Quantitative muscle ultrasound assessment using automatic thresholding methods in amyotrophic lateral sclerosis

OBJECTIVETo evaluate the usefulness of automatic thresholding methods for quantitative assessments of muscle echogenicity in amyotrophic lateral sclerosis (ALS) patients. METHODSThirty-one ALS patients and 31 matched healthy controls underwent ultrasound examination of the biceps brachii, rectus fem...

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Bibliographic Details
Published inClinical neurophysiology Vol. 142; pp. 236 - 243
Main Authors Spiliopoulos, Kanellos C., Veltsista, Dimitra, Chroni, Elisabeth
Format Journal Article
LanguageEnglish
Published 01.10.2022
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Summary:OBJECTIVETo evaluate the usefulness of automatic thresholding methods for quantitative assessments of muscle echogenicity in amyotrophic lateral sclerosis (ALS) patients. METHODSThirty-one ALS patients and 31 matched healthy controls underwent ultrasound examination of the biceps brachii, rectus femoris and tibialis anterior muscles. Muscle echogenicity was evaluated using grayscale analysis and the 16 automatic thresholding methods of ImageJ program. The diagnostic value and correlations between ultrasound parameters and muscle strength were investigated. RESULTSMean grayscale values (GSV) and mean hyperechoic fractions of 8 out of the 16 automatic thresholding methods were significantly different between patients and controls in all 3 muscles (p < 0.05 for all). Four thresholding methods (Default, Li, Moments, Otsu) showed a significant correlation between hyperechoic fractions and muscle strength, and diagnostic accuracy comparable or superior to GSVs. Otsu method was the only technique that detected ultrasound changes in normal strength muscles of ALS patients. CONCLUSIONSOur findings support the utility of automatic thresholding methods in muscle echogenicity studies as a supplementary ultrasound image analysis in ALS. SIGNIFICANCEIn an era of advances in developing neurophysiological diagnostic tools and biomarkers in ALS, muscle ultrasonography and echogenicity analysis using automatic thresholding methods could be effectively implemented in clinical research.
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ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2022.08.008