cris.boxmetadata.label.title
Real-Time Crawling Wave Sonoelastography for Human Muscle Characterization: Initial Results
cris.boxmetadata.label.dateissued
01 browse.startsWith.months.march 2019
cris.boxmetadata.label.accesslevel
metadata only access
cris.boxmetadata.label.resourcetype
journal article
cris.boxmetadata.label.publisher
Institute of Electrical and Electronics Engineers Inc.
cris.boxmetadata.label.abstract
Imaging of musculoskeletal tissue dynamics is currently an exploratory field with the goal of aiding rehabilitation and performance evaluation of pathological or asymptomatic patients. In this pilot study, initial elasticity assessments of the biceps brachii were conducted in a novel crawling wave sonoelastography (CWS) system implemented on a research ultrasound instrument with graphical processing unit capabilities, displaying quantitative elasticity values at 4 frames per second. The CWS system computes the tissue stiffness with the generation of an interference pattern from external vibrators, which can overcome depth limitations of imaging systems with internal excitation sources. Validation on gelatin-based phantoms reported low bias of elasticity values (4.7%) at low excitation frequencies. Preliminary results on in vivo muscle characterization are in accordance with average elasticity values for relaxed and contracted tissues found in the literature, as well as for a range of weight loads.
cris.boxmetadata.label.citationstartpage
563
cris.boxmetadata.label.citationendpage
571
cris.boxmetadata.label.volume
66
cris.boxmetadata.label.issue
3
cris.boxmetadata.label.language
English
cris.boxmetadata.label.ocdeknowledgeArea
Tecnología médica de laboratorio (análisis de muestras, tecnologías para el diagnóstico)
cris.boxmetadata.label.doi
cris.boxmetadata.label.scopusidentifier
2-s2.0-85050374596
cris.boxmetadata.label.pubmedidentifier
cris.boxmetadata.label.source
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
cris.boxmetadata.label.containerissn
08853010
cris.boxmetadata.label.sponsor
Manuscript received July 5, 2018; accepted July 19, 2018. Date of publication July 23, 2018; date of current version March 14, 2019. This work was supported by the Fondo Nacional de Desarrollo Cientifico y Tecnologico-PERU, Peruvian Government under Grant 012-2014-FONDECYT-C1. (Corresponding author: Eduardo A. Gonzalez.) E. A. Gonzalez was with the Department of Electrical and Electronic Engineering, Pontificia Universidad Catolica del Peru, Lima 32, Peru. He is now with the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287 USA (e-mail: egonza31. . i.edu).
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