Diffusion Tensor Imaging Shows Differences Between Myotonic Dystrophy Type 1 and Type 2

Compositional and Functional MRI of Skeletal Muscle: A Review

Due to its exceptional sensitivity to soft tissues, MRI has been extensively utilized to assess anatomical muscle parameters such as muscle volume and cross-sectional area. Quantitative Magnetic Resonance Imaging (qMRI) adds to the capabilities of MRI, by providing information on muscle composition such as fat content, water content, microstructure, hypertrophy, atrophy, as well as muscle architecture. In addition to compositional changes, qMRI can also be used to assess function for example by measuring muscle quality or through characterization of muscle deformation during passive lengthening/shortening and active contractions. The overall aim of this review is to provide an updated overview of qMRI techniques that can quantitatively evaluate muscle structure and composition, provide insights into the underlying biological basis of the qMRI signal, and illustrate how qMRI biomarkers of muscle health relate to function in healthy and diseased/injured muscles. While some applications still require systematic clinical validation, qMRI is now established as a comprehensive technique, that can be used to characterize a wide variety of structural and compositional changes in healthy and diseased skeletal muscle. Taken together, multiparametric muscle MRI holds great potential in the diagnosis and monitoring of muscle conditions in research and clinical applications. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Applications of Diffusion-Weighted MRI to the Musculoskeletal System

Diffusion-weighted imaging (DWI) is an established MRI technique that can investigate tissue microstructure at the scale of a few micrometers. Musculoskeletal tissues typically have a highly ordered structure to fulfill their functions and therefore represent an optimal application of DWI. Even more since disruption of tissue organization affects its biomechanical properties and may indicate irreversible damage. The application of DWI to the musculoskeletal system faces application-specific challenges on data acquisition including susceptibility effects, the low T2 relaxation time of most musculoskeletal tissues (2-70 msec) and the need for sub-millimetric resolution. Thus, musculoskeletal applications have been an area of development of new DWI methods. In this review, we provide an overview of the technical aspects of DWI acquisition including diffusion-weighting, MRI pulse sequences and different diffusion regimes to study tissue microstructure. For each tissue type (growth plate, articular cartilage, muscle, bone marrow, intervertebral discs, ligaments, tendons, menisci, and synovium), the rationale for the use of DWI and clinical studies in support of its use as a biomarker are presented. The review describes studies showing that DTI of the growth plate has predictive value for child growth and that DTI of articular cartilage has potential to predict the radiographic progression of joint damage in early stages of osteoarthritis. DTI has been used extensively in skeletal muscle where it has shown potential to detect microstructural and functional changes in a wide range of muscle pathologies. DWI of bone marrow showed to be a valuable tool for the diagnosis of benign and malignant acute vertebral fractures and bone metastases. DTI and diffusion kurtosis have been investigated as markers of early intervertebral disc degeneration and lower back pain. Finally, promising new applications of DTI to anterior cruciate ligament grafts and synovium are presented. The review ends with an overview of the use of DWI in clinical routine. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.

Associations between lower extremity muscle fat fraction and motor performance in myotonic dystrophy type 2: A pilot study

INTRODUCTION/AIMS

Although muscle structure measures from magnetic resonance imaging (MRI) have been used to assess disease severity in muscular dystrophies, little is known about how these measures are affected in myotonic dystrophy type 2 (DM2). We aim to characterize lower extremity muscle fat fraction (MFF) as a potential biomarker of disease severity, and evaluate its relationship with motor performance in DM2.

METHODS

3-Tesla MRIs were obtained from nine patients with DM2 and six controls using a T1W-Dixon protocol. To calculate MFF, muscle volumes were segmented from proximal, middle, and distal regions of the thigh and calf. Associations between MFF and motor performance were calculated using Spearman's correlations (ρ).

RESULTS

Mean age of DM2 participants was 62 ± 11 y (89% female), and mean symptom duration was 20 ± 12 y. Compared to controls, the DM2 group had significantly higher MFF in the thigh and the calf segments (p-value = .002). The highest MFF at the thigh in DM2 was located in the posterior compartment (39.7 ± 12.9%) and at the calf was the lateral compartment (31.5 ± 8.7%). In the DM2 group, we found a strong correlation between the posterior thigh MFF and the 6-min walk test (ρ = -.90, p-value = .001). The lateral calf MFF was also strongly correlated with the step test (ρ = -0.82, p-value = .006).

DISCUSSION

Our pilot data suggest a potential correlation between lower extremity MFF and some motor performance tests in DM2. Longitudinal studies with larger sample sizes are required to validate MFF as a marker of disease severity in DM2.

Quantitative muscle MRI displays clinically relevant myostructural abnormalities in long-term ICU-survivors: a case-control study

BACKGROUND

Long-term data on ICU-survivors reveal persisting sequalae and a reduced quality-of-life even after years. Major complaints are neuromuscular dysfunction due to Intensive care unit acquired weakness (ICUAW). Quantitative MRI (qMRI) protocols can quantify muscle alterations in contrast to standard qualitative MRI-protocols.

METHODS

Using qMRI, the aim of this study was to analyse persisting myostructural abnormalities in former ICU patients compared to controls and relate them to clinical assessments. The study was conducted as a cohort/case-control study. Nine former ICU-patients and matched controls were recruited (7 males; 54.8y ± 16.9; controls: 54.3y ± 11.1). MRI scans were performed on a 3T-MRI including a mDTI, T2 mapping and a mDixonquant sequence. Water T2 times, fat-fraction and mean values of the eigenvalue (λ₁), mean diffusivity (MD), radial diffusivity (RD) and fractional anisotropy (FA) were obtained for six thigh and seven calf muscles bilaterally. Clinical assessment included strength testing, electrophysiologic studies and a questionnaire on quality-of-life (QoL). Study groups were compared using a multivariate general linear model. qMRI parameters were correlated to clinical assessments and QoL questionnaire using Pearson´s correlation.

RESULTS

qMRI parameters were significantly higher in the patients for fat-fraction (p < 0.001), water T2 time (p < 0.001), FA (p = 0.047), MD (p < 0.001) and RD (p < 0.001). Thighs and calves showed a different pattern with significantly higher water T2 times only in the calves. Correlation analysis showed a significant negative correlation of muscle strength (MRC sum score) with FA and T2-time. The results were related to impairment seen in QoL-questionnaires, clinical testing and electrophysiologic studies.

CONCLUSION

qMRI parameters show chronic next to active muscle degeneration in ICU survivors even years after ICU therapy with ongoing clinical relevance. Therefore, qMRI opens new doors to characterize and monitor muscle changes of patients with ICUAW. Further, better understanding on the underlying mechanisms of the persisting complaints could contribute the development of personalized rehabilitation programs.

Quantitative muscle MRI captures early muscle degeneration in calpainopathy

To evaluate differences in qMRI parameters of muscle diffusion tensor imaging (mDTI), fat-fraction (FF) and water T2 time in leg muscles of calpainopathy patients (LGMD R1/D4) compared to healthy controls, to correlate those findings to clinical parameters and to evaluate if qMRI parameters show muscle degeneration in not-yet fatty infiltrated muscles. We evaluated eight thigh and seven calf muscles of 19 calpainopathy patients and 19 healthy matched controls. MRI scans were performed on a 3T MRI including a mDTI, T2 mapping and mDixonquant sequence. Clinical assessment was done with manual muscle testing, patient questionnaires (ACTIVLIM, NSS) as well as gait analysis. Average FF was significantly different in all muscles compared to controls (p < 0.001). In muscles with less than 8% FF a significant increase of FA (p < 0.005) and decrease of RD (p < 0.004) was found in high-risk muscles of calpainopathy patients. Water T2 times were increased within the low- and intermediate-risk muscles (p ≤ 0.045) but not in high-risk muscles (p = 0.062). Clinical assessments correlated significantly with qMRI values: QMFM vs. FF: r = - 0.881, p < 0.001; QMFM versus FA: r = - 0.747, p < 0.001; QMFM versus MD: r = 0.942, p < 0.001. A good correlation of FF and diffusion metrics to clinical assessments was found. Diffusion metrics and T2 values are promising candidates to serve as sensitive early and non-invasive methods to capture early muscle degeneration in non-fat-infiltrated muscles in calpainopathies.

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset

Quantitative MRI combines non-invasive imaging techniques to reveal alterations in muscle pathophysiology. Creating muscle-specific labels manually is time consuming and requires an experienced examiner. Semi-automatic and fully automatic methods reduce segmentation time significantly. Current machine learning solutions are commonly trained on data from healthy subjects using homogeneous databases with the same image contrast. While yielding high Dice scores (DS), those solutions are not applicable to different image contrasts and acquisitions. Therefore, the aim of our study was to evaluate the feasibility of automatic segmentation of a heterogeneous database. To create a heterogeneous dataset, we pooled lower leg muscle images from different studies with different contrasts and fields-of-view, containing healthy controls and diagnosed patients with various neuromuscular diseases. A second homogenous database with uniform contrasts was created as a subset of the first database. We trained three 3D-convolutional neuronal networks (CNN) on those databases to test performance as compared to manual segmentation. All networks, training on heterogeneous data, were able to predict seven muscles with a minimum average DS of 0.75. U-Net performed best when trained on the heterogeneous dataset (DS: 0.80 ± 0.10, AHD: 0.39 ± 0.35). ResNet and DenseNet yielded higher DS, when trained on a heterogeneous dataset (both DS: 0.86), as compared to a homogeneous dataset (ResNet DS: 0.83, DenseNet DS: 0.76). In conclusion, a CNN trained on a heterogeneous dataset achieves more accurate labels for predicting a heterogeneous database of lower leg muscles than a CNN trained on a homogenous dataset. We propose that a large heterogeneous database is needed, to make automated segmentation feasible for different kinds of image acquisitions.