Recent years have seen tremendous progress towards therapy of many previously incurable neuromuscular diseases. This new context has acted as a driving force for the development of novel non-invasive outcome measures. These can be organized in three main categories: functional tools, fluid biomarkers and imagery. In the latest category, nuclear magnetic resonance imaging (NMRI) offers a considerable range of possibilities for the characterization of skeletal muscle composition, function and metabolism. Nowadays, three NMR outcome measures are frequently integrated in clinical research protocols. They are: 1/ the muscle cross sectional area or volume, 2/ the percentage of intramuscular fat and 3/ the muscle water T2, which quantity muscle trophicity, chronic fatty degenerative changes and oedema (or more broadly, “disease activity”), respectively. A fourth biomarker, the contractile tissue volume is easily derived from the first two ones. The fat fraction maps most often acquired with Dixon sequences have proven their capability to detect small changes in muscle composition and have repeatedly shown superior sensitivity over standard functional evaluation. This outcome measure will more than likely be the first of the series to be validated as an endpoint by regulatory agencies. The versatility of contrast generated by NMR has opened many additional possibilities for characterization of the skeletal muscle and will result in the proposal of more NMR biomarkers. Ultra-short TE (UTE) sequences, late gadolinium enhancement and NMR elastography are being investigated as candidates to evaluate skeletal muscle interstitial fibrosis. Many options exist to measure muscle perfusion and oxygenation by NMR. Diffusion NMR as well as texture analysis algorithms could generate complementary information on muscle organization at microscopic and mesoscopic scales, respectively. 31P NMR spectroscopy is the reference technique to assess muscle energetics non-invasively during and after exercise. In dystrophic muscle, 31P NMR spectrum at rest is profoundly perturbed, and several resonances inform on cell membrane integrity. Considerable efforts are being directed towards acceleration of image acquisitions using a variety of approaches, from the extraction of fat content and water T2 maps from one single acquisition to partial matrices acquisition schemes. Spectacular decreases in examination time are expected in the near future. They will reinforce the attractiveness of NMR outcome measures and will further facilitate their integration in clinical research trials.

Spinal muscular atrophy (SMA) is a monogenic disorder caused by loss of function mutations in the survival motor neuron 1 gene, which results in a broad range of disease severity, from neonatal to adult onset. There is currently a concerted effort to define the natural history of the disease and develop outcome measures that accurately capture its complexity. As several therapeutic strategies are currently under investigation and both the FDA and EMA have recently approved the first medical treatment for SMA, there is a critical need to identify the right association of responsive outcome measures and biomarkers for individual patient follow-up. As an approved treatment becomes available, untreated patients will soon become rare, further intensifying the need for a rapid, prospective and longitudinal study of the natural history of SMA Type 2 and 3. Here we present the baseline assessments of 81 patients aged 2 to 30 years of which 19 are non-sitter SMA Type 2, 34 are sitter SMA Type 2, 9 non-ambulant SMA Type 3 and 19 ambulant SMA Type 3. Collecting these data at nine sites in France, Germany and Belgium established the feasibility of gathering consistent data from numerous and demanding assessments in a multicenter SMA study. Most assessments discriminated between the four groups well. This included the Motor Function Measure (MFM), pulmonary function testing, strength, electroneuromyography, muscle imaging and workspace volume. Additionally, all of the assessments showed good correlation with the MFM score. As the untreated patient population decreases, having reliable and valid multi-site data will be imperative for recruitment in clinical trials. The pending two-year study results will evaluate the sensitivity of the studied outcomes and biomarkers to disease progression.Trial Registration: ClinicalTrials.gov (NCT02391831).

Skeletal muscle inflammation/necrosis and fat infiltration are strong indicators of disease activity and progression in many neuromuscular disorders. They can be assessed by muscle T2 relaxometry and water-fat separation techniques, respectively. In the present work, we exploited differences between water and fat T1 and T2 relaxivities by applying a bi-component extended phase graph (EPG) fitting approach to simultaneously quantify the muscle water T2 and fat fraction from standard multi-slice multi-echo (MSME) acquisitions in the presence of stimulated echoes. Experimental decay curves were adjusted to the theoretical model using either an iterative non-negative least-squares (NNLS) procedure or a pattern recognition approach. Twenty-two patients (age, 49 ± 18 years) were selected to cover a large range of muscle fat infiltration. Four cases of chronic or subchronic juvenile dermatomyositis (age, 8 ± 3 years) were investigated before and 3 months following steroid treatment. For control, five healthy volunteers (age, 25 ± 2 years) were recruited. All subjects underwent the MSME sequence and EPG fitting procedure. The EPG fitting algorithm allowed a precise estimation of water T2 and fat fraction in diseased muscle, even in the presence of large B1(+) inhomogeneities. In the whole cohort of patients, there was no overall correlation between water T2 values obtained with the proposed method and the fat fraction estimated inside muscle tissues (R(2) = 0.02). In the patients with dermatomyositis, there was a significant decrease in water T2 (-4.09 ± 3.7 ms) consequent to steroid treatment. The pattern recognition approach resulted in a 20-fold decrease in processing time relative to the iterative NNLS procedure. The fat fraction derived from the EPG fitting approach correlated well with the fat fraction derived from a standard three-point Dixon method (≈1.5% bias). The bi-component EPG fitting analysis is a precise tool to monitor muscle tissue disease activity and is able to handle bias introduced by fat infiltration and B1(+) inhomogeneities.

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In this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is represented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization system. We reveal the potential of our approach on a challenging set of real clinical data.