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.

Background: Quantitative muscle MRI (qMRI) is a promising tool for evaluating and monitoring neuromuscular disorders (NMD). However, the application of different imaging protocols and processing pipelines restricts comparison between patient cohorts and disorders. In this qMRI study, we aim to compare dystrophic (limb-girdle muscular dystrophy), inflammatory (inclusion body myositis), and metabolic myopathy (Pompe disease) as well as patients with post-COVID-19 conditions suffering from myalgia to healthy controls. Methods: Ten subjects of each group underwent a 3T lower extremity muscle MRI, including a multi-echo, gradient-echo, Dixon-based sequence, a multi-echo, spin-echo (MESE) T2 mapping sequence, and a spin-echo EPI diffusion-weighted sequence. Furthermore, the following clinical assessments were performed: Quick Motor Function Measure, patient questionnaires for daily life activities, and 6-min walking distance. Results: Different involvement patterns of conspicuous qMRI parameters for different NMDs were observed. qMRI metrics correlated significantly with clinical assessments. Conclusions: qMRI metrics are suitable for evaluating patients with NMD since they show differences in muscular involvement in different NMDs and correlate with clinical assessments. Still, standardisation of acquisition and processing is needed for broad clinical use.

Introduction: Muscle diffusion tensor imaging (mDTI) is a quantitative MRI technique that can provide information about muscular microstructure and integrity. Ultrasound and DTI studies have shown intramuscular differences, and therefore separation of different muscles for analysis is essential. The commonly used methods to assess DTI metrics in muscles are manual segmentation and tract‐based analysis. Recently methods such as volume‐based tractography have been applied to optimize muscle architecture estimation, but can also be used to assess DTI metrics.Purpose: To evaluate diffusion metrics obtained using three different methods—volume‐based tractography, manual segmentation‐based analysis and tract‐based analysis—with respect to their interrater reliability and their ability to detect intramuscular variance.Materials and methods: 30 volunteers underwent an MRI examination in a 3 T scanner using a 16‐channel Torso XL coil. Diffusion‐weighted images were acquired to obtain DTI metrics. These metrics were evaluated in six thigh muscles using volume‐based tractography, manual segmentation and standard tractography. All three methods were performed by two independent raters to assess interrater reliability by ICC analysis and Bland‐Altman plots. Ability to assess intramuscular variance was compared using an ANOVA with muscle as a between‐subjects factor.Results: Interrater reliability for all methods was found to be excellent. The highest interrater reliability was found for volume‐based tractography (ICC ≥ 0.967). Significant differences for the factor muscle in all examined diffusion parameters were shown in muscles using all methods (main effect p < 0.001).Conclusions: Diffusion data can be assessed by volume tractography, standard tractography and manual segmentation with high interrater reliability. Each method produces different results for the investigated DTI parameters. Volume‐based tractography was superior to conventional manual segmentation and tractography regarding interrater reliability and detection of intramuscular variance, while tract‐based analysis showed the lowest coefficients of variation.

Background: Sporadic inclusion body myositis (sIBM) is the predominant idiopathic inflammatory myopathy (IIM) in older people. Limitations of classical clinical assessments have been discussed as possible explanations for failed clinical trials, underlining the need for more sensitive outcome measures. Quantitative muscle MRI (qMRI) is a promising candidate for evaluating and monitoring sIBM. Objective: Longitudinal assessment of qMRI in sIBM patients. Methods: We evaluated fifteen lower extremity muscles of 12 sIBM patients (5 females, mean age 69.6, BMI 27.8) and 12 healthy age- and gender-matched controls. Seven patients and matched controls underwent a follow-up evaluation after one year. Clinical assessment included testing for muscle strength with Quick Motor Function Measure (QMFM), IBM functional rating scale (IBM-FRS), and gait analysis (6-minute walking distance). 3T-MRI scans of the lower extremities were performed, including a Dixon-based sequence, T2 mapping and Diffusion Tensor Imaging. The qMRI-values fat-fraction (FF), water T2 relaxation time (wT2), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity ( λ₁), and radial diffusivity (RD) were analysed. Results: Compared to healthy controls, significant differences for all qMRI parameters averaged over all muscles were found in sIBM using a MANOVA ( p < 0.001). In low-fat muscles (FF < 10%), a significant increase of wT2 and FA with an accompanying decrease of MD, λ₁, and RD was observed ( p≤0.020). The highest correlation with clinical assessments was found for wT2 values in thigh muscles ( r≤–0.634). Significant changes of FF (+3.0%), wT2 (+0.6 ms), MD (–0.04 10^-3mm²/s), λ₁ (–0.05 10^-3mm²/s), and RD (–0.03 10^-3mm²/s) were observed in the longitudinal evaluation of sIBM patients ( p≤0.001). FA showed no significant change ( p = 0.242). Conclusion: qMRI metrics correlate with clinical findings and can reflect different ongoing pathophysiological mechanisms. While wT2 is an emerging marker of disease activity, the role of diffusion metrics, possibly reflecting changes in fibre size and intracellular deposits, remains subject to further investigations.

Quantitative muscle magnetic resonance imaging (qMRI) is a valuable methodology for assessing muscular injuries and neuromuscular disorders. Notably, muscle diffusion tensor imaging (DTI) gives insights into muscle microstructural and macrostructural characteristics. However, the long‐term reproducibility and robustness of these measurements remain relatively unexplored. The purpose of this prospective longitudinal cohort study was to assess the long‐term robustness and range of variation of qMRI parameters, especially DTI metrics, in the lower extremity muscles of healthy controls under real‐life conditions. Twelve volunteers (seven females, age 44.1 ± 12.1 years, body mass index 23.3 ± 2.0 kg/m²) underwent five leg muscle MRI sessions every 20 ± 4 weeks over a total period of 1.5 years. A multiecho gradient‐echo Dixon‐based sequence, a multiecho spin‐echo T2‐mapping sequence, and a spin‐echo echo planar imaging diffusion‐weighted sequence were acquired bilaterally with a Philips 3‐T Achieva MR System using a 16‐channel torso coil. Fifteen leg muscles were segmented in both lower extremities. qMRI parameters, including fat fraction (FF), water T2 relaxation time, and the diffusion metrics fractional anisotropy (FA) and mean diffusivity (MD), were evaluated. Coefficients of variance (wsCV) and intraclass correlation coefficients (ICCs) were calculated to assess the reproducibility of qMRI parameters. The standard error of measurement (SEM) and the minimal detectable change (MDC) were calculated to determine the range of variation. All tests were applied to all muscles and, subsequently, to each muscle separately. wsCV showed good reproducibility (≤ 10%) for all qMRI parameters in all muscles. The ICCs revealed excellent agreement between time points (FF = 0.980, water T2 = 0.941, FA = 0.952, MD = 0.948). Random measurement errors assessed by SEM and the MDC were low (< 12%). In conclusion, in this study, we showed that qMRI parameters in healthy volunteers living normal lives are stable over 18 months, thereby defining a benchmark for the expected range of variation over time.