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Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles., Material and Methods: U-Net architectures with different complexities have been compared for the quantification of the fat fraction in each muscle group selected in the central part of the thigh region. The corresponding performance has been assessed in terms of Dice score (DSC) and FF quantification error. The database contained 1450 thigh images from 59 patients and 14 healthy subjects (age: 47 ± 17 years, sex: 36F, 37M). Ten individual muscles were segmented in each image. The performance of each model was compared to nnU-Net, a complex architecture with 4.35 × 10 7 parameters, 12.8 Gigabytes of peak memory usage and 167 h of training time., Results: As expected, nnU-Net achieved the highest DSC (94.77 ± 0.13%). A simpler U-Net (5.81 × 10 5 parameters, 2.37 Gigabytes, 14 h of training time) achieved a lower DSC but still above 90%. Surprisingly, both models achieved a comparable FF estimation., Discussion: The poor correlation between observed DSC and FF indicates that less complex architectures, reducing GPU memory utilization and training time, can still accurately quantify FF., Competing Interests: Declarations. Conflict of interest: The authors have no conflict of interest to declare. Ethical standards: The study was approved by the local research committee and was conducted in conformity with the Declaration of Helsinki (version October 2013) and the Medical Research Involving Human Subjects Act. A written informed consent was obtained from each subject regarding their participation in the project and the retrospective analysis of the MR images., (© 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Background and Objectives: Intramuscular fat fraction (FF), assessed using quantitative MRI (qMRI), has emerged as a promising biomarker for hereditary transthyretin amyloidosis (ATTRv) patients. Currently, the main drawbacks to its use in future therapeutic trials are its sensitivity to change over a short period of time and the time-consuming manual segmentation step to extract quantitative data. This pilot study aimed to demonstrate the suitability of an Artificial Intelligence-based (AI) segmentation technique to assess disease progression in a real-life cohort of ATTRv patients over 1 year., Methods: Fifteen ATTRv patients were included in this monocentric, observational, prospective study. FF, magnetization transfer ratio (MTR), and quantitative T2 were extracted from patients' lower limb qMRI at two time points, 1 year apart, at thigh and leg levels. qMRI parameters were correlated with clinical and electrophysiological parameters assessed at the same time., Results: Global FF at leg level significantly progressed over 1 year: +1.28 ± 2.62% (p = 0.017). At thigh level, no significant change in global FF, MTR, or T2 was measured. The leg FF was strongly correlated with the main clinical and electrophysiological scores., Conclusion: AI-based CNN network segmentation combined with qMRI can be used to obtain quantitative metrics for longitudinal studies in ATTRv patients. Global FF at the leg level seems to be the most sensitive MRI biomarker to track disease progression in a 1-year period. Larger studies with treatment-specific groups will now be necessary to determine the place of qMRI markers compared to the current clinical and electrophysiological scores., (© 2024 The Author(s). European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Background and Objectives: Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), has emerged as one of the few responsive outcome measures in CMT1A patients. The main limitation for its use in future therapeutic trials is the time required for the manual segmentation of individual muscles. This study aimed to evaluate the accuracy and responsiveness of a fully automatic artificial intelligence (AI)-based segmentation pipeline to assess disease progression in a cohort of CMT1A patients over 1 year., Methods: Twenty CMT1A patients were included in this observational, prospective, longitudinal study. FF was measured twice a year apart using qMRI in the lower limbs. Individual muscle segmentation was performed fully automatically using a trained convolutional neural network with or without human quality check (QC). The corresponding results were compared with those obtained by fully manual (FM) segmentation using the Dice similarity coefficient (DSC). FF progression and its standardized response mean (SRM) were also computed in individual muscles over the single central slice and a 3D volume to define the most sensitive region of interest., Results: AI-based segmentation showed excellent DSC values (>0.90). Significant global FF progression was observed at thigh (+0.71% ± 1.28%; p = 0.016) and leg (+1.73% ± 2.88%, p = 0.007) levels, similarly to that calculated using the FM technique ( p = 0.363 and p = 0.634). FF progression of each individual muscle was comparable when computed from either the central slice or the 3D volume. The best SRM value (0.70) was obtained for the FF progression computed using the AI-based technique with human QC in the 3D volume at the leg level. The time required for fully automatic segmentation using AI with a QC was 10 hours for the entire data set compared with 90 hours for the FM., Discussion: qMRI combined with AI-based segmentation can be considered as a process ready for assessing longitudinal FF changes in CMT1A patients. Given the slow FF progression at a thigh level and the large heterogeneity between muscles and individuals, FF should be quantified from a 3D volume at the leg level for longitudinal analyses. A QC performed after the AI-based segmentation is still advised given the increased SRM value.

Muscle weakness following anterior cruciate ligament reconstruction (ACLR) increases the risk of posttraumatic osteoarthritis (OA). However, focusing solely on muscle weakness overlooks other aspects like muscle composition, which could hinder strength recovery. Intramuscular fat is a non-contractile element linked to joint degeneration in idiopathic OA, but its role post-ACLR has not been thoroughly investigated. To bridge this gap, we aimed to characterize quadriceps volume and intramuscular fat in participants with ACLR (male/female = 15/9, age = 22.8 ± 3.6 years, body mass index [BMI] = 23.2 ± 1.9, time since surgery = 3.3 ± 0.9 years) and in controls (male/female = 14/10, age = 22.0 ± 3.1 years, BMI = 23.3 ± 2.6) while also exploring the associations between intramuscular fat and muscle volume with isometric strength. Linear mixed effects models assessed (I) muscle volume, (II) intramuscular fat, and (III) strength between limbs (ACLR vs. contralateral vs. control). Regression analyses were run to determine if intramuscular fat or volume were associated with quadriceps strength. The ACLR limb was 8%-11% smaller than the contralateral limb (p < 0.05). No between-limb differences in intramuscular fat were observed (p = 0.091-0.997). Muscle volume but not intramuscular fat was associated with strength in the ACLR and control limbs (p < 0.001-0.002). We demonstrate that intramuscular fat does not appear to be an additional source of quadriceps dysfunction following ACLR and that muscle size only explains some of the variance in muscle strength., (© 2024 Orthopaedic Research Society.)

Sickle cell disease (SCD) is an hemoglobinopathy resulting in the production of an abnormal Hb (HbS) which can polymerize in deoxygenated conditions, leading to the sickling of red blood cells (RBC). These alterations can decrease the oxygen-carrying capacity leading to impaired function and energetics of skeletal muscle. Any strategy which could reverse the corresponding defects could be of interest. In SCD, endurance training is known to improve multiples muscle properties which restores patient's exercise capacity but present reduced effects in anemic patients. Hydroxyurea (HU) can increase fetal hemoglobin production which can reduce anemia in patients. The present study was conducted to determine whether HU can improve the effects of endurance training to improve muscle function and energetics. Twenty SCD Townes mice have been trained for 8 weeks with (n = 11) or without (n = 9) HU. SCD mice muscle function and energetics were analyzed during a standardized rest-exercise-recovery protocol, using Phosphorus-31 Magnetic resonance spectroscopy ( 31 P-MRS) and transcutaneous stimulation. The combination of training and HU specifically decreased fatigue index and PCr consumption while muscle oxidative capacity was improved. These results illustrate the potential synergistic effects of endurance training and HU on muscle function and energetics in sickle cell disease., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients., Purpose: Evaluate the influence of fat infiltration on convolutional neural network (CNN) segmentation of MRIs from NMD patients., Study Type: Retrospective study., Subjects: Data were collected from a hospital database of 67 patients with NMDs and 14 controls (age: 53 ± 17 years, sex: 48 M, 33 F). Ten individual muscles were segmented from the thigh and six from the calf (20 slices, 200 cm section)., Field Strength/sequence: A 1.5 T. Sequences: 2D T 1 -weighted fast spin echo. Fat fraction (FF): three-point Dixon 3D GRE, magnetization transfer ratio (MTR): 3D MT-prepared GRE, T2: 2D multispin-echo sequence., Assessment: U-Net 2D, U-Net 3D, TransUNet, and HRNet were trained to segment thigh and leg muscles (101/11 and 95/11 training/validation images, 10-fold cross-validation). Automatic and manual segmentations were compared based on geometric criteria (Dice coefficient [DSC], outlier rate, absence rate) and reliability of measured MRI quantities (FF, MTR, T2, volume)., Statistical Tests: Bland-Altman plots were chosen to describe agreement between manual vs. automatic estimated FF, MTR, T2 and volume. Comparisons were made between muscle populations with an FF greater than 20% (G20+) and lower than 20% (G20-)., Results: The CNNs achieved equivalent results, yet only HRNet recognized every muscle in the database, with a DSC of 0.91 ± 0.08, and measurement biases reaching -0.32% ± 0.92% for FF, 0.19 ± 0.77 for MTR, -0.55 ± 1.95 msec for T2, and - 0.38 ± 3.67 cm 3 for volume. The performances of HRNet, between G20- and G20+ decreased significantly., Data Conclusion: HRNet was the most appropriate network, as it did not omit any muscle. The accuracy obtained shows that CNNs could provide fully automated methods for studying NMDs. However, the accuracy of the methods may be degraded on the most infiltrated muscles (>20%)., Evidence Level: 4., Technical Efficacy: Stage 1., (© 2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: Although enthesitis is a hallmark of several rheumatologic conditions, current imaging methods are still unable to characterize entheses changes because of the corresponding short transverse relaxation times (T2). A growing number of MR studies have used Ultra-High Field (UHF) MRI in order to assess low-T2 tissues e.g., tendon but never in humans. The purpose of the present study was to assess in vivo the enthesis of the quadriceps tendon in healthy subjects using UHF MRI., Methods: Eleven healthy subjects volunteered in an osteoarthritis imaging study. The inclusion criteria were: no knee trauma, Lequesne index = 0, less than 3 h of sport activities per week, and Kellgren and Lawrence grade = 0. 3D MR images were acquired at 7 T using GRE sequences and a T2* mapping. Regions of interest i.e., trabecular bone, subchondral bone, enthesis, and tendon body were identified, and T2* values were quantified and compared., Results: Quadriceps tendon enthesis was visible as a hyper-intense signal. The largest and the lowest T2* values were quantified in the subchondral bone region and the tendon body respectively. T2* value within subchondral bone was significantly higher than T2* value within the enthesis. T2* in subchondral bone region was significantly higher than the whole tendon body T2*., Conclusion: A T2* gradient was observed along the axis from the enthesis toward the tendon body. It illustrates different water biophysical properties. These results provide normative values which could be used in the field of inflammatory rheumatologic diseases and mechanical disorders affecting the tendon., (© 2023. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)

Hydroxyurea (HU) is commonly used as a treatment for patients with sickle cell disease (SCD) to enhance fetal hemoglobin production. This increased production is expected to reduce anemia (which depresses oxygen transport) and abnormal Hb content alleviating clinical symptoms such as vaso-occlusive crisis and acute chest syndrome. The effects of HU on skeletal muscle bioenergetics in vivo are still unknown. Due to the beneficial effects of HU upon oxygen delivery, improved skeletal muscle energetics and function in response to a HU treatment have been hypothesized. Muscle energetics and function were analyzed during a standardized rest-exercise-recovery protocol, using 31 P-magnetic resonance spectroscopy in Townes SCD mice. Measurements were performed in three groups of mice: one group of 2-mo-old mice (SCD 2 m , n = 8), another one of 4-mo-old mice (SCD 4 m , n = 8), and a last group of 4-mo-old mice that have been treated from 2 mo of age with HU at 50 mg/kg/day (SCD 4 m-HU , n = 8). As compared with SCD 2 m mice, SCD 4 m mice were heavier and displayed a lower acidosis. As lower specific forces were developed by SCD 4 m compared with SCD 2 m , greater force-normalized phosphocreatine consumption and oxidative and nonoxidative costs of contraction were also reported. HU-treated mice (SCD 4 m-HU ) displayed a significantly higher specific force production as compared with untreated mice (SCD 4 m ), whereas muscle energetics was unchanged. Overall, our results support a beneficial effect of HU on muscle function. NEW & NOTEWORTHY Our results highlighted that force production decreases between 2 and 4 mo of age in SCD mice thereby indicating a decrease of muscle function during this period. Of interest, HU treatment seemed to blunt the observed age effect given that SCD 4 m-HU mice displayed a higher specific force production as compared with SCD 4 m mice. In that respect, HU treatment would help to maintain a higher capacity of force production during aging in SCD.

Hydroxyurea (HU) is a ribonucleotide reductase inhibitor most commonly used as a therapeutic agent in sickle cell disease (SCD) with the aim of reducing the risk of vaso-occlusion and improving oxygen transport to tissues. Previous studies suggest that HU may be even beneficial in mild anemia. However, the corresponding effects on skeletal muscle energetics and function have never been reported in such a mild anemia model. Seventeen mildly anemic HbAA Townes mice were subjected to a standardized rest-stimulation (transcutaneous stimulation)-protocol while muscle energetics using 31 Phosphorus magnetic resonance spectroscopy and muscle force production were assessed and recorded. Eight mice were supplemented with hydroxyurea (HU) for 6 weeks while 9 were not (CON). HU mice displayed a higher specific total force production compared to the CON, with 501.35 ± 54.12 N/mm 3 and 437.43 ± 57.10 N/mm 3 respectively (+14.6%, p < 0.05). Neither the total rate of energy consumption nor the oxidative metabolic rate were significantly different between groups. The present results illustrated a positive effect of a HU chronic supplementation on skeletal muscle function in mice with mild anemia., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Michel, Messonnier, Giannesini, Chatel, Vilmen, Le Fur and Bendahan.)

Purpose: To propose a novel segmentation framework that is dedicated to the follow-up of fat infiltration in individual muscles of patients with neuromuscular disorders., Methods: We designed a semi-automatic segmentation pipeline of individual leg muscles in MR images based on automatic propagation through nonlinear registrations of initial delineation in a minimal number of MR slices. This approach has been validated for the segmentation of individual muscles from MRI data sets, acquired over a 10-month period, from thighs and legs in 10 patients with muscular dystrophy. The robustness of the framework was evaluated using conventional metrics related to muscle volume and clinical metrics related to fat infiltration., Results: High accuracy of the semi-automatic segmentation (mean Dice similarity coefficient higher than 0.89) was reported. The provided method has excellent reliability regarding the reproducibility of the fat fraction estimation, with an average intraclass correlation coefficient score of 0.99. Furthermore, the present segmentation framework was determined to be more reliable than the intra-expert performance, which had an average intraclass correlation coefficient of 0.93., Conclusion: The proposed framework of segmentation can successfully provide an effective and reliable tool for accurate follow-up of any MRI biomarkers in neuromuscular disorders. This method could assist the quantitative assessment of muscular changes occurring in such diseases., (© 2019 International Society for Magnetic Resonance in Medicine.)