Ultrasound texture-based CAD system for detecting neuromuscular diseases

Validity of skeletal muscle ultrasound as a screening tool in the assessment of patients with suspected limb-girdle muscular dystrophy

This cross-sectional study measured the sensitivity and specificity of muscle ultrasound (MUS) in the assessment of patients with suspected limb-girdle muscular dystrophy (LGMD). Sixty patients with suspected LGMD from the Neuromuscular Unit, Myology Clinic, Ain Shams University Hospital, Cairo, Egypt, and a series of healthy subjects were included. The patients underwent real-time B-mode ultrasonography performed using a General Electric ultrasound machine (GE Logiq P7) and a General Electric 7.5 MHz linear array ultrasound probe (USA). All images were obtained and scored by a single examiner, and muscle echo intensity was visually graded semiquantitatively using Heckmatt's scale. The examiner was blinded to the clinical evaluations and patients' investigations. Statistical analysis using receiver operating characteristic (ROC) curve analysis revealed that the total upper-limb (UL) Heckmatt's US score at a cutoff point >1 predicted patients with dystrophy, with good (88%) accuracy and with sensitivity and specificity of 100% and 75%, respectively (p < 0.01). Moreover, the total lower-limbs (LL) Heckmatt's US score at a cutoff point >1 predicted patients with dystrophy, with excellent (91%) accuracy and with sensitivity and specificity of 100% and 75%, respectively (p < 0.01). Finally, the total Heckmatt's US score at a cutoff point >2 predicted patients with dystrophy, with good (89%) accuracy and with sensitivity and specificity of 100% and 75%, respectively (p < 0.01). Thus, MUS can be considered a valid screening tool in the assessment of patients with suspected LGMD.

MRI and muscle imaging for idiopathic inflammatory myopathies

Although idiopathic inflammatory myopathies (IIM) are a heterogeneous group of diseases nearly all patients display muscle inflammation. Originally, muscle biopsy was considered as the gold standard for IIM diagnosis. The development of muscle imaging led to revisiting not only the IIM diagnosis strategy but also the patients' follow-up. Different techniques have been tested or are in development for IIM including positron emission tomography, ultrasound imaging, ultrasound shear wave elastography, though magnetic resonance imaging (MRI) remains the most widely used technique in routine. Whereas guidelines on muscle imaging in myositis are lacking here we reviewed the relevance of muscle imaging for both diagnosis and myositis patients' follow-up. We propose recommendations about when and how to perform MRI on myositis patients, and we describe new techniques that are under development.

Muscle Composition Analysis of Ultrasound Images: A Narrative Review of Texture Analysis

Skeletal muscle composition, often characterized by the degree of intramuscular adipose tissue, deteriorates with aging and disease and contributes to impairments in function and metabolism. Ultrasound can provide surrogate measures of muscle composition through measurement of echo intensity; however, there are several limitations associated with its analysis. More complex image processing features, broadly known as texture analysis, can also provide surrogates of muscle composition and may circumvent some of the limitations associated with muscle echo intensity. Here, texture features from the intensity histogram, gray-level co-occurrence matrix, run-length matrix, local binary pattern, blob analysis, texture anisotropy index and wavelet analysis are discussed. The purpose of this review was to provide a conceptual understanding of texture analysis as it pertains to muscle composition of ultrasound images.

Quantitative Ultrasound Texture Analysis to Assess the Spastic Muscles in Stroke Patients

This study aimed to investigate the feasibility of sonoelastography for determining echotexture in post-stroke patients. Moreover, the relationships of muscle echotexture features, muscle stiffness, and functional performance in spastic muscle were explored. The study population comprised 22 males with stroke. The echotexture features (entropy and energy) of the biceps brachii muscles (BBM) in both arms were extracted by local binary pattern (LBP) from ultrasound images, whereas the stiffness of BBM was assessed by shear wave velocity (SWV) in the transverse and longitudinal planes. The Fugl–Meyer assessment (FMA) was used to assess the functional performance of the upper arm. The results showed that echotexture was more inhomogeneous in the paretic BBM than in the non-paretic BBM. SWV was significantly faster in paretic BBM than in non-paretic BBM. Both echotexture features were significantly correlated with SWV in the longitudinal plane. The feature of energy was significantly negatively correlated with FMA in the longitudinal plane and was significantly positively correlated with the duration from stroke onset in the transverse plane. The echotexture extracted by LBP may be a promising approach for quantitative assessment of the spastic BBM in post-stroke patients.

Diagnostic Value of Muscle Ultrasound for Myopathies and Myositis

Purpose of Review

The purpose of this review is to critically discuss the use of ultrasound in the evaluation of muscle disorders with a particular focus on the emerging use in inflammatory myopathies.

Recent Findings

In myopathies, pathologic muscle shows an increase in echogenicity. Muscle echogenicity can be assessed visually, semi-quantitatively, or quantitatively using grayscale analysis. The involvement of specific muscle groups and the pattern of increase in echogenicity can further point to specific diseases. In pediatric neuromuscular disorders, the value of muscle ultrasound for screening and diagnosis is well-established. It has also been found to be a responsive measure of disease change in muscular dystrophies. In chronic forms of myositis like inclusion body myositis, ultrasound is very suitable for detecting markedly increased echogenicity and atrophy in affected muscles. Acute cases of muscle edema show only a mild increase in echogenicity, which can also reverse with successful treatment.

Summary

Muscle ultrasound is an important imaging modality that is highly adaptable to study various muscle conditions. Although its diagnostic value for neuromuscular disorders is high, the evidence in myositis has only begun to accrue in earnest. Further systematic studies are needed, especially in its role for detecting muscle edema.

Efficacy of Quantitative Muscle Ultrasound Using Texture-Feature Parametric Imaging in Detecting Pompe Disease in Children

Pompe disease is a hereditary neuromuscular disorder attributed to acid α-glucosidase deficiency, and accurately identifying this disease is essential. Our aim was to discriminate normal muscles from neuropathic muscles in children affected by Pompe disease using a texture-feature parametric imaging method that simultaneously considers microstructure and macrostructure. The study included 22 children aged 0.02-54 months with Pompe disease and six healthy children aged 2-12 months with normal muscles. For each subject, transverse ultrasound images of the bilateral rectus femoris and sartorius muscles were obtained. Gray-level co-occurrence matrix-based Haralick's features were used for constructing parametric images and identifying neuropathic muscles: autocorrelation (AUT), contrast, energy (ENE), entropy (ENT), maximum probability (MAXP), variance (VAR), and cluster prominence (CPR). Stepwise regression was used in feature selection. The Fisher linear discriminant analysis was used for combination of the selected features to distinguish between normal and pathological muscles. The VAR and CPR were the optimal feature set for classifying normal and pathological rectus femoris muscles, whereas the ENE, VAR, and CPR were the optimal feature set for distinguishing between normal and pathological sartorius muscles. The two feature sets were combined to discriminate between children with and without neuropathic muscles affected by Pompe disease, achieving an accuracy of 94.6%, a specificity of 100%, a sensitivity of 93.2%, and an area under the receiver operating characteristic curve of 0.98 ± 0.02. The CPR for the rectus femoris muscles and the AUT, ENT, MAXP, and VAR for the sartorius muscles exhibited statistically significant differences in distinguishing between the infantile-onset Pompe disease and late-onset Pompe disease groups (p < 0.05). Texture-feature parametric imaging can be used to quantify and map tissue structures in skeletal muscles and distinguish between pathological and normal muscles in children or newborns.

Advances in Quantitative Imaging of Genetic and Acquired Myopathies: Clinical Applications and Perspectives

In the last years, magnetic resonance imaging (MRI) has become fundamental for the diagnosis and monitoring of myopathies given its ability to show the severity and distribution of pathology, to identify specific patterns of damage distribution and to properly interpret a number of genetic variants. The advances in MR techniques and post-processing software solutions have greatly expanded the potential to assess pathological changes in muscle diseases, and more specifically of myopathies; a number of features can be studied and quantified, ranging from composition, architecture, mechanical properties, perfusion, and function, leading to what is known as quantitative MRI (qMRI). Such techniques can effectively provide a variety of information beyond what can be seen and assessed by conventional MR imaging; their development and application in clinical practice can play an important role in the diagnostic process and in assessing disease course and treatment response. In this review, we briefly discuss the current role of muscle MRI in diagnosing muscle diseases and describe in detail the potential and perspectives of the application of advanced qMRI techniques in this field.

Local Texture Anisotropy as an Estimate of Muscle Quality in Ultrasound Imaging

This study introduces local pattern texture anisotropy as a novel parameter to differentiate healthy and disordered muscle and to gauge the severity of muscle impairments based on B-mode ultrasound images. Preliminary human results are also presented. A local pattern texture anisotropy index (TAI) was computed in one region of interest in the short head of the biceps brachii. The effects of gain settings and box sizes required for TAI computation were investigated. Between-day reliability was studied in patients with sporadic inclusion body myositis (n = 26). The ability of the TAI to discriminate dystrophic from healthy muscle was evaluated in patients with Duchenne muscular dystrophy and healthy controls (n = 16). TAI values were compared with a gray-scale index (GSI). TAI values were less influenced by gain settings than were GSI values. TAI had lower between-day variability (typical error = 2.3%) compared with GSI (typical error = 2.3% vs. 8.3%, respectively). Patients with Duchenne muscular dystrophy had lower TAIs than controls (0.76 ± 0.06 vs. 0.87 ± 0.03, respectively, p <0.05). At 40% gain, TAI values correlated with percentage predicted elbow flexor strength in inclusion body myositis (R = 0.63, p <0.001). The TAI may be a promising addition to other texture-based approaches for quantitative muscle ultrasound imaging.

Machine learning for medical ultrasound: status, methods, and future opportunities

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

[Ultrasound of muscular diseases in children and adolescents]

BACKGROUND

The increasing number of treatable hereditary neuromuscular diseases in children requires a diagnostic tool that can quickly, safely, and noninvasively identify affected patients directly after birth or when showing initial clinical symptoms. With clinical analysis alone, this is very difficult.

IMAGING MODALITY

Near-field sonography of skeletal muscles has gradually become established as a successful method over the last 35 years.

METHODOLOGICAL INNOVATIONS

Examination is performed using a strictly standardized protocol in isotopic muscle regions and with standardized sections and application parameters. Interpretation is performed with specific assessment criteria and nomenclature for the description of normal and pathological muscle architecture and echogenicity. This is sonographic tissue characterization.

PERFORMANCE

Using case studies, the sonoanatomy and sonopathology of selected myo- and neuropathies, metabolic, inflammatory, and other lesions are illustrated. We present their differential diagnosis by texture and echogenicity analysis. Affected persons are identified in 70% up to 100% of cases, depending on the entity; specificity is less dependent on experience and training. Of the 12 disorders presented in this article, 6 are causally/symptomatically treatable today.

ACHIEVEMENTS

Standardized myosonography is the imaging modality of first choice for detection of neuromuscular diseases.

PRACTICAL RECOMMENDATIONS

High frequency (8-22 MHz) linear array transducer. Highly standardized examination modality. Simultaneous, paired comparison of affected persons and controls. If necessary, muscle tissue biopsy only after ultrasonic determination of a suitable area.

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

OBJECTIVE

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis.

METHODS

Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.

RESULTS

The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C).

CONCLUSIONS

This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

Neurogenic and Myogenic Diseases: Quantitative Texture Analysis of Muscle US Data for Differentiation

Purpose To assess the multiple texture features of skeletal muscles in neurogenic and myogenic diseases by using ultrasonography (US). Materials and Methods After institutional review board approval, muscle US studies of the medial head of the gastrocnemius were performed prospectively in patients with neurogenic diseases (n = 25 [18 men]; mean age, 66.0 years ± 12.3 [standard deviation]), in patients with myogenic diseases (n = 21 [12 men]; mean age, 68.3 years ± 11.5), and in healthy control subjects (n = 21 [11 men]; mean age, 70.5 years ± 8.4) between January 2013 and May 2016. Written informed consent was obtained. Muscle texture parameters were obtained, and five algorithms were used to classify the groups. Results The neurogenic and myogenic disease groups showed higher echo intensities than the control subjects. The histogram-derived texture parameters had overlaps between the neurogenic and myogenic groups and thus had a low discrimination rate. With assessment of more classes of texture parameters, three groups were correctly classified (100% correct, according to four of five classification algorithms). Tenfold cross validation showed 93.5%-95.7% correct classification between the neurogenic and myogenic groups. The run-length matrix, autoregressive model, and co-occurrence matrix were particularly useful in distinguishing the neurogenic and myogenic groups. Conclusion Texture analysis of muscle US data can enable differentiation between neurogenic and myogenic diseases and is useful in noninvasively assessing underlying disease mechanisms. ^© RSNA, 2017 Online supplemental material is available for this article.