Heart area positioning method based on electrical impedance tomography technology

Abstract

The invention discloses a heart area positioning method based on an electrical impedance tomography technology, which belongs to the technical field of electrical impedance tomography and comprises the following steps: collecting EIT data, and dividing into a positioning group and a control group; performing EIT data reconstruction to obtain an EIT time sequence signal diagram; processing each pixel in the EIT time sequence signal diagram to obtain a time sequence signal intensity diagram of each pixel; performing standard deviation normalization calculation on the time sequence signal intensity of each pixel to obtain a time sequence signal intensity distribution diagram, selecting a pixel point where the highest intensity value is located as the central position of the heart region of interest, and calculating the phase difference between each pixel position and the central position; obtaining heart area positioning results of the positioning group and the control group according to the phase difference and the time sequence signal intensity graph; the results of the positioning group and the control group are compared, the final heart positioning area is determined after verification, simple signal intensity threshold division is not depended on any more, the EIT clinical application scene can be expanded, and the noninvasive superiority of the EIT technology is further enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of electrical impedance tomography, and in particular to a heart region positioning method based on electrical impedance tomography technology. Background Art

[0002] Electrical Impedance Tomography (EIT) is a technology that uses the internal impedance distribution of an object to form an image. It is currently widely used to monitor the patient's pulmonary ventilation and cardiac function in real time, and can provide dynamic information on pulmonary ventilation and perfusion as well as non-invasive measurement of cardiac output. However, the EIT technology itself has problems with resolution and image quality, as well as errors in the human body impedance distribution model. Therefore, accurate information cannot be obtained when collecting real-time data from clinical patients. In particular, the measurement accuracy of cardiac output and various pulmonary indicators is heavily dependent on the accuracy of the division of the cardiac region of interest and the pulmonary region of interest, which will affect clinical diagnosis.

[0003] At present, the cardiac region is located by using an empirical double threshold that defines the global normalized signal intensity to divide the heart-lung junction and the heart-lung joint area, and indirectly segmenting the cardiac region of interest (ROI). heart ), but this method has two problems: first, the empirical threshold is not universally applicable, and the distribution of cardiopulmonary signals in different patients may have structural differences; second, the signal intensity cannot fully reflect the differences in cardiopulmonary regions, and more information hidden in the EIT signal is ignored. At present, there is no accurate and standardized way to locate the cardiac region of interest (ROI) in the industry. heart ). Summary of the invention

[0004] In view of the above problems, the present invention provides a cardiac region positioning method based on electrical impedance tomography technology, which can improve the accuracy of cardiac region of interest division and can effectively assist clinical analysis and treatment.

[0005] The technical solution of the present invention is:

[0006] The method for locating the cardiac region based on electrical impedance tomography technology comprises the following steps:

[0007] S1, collect EIT data and divide the positioning group and the control group;

[0008] S2, EIT data reconstruction to obtain EIT timing signal diagram;

[0009] S3, using a high-pass filter to remove linear drift signals from each pixel in the EIT time series signal diagram, and then using a band-pass filter to extract signals near the perfusion frequency to obtain a time series signal intensity diagram of each pixel;

[0010] S4, performing standard deviation normalization calculation on the time series signal intensity of each pixel to obtain a time series signal intensity distribution diagram, selecting the pixel point with the highest intensity value as the center position of the cardiac region of interest, and calculating the phase difference between each pixel position and the center position;

[0011] S5. Obtaining the cardiac region positioning results of the positioning group and the control group according to the phase difference and the time series signal intensity diagram;

[0012] S6. Compare the results of the positioning group and the control group, and determine the final heart positioning area after verification.

[0013] The positioning group was a spontaneous breathing group, and the control group was an apnea group.

[0014] In step S3, the 3dB cutoff frequency of the high-pass filter is 0.1-0.3Hz, and the 3dB cutoff frequency of the band-pass filter is ±0.1Hz of the average heart rate during the sampling period.

[0015] In step S4, before normalization calculation is performed, the center position of the cardiac region of interest needs to be determined in combination with prior information.

[0016] In the step S4, the standard deviation normalization calculation is performed using Fourier transform.

[0017] In step S5, a threshold is set for the positioning group, an optimal threshold condition is determined, the positioning result of the positioning group is calculated under the optimal threshold, the positioning result of the control group is calculated using the optimal threshold, and a manual review is performed to obtain the final positioning result of the control group.

[0018] The control group needs to fill the internal holes of the cardiac region of interest before manual review.

[0019] The beneficial effects of the present invention are:

[0020] This method effectively improves the traditional empirical ROI of EIT chest imaging. heart Positioning accuracy. ROI heart Positioning no longer relies on simple signal strength threshold division. By adding the use of EIT phase information, the defect of "different for different people" in cardiac area positioning is overcome, while ensuring ROI heart Based on the robustness of positioning technology, the effectiveness and integrity of scene usage can be effectively improved. For patients receiving diagnosis and treatment, this technology does not require apnea and is suitable for spontaneously breathing patients. It can expand the clinical application scenarios of EIT and further enhance the non-invasive superiority of EIT technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of a cardiac region positioning method based on electrical impedance tomography technology according to an embodiment of the present invention;

[0022] Figure 2 It is a logic flow chart of the cardiac region positioning method based on electrical impedance tomography technology described in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0024] Example:

[0025] like Figure 1 , Figure 2 As shown, the cardiac region positioning method based on electrical impedance tomography technology includes the following steps:

[0026] S1, EIT data collection, division into positioning group and control group;

[0027] The patient lies flat, and an electrode belt suitable for the patient's chest size is properly fixed at the T4 / 5 intercostal space. For each patient, 120 seconds of EIT data (EIT spontaneous ); After general anesthesia was implemented and mechanical ventilation was stable, mechanical ventilation was suspended for 30 seconds using breath-holding technique, and EIT data were collected (EIT apnea ).

[0028] The positioning group was the spontaneous breathing group, and the control group was the apnea group.

[0029] S2, EIT data reconstruction to obtain EIT timing signal diagram;

[0030] Using the human chest model reconstruction algorithm provided by Draeger, an EIT timing signal with a resolution of 32*32*N is generated. (32*32 represents the spatial resolution of the reconstructed image, and N represents the number of signal frames collected. N=t*Fs, where t is the acquisition time in seconds, and Fs represents the sampling frequency of the EIT device, which is generally 50Hz).

[0031] S3. Use a high-pass filter to remove the linear drift signal from each pixel in the EIT time series signal diagram, and then use a band-pass filter to extract the signal near the perfusion frequency to obtain the time series signal intensity diagram of each pixel:

[0032] The details are as follows: The collected time series signal is regarded as a digital signal of length N with a resolution of 32*32 pixels. A high-pass filter is used to remove linear drift from the signal of each pixel, and then a band-pass filter is used to extract the signal near the perfusion frequency to obtain the time series signal intensity map of each pixel. A high-pass filter (3dB cutoff frequency is set to 0.2Hz, filter type is IIR Butterworth, order is 50) is used to filter the signal from EIT. spontaneous and EITapnea The timing signal is filtered to remove the linear signal drift and the signal from EIT spontaneous An additional high-pass filter is set (3dB cutoff frequency is the average heart rate during the sampling period ±0.1Hz, filter type is FIR EQUIRIPPLE, order is 1500). If there is heart rate data, it is directly read in the standard input signal; if the heart rate is unknown, the time series signal corresponding to the pixel with the largest signal intensity after high-pass filtering is selected for Fourier transform, and the frequency corresponding to the maximum value greater than 0.8Hz is empirically regarded as the average heart rate. Both filterings need to remove the signal of the filter-related delay. The standard deviation of the time series signal after filtering (represented by a 32*32*N array) is calculated pixel by pixel, and the standard deviation value of each pixel of the entire image is normalized to generate a 32*32 time series signal intensity distribution map, recorded as Imap (Intensity map).

[0033] S4. Perform standard deviation normalization calculation on the time series signal intensity of each pixel to obtain a time series signal intensity distribution diagram, select the pixel point with the highest intensity value as the center position of the cardiac region of interest, and calculate the phase difference between each pixel position and the center position:

[0034] The phase of the timing signal at any pixel on the perfusion frequency is obtained in the following way: the timing signal is converted from the time domain to the frequency domain using Fourier transform, and the phase value corresponding to the perfusion frequency (heart rate) is found on the phase spectrum of the frequency domain signal, which is the obtained phase. The position of the maximum pixel value in Imap is considered to be the "center position" of the heart area. The phase of the timing signal corresponding to each pixel is subtracted from the phase of the pixel at the "center position" of the heart area, and the "phase difference" between the two is calculated at each pixel corresponding position (theoretically in the range of [-π,π)), and its absolute value is calculated (in the range of [0,π)). The 32*32 phase distribution map represented by the absolute value of the phase difference is recorded as Pmap (Phase map).

[0035] S5. Obtaining the cardiac region positioning results of the positioning group and the control group according to the phase error and time series signal strength diagram;

[0036] The specific steps are as follows:

[0037] Step 1: EIT apneaThe intensity distribution map (Imap) and phase distribution map (Pmap) set a set of initial intensity thresholds (denoted as I_t, between [0,1]) and phase delay thresholds (denoted as P_t, between [0,π)), marked as I_t / P_t. In Imap, all pixels with values ​​greater than I_t are selected to form a pixel set, denoted as RI (Region of Intensity-valid); in Pmap, all pixels with values ​​less than P_t are selected to form a pixel set, denoted as RP (Region of Phase-valid). The intersection of pixel sets RI and RP is taken, denoted as RIP (Region of Intensity-and Phase-valid). By generating a binary mask (32*32), the RIP and non-RIP areas are located (represented by 1 and 0, respectively); to ensure connectivity, all pixels in the RIP that cannot be adjacent to the "center position" pixel of the heart area through any other RIP pixel are removed, and all holes in the RIP area are further filled. The final binary mask is recorded as RIP mask:i (i=1) to represent ROI heart In Imap, set the area with a value of 1 in RIP mask:i to 0, that is, the output includes ROI heart Perfusion signal intensity distribution map for localization information.

[0038] Step 2: Repeat step 1 and adjust the I_t / P_t value one by one. Obtain a new RIP mask:i (i = number of detections) by manually measuring the accuracy of the detection results (to determine whether to adjust the I_t / P_t value up or down in the next repetition). Repeat the adjustment until it is subjectively determined that the positioning accuracy of the heart area cannot be significantly improved by changing I_t / P_t. The I_t / P_t value selected at this time is selected as the optimal value.

[0039] Step 3: EIT spontaneous The Imap and Pmap use the best I_t / P_t values ​​obtained in step 2, and then execute step 1 to finally obtain the ROI under spontaneous breathing. heart Perfusion signal intensity distribution map for localization information.

[0040] Perform manual review simultaneously. Compare the Imap obtained by digital signal processing in S3 with the RIP mask result obtained in step three to check whether the positioning result of the heart area is credible. In existing cases, the RIP mask is usually reliable (95%). In the event of obvious errors (mostly due to errors in the automatically selected "center position" of the heart area), a backup plan can be run, that is, manually select the position of the pixel of the "center position" of the heart area through the visual interface for correction. The rest of the technical route is consistent with the aforementioned plan.

[0041] S6. After verification, the final heart positioning area is determined.

[0042] By comparing the EIT apnea and EIT spontaneous The consistency of the RIP mask under the same I_t / P_t setting of the data is used to evaluate the patent ROI heart The effectiveness of the positioning algorithm. Mark EIT spontaneous The RIP mask result is R1, EIT apnea The RIP mask result is R2. The IoU (Intersection of Union) metric is used to quantify the consistency.

[0043]

[0044] The closer the value is to 1, the better the consistency between the two. IoU_mean is used to represent the IoU in all EITs. apnea The calculated average value in is used to measure whether the algorithm is effective enough.

[0045] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A cardiac region positioning method based on electrical impedance tomography technology, characterized in that: The steps include: S1, collect EIT data and divide the positioning group and the control group; S2, EIT data reconstruction to obtain EIT timing signal diagram; S3, using a high-pass filter to remove linear drift signals from each pixel in the EIT time series signal diagram, and then using a band-pass filter to extract signals near the perfusion frequency to obtain a time series signal intensity diagram of each pixel; S4, performing standard deviation normalization calculation on the time series signal intensity of each pixel to obtain a time series signal intensity distribution diagram, selecting the pixel point with the highest intensity value as the center position of the cardiac region of interest, and calculating the phase difference between each pixel position and the center position; S5. Obtaining the cardiac region positioning results of the positioning group and the control group according to the phase difference and the time series signal intensity diagram; S6. Compare the results of the positioning group and the control group, and determine the final heart positioning area after verification.

2. The method for locating the cardiac region based on electrical impedance tomography according to claim 1, characterized in that: The positioning group was a spontaneous breathing group, and the control group was an apnea group.

3. The method for locating the cardiac region based on electrical impedance tomography according to claim 1, characterized in that: In step S3, the 3dB cutoff frequency of the high-pass filter is 0.1-0.3Hz, and the 3dB cutoff frequency of the band-pass filter is ±0.1Hz of the average heart rate during the sampling period.

4. The method for locating the cardiac region based on electrical impedance tomography according to claim 1, characterized in that: In step S4, before normalization calculation is performed, the center position of the cardiac region of interest needs to be determined in combination with prior information.

5. The method for locating the cardiac region based on electrical impedance tomography according to claim 1, characterized in that: In step S4, the standard deviation normalization calculation is performed using Fourier transform.

6. The method for locating the cardiac region based on electrical impedance tomography according to claim 1, characterized in that: In step S5, a threshold is set for the positioning group, an optimal threshold condition is determined, the positioning result of the positioning group is calculated under the optimal threshold, the positioning result of the control group is calculated using the optimal threshold, and a manual review is performed to obtain the final positioning result of the control group.

7. The method for locating the cardiac region based on electrical impedance tomography according to claim 6, characterized in that: The control group needs to fill the internal holes of the cardiac region of interest before manual review.

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