Quantification of Endogenous Brain Tissue Displacement Imaging by Radiofrequency Ultrasound

Abstract

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.

Diagnostic Ability of Radiofrequency Ultrasound in Parkinson’s Disease Compared to Conventional Transcranial Sonography and Magnetic Resonance Imaging

“…The classical 1D cross-correlation [ 18 , 19 , 20 ] was used to obtain spatial point displacements in ROI along the scanning line from the acquired US RF signals. The calculation options used at this step were the same as described in our earlier article [ 14 ]. The displacement signal processing was the same as in our recent study [ 15 ]: the coordinate system was changed into Lagrangian [ 21 ], the median for each scanning frame’s line was subtracted separately and high-pass filtered with cutoff at 0.75 Hz and only confidently repeatable moving spatial points were selected for further analysis.…”

“…These brain tissue displacements can be evaluated by detailed analysis of raw ultrasound (US) radiofrequency (RF) signal changes of the same tissue region over time [ 13 ]. We developed such an approach further [ 14 ] and in our recent study [ 15 ] we demonstrated that evaluation of complex interactions between the set of RF TCS-based brain tissue displacement parameters allows us to discern the medial temporal lobe of an Alzheimer’s disease patient from that of a healthy control (HC) subject with excellent diagnostic ability.…”

Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

“…Amplitudes of displacements can be used for the strain estimates in intracranial structures [14,18]. The waveform of displacement can be characterized by amplitude [19], by root mean square [20], or by the parameters of frequency spectra [21][22][23][24]. The frequency spectrum of signals obtained using different monitoring technologies-invasive CSF pressure [25], intracranial pressure [26][27][28][29][30][31], functional magnetic resonance imaging [32,33], transcranial color Doppler imaging [23]-reveals a wide frequency bandwidth of intracranial dynamics.…”

“…Therefore, comprehensive analysis of US RF signals obtained from the pulsating brain tissue and calculation of new parameters of brain tissue displacements could help to develop more accurate US RF-based diagnostic methods. During our previous study, while developing a US RF method of displacement quantification [20], we presented a possibility to apply this method for one patient with Alzheimer's disease (AD) however, to our knowledge, the tissue displacement method was not previously tested for AD diagnostics in a sample of patients.…”

“…Brain tissue displacement parameters obtained from US RF signals are used to assess the diagnostic ability to differentiate patients with AD from healthy controls. To improve the diagnostic value of the method, we also present essential modifications of our previous method [20] in US waveform parametrization, increasing the statistical certainty of micrometer-range displacement signals in the presence of noise, and analysis of regression models.…”