CN113499053B - Brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method - Google Patents
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- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
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Abstract
The invention discloses a brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method, which comprises the steps of obtaining mutual inductance data corresponding to each frequency, obtaining frequency difference data before algorithm processing, arranging the frequency difference data according to relative antenna positions, obtaining trend signals of the frequency difference data of receiving antennas corresponding to each transmitting antenna, subtracting the trend signals, and restoring the data from which the trend signals are removed into an arrangement sequence of absolute antenna positions to obtain processed data. According to the invention, through a specific algorithm for extracting the noise signals in the mutual inductance data, the noise signals with stronger signals in the differential data are removed, so that the weak characteristic signals can be highlighted in the value range close to the weak characteristic signals. The frequency difference data are arranged according to the relative antenna positions, so that the rearranged data are more in accordance with physical significance, meanwhile, the rearranged data are also more in accordance with quadratic function curves, and the second-order polynomial fitting effect is better.
Description
Technical Field
The invention belongs to the field of multi-frequency imaging, and particularly relates to a brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method.
Background
In the prior art, brain electrical impedance magnetic induction multi-frequency imaging is realized by directly acquiring mutual inductance data of 2 frequencies and directly imaging by using the difference value. The mutual inductance data of one frequency is subjected to polynomial fitting to the mutual inductance data of the other frequency, the value ranges of the mutual inductance data are close, the fitted frequency difference value is used for imaging, and the difference value between the frequencies or the difference value after polynomial fitting cannot be directly used for extracting a tiny signal such as focus change in a complex environment such as a human brain. The existing imaging method has low resolution, and weak focus signals are submerged under the influence of strong noise and cannot be imaged clearly.
FIG. 1 is a diagram of two frequency direct differential data; FIG. 2 is the difference data after two frequency polynomial fits; it follows that in the prior art, the amplitude of the data obtained using the difference between frequencies is about 0.5, the amplitude of the method using polynomial fitting is about 0.4, and the signal for lesion variation is about 0.1, the signal strength differs by a factor of 4-5, thus submerging the lesion signal in strong noise. Imaging after using such data processing methods will take noise as the main imaging subject and will not show the faint signal of the lesion.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method which can remove noise signals with larger imaging interference, retain weak characteristic signals and improve signal-to-noise ratio on the premise of not influencing the characteristics of data.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme:
the invention discloses a brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method, which comprises the following steps:
1) Acquiring mutual inductance data corresponding to each frequency;
2) Acquiring frequency difference data before algorithm processing;
3) Arranging frequency difference data according to the relative antenna positions;
4) Obtaining trend signals of frequency difference data of receiving antennas corresponding to each transmitting antenna and subtracting the trend signals;
5) And restoring the data from which the trend signals are removed into the arrangement sequence of the absolute antenna positions to obtain the processed data.
As a further improvement, the method for acquiring mutual inductance data corresponding to each frequency specifically includes: and subtracting the air mutual inductance data and the detection mutual inductance data corresponding to each frequency to obtain the basic calibrated mutual inductance data.
As a further improvement, the frequency difference data is obtained by subtracting mutual inductance data after basic calibration of 2 frequencies.
As a further improvement, the frequency difference data arranged according to the relative antenna positions according to the present invention is: according to the distance sequence of the receiving antennas to the transmitting antennas, n pieces of the n pieces of antenna data are n times n, the data of the same receiving and transmitting antennas are removed, the near-far arrangement is seen from one direction according to the position of the receiving antennas from the transmitting antennas, and the rearranged frequency difference data are n pieces of data of n transmitting antennas corresponding to n-1 receiving antennas=n times (n-1).
As a further improvement, the distance ordering of the receiving antenna to the transmitting antenna is specifically as follows: the antenna number is 0-n-1, the quality detection signal of the data signal which is transmitted and received does not pass through the target so that the quality detection signal basically has no influence of target characteristics, the default is 0, the calculation is not participated, the receiving antenna sequence corresponding to the transmitting antenna number 0 is 1,2, 3 … … n-1, n, the receiving antenna sequence corresponding to the transmitting antenna number 1 is 2, 3 and 4 … … n, the receiving antenna sequence corresponding to the transmitting antenna number 0 and 2 is 3, 4 and 5 … … 0, and the receiving antenna sequence corresponding to the transmitting antenna number 1 and n-1 is 0, 1 and 2 … … n-3 and n-2.
As a further improvement, the present invention obtains a trend signal of frequency difference data of a receiving antenna corresponding to each transmitting antenna and subtracts the trend signal from the trend signal specifically: and respectively carrying out 2-order polynomial fitting on the data of the receiving antenna corresponding to each transmitting antenna on the y=x function (x=1, 2, and the number of the receiving antennas, obtaining a trend line corresponding to each antenna, and subtracting the calculated trend from the original data.
As a further improvement, the method for restoring the data from which the trend signal is removed to the arrangement sequence of the absolute antenna positions specifically includes: the original n (n-1) data are arranged according to the sequence from the 0 # n-1 receiving antenna, and the data of the transmitting and receiving antennas with the same sequence number are set as 0: for example, the number 0 transmission corresponds to the number 0-n-1 reception, the number 1 transmission corresponds to the number 0-n-1 reception, and so on, to obtain n×n data after the preprocessing is completed.
The beneficial effects of the invention are as follows:
according to the invention, through a specific algorithm for extracting the noise signals in the mutual inductance data, the noise signals with stronger signals in the differential data are removed, so that the weak characteristic signals can be highlighted in the value range close to the weak characteristic signals. Specifically, the noise signal with larger amplitude in the acquired mutual inductance data difference value of 2 frequencies is removed. Because the characteristic signal and the noise signal are in a superposition state, the noise signal has a much larger duty ratio than the characteristic signal, and the removal of the noise signal with larger amplitude according to the overall trend does not influence the extraction of the characteristic signal. The data is used for being more fit with an imaging algorithm, so that the proportion of noise in an image is greatly reduced.
The frequency difference data are arranged according to the relative antenna positions, so that the rearranged data are more in accordance with physical significance, meanwhile, the rearranged data are also more in accordance with quadratic function curves, and the second-order polynomial fitting effect is better.
Drawings
FIG. 1 is a diagram of two frequency direct differential data;
FIG. 2 is a graph of differential data after fitting two frequency polynomials;
FIG. 3 is a graph comparing 15 raw data of the number 0 emission with the fitted data;
FIG. 4 is a data plot of 15 emissions number 0 after algorithmic processing;
FIG. 5 is a graph comparing all raw data to post-fit data;
FIG. 6 is a full data graph after algorithmic processing;
FIG. 7 is a CT image of a patient with cerebral hemorrhage;
FIG. 8 is a graph of direct 2 frequency differential imaging without processing;
FIG. 9 is a plot of 2 frequency data polynomial fits;
fig. 10 is an image using the algorithm of the present invention.
Detailed Description
The invention discloses a brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method, which comprises the following steps:
1) Acquiring mutual inductance data corresponding to each frequency;
2) Acquiring frequency difference data before algorithm processing;
3) Arranging frequency difference data according to the relative antenna positions;
4) Obtaining trend signals of frequency difference data of receiving antennas corresponding to each transmitting antenna and subtracting the trend signals;
5) And restoring the data from which the trend signals are removed into the arrangement sequence of the absolute antenna positions to obtain the processed data.
Specifically:
1) Obtaining mutual inductance data corresponding to each frequency: and subtracting the air reference mutual inductance data and the detection mutual inductance data corresponding to each frequency to obtain basic calibrated mutual inductance data.
2) Acquiring frequency difference data before algorithm processing: the mutual inductance data after basic calibration corresponding to 2 frequencies are subtracted to obtain frequency difference data before algorithm processing, for example, the frequency 1 data is f1, the frequency 2 data is f2, and the frequency 2-f1 data is obtained.
3) The distance ordering of the receiving antenna to the transmitting antenna is specifically as follows:
n pieces of antenna data are n times n, the data of the same receiving and transmitting antenna are removed, the data are arranged from near to far according to the position of the receiving antenna from the transmitting antenna (antenna numbers are 0-n-1, the quality detection signals of the data signals received and transmitted by the same transmit have no target characteristics basically because the quality detection signals do not pass through the target, and the quality detection signals are default to 0 and do not participate in calculation): the receiving antenna sequence corresponding to the transmitting antenna number 0 is 1,2, 3 … … n-1, n, the receiving antenna sequence corresponding to the transmitting antenna number 1 is 2, 3, 4 … … n, the receiving antenna sequence corresponding to the transmitting antenna number 0,2 is 3, 4, 5 … … 0, 1, and so on, and the receiving antenna sequence corresponding to the transmitting antenna number n-1 is 0, 1,2 … … n-3, n-2. The rearranged frequency offset data is n transmit antennas corresponding to n-1 receive antennas = n x (n-1) data. For example, 16 antenna data are 16 by 16, the data of the same receiving and transmitting antenna are removed, the data are arranged from near to far according to the position of the receiving antenna from the transmitting antenna (antenna number is 0-15, the quality detection signal of the data signal received and transmitted does not pass through the target so as to have basically no target characteristic influence, and the default is 0 and does not participate in calculation): the receiving antenna sequence corresponding to the transmitting antenna No. 0 is 1,2, 3 … … 14, 15, the receiving antenna sequence corresponding to the transmitting antenna No. 1 is 2, 3, 4 … …, the receiving antenna sequence corresponding to the transmitting antenna No. 0,2 is 3, 4, 5 … … 0, 1, and so on, and the receiving antenna sequence corresponding to the transmitting antenna No. 15 is 0, 1,2 … …, 14. Rearranging the frequency offset data to 16 transmit antennas corresponds to 15 receive antennas = 240 data.
4) The trend signal of the frequency difference data of the receiving antenna corresponding to each transmitting antenna is obtained and subtracted specifically as follows:
and respectively carrying out 2-order polynomial fitting on the data of the receiving antenna corresponding to each transmitting antenna on the y=x function (x=1, 2, and the number of the receiving antennas, obtaining a trend line corresponding to each antenna, and subtracting the calculated trend from the original data. Fig. 3 is a comparison of 15 raw data of the number 0 emission and the fitted data, and the number 1-15 corresponding to the number 0 emission is selected for receiving, and the other 15 emissions are similar, so that the amplitude of the overall trend is about 0.5, the amplitude of the raw data is about 0.6, and the amplitude ratio of the trend exceeds 80%.
Fig. 4 is a plot of data for 15 emissions number 0 after algorithm processing, raw data minus trend after fitting.
Fig. 5 is a graph comparing all raw data and post-fit data, and is a graph comparing raw data and post-fit data trend signal curves.
5) The data from which the trend signal is removed is restored to the arrangement sequence of the absolute antenna positions, and the data after the processing is completed is specifically: the original n (n-1) data are arranged according to the sequence from the 0 # n-1 receiving antenna, and the data of the transmitting and receiving antennas with the same sequence number are set as 0: for example, the number 0 transmission corresponds to the number 0-n-1 reception, the number 1 transmission corresponds to the number 0-n-1 reception, and so on, to obtain n×n data after the preprocessing is completed.
Such as: the original 240 data are arranged according to the sequence of the 0 # to 15 # receiving antennas, and the data of the transmitting and receiving antennas with the same serial numbers are set as 0: for example, the number 0 transmission corresponds to the number 0-15 reception, the number 1 transmission corresponds to the number 0-15 reception, and so on, to obtain 256 pieces of data after preprocessing is completed.
The processed data are shown in fig. 6, fig. 6 is a graph of all the data processed by the algorithm, the data fluctuation oscillates up and down in a straight line with y=0, no obvious overall fluctuation exists, and small signals in the data are highlighted.
The specific application is as follows: performing brain electrical impedance magnetic induction multi-frequency imaging on a patient with brain hemorrhage:
FIG. 7 is a CT image of a patient with cerebral hemorrhage, corresponding to the graph of FIG. 1, with lesions in the left white block area;
FIG. 8 is a graph of direct 2 frequency differential imaging without processing, corresponding to the graph of FIG. 2, with no observable lesion sites;
FIG. 9 is a plot of 2 frequency data polynomial fits corresponding to the plot of FIG. 6 with small lesion locations and no primary imaging;
fig. 10 is an image showing the position of a lesion accurately shown, coincident with a CT using the algorithm of the present invention.
It is obvious that the direct difference and the direct polynomial fit images on the imaging are mainly dark images with noise and cannot correspond to focus parts, and the contrast noise is too tiny although the polynomial fit image has a small part of light focus images at the upper left. The dark image with noise can be eliminated under a large condition by using the current algorithm, and the light image of the focus signal is highlighted.
The foregoing is merely a preferred embodiment of the present invention, and the present invention is not limited to the above examples, and other modifications and variations, which are directly derived or suggested to those skilled in the art, should be considered to be included in the scope of the present invention without departing from the spirit and concept of the present invention.
Claims (4)
1. The brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method is characterized by comprising the following steps of:
1) Acquiring mutual inductance data corresponding to each frequency; the step of obtaining the mutual inductance data corresponding to each frequency specifically comprises the following steps: subtracting the air mutual inductance data and the detection mutual inductance data corresponding to each frequency to obtain basic calibrated mutual inductance data;
2) Acquiring frequency difference data; the frequency difference data is obtained by subtracting mutual inductance data after basic calibration of 2 frequencies;
3) Arranging frequency difference data according to the relative antenna positions; the frequency difference data arranged according to the relative antenna positions are as follows: according to the distance sequence of the receiving antennas to the transmitting antennas, n pieces of antenna data are n times n, the data of the same receiving and transmitting antennas are removed, the near-far arrangement is seen from one direction according to the position of the receiving antennas from the transmitting antennas, and the rearranged frequency difference data are n pieces of data of n transmitting antennas corresponding to n-1 receiving antennas = n times (n-1); obtaining the arranged frequency difference data;
4) Fitting the arranged frequency difference data obtained in the step 3) to obtain fitting data, and subtracting the fitting data from the arranged frequency difference data obtained in the step 3) to obtain data for removing trend signals;
5) And restoring the data from which the trend signals are removed into the arrangement sequence of the absolute antenna positions to obtain the processed data.
2. The brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method according to claim 1, wherein the distance sequence of the receiving antenna to the transmitting antenna is specifically as follows: the antenna number is 0~n-1, the data signal quality detection signal which is transmitted and received does not pass through the target so as to basically have no influence of target characteristics, the data signal quality detection signal is defaulted to 0 and does not participate in calculation, the receiving antenna sequence corresponding to the transmitting antenna number 0 is 1,2, 3 … … n-1, n, the receiving antenna sequence corresponding to the transmitting antenna number 1 is 2, 3, 4 … … n, the receiving antenna sequence corresponding to the transmitting antenna number 0,2 is 3, 4, 5 … … 0, 1, and the receiving antenna sequence corresponding to the transmitting antenna number n-1 is 0, 1,2 … … n-3, n-2.
3. The brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method according to claim 1 or 2, wherein the trend signal of the frequency difference data of the receiving antenna corresponding to each transmitting antenna is obtained and subtracted specifically: and respectively carrying out 2-order polynomial fitting on the data of the receiving antenna corresponding to each transmitting antenna on the y=x function (x=1, 2, and the number of the receiving antennas, obtaining a trend line corresponding to each antenna, and subtracting the calculated trend from the original data.
4. The brain electrical impedance magnetic induction multi-frequency imaging data preprocessing method according to claim 3, wherein the data of removing trend signals is restored to an arrangement sequence of absolute antenna positions, and the processed data is specifically:
the original n (n-1) data are arranged according to the sequence from the 0 # to the n-1 # receiving antennas, and the data of the transmitting and receiving antennas with the same sequence number are set as 0: for example, transmission number 0 corresponds to reception number 0~n-1, transmission number 1 corresponds to reception number 0~n-1, and so on, to obtain n×n data after preprocessing is completed.
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| CN108957439A (en) * | 2017-04-20 | 2018-12-07 | 奥托立夫Asp公司 | For by making the double frequency difference of radar signal in car radar sensor balance the device and method weaken low coverage radar signal |
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| EP2225579A1 (en) * | 2007-11-27 | 2010-09-08 | Arjae Spectral Enterprises, Inc. | Noise reduction by means of spectral parallelism |
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| US5228009A (en) * | 1992-04-10 | 1993-07-13 | Diasonics, Inc. | Parametric clutter elimination |
| CN108957439A (en) * | 2017-04-20 | 2018-12-07 | 奥托立夫Asp公司 | For by making the double frequency difference of radar signal in car radar sensor balance the device and method weaken low coverage radar signal |
| CN110687555A (en) * | 2019-09-23 | 2020-01-14 | 西安空间无线电技术研究所 | Navigation satellite atomic clock weak frequency hopping on-orbit autonomous rapid detection method |
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