Quantitative Muscle Ultrasonography Using 2D Textural Analysis: A Novel Approach to Assess Skeletal Muscle Structure and Quality in Chronic Kidney Disease

Abstract

Chronic kidney disease (CKD) is characterized by progressive reductions in skeletal muscle function and size. The concept of muscle quality is increasingly being used to assess muscle health, although the best means of assessment remains unidentified. The use of muscle echogenicity is limited by an inability to be compared across devices. Gray level of co-occurrence matrix (GLCM), a form of image texture analysis, may provide a measure of muscle quality, robust to scanner settings. This study aimed to identify GLCM values from skeletal muscle images in CKD and investigate their association with physical performance and strength (a surrogate of muscle function). Transverse images of the rectus femoris muscle were obtained using B-mode 2D ultrasound imaging. Texture analysis (GLCM) was performed using ImageJ. Five different GLCM features were quantified: energy or angular second moment (ASM), entropy, homogeneity, or inverse difference moment (IDM), correlation, and contrast. Physical function and strength were assessed using tests of handgrip strength, sit to stand-60, gait speed, incremental shuttle walk test, and timed up-and-go. Correlation coefficients between GLCM indices were compared to each objective functional measure. A total of 90 CKD patients (age 64.6 (10.9) years, 44% male, eGFR 33.8 (15.7) mL/minutes/1.73 m2) were included. Better muscle function was largely associated with those values suggestive of greater image texture homogeneity (i.e., greater ASM, correlation, and IDM, lower entropy and contrast). Entropy showed the greatest association across all the functional assessments ( r = −.177). All GLCM parameters, a form of higher-order texture analysis, were associated with muscle function, although the largest association as seen with image entropy. Image homogeneity likely indicates lower muscle infiltration of fat and fibrosis. Texture analysis may provide a novel indicator of muscle quality that is robust to changes in scanner settings. Further research is needed to substantiate our findings.

High-frequency Quantitative Ultrasound Imaging of Human Rotator Cuff Muscles: Assessment of Repeatability and Reproducibility

“…This is an early if not the first study to evaluate BSC using raw RF data in normal and pathologic rotator cuff musculature. QUS, grayscale and GLCM textural analysis have been performed on skeletal muscles through grayscale [14][15][16][17] and GLCM textural 18 analyses on uncalibrated B-mode images, though the repeatability and reproducibility testing of these measurements are lacking, particularly under varying conditions in accordance with guidelines endorsed by QIBA. It has been suggested that grayscale and GLCM textural analyses are much more dependent on the settings adjusted by the operators 19,35 than QUS features.…”

“…For US quantification of muscle tissue, typically post-processed B-mode images have been used, with one of the most common measures being grayscale analysis, referred to as “echo intensity.” 14–17 Some authors have also extracted texture parameters from B-mode images, such as through gray-level co-occurrence matrix (GLCM) analyses. 15,18 Notably, virtually all prior studies using US to quantify muscle tissue maintain constant system settings between participants regardless of size or pathology, as it is generally accepted that B-mode-based analysis is sensitive to varying settings such as beam focus, frequency, transmit and receiver gains, and time-gain compensation (TGC). 19 However, in clinical settings, radiologists and sonographers adjust these settings to optimize image quality and contrast.…”

The Application of Texture Feature Analysis of Rectus Femoris Based on Local Binary Pattern (LBP) Combined With Gray‐Level Co‐Occurrence Matrix (GLCM) in Sarcopenia

“…In the field of ultrasound, texture analysis has been applied in the differentiation of benign and malignant lymph nodes and masses, [30][31][32] the inflammatory state and damage of various glands, [33][34][35] and the fatty infiltration of solid organs like liver. 36 In recent years, the research on muscle texture in musculoskeletal ultrasound [21][22][23][24][25]37,38 has aroused our attention and research interest.…”

“…16,17 In recent years, texture analysis in radiological examinations, including X-ray, Computed Tomography, and other images, has become a new breakthrough in disease assessment. [18][19][20] In the field of musculoskeletal ultrasound, we have noticed that some researchers incorporated the results of muscle texture analysis into the evaluation of muscle function [21][22][23][24] ; in addition, researchers have found that the results of muscle texture analysis can help identify neurogenic and myogenic diseases, 25 which provide new ideas for the diagnosis and evaluation of sarcopenia. Texture analysis can quantify the intuitive characteristics described by rough, smooth, or uneven perceptual vocabulary.…”

Application of Artificial Intelligence to Ultrasonography

“…Wilkinson et al investigated the use of computer texture analysis technology to quantitatively identify the texture features of skeletal muscle ultrasound images under vision, and their self-developed intensity interface multilevel decomposition method for quantitative analysis of skeletal muscle injury ultrasound images was demonstrated to be effective (34). Yu et al proposed a DCNN to identify fetal facial standard planes.…”