Quantitative Sonography of Muscle

1989 ◽  
Vol 4 (1_suppl) ◽  
pp. S101-S106 ◽  
Author(s):  
John Heckmatt ◽  
E. Rodillo ◽  
Mark Doherty ◽  
Keith Willson ◽  
Sidney Leeman
Keyword(s):  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Ultrasound Imaging ◽  
Thigh Muscle ◽  
Ultrasound Transducer ◽  
Scanning System ◽  
Ultrasound Scan ◽  
Pathologic Change ◽  
Increased Strength ◽  
Commercial Equipment

Ultrasound imaging allows detection of pathologic change in muscle on the basis of increased strength of echoes. With current commercial equipment, however, there is no method of quantitation of the echoes representing muscle, and there is lack of uniformity in scanning methodology. We describe a specially constructed scanning system, designed to access the raw echo data directly from the ultrasound transducer, and allow display and measurement of the echo signals on a computer. In a study of 38 boys with Duchenne muscular dystrophy, aged 1 to 11 years, who had an ultrasound scan of the thigh muscle, 32 (84%) had abnormality on quantitation of the ultrasound echoes. The quantitative techniques we describe could easily be incorporated into the design of ultrasound scanners. (J Child Neurol 1989;4:S101-S106).

Towards objective and reproducible measures of thigh muscle fat fraction in patients with Duchenne Muscular Dystrophy

2017 ◽  
Vol 27 ◽  
pp. S7
Author(s):  
A. Fischmann ◽  
C. Trentin ◽  
M. Gloor ◽  
R. Andriantsimiavona ◽  
D. Fischer ◽  
...  
Keyword(s):  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Thigh Muscle ◽  
Fat Fraction

scholarly journals Deep Learning of Ultrasound Imaging for Evaluating Ambulatory Function of Individuals with Duchenne Muscular Dystrophy

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 963
Author(s):  
Ai-Ho Liao ◽  
Jheng-Ru Chen ◽  
Shi-Hong Liu ◽  
Chun-Hao Lu ◽  
Chia-Wei Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Ultrasound Imaging ◽  
Roc Curve ◽  
Confusion Matrix ◽  
Automatic Generation ◽  
Classification Performance ◽  
Fine Tuning ◽  
Muscle Ultrasound

Duchenne muscular dystrophy (DMD) results in loss of ambulation and premature death. Ultrasound provides real-time, safe, and cost-effective routine examinations. Deep learning allows the automatic generation of useful features for classification. This study utilized deep learning of ultrasound imaging for classifying patients with DMD based on their ambulatory function. A total of 85 individuals (including ambulatory and nonambulatory subjects) underwent ultrasound examinations of the gastrocnemius for deep learning of image data using LeNet, AlexNet, VGG-16, VGG-16TL, VGG-19, and VGG-19TL models (the notation TL indicates fine-tuning pretrained models). Gradient-weighted class activation mapping (Grad-CAM) was used to visualize features recognized by the models. The classification performance was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve analysis. The results show that each deep learning model endows muscle ultrasound imaging with the ability to enable DMD evaluations. The Grad-CAMs indicated that boundary visibility, muscular texture clarity, and posterior shadowing are relevant sonographic features recognized by the models for evaluating ambulatory function. Of the proposed models, VGG-19 provided satisfying classification performance (the area under the ROC curve: 0.98; accuracy: 94.18%) and feature recognition in terms of physical characteristics. Deep learning of muscle ultrasound is a potential strategy for DMD characterization.

scholarly journals Relationships of thigh muscle contractile and non-contractile tissue with function, strength, and age in boys with Duchenne muscular dystrophy

2012 ◽  
Vol 22 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Hiroshi Akima ◽  
Donovan Lott ◽  
Claudia Senesac ◽  
Jasjit Deol ◽  
Sean Germain ◽  
...  
Keyword(s):  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Thigh Muscle ◽  
Contractile Tissue ◽  
Muscle Contractile

scholarly journals Clinical Evaluation of Duchenne Muscular Dystrophy Severity Using Ultrasound Small-Window Entropy Imaging

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 715 ◽  
Author(s):  
Dong Yan ◽  
Qiang Li ◽  
Chia-Wei Lin ◽  
Jeng-Yi Shieh ◽  
Wen-Chin Weng ◽  
...  
Keyword(s):  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Ultrasound Imaging ◽  
Characteristic Curve ◽  
Image Data ◽  
Control Group ◽  
Information Uncertainty ◽  
Small Window ◽  
Stage 1

Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98–5.00) for the control group, 5.04 (IQR: 5.01–5.05) for stage 1 patients, 5.07 (IQR: 5.06–5.07) for stage 2 patients, and 5.07 (IQR: 5.06–5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD.

scholarly journals Quantitative muscle MRI biomarkers in Duchenne muscular dystrophy: cross-sectional correlations with age and functional tests

2021 ◽  
Author(s):  
Sarah P Sherlock ◽  
Yao Zhang ◽  
Michael Binks ◽  
Shannon Marraffino
Keyword(s):  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
T2 Mapping ◽  
Quantitative Mri ◽  
Thigh Muscle ◽  
Ambulatory Assessment ◽  
Functional Tests ◽  
Muscle Mri ◽  
Cross Sectional ◽  
Fat Fraction

Aim: Using baseline data from a clinical trial of domagrozumab in Duchenne muscular dystrophy, we evaluated the correlation between functional measures and quantitative MRI assessments of thigh muscle. Patients & methods: Analysis included timed functional tests, knee extension/strength and North Star Ambulatory Assessment. Patients (n = 120) underwent examinations of one thigh, with MRI sequences to enable measurements of muscle volume (MV), MV index, mean T2 relaxation time via T2-mapping and fat fraction. Results: MV was moderately correlated with strength assessments. MV index, fat fraction and T2-mapping measures had moderate correlations ( r ∼ 0.5) to all functional tests, North Star Ambulatory Assessment and age. Conclusion: The moderate correlation between functional tests, age and baseline MRI measures supports MRI as a biomarker in Duchenne muscular dystrophy clinical trials. Trial registration: ClinicalTrials.gov , NCT02310763 ; registered 4 November 2014.

Sign in / Sign up

Export Citation Format

Share Document