BACKGROUND AND PURPOSE: The inhomogeneous magnetization transfer technique has demonstrated high specificity for myelin, and has shown sensitivity to multiple sclerosis-related impairment in brain tissue. Our aim was to investigate its sensitivity to spinal cord impairment in MS relative to more established MR imaging techniques (volumetry, magnetization transfer, DTI). MATERIALS AND METHODS: Anatomic images covering the cervical spinal cord from the C1 to C6 levels and DTI, magnetization transfer/inhomogeneous magnetization transfer images at the C2/C5 levels were acquired in 19 patients with MS and 19 paired healthy controls. Anatomic images were segmented in spinal cord GM and WM, both manually and using the AMU 40 atlases. MS lesions were manually delineated. MR metrics were analyzed within normal-appearing and lesion regions in anterolateral and posterolateral WM and compared using Wilcoxon rank tests and z scores. Correlations between MR metrics and clinical scores in patients with MS were evaluated using the Spearman rank correlation. RESULTS: AMU 40-based C1-to-C6 GM/WM automatic segmentations in patients with MS were evaluated relative to manual delineation. Mean Dice coefficients were 0.75/0.89, respectively. All MR metrics (WM/GM cross-sectional areas, normal-appearing and lesion diffusivities, and magnetization transfer/inhomogeneous magnetization transfer ratios) were observed altered in patients compared with controls (P , .05). Additionally, the absolute inhomogeneous magnetization transfer ratio z scores were significantly higher than those of the other MR metrics (P , .0001), suggesting a higher inhomogeneous magnetization transfer sensitivity toward spinal cord impairment in MS. Significant correlations with the Expanded Disability Status Scale (r ¼-0.73/P ¼ .02, r ¼-0.81/P ¼ .004) and the total Medical Research Council scale (r ¼ 0.80/P ¼ .009, r ¼-0.74/P ¼ .02) were observed for inhomogeneous magnetization transfer and magnetization transfer ratio z scores, respectively, in normal-appearing WM regions, while weaker and nonsignificant correlations were obtained for DTI metrics. CONCLUSIONS: With inhomogeneous magnetization transfer being highly sensitive to spinal cord damage in MS compared with conventional magnetization transfer and DTI, it could generate great clinical interest for longitudinal follow-up and potential remyelinating clinical trials. In line with other advanced myelin techniques with which it could be compared, it opens perspectives for multicentric investigations. ABBREVIATIONS: AMU ¼ Aix-Marseille University; CSA ¼ cross-sectional areas; EDSS ¼ Expanded Disability Status Scale; FA ¼ fractional anisotropy; HC ¼ healthy controls; ihMT ¼ inhomogeneous magnetization transfer; ihMTR ¼ inhomogeneous magnetization transfer ratio; l // ¼ axial diffusivity; l \ ¼ radial diffusivity; MRC ¼ Medical Research Council; MT ¼ magnetization transfer; MTR ¼ magnetization transfer ratio; NA ¼ normal-appearing; SC ¼ spinal cord; TWT ¼ Timed 25-Foot Walk Test; SCT ¼ Spinal Cord Toolbox M S is a ...

Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI’s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot–Marie–Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

Magnetic resonance imaging (MRI), a non-invasive and safe imaging method, is largely used for the assessment of multiple organs including knee. Due to the complexity of the knee joint, better images quality are required. Over the last decades, a variety of coil solutions has been proposed to improve MR image quality. Interestingly, dielectric or metamaterial structures have been used as additional devices for their ability to tailor electromagnetic field at a given scale. However, the use of these devices is often limited by their complexity and bulkiness. The present study aimed at improving the B1 transmit field for knee imaging at 3T through the design and manufacturing of a convenient and comfortable passive metasurface. A cylindrical array of conductive stripes was used to redistribute by inductive coupling the radiofrequency field generated by the body coil of the MRI scanner. This design takes no more space than a thin sheet placed around the leg of the patient. We have shown in simulation and experimentally the accuracy of this solution. For a given flip angle during signal acquisition, the improved transmit field allowed a reduction of the necessary input power. In addition to that, the structure had a negligible influence on the electric field inside the tissue and so did not significantly increase the specific absorption rate (SAR).

Neuromuscular diseases are genetic conditions which result in a progressive loss of muscle function. One of the hallmarks is the replacement of muscle by fat tissue which can be quantified using Magnetic Resonance Imaging (qMRI). Although individual muscles are generally affected by this replacement, the corresponding degree of fat infiltration differs from one muscle to another so that Fat Fraction quantification in individual muscles is of importance and this requires a delineation procedure to be performed. Given that the manual delineation is tedious and time consuming, semi-automatic and automatic approaches have been developed over the last decade. More specifically, deep learning approaches have provided promising results for automatic segmentation of medical images and U-Net has been the most largely used Convolutional Neural Network. A modified version of U-Net incorporating an "attention" block (Attention U-Net) has been proposed recently. It has been initially used for the automatic delineation of Pancreas on CT images. In the present work, we intended to compare the performance of 2D U-Net and 2D Attention U-Net for i) the segmentation of individual thigh muscles on MR images from neuropathic patients and controls and ii) the quantification of FF. Our results illustrate that both Attention U-Net and U-Net provide very high Dice scores with a significantly higher value for Attention U-Net (90% to 94.4%) in comparison with U-Net (86% to 94.2%). Nevertheless, a statistical analysis shows that the FF estimation is not significantly impacted by the deviation of the Dice score between the networks. This statistical analysis also shows that Attention U-Net and U-Net allow to estimate a fat fraction comparable with those computed by using the segmentation mask performed by experts.

MRI's T2 relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T2 relaxation time. In this proof‐of‐concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T2 values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary‐based technique that offers accurate and reproducible mapping of T2 relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T2 and proton‐density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p < 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T2 value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression.

Facioscapulohumeral dystrophy is the third genetic myopathy. Given that no therapeutic strategy has proved to be successful, rehabilitation has been considered as an interesting alternative. The aim of this study was to quantitatively evaluate the effects of a personalized rehabilitation strategy using MRI by tracking fat fraction (FF) and contractile volume (CV) over time. The lower limb was scanned before and after a rehabilitation program. FF and CV remained stable over the training period. Clinical parameters evolved positively. FF and CV were correlated to isokinetic strength thereby indicating that both metrics could be considered as biomarkers of the pathology progression.

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We assessed electromagnetic behavior of metallic implant used for distal femur fracture in typical MRI situation. |B1+| field, SAR and temperature variations were computed at 3T using a surface coil on human model for different positions of the implant relative to the coil. To validate the simulation, we measured the E-field and compared it to simulated E-field. |B1+| field maps showed an interesting augmentation near the implant. Both global SAR and local SAR levels proved that it is possible to safely image bone repair. However, temperature elevation near the tip of the implant was important and is to be considerate.