Background: Spinal cord injuries (SCIs) cause debilitating secondary conditions such as severe muscle deterioration, cardiovascular, and metabolic dysfunctions, significantly impacting patients’ quality of life. Functional electrical stimulation (FES) combined with cycling exercise (FES-cycling) has shown promise in improving muscle function and health in individuals with SCI. Objective: This pilot study aimed to investigate the potential role of multiparametric magnetic resonance imaging (MRI) to assess muscle health during and after an FES-cycling rehabilitation program. Methods: Four male participants with chronic SCI underwent a 6-month FES-cycling training program, consisting of two 30-minute sessions per week. MRI scans were performed at baseline (T₀), after 3 months (T₁), at the end of the training (T₂), and 1-month posttraining (T₃). The MRI protocol included T₁-weighted imaging for volume quantification, Dixon imaging for fat fraction, multi-echo spin echo for T₂ relaxation times, and diffusion tensor imaging to assess diffusion parameters. Results: Muscle hypertrophy was observed, with an average increase in muscle volume of 22.3% at T₁ and 36.7% at T₂ compared with baseline. One month posttraining, muscle volume remained 23.2% higher than baseline. Fat fraction decreased from 11.1% at T₀ to 9.1% at T₂, with a rebound to 10.9% at T₃. T₂ relaxation times showed a reduction even though this was not consistent among participants. Diffusion tensor imaging parameters revealed subtle changes in muscle tissue microstructure, with a decrease in fractional anisotropy mainly associated to an increase of radial diffusivity. Conclusions: Although preliminary, this study provides evidence that 6 months of low-intensity FES-bike training can increase muscle volume and decrease fat infiltration in individuals with SCI. The study demonstrates that the use of a multiparametric MRI provides comprehensive insights into both macroscopic and microscopic changes within muscle tissues, supporting its integration into clinical practice for assessing the efficacy of rehabilitation interventions.

Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb–girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.

Deep learning (DL) models have shown promise for skeletal muscle (SM) segmentation in MR images, which is crucial for extracting biomarkers in neuromuscular disorders (NMDs). However, to ensure safe clinical use, models should provide uncertainty estimates, allowing radiologists to assess predictions and intervene when needed. Foundation Models (FMs) have the potential to play a significant role due to their strong generalization capabilities and well-calibrated predictions. However, their applicability in this context has not yet been explored. This study aims to develop an accurate and trustworthy technique by fine-tuning FMs to delineate fatty-infiltrated SM fascicles in NMD patients. We fine-tuned Segment Anything Model (SAM) and MedSAM using two configurations - encoder/decoder and decoder only - and compared their performance against state-of-the-art 2D and 3D nnU-Net using a dataset of thigh MR images from 76 NMD patients, categorized into Early, Moderate, and Severe fatty infiltration groups. Accuracy was evaluated using the Dice Similarity Coefficient (DSC), while Uncertainty Quantification (UQ) was evaluated using the Expected Calibration Error (ECE) and the Negative Log-Likelihood (NLL). Deep Ensembles were used to convey epistemic uncertainty in addition to the aleatoric counterpart. SAM's fine-tuned encoder/decoder outperformed nnU-Net 3D in Moderate and Severe cases (DSC: 0.886 vs 0.883 and 0.857 vs 0.850) and was comparable in Early (DSC: 0.925). MedSAM did not show an advantage over SAM. Regarding UQ, SAM exhibited superior calibration in Moderate and Severe groups (ECE: 3.6% vs. 5.1% and 3.3% vs. 7.1%), CONCLUSIONS:: In conclusion, our findings demonstrate that fine-tuning SAM yields superior performance, considering both accuracy and UQ metrics, highlighting its enhanced reliability in challenging NMD imaging scenarios.

This study aimed to explore the use of Foundation Models (FM) for skeletal muscle segmentation from magnetic resonance (MR) images, which is a critical step in extracting morphological and functional biomarkers in neuromuscular disorders (NMDs). The research addresses challenges such as fat infiltration and ambiguous muscle boundaries by comparing the performance of traditional convolutional neural networks (CNNs) and emerging FMs like the Segment Anything Model (SAM) and its medical adaptation, MedSAM. A dataset of MR thigh images from 76 NMD patients, splitted in Early, Moderate, and Severe according to the degree of fat infiltration, was annotated for 12 muscle groups across 152 volumes. Fine-tuned SAM and MedSAM configurations (encoder-decoder and decoderonly) were evaluated alongside 2D and 3D nnU-Net CNN models. Performance was assessed using Dice Similarity Coefficient (DSC), Average Symmetric Surface Distance (ASSD), and 95% Hausdorff Distance (HD95). Additionally, uncertainty quantification (UQ) metrics, including Expected Calibration Error (ECE) and Negative Log-Likelihood (NLL), were employed to assess model reliability. SAM's finetuned encoder-decoder configuration achieved segmentation accuracy comparable to state-of-the-art 3D nnU-Net models (DSC: 0.925, 0.883, and 0.857 for Early, Moderate, and Severe cases) and significantly outperformed 2D nnU-Net model. SAM achieved superior calibration, particularly in the Severe group (ECE: 0.032 vs. 0.071 of nnU-Net 3D). Deep ensemble methods further improved segmentation reliability and UQ assessment. MedSAM did not surpass the SAM performance. In conclusion, SAM has demonstrated accurate and better calibrated segmentation, demonstrating its potential for medical imaging applications in challenging NMD datasets.