Characterization of local muscle fiber anisotropy using shear wave elastography in patients with chronic myofascial pain

Bird, Matthew D.; Le, David; Shah, Jay P.; Gerber, Lynn H.; Tandon, Hannah; DeStefano, Secili; Sikdar, Siddhartha

doi:10.1109/ultsym.2017.8091631

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…This could be attributed to the fact that the Gabor feature measures the gray level of US images [47], and there were similar mean and standard deviations seen in the A-MTrP and L-MTrP groups as indicated in Table 5. These findings align with previous studies that have reported muscle with MTrPs to exhibit anisotropy [10].…”

Section: Discussionsupporting

confidence: 93%

“…Thus, texture features have been used to mitigate this issue and have been widely used to discriminate variables in B-mode US images. Texture features play a vital role in radiomics, providing information such as muscle fiber orientation, normal anatomy, and the extent of adipose, fibrous, and other connective tissues within muscle [ 10 ]. Previous studies have suggested that the muscle fibers within the MTrPs in the affected zone and the muscle fibers in the surrounding regions have different orientations in comparison with normal skeletal muscle [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is a non-invasive way to assess muscles, tendons, and ligaments [7][8][9] and is relatively low cost. Doppler and elastography US have been used to visualize and distinguish MTrPs from normal tissue [8,[10][11][12]. Unfortunately, not all clinical US machines Sensors 2023, 23,9873 2 of 17 are equipped with elastography capabilities, and these approaches require comprehensive training to use and interpret.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers

Shomal Zadeh,

Koh,

Dilek

et al. 2023

Sensors

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Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (n = 30), latent MTrPs (n = 30), and healthy muscle (n = 30). Machine learning (ML) and one-way analysis of variance were used to investigate the discrimination ability of the different approaches. Statistically significant results were seen in almost all examined features for each texture feature approach, but, in contrast, ML techniques struggled to produce robust discrimination. The ML techniques showed that two texture features (i.e., correlation and mean) within the combination of texture features were most important in classifying the three groups. This discrepancy between traditional statistical analysis and ML techniques prompts the need for further investigation of texture-based approaches in US for the discrimination of MTrPs.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers

Shomal Zadeh,

Koh,

Dilek

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…Opisywane badanie dostarcza informacji na temat sztywności tkanek i jest niezależne od impedancji akustycznej oraz informacji o przepływie naczyniowym dostarczanym przez tryb B i obrazowanie dopplerowskie, otwierając tym samym nowy wymiar w obrazowaniu diagnostycznym [85]. Dostępne są różne techniki elastografii, a każda z nich opiera się na odmiennych metodach wytwarzania i pomiaru zniekształcenia tkanek.…”

Section: Elastografiaunclassified

Objective methods of diagnosing myofascial pain syndrome

Piętka¹,

Onik²,

Knapik³

et al. 2021

Med Og Nauk Zdr.

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Wprowadzenie i cel pracy. Zespół bólu mięśniowo-powięziowego (ang. myofascial pain syndrome, MPS) to istotny problem kliniczny, który pomimo licznych doniesień naukowych pozostaje nadal nie w pełni wyjaśniony. Dotyczy to szczególnie zagadnień związanych z patomechanizmem oraz tworzeniem standardów diagnostycznych i terapeutycznych. W praktyce klinicznej do najczęściej stosowanych metod rozpoznawania MPS zalicza się badanie palpacyjne. Niemniej jednak badanie to obarczone jest ryzykiem dużego błędu. W związku z powyższym podczas diagnostyki MPS potrzeba rzetelnych, powtarzalnych pomiarów, które powinny charakteryzować się wysoką czułością, aby umożliwić wykrycie klinicznie ważnych zmian. Celem artykułu jest przegląd obiektywnych metod diagnostycznych istotnych dla prawidłowego rozpoznania MPS. Opis stanu wiedzy. Postęp medycyny klinicznej zależy od umiejętności dokładnego diagnozowania choroby i bezstronnej oceny efektów leczenia. Wydaje się zatem kluczowe opracowanie skutecznych, obiektywnych metod diagnozowania MPS. Obecnie brak jest konsensusu wśród klinicystów dotyczącego schematu diagnostycznego MPS. Podsumowanie. Najbardziej rzetelnym badaniem wydaje się badanie palpacyjne wykonane przez doświadczonego terapeutę. Następnie ustalając na jego podstawie kryteria diagnostyczne D.G. Simonsa wraz z podparciem diagnozy za pomocą jednego z obiektywnych narzędzi oceniających zmiany w tkance mięśniowej sugerujące obecność punktu spustowego.

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“…Because the shear wave propagation speed is affected by the fiber orientation. Previous studies have attempted to use asymmetry of SWV with fiber orientation to determine anisotropy in muscle fibers, suggesting that disorientated fiber architecture may be a useful feature for distinguishing active myofascial trigger points [28] and Duchenne muscular dystrophy from normal muscle [7]. Although SWV is feasible for measuring spasticity, most SWV algorithms are designed assuming that soft tissues are elastic, isotropic, and locally homogeneous.…”

Section: Fiber Orientation and Shear Wave Velocitymentioning

confidence: 99%

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

2020

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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.

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Characterization of local muscle fiber anisotropy using shear wave elastography in patients with chronic myofascial pain

Cited by 4 publications

References 11 publications

Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers

Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers

Objective methods of diagnosing myofascial pain syndrome

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

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