sensitive to detect muscle damage. It indicates that the changes in diffusion parameters post-marathon reflect changes in muscle micro structure rather than edema or perfusion. These findings agree with previous studies which reported a change in diffusion parameters and a less defined pattern for T2^2,3. Localized assessment of muscle damage showed more pronounced effects, going up to 15% with respect to baseline, compared with whole volume measurements, indicating that heterogeneity within individual muscles needs to be considered during muscle injury evaluation.

Conclusion

Diffusion parameters are sensitive to detect micro-structural changes on a localized and whole muscle volume basis. The multi-parametric approach used here is essential to understand the underlying pathophysiology of muscle injury and recovery and could prove valuable for improved outcome prediction and risk assessment of sports-related injuries.

Acknowledgements

No acknowledgement found.

References

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Figures

Figure 1. Schematic representation of the study design. A. Timeline of the study specifying the MR scans with respect to the marathon. B. Schematic view of the scan set-up, showing three stacks of 31 slices with 30mm section overlap covering the full upper legs specifying the sequences per stack. Out-of-Phase Dixon images showing manually drawn regions-of interest for 8 upper leg muscles 1. Biceps Femoris Short Head; 2. Biceps Femoris Long Head; 3. Semitendinosus; 4. Semimenbranosus; 5. Vastus Medialis; 6. Vastus intermedius; 7. Vastus lateralis; 8. Rectus Femoris; at mid-thigh level.

Table 1. Scan parameters for the acquired quantitative T2, DWI, Dixon and T2w SPIR sequences.

Figure 2. Raw and processed example images of a representative subject for the different time points. Left column: baseline data, middle column: post-marathon and right column: follow-up. Top: water images from Dixon sequence and processed PDFF-maps. Middle: raw diffusion images for b-value 0 s/mm² and processed MD-maps. Bottom: raw images from first TE of T2 sequence and processed EPG T2 maps.

Figure 3. Time courses of FA, MD, λ3, qT2 and perfusion fraction for all upper leg muscles separately (left and right leg taken together; left column) and for all subjects separately for the VM and BFL muscle. Each muscle (left column) or subject (middle and right column) are shown in grey while the mean and standard deviation are shown in black. Asterisks indicate an overall time-effect in the left column and a significant change between time points in the middle and right column.

Figure 4. Comparison between whole muscle volume and localized measurements. Left: boxplots showing MD and qT2 for the BFL and VM muscle for all subjects based on a whole volume assessment. Middle: line graphs of MD and qT2 for five segments of the BFL and VM muscle averaged over all subjects at baseline (black), post-marathon (red) and follow-up (grey). Asterisks indicate significant time-effects. Right: showing the location of the five muscle segments for the BFL and VM muscle. Note that the difference in MD and qT2 values between time points is more pronounced for the localized measurements indicating the higher sensitivity to detect change.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)

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