Muscle Ultrasound Texture Parameters Correlate With EMG Findings: How an Image Translates Into Electrophysiology

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Authors: Michał Błaż, Monika Ostrowska, Agnieszka Kułaga, Ewa Maludzińska, Michał Michalski
Cited by: 0
References: 20

Abstract

INTRODUCTION/AIMS: It has been demonstrated that muscle echo intensity quantification and needle electromyography (EMG) produce disparate results. The aim of this study was to investigate whether a more detailed muscle ultrasound texture analysis is associated with electrophysiological parameters in EMG. METHODS: We retrospectively analyzed muscle ultrasound and EMG data from consecutive subjects > 18 years of age suspected of myopathy or neurogenic disease, and healthy controls. Muscles with inconsistent ultrasound presets or missing EMG data were excluded. Texture parameters encompassed grayscale level histograms, second-order features (contrast, entropy, correlation), and higher-order features (short-run frequency and gray-level distribution, local entropy). EMG data included motor unit size index (SI), polyphasia, recruitment, and the presence of active denervation. RESULTS: We analyzed 156 muscles from 64 individuals (median age 55 years [IQR 45-69], 51% female). In neurogenic processes the SI correlated moderately and positively with contrast (r = 0.62, p = 0.002), whereas reduced recruitment was associated with a higher frequency of short runs. In non-inflammatory myopathies polyphasia was moderately and negatively correlated with entropy (r = -0.46, p = 0.003), while in inflammatory myopathies brightness correlated weakly and negatively with SI (r = -0.39, p = 0.014) and was increased in muscles that showed active denervation on EMG (p = 0.018). DISCUSSION: Muscle ultrasound texture parameters correlate with certain EMG findings, possibly reflecting underlying pathology. This study advances the use of ultrasound textural parameters beyond grayscale measurements to potentially bridge the gap between imaging and electrophysiology.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging Muscle Physiology and Disorders Peripheral Nerve Disorders

Publication details

Journal / source
Muscle & Nerve
Volume
73
Issue
5
Pages
772–780
Publication date
27 January 2026
Language
EN