Paraspinal Muscle DTI Metrics Predict Muscle Strength.

BACKGROUND: The paraspinal muscles play an important role in the onset and progression of lower back pain. It would be of clinical interest to identify imaging biomarkers of the paraspinal musculature that are related to muscle function and strength. Diffusion tensor imaging (DTI) enables the microstructural examination of muscle tissue and its pathological changes. PURPOSE: To investigate associations of DTI parameters of the lumbar paraspinal muscles with isometric strength measurements in healthy volunteers. STUDY TYPE: Prospective. SUBJECTS: Twenty-one healthy subjects (12 male, 9 female; age = 30.1 ± 5.6 years; body mass index [BMI] = 27.5 ± 2.6 kg/m2 ) were recruited. FIELD STRENGTH/SEQUENCE: 3 T/single-shot echo planar imaging (ss-EPI) DTI in 24 directions; six-echo 3D spoiled gradient echo sequence for chemical shift encoding-based water-fat separation. ASSESSMENT: Paraspinal muscles at the lumbar spine were examined. Erector spinae muscles were segmented bilaterally; cross-sectional area (CSA), proton density fat fraction (PDFF), and DTI parameters were calculated. Muscle flexion and extension maximum isometric torque values [Nm] at the back were measured with an isokinetic dynamometer and the ratio of extension to flexion strength (E/F) calculated. STATISTICAL TESTS: Pearson correlation coefficients; multivariate regression models. RESULTS: Significant positive correlations were found between the ratio of extension to flexion (E/F) strength and mean diffusivity (MD) (P = 0.019), RD (P = 0.02) and the eigenvalues (λ1: P = 0.026, λ2: P = 0.033, λ3: P = 0.014). In multivariate regression models λ3 of the erector spinae muscle λ3 and gender remained statistically significant predictors of E/F (R2adj = 0.42, P = 0.003). DATA CONCLUSION: DTI allowed the identification of muscle microstructure differences related to back muscle function that were not reflected by CSA and PDFF. DTI may potentially track subtle changes of back muscle tissue composition. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:816-823.

Association of paraspinal muscle water-fat MRI-based measurements with isometric strength measurements.

OBJECTIVES: Chemical shift encoding-based water-fat MRI derived proton density fat fraction (PDFF) of the paraspinal muscles has been emerging as a surrogate marker in subjects with sarcopenia, lower back pain, injuries and neuromuscular disorders. The present study investigates the performance of paraspinal muscle PDFF and cross-sectional area (CSA) in predicting isometric muscle strength. METHODS: Twenty-six healthy subjects (57.7% women; age: 30 ± 6 years) underwent 3T axial MRI of the lumbar spine using a six-echo 3D spoiled gradient echo sequence for chemical shift encoding-based water-fat separation. Erector spinae and psoas muscles were segmented bilaterally from L2 level to L5 level to determine CSA and PDFF. Muscle flexion and extension maximum isometric torque values [Nm] at the back were measured with an isokinetic dynamometer. RESULTS: Significant correlations between CSA and muscle strength measurements were observed for erector spinae muscle CSA (r = 0.40; p = 0.044) and psoas muscle CSA (r = 0.61; p = 0.001) with relative flexion strength. Erector spinae muscle PDFF correlated significantly with relative muscle strength (extension: r = -0.51; p = 0.008; flexion: r = -0.54; p = 0.005). Erector spinae muscle PDFF, but not CSA, remained a statistically significant (p < 0.05) predictor of relative extensor strength in multivariate regression models (R2adj = 0.34; p = 0.002). CONCLUSIONS: PDFF measurements improved the prediction of paraspinal muscle strength beyond CSA. Therefore, chemical shift encoding-based water-fat MRI may be used to detect subtle changes in the paraspinal muscle composition. KEY POINTS: • We investigated the association of paraspinal muscle fat fraction based on chemical shift encoding-based water-fat MRI with isometric strength measurements in healthy subjects. • Erector spinae muscle PDFF correlated significantly with relative muscle strength. • PDFF measurements improved prediction of paraspinal muscle strength beyond CSA.

Automated assessment of paraspinal muscle fat composition based on the segmentation of chemical shift encoding-based water/fat-separated images.

Proton-density fat fraction (PDFF) of the paraspinal muscles, derived from chemical shift encoding-based water-fat magnetic resonance imaging, has emerged as an important surrogate biomarker in individuals with intervertebral disc disease, osteoporosis, sarcopenia and neuromuscular disorders. However, quantification of paraspinal muscle PDFF is currently limited in clinical routine due to the required time-consuming manual segmentation procedure. The present study aimed to develop an automatic segmentation algorithm of the lumbar paraspinal muscles based on water-fat sequences and compare the performance of this algorithm to ground truth data based on manual segmentation. The algorithm comprised an average shape model, a dual feature model, associating each surface point with a fat and water image appearance feature, and a detection model. Right and left psoas, quadratus lumborum and erector spinae muscles were automatically segmented. Dice coefficients averaged over all six muscle compartments amounted to 0.83 (range 0.75-0.90).

Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.

Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.