Normalized STEAM-based diffusion tensor imaging provides a robust assessment of muscle tears in football players: preliminary results of a new approach to evaluate muscle injuries

Normalized STEAM-based diffusion tensor imaging provides a robust assessment of muscle tears in... Objectives To assess acute muscle tears in professional football players by diffusion tensor imaging (DTI) and evaluate the impact of normalization of data. Methods Eight football players with acute lower limb muscle tears were examined. DTI metrics of the injured muscle and corresponding healthy contralateral muscle and of ROIs drawn in muscle tears (ROI ) in the corresponding healthy contralat- tear eral muscle (ROI ) in a healthy area ipsilateral to the injury (ROI ) and in a corresponding contralateral area (ROI )were hc_t hi hc_i compared. The same comparison was performed for ratios of the injured (ROI /ROI ) and contralateral sides (ROI /ROI ). tear hi hc_t hc_i ANOVA, Bonferroni-corrected post-hoc and Student’s t-tests were used. Results Analyses of the entire muscle did not show any differences (p>0.05 each) except for axial diffusivity (AD; p=0.048). ROI showed higher mean diffusivity (MD) and AD than ROI (p<0.05). Fractional anisotropy (FA) was lower in ROI tear hc_t tear than in ROI and ROI (p<0.05). Radial diffusivity (RD) was higher in ROI than in any other ROI (p<0.05). Ratios revealed hi hc_t tear higher MD and RD and lower FA and reduced number and length of fibre tracts on the injured side (p<0.05 each). Conclusions DTI allowed a robust assessment of muscle tears in athletes especially after normalization to healthy muscle tissue. Key Points � STEAM-based DTI allows the investigation of muscle tears affecting professional football players. � Fractional anisotropy and mean diffusivity differ between injured and healthy muscle areas. � Only normalized data show differences of fibre tracking metrics in muscle tears. � The normalization of DTI-metrics enables a more robust characterization of muscle tears. . . . . Keywords Diffusion tensor imaging Magnetic resonance imaging Muscle Injury Athletes Abbreviations ROI Region of interest (ROI) drawn on the muscle tear tear ROI ROI drawn on the corresponding healthy contralat- hc_t eral muscle * Chiara Giraudo ROI ROI drawn on a healthy area ipsilateral to the injury hi chiara_giraudo@hotmail.it ROI ROI drawn on an area matching with ROI on the hc_i hi contralateral limb High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria Orthopedic Department, Evangelisches Krankenhaus Wien, Introduction Vienna, Austria Siemens Healthcare GmbH, Erlangen, Germany Acute muscle injuries are very common in elite and non-elite athletes, and tears due to indirect active traumatic events espe- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Medical University of Vienna, Vienna, Austria cially have a high prevalence [1, 2]. In the last decades, the Eur Radiol (2018) 28:2882–2889 2883 clinical evaluation of muscle strains has increasingly been as- matrix coil and a 12-channel spine coil. Both limbs (i.e. the sociated with imaging-based assessment [2, 3]. Several grading injured and the healthy contralateral) were covered by a single systems of muscle injuries have been proposed in the clinical STEAM-DTI scan with the following parameters: repetition and radiological literature [4–6] and recently the Munich time/echo time/TM (TR/TE/TM) 6,100 ms/30 ms/186 ms, Consensus Statement highly recommended the use of an accu- 128 × 96 matrix, field of view (FOV) 440 × 330 mm , rate terminology about muscle lesions [7]. Nevertheless, the GeneRalized Autocalibrating Partial Parallel Acquisition-2 prevalent MRI-based classification is still based on a rough (GRAPPA-2), diffusion time 200 ms, fat saturation (frequency quantification of the amount of torn fibres [8] preventing a high selective suppression and gradient reversal), b-values 0 and imaging-based accuracy in both therapeutic and prognostic 500 s/mm , six averages, 12 directions; 30 adjacent axial management of patients [9]. Diffusion tensor imaging (DTI) slices of 3.5-mm thickness, time of acquisition (TA) 8:10 [10–12] has been successfully used to investigate muscle tears min; voxel volume 3.4 × 3.4 × 3.5 mm . on an animal model (i.e. dystrophic and wild mice) [13]aswell For the morphological assessment, only the injured limb as in patients (i.e. two patients with acute muscle tears) [14]. was imaged via positioning-matched, axial (TR/TE 3,000 ms/ Even though DTI allows an accurate assessment of muscle 26 ms, matrix 384 × 384, FOV 220 × 220mm , TA=1:18 anatomy [15–18]and disorders[13, 14, 19–21], it is affected min), coronal and sagittal proton density fat-sat (TR/TE by challenges (i.e. short T2 relaxation times of muscle) [22]and 4,600 ms/26 ms, matrix 384 × 384, FOV 400 × 400 mm , artifacts [23–25]. The development of new techniques for mus- TA=4:18min, each) and axial T1-weighted TSE (TR/TE 921 cle fibre-tracking is, therefore, an active field of research ms/11 ms, matrix 448 × 448, FOV 220 × 220 mm , TA=4:23 [26–28]. Promising results were recently obtained using a min) with 3-mm slice thickness. Stimulated Echo Acquisition Mode (STEAM) sequence, which, among other advantages, is hardly affected by eddy Morphological assessment current distortions and enables long diffusion times without strong T2-induced signal-to-noise ratio (SNR) loss via the ap- Each injury was rated according to the Munich Consensus plication of mixing time (TM) [25, 29]. classification (i.e. minor partial, moderate partial and Despite the above-mentioned encouraging results and tech- (sub)total muscle tear/tendinous avulsion) [7] by a musculo- nical improvements, to the best of our knowledge a prospec- skeletal radiologist (C.G., 6 years of experience in musculo- tive study applying STEAM-DTI for investigating acute mus- skeletal radiology) using all morphological datasets. cle tears in athletes has not been performed yet. Therefore, the main aim of this study was to assess and quantify acute muscle DTI post-processing tears affecting the lower limb of professional football players with STEAM-DTI. As it has been demonstrated that in ath- DTI images with the same contrast were co-registered to cor- letes differences between the muscles of the preferred and rect gross motion artifacts and/or misalignment [25]. Since non-preferred leg occur [30–33], the second aim of this study STEAM-DTI images are affected by random artifacts due to was to evaluate the impact of a normalization of the data by involuntary muscle contractions [23, 25], a recent correction deriving a ratio between injured and healthy areas on the in- method, based on the weighted mean of voxels’ signal inten- jured limb and healthy areas on the contralateral extremity. sity (WMSI), was applied [25]. Then, a second co-registration among images from the same slice but with different diffusion gradient directions was used [25]. Materials and methods Masking was performed by multiplying MD and RD maps [25]. Matlab (The Mathworks, Natick, MA, USA) was used Patients and study design for the artifact correction, for both co-registrations and masking. Eight professional football players (all males, age range 20–36 A fourth-order Runge-Kutta (RK4) tracking algorithm years) with clinically diagnosed acute muscle tears (i.e. < 1 (DSI Studio,http://dsi-studio.labsolver.org) (FA and angular week) of the lower limb were enrolled in this prospective, threshold 0.12 and 17°, respectively) [25, 34] was applied. IRB-approved study. Written informed consent was obtained from each patient. DTI quantitative evaluation MR protocol Entire muscle analyses Each patient was investigated on a 3T MAGNETOM Trio, a DTI metrics (i.e. fractional anisotropy (FA), mean (MD), radial Tim system MRI Scanner (Siemens Healthcare, Erlangen, (RD) and axial (AD) diffusivity, number, length and volume of Germany) using a combination of an anterior four-channel fibre tracts) were collected, after manual segmentation, from 2884 Eur Radiol (2018) 28:2882–2889 Fig. 1 Drawing of the muscles of the thigh representing the regions of (ROI ). The same ROI has been drawn on a healthy ipsilateral area tear interest (ROIs) used for the ratio analysis. In this example, an injured area (blue ROI in a; i.e., ROI ). The same areas were then investigated on hi on the right rectus femoris muscle is represented (yellow star) where a the contralateral side (red and blue ROIs in b, respectively ROI and hc_t manual ROI (red ROI in a, indicated by the yellow arrow) has been drawn ROI ) hc_i the entire examined section of the injured muscle and from the Statistical analysis healthy contralateral corresponding muscle using DSI Studio (i.e. using b0 and PD-FS images in the background as anatom- Descriptive statistics were applied for categorical data. One- ical reference). The contralateral leg was chosen as control, way repeated measures analysis of variance (ANOVA) with rather than control participants, because of the high inter- Greenhouse-Geisser correction and Bonferroni post-hoc tests subject variability in DTI measurements [35–38]. were used to evaluate differences among all the examined ROIs. Student’s t-tests were applied to compare DTI metrics Region of interest (ROI) analyses of the entire muscles as well as ratios of DTI metrics of the injured side (ROI /ROI ) and of the corresponding contra- tear hi Freehand regions of interest (ROIs) were drawn along the lateral healthy areas (ROI /ROI ). hc_t hc_i margins of each muscle tear (ROI ) (i.e. using b0 and PD- tear FS images in the background as anatomical reference) and the Table 1 Demographic and clinical findings of the patients with muscle same ROI was applied on the corresponding healthy contra- tears enrolled in the study lateral muscle (ROI ). To rule out any physiological differ- hc_t ence between right and left limbs, two other ROIs were drawn, Gender 8 males both in healthy tissue: one in a healthy area ipsilateral to the Age range 20–36 years injury (ROI ) and one in a matching area in the contralateral hi Injured muscle limb (ROI )(Fig. 1). hc_i Gastrocnemius medialis 2 Rectus femoris 2 Ratio Semimembranosus 1 Semitendinosus 1 As it has already been demonstrated in the literature, differ- Soleus 1 ences between the muscles of the dominant and contralateral Biceps femoris 1 limb may occur in professional athletes [30–33]. Thus, to # Grading Minor partial tear 2 avoid any bias, an intra-subject normalization of DTI metrics Moderate partial 6 was performed: ratios of DTI metrics of the injured side (Sub)Total rupture / (ROI /ROI ) and of the two corresponding contralateral tear hi According to the Munich Consensus’ classification healthy areas (ROI /ROI )were compared. hc_t hc_i Eur Radiol (2018) 28:2882–2889 2885 Fig. 2 Axial proton density fat-sat image showing a grade I muscle tear of fibre tracking of both semitendinosus muscles (i.e. blue dotted line in a), the right semitendinosus muscle (blue arrow in a) of a 20-year-old pro- which do not demonstrate any difference for diffusion tensor imaging fessional football player. In (b) and (c), the colour-coded maps of the right (DTI) metrics at the statistical analyses (i.e. Student’st-tests) and left thigh, respectively, are presented along with the corresponding All statistical analyses were performed with SPSS Statistics volume of the fibre tracts were 8,117 ± 6,348, 44.6 ± 21.0 (IBM Corp, Armonk, NY, USA), and the level of signif- 19.2 mm and 93,958 ± 57,292 mm for the injured muscles, icance was set at p<0.05. and 8,795 ± 6,402, 46.7 ± 20.9 mm, 108,564 ± 66,799 mm for the healthy contralateral. No differences emerged for any of the DTI metrics (p>0.05, each) (Fig. 2) except for AD (p=0.048) (Table 2). Results Five out of the eight investigated patients showed an injury of ROI the thigh and three one of the calf. Seven lesions affected the right side and one the left. Two tears were rated as minor ROI analyses allowed an improved characterization of muscle partial and six as moderate [7](Table 1). injuries as listed in Table 3. The average volume and amount of voxels of the ROIs were 3,942 ± 2,915mm and 381 ± 282. DTI quantitative evaluation No differences in DTI metrics were found between ROIs placed in healthy tissue areas (p>0.05, each). Entire muscle The injured areas (i.e. ROI ) showed higher MD tear (+10.3% than ROI and +12.3% than ROI ,respectively; hc_t hc_i The MD, FA, AD and RD values (mean ± SD) of the injured p<0.05 each) and higher AD values (+6.6% than ROI and hc_t and corresponding contralateral muscles were 1.35 ± 0.10 +9.1% than ROI ,respectively; p<0.05, each) than the con- hc_i -3 2 (×10 mm /s), 0.20 ± 0.06, 1.73 ± 0.16, 1.16 ± 0.09 and tralateral healthy areas. There were no differences compared -3 2 1.30 ± 0.05 (×10 mm /s), 0.20 ± 0.05, 1.67 ± 0.12, 1.11 ± to the ipsilateral healthy regions (i.e. ROI )(p>0.05 for each hi 0.05, respectively. The mean ± SD of number, length and DTI metric). Table 2 Entire muscle analyses. Entire muscle with tear (mean ± SD) Entire contralateral healthy Student’st-test Comparison between the injured muscle (mean ± SD) p value* muscle and the contralateral corresponding healthy muscle tr 8116 ± 6347 8794 ± 6402 0.396 tr (mm) 44.6 ± 19.16 46.74 ± 20.85 0.496 tr (mm ) 93,957.61 ± 57,291.74 108,564.36 ± 66,799.11 0.189 FA 0.20 ± 0.06 0.20 ± 0.05 0.858 -3 2 MD (10 mm /s) 1.35 ± 0.10 1.30 ± 0.05 0.078 AD 1.73 ± 0.16 1.67 ± 0.11 0.048 RD 1.16 ± 0.09 1.11 ± 0.05 0.106 tr number of tracks, tr length of tracks, tr volume of tracks, FA fractional anisotropy, MD mean diffusivity, AD n l v axial diffusivity, RD radial diffusivity *Bold type indicates statistically significant values (p<0.05) 2886 Eur Radiol (2018) 28:2882–2889 Also concerning FA, the differences were inhomogeneous. Even if FA was lower in the injured areas (i.e. ROI )than in tear the ipsilateral healthy ones (-19.8 % than in ROI ; p=0.002) hi (Fig. 3), differences in the contralateral side emerged only with the healthy ROIs specular to the tear (-11.5 % than in ROI ; p=0.003). No differences of FA were found between hc_t tears (ROI ) and contralateral areas corresponding to the tear healthy ROI on the injured side (ROI ; p>0.05). hc_i RD was higher in muscle tears than in any other examined ROIs (+13.1 % than ROI , +10.5 % than ROI , and +14.8 hc_t hi %than ROI ; p<0.05). hc_i There were no differences for number, length and volume for fibre tracts in any of the performed comparisons (p>0.05, each) (Fig. 4). Ratio The differences between healthy and injured muscles, partic- ularly the fibre-tracking parameters, were more pronounced after normalization (Table 4). Comparison of the ratios (ROI /ROI and ROI /ROI ) revealed higher MD and tear hi hc_t hc_i RD (+6 % and +8.7 %, respectively; p<0.05 each) and lower FA (-19.5 %, p=0.07) as well as a reduced number and length of fibre tracts on the injured side (-55.6 % and -39.5 %, re- spectively; p<0.05) (Fig. 4). There were no differences for AD and fibre tract volume (p>0.05, each). Discussion Our results suggest that normalized DTI/fibre-tracking metrics obtained via artifact-corrected STEAM-DTI are insensitive to possible bias due to laterality, being thus well suited for quan- titative diagnostic assessment of muscle tears. Acute muscle tears are characterized by alterations of the myofibrillar structure and inflammation [39, 40]. DTI is uniquely sensitive to changes in the magnitude and direction- ality of intramuscular water diffusivity occurring in acute muscle tears. Hence, these alterations are expected to be easily detected and quantified by this technique. Our results show an absence of significant differences (i.e. besides higher AD on the injured side) comparing entire and injured muscles. This is consistent with observations by McMillan et al. on an animal model for injuries of the tibialis anterior [13]. Indeed, these authors found significant differences in DTI metrics only comparing wild and dystrophic mice with muscle injury or comparing injured and non-injured dystrophic animals, whereas differences between injured and non-injured wild an- imals did not occur [13]. In contrast, Zaraiskaya et al. [14] showed significant differ- ences in FA, MD and eigenvalues (i.e. λ ,λ ,λ )comparing 1 2 3 DTI measures from entire healthy muscles (i.e. eight volun- teers) with those obtained in ROIs drawn in injured muscle Table 3 Region of interest (ROI)-based diffusion tensor imaging (DTI) analyses 1-Way ANOVA (P) Post-hoc tests^ ROI ROI ROI ROI ROI vs. ROI vs. ROI vs. ROI vs. ROI vs. ROI vs. tear hc_t hi hc_i tear tear tear hc_t hc_t hi ROI ROI ROI ROI ROI ROI hc_t hi hc_i hi hc_i hc_i tr 972 ± 997 1,633 ± 1317 1,002 ± 669 893 ± 661 0.182 - - - - - - tr (mm) 37.30 ± 22 54.15 ± 24.83 48.08 ± 23.1 44.10 ± 23.7 0.043 0.065 0.596 1.000 1.000 0.366 1.000 tr (mm ) 14,530 ± 13,925 22,424 ± 12,960 14,371 ± 10,191 13,482 ± 11,349 0.102 - - - - - - FA 0.18 ± 0.05 0.20 ± 0.04 0.22 ± 0.05 0.20 ± 0.04 0.008 0.018 0.015 0.098 0.454 1.000 1.000 -3 2 MD (10 mm /s) 1.44 ± 0.11 1.31 ± 0.07 1.33 ± 0.13 1.28 ± 0.1 0.002 0.003 0.084 0.002 1.000 1.000 1.000 AD 1.8 ± 0.15 1.7 ± 0.15 1.7 ± 0.18 1.65 ± 0.17 0.001 0.007 0.336 0.007 1.000 0.784 0.282 RD 1.27 ± 0.12 1.12 ± 0.07 1.14 ± 0.13 1.10 ± 0.08 0.003 0.005 0.047 0.003 1.000 1.000 1.000 Comparison between the muscle tear and the healthy contralateral and ipsilateral muscle areas tr number of tracks, tr length of tracks, tr volume of tracks, FA fractional anisotropy, MD mean diffusivity, AD axial diffusivity, RD radial diffusivity n l v ROI region of interest drawn on the muscle tear, ROI ROI drawn on the on the corresponding healthy contralateral muscle, ROI ROI drawn on a healthy area ipsilateral to the injury, ROI ROI tear hc_t hi hc_i drawnonanareamatchingthe ROI on the contralateral limb, ^ Greenhouse Geisser hi Bold type indicates statistically significant values (p<0.05) Eur Radiol (2018) 28:2882–2889 2887 Fig. 3 Grade II muscle tear of the right rectus femoris muscle (blue arrow fractional anisotropy (FA) (blue arrow on the FA map in b) than the on the axial proton density fat-saturated image in a) of a 23-year-old corresponding healthy contralateral muscle (white arrow on the FA map professional football player. The injured area demonstrates lower in c) areas of the calves of four patients (i.e. two with haematomas dominant and non-dominant side has been shown [30–33], it and two with muscle tears). These results are in accordance appears reasonable that the laterality is a biasing factor in with the differences in FA, MD, RD and AD found in our quantitative DTI assessments in muscles. The results obtained population comparing the injured areas with the healthy ones after applying the normalization seem to confirm this assump- (i.e. ipsi- and contralateral ROIs), even if it has to be taken into tion, as differences in length, number and volume of the account that the presence of oedema may lead just to an ap- tracked fibres due to the injury were apparent only in normal- parent decrease of AD and FA [41]. ized data. Zaraiskaya et al. performed fibre tracking only in healthy Our study results are preliminary and could not yet validate controls, but no such data were presented for patients [14]. the fact that STEAM-DTI brings any additional benefits com- Froeling et al. [35] evaluated fibre-tracking changes at differ- pared to conventional MRI. Since one of the more challenging ent time points in marathon runners, but performed no sepa- aspects of muscle tear assessment is represented by the prog- rate assessments for muscle strains already visible on anatom- nosis of the recovery interval [42], we strongly believe that the ical images (i.e. T2w images). To the best of our knowledge, application of the ratio could also provide essential benefits there are no previous studies that have investigated fibre- for the longitudinal evaluation of muscle strains during the tracking metrics (i.e. number, length and volume of tracked recovery phase and thus improve the prediction of the recov- fibres) of muscle tears. In our cohort, no differences in fibre ery interval and reduce the risk of recurrence. tracking emerged, either in the entire muscle, or in the ROI- based analyses. The tracked muscle fibres of the injured side turned out to be significantly less numerous (-55 %) and Limitations shorter (-39 %) only after normalization of the data. Considering that in athletes an asymmetry in the characteris- Despite our very promising results, there are some limitations tics and metabolic activity of muscle belonging to the to our study. All patients were scanned within 1 week after the Fig. 4 Grade II lesion of the right medial gastrocnemius (blue arrow on in our population comparing the tears with all healthy areas. The statisti- the axial proton density fat-saturated image in a) of a 35-year-old football cal analyses revealed significant differences in terms of length and player. Fibre tracking of the injured area is illustrated (blue arrow in b) amount of fibre tracts only when a ratio between the ROIs on the injured and of the ipsi- (b) and contralateral healthy areas (c). Although visually (i.e. represented here by the fibre tracts on the right calf in b)and contra- the fibre tracking of the injured muscle area seems to demonstrate shorter lateral extremity (i.e. represented here by the fibre tracts on the left calf in and less numerous fibres, no statistically significant differences occurred c) was calculated 2888 Eur Radiol (2018) 28:2882–2889 Table 4 Comparison of diffusion tensor imaging (DTI) metrics’ ratio Acknowledgements The preliminary results of this study have been pre- between the injured leg and the contralateral healthy one sented as an electronic poster at the ISMRM 25th Annual Meeting & Exhibition (22–27 April 2017, Hawaii, USA). ROI /ROI ROI /ROI Students’ t-test tear hi hc_t hc_i Funding Stanislav Motyka was supported by the OeNB tr 0.55 ± 0.45 1.24 ± 0.53 0.028 Jubilaeumsfond (Grant #16133) and Giraudo Chiara by the Austrian Science Fund (FWF; Project KLIF 382). Open Access Funding provided tr (mm) 0.69 ± 0.22 1.14 ± 0.28 0.005 by Medical University of Vienna. tr (mm ) 0.62 ± 0.40 1.26 ± 0.65 0.056 FA 0.88 ± 0.07 1.09 ± 0.15 0.007 Compliance with ethical standards -3 2 MD (10 mm /s) 1.10 ± 0.04 1.04 ± 0.07 0.028 AD 1.06 ± 0.03 1.04 ± 0.04 0.241 Guarantor The scientific guarantor of this publication is Prof. Wolfgang Bogner. RD 1.13 ± 0.06 1.03 ± 0.10 0.014 tr number of tracks, tr length of tracks, tr volume of tracks, FA frac- Conflict of interest Thorsten Feiweier is senior researcher at Siemens n l v tional anisotropy, MD mean diffusivity, AD axial diffusivity, RD radial Healthcare GmbH (Siemens, Germany). diffusivity, ROI /ROI ratio between the ROI drawn on the tear and the The other authors of this manuscript declare no relationships with any tear hi one drawn on a ipsilateral healthy area, ROI /ROI ratio of the two companies whose products or services may be related to the subject mat- hc_t hc_i corresponding contralateral healthy areas ter of the article. Bold type indicates statistically significant values (p<0.05) Statistics and biometry Michael Weber, co-author of this manuscript, has significant statistical expertise. injury; however, DTI/fibre-tracking metrics may change quite quickly (e.g. inflammation may occur in a few days). Thus, a Informed consent Written informed consent was obtained from all sub- jects (patients) in this study. more standardized recruitment (i.e. a fixed number of days after the injury for all patients) may be beneficial, especially Ethical approval Institutional Review Board approval was obtained. for entire muscle analyses. Despite the evidence that in volun- teers different ranges of DTI metrics values occur in different Methodology muscles [15, 16], in the present study separate analyses ac- � prospective cording to the injured muscles were not performed, because � experimental � performed at one institution of the low number of examined patients. Future studies includ- ing larger patient populations should focus on muscle-specific analyses to provide even more accurate results. Nevertheless, Open Access This article is distributed under the terms of the Creative normalization will certainly also reduce such differences be- Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, tween muscle groups. distribution, and reproduction in any medium, provided you give appro- Finally, the quite long acquisition time (i.e. ca. 8 min) priate credit to the original author(s) and the source, provide a link to the might represent a limit with very extensive lesions, since mo- Creative Commons license, and indicate if changes were made. tion artifacts may occur. However, recent developments in simultaneous-multi-slice (SMS) DTI have translated into References ~threefold acceleration of clinically available DTI sequences [43]. SMS was not yet implemented into our STEAM-DTI 1. Page P (1995) Pathophysiology of acute exercise-induced muscular sequence when our study was performed, but future studies injury: clinical implications. J Athl Train 30:29–34 2. Lee JC, Mitchell AWM, Healy JC (2012) Imaging of muscle injury aiming for larger FOVs should directly benefit from this new in the elite athlete. Br J Radiol 85:1173–1185 technology. 3. Cross TM, Gibbs N, Dip G et al (2004) Acute quadriceps muscle strains. Magnetic Resonance Imaging features and prognosis. Am J Sports Med 32:710–719 4. O’Donoghue DO (1962) Treatment of injuries to athletes. WB Saunders, Philadelphia Conclusion 5. Ryan AJ (1969) Quadriceps strain, rupture and charlie horse. Med Sci Sports 1:106–111 In conclusion, STEAM-based DTI allowed a precise assess- 6. Takebayashi S, Takasawa H, Banzai Y et al (1995) Sonographic ment of the injured fibres in athletes especially when a ratio findings in muscle strain injury: clinical and MR imaging correla- between the injured and the contralateral muscles was applied. tion. J Ultrasound Med 14:899–905 7. Mueller-Wohlfahrt HW, Haensel L, Mithoefer K et al (2013) Aiming to improve the current imaging-based classification of Terminology and classification of muscle injuries in sport: The muscle tears and to increase the accuracy of the therapeutic Munich consensus statement. Br J Sports Med 47:342–350 and prognostic management of injured athletes, future studies 8. Pedowitz R, Chung CB, Resnick D (2009) Muscle. In Magnetic including a larger population and evaluating muscle tears, also Resonance Imaging in Orthopedic Sports Medicine. Springer- Verlag GmbH during follow-up, are necessary. Eur Radiol (2018) 28:2882–2889 2889 9. Hamilton B, Valle X, Rodas G et al (2015) Classification and grad- 27. Karampinos DC, Banerjee S, King KF et al (2012) Considerations in high-resolution skeletal muscle diffusion tensor imaging using ing of muscle injuries: a narrative review. Br J Sports Med 49:306 10. Alexander AL, Lee JE, Lazar M et al (2007) Diffusion Tensor single-shot echo planar imaging with stimulated-echo preparation Imaging of the Brain. Neurotherapeutics 4:316–329 and sensitivity encoding. NMR Biomed 25:766–778 11. Li K, Dortch RD, Brian E et al (2014) Multi-parametric MRI 28. Filli L, Piccirelli M, Kenkel D et al (2015) Simultaneous multislice Characterization of Healthy Human Thigh Muscles at 3.0T - echo planar imaging with blipped controlled aliasing in parallel Relaxation, Magnetization Transfer, Fat/Water, and Diffusion imaging results in higher acceleration. A promising technique for Tensor Imaging. NMR Biomed 27:1070–1084 accelerated diffusion tensor imaging of skeletal muscle. Invest 12. Scheel M, von Roth P, Winkler T et al (2013) Fiber type character- Radiol 50:456–463 ization in skeletal muscle by diffusion tensor imaging. NMR 29. Noehren B, Andersen A, Feiweier T et al (2015) Comparison of Biomed 10:1220–1224 twice refocused spin echo versus stimulated echo diffusion tensor 13. McMillan A, Shi D, Pratt S et al (2011) Diffusion Tensor MRI to imaging for tracking muscle fibers. J Magn Reson Imaging 41:624– Assess Damage in Healthy and Dystrophic Skeletal Muscle after Lengthening Contractions. J Biomed Biotechnol 970726 30. Rahnama N, Lees A, Bambaecichi E (2005) Comparison of muscle 14. Zaraiskaya T, Kumbhare D, Noseworthy M (2006) Diffusion strength and flexibility between the preferred and non-preferred leg Tensor Imaging in evaluation of human skeletal muscle injury. J in English soccer players. Ergonomics 48:1568–1575 Magn Reson Imaging 24:402–408 31. Kearns CF, Isokawa M, Abe T (2001) Architectural characteristics 15. Sinha S, Sinha U, Edgerton VR (2006) In vivo diffusion tensor of dominant leg muscles in junior soccer players. Eur J Appl imaging of the human calf muscle. J Magn Reson Imaging 24: Physiol 85:240–243 182–190 32. Zoladz JA, Kulinowski P, Zapart-Bukowska J et al (2007) 16. Budzik JF, Le Thuc V, Demondion X et al (2007) In vivo MR Phosphorylation potential in the dominant leg is lower, and tractography of thigh muscles using diffusion imaging: initial re- [ADPfree] is higher in calf muscles at rest in endurance athletes sults. Eur Radiol 17:3079–3085 than in sprinters and in untrained subjects. J Physiol Pharmacol 58: 17. Kermarrec E, Budzik JF, Khalil C et al (2010) In vivo diffusion 803–819 tensor imaging and tractography of human thigh muscles in healthy 33. Hides J, Fan T, Stanton W et al (2010) Psoas and quadratus subjects. AJR Am J Roentgenol 195:352–356 lumborum muscle asymmetry among elite Australian Football 18. Froeling M, Nederveen AJ, Heijtel DF et al (2012) Diffusion-tensor League players. Br J Sports Med 44:563–567 MRI reveals the complex muscle architecture of the human fore- 34. Basser PJ, Pajevic S, Pierpaoli C et al (2000) In vivo fiber arm. J Magn Reson Imaging 36:237–248 tractography using DT-MRI Data. Magn Reson Med 44:625–632 19. Ponrartana S, Ramos-Platt L, Wren TA et al (2015) Effectiveness of 35. Froeling M, Oudeman J, Strijkers GJ et al (2015) Muscle changes diffusion tensor imaging in assessing disease severity in Duchenne detected with diffusion-tensor imaging after long-distance running. muscular dystrophy: preliminary study. Pediatr Radiol 45:582–589 Radiology 274:548–562 20. Sigmund EE, Sui D, Ukpebor O et al (2013) Stimulated echo dif- 36. Lansdown DA, Ding Z, Wadington M et al (1985) (2007) fusion tensor imaging and SPAIR T2-weighted imaging in chronic Quantitative diffusion tensor MRI-based fiber tracking of human exertional compartment syndrome of the lower leg muscles. J Magn skeletal muscle. J Appl Physiol 103:673–668 Reson Imaging 38:1073–1082 37. Okamoto Y, Kunimatsu A, Kono T et al (2010) Gender differences 21. Budzik JF, Balbi V, Verclytte S (2014) Diffusion tensor imaging in in MR muscle tractography. Magn Reson Med Sci 9:111–118 musculoskeletal disorders. Radiographics 34:56–72 38. Galbán CJ, Maderwald S, Stock F et al (2007) Age-related changes 22. Schick F, Eismann B, Jung WI et al (1993) Comparison of localized in skeletal muscle as detected by diffusion tensor magnetic reso- proton NMR signals of skeletal muscle and fat tissue in vivo: two nance imaging. J Gerontol A Biol Sci Med Sci 62:453–458 lipid compartments in muscle tissue. Magn Reson Med 29:158–167 39. Fernandes TL, Pedrinelli A, Hernandes AJ (2011) Muscle injury – 23. Steidle G, Schick F (2015) Addressing spontaneous signal voids in physiopathology, diagnosis, treatment and clinical presentation. repetitive single-shot DWI of musculature: spatial and temporal Rev Bras Ortop 46:247–255 patterns in the calves of healthy volunteers and consideration of 40. Delos D, Maak TG, Rodeo SA (2013) Muscle Injuries in Athletes: unintended muscle activities as underlying mechanism. NMR Enhancing Recovery Through Scientific Understanding and Novel Biomed 28:801–810 Therapies. Sports Health 5:346–352 24. Chang LC, Walker L, Pierpaoli C (2012) Informed RESTORE: a 41. Froeling M, Nederveen AJ, Nicolay K et al (2013) DTI of human method for robust estimation of diffusion tensor from low redun- skeletal muscle: the effects of diffusion encoding parameters, dancy datasets in the presence of physiological noise artifacts. signal-to-noise ratio and T2 on tensor indices and fiber tracts. Magn Reson Med 68:1654–1663 NMR Biomed 26:1339–1352 25. Giraudo C, Motyka S, Weber M et al (2017) Weighted Mean of 42. Slavotinek JP (2010) Muscle Injury: The Role of Imaging in Signal Intensity for Unbiased Fiber Tracking of Skeletal Muscles: Prognostic Assignment and Monitoring of Muscle Repair. Semin Development of a New Method and Comparison With Other Musculoskelet Radiol 14:194–200 Correction Techniques. Invest Radiol 52:488–497 26. Saupe N, White LM, Sussman MS (2008) Diffusion tensor mag- 43. Setsompop K, Cohen-Adad J, Gagoski BA et al (2012) Improving netic resonance imaging of the human calf: comparison between 1.5 diffusion MRI using simultaneous multi-slice echo planar imaging. T and 3.0 T-preliminary results. Invest Radiol 43:612–618 NeuroImage 63:569–580 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Radiology Springer Journals

Normalized STEAM-based diffusion tensor imaging provides a robust assessment of muscle tears in football players: preliminary results of a new approach to evaluate muscle injuries

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Springer Berlin Heidelberg
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Copyright © 2018 by The Author(s)
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Medicine & Public Health; Imaging / Radiology; Diagnostic Radiology; Interventional Radiology; Neuroradiology; Ultrasound; Internal Medicine
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0938-7994
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1432-1084
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10.1007/s00330-017-5218-9
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Abstract

Objectives To assess acute muscle tears in professional football players by diffusion tensor imaging (DTI) and evaluate the impact of normalization of data. Methods Eight football players with acute lower limb muscle tears were examined. DTI metrics of the injured muscle and corresponding healthy contralateral muscle and of ROIs drawn in muscle tears (ROI ) in the corresponding healthy contralat- tear eral muscle (ROI ) in a healthy area ipsilateral to the injury (ROI ) and in a corresponding contralateral area (ROI )were hc_t hi hc_i compared. The same comparison was performed for ratios of the injured (ROI /ROI ) and contralateral sides (ROI /ROI ). tear hi hc_t hc_i ANOVA, Bonferroni-corrected post-hoc and Student’s t-tests were used. Results Analyses of the entire muscle did not show any differences (p>0.05 each) except for axial diffusivity (AD; p=0.048). ROI showed higher mean diffusivity (MD) and AD than ROI (p<0.05). Fractional anisotropy (FA) was lower in ROI tear hc_t tear than in ROI and ROI (p<0.05). Radial diffusivity (RD) was higher in ROI than in any other ROI (p<0.05). Ratios revealed hi hc_t tear higher MD and RD and lower FA and reduced number and length of fibre tracts on the injured side (p<0.05 each). Conclusions DTI allowed a robust assessment of muscle tears in athletes especially after normalization to healthy muscle tissue. Key Points � STEAM-based DTI allows the investigation of muscle tears affecting professional football players. � Fractional anisotropy and mean diffusivity differ between injured and healthy muscle areas. � Only normalized data show differences of fibre tracking metrics in muscle tears. � The normalization of DTI-metrics enables a more robust characterization of muscle tears. . . . . Keywords Diffusion tensor imaging Magnetic resonance imaging Muscle Injury Athletes Abbreviations ROI Region of interest (ROI) drawn on the muscle tear tear ROI ROI drawn on the corresponding healthy contralat- hc_t eral muscle * Chiara Giraudo ROI ROI drawn on a healthy area ipsilateral to the injury hi chiara_giraudo@hotmail.it ROI ROI drawn on an area matching with ROI on the hc_i hi contralateral limb High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria Orthopedic Department, Evangelisches Krankenhaus Wien, Introduction Vienna, Austria Siemens Healthcare GmbH, Erlangen, Germany Acute muscle injuries are very common in elite and non-elite athletes, and tears due to indirect active traumatic events espe- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Medical University of Vienna, Vienna, Austria cially have a high prevalence [1, 2]. In the last decades, the Eur Radiol (2018) 28:2882–2889 2883 clinical evaluation of muscle strains has increasingly been as- matrix coil and a 12-channel spine coil. Both limbs (i.e. the sociated with imaging-based assessment [2, 3]. Several grading injured and the healthy contralateral) were covered by a single systems of muscle injuries have been proposed in the clinical STEAM-DTI scan with the following parameters: repetition and radiological literature [4–6] and recently the Munich time/echo time/TM (TR/TE/TM) 6,100 ms/30 ms/186 ms, Consensus Statement highly recommended the use of an accu- 128 × 96 matrix, field of view (FOV) 440 × 330 mm , rate terminology about muscle lesions [7]. Nevertheless, the GeneRalized Autocalibrating Partial Parallel Acquisition-2 prevalent MRI-based classification is still based on a rough (GRAPPA-2), diffusion time 200 ms, fat saturation (frequency quantification of the amount of torn fibres [8] preventing a high selective suppression and gradient reversal), b-values 0 and imaging-based accuracy in both therapeutic and prognostic 500 s/mm , six averages, 12 directions; 30 adjacent axial management of patients [9]. Diffusion tensor imaging (DTI) slices of 3.5-mm thickness, time of acquisition (TA) 8:10 [10–12] has been successfully used to investigate muscle tears min; voxel volume 3.4 × 3.4 × 3.5 mm . on an animal model (i.e. dystrophic and wild mice) [13]aswell For the morphological assessment, only the injured limb as in patients (i.e. two patients with acute muscle tears) [14]. was imaged via positioning-matched, axial (TR/TE 3,000 ms/ Even though DTI allows an accurate assessment of muscle 26 ms, matrix 384 × 384, FOV 220 × 220mm , TA=1:18 anatomy [15–18]and disorders[13, 14, 19–21], it is affected min), coronal and sagittal proton density fat-sat (TR/TE by challenges (i.e. short T2 relaxation times of muscle) [22]and 4,600 ms/26 ms, matrix 384 × 384, FOV 400 × 400 mm , artifacts [23–25]. The development of new techniques for mus- TA=4:18min, each) and axial T1-weighted TSE (TR/TE 921 cle fibre-tracking is, therefore, an active field of research ms/11 ms, matrix 448 × 448, FOV 220 × 220 mm , TA=4:23 [26–28]. Promising results were recently obtained using a min) with 3-mm slice thickness. Stimulated Echo Acquisition Mode (STEAM) sequence, which, among other advantages, is hardly affected by eddy Morphological assessment current distortions and enables long diffusion times without strong T2-induced signal-to-noise ratio (SNR) loss via the ap- Each injury was rated according to the Munich Consensus plication of mixing time (TM) [25, 29]. classification (i.e. minor partial, moderate partial and Despite the above-mentioned encouraging results and tech- (sub)total muscle tear/tendinous avulsion) [7] by a musculo- nical improvements, to the best of our knowledge a prospec- skeletal radiologist (C.G., 6 years of experience in musculo- tive study applying STEAM-DTI for investigating acute mus- skeletal radiology) using all morphological datasets. cle tears in athletes has not been performed yet. Therefore, the main aim of this study was to assess and quantify acute muscle DTI post-processing tears affecting the lower limb of professional football players with STEAM-DTI. As it has been demonstrated that in ath- DTI images with the same contrast were co-registered to cor- letes differences between the muscles of the preferred and rect gross motion artifacts and/or misalignment [25]. Since non-preferred leg occur [30–33], the second aim of this study STEAM-DTI images are affected by random artifacts due to was to evaluate the impact of a normalization of the data by involuntary muscle contractions [23, 25], a recent correction deriving a ratio between injured and healthy areas on the in- method, based on the weighted mean of voxels’ signal inten- jured limb and healthy areas on the contralateral extremity. sity (WMSI), was applied [25]. Then, a second co-registration among images from the same slice but with different diffusion gradient directions was used [25]. Materials and methods Masking was performed by multiplying MD and RD maps [25]. Matlab (The Mathworks, Natick, MA, USA) was used Patients and study design for the artifact correction, for both co-registrations and masking. Eight professional football players (all males, age range 20–36 A fourth-order Runge-Kutta (RK4) tracking algorithm years) with clinically diagnosed acute muscle tears (i.e. < 1 (DSI Studio,http://dsi-studio.labsolver.org) (FA and angular week) of the lower limb were enrolled in this prospective, threshold 0.12 and 17°, respectively) [25, 34] was applied. IRB-approved study. Written informed consent was obtained from each patient. DTI quantitative evaluation MR protocol Entire muscle analyses Each patient was investigated on a 3T MAGNETOM Trio, a DTI metrics (i.e. fractional anisotropy (FA), mean (MD), radial Tim system MRI Scanner (Siemens Healthcare, Erlangen, (RD) and axial (AD) diffusivity, number, length and volume of Germany) using a combination of an anterior four-channel fibre tracts) were collected, after manual segmentation, from 2884 Eur Radiol (2018) 28:2882–2889 Fig. 1 Drawing of the muscles of the thigh representing the regions of (ROI ). The same ROI has been drawn on a healthy ipsilateral area tear interest (ROIs) used for the ratio analysis. In this example, an injured area (blue ROI in a; i.e., ROI ). The same areas were then investigated on hi on the right rectus femoris muscle is represented (yellow star) where a the contralateral side (red and blue ROIs in b, respectively ROI and hc_t manual ROI (red ROI in a, indicated by the yellow arrow) has been drawn ROI ) hc_i the entire examined section of the injured muscle and from the Statistical analysis healthy contralateral corresponding muscle using DSI Studio (i.e. using b0 and PD-FS images in the background as anatom- Descriptive statistics were applied for categorical data. One- ical reference). The contralateral leg was chosen as control, way repeated measures analysis of variance (ANOVA) with rather than control participants, because of the high inter- Greenhouse-Geisser correction and Bonferroni post-hoc tests subject variability in DTI measurements [35–38]. were used to evaluate differences among all the examined ROIs. Student’s t-tests were applied to compare DTI metrics Region of interest (ROI) analyses of the entire muscles as well as ratios of DTI metrics of the injured side (ROI /ROI ) and of the corresponding contra- tear hi Freehand regions of interest (ROIs) were drawn along the lateral healthy areas (ROI /ROI ). hc_t hc_i margins of each muscle tear (ROI ) (i.e. using b0 and PD- tear FS images in the background as anatomical reference) and the Table 1 Demographic and clinical findings of the patients with muscle same ROI was applied on the corresponding healthy contra- tears enrolled in the study lateral muscle (ROI ). To rule out any physiological differ- hc_t ence between right and left limbs, two other ROIs were drawn, Gender 8 males both in healthy tissue: one in a healthy area ipsilateral to the Age range 20–36 years injury (ROI ) and one in a matching area in the contralateral hi Injured muscle limb (ROI )(Fig. 1). hc_i Gastrocnemius medialis 2 Rectus femoris 2 Ratio Semimembranosus 1 Semitendinosus 1 As it has already been demonstrated in the literature, differ- Soleus 1 ences between the muscles of the dominant and contralateral Biceps femoris 1 limb may occur in professional athletes [30–33]. Thus, to # Grading Minor partial tear 2 avoid any bias, an intra-subject normalization of DTI metrics Moderate partial 6 was performed: ratios of DTI metrics of the injured side (Sub)Total rupture / (ROI /ROI ) and of the two corresponding contralateral tear hi According to the Munich Consensus’ classification healthy areas (ROI /ROI )were compared. hc_t hc_i Eur Radiol (2018) 28:2882–2889 2885 Fig. 2 Axial proton density fat-sat image showing a grade I muscle tear of fibre tracking of both semitendinosus muscles (i.e. blue dotted line in a), the right semitendinosus muscle (blue arrow in a) of a 20-year-old pro- which do not demonstrate any difference for diffusion tensor imaging fessional football player. In (b) and (c), the colour-coded maps of the right (DTI) metrics at the statistical analyses (i.e. Student’st-tests) and left thigh, respectively, are presented along with the corresponding All statistical analyses were performed with SPSS Statistics volume of the fibre tracts were 8,117 ± 6,348, 44.6 ± 21.0 (IBM Corp, Armonk, NY, USA), and the level of signif- 19.2 mm and 93,958 ± 57,292 mm for the injured muscles, icance was set at p<0.05. and 8,795 ± 6,402, 46.7 ± 20.9 mm, 108,564 ± 66,799 mm for the healthy contralateral. No differences emerged for any of the DTI metrics (p>0.05, each) (Fig. 2) except for AD (p=0.048) (Table 2). Results Five out of the eight investigated patients showed an injury of ROI the thigh and three one of the calf. Seven lesions affected the right side and one the left. Two tears were rated as minor ROI analyses allowed an improved characterization of muscle partial and six as moderate [7](Table 1). injuries as listed in Table 3. The average volume and amount of voxels of the ROIs were 3,942 ± 2,915mm and 381 ± 282. DTI quantitative evaluation No differences in DTI metrics were found between ROIs placed in healthy tissue areas (p>0.05, each). Entire muscle The injured areas (i.e. ROI ) showed higher MD tear (+10.3% than ROI and +12.3% than ROI ,respectively; hc_t hc_i The MD, FA, AD and RD values (mean ± SD) of the injured p<0.05 each) and higher AD values (+6.6% than ROI and hc_t and corresponding contralateral muscles were 1.35 ± 0.10 +9.1% than ROI ,respectively; p<0.05, each) than the con- hc_i -3 2 (×10 mm /s), 0.20 ± 0.06, 1.73 ± 0.16, 1.16 ± 0.09 and tralateral healthy areas. There were no differences compared -3 2 1.30 ± 0.05 (×10 mm /s), 0.20 ± 0.05, 1.67 ± 0.12, 1.11 ± to the ipsilateral healthy regions (i.e. ROI )(p>0.05 for each hi 0.05, respectively. The mean ± SD of number, length and DTI metric). Table 2 Entire muscle analyses. Entire muscle with tear (mean ± SD) Entire contralateral healthy Student’st-test Comparison between the injured muscle (mean ± SD) p value* muscle and the contralateral corresponding healthy muscle tr 8116 ± 6347 8794 ± 6402 0.396 tr (mm) 44.6 ± 19.16 46.74 ± 20.85 0.496 tr (mm ) 93,957.61 ± 57,291.74 108,564.36 ± 66,799.11 0.189 FA 0.20 ± 0.06 0.20 ± 0.05 0.858 -3 2 MD (10 mm /s) 1.35 ± 0.10 1.30 ± 0.05 0.078 AD 1.73 ± 0.16 1.67 ± 0.11 0.048 RD 1.16 ± 0.09 1.11 ± 0.05 0.106 tr number of tracks, tr length of tracks, tr volume of tracks, FA fractional anisotropy, MD mean diffusivity, AD n l v axial diffusivity, RD radial diffusivity *Bold type indicates statistically significant values (p<0.05) 2886 Eur Radiol (2018) 28:2882–2889 Also concerning FA, the differences were inhomogeneous. Even if FA was lower in the injured areas (i.e. ROI )than in tear the ipsilateral healthy ones (-19.8 % than in ROI ; p=0.002) hi (Fig. 3), differences in the contralateral side emerged only with the healthy ROIs specular to the tear (-11.5 % than in ROI ; p=0.003). No differences of FA were found between hc_t tears (ROI ) and contralateral areas corresponding to the tear healthy ROI on the injured side (ROI ; p>0.05). hc_i RD was higher in muscle tears than in any other examined ROIs (+13.1 % than ROI , +10.5 % than ROI , and +14.8 hc_t hi %than ROI ; p<0.05). hc_i There were no differences for number, length and volume for fibre tracts in any of the performed comparisons (p>0.05, each) (Fig. 4). Ratio The differences between healthy and injured muscles, partic- ularly the fibre-tracking parameters, were more pronounced after normalization (Table 4). Comparison of the ratios (ROI /ROI and ROI /ROI ) revealed higher MD and tear hi hc_t hc_i RD (+6 % and +8.7 %, respectively; p<0.05 each) and lower FA (-19.5 %, p=0.07) as well as a reduced number and length of fibre tracts on the injured side (-55.6 % and -39.5 %, re- spectively; p<0.05) (Fig. 4). There were no differences for AD and fibre tract volume (p>0.05, each). Discussion Our results suggest that normalized DTI/fibre-tracking metrics obtained via artifact-corrected STEAM-DTI are insensitive to possible bias due to laterality, being thus well suited for quan- titative diagnostic assessment of muscle tears. Acute muscle tears are characterized by alterations of the myofibrillar structure and inflammation [39, 40]. DTI is uniquely sensitive to changes in the magnitude and direction- ality of intramuscular water diffusivity occurring in acute muscle tears. Hence, these alterations are expected to be easily detected and quantified by this technique. Our results show an absence of significant differences (i.e. besides higher AD on the injured side) comparing entire and injured muscles. This is consistent with observations by McMillan et al. on an animal model for injuries of the tibialis anterior [13]. Indeed, these authors found significant differences in DTI metrics only comparing wild and dystrophic mice with muscle injury or comparing injured and non-injured dystrophic animals, whereas differences between injured and non-injured wild an- imals did not occur [13]. In contrast, Zaraiskaya et al. [14] showed significant differ- ences in FA, MD and eigenvalues (i.e. λ ,λ ,λ )comparing 1 2 3 DTI measures from entire healthy muscles (i.e. eight volun- teers) with those obtained in ROIs drawn in injured muscle Table 3 Region of interest (ROI)-based diffusion tensor imaging (DTI) analyses 1-Way ANOVA (P) Post-hoc tests^ ROI ROI ROI ROI ROI vs. ROI vs. ROI vs. ROI vs. ROI vs. ROI vs. tear hc_t hi hc_i tear tear tear hc_t hc_t hi ROI ROI ROI ROI ROI ROI hc_t hi hc_i hi hc_i hc_i tr 972 ± 997 1,633 ± 1317 1,002 ± 669 893 ± 661 0.182 - - - - - - tr (mm) 37.30 ± 22 54.15 ± 24.83 48.08 ± 23.1 44.10 ± 23.7 0.043 0.065 0.596 1.000 1.000 0.366 1.000 tr (mm ) 14,530 ± 13,925 22,424 ± 12,960 14,371 ± 10,191 13,482 ± 11,349 0.102 - - - - - - FA 0.18 ± 0.05 0.20 ± 0.04 0.22 ± 0.05 0.20 ± 0.04 0.008 0.018 0.015 0.098 0.454 1.000 1.000 -3 2 MD (10 mm /s) 1.44 ± 0.11 1.31 ± 0.07 1.33 ± 0.13 1.28 ± 0.1 0.002 0.003 0.084 0.002 1.000 1.000 1.000 AD 1.8 ± 0.15 1.7 ± 0.15 1.7 ± 0.18 1.65 ± 0.17 0.001 0.007 0.336 0.007 1.000 0.784 0.282 RD 1.27 ± 0.12 1.12 ± 0.07 1.14 ± 0.13 1.10 ± 0.08 0.003 0.005 0.047 0.003 1.000 1.000 1.000 Comparison between the muscle tear and the healthy contralateral and ipsilateral muscle areas tr number of tracks, tr length of tracks, tr volume of tracks, FA fractional anisotropy, MD mean diffusivity, AD axial diffusivity, RD radial diffusivity n l v ROI region of interest drawn on the muscle tear, ROI ROI drawn on the on the corresponding healthy contralateral muscle, ROI ROI drawn on a healthy area ipsilateral to the injury, ROI ROI tear hc_t hi hc_i drawnonanareamatchingthe ROI on the contralateral limb, ^ Greenhouse Geisser hi Bold type indicates statistically significant values (p<0.05) Eur Radiol (2018) 28:2882–2889 2887 Fig. 3 Grade II muscle tear of the right rectus femoris muscle (blue arrow fractional anisotropy (FA) (blue arrow on the FA map in b) than the on the axial proton density fat-saturated image in a) of a 23-year-old corresponding healthy contralateral muscle (white arrow on the FA map professional football player. The injured area demonstrates lower in c) areas of the calves of four patients (i.e. two with haematomas dominant and non-dominant side has been shown [30–33], it and two with muscle tears). These results are in accordance appears reasonable that the laterality is a biasing factor in with the differences in FA, MD, RD and AD found in our quantitative DTI assessments in muscles. The results obtained population comparing the injured areas with the healthy ones after applying the normalization seem to confirm this assump- (i.e. ipsi- and contralateral ROIs), even if it has to be taken into tion, as differences in length, number and volume of the account that the presence of oedema may lead just to an ap- tracked fibres due to the injury were apparent only in normal- parent decrease of AD and FA [41]. ized data. Zaraiskaya et al. performed fibre tracking only in healthy Our study results are preliminary and could not yet validate controls, but no such data were presented for patients [14]. the fact that STEAM-DTI brings any additional benefits com- Froeling et al. [35] evaluated fibre-tracking changes at differ- pared to conventional MRI. Since one of the more challenging ent time points in marathon runners, but performed no sepa- aspects of muscle tear assessment is represented by the prog- rate assessments for muscle strains already visible on anatom- nosis of the recovery interval [42], we strongly believe that the ical images (i.e. T2w images). To the best of our knowledge, application of the ratio could also provide essential benefits there are no previous studies that have investigated fibre- for the longitudinal evaluation of muscle strains during the tracking metrics (i.e. number, length and volume of tracked recovery phase and thus improve the prediction of the recov- fibres) of muscle tears. In our cohort, no differences in fibre ery interval and reduce the risk of recurrence. tracking emerged, either in the entire muscle, or in the ROI- based analyses. The tracked muscle fibres of the injured side turned out to be significantly less numerous (-55 %) and Limitations shorter (-39 %) only after normalization of the data. Considering that in athletes an asymmetry in the characteris- Despite our very promising results, there are some limitations tics and metabolic activity of muscle belonging to the to our study. All patients were scanned within 1 week after the Fig. 4 Grade II lesion of the right medial gastrocnemius (blue arrow on in our population comparing the tears with all healthy areas. The statisti- the axial proton density fat-saturated image in a) of a 35-year-old football cal analyses revealed significant differences in terms of length and player. Fibre tracking of the injured area is illustrated (blue arrow in b) amount of fibre tracts only when a ratio between the ROIs on the injured and of the ipsi- (b) and contralateral healthy areas (c). Although visually (i.e. represented here by the fibre tracts on the right calf in b)and contra- the fibre tracking of the injured muscle area seems to demonstrate shorter lateral extremity (i.e. represented here by the fibre tracts on the left calf in and less numerous fibres, no statistically significant differences occurred c) was calculated 2888 Eur Radiol (2018) 28:2882–2889 Table 4 Comparison of diffusion tensor imaging (DTI) metrics’ ratio Acknowledgements The preliminary results of this study have been pre- between the injured leg and the contralateral healthy one sented as an electronic poster at the ISMRM 25th Annual Meeting & Exhibition (22–27 April 2017, Hawaii, USA). ROI /ROI ROI /ROI Students’ t-test tear hi hc_t hc_i Funding Stanislav Motyka was supported by the OeNB tr 0.55 ± 0.45 1.24 ± 0.53 0.028 Jubilaeumsfond (Grant #16133) and Giraudo Chiara by the Austrian Science Fund (FWF; Project KLIF 382). Open Access Funding provided tr (mm) 0.69 ± 0.22 1.14 ± 0.28 0.005 by Medical University of Vienna. tr (mm ) 0.62 ± 0.40 1.26 ± 0.65 0.056 FA 0.88 ± 0.07 1.09 ± 0.15 0.007 Compliance with ethical standards -3 2 MD (10 mm /s) 1.10 ± 0.04 1.04 ± 0.07 0.028 AD 1.06 ± 0.03 1.04 ± 0.04 0.241 Guarantor The scientific guarantor of this publication is Prof. Wolfgang Bogner. RD 1.13 ± 0.06 1.03 ± 0.10 0.014 tr number of tracks, tr length of tracks, tr volume of tracks, FA frac- Conflict of interest Thorsten Feiweier is senior researcher at Siemens n l v tional anisotropy, MD mean diffusivity, AD axial diffusivity, RD radial Healthcare GmbH (Siemens, Germany). diffusivity, ROI /ROI ratio between the ROI drawn on the tear and the The other authors of this manuscript declare no relationships with any tear hi one drawn on a ipsilateral healthy area, ROI /ROI ratio of the two companies whose products or services may be related to the subject mat- hc_t hc_i corresponding contralateral healthy areas ter of the article. Bold type indicates statistically significant values (p<0.05) Statistics and biometry Michael Weber, co-author of this manuscript, has significant statistical expertise. injury; however, DTI/fibre-tracking metrics may change quite quickly (e.g. inflammation may occur in a few days). Thus, a Informed consent Written informed consent was obtained from all sub- jects (patients) in this study. more standardized recruitment (i.e. a fixed number of days after the injury for all patients) may be beneficial, especially Ethical approval Institutional Review Board approval was obtained. for entire muscle analyses. Despite the evidence that in volun- teers different ranges of DTI metrics values occur in different Methodology muscles [15, 16], in the present study separate analyses ac- � prospective cording to the injured muscles were not performed, because � experimental � performed at one institution of the low number of examined patients. Future studies includ- ing larger patient populations should focus on muscle-specific analyses to provide even more accurate results. Nevertheless, Open Access This article is distributed under the terms of the Creative normalization will certainly also reduce such differences be- Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, tween muscle groups. distribution, and reproduction in any medium, provided you give appro- Finally, the quite long acquisition time (i.e. ca. 8 min) priate credit to the original author(s) and the source, provide a link to the might represent a limit with very extensive lesions, since mo- Creative Commons license, and indicate if changes were made. tion artifacts may occur. However, recent developments in simultaneous-multi-slice (SMS) DTI have translated into References ~threefold acceleration of clinically available DTI sequences [43]. SMS was not yet implemented into our STEAM-DTI 1. Page P (1995) Pathophysiology of acute exercise-induced muscular sequence when our study was performed, but future studies injury: clinical implications. J Athl Train 30:29–34 2. Lee JC, Mitchell AWM, Healy JC (2012) Imaging of muscle injury aiming for larger FOVs should directly benefit from this new in the elite athlete. Br J Radiol 85:1173–1185 technology. 3. Cross TM, Gibbs N, Dip G et al (2004) Acute quadriceps muscle strains. Magnetic Resonance Imaging features and prognosis. Am J Sports Med 32:710–719 4. O’Donoghue DO (1962) Treatment of injuries to athletes. WB Saunders, Philadelphia Conclusion 5. Ryan AJ (1969) Quadriceps strain, rupture and charlie horse. Med Sci Sports 1:106–111 In conclusion, STEAM-based DTI allowed a precise assess- 6. Takebayashi S, Takasawa H, Banzai Y et al (1995) Sonographic ment of the injured fibres in athletes especially when a ratio findings in muscle strain injury: clinical and MR imaging correla- between the injured and the contralateral muscles was applied. tion. J Ultrasound Med 14:899–905 7. Mueller-Wohlfahrt HW, Haensel L, Mithoefer K et al (2013) Aiming to improve the current imaging-based classification of Terminology and classification of muscle injuries in sport: The muscle tears and to increase the accuracy of the therapeutic Munich consensus statement. Br J Sports Med 47:342–350 and prognostic management of injured athletes, future studies 8. Pedowitz R, Chung CB, Resnick D (2009) Muscle. In Magnetic including a larger population and evaluating muscle tears, also Resonance Imaging in Orthopedic Sports Medicine. Springer- Verlag GmbH during follow-up, are necessary. Eur Radiol (2018) 28:2882–2889 2889 9. Hamilton B, Valle X, Rodas G et al (2015) Classification and grad- 27. Karampinos DC, Banerjee S, King KF et al (2012) Considerations in high-resolution skeletal muscle diffusion tensor imaging using ing of muscle injuries: a narrative review. Br J Sports Med 49:306 10. Alexander AL, Lee JE, Lazar M et al (2007) Diffusion Tensor single-shot echo planar imaging with stimulated-echo preparation Imaging of the Brain. Neurotherapeutics 4:316–329 and sensitivity encoding. NMR Biomed 25:766–778 11. Li K, Dortch RD, Brian E et al (2014) Multi-parametric MRI 28. Filli L, Piccirelli M, Kenkel D et al (2015) Simultaneous multislice Characterization of Healthy Human Thigh Muscles at 3.0T - echo planar imaging with blipped controlled aliasing in parallel Relaxation, Magnetization Transfer, Fat/Water, and Diffusion imaging results in higher acceleration. A promising technique for Tensor Imaging. NMR Biomed 27:1070–1084 accelerated diffusion tensor imaging of skeletal muscle. Invest 12. Scheel M, von Roth P, Winkler T et al (2013) Fiber type character- Radiol 50:456–463 ization in skeletal muscle by diffusion tensor imaging. NMR 29. Noehren B, Andersen A, Feiweier T et al (2015) Comparison of Biomed 10:1220–1224 twice refocused spin echo versus stimulated echo diffusion tensor 13. McMillan A, Shi D, Pratt S et al (2011) Diffusion Tensor MRI to imaging for tracking muscle fibers. J Magn Reson Imaging 41:624– Assess Damage in Healthy and Dystrophic Skeletal Muscle after Lengthening Contractions. J Biomed Biotechnol 970726 30. Rahnama N, Lees A, Bambaecichi E (2005) Comparison of muscle 14. Zaraiskaya T, Kumbhare D, Noseworthy M (2006) Diffusion strength and flexibility between the preferred and non-preferred leg Tensor Imaging in evaluation of human skeletal muscle injury. J in English soccer players. Ergonomics 48:1568–1575 Magn Reson Imaging 24:402–408 31. Kearns CF, Isokawa M, Abe T (2001) Architectural characteristics 15. Sinha S, Sinha U, Edgerton VR (2006) In vivo diffusion tensor of dominant leg muscles in junior soccer players. Eur J Appl imaging of the human calf muscle. J Magn Reson Imaging 24: Physiol 85:240–243 182–190 32. Zoladz JA, Kulinowski P, Zapart-Bukowska J et al (2007) 16. Budzik JF, Le Thuc V, Demondion X et al (2007) In vivo MR Phosphorylation potential in the dominant leg is lower, and tractography of thigh muscles using diffusion imaging: initial re- [ADPfree] is higher in calf muscles at rest in endurance athletes sults. Eur Radiol 17:3079–3085 than in sprinters and in untrained subjects. J Physiol Pharmacol 58: 17. Kermarrec E, Budzik JF, Khalil C et al (2010) In vivo diffusion 803–819 tensor imaging and tractography of human thigh muscles in healthy 33. Hides J, Fan T, Stanton W et al (2010) Psoas and quadratus subjects. AJR Am J Roentgenol 195:352–356 lumborum muscle asymmetry among elite Australian Football 18. Froeling M, Nederveen AJ, Heijtel DF et al (2012) Diffusion-tensor League players. Br J Sports Med 44:563–567 MRI reveals the complex muscle architecture of the human fore- 34. Basser PJ, Pajevic S, Pierpaoli C et al (2000) In vivo fiber arm. J Magn Reson Imaging 36:237–248 tractography using DT-MRI Data. Magn Reson Med 44:625–632 19. Ponrartana S, Ramos-Platt L, Wren TA et al (2015) Effectiveness of 35. Froeling M, Oudeman J, Strijkers GJ et al (2015) Muscle changes diffusion tensor imaging in assessing disease severity in Duchenne detected with diffusion-tensor imaging after long-distance running. muscular dystrophy: preliminary study. Pediatr Radiol 45:582–589 Radiology 274:548–562 20. Sigmund EE, Sui D, Ukpebor O et al (2013) Stimulated echo dif- 36. Lansdown DA, Ding Z, Wadington M et al (1985) (2007) fusion tensor imaging and SPAIR T2-weighted imaging in chronic Quantitative diffusion tensor MRI-based fiber tracking of human exertional compartment syndrome of the lower leg muscles. J Magn skeletal muscle. J Appl Physiol 103:673–668 Reson Imaging 38:1073–1082 37. Okamoto Y, Kunimatsu A, Kono T et al (2010) Gender differences 21. Budzik JF, Balbi V, Verclytte S (2014) Diffusion tensor imaging in in MR muscle tractography. Magn Reson Med Sci 9:111–118 musculoskeletal disorders. Radiographics 34:56–72 38. Galbán CJ, Maderwald S, Stock F et al (2007) Age-related changes 22. Schick F, Eismann B, Jung WI et al (1993) Comparison of localized in skeletal muscle as detected by diffusion tensor magnetic reso- proton NMR signals of skeletal muscle and fat tissue in vivo: two nance imaging. J Gerontol A Biol Sci Med Sci 62:453–458 lipid compartments in muscle tissue. Magn Reson Med 29:158–167 39. Fernandes TL, Pedrinelli A, Hernandes AJ (2011) Muscle injury – 23. Steidle G, Schick F (2015) Addressing spontaneous signal voids in physiopathology, diagnosis, treatment and clinical presentation. repetitive single-shot DWI of musculature: spatial and temporal Rev Bras Ortop 46:247–255 patterns in the calves of healthy volunteers and consideration of 40. Delos D, Maak TG, Rodeo SA (2013) Muscle Injuries in Athletes: unintended muscle activities as underlying mechanism. NMR Enhancing Recovery Through Scientific Understanding and Novel Biomed 28:801–810 Therapies. Sports Health 5:346–352 24. Chang LC, Walker L, Pierpaoli C (2012) Informed RESTORE: a 41. Froeling M, Nederveen AJ, Nicolay K et al (2013) DTI of human method for robust estimation of diffusion tensor from low redun- skeletal muscle: the effects of diffusion encoding parameters, dancy datasets in the presence of physiological noise artifacts. signal-to-noise ratio and T2 on tensor indices and fiber tracts. Magn Reson Med 68:1654–1663 NMR Biomed 26:1339–1352 25. Giraudo C, Motyka S, Weber M et al (2017) Weighted Mean of 42. Slavotinek JP (2010) Muscle Injury: The Role of Imaging in Signal Intensity for Unbiased Fiber Tracking of Skeletal Muscles: Prognostic Assignment and Monitoring of Muscle Repair. Semin Development of a New Method and Comparison With Other Musculoskelet Radiol 14:194–200 Correction Techniques. Invest Radiol 52:488–497 26. Saupe N, White LM, Sussman MS (2008) Diffusion tensor mag- 43. Setsompop K, Cohen-Adad J, Gagoski BA et al (2012) Improving netic resonance imaging of the human calf: comparison between 1.5 diffusion MRI using simultaneous multi-slice echo planar imaging. T and 3.0 T-preliminary results. Invest Radiol 43:612–618 NeuroImage 63:569–580

Journal

European RadiologySpringer Journals

Published: Feb 8, 2018

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