Institute of Fundamental Technological Research

Acoustocerebrography is a novel, non-invasive, transcranial ultrasonic diagnostic method based on the transmission of multispectral ultrasound signals propagating through the brain tissue. Dedicated signal processing enables the estimation of absorption coefficient, frequency-dependent attenuation, speed of sound and tissue elasticity. Hypertension and atrial fibrillation are well known factors correlated with white matter lesions, intracerebral hemorrhage and cryptogenic stroke numbers. The aim of this study was to compare the acoustocerebrography signal in the brains of asymptomatic atrial fibrillation patients with and without hypertension. The study included 97 asymptomatic patients (40 female and 57 male, age 66.26 +/- 6.54 years) who were clinically monitored for atrial fibrillation. The patients were divided into two groups: group I (patients with hypertension) n = 75, and group II (patients without hypertension) n = 22. Phase and amplitude of all spectral components for the received signals from the brain path were extracted and compared to the phase and amplitude of the transmitted pulse. Next, the time of flight and the attenuation of each frequency component were calculated. Additionally, a fast Fourier transformation was performed and its features were extracted. After introducing a machine learning technique, the ROC plot of differentiations between group I and group II with an AUC of 0.958 (sensitivity 0.99 and specificity 0.968) was obtained. It can be assumed that the significant difference in the acoustocerebrography signals in patients with hypertension is due to changes in the brain tissue, and it allows for the differentiating of high-risk patients with asymptomatic atrial fibrillation and hypertension.

changes in the brain, hypertension in atrial, acoustocerebrography

A new ultrasound digital transcranial Doppler system (digiTDS) is introduced. The digiTDS enables diagnosis of intracranial vessels which are rather difficult to penetrate for standard systems. The device can display a color map of flow velocities (in time-depth domain) and a spectrogram of a Doppler signal obtained at particular depth. The system offers a multigate processing which allows to display a number of spectrograms simultaneously and to reconstruct a flow velocity profile.
The digital signal processing in digiTDS is partitioned between hardware and software parts. The hardware part (based on FPGA) executes a signal demodulation and reduces data stream. The software part (PC) performs the Doppler processing and display tasks. The hardware-software partitioning allowed to build a flexible Doppler platform at a relatively low cost.
The digiTDS design fulfills all necessary medical standards being a new useful tool in the transcranial field as well as in heart velocimetry research.

Doppler system, digital signal processing, hardware-software partitioning, field programmable gate arrays

For the last 20 years the world cardiosurgery has presented a considerable change of attitude to mechanical circulatory support. In spite of technological progress the main problems in ventricular assist devices are: thrombosis and low accuracy of flow measurements. In this paper the prototype of multi-gate Doppler flowmeter intended for cardiac assist system ReligaHeart EXT has been presented as well as the possibility of ultrasonic micro embolus detection.

artificial heart, microemboli, ultrasound Doppler

Background: Hypertension (HT) is the leading cause of global disease burden and overall health loss. The brain is one of the main target organs affected by HT. HT is a potentially modifiable risk factor that leads to the formation of large vessel macroangiopathy, small vessel disease, microangiopathy, and microhemorrhages. Early detection of the brain changes (BC) gives a chance to receive appropriate treatment and protection from irreversible damage. Acoustocerebrography (ACG) is a set of techniques to capture the states of human brain tissue, and its changes on its molecular and cellular level. It is based on noninvasive measurements of various parameters obtained by analyzing an ultrasound pulse emitted across the human's skull. The main idea of this method relies in the relation between the tissue density, bulk modulus, and speed of propagation, for ultrasound waves in this medium. In our previous studies we showed that ACG is an effective method for detecting white matter lesions compared to the Magnetic Resonance Imaging. Additionally we showed that ACG allows to obtain a differentiated signal originates from atrial fibrillation (AF) patients and high-risk patients wit AF and HT.
Aim: The aim of the study was early detection of the BC in patients with HT using ACG.
Methods: The study included 136 female and 98 male patients (age 43.6±15.7 years) who were surveyed in the clinical research. The patients were divided into two groups: group I (patients with HT) n=33, and control group II (patients without HT) n=201. Phase and amplitude of all frequency components of the received signals from the brain path were extracted and compared to the phase and amplitude of the transmitted pulse. By doing so, the time of flight and the attenuation of each frequency component were calculated. Additionally, a fast Fourier transformation (FFT) was performed and its features were extracted.
Results: After introducing a machine learning technique, the ROC plot with an AUC of 0.929 with sensitivity 0.879 and specificity 0.831 was obtained (Fig. 1).
Conclusion: ACG is new promising method, which allows for early detection of change in the brain in the patients with HT.

A new ultrasound digital transcranial Doppler system (digiTDS) is introduced. The digiTDS enables diagnosis of intracranial vessels which are difficult to penetrate for standard systems. The device can display a color map of flow velocities in time-depth domain and a spectrogram of a Doppler signal received from a particular depth. The system offers a multigate processing that allows to display simultaneously a number of spectrograms and to reconstruct a flow velocity profile. The digital signal processing in digiTDS is partitioned between hardware and software parts. The hardware part (based on FPGA) executes a signal demodulation and reduces data stream. The software part (PC) performs the Doppler processing and display tasks. The hardware-software partitioning allowed to build a flexible Doppler platform at a relatively low cost. The digiTDS design fulfills all necessary medical standards being a new useful tool in transcranial field as well as in heart velocimetry research.

ultrasound transcranial Doppler, RF signal processing, DSP, FPGA