Ultrasound-Based Detection of Fasciculations in Healthy and Diseased Muscles

Involuntary muscle activations are diagnostic indicators of neurodegenerative pathologies. Currently detected by invasive intramuscular electromyography, these muscle twitches are found to be visible in ultrasound images. We present an automated computational approach for the detection of muscle twitches, and apply this to two muscles in healthy and motor neuron disease-affected populations. The technique relies on motion tracking within ultrasound sequences, extracting local movement information from muscle. A statistical analysis is applied to classify the movement, either as noise or as more coherent movement indicative of a muscle twitch. The technique is compared to operator identified twitches, which are also assessed to ensure operator agreement. We find that, when two independent operators manually identified twitches, higher interoperator agreement (Cohen's k) occurs when more twitches are present (k = 0.94), compared to a lower number (k = 0.49). Finally, we demonstrate, via analysis of receiver operating characteristics, that our computational technique detects muscle twitches across the entire dataset with a high degree of accuracy (0.83 <; accuracy <; 0.96)

Foreground Detection Analysis of Ultrasound Image Sequences Identifies Markers of Motor Neurone Disease across Diagnostically Relevant Skeletal Muscles

© 2019 The Authors Diagnosis of motor neurone disease (MND) includes detection of small, involuntary muscle excitations, termed fasciculations. There is need to improve diagnosis and monitoring of MND through provision of objective markers of change. Fasciculations are visible in ultrasound image sequences. However, few approaches that objectively measure their occurrence have been proposed; their performance has been evaluated in only a few muscles; and their agreement with the clinical gold standard for fasciculation detection, intramuscular electromyography, has not been tested. We present a new application of adaptive foreground detection using a Gaussian mixture model (GMM), evaluating its accuracy across five skeletal muscles in healthy and MND-affected participants. The GMM provided good to excellent accuracy with the electromyography ground truth (80.17%–92.01%) and was robust to different ultrasound probe orientations. The GMM provides objective measurement of fasciculations in each of the body segments necessary for MND diagnosis and hence could provide a new, clinically relevant disease marker

Usefulness of Ultrasound Assessment of Fasciculations in Neurological Disease.

Aim: To study and analyze the fasciculations in multiple sites clinically, by EMG and by ultrasound. Find the usefulness of neuro ultrasound in identifying fasiculations both occult and manifest. Materials and Methods : Patients who are admitted with fasciculations with neurological illness in RGGGH. Patients with age group between 18yrs to 70 yrs. Totally 30 patients were examined. The duration of study was between December 2012 to Decmber 2013 Assessment by detailed history, neurological examination by standard proforma and criteria, EMG by standardized protocol (AAN), ultrasound examination of muscle using transducer of 7.5 MHZ for 30 seconds in multiple sites were studied. Correlation of EMG, Ultrasound and clinical examination were done Results and observation : In our study of 30 patients EMG was compared with ultrasonography in the detection of fasciculations. The correlation coefficient between EMG and USG was 0.024.High degree of correlation was found between the EMG detection of fasciculations and Ultrasound detection of fasciculations which was statistically significant with a p value of <0.005. USG detected more percent of fasciculations when compared to EMG. USG is noninvasive and fasciculation can be easily detected. USG can detect occult fasciculation which is difficult to detect by EMG. Different examination by EMG to search for occult fasciculations is difficult since patient may experience pain and technically difficult. But USG can be judiciously used to detect occult fasciculations. Conclusion : Neuromuscular ultrasound is a useful technique in neurology especially in confirmation of fasciculations in both clinical and subclinical. The utility of neuromuscular ultrasound in detecting occult fasciculations enables diagnostic accuracy of anterior horn cell disease. It is comparable to needle EMG in detecting fasciculations . Neuromuscular ultrasound is a non invasive, painless tool in the evaluation of fasciculations

Motor Unit Magnetic Resonance Imaging (MUMRI) In Skeletal Muscle

\ua9 2024 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.Magnetic resonance imaging (MRI) is routinely used in the musculoskeletal system to measure skeletal muscle structure and pathology in health and disease. Recently, it has been shown that MRI also has promise for detecting the functional changes, which occur in muscles, commonly associated with a range of neuromuscular disorders. This review focuses on novel adaptations of MRI, which can detect the activity of the functional sub-units of skeletal muscle, the motor units, referred to as “motor unit MRI (MUMRI).” MUMRI utilizes pulsed gradient spin echo, pulsed gradient stimulated echo and phase contrast MRI sequences and has, so far, been used to investigate spontaneous motor unit activity (fasciculation) and used in combination with electrical nerve stimulation to study motor unit morphology and muscle twitch dynamics. Through detection of disease driven changes in motor unit activity, MUMRI shows promise as a tool to aid in both earlier diagnosis of neuromuscular disorders and to help in furthering our understanding of the underlying mechanisms, which proceed gross structural and anatomical changes within diseased muscle. Here, we summarize evidence for the use of MUMRI in neuromuscular disorders and discuss what future research is required to translate MUMRI toward clinical practice. Level of Evidence: 5. Technical Efficacy: Stage 3

Comparison between surface electrodes and ultrasound monitoring to measure TMS evoked muscle contraction

INTRODUCTION: Transcranial magnetic stimulation (TMS) is widely employed to explore cortical physiology in health and disease. Surface electromyography (sEMG) is appropriate for superficial muscles, but cannot be applied easily to less accessible muscles. Muscle ultrasound (mUS) may provide an elegant solution to this problem, but fundamental questions remain. We explore the relationship between TMS evoked muscle potentials and TMS evoked muscle contractions measured with mUS. METHODS: In 10 participants we performed a TMS recruitment curve, simultaneously measuring motor evoked potentials (MEPs) and mUS in biceps (BI), first dorsal interosseous (FDI), tibialis anterior (TA) and the tongue (TO). RESULTS: Resting motor threshold (RMT) measurements and recruitment curves were found to be consistent across sEMG and mUS. DISCUSSION: This work supports the use of TMS-US to study less accessible muscles. The implications are broad but could include the study of a new range of muscles in disorders such as amyotrophic lateral sclerosis

The utility of b-mode ultrasound for the diagnosis of motor neurone disease: automated detection and analysis of muscle twitches in ultrasound Images of motor neurone disease affected participants and healthy controls

Motor Neurone Disease (MND) is a progressive, neurodegenerative disease, for which there is no known cure. Electromyography (EMG) is the standard technique for the detection of diagnostic indicators, such as fasciculations (twitches). Ultrasound (US) imaging may provide a more sensitive alternative to EMG for detection of fasciculations. However, only one computational technique has previously been applied to image sequences to provide an objective measure of fasciculation occurrence. The work presented here therefore describes the development and evaluation of a new computational approach, based on foreground detection using a mixture of Gaussians (GMM). In addition, the only other computational analysis approach available, which is based on feature tracking and mutual information analysis (KLT/MI) was further evaluated. Two data sets were used to evaluate the computational approaches. The first data set had previously been collected and comprised US images from medial gastrocnemius (MG) and biceps brachii (BB) from healthy (n = 20) and MND affected (n = 5) participants. The second data set comprised simultaneously recorded US images and intramuscular EMG from five muscles (medial gastrocnemius (MG), biceps brachii (BB), rectus femoris (RF), trapezius (TRAP), rectus abdominis (RA) and thoracic paraspinal (TP)) of healthy (n = 20) and MND affected (n = 20) participants. Accuracy of the approaches for fasciculation detection was evaluated against two measures of ground-truth: i) manual identification; ii) intramuscular EMG. Accuracy was defined as the area under the receiver operator curve and comparisons made between the performance of GMM and KLT/MI. Initial analysis was completed on the large limb muscles, MG and BB. The GMM had better accuracy than the KLT/MI when compared against operator identifications as the ground truth signal (88 – 94 % vs. 82 – 90 %). When EMG was used as the ground truth the GMM again had higher accuracy (81 – 88 % vs. 70 – 79 This thesis has shown a GMM computational analysis can detect fasciculations across a wide range of muscles and also can be used for the characterisation of fasciculations as they appear in ultrasound images, with significant differences being found between the healthy and MND affected participant groups. It has provided a foundation from which to build, with suggestions for future work being collecting images of stimulated twitches in a wide range of muscles for further characterisation and also a larger scale study prior to an official diagnosis being made to determine sensitivity and specificity values for this method as a diagnostic test

Electrodes' Configuration Influences the Agreement Between Surface EMG and B-Mode Ultrasound Detection of Motor Unit Fasciculation

Muscle fasciculations, resulting from the spontaneous activation of motor neurons, may be associated with neurological disorders, and are often assessed with intramuscular electromyography (EMG). Recently, however, both ultrasound (US) imaging and multichannel surface EMG have been shown to be more sensitive to fasciculations. In this study we combined these two techniques to compare their detection sensitivity to fasciculations occurring in different muscle regions and to investigate the effect of EMG electrodes' configuration on their agreement. Monopolar surface EMGs were collected from medial gastrocnemius and soleus with an array of 32 electrodes (10 mm Inter-Electrode Distance, IED) simultaneously with b-mode US images detected alongside either proximal, central or distal electrodes groups. Fasciculation potentials (FP) were identified from single differential EMGs with 10 mm (SD1), 20 mm (SD2) and 30 mm (SD3) IEDs, and fasciculation events (FE) from US image sequences. The number, location, and size of FEs and FPs in 10 healthy participants were analyzed. Overall, the two techniques showed similar sensitivities to muscle fasciculations. US was equally sensitive to FE occurring in the proximal and distal calf regions, while the number of FP revealed by EMG increased significantly with the IED and was larger distally, where the depth of FE decreased. The agreement between the two techniques was relatively low, with a percentage of fasciculation classified as common ranging from 22% for the smallest IED to 68% for the largest IED. The relevant number of events uniquely detected by the two techniques is discussed in terms of different spatial sensitivities of EMG and US, which suggest that a combination of US-EMG is likely to maximise the sensitivity to muscle fasciculations

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions

Objective analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning

Objective: To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia. Methods: We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range of pitch, yaw, and roll head movements. Using previously validated methods, we used 60-fold cross validation to train, validate and test a deep neural network (U-net) to classify pixels to 13 categories (five paired neck muscles, skin, ligamentum nuchae, vertebra). For all participants for their normal standing posture, we segmented US images and classified condition (Dystonia/Control), sex and age (higher/lower) from segment boundaries. We performed an explanatory, visualization analysis of dystonia muscle-boundaries. Results: For all segments, agreement with manual labels was Dice Coefficient (64±21%) and Hausdorff Distance (5.7±4 mm). For deep muscle layers, boundaries predicted central injection sites with average precision 94±3%. Using leave-one-out cross-validation, a support-vector-machine classified condition, sex, and age from predicted muscle boundaries at accuracy 70.5%, 67.2%, 52.4% respectively, exceeding classification by manual labels. From muscle boundaries, Dystonia clustered optimally into three sub-groups. These sub-groups are visualized and explained by three eigen-patterns which correlate significantly with truncal and head posture. Conclusion: Using US, neck muscle shape alone discriminates dystonia from healthy controls. Significance: Using deep learning, US imaging allows online, automated visualization, and diagnostic analysis of cervical dystonia and segmentation of individual muscles for targeted injection. The dataset is available (DOI: 10.23634/MMUDR.00624643)