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Open AccessArticle

Quantification of Endogenous Brain Tissue Displacement Imaging by Radiofrequency Ultrasound

1
Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59-455, LT-51423 Kaunas, Lithuania
2
Department of Neurology, Lithuanian University of Health Sciences, A. Mickevičiaus Str. 9, LT-44307 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(2), 57; https://doi.org/10.3390/diagnostics10020057
Received: 30 November 2019 / Revised: 15 January 2020 / Accepted: 16 January 2020 / Published: 21 January 2020
(This article belongs to the Special Issue Elastography)
The purpose of this paper is a quantification of displacement parameters used in the imaging of brain tissue endogenous motion using ultrasonic radiofrequency (RF) signals. In a preclinical study, an ultrasonic diagnostic system with RF output was equipped with dedicated signal processing software and subject head–ultrasonic transducer stabilization. This allowed the use of RF scanning frames for the calculation of micrometer-range displacements, excluding sonographer-induced motions. Analysis of quantitative displacement estimates in dynamical phantom experiments showed that displacements of 55 µm down to 2 µm were quantified as confident according to Pearson correlation between signal fragments (minimum p ≤ 0.001). The same algorithm and scanning hardware were used in experiments and clinical imaging which allows translating phantom results to Alzheimer’s disease patients and healthy elderly subjects as examples. The confident quantitative displacement waveforms of six in vivo heart-cycle episodes ranged from 8 µm up to 263 µm (Pearson correlation p ≤ 0.01). Displacement time sequences showed promising possibilities to evaluate the morphology of endogenous displacement signals at each point of the scanning plane, while displacement maps—regional distribution of displacement parameters—were essential for tissue characterization. View Full-Text
Keywords: brain; transcranial sonography; radiofrequency ultrasound; tissue displacements brain; transcranial sonography; radiofrequency ultrasound; tissue displacements
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MDPI and ACS Style

Jurkonis, R.; Makūnaitė, M.; Baranauskas, M.; Lukoševičius, A.; Sakalauskas, A.; Matijošaitis, V.; Rastenytė, D. Quantification of Endogenous Brain Tissue Displacement Imaging by Radiofrequency Ultrasound. Diagnostics 2020, 10, 57.

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