Muscle quantitative MRI as a novel biomarker in hereditary transthyretin amyloidosis with polyneuropathy: a cross-sectional study.

BACKGROUND: The development of reproducible and sensitive outcome measures has been challenging in hereditary transthyretin (ATTRv) amyloidosis. Recently, quantification of intramuscular fat by magnetic resonance imaging (MRI) has proven as a sensitive marker in patients with other genetic neuropathies. The aim of this study was to investigate the role of muscle quantitative MRI (qMRI) as an outcome measure in ATTRv. METHODS: Calf- and thigh-centered multi-echo T2-weighted spin-echo and gradient-echo sequences were obtained in patients with ATTRv amyloidosis with polyneuropathy (n = 24) and healthy controls (n = 12). Water T2 (wT2) and fat fraction (FF) were calculated. Neurological assessment was performed in all ATTRv subjects. Quantitative MRI parameters were correlated with clinical and neurophysiological measures of disease severity. RESULTS: Quantitative imaging revealed significantly higher FF in lower limb muscles in patients with ATTRv amyloidosis compared to controls. In addition, wT2 was significantly higher in ATTRv patients. There was prominent involvement of the posterior compartment of the thighs. Noticeably, FF and wT2 did not exhibit a length-dependent pattern in ATTRv patients. MRI biomarkers correlated with previously validated clinical outcome measures, Polyneuropathy Disability scoring system, Neuropathy Impairment Score (NIS) and NIS-lower limb, and neurophysiological parameters of axonal damage regardless of age, sex, treatment and TTR mutation. CONCLUSIONS: Muscle qMRI revealed significant difference between ATTRv and healthy controls. MRI biomarkers showed high correlation with clinical and neurophysiological measures of disease severity making qMRI as a promising tool to be further investigated in longitudinal studies to assess its role at monitoring onset, progression, and therapy efficacy for future clinical trials on this treatable condition.

New-onset myasthenia gravis after mRNA SARS-CoV-2 vaccination: a case series.

BACKGROUND: Myasthenia gravis (MG) is an autoimmune disease that targets acetylcholine receptor (AChR) of the neuromuscular junction. New-onset MG after SARS-CoV-2 vaccination has rarely been reported. CASE PRESENTATION: We report about three patients who presented new-onset myasthenia gravis after receiving mRNA SARS-CoV-2 vaccination. The patients were all males and older than 55 years. All the patients presented with ocular and bulbar symptoms. The interval between vaccine administration and MG onset ranged from 3 days after the first dose to 10 days after the second dose. All the patients had elevated serum AChR antibodies and responded to pyridostigmine. Two out of three patients were successfully treated with IVIG or plasma exchange and with long-term immunosuppression. CONCLUSIONS: MG is a rare disease; clinicians should be aware of possible new-onset MG after SARS-CoV-2 vaccination, especially with the current recommendation of booster doses. The hyperstimulation of the innate immune system or the exacerbation of a subclinical pre-existing MG could be possible explanations.

Dynamic magnetic resonance imaging of muscle contraction in facioscapulohumeral muscular dystrophy.

Quantitative muscle MRI (water-T2 and fat mapping) is being increasingly used to assess disease involvement in muscle disorders, while imaging techniques for assessment of the dynamic and elastic muscle properties have not yet been translated into clinics. In this exploratory study, we quantitatively characterized muscle deformation (strain) in patients affected by facioscapulohumeral muscular dystrophy (FSHD), a prevalent muscular dystrophy, by applying dynamic MRI synchronized with neuromuscular electrical stimulation (NMES). We evaluated the quadriceps muscles in 34 ambulatory patients and 13 healthy controls, at 6-to 12-month time intervals. While a subgroup of patients behaved similarly to controls, for another subgroup the median strain decreased over time (approximately 57% over 1.5 years). Dynamic MRI parameters did not correlate with quantitative MRI. Our results suggest that the evaluation of muscle contraction by NMES-MRI is feasible and could potentially be used to explore the elastic properties and monitor muscle involvement in FSHD and other neuromuscular disorders.

Imaging features and ultraearly hematoma growth in intracerebral hemorrhage associated with COVID-19.

PURPOSE: Intracerebral hemorrhage (ICH) is an uncommon but deadly event in patients with COVID-19 and its imaging features remain poorly characterized. We aimed to describe the clinical and imaging features of COVID-19-associated ICH. METHODS: Multicenter, retrospective, case-control analysis comparing ICH in COVID-19 patients (COV19 +) versus controls without COVID-19 (COV19 -). Clinical presentation, laboratory markers, and severity of COVID-19 disease were recorded. Non-contrast computed tomography (NCCT) markers (intrahematoma hypodensity, heterogeneous density, blend sign, irregular shape fluid level), ICH location, and hematoma volume (ABC/2 method) were analyzed. The outcome of interest was ultraearly hematoma growth (uHG) (defined as NCCT baseline ICH volume/onset-to-imaging time), whose predictors were explored with multivariable linear regression. RESULTS: A total of 33 COV19 + patients and 321 COV19 - controls with ICH were included. Demographic characteristics and vascular risk factors were similar in the two groups. Multifocal ICH and NCCT markers were significantly more common in the COV19 + population. uHG was significantly higher among COV19 + patients (median 6.2 mL/h vs 3.1 mL/h, p = 0.027), and this finding remained significant after adjustment for confounding factors (systolic blood pressure, antiplatelet and anticoagulant therapy), in linear regression (B(SE) = 0.31 (0.11), p = 0.005). This association remained consistent also after the exclusion of patients under anticoagulant treatment (B(SE) = 0.29 (0.13), p = 0.026). CONCLUSIONS: ICH in COV19 + patients has distinct NCCT imaging features and a higher speed of bleeding. This association is not mediated by antithrombotic therapy and deserves further research to characterize the underlying biological mechanisms.

Deep learning for automatic segmentation of thigh and leg muscles.

OBJECTIVE: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach. MATERIAL AND METHODS: The application of quantitative imaging in neuromuscular diseases requires the availability of regions of interest (ROI) drawn on muscles to extract quantitative parameters. Up to now, manual drawing of ROIs has been considered the gold standard in clinical studies, with no clear and universally accepted standardized procedure for segmentation. Several automatic methods, based mainly on machine learning and deep learning algorithms, have recently been proposed to discriminate between skeletal muscle, bone, subcutaneous and intermuscular adipose tissue. We develop a supervised deep learning approach based on a unified framework for ROI segmentation. RESULTS: The proposed network generates segmentation maps with high accuracy, consisting in Dice Scores ranging from 0.89 to 0.95, with respect to "ground truth" manually segmented labelled images, also showing high average performance in both mild and severe cases of disease involvement (i.e. entity of fatty replacement). DISCUSSION: The presented results are promising and potentially translatable to different skeletal muscle groups and other MRI sequences with different contrast and resolution.

Longitudinal Quantitative MRI Evaluation of Muscle Involvement in Amyotrophic Lateral Sclerosis.

Background: Biomarkers of disease progression and outcome measures are still lacking for patients with amyotrophic lateral sclerosis (ALS). Muscle MRI can be a promising candidate to track longitudinal changes and to predict response to the therapy in clinical trials. Objective: Our aim is to apply quantitative muscle MRI in the evaluation of disease progression, focusing on thigh and leg muscles of patients with ALS, and to explore the correlation between radiological and clinical scores. Methods: We enrolled newly diagnosed patients with ALS, longitudinally scored using the ALS Functional Rating Scale-Revised (ALSFRS-R), who underwent a 3T muscle MRI protocol including a 6-point Dixon gradient-echo sequence and multi-echo turbo spin echo (TSE) T2-weighted sequence for quantification of fat fraction (FF), cross-sectional area (CSA), and water T2 (wT2). A total of 12 muscles of the thigh and six muscles of the leg were assessed by the manual drawing of 18 regions of interest (ROIs), for each side. A group of 11 age-matched healthy controls (HCs) was enrolled for comparison. Results: 15 patients (M/F 8/7; mean age 62.2 years old, range 29-79) diagnosed with possible (n = 2), probable (n = 12), or definite (n = 1) ALS were enrolled. Eleven patients presented spinal onset, whereas four of them had initial bulbar involvement. All patients performed MRI at T0, nine of them at T1, and seven of them at T2. At baseline, wT2 was significantly elevated in ALS subjects compared to HCs for several muscles of the thigh and mainly for leg muscles. By contrast, FF was elevated in few muscles, and mainly at the level of the thigh. The applied mixed effects model showed that FF increased significantly in the leg muscles over time (mainly in the triceps surae) and that wT2 decreased significantly in line with worsening in the leg subscore of ALSFRS-R, mainly at the leg level and in the anterior and medial compartment of the thigh. Conclusions: Quantitative MRI represents a non-invasive tool that is able to outline the trajectory of pathogenic modifications at the muscle level in ALS. In particular, wT2 was found to be increased early in the clinical history of ALS and also tended to decrease over time, also showing a positive correlation with leg subscore of ALSFRS-R.

Reproducibility of manual segmentation in muscle imaging.

PURPOSE: To assess the reproducibility of a manual muscle MRI segmentation method that follows a specific set of recommendations developed in our center. MATERIALS AND METHODS: Nine healthy volunteers underwent a muscle MRI examination that included a TSE T2 sequence of the thighs. Muscle segmentation was performed by three operators: an expert operator (OP1) with 3 years of experience and two radiology residents (OP2 and 3) who were both given basic segmentation instructions, whereas only OP2 underwent additional supervised training from OP1. Intra- and inter-operator Dice similarity coefficient (DSC) was calculated. RESULTS: OP1 showed the highest average intra-operator DSC values (0.885), whereas OP2 had higher average DSC (0.856) compared to OP3 (0.818). The highest inter-operator agreement was observed between Operators 1 and 2 (0.814) and the lowest between OP2 and OP3 (0.702). Confidence interval (CI) analysis showed that the most experienced operator also had the least variability in drawing the ROIs, whereas OP2 showed both higher intra-operator reproducibility compared to OP3 and higher inter-operator agreement with OP1. The muscles that showed the least reproducibility were the semimembranosus and the short head of the biceps femoris. DISCUSSION: Following specific recommendations such as these ones derived from our single-center experience leads to an overall high reproducibility of manual muscle segmentation and is helpful in improving both intra-operator and inter-operator reproducibility in less experienced operators.

Pilot Study on Quantitative Cervical Cord and Muscular MRI in Spinal Muscular Atrophy: Promising Biomarkers of Disease Evolution and Treatment?

Introduction: Nusinersen is a recent promising therapy approved for the treatment of spinal muscular atrophy (SMA), a rare disease characterized by the degeneration of alpha motor neurons (αMN) in the spinal cord (SC) leading to progressive muscle atrophy and dysfunction. Muscle and cervical SC quantitative magnetic resonance imaging (qMRI) has never been used to monitor drug treatment in SMA. The aim of this pilot study is to investigate whether qMRI can provide useful biomarkers for monitoring treatment efficacy in SMA. Methods: Three adult SMA 3a patients under treatment with nusinersen underwent longitudinal clinical and qMRI examinations every 4 months from baseline to 21-month follow-up. The qMRI protocol aimed to quantify thigh muscle fat fraction (FF) and water-T2 (w-T2) and to characterize SC volumes and microstructure. Eleven healthy controls underwent the same SC protocol (single time point). We evaluated clinical and imaging outcomes of SMA patients longitudinally and compared SC data between groups transversally. Results: Patient motor function was stable, with only Patient 2 showing moderate improvements. Average muscle FF was already high at baseline (50%) and progressed over time (57%). w-T2 was also slightly higher than previously published data at baseline and slightly decreased over time. Cross-sectional area of the whole SC, gray matter (GM), and ventral horns (VHs) of Patients 1 and 3 were reduced compared to controls and remained stable over time, while GM and VHs areas of Patient 2 slightly increased. We found altered diffusion and magnetization transfer parameters in SC structures of SMA patients compared to controls, thus suggesting changes in tissue microstructure and myelin content. Conclusion: In this pilot study, we found a progression of FF in thigh muscles of SMA 3a patients during nusinersen therapy and a concurrent slight reduction of w-T2 over time. The SC qMRI analysis confirmed previous imaging and histopathological studies suggesting degeneration of αMN of the VHs, resulting in GM atrophy and demyelination. Our longitudinal data suggest that qMRI could represent a feasible technique for capturing microstructural changes induced by SMA in vivo and a candidate methodology for monitoring the effects of treatment, once replicated on a larger cohort.

Fast Open-Source Toolkit for Water T2 Mapping in the Presence of Fat From Multi-Echo Spin-Echo Acquisitions for Muscle MRI.

Imaging has become a valuable tool in the assessment of neuromuscular diseases, and, specifically, quantitative MR imaging provides robust biomarkers for the monitoring of disease progression. Quantitative evaluation of fat infiltration and quantification of the T2 values of the muscular tissue's water component (wT2) are two of the most essential indicators currently used. As each voxel of the image can contain both water and fat, a two-component model for the estimation of wT2 must be used. In this work, we present a fast method for reconstructing wT2 maps obtained from conventional multi-echo spin-echo (MESE) acquisitions and released as Free Open Source Software. The proposed software is capable of fast reconstruction thanks to extended phase graphs (EPG) simulations and dictionary matching implemented on a general-purpose graphic processing unit. The program can also perform more conventional biexponential least-squares fitting of the data and incorporate information from an external water-fat acquisition to increase the accuracy of the results. The method was applied to the scans of four healthy volunteers and five subjects suffering from facioscapulohumeral muscular dystrophy (FSHD). Conventional multi-slice MESE acquisitions were performed with 17 echoes, and additionally, a 6-echo multi-echo gradient-echo (MEGE) sequence was used for an independent fat fraction calculation. The proposed reconstruction software was applied on the full datasets, and additionally to reduced number of echoes, respectively, to 8, 5, and 3, using EPG and biexponential least-squares fitting, with and without incorporating information from the MEGE acquisition. The incorporation of external fat fraction maps increased the robustness of the fitting with a reduced number of echoes per datasets, whereas with unconstrained fitting, the total of 17 echoes was necessary to retain an independence of wT2 from the level of fat infiltration. In conclusion, the proposed software can successfully be used to calculate wT2 maps from conventional MESE acquisition, allowing the usage of an optimized protocol with similar precision and accuracy as a 17-echo acquisition. As it is freely released to the community, it can be used as a reference for more extensive cohort studies.

Variable echo time imaging for detecting the short T2* components of the sciatic nerve: a validation study.

OBJECTIVE: The aim of this study was to develop and validate an MRI protocol based on a variable echo time (vTE) sensitive to the short T2* components of the sciatic nerve. MATERIALS AND METHODS: 15 healthy subjects (M/F: 9/6; age: 21-62) were scanned at 3T targeting the sciatic nerve at the thigh bilaterally, using a dual echo variable echo time (vTE) sequence (based on a spoiled gradient echo acquisition) with echo times of 0.98/5.37 ms. Apparent T2* (aT2*) values of the sciatic nerves were calculated with a mono-exponential fit and used for data comparison. RESULTS: There were no significant differences in aT2* related to side, sex, age, and BMI, even though small differences for side were reported. Good-to-excellent repeatability and reproducibility were found for geometry of ROIs (Dice indices: intra-rater 0.68-0.7; inter-rater 0.70-0.72) and the related aT2* measures (intra-inter reader ICC 0.95-0.97; 0.66-0.85) from two different operators. Side-related signal-to-noise-ratio non-significant differences were reported, while contrast-to-noise-ratio measures were excellent both for side and echo. DISCUSSION: Our study introduces a novel MR sequence sensitive to the short T2* components of the sciatic nerve and may be used for the study of peripheral nerve disorders.

Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy.

PURPOSE: Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning. METHOD: Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset. RESULTS: Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2. CONCLUSION: This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.