# Quantitative Ultrasound Texture Analysis to Assess the Spastic Muscles in Stroke Patients
###### Abstract  
This study aimed to investigate the feasibility of sonoelastography for determining echo-texture 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.  
Keywords: echotexture; local binary pattern (LBP); shear wave velocity; spasticity; stroke +
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In contrast to EI, textural descriptors are less affected by ultrasound scanner settings and may have an advantage of providing more complete description of tissue composition [7]. Several texture features, such as the Haralick and Galloway features, are useful for the diagnosis, characterization, and detection of neuromuscular diseases [8; 9; 10; 11]. Another potential technique for texture analysis is the local binary pattern (LBP), which has been used for face recognition, characterization, and classification of fatty liver [12], plaques in the carotid artery [13], breast tumors [14], and knee osteoarthritis [15]. Its advantages include ease of implementation, invariance to illumination changes, and low computational complexity. However, little is known about its applications for muscle texture analysis.  
In recent years, sonoelastography becomes commonly used as a surrogate tool to define post-stroke muscle stiffness by estimating shear wave velocity (SWV) in the muscle [16; 17]. Stiffness is a commonly used factor to describe the material property of muscle. However, the correlation between eductexture and sonoelastography post-stroke has not been reported.  
Based on the facts of imaging texture is influenced by acoustic parameters (speed, attenuation, and backscatter amplitude), which are closely related to muscle properties [18]. We hypothesize that eductexture features can be used to interpret the uniformity of the muscle pattern. To address this gap, the present study investigated the feasibility of sonoelastography for the determination of eductexture and muscle stiffness in post-stroke patients. Moreover, we assessed the relationships among sonoelastography findings, muscle echotexture features, and functional performance in spastic muscle.