CN112220494B - Acoustic-electric nerve imaging system based on pulse repetition frequency - Google Patents

Acoustic-electric nerve imaging system based on pulse repetition frequency Download PDF

Info

Publication number
CN112220494B
CN112220494B CN202011026933.XA CN202011026933A CN112220494B CN 112220494 B CN112220494 B CN 112220494B CN 202011026933 A CN202011026933 A CN 202011026933A CN 112220494 B CN112220494 B CN 112220494B
Authority
CN
China
Prior art keywords
pulse repetition
repetition frequency
signal
module
prf
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011026933.XA
Other languages
Chinese (zh)
Other versions
CN112220494A (en
Inventor
宋西姊
周伊婕
明东
韩港男
许敏鹏
何峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University
Original Assignee
Tianjin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University filed Critical Tianjin University
Priority to CN202011026933.XA priority Critical patent/CN112220494B/en
Publication of CN112220494A publication Critical patent/CN112220494A/en
Application granted granted Critical
Publication of CN112220494B publication Critical patent/CN112220494B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0808Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of the brain
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/0035Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/725Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A61B8/5238Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for combining image data of patient, e.g. merging several images from different acquisition modes into one image
    • A61B8/5261Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for combining image data of patient, e.g. merging several images from different acquisition modes into one image combining images from different diagnostic modalities, e.g. ultrasound and X-ray

Abstract

The invention relates to an acoustic-electric nerve imaging system based on pulse repetition frequency, which comprises a pulse repetition frequency coding and scanning module, an acoustic-electric signal acquisition module, a pulse repetition frequency characteristic extraction module, an interpolation imaging module and a pulse repetition frequency demodulation reconstruction module, wherein the pulse repetition frequency coding and scanning module is used for adjusting a coding mode of the pulse repetition frequency and transmitting focused ultrasound, and comprises a pulse repetition frequency coding module and a programmable phased array; the sound-electricity signal acquisition module is used for acquiring sound-electricity signals coded by pulse repetition frequency while the focused ultrasonic emission and scanning module scans the target area; the pulse repetition frequency feature extraction module is used for extracting effective features of the collected acousto-optic signals; the interpolation imaging module is used for converting the extracted sound-electricity signal characteristics into sound-electricity images; the pulse repetition frequency demodulation and reconstruction module is used for demodulating the acousto-electric signal and reconstructing a source signal.

Description

Acoustic-electric nerve imaging system based on pulse repetition frequency
Technical Field
The invention relates to an acoustic-electric nerve imaging system based on pulse repetition frequency.
Background
Existing neural function imaging techniques, such as electroencephalogram (EEG), functional magnetic resonance imaging, functional near infrared spectroscopy, etc., often have difficulty in combining high spatial-temporal resolution. Among them, the synthesized signal of the postsynaptic potential of the neuron clusters synchronously occurs when EEG records brain activities is an overall reflection of the brain cortex nerve electrophysiological activities, and has been widely applied to clinical practice. Current EEG techniques based on scalp electrode acquisition can obtain electrophysiological data with frequencies up to 1 kHz. Since the endogenous activity of most EEG is below 100Hz, EEG has great advantage in time resolution. But is affected by the effects of conductors in the volume of intracranial tissue, the EEG spatial resolution is relatively low, typically on the order of centimeters. This makes it almost impossible to monitor deep brain functions extracranial based on scalp EEG. To break through this limitation, sonoelectric imaging is expected to be used to improve the spatial resolution of EEG as an emerging imaging technique, enabling high spatial-temporal resolution neuroimaging.
By utilizing the advantages of the acoustic-electric effect principle and transcranial focusing ultrasonic targeting positioning, the acoustic-electric nerve imaging skillfully combines the high time resolution of electroencephalogram with the high spatial resolution of focused ultrasonic by the coupling action of two physical fields of a sound field and an electric field, and performs spatial encoding on the electroencephalogram from an activation source to obtain the electroencephalogram with accurate position information. The pulse repetition frequency is used as an important ultrasonic field parameter and can be used for high-efficiency coding of the brain electrical signals. In order to find out how to realize electroencephalogram space coding and acoustic-electric nerve imaging by using the pulse repetition frequency, an acoustic-electric nerve imaging system based on the pulse repetition frequency is provided.
Transcranial focused ultrasound (transcranial focus ultrasound, tFUS) has the characteristic of non-invasive spatial focusing on the intracranial cerebral cortex, can modulate tissue electrophysiological signals in the focused space through sound field effect, and endows the tissue electrophysiological signals with high spatial resolution characteristics, so that the electrophysiological nerve imaging spatial resolution of the intracranial deep EEG or MEG is enhanced.
Disclosure of Invention
The invention aims to provide an acoustic-electric nerve imaging system based on pulse repetition frequency. The technical scheme adopted by the invention is as follows:
an acoustic-electric nerve imaging system based on pulse repetition frequency comprises a pulse repetition frequency coding and scanning module, an acoustic-electric signal acquisition module, a pulse repetition frequency characteristic extraction module, an interpolation imaging module and a pulse repetition frequency demodulation reconstruction module, wherein the pulse repetition frequency coding and scanning module is used for adjusting a coding mode of the pulse repetition frequency and transmitting focused ultrasound, and comprises a pulse repetition frequency coding module and a programmable phased array; the sound-electricity signal acquisition module is used for acquiring sound-electricity signals coded by pulse repetition frequency while the focused ultrasonic emission and scanning module scans the target area; the pulse repetition frequency feature extraction module is used for extracting effective features of the collected acousto-optic signals; the interpolation imaging module is used for converting the extracted sound-electricity signal characteristics into sound-electricity images; the pulse repetition frequency demodulation and reconstruction module is used for demodulating the acousto-electric signal and reconstructing a source signal.
The pulse repetition frequency coding and scanning module utilizes a programmable phased array to set different pulse repetition frequency coding modes for high-precision three-dimensional scanning of focused ultrasound, and the codes meet the following mathematical relationship:
Figure BDA0002702388500000021
/>
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0002702388500000022
is an acoustic-electric signal, J I =J I (x, y, z) is a distributed current source, ">
Figure BDA0002702388500000023
For the lead field of lead i, σ 0 For initial conductivity, K is the acoustoelectric effect coefficient, ΔP is the sound pressure, f PRF Let t be the ultrasonic propagation time and (x, y, z) be the three-dimensional rectangular coordinates of the ultrasonic focusing domain.
The sound and electricity signal acquisition module is used for: and collecting the acousto-optic signals of the pulse repetition frequency codes while the pulse repetition frequency codes and the scanning module scan the target brain region, amplifying and filtering the weak acousto-optic signals, and storing the weak acousto-optic signals.
The pulse repetition frequency feature extraction module: for collecting the acoustic and electric signals
Figure BDA0002702388500000024
Acoustic-electric signal by pulse repetition frequency band-pass filtering>
Figure BDA0002702388500000025
Conversion to a pulse repetition frequency encoded signal +.>
Figure BDA0002702388500000026
Encoding a signal for pulse repetition frequency>
Figure BDA0002702388500000027
Obtaining a pulse repetition frequency modulated signal comprising the frequency and amplitude characteristics of the source signal by means of a Hilbert transform>
Figure BDA0002702388500000028
Pulse repetition frequency modulated signal->
Figure BDA0002702388500000029
The method comprises the following steps: the focused ultrasonic wave acts on the effective focal domain to change the conductivity delta sigma, so that the electroencephalogram signal after acoustic-electric coupling at the measuring electrode is changed, and an activation source signal is set as V s With a frequency of omega s Amplitude A s The focused ultrasonic signal is V US (f PRF ) Focusing ultrasonic scanning brain region containing an activating source, and obtaining pulse repetition frequency coding signal corresponding to scanning position by an acousto-electric acquisition module>
Figure BDA00027023885000000210
Pulse repetition frequency modulated signal->
Figure BDA00027023885000000211
By focused ultrasound and an active source signal V at the corresponding scanning position s The interaction is formed, the space position information of the focused ultrasonic wave focus is provided, and the following mathematical relationship is satisfied:
Figure BDA00027023885000000212
wherein the BPF PRF At pulse repetition frequency f PRF Band-pass filter for center frequency, pulse repetition frequency modulated signal
Figure BDA00027023885000000213
Encoding the signal by the pulse repetition frequency>
Figure BDA00027023885000000214
The Hilbert transform results in the following mathematical relationship:
Figure BDA00027023885000000215
interpolation imaging module: taking pulse repetition frequency modulated signal
Figure BDA00027023885000000216
The absolute value of the (a) is taken as the acousto-electric signal amplitude at the focusing position, and the acousto-electric signal amplitude at each scanning position is similarly deduced to be taken as the acousto-electric imaging pixel value at each scanning position, and as the scanning position is known, two-dimensional pixel value distribution with spatial position information is obtained, multi-source activated imaging is reconstructed through pixel value normalization and two-dimensional cubic interpolation, and the current source distribution of multi-source activation is reflected.
A pulse repetition frequency demodulation reconstruction module: modulating signals with pulse repetition frequency
Figure BDA00027023885000000217
At pulse repetition frequency f PRF Amplitude demodulation is carried out on the carrier wave to obtain a pulse repetition frequency demodulation reconstruction signal +.>
Figure BDA00027023885000000218
The signal->
Figure BDA00027023885000000219
Should be V with the activation source signal s Is positively correlated with the amplitude, frequency and phase of (a).
Compared with the traditional acousto-electric imaging method, the method can fully excavate and utilize the effective parameter pulse repetition frequency of the ultrasonic field, and provides a new thought for acousto-electric imaging. Reconstructing the activation source is expected to provide key technical support for novel multi-mode neural function imaging, and lays a foundation for the focused ultrasound to be fused into the application stage of the neural imaging technology as soon as possible.
Drawings
FIG. 1 is a schematic diagram of a system architecture of the present invention;
FIG. 2 is a workflow diagram of the present invention;
FIG. 3 is a schematic diagram of the present invention;
FIG. 4 shows the results of the acoustic-electric imaging and source signal reconstruction of experimental data according to the present invention.
Detailed Description
The nerve imaging is used as an important brain science and technology means, not only can noninvasively (minimally invasively) detect the structural and functional changes of living brain, but also can be used as an intermediate junction to integrate macroscopic structure, function, microscopic molecule, metabolism and other information, so that the nerve imaging is widely applied to clinical diagnosis and neuroscience research. The development of new neuroimaging techniques with high spatial-temporal resolution plays a vital role in neuroscience and clinical medicine research. The focused ultrasound can be used for obtaining the space distribution of multi-source discharge by acoustic-electric imaging due to the advantages of high targeting and noninvasive property, so as to realize neural imaging with ultrahigh space-time resolution.
The pulse repetition frequency based acoustic-electric neuroimaging system of the present invention is described with reference to the accompanying drawings and examples.
The invention realizes the acoustic-electric nerve imaging and the active source reconstruction by means of pulse repetition frequency. The deep cortical tissue at the interested position is scanned by utilizing transcranial focusing ultrasonic waves, the acousto-electric signals coded by the pulse repetition frequency of the target region are focused, the deep cortical electrophysiological activities are detected, and the acousto-electric imaging and the source signal reconstruction are realized.
The invention discloses an acoustic-electric nerve imaging system based on pulse repetition frequency, which is shown in figure 1 and mainly comprises a pulse repetition frequency coding and scanning module, an acoustic-electric signal acquisition module, a pulse repetition frequency characteristic extraction module, an interpolation imaging module and a pulse repetition frequency demodulation reconstruction module, wherein the pulse repetition frequency coding and scanning module is used for adjusting a coding mode of the pulse repetition frequency and transmitting focused ultrasound and consists of a pulse repetition frequency coding module and a programmable phased array. And the acousto-electric signal acquisition module is used for acquiring the acousto-electric signals encoded by the pulse repetition frequency while the focused ultrasonic emission and scanning module scans the target area. The pulse repetition frequency feature extraction module is used for extracting effective features of the collected acousto-optic signals. The interpolation imaging module is used for converting the extracted sound and electricity signal characteristics into sound and electricity images. The pulse repetition frequency demodulation and reconstruction module is used for demodulating the acousto-electric signal and reconstructing a source signal.
The workflow of the acoustic-electric neuroimaging system based on pulse repetition frequency is shown in fig. 2, and may be roughly divided into the following steps:
1) Pulse repetition frequency coding and scanning module: setting different pulse repetition frequency coding modes by using a programmable phased array; the high-precision three-dimensional scanning method is used for focusing ultrasonic, and the scanning precision is 0.05mm; the coding algorithm satisfies the following mathematical relationship:
Figure BDA0002702388500000031
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0002702388500000032
is an acoustic-electric signal, J I =J I (x, y, z) is a distributed current source, ">
Figure BDA0002702388500000033
For the lead field of lead i, σ 0 For initial conductivity, K is the acoustoelectric effect coefficient, ΔP is the sound pressure, f PRF Let t be the ultrasonic propagation time and (x, y, z) be the three-dimensional rectangular coordinates of the ultrasonic focusing domain.
2) The sound and electricity signal acquisition module is used for: collecting the acousto-electric signals of the pulse repetition frequency codes while the pulse repetition frequency codes and the scanning module scan the target brain region, amplifying and filtering the weak acousto-electric signals, and storing the weak acousto-electric signals with the sampling rate of 20kHz;
3) The pulse repetition frequency feature extraction module: for collecting the acoustic and electric signals
Figure BDA0002702388500000034
Acoustic-electric signal by pulse repetition frequency band-pass filtering>
Figure BDA0002702388500000035
Conversion to a pulse repetition frequency encoded signal +.>
Figure BDA0002702388500000036
Encoding a signal for pulse repetition frequency>
Figure BDA0002702388500000037
Obtaining a pulse repetition frequency modulated signal comprising the frequency and amplitude characteristics of the source signal by means of a Hilbert transform>
Figure BDA0002702388500000038
Pulse repetition frequency modulated signal->
Figure BDA0002702388500000039
The method comprises the following steps: the focused ultrasonic wave acts on the effective focal domain to change the conductivity delta sigma, so that the electroencephalogram signal after acoustic-electric coupling at the measuring electrode is changed. Is provided with an activation source signal V s With a frequency of omega s Amplitude A s The focused ultrasonic signal is V US (f PRF ) Focusing ultrasonic scanning brain region containing an activating source, and obtaining pulse repetition frequency coding signal corresponding to scanning position by an acousto-electric acquisition module>
Figure BDA00027023885000000310
Pulse repetition frequency modulated signal->
Figure BDA00027023885000000311
By focused ultrasound and an active source signal V at the corresponding scanning position s The interaction is formed, the space position information of the focused ultrasonic wave focus is provided, and the following mathematical relationship is satisfied:
Figure BDA0002702388500000041
wherein the BPF PRF At pulse repetition frequency f PRF Band-pass filter for center frequency, pulse repetition frequency modulated signal
Figure BDA0002702388500000042
Encoding the signal by the pulse repetition frequency>
Figure BDA0002702388500000043
The Hilbert transform results in the following mathematical relationship:
Figure BDA0002702388500000044
4) Interpolation imaging module: taking pulse repetition frequency modulated signal
Figure BDA0002702388500000045
The absolute value of the (a) is taken as the acousto-electric signal amplitude at the focusing position, and the acousto-electric signal amplitude at each scanning position is similarly deduced to be taken as the acousto-electric imaging pixel value at each scanning position, and as the scanning position is known, two-dimensional pixel value distribution with spatial position information is obtained, multi-source activated imaging is reconstructed through pixel value normalization and two-dimensional cubic interpolation, and the current source distribution of multi-source activation is reflected.
5) A pulse repetition frequency demodulation reconstruction module: modulating signals with pulse repetition frequency
Figure BDA0002702388500000046
At pulse repetition frequency f PRF Amplitude demodulation is carried out on the carrier wave to obtain a pulse repetition frequency demodulation reconstruction signal +.>
Figure BDA0002702388500000047
The signal->
Figure BDA0002702388500000048
Should be V with the activation source signal s Is positive in amplitude, frequency and phaseAnd (3) closing.
Fig. 3 is a schematic diagram of the present invention, in which pulse repetition frequency encoding is applied to a focused ultrasound transducer to transmit focused ultrasound waves, which are focused on a target brain region (light gray dashed line), including an activated brain region (black dashed line) and an inactivated brain region (dark gray dashed line). Scalp electroencephalogram is acquired while the brain area is scanned ultrasonically. The activated brain region generates high-frequency acousto-electric signals after being subjected to ultrasonic modulation, and the non-activated brain region outputs noise. Thus obtaining the electroencephalogram signal which is encoded by pulse repetition frequency and has accurate position information.
FIG. 4 shows the results of the acoustic-electric imaging and source signal reconstruction of experimental data according to the present invention, and one of the results of the acoustic-electric imaging of the active source is shown in FIG. 4 (a). Fig. 4 (b) reconstructs the source signal for different phases of the source signal at source point s+ (0, 0) and the corresponding demodulation. From this, the frequency, amplitude and phase of the demodulated reconstructed source signal are positively correlated with the source signal.

Claims (2)

1. An acoustic-electric nerve imaging system based on pulse repetition frequency comprises a pulse repetition frequency coding and scanning module, an acoustic-electric signal acquisition module, a pulse repetition frequency characteristic extraction module, an interpolation imaging module and a pulse repetition frequency demodulation reconstruction module, wherein the pulse repetition frequency coding and scanning module is used for adjusting a coding mode of the pulse repetition frequency and transmitting focused ultrasound, and comprises a pulse repetition frequency coding module and a programmable phased array; the sound-electricity signal acquisition module is used for acquiring sound-electricity signals coded by pulse repetition frequency while the focused ultrasonic emission and scanning module scans the target area; the pulse repetition frequency feature extraction module is used for extracting effective features of the collected acousto-optic signals; the interpolation imaging module is used for converting the extracted sound-electricity signal characteristics into sound-electricity images; the pulse repetition frequency demodulation and reconstruction module is used for demodulating the acousto-electric signal and reconstructing a source signal;
the pulse repetition frequency coding and scanning module utilizes a programmable phased array to set different pulse repetition frequency coding modes for high-precision three-dimensional scanning of focused ultrasound, and the codes meet the following mathematical relationship:
Figure QLYQS_1
wherein V is i AE Is an acoustic-electric signal, J I =J I (x, y, z) is a distributed current source,
Figure QLYQS_2
for the lead field of lead i, σ 0 For initial conductivity, K is the acoustoelectric effect coefficient, ΔP is the sound pressure, f PRF The pulse repetition frequency is given, t is the ultrasonic propagation time, and (x, y, z) is the three-dimensional rectangular coordinates of the ultrasonic focusing domain;
the pulse repetition frequency feature extraction module: for collecting the sound and electricity signal V i AE Acousto-electric signal V by pulse repetition frequency band-pass filtering i AE Conversion to pulse repetition frequency encoded signal V i PRF The method comprises the steps of carrying out a first treatment on the surface of the Encoding a signal V for pulse repetition frequency i PRF Obtaining a pulse repetition frequency modulation signal V containing source signal frequency and amplitude characteristics through Hilbert transformation i PRF-Hil The method comprises the steps of carrying out a first treatment on the surface of the Pulse repetition frequency modulation signal V i PRF-Hil The method comprises the following steps: the focused ultrasonic wave acts on the effective focal domain to change the conductivity delta sigma, so that the electroencephalogram signal after acoustic-electric coupling at the measuring electrode is changed, and an activation source signal is set as V s With a frequency of omega s Amplitude A s The focused ultrasonic signal is V US (f PRF ) The focused ultrasonic scanning comprises a brain region of an activation source, and a pulse repetition frequency coding signal V corresponding to the scanning position is obtained by an acousto-electric acquisition module i PRF Pulse repetition frequency modulated signal V i PRF-Hil By focused ultrasound and an active source signal V at the corresponding scanning position s The interaction is formed, the space position information of the focused ultrasonic wave focus is provided, and the following mathematical relationship is satisfied:
Figure QLYQS_3
wherein the BPF PRF At pulse repetition frequency f PRF Band-pass filter with center frequency, pulse repetition frequency modulating signal V i PRF-Hil Encoding signal V by pulse repetition frequency i PRF The Hilbert transform results in the following mathematical relationship:
V i PRF-Hil (A s ,ω s ,f PRF )=Hilbert{V i PRF (A s ,ω s ,f PRF )} (3)
interpolation imaging module: taking pulse repetition frequency modulated signal V i PRF-Hil The absolute value of the (a) is taken as the acoustic-electric signal amplitude at the focusing position, and the acoustic-electric signal amplitude at each scanning position is similarly deduced to be taken as the acoustic-electric imaging pixel value at each scanning position, and as the scanning position is known, two-dimensional pixel value distribution with spatial position information is obtained, and multisource activated imaging is reconstructed through pixel value normalization and two-dimensional cubic interpolation, so as to reflect the current source distribution of multisource activation;
a pulse repetition frequency demodulation reconstruction module: modulating signal V with pulse repetition frequency i PRF-Hil At pulse repetition frequency f PRF Amplitude demodulation is carried out on the carrier wave to obtain a pulse repetition frequency demodulation reconstruction signal V i PRF-Dem The signal V i PRF-Dem Should be V with the activation source signal s Is positively correlated with the amplitude, frequency and phase of (a).
2. The acoustic-electric neuroimaging system of claim 1, wherein the acoustic-electric signal acquisition module: and collecting the acousto-optic signals of the pulse repetition frequency codes while the pulse repetition frequency codes and the scanning module scan the target brain region, amplifying and filtering the weak acousto-optic signals, and storing the weak acousto-optic signals.
CN202011026933.XA 2020-09-25 2020-09-25 Acoustic-electric nerve imaging system based on pulse repetition frequency Active CN112220494B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011026933.XA CN112220494B (en) 2020-09-25 2020-09-25 Acoustic-electric nerve imaging system based on pulse repetition frequency

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011026933.XA CN112220494B (en) 2020-09-25 2020-09-25 Acoustic-electric nerve imaging system based on pulse repetition frequency

Publications (2)

Publication Number Publication Date
CN112220494A CN112220494A (en) 2021-01-15
CN112220494B true CN112220494B (en) 2023-05-05

Family

ID=74108220

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011026933.XA Active CN112220494B (en) 2020-09-25 2020-09-25 Acoustic-electric nerve imaging system based on pulse repetition frequency

Country Status (1)

Country Link
CN (1) CN112220494B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115813344A (en) * 2022-12-09 2023-03-21 天津大学 Deep brain stimulation noninvasive monitoring system based on sound computer imaging

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014179681A1 (en) * 2013-05-03 2014-11-06 Sunnybrook Health Sciences Centre Systems and methods for super-resolution ultrasound imaging
CN109890267A (en) * 2016-07-07 2019-06-14 加州大学评议会 Use the implantation material of ultrasound backscatter device detection electricity physiological signal
CN111643110A (en) * 2020-03-31 2020-09-11 天津大学 Electroencephalogram detection device based on focused ultrasound spatial coding

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1553426A1 (en) * 2004-01-08 2005-07-13 Institut de Microtechnique de l'Université de Neuchâtel Method and receiver apparatus for wireless data communication with Ultra Wide Band time coded signals
WO2014127091A1 (en) * 2013-02-14 2014-08-21 Thync, Inc. Transcranial ultrasound systems
US20200107817A1 (en) * 2017-03-22 2020-04-09 Institut National De La Sante Et De La Recherche Medical (Inserm) Method for imaging an area of a medium with ultrasound contrast agents and associated device
CN109645999B (en) * 2018-11-29 2022-04-26 天津大学 4D transcranial focused ultrasound nerve imaging method based on acoustoelectric effect
CN111227801B (en) * 2020-02-03 2022-06-10 天津大学 Acoustic-electric nerve imaging system for multi-source activation detection

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014179681A1 (en) * 2013-05-03 2014-11-06 Sunnybrook Health Sciences Centre Systems and methods for super-resolution ultrasound imaging
CN109890267A (en) * 2016-07-07 2019-06-14 加州大学评议会 Use the implantation material of ultrasound backscatter device detection electricity physiological signal
CN111643110A (en) * 2020-03-31 2020-09-11 天津大学 Electroencephalogram detection device based on focused ultrasound spatial coding

Also Published As

Publication number Publication date
CN112220494A (en) 2021-01-15

Similar Documents

Publication Publication Date Title
CN109645999B (en) 4D transcranial focused ultrasound nerve imaging method based on acoustoelectric effect
US20190380686A1 (en) Method for multi-frequency imaging and composite image display using high-bandwidth transducer outputs
CN211511858U (en) Optical ultrasonic double-catheter endoscopic imaging system
US20190083048A1 (en) Massively Multi-Frequency Ultrasound-Encoded Tomography
CN107550458B (en) Biological tissue multi-characteristic imaging method based on acoustoelectric effect and acoustic radiation force
EP2934333B1 (en) Method for multi-frequency imaging using high-bandwidth transducer outputs
CN103202713A (en) Image optimization method by blending of ultrasound fundamental wave and harmonic wave
Qin et al. Performance of a transcranial US array designed for 4D acoustoelectric brain imaging in humans
CN111227801B (en) Acoustic-electric nerve imaging system for multi-source activation detection
CN112220494B (en) Acoustic-electric nerve imaging system based on pulse repetition frequency
Song et al. Living rat SSVEP mapping with acoustoelectric brain imaging
US20090171210A1 (en) Sonoelectric tomography using a frequency-swept ultrasonic wave
CN100509083C (en) Method and device for controlling image synchronization utilizing respiratory signal
Deng et al. Image quality improvement of magneto-acousto-electrical tomography with Barker coded excitation
Du et al. Nonlinear imaging of microbubble contrast agent using the volterra filter: In vivo results
Hossain et al. Imaging of single transducer-harmonic motion imaging-derived displacements at several oscillation frequencies simultaneously
CN105249933A (en) Photo-acoustic molecule three-dimensional image instrument
Qin et al. 4D acoustoelectric imaging of current sources in a human head phantom
US20210059644A1 (en) Retrospective multimodal high frame rate imaging
Qiu et al. A novel modulated excitation imaging system for microultrasound
Zhou et al. A source signal modulation mechanism with pulse focused ultrasound for acoustoelectric brain imaging
Burton et al. Development of a mobile platform for acoustoelectric brain imaging in rats
Yang et al. Sensitivity enhanced photoacoustic imaging using a high-frequency PZT transducer with an integrated front-end amplifier
Jonveaux et al. Review of Current Simple Ultrasound Hardware Considerations, Designs, and Processing Opportunities.
US20210059535A1 (en) Handheld ultrasound transducer array for 3d transcranial and transthoracic ultrasound and acoustoelectric imaging and related modalities

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant