Development of an initial training and evaluation programme for manual lower limb muscle MRI segmentation.

BACKGROUND: Magnetic resonance imaging (MRI) quantification of intramuscular fat accumulation is a responsive biomarker in neuromuscular diseases. Despite emergence of automated methods, manual muscle segmentation remains an essential foundation. We aimed to develop a training programme for new observers to demonstrate competence in lower limb muscle segmentation and establish reliability benchmarks for future human observers and machine learning segmentation packages. METHODS: The learning phase of the training programme comprised a training manual, direct instruction, and eight lower limb MRI scans with reference standard large and small regions of interest (ROIs). The assessment phase used test-retest scans from two patients and two healthy controls. Interscan and interobserver reliability metrics were calculated to identify underperforming outliers and to determine competency benchmarks. RESULTS: Three experienced observers undertook the assessment phase, whilst eight new observers completed the full training programme. Two of the new observers were identified as underperforming outliers, relating to variation in size or consistency of segmentations; six had interscan and interobserver reliability equivalent to those of experienced observers. The calculated benchmark for the Sørensen-Dice similarity coefficient between observers was greater than 0.87 and 0.92 for individual thigh and calf muscles, respectively. Interscan and interobserver reliability were significantly higher for large than small ROIs (all p < 0.001). CONCLUSIONS: We developed, implemented, and analysed the first formal training programme for manual lower limb muscle segmentation. Large ROI showed superior reliability to small ROI for fat fraction assessment. RELEVANCE STATEMENT: Observers competent in lower limb muscle segmentation are critical to application of quantitative muscle MRI biomarkers in neuromuscular diseases. This study has established competency benchmarks for future human observers or automated segmentation methods. KEY POINTS: • Observers competent in muscle segmentation are critical for quantitative muscle MRI biomarkers. • A training programme for muscle segmentation was undertaken by eight new observers. • We established competency benchmarks for future human observers or automated segmentation methods.

Skeletal muscle MRI differentiates SBMA and ALS and correlates with disease severity.

OBJECTIVE: To investigate the use of muscle MRI for the differential diagnosis and as a disease progression biomarker for 2 major forms of motor neuron disorders: spinal bulbar muscular atrophy (SBMA) and amyotrophic lateral sclerosis (ALS). METHODS: We applied quantitative 3-point Dixon and semiquantitative T1-weighted and short tau inversion recovery (STIR) imaging to bulbar and lower limb muscles and performed clinical and functional assessments in ALS (n = 21) and SBMA (n = 21), alongside healthy controls (n = 16). Acquired images were analyzed for the presence of fat infiltration or edema as well as specific patterns of muscle involvement. Quantitative MRI measurements were correlated with clinical measures of disease severity in ALS and SBMA. RESULTS: Quantitative imaging revealed significant fat infiltration in bulbar (p < 0.001) and limb muscles in SBMA compared to controls (thigh: p < 0.001; calf: p = 0.001), identifying a characteristic pattern of muscle involvement. In ALS, semiquantitative STIR imaging detected marked hyperintensities in lower limb muscles, distinguishing ALS from SBMA and controls. Finally, MRI measurements correlated significantly with clinical scales of disease severity in both ALS and SBMA. CONCLUSIONS: Our findings show that muscle MRI differentiates between SBMA and ALS and correlates with disease severity, supporting its use as a diagnostic tool and biomarker for disease progression. This highlights the clinical utility of muscle MRI in motor neuron disorders and contributes to establish objective outcome measures, which is crucial for the development of new drugs.

Modeling and analysis of fractional order DC-DC converter.

Due to the non-idealities of commercial inductors, the demand for a better model that accurately describe their dynamic response is elevated. So, the fractional order models of Buck, Boost and Buck-Boost DC-DC converters are presented in this paper. The detailed analysis is made for the two most common modes of converter operation: Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). Closed form time domain expressions are derived for inductor currents, voltage gain, average current, conduction time and power efficiency where the effect of the fractional order inductor is found to be strongly present. For example, the peak inductor current at steady state increases with decreasing the inductor order. Advanced Design Systems (ADS) circuit simulations are used to verify the derived formulas, where the fractional order inductor is simulated using Valsa Constant Phase Element (CPE) approximation and Generalized Impedance Converter (GIC). Different simulation results are introduced with good matching to the theoretical formulas for the three DC-DC converter topologies under different fractional orders. A comprehensive comparison with the recently published literature is presented to show the advantages and disadvantages of each approach.

Upper Limb Evaluation in Duchenne Muscular Dystrophy: Fat-Water Quantification by MRI, Muscle Force and Function Define Endpoints for Clinical Trials.

OBJECTIVE: A number of promising experimental therapies for Duchenne muscular dystrophy (DMD) are emerging. Clinical trials currently rely on invasive biopsies or motivation-dependent functional tests to assess outcome. Quantitative muscle magnetic resonance imaging (MRI) could offer a valuable alternative and permit inclusion of non-ambulant DMD subjects. The aims of our study were to explore the responsiveness of upper-limb MRI muscle-fat measurement as a non-invasive objective endpoint for clinical trials in non-ambulant DMD, and to investigate the relationship of these MRI measures to those of muscle force and function. METHODS: 15 non-ambulant DMD boys (mean age 13.3 y) and 10 age-gender matched healthy controls (mean age 14.6 y) were recruited. 3-Tesla MRI fat-water quantification was used to measure forearm muscle fat transformation in non-ambulant DMD boys compared with healthy controls. DMD boys were assessed at 4 time-points over 12 months, using 3-point Dixon MRI to measure muscle fat-fraction (f.f.). Images from ten forearm muscles were segmented and mean f.f. and cross-sectional area recorded. DMD subjects also underwent comprehensive upper limb function and force evaluation. RESULTS: Overall mean baseline forearm f.f. was higher in DMD than in healthy controls (p<0.001). A progressive f.f. increase was observed in DMD over 12 months, reaching significance from 6 months (p<0.001, n = 7), accompanied by a significant loss in pinch strength at 6 months (p<0.001, n = 9) and a loss of upper limb function and grip force observed over 12 months (p<0.001, n = 8). CONCLUSIONS: These results support the use of MRI muscle f.f. as a biomarker to monitor disease progression in the upper limb in non-ambulant DMD, with sensitivity adequate to detect group-level change over time intervals practical for use in clinical trials. Clinical validity is supported by the association of the progressive fat transformation of muscle with loss of muscle force and function.