A ct real-time 4D gating imaging method based on projection data

Abstract

The application discloses a CT real-time 4D gating imaging method based on projection data, and steps of the method comprise the following steps: 1) collecting CT projection of a target to be detected, and dividing the CT projection into multiple groups according to the collection angle; representing each CT projection by using an n-dimensional vector after pixel merging; constructing an original matrix according to the vectors corresponding to the same angle group, extracting principal component information from the matrix based on a PCA method, and obtaining superimposed signals containing respiration and heartbeat; 2) separating the respiration and heartbeat signals from the superimposed signals; performing respiration gating processing on the CT projection according to the respiration signal, and performing gating division on the CT projection in the respiration motion period; performing heartbeat gating processing on the CT projection in the respiration stable period according to the heartbeat signal; 3) reconstructing the CT projection groups after the respiration and heartbeat gating processing to obtain 4D gating imaging of the target to be detected. The application realizes the real-time of CT gating imaging, and improves the gating information extraction precision.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of medical imaging and X-ray imaging, and relates to a CT real-time 4D gating imaging method based on projection data. BACKGROUND

[0002] Computed Tomography (CT) is an imaging method that uses an X-ray source to perform transmission scanning on an object, uses a detector to detect the attenuation of the rays, and then uses a reconstruction algorithm to reconstruct a tomographic image by a computer. CT imaging has important applications in clinical diagnosis, preclinical research, and industrial non-destructive testing. In the field of small animal imaging, CT imaging can provide high-contrast images of chest structures such as lung tissue, bone, enhanced contrast heart cavities and blood vessels, and is also suitable for most pathological detection related to chest organs, making it an important imaging technology.

[0003] However, due to the periodic expansion and contraction of small animal breathing and heartbeat, the CT data collected is the superimposed result of multiple cycles of motion. When reconstructing the CT image, it results in image blurring, i.e., motion artifacts. When there is a lesion point on the lung, heart or surrounding tissue organs, the periodic motion will cause the lesion point to expand in size on the reconstructed image, and the contrast of the lesion point will decrease or even be submerged in the background, affecting the diagnostic accuracy. To reduce the errors caused by the above motion, the CT data collected can be divided into multiple phase data according to the motion cycle, and then dynamic imaging is performed, i.e., 4D gating technology considering the breathing and heartbeat motion. Through the gating technology, the CT data can be grouped and reconstructed, which can effectively remove the motion artifacts and can be used to realize 4D dynamic CT reconstruction.

[0004] Data-driven gating (DDG) is a technology that directly extracts the breathing and heart motion signals from the changes in information in CT chest projections. In contrast, external device gating (EDG) requires additional monitoring devices to be connected to the patient, which increases the discomfort of the subject. And due to the complexity of its operation and the difficulty of accurately aligning the motion signal with the CT data in time, it has been gradually replaced by data-driven gating.

[0005] The commonly used CT data-driven gating methods include the center of mass method (COM), the frequency analysis method and the principal component analysis (PCA) method. In the process of extracting motion information, the three methods are all based on the complete CT projection data. The COM method needs to manually draw a region of interest (ROI) or to automatically determine the ROI position through multi-cycle acquisition, which increases the acquisition time and the radiation dose. The frequency analysis method determines the ROI and the respiratory frequency by analyzing the Fourier spectrum of each image or sinusoidal image pixel, and estimates the respiratory signal by the total count in the region over time. This method has the characteristics of many parameters and multiple calculation stages, and thus has a relatively large amount of calculation. The overall PCA method directly processes the complete CT projection data, which reduces the accuracy of motion information extraction. When the projection is large and the projection number is large, the large amount of calculation leads to a long data processing time.

[0006] Problems of the prior art:

[0007] (1) Multi-cycle acquisition is needed, which increases the acquisition time and the radiation dose;

[0008] (2) The accuracy of motion information extraction is not high;

[0009] (3) The additional data processing time caused by the large amount of calculation cannot realize real-time. Summary

[0010] In view of the problems in the prior art, the purpose of the present application is to provide a CT real-time 4D gating imaging method based on projection data, which solves the problem of inaccurate information caused by additional rotation information due to the change of projection angle in the overall PCA gating information extraction, and the problem of additional time consumption caused by large amount of calculation. The present application realizes the universal applicability, real-time and high accuracy of CT cardiothoracic imaging.

[0011] The process of the CT real-time 4D gating imaging method of the present application is as follows: first, the CT projection data is preprocessed, which includes pixel merging and filtering of CT projections at the same angle, and then the motion information is extracted by using the PCA-based small interval data-driven gating technology; second, a bidirectional Hamming window FIR filter is used to band-pass filter the extracted respiratory and heartbeat superimposed signal; third, the separated respiratory signal is subjected to respiratory gating processing; fourth, the stable period information obtained by respiratory gating is combined with the heartbeat signal to perform heartbeat gating processing; and finally, the CT projection after gating processing is grouped and reconstructed to obtain 4D gating imaging.

[0012] Before extracting motion information by CT projection, data preprocessing, pixel merging and filtering are carried out, so as to weaken noise and greatly reduce calculation amount.

[0013] The preprocessed CT projection is subjected to small inter-group PCA data-driven gating of overlapping grouping, so as to extract more accurate superimposed signals of respiration and heartbeat.

[0014] The respiration and heartbeat superimposed signals are subjected to band-pass filtering by using a bidirectional Hamming window FIR filter, so as to separate the respiration and heartbeat signals based on frequency.

[0015] Periodic step-by-step respiration gating is adopted, so as to accurately determine the respiration stable period and the inspiration and expiration boundary, and then the respiration motion period is subjected to gating grouping, so as to obtain complete respiration grouping.

[0016] The respiration and heartbeat double-gating method is adopted, so as to perform heartbeat gating in the respiration stable period, and to confirm the heartbeat period and complete grouping according to periodic peak detection, so as to obtain CT projection grouping of different states of the heart, including atrial systole CT projection, ventricular systole CT projection and full cardiac diastole CT projection.

[0017] The grouping projection obtained by the method can be used for CT imaging analysis imaging algorithm and iterative imaging algorithm and other reconstruction methods, and the method is not limited by the reconstruction method.

[0018] The technical scheme of the present application is as follows:

[0019] A CT real-time 4D gating imaging method based on projection data, comprising the following steps:

[0020] 1) Collecting CT projection of a target to be detected and dividing the CT projection into a plurality of groups according to the collection angle; performing pixel merging on each CT projection in the same angle group, representing each merged CT projection by an n-dimensional vector, n being the number of pixels in the merged image; arranging the n-dimensional vectors corresponding to the same angle group to construct an original matrix according to the scanning time of the CT projection data in the same angle group, and then extracting principal component information from the original matrix based on the PCA method, and arranging the weight factors corresponding to the selected principal components in the CT projection collection order to obtain superimposed signals containing respiration and heartbeat;

[0021] 2) using a bidirectional Hamming window FIR filter to band-pass filter the superimposed signal, separating out the respiratory signal and the heartbeat signal; according to the respiratory signal, performing respiratory gating processing on the CT projection, dividing out the CT projection of the respiratory motion period and the CT projection of the respiratory stable period, and performing gating division on the CT projection of the respiratory motion period to obtain the CT projection grouping in the same state of the chest cavity in the respiratory process; according to the heartbeat signal, performing heartbeat gating processing on the CT projection in the respiratory stable period to obtain the CT projection grouping corresponding to different heartbeat stages;

[0022] 3) according to the CT projection grouping after the respiratory gating processing and the heartbeat gating processing, reconstructing to obtain the 4D gated imaging of the target to be detected.

[0023] Further, the method for performing gating grouping on the CT projection of the respiratory motion period to obtain the CT projection grouping in the same state of the chest cavity in the respiratory process is as follows:

[0024] 21) find all peak positions peak by using the AMPD peak searching algorithm, and determine the demarcation of inspiration and expiration in each respiratory cycle according to the peak positions peak; wherein, taking the coordinate positions kpeak[i]+1 and peak[i+1]-1 corresponding to one CT projection on the left and right of the ith peak position peak[i] as the boundaries highLeft and hightRight of the ith interval;

[0025] 22) taking the ith peak position peak[i] as a reference, moving the coordinates corresponding to p CT projections in the direction of the next peak position peak[i+1] as the left boundary stableLeft of the ith respiratory stable period, and moving the coordinates corresponding to q CT projections in the direction of the peak position peak[i] as the right boundary stableRight of the ith respiratory stable period;

[0026] 23) taking the region between the adjacent two peak positions peak[i] and peak[i+1] as a period, taking max(stableLeft, stableRight) in the period as the lower limit of the respiratory motion period in the period, and taking min(highLeft, hightRight) in the period as the upper limit of the respiratory motion period in the period;

[0027] 24) dividing the respiratory motion period in the period at equal intervals according to a preset grouping number, to obtain the CT projection grouping in the same state of the chest cavity in the respiratory process.

[0028] Further, the method for performing heartbeat gating processing on the CT projection in the respiratory stable period according to the heartbeat signal to obtain the CT projection grouping of different heartbeat stages is as follows:

[0029] 31) Calculate the detection threshold k=(f2 / f1) by CT scanning the heartbeat frequency f1 of the target to be detected and the acquisition frequency f2 of the CT projection data; detect the CT projection in the i-th respiratory plateau stable[i] to obtain the position of the first valley in the respiratory plateau stable[i] valley[0] as the initial detection position;

[0030] 32) Detect from the current detection position backward;

[0031] 321) If the position index of the current valley exceeds the right boundary of the respiratory plateau stable[i], change the last value of the valley valley[i] before the position index to the opposite number, and jump to the next respiratory plateau stable[i+1] to start detection;

[0032] 322) If the current valley[i] is a negative value, store the value of the first valley after detecting the negative value of valley[i] into the array valley, and return to step 32) for execution;

[0033]

[0034] 323) If the current valley[i] is a positive value, record the distance between the position index of the detected valley and valley[i], and when

[0035] |index-valley[i]-k| is the smallest, store the position index of the valley into the array valley, and return to step 32) for execution;

[0036] 33) After detecting all the CT projections in the respiratory plateau stable[i], end the detection and return the array valley;

[0037] 34) Based on the array valley, divide the CT projections in the respiratory plateau stable[i] into groups with equal time intervals to obtain CT projection groups in different heartbeat stages.

[0038] Further, the CT projections are divided into multiple groups by using an overlapping grouping method, wherein the CT projections of adjacent groups partially overlap.

[0039] Further, the selected principal components are the first two principal components p1 and p2 in the obtained principal components.

[0040] Further, based on the CT projection data after the respiratory gating processing and the heartbeat gating processing, 4D gated imaging of the target to be detected is reconstructed by using analytical imaging, iterative imaging or filtered back projection.

[0041] ​Further, the CT projection data of each group is respectively reconstructed, and then the reconstruction results of each group are spliced to generate a 4D dynamic image changing with motion.

[0042] The advantages of the present application are as follows:

[0043] The method adopts the inter-cell PCA mode, reduces the calculation time cost, improves the accuracy of motion information extraction and CT projection grouping, and thus realizes accurate real-time grouping, has universal applicability and high precision characteristics.

[0044] (1) It can be executed simultaneously with CT acquisition, and has real-time performance;

[0045] (2) The accuracy of CT data-driven gating is effectively improved;

[0046] (3) The method has universality. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a flow chart of real-time 4D gating imaging.

[0048] Figure 2 It is w corresponding to the first principal component ik ;

[0049] (a) only contains respiratory motion; (b) contains respiratory and heartbeat motion.

[0050] Figure 3 It is a projection graph before and after pixel merging and w ik Comparison chart;

[0051] (a) before merging; (b) after merging.

[0052] Figure 4 It is w of overall PCA and inter-cell PCA ik Comparison chart;

[0053] (a) is the motion waveform selected from the corresponding interval in the overall PCA; (b) is the inter-cell PCA, and the red dots on the waveform mark the true ventricular systole.

[0054] Figure 5 It is a band-pass filtering of simulation data;

[0055] (a) is a simulation graph; (b) is a heartbeat signal graph, (c) is a respiratory signal graph; wherein, blue is the original signal, and orange is the filtered signal.

[0056] Figure 6 It is a comparison chart of periodic step-by-step respiratory gating and equal data amount amplitude gating method;

[0057] (a) is the equal data amount amplitude gating method; (b) is the present method.

[0058] Figure 7 4D cardiac coronal view based on the periodic time interval gating method. DETAILED DESCRIPTION

[0059] The application will be described in further detail below with reference to the drawings, which are provided by way of illustration only and thus are not intended to limit the scope of the application.

[0060] The CT real-time 4D gating imaging method based on projection data can be applied to real-time dynamic reconstruction of CT projection. The method flow of the application is shown in Figure 1 The steps will be described in detail below.

[0061] Step 1, data-driven gating

[0062] The core of the CT real-time 4D gating imaging method based on projection data is to extract data-driven information based on the PCA method, and to process the small-angle interval grouping. Compared with the traditional overall processing method, it has real-time and higher accuracy. For example, 2000 CT projections are collected in a single circle, and every 200 CT projections are processed as a group based on the PCA method, and then the principal component information is extracted from each group of images as motion information.

[0063] The PCA method is a commonly used data dimension reduction method. This method converts the observation data represented by linearly related variables into a few linearly independent variables by orthogonal transformation, which are called principal components. These principal components are also called basis vectors to represent the maximum contribution to the change in the data set. During the chest CT scanning process, the CT projection data changes due to respiratory motion and heartbeat motion, so PCA can detect such changes and extract motion information.

[0064] The application takes a single CT scan data as x i , x i is an n-dimensional vector representing the number of pixels of a single CT projection, i∈[1,m], representing m CT projections. X n×m =(x1, x2, …, x m ) represents the original matrix with pixels as samples and scanning time as features. The principal components are solved according to the following steps.

[0065] 1. Normalize the original matrix;

[0066] 2. Get the covariance matrix ∑=(X T X) / (m-1)

[0067] 3. Solve the eigenvectors α k of the covariance matrix and sort them according to the corresponding eigenvalues.

[0068] 4. Obtain principal components p k = Xa k .

[0069] where the original data x i can be approximated by the principal components p k with the weight factors w i corresponding to x ik , as follows:

[0070]

[0071] is the mean of the i-th row in the matrix X. If all principal components are retained, i.e. K equals m in equation (1), the equality holds. Since the principal components are orthogonal to each other, equation (1) can also be expressed as:

[0072]

[0073] The weight factors w ik correspond to the i-th CT projection in the k-th principal component basis, as shown in Fig. 2, where the first two principal components p1, p2 correspond to the weight factors w i1 , w i2 contain most of the motion information, and arranging the weight factors in the order of CT projection acquisition yields the breathing and heartbeat motion waveforms. Figure 2 Pixel binning is a common technique in image processing, which reduces the image size, facilitates faster processing and analysis, and also reduces image noise, as shown in Fig. 3. Therefore, the present application considers performing pixel binning preprocessing on the CT projections before data-driven gating.

[0074] Figure 3 Pixel binning is a common technique in image processing, which reduces the image size, facilitates faster processing and analysis, and also reduces image noise, as shown in Fig. 3. Therefore, the present application considers performing pixel binning preprocessing on the CT projections before data-driven gating.

[0075] Pixel binning is a common technique in image processing, which reduces the image size, facilitates faster processing and analysis, and also reduces image noise, as shown in Fig. 3. Therefore, the present application considers performing pixel binning preprocessing on the CT projections before data-driven gating.

[0076] The advantage of small-angle interval grouping PCA is that it eliminates the motion in CT projections caused by changes in angle during rotation, which is not affected by breathing and heartbeat.

[0077] Figure 4 ​​It can be seen that the w obtained by small-angle interval grouping PCA ik The accuracy is obviously improved while the calculation time is greatly shortened. However, due to the existence of boundaries in different grouping intervals, boundary information may be lost, and the more the grouping is, the more serious the loss is. Therefore, an overlap grouping method is adopted. In the present application, each group contains 10% of the data at the end of the previous group, which solves the boundary problem with only a small calculation cost.

[0078] Step 2, band-pass filtering

[0079] The chest CT projection is jointly affected by the respiratory and heartbeat motions, so that the extracted motion waveform is a superimposed signal of the respiratory motion and the heartbeat motion. However, the influence of the respiratory motion is much greater than that of the heartbeat motion in the projection, and the respiratory amplitude in the extracted superimposed signal is also much greater than the heartbeat amplitude, so that the heartbeat signal needs to be separated before the cardiac gating.

[0080] The Hamming window is a commonly used window function, has good frequency response characteristics, can realize a relatively smooth transition region in the frequency domain, and can effectively suppress the sidelobe response of the filter.

[0081]

[0082] The present application adopts a bidirectional Hamming window FIR filter for band-pass filtering to realize the separation of the respiratory and heartbeat signals. The frequencies of the respiratory and heartbeat signals are determined by Fourier transform to convert the motion signal to the frequency domain, as shown in the formula (1). Figure 5

[0083] Step 3, respiratory gating / cardiac gating

[0084] After the motion signal is obtained by the data-driven gating, the gating grouping technology needs to divide the CT data into different groups according to the corresponding motion signal, so that the CT data in the same group corresponding to the same motion signal belongs to the same motion state, the CT data set in the same group corresponds to the range with negligible motion difference in space, and then the data of each group is reconstructed to obtain the 4D dynamic image varying with the motion. In the data grouping, the respiratory gating processing mainly adopts the amplitude-based method, and the cardiac gating processing mainly adopts the time-based method. The gating grouping technology that performs better in the respiratory motion is the amplitude-based division method, because the respiratory motion is more likely to appear unstable in the actual acquisition process, such as the change of the respiratory amplitude, the change of the respiratory period, etc. These changes will cause the problem that the data divided into the same group does not match in the actual space when the time-based division method is used.

[0085] ​In amplitude-based gating grouping techniques, the global equal amplitude gating method only requires determining the upper and lower boundaries and the number of groups, making it relatively simple to implement. It also ensures that the CT data within each group is spatially uniform. However, due to the characteristics of respiratory motion, there will be a significant difference in the amount of data in the groups during the stable respiratory period and the motion period, which leads to a large difference in the signal-to-noise ratio of the reconstructed images. The global equal data volume amplitude gating method first performs histogram statistics on the data, and then divides the data volume boundaries according to the data volume information reflected by the histogram and the number of groups. This method can ensure that the amount of CT data within each group is approximately the same, and the signal-to-noise ratio of the reconstructed images is basically uniform. However, the data within each group is not spatially uniform, and motion blur may occur in the region with the fastest motion speed.

[0086] This invention, based on the physiological characteristics of respiratory movement, employs a novel periodic respiratory gating grouping method—periodic step-by-step respiratory gating. Respiratory movement can be divided into an exercise phase and a steady phase. The exercise phase includes two processes: inhalation (chest cavity expansion, diaphragm descent) and exhalation (chest cavity contraction, diaphragm rise). The steady phase is the period from the end of exhalation to the beginning of the next inhalation, during which the chest cavity and diaphragm are in a static state. Therefore, the method adopted in this invention is to first determine two sets of data: the steady phase and the boundary region between inhalation and exhalation (also the static state), and then group the data within the exercise phase. The specific algorithm is as follows:

[0087] 1. Determine all peak positions (corresponding to the boundary between inhalation and exhalation in each respiratory cycle) using the AMPD peak-finding algorithm, and use the amplitudes corresponding to the positions peak[i]+1 and peak[i+1]-1 as the boundaries of the i-th interval, highLeft and hightRight.

[0088] 2. Using the position of peak[i] as a reference, move p to the right of peak[i+1] and q to the left of peak[i+1] as a reference. These serve as the left and right boundaries stableLeft and stableRight for the i-th respiratory stabilization period (p and q are the horizontal coordinates of the points in the motion waveform, which also correspond to the number of CT projections; the values ​​of p and q are related to the animal species, the acquisition frame rate, and the depth of anesthesia).

[0089] 3. Taking the region between two peaks as a period (one period includes a stable period and an exercise period), take max(stableLeft, stableRight) as the lower bound of the respiratory exercise period, and take min(highLeft, highRight).

[0090] As the upper limit of the movement period;

[0091] 4. The respiratory motion period interval is divided into equal intervals according to a preset grouping number.

[0092] Compared with the global equal amplitude gating method and the global equal data volume amplitude gating method, the method comprehensively considers the uniformity of the reconstruction signal-to-noise ratio (ensuring the motion period) and the physiological characteristics of the respiratory motion, so that the grouping of the stationary period and the motion period is more reasonable. Moreover, due to the influence of rotation on the respiratory signal obtained by the data-driven gating, there is a baseline drift problem. Compared with the overall amplitude gating method, the separate grouping of each cycle in the method will not be affected, and accurate grouping can be obtained.

[0093] The heartbeat motion is more commonly used in the time-based gating method. A complete heartbeat cycle is composed of atrial systole, ventricular systole and full cardiac diastole, and has strong stability in terms of cycle length and heartbeat mode within a short time. However, due to the existence of respiratory motion, the heart shape changes greatly during the motion period of respiratory motion, which will cause serious motion artifacts during reconstruction. Therefore, the present application adopts a respiratory and heart dual gating method. First, the respiratory motion stationary period is determined by the respiratory gating technology, and the CT projection in the respiratory stationary period is processed by heartbeat gating according to the heartbeat signal to obtain CT projection grouping corresponding to different heartbeat stages. Compared with the global equal time interval gating method, all data can be fully utilized, and the physiological characteristics of the heartbeat are also more in line with the heartbeat. The specific algorithm is as follows:

[0094] The detection threshold k is calculated by the heartbeat frequency (average) f1 of the CT scanned object and the acquisition frequency f2 of the CT data, k=(f2 / f1). The first valley position valley[0] of the respiratory stationary period stable[i] is detected in the i-th respiratory stationary period stable[i];

[0095] 1. Peak or valley detection is performed from the current position backward;

[0096] 2. Take the valley as an example for judgment:

[0097] (1) If the current valley position index exceeds the right boundary of the respiratory stationary period stable[i], the current valley[i] is changed to the opposite number, and the next respiratory stationary period stable[i+1] is jumped to start, and the first step is returned to execute;

[0098] The last value is changed to the opposite number, and the next respiratory stationary period stable[i+1] is jumped to start, and the first step is returned to execute; index is a number for recording the position of the current valley (not detected), valley

[0099] The array saves the positions of the valleys that have been detected without problems, and valley[i] is the i-th value in the valley array.

[0100] (2) If the current valley[i] is negative, then the first valley value after detecting the valley[i] is negative is stored in the array valley, and the execution of step 1 is returned;

[0101] (3) If the current valley[i] is positive, the distance of the detected valley position index from the previous valley[i] is recorded, and when |index-valley[i]-k| is the smallest, the position index of the valley is stored in the array valley, and the execution of step 1 is returned;

[0102] 3. After detecting all the stable period data, the detection is ended, and the array valley is returned;

[0103] 4. Based on the array valley, the period time interval is divided, and the range of each stable period starts from the position after detecting the previous negative value to the position of detecting the negative value again.

[0104] Step 4, image reconstruction

[0105] Using the respiratory and heartbeat gating results in step 3, analytical imaging, iterative imaging or other imaging methods can be used, and in the embodiment, the filtered back-projection (FBP) method is used for image reconstruction.

[0106] Embodiment:

[0107] The present application is based on the projection data collected by the photon counting small animal spectral CT developed by the Institute of High Energy Physics, Chinese Academy of Sciences, and the real-time 4D gating imaging proposed in the present application.

[0108] Detector related parameters: equivalent number of rows of detector (252), equivalent number of columns of detector (2062), pixel size of detector (0.1mm), effective field of view of detector (80.91x80.91x7.59mm 3 ).

[0109] Experimental conditions: tube voltage (90kV), tube current (0.2mA), frame frequency (40Hz).

[0110] The results of using the KM mouse CT projection data for 4D gating and then using the FBP method for reconstruction are as follows. Figure 7 The results of using the present application for 4D heartbeat gating imaging show that the heartbeat period is divided into 8 groups, and 12.5% of the data is used for image reconstruction, and the movement of the myocardium during the systole and diastole of the heart chamber can be clearly seen.

[0111] When the heartbeat and respiratory signals are separated, the motion calibration method can also be used, the respiratory signal is extracted first, and then the heartbeat signal is extracted after the original data is calibrated. The calibration method is to regard the respiratory motion as an axial rigid body motion, calibrate the original CT projection data (i.e. move the pixels in the motion region), and then use the same method to extract the heartbeat signal from the calibrated new projection.

[0112] The search for the respiratory gating plateau can also be determined by the statistical histogram method. The motion waveform is statistically histogramed according to the amplitude, and the plateau is determined according to the respiratory type of different species and the proportion of the plateau.

[0113] The reconstruction algorithm can also use the SART algorithm, and the total variation image denoising algorithm (Total Variation, TV) is added in the reconstruction process.

[0114] Although the specific embodiments of the present application are disclosed for the purpose of illustrating the present application, the purpose is to help understand the content of the present application and to implement the same, those skilled in the art can understand that various substitutions, changes and modifications are possible without departing from the spirit and scope of the present application and the appended claims. Therefore, the present application should not be limited to the disclosed content of the best mode, and the scope of the present application claimed is the scope defined by the claims.

Claims

1. A CT real-time 4D gating imaging method based on projection data, comprising the steps of: 1) acquiring CT projections of a target to be detected and dividing the CT projections into a plurality of groups according to the acquisition angle; performing pixel merging on each CT projection in the same angle group, representing each merged CT projection by an n-dimensional vector, n being the number of pixels in the merged image; arranging the n-dimensional vectors corresponding to the same angle group to construct an original matrix according to the scanning time of the CT projection data in the same angle group, and then extracting principal component information from the original matrix based on the PCA method to obtain a superimposed signal containing breathing and heartbeat by arranging the weight factors corresponding to the selected principal components in the CT projection acquisition order; 2) performing band-pass filtering on the superimposed signal by using a bidirectional Hamming window FIR filter to separate the breathing signal and the heartbeat signal; performing breathing gating processing on the CT projections according to the breathing signal to divide the CT projections into those in the breathing motion period and those in the breathing stable period, and performing gating division on the CT projections in the breathing motion period to obtain CT projection groups in the same state of the thoracic cavity during the breathing process; performing heartbeat gating processing on the CT projections in the breathing stable period according to the heartbeat signal to obtain CT projection groups corresponding to different heartbeat stages; 3) reconstructing the CT projection groups after the breathing gating processing and the heartbeat gating processing to obtain 4D gated imaging of the target to be detected.

2. The method of claim 1, wherein, The method for performing gating division on the CT projections in the breathing motion period to obtain CT projection groups in the same state of the thoracic cavity during the breathing process is as follows: 21) finding all peak positions peak by using an AMPD peak searching algorithm, and determining the boundaries of inspiration and expiration in each breathing cycle according to the peak positions peak; wherein the coordinate positions kpeak[i]+1 and peak[i+1]-1 corresponding to one CT projection on the left and right of the i th peak position peak[i] are taken as the boundaries highLeft and hightRight of the i th interval; 22) taking the i th peak position peak[i] as a reference, moving the coordinates corresponding to p CT projections in the direction of the next peak position peak[i+1] as the left boundary stableLeft of the i th breathing stable period, and moving the coordinates corresponding to q CT projections in the direction of the peak position peak[i] as the right boundary stableRight of the i th breathing stable period; 23) taking the region between the adjacent two peak positions peak[i] and peak[i+1] as a period, taking max(stableLeft, stableRight) in the period as the lower limit of the breathing motion period in the period, and taking min(highLeft, hightRight) in the period as the upper limit of the breathing motion period in the period; 24) dividing the breathing motion period in the period into CT projection groups in the same state of the thoracic cavity during the breathing process at equal intervals according to a preset grouping number.

3. The method of claim 1, wherein, The heartbeat-gated CT projection in the respiratory plateau is processed according to the heartbeat signal, and a method for grouping the CT projections in different heartbeat stages is as follows: 31) The detection threshold k=(f2 / f1) is calculated by CT scanning the heartbeat frequency f1 of the target to be detected and the acquisition frequency f2 of the CT projection data; the CT projection in the i-th respiratory plateau stable[i] is detected to obtain the position valley[0] of the first valley in the respiratory plateau stable[i] as the initial detection position; 32) The detection is performed from the current detection position backward; 321) If the position index of the current valley exceeds the right boundary of the respiratory plateau stable[i], the last value of the valley valley[i] before the position index is changed to the opposite number, and the detection is started in the next respiratory plateau stable[i+1]; 322) If the current valley[i] is a negative value, the first valley value after the valley[i] is detected to be a negative value is stored in the array valley, and the step 32) is returned to be executed; 323) If the current valley[i] is a positive value, the distance between the position index of the detected valley and the valley[i] is recorded, when |index-valley[i]-k| is the minimum, the position index of the valley is stored in the array valley, and the step 32) is returned to be executed; 33) After the detection of all the CT projections in the respiratory plateau stable[i] is completed, the detection is ended, and the array valley is returned; 34) The CT projections in the respiratory plateau stable[i] are divided into groups at equal time intervals based on the array valley, and the CT projection groups in different heartbeat stages are obtained.

4. The method according to claim 1 or 2 or 3, characterized in that, The CT projections are divided into multiple groups by using the overlapping grouping method, and the CT projections of adjacent groups are partially overlapped.

5. The method according to claim 1 or 2 or 3, characterized in that, The selected principal components are the first two principal components p1 and p2 in the obtained principal components.

6. The method of claim 1, wherein, The 4D gated imaging of the target to be detected is reconstructed by using the analytical imaging, the iterative imaging or the filtered back projection method based on the CT projection data after the respiratory gating and the heartbeat gating.

7. The method according to claim 1 or 6, characterized in that, The CT projection data of each group is reconstructed respectively, and then the reconstruction results of the groups are spliced to generate the 4D dynamic image changing with the motion.

Images