CN117148416A - Pixel counting rate correction method for CdZnTe photon counting detector - Google Patents
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Abstract
The invention relates to the technical field of detector pixel correction, in particular to a method for correcting the pixel count rate of a CdZnTe photon counting detector, which is used for presetting a count rate linear interval of each pixel of the detector and ensuring that the X-ray dose detected by each pixel in an imaging process is in the linear interval; performing full-threshold scanning on each tube voltage to obtain an integral graph of the energy spectrum, and subtracting the count rate under the next threshold from the count rate under the previous threshold based on the integral graph to obtain a differential graph of each energy spectrum; according to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage, and performing least square straight line fitting to obtain an expression between the energy and the threshold value; the optimal threshold value is selected for the photon counting detector according to the expression between the energy and the threshold value, and the method can eliminate the nonlinear response and the deviation of the counting rate in the detector and improve the accuracy and the reliability of energy spectrum measurement.
Description
Technical Field
The invention relates to the technical field of detector pixel correction, in particular to a method for correcting the pixel count rate of a CdZnTe photon counting detector.
Background
Photon counting detectors with spectral resolution have attracted a great deal of interest to more and more researchers in recent years. In X-ray imaging applications, such detectors can significantly improve image quality, improve signal-to-noise ratio and dose efficiency, and have extremely high spatial resolution. The compound semiconductor CdZnTe is considered one of the most promising photon counting detector materials because of its high effective atomic number, high resistivity and suitable band gap width. Furthermore, the energy detection range of CdZnTe detectors is 10 keV to 3 MeV, covering essentially all the range required for X-ray detection applications. But because of the complexity of the CdZnTe crystal growth process, there is an inconsistency in the response of individual pixels of its detector to X-rays. That is, different pixel locations of the detector may behave differently for photons of the same energy received, resulting in erroneous counts. In order to reduce such non-uniformity in counting performance among pixels due to non-uniformity in crystal performance, the detector needs to be calibrated.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a pixel counting rate correction method of a CdZnTe photon counting detector.
The technical scheme adopted by the invention for achieving the purpose is as follows:
the first aspect of the invention discloses a method for correcting the pixel count rate of a CdZnTe photon counting detector, which comprises the following steps:
s102: presetting a counting rate linear interval of each pixel of the detector, and ensuring that the X-ray dose detected by each pixel in the imaging process is in the linear interval; prefabricating an ideal counting rate curve, and defining an interval between the ideal counting rate curve and 20% of the interval deviated from the ideal counting rate curve as an effective linear area of each pixel;
s104: performing full-threshold scanning when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, obtaining an integral graph of energy spectrums under the condition of each tube voltage, and subtracting the count rate under the former threshold from the count rate under the latter threshold based on the integral graph to obtain a differential graph of each energy spectrum;
s106: according to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage;
s108: obtaining detector thresholds corresponding to the tube voltages of 50kVp, 60kVp, 70kVp and 80kVp respectively according to the step S106, and performing least square straight line fitting on the four detector thresholds to obtain an expression between energy and the thresholds;
S110: and selecting an optimal threshold value for the photon counting detector according to an expression between the energy and the threshold value, and correcting the counting rate of each pixel of the detector according to the optimal threshold value.
Further, in a preferred embodiment of the present invention, the threshold corresponding to the maximum energy of the detector under each tube voltage is retrieved according to the differential graph of each energy spectrum, specifically:
searching in a differential graph of the energy spectrum to obtain a corresponding energy peak, and marking the energy peak as MaxC; wherein the energy peak is the maximum count value;
searching in a differential graph of the energy spectrum to obtain A, B points with the count of 0.2 times of MaxC and 0.1 times of MaxC;
the A, B points are connected and prolonged, and the horizontal axis is intersected with the C point, and the horizontal axis of the C point is the threshold value corresponding to the maximum energy of the detector under the current tube voltage.
Further, in a preferred embodiment of the present invention, the expression between the energy and the threshold is: t=k×e+v; wherein K, V is two coefficient matrixes in the primary term expression respectively; t represents a threshold value; e represents energy; the threshold value corresponding to each detector pixel under unified energy can be calculated through the expression between the energy and the threshold value.
Further, in a preferred embodiment of the present invention, an optimal threshold is selected for the photon counting detector according to the expression between the energy and the threshold, specifically:
Because of the inconsistency among the detector pixels, the energy corresponding to the detector pixels in different thresholds is calculated according to the expression between the energy and the thresholds, the average energy is obtained, and finally the average energy is substituted into the expression between the energy and the thresholds to obtain the optimal threshold corresponding to each detector pixel.
Further, in a preferred embodiment of the present invention, full threshold scanning is performed at tube voltages of 50kVp, 60kVp, 70kVp and 80kVp, respectively, resulting in an integral map of the energy spectrum under each tube voltage condition, specifically:
setting the threshold voltage of the detector to a preset value;
setting the tube voltages to 50kVp, 60kVp, 70kVp, and 80kVp in sequence, and at each tube voltage condition, ensuring that energy data for the radiation event is acquired within a full threshold range and delivering the acquired energy data into a database;
after the collection is finished, classifying the energy data belonging to different radiation events in the database to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube;
and carrying out accumulated summation on energy data corresponding to different radiation events under each tube voltage condition to draw an integral graph of energy spectrum under each tube voltage condition.
Further, in a preferred embodiment of the present invention, the energy data belonging to different radiation events in the database is classified to obtain the energy data corresponding to different radiation events under each tube voltage condition, which specifically includes:
s202: acquiring each energy data in a database, and performing dimension reduction processing on each energy data so that each energy data is represented in a data vector form; constructing a coordinate system, mapping the data vector into the coordinate system, so that each energy data is represented in the coordinate system in the form of data points;
s204: regarding each data point as an independent cluster, acquiring a coordinate value of each cluster, calculating Manhattan distance between clusters according to the coordinate value, and establishing a distance matrix according to the Manhattan distance between the clusters;
s206: screening out two clusters with the Manhattan distance nearest to the distance matrix, and combining the two clusters with the Manhattan distance nearest to the distance matrix into one cluster; after the combination is finished, updating the distance matrix and recalculating the Manhattan distance between the aggregation; stopping iteration until the aggregation is equal to the preset aggregation quantity, and generating an iteration result;
S208: and visualizing the iteration result into a tree diagram, and acquiring energy data corresponding to different radiation events by intercepting the tree diagram.
Further, in a preferred embodiment of the present invention, the step after obtaining the integral map of the energy spectrum under each tube voltage condition further comprises the steps of:
singular value decomposition is carried out on the integral graph, so that an orthogonal matrix formed by a left singular vector and a right singular vector and a diagonal matrix arranged from large to small according to singular values are obtained;
selecting any limit vector in the orthogonal matrix and the diagonal matrix as a construction datum point, and establishing a two-dimensional coordinate system according to the datum point;
the orthogonal matrix formed by the left singular vector and the right singular vector and the diagonal matrix arranged from large to small according to singular values are imported into the two-dimensional coordinate system for feature transformation, and feature vectors of the orthogonal matrix and the diagonal matrix are generated;
and acquiring coordinate information of the feature vector in the two-dimensional coordinate system, generating a new coordinate number set according to the coordinate information of the feature vector, acquiring a limit coordinate point number set of the new coordinate number set, and importing the limit coordinate point number set into a world coordinate system for recombination to obtain an integration map after redundancy reduction.
The second aspect of the present invention discloses a system for correcting the pixel count rate of a CdZnTe photon count detector, the system for correcting the pixel count rate comprises a memory and a processor, wherein a pixel count rate correction method program is stored in the memory, and when the pixel count rate correction method program is executed by the processor, the following steps are realized:
presetting a counting rate linear interval of each pixel of the detector, and ensuring that the X-ray dose detected by each pixel in the imaging process is in the linear interval; prefabricating an ideal counting rate curve, and defining an interval between the ideal counting rate curve and 20% of the interval deviated from the ideal counting rate curve as an effective linear area of each pixel;
performing full-threshold scanning when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, obtaining an integral graph of energy spectrums under the condition of each tube voltage, and subtracting the count rate under the former threshold from the count rate under the latter threshold based on the integral graph to obtain a differential graph of each energy spectrum;
according to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage;
obtaining detector thresholds corresponding to the tube voltages of 50kVp, 60kVp, 70kVp and 80kVp respectively according to the step S106, and performing least square straight line fitting on the four detector thresholds to obtain an expression between energy and the thresholds;
And selecting an optimal threshold value for the photon counting detector according to an expression between the energy and the threshold value, and correcting the counting rate of each pixel of the detector according to the optimal threshold value.
Further, in a preferred embodiment of the present invention, full threshold scanning is performed at tube voltages of 50kVp, 60kVp, 70kVp and 80kVp, respectively, resulting in an integral map of the energy spectrum under each tube voltage condition, specifically:
setting the threshold voltage of the detector to a preset value;
setting the tube voltages to 50kVp, 60kVp, 70kVp, and 80kVp in sequence, and at each tube voltage condition, ensuring that energy data for the radiation event is acquired within a full threshold range and delivering the acquired energy data into a database;
after the collection is finished, classifying the energy data belonging to different radiation events in the database to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube;
and carrying out accumulated summation on energy data corresponding to different radiation events under each tube voltage condition to draw an integral graph of energy spectrum under each tube voltage condition.
Further, in a preferred embodiment of the present invention, the energy data belonging to different radiation events in the database is classified to obtain the energy data corresponding to different radiation events under each tube voltage condition, which specifically includes:
Acquiring each energy data in a database, and performing dimension reduction processing on each energy data so that each energy data is represented in a data vector form; constructing a coordinate system, mapping the data vector into the coordinate system, so that each energy data is represented in the coordinate system in the form of data points;
regarding each data point as an independent cluster, acquiring a coordinate value of each cluster, calculating Manhattan distance between clusters according to the coordinate value, and establishing a distance matrix according to the Manhattan distance between the clusters;
screening out two clusters with the Manhattan distance nearest to the distance matrix, and combining the two clusters with the Manhattan distance nearest to the distance matrix into one cluster; after the combination is finished, updating the distance matrix and recalculating the Manhattan distance between the aggregation; stopping iteration until the aggregation is equal to the preset aggregation quantity, and generating an iteration result;
and visualizing the iteration result into a tree diagram, and acquiring energy data corresponding to different radiation events by intercepting the tree diagram.
The invention solves the technical defects existing in the background technology, and has the following beneficial effects: the method can eliminate the deviation of nonlinear response and counting rate in the detector and improve the accuracy and reliability of energy spectrum measurement; through pixel count rate correction, more accurate and consistent pixel count rate data can be obtained, so that the CdZnTe photon count detector can more uniformly detect and measure photons among pixels, and the accuracy and reliability of spectrum analysis, imaging and other applications can be improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a first method flow diagram of a method for correcting the pixel count rate of a CdZnTe photon-counting detector;
FIG. 2 is a second method flow chart of a method for correcting the pixel count rate of a CdZnTe photon-counting detector;
FIG. 3 is a third method flow chart of a method for correcting the pixel count rate of a CdZnTe photon-counting detector;
FIG. 4 is a system block diagram of a CdZnTe photon-counting detector pixel-counting-rate correction system.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention discloses a method for correcting the pixel count rate of a CdZnTe photon count detector, comprising the following steps:
s102: presetting a counting rate linear interval of each pixel of the detector, and ensuring that the X-ray dose detected by each pixel in the imaging process is in the linear interval; prefabricating an ideal counting rate curve, and defining an interval between the ideal counting rate curve and 20% of the interval deviated from the ideal counting rate curve as an effective linear area of each pixel;
s104: performing full-threshold scanning when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, obtaining an integral graph of energy spectrums under the condition of each tube voltage, and subtracting the count rate under the former threshold from the count rate under the latter threshold based on the integral graph to obtain a differential graph of each energy spectrum;
s106: according to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage;
S108: obtaining detector thresholds corresponding to the tube voltages of 50kVp, 60kVp, 70kVp and 80kVp respectively according to the step S106, and performing least square straight line fitting on the four detector thresholds to obtain an expression between energy and the thresholds;
s110: and selecting an optimal threshold value for the photon counting detector according to an expression between the energy and the threshold value, and correcting the counting rate of each pixel of the detector according to the optimal threshold value.
It should be noted that the counting principle of the CdZnTe photon counting detector and the energy spectrum type CdZnTe detector have no essential difference in the working principle. Similarly, after the radiation interacts with the CdZnTe crystal, a certain number of electron-hole pairs are excited. Under the action of the external electric field, electrons and holes drift towards the detector in two stages to form induced charges. The main difference between the working principle of photon counting type CdZnTe detector and the working principle of energy spectrum type CdZnTe detector is the difference of back-end signal processing electronics. For a photon counting type CdZnTe detector, after the formed induced charge pulse sequentially enters a sensitive charge amplifying circuit and a pulse shaping circuit, a Gaussian pulse signal which is in direct proportion to the energy of the incident photon is output, then the pulse signal is input into a comparator, the pulse signal is compared with a set threshold voltage, and finally photons falling into each threshold range (energy zone) are counted, and a counting result is output. By using the counting result of the multi-energy region, the multi-energy spectrum imaging of the X-rays can be realized. Photon counting type detectors are often used in the field of X-ray imaging, and are often designed as pixel type detectors according to the requirements of application scenes on image resolution. Typically, the resolution of the final X-ray imaging is determined by the X-ray source focus and detector pixel size together. It is worth noting that X-ray imaging modules typically require miniaturization, portability, and integration, with the electronics being partially concentrated in a dedicated readout chip (Application Specific Integrated Circuit, ASIC). Parameters such as the channel number, the single-channel saturation count rate, the count rate linear interval, the energy linearity, the number of energy regions, the dividing mode and the like of the chip can influence the application effect of the CdZnTe photon counting detection system.
According to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage, wherein the threshold value is specifically as follows:
searching in a differential graph of the energy spectrum to obtain a corresponding energy peak, and marking the energy peak as MaxC; wherein the energy peak is the maximum count value;
searching in a differential graph of the energy spectrum to obtain A, B points with the count of 0.2 times of MaxC and 0.1 times of MaxC;
the A, B points are connected and prolonged, and the horizontal axis is intersected with the C point, and the horizontal axis of the C point is the threshold value corresponding to the maximum energy of the detector under the current tube voltage.
It should be noted that, the integral graph of the energy spectrum refers to a graph obtained by cumulatively summing the energy spectrum, in the energy spectrum, each energy channel records the number of radiation events detected in a corresponding energy range, and the integral graph of the energy spectrum cumulatively sums the counts of each energy channel to obtain a cumulative count that increases with the change of energy. An integral map of the energy spectrum may be used to infer the characteristics and properties of the radiation source by extracting important information about the energy characteristics, such as energy peak position and relative intensity.
It should be noted that, the differential graph of the energy spectrum refers to a derivative graph of the energy spectrum, which represents the change rate of energy distribution relative to energy, in the energy spectrum, each energy channel records the number of radiation events detected in a corresponding energy range, and the differential graph of the energy spectrum performs differential operation on the energy spectrum, so as to calculate the change rate of energy channel count; a differential plot of the energy spectrum may be used to show the rate of change, i.e., slope or rate of change, between the energy channels. Which shows the relative intensity variation of radiation events over different energy ranges. It helps to detect the energy peak position, peak shape and energy distribution characteristics of the radiation event, thereby providing energy information of the radiation source and the relative intensity of the radiation event.
Wherein the expression between the energy and the threshold is: t=k×e+v; wherein K, V is two coefficient matrixes in the primary term expression respectively; t represents a threshold value; e represents energy; the threshold value corresponding to each detector pixel under unified energy can be calculated through the expression between the energy and the threshold value.
Wherein, the optimal threshold value is selected for the photon counting detector according to the expression between the energy and the threshold value, specifically:
because of the inconsistency among the detector pixels, the energy corresponding to the detector pixels in different thresholds is calculated according to the expression between the energy and the thresholds, the average energy is obtained, and finally the average energy is substituted into the expression between the energy and the thresholds to obtain the optimal threshold corresponding to each detector pixel.
After obtaining the relation between the energy of each pixel and the threshold value, a suitable high-energy threshold value and a suitable low-energy threshold value can be selected for the photon counting detector. Because of the inconsistency between the detector pixels, the energy corresponding to each detector pixel at different thresholds needs to be calculated according to the relation between the energy and the threshold. And obtaining the average energy, and finally substituting the average energy into a relation between the energy and the threshold value to obtain the threshold value corresponding to each detector pixel.
Wherein, when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, the full threshold scanning is carried out, and an integral graph of the energy spectrum under each tube voltage condition is obtained, specifically:
setting the threshold voltage of the detector to a preset value to ensure that as many radiation events as possible can be detected;
setting the tube voltages to 50kVp, 60kVp, 70kVp, and 80kVp in sequence, and at each tube voltage condition, ensuring that energy data for the radiation event is acquired within a full threshold range and delivering the acquired energy data into a database;
after the collection is finished, classifying the energy data belonging to different radiation events in the database to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube;
and carrying out accumulated summation on energy data corresponding to different radiation events under each tube voltage condition to draw an integral graph of energy spectrum under each tube voltage condition.
Where a radiation event refers to a process in which a radiation particle or photon interacts with a substance, energy transfer and/or scattering occurs when the radiation particle or photon interacts with the substance, these processes are referred to as radiation events. The energy data includes energy characteristics, intensity, etc.
It should be noted that the tube voltages were set to 50kVp, 60kVp, 70kVp, and 80kVp in order, ensuring that each tube voltage was stable; setting the threshold voltage of the detector to a lower value to ensure that as many radiation events as possible can be detected, and gradually increasing the threshold voltage to adjust the sensitivity of the detector; using appropriate data acquisition software or instrumentation to begin acquiring energy data for the radiation event, ensuring data acquisition within a full threshold range (i.e., without energy cutting) under each tube voltage condition; for each tube voltage condition, the counts of each energy channel are cumulatively summed to obtain an integral map, the energy channels are gradually increased, and the count of the current energy channel is added to the count of the last channel. And respectively drawing curves between the energy channels under each tube voltage condition and the corresponding accumulated count value according to the accumulated count value, wherein each tube voltage condition corresponds to one integral graph. The energy distribution at different tube voltages can be compared and analyzed by means of the integral map to obtain information about the characteristics of the radiation event and the nature of the radiation source.
The energy data belonging to different radiation events in the database are classified to obtain energy data corresponding to different radiation events under the voltage condition of each tube, as shown in fig. 2, specifically:
S202: acquiring each energy data in a database, and performing dimension reduction processing on each energy data so that each energy data is represented in a data vector form; constructing a coordinate system, mapping the data vector into the coordinate system, so that each energy data is represented in the coordinate system in the form of data points;
s204: regarding each data point as an independent cluster, acquiring a coordinate value of each cluster, calculating Manhattan distance between clusters according to the coordinate value, and establishing a distance matrix according to the Manhattan distance between the clusters;
s206: screening out two clusters with the Manhattan distance nearest to the distance matrix, and combining the two clusters with the Manhattan distance nearest to the distance matrix into one cluster; after the combination is finished, updating the distance matrix and recalculating the Manhattan distance between the aggregation; stopping iteration until the aggregation is equal to the preset aggregation quantity, and generating an iteration result;
s208: and visualizing the iteration result into a tree diagram, and acquiring energy data corresponding to different radiation events by intercepting the tree diagram.
When the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, the energy data detected by the continuous scanning needs to be continuously transmitted to the database which is built by extraction in order to improve the scanning efficiency, and after the scanning is finished, the energy data in the database is disordered, if the energy data corresponding to 50kVp are not known, the energy data corresponding to 60kVp are not known, and the energy data detected by the energy channels are not known, so the energy data in the database need to be classified by a hierarchical clustering method to obtain the energy data corresponding to different radiation events under each tube voltage condition. By the method, the disordered energy data in the database can be classified rapidly, the robustness of the system is improved, and the data processing efficiency is improved.
As shown in fig. 3, the steps after obtaining the integral map of the energy spectrum under each tube voltage condition further include the steps of:
s302: singular value decomposition is carried out on the integral graph, so that an orthogonal matrix formed by a left singular vector and a right singular vector and a diagonal matrix arranged from large to small according to singular values are obtained;
s304: selecting any limit vector in the orthogonal matrix and the diagonal matrix as a construction datum point, and establishing a two-dimensional coordinate system according to the datum point;
s306: the orthogonal matrix formed by the left singular vector and the right singular vector and the diagonal matrix arranged from large to small according to singular values are imported into the two-dimensional coordinate system for feature transformation, and feature vectors of the orthogonal matrix and the diagonal matrix are generated;
s308: and acquiring coordinate information of the feature vector in the two-dimensional coordinate system, generating a new coordinate number set according to the coordinate information of the feature vector, acquiring a limit coordinate point number set of the new coordinate number set, and importing the limit coordinate point number set into a world coordinate system for recombination to obtain an integration map after redundancy reduction.
It should be noted that in the integral graph of the energy spectrum, the energy distribution of the signal is generally uneven, and some frequencies may have higher energy, while other frequencies are lower. The integration of the signal in the frequency domain results in a concentration of frequency energy. This means that some high frequency components will be inundated with energy of lower frequency components during integration, resulting in a redundant representation. This redundancy may affect the accurate extraction and reconstruction of the frequency information. In addition, in an integral graph of the energy spectrum, the frequency components of the signal generally change over time. In the integral map processing, time introduces additional phase drift, which leads to distortion and distortion of the frequency components. Such phase drift may affect the accuracy of the frequency information, resulting in distortion and distortion of the frequency spectrum. Therefore, in order to reduce the influence of redundancy and drift, the integral graph needs to be corrected and subjected to redundancy reduction processing so as to better process the integral graph of the spectrum and improve the effect thereof, so that more accurate and stable frequency representation can be obtained, and the effect of signal processing is improved.
In addition, the pixel count rate correction method of the CdZnTe photon count detector further comprises the following steps of:
acquiring imaging image information corrected by the pixel count rate of a detector, and performing feature extraction processing on the imaging image information through a SIFT algorithm to obtain a plurality of corner points;
calculating outlier scores of the corner points through an isolated forest algorithm, and eliminating the corner points with the outlier scores larger than a preset outlier score to obtain sparse corner points;
randomly selecting one sparse angular point as a coordinate origin, establishing a three-dimensional coordinate system according to the coordinate origin, acquiring three-dimensional coordinate values of each sparse angular point, and calculating according to the three-dimensional coordinate values to obtain Chebyshev distances among the sparse angular points;
pairing every two sparse corner points according to the Chebyshev distance to obtain a plurality of sparse corner point pairs; acquiring coordinate midpoints of each sparse corner pair, and marking the coordinate midpoints as supplementary corner points;
generating dense angular points according to the complement angular points and the sparse angular points, acquiring three-dimensional coordinate values of each dense angular point, importing the three-dimensional coordinate values of each dense angular point into three-dimensional modeling software, and reconstructing to obtain an imaging three-dimensional model diagram;
Prefabricating a standard imaging three-dimensional model diagram, constructing a pairing space, and importing the standard imaging three-dimensional model diagram and the imaging three-dimensional model diagram into the pairing space for pairing;
after pairing is completed, rejecting a model superposition area of the standard imaging three-dimensional model diagram and the imaging three-dimensional model diagram, and reserving a model non-superposition area of the standard imaging three-dimensional model diagram and the imaging three-dimensional model diagram to obtain an imaging model deviation diagram;
calculating a volume value of the imaging model deviation graph, and comparing the volume value with a preset volume value; if the volume value is larger than a preset volume value, determining a model deviation area based on the imaging model deviation graph, determining a corresponding correction abnormal pixel area according to the model deviation area, and re-correcting the pixel count rate of the correction abnormal pixel area.
It should be noted that SIFT, which is collectively referred to as "Scale-Invariant Feature Transform", is a feature extraction algorithm commonly used in the field of computer vision to detect key points in an image and generate corner points having invariance to Scale, rotation, and brightness variations. The corner points of outlier drift can be screened out through an isolated forest algorithm, so that the subsequent modeling accuracy is improved. The main idea of the isolated forest is to identify outliers by constructing a random binary tree, so that outliers in the data can be detected quickly and effectively. By analyzing the abnormal region in the imaging effect graph, the benefit of the pixel count rate correction effect of the photon count detector can be judged, if the abnormal region is obviously reduced or eliminated, and the imaging result is more uniform, has no artifact and no geometric distortion, which indicates that the pixel count rate correction is effective for improving the imaging quality and accuracy, and is beneficial to the performance optimization method of the photon count detector, otherwise, the pixel region corresponding to the abnormal region needs to be corrected continuously.
The method specifically comprises the following steps of:
acquiring correction abnormal pixel areas corresponding to each preset model deviation graph through a big data network, constructing a knowledge graph, and importing the correction abnormal pixel areas corresponding to each preset model deviation graph into the knowledge graph;
importing the imaging model deviation graph into the knowledge graph, and calculating the similarity between the imaging model deviation graph and each preset model deviation graph through a Euclidean distance algorithm to obtain a plurality of similarities;
and extracting the maximum similarity from the plurality of similarities, acquiring a preset model deviation map corresponding to the maximum similarity, and determining a corresponding correction abnormal pixel region according to the preset model deviation map corresponding to the maximum similarity.
By means of the method, the corresponding abnormal correction pixel area can be rapidly determined through the knowledge graph construction mode, the system operation efficiency can be improved, and the robustness is improved.
As shown in fig. 4, the second aspect of the present invention discloses a CdZnTe photon counting detector pixel counting rate correction system, which comprises a memory 41 and a processor 62, wherein a pixel counting rate correction method program is stored in the memory 41, and when the pixel counting rate correction method program is executed by the processor 62, the following steps are implemented:
Presetting a counting rate linear interval of each pixel of the detector, and ensuring that the X-ray dose detected by each pixel in the imaging process is in the linear interval; prefabricating an ideal counting rate curve, and defining an interval between the ideal counting rate curve and 20% of the interval deviated from the ideal counting rate curve as an effective linear area of each pixel;
performing full-threshold scanning when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, obtaining an integral graph of energy spectrums under the condition of each tube voltage, and subtracting the count rate under the former threshold from the count rate under the latter threshold based on the integral graph to obtain a differential graph of each energy spectrum;
according to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage;
obtaining detector thresholds corresponding to the tube voltages of 50kVp, 60kVp, 70kVp and 80kVp respectively according to the step S106, and performing least square straight line fitting on the four detector thresholds to obtain an expression between energy and the thresholds;
and selecting an optimal threshold value for the photon counting detector according to an expression between the energy and the threshold value, and correcting the counting rate of each pixel of the detector according to the optimal threshold value.
Further, in a preferred embodiment of the present invention, full threshold scanning is performed at tube voltages of 50kVp, 60kVp, 70kVp and 80kVp, respectively, resulting in an integral map of the energy spectrum under each tube voltage condition, specifically:
Setting the threshold voltage of the detector to a preset value to ensure that as many radiation events as possible can be detected;
setting the tube voltages to 50kVp, 60kVp, 70kVp, and 80kVp in sequence, and at each tube voltage condition, ensuring that energy data for the radiation event is acquired within a full threshold range and delivering the acquired energy data into a database;
after the collection is finished, classifying the energy data belonging to different radiation events in the database to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube;
and carrying out accumulated summation on energy data corresponding to different radiation events under each tube voltage condition to draw an integral graph of energy spectrum under each tube voltage condition.
Further, in a preferred embodiment of the present invention, the energy data belonging to different radiation events in the database is classified to obtain the energy data corresponding to different radiation events under each tube voltage condition, which specifically includes:
acquiring each energy data in a database, and performing dimension reduction processing on each energy data so that each energy data is represented in a data vector form; constructing a coordinate system, mapping the data vector into the coordinate system, so that each energy data is represented in the coordinate system in the form of data points;
Regarding each data point as an independent cluster, acquiring a coordinate value of each cluster, calculating Manhattan distance between clusters according to the coordinate value, and establishing a distance matrix according to the Manhattan distance between the clusters;
screening out two clusters with the Manhattan distance nearest to the distance matrix, and combining the two clusters with the Manhattan distance nearest to the distance matrix into one cluster; after the combination is finished, updating the distance matrix and recalculating the Manhattan distance between the aggregation; stopping iteration until the aggregation is equal to the preset aggregation quantity, and generating an iteration result;
and visualizing the iteration result into a tree diagram, and acquiring energy data corresponding to different radiation events by intercepting the tree diagram.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The method for correcting the pixel count rate of the CdZnTe photon count detector is characterized by comprising the following steps of:
s102: presetting a counting rate linear interval of each pixel of the detector, and ensuring that the X-ray dose detected by each pixel in the imaging process is in the linear interval; prefabricating an ideal counting rate curve, and defining an interval between the ideal counting rate curve and 20% of the interval deviated from the ideal counting rate curve as an effective linear area of each pixel;
s104: performing full-threshold scanning when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, obtaining an integral graph of energy spectrums under the condition of each tube voltage, and subtracting the count rate under the former threshold from the count rate under the latter threshold based on the integral graph to obtain a differential graph of each energy spectrum;
s106: according to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage;
s108: obtaining detector thresholds corresponding to the tube voltages of 50kVp, 60kVp, 70kVp and 80kVp respectively according to the step S106, and performing least square straight line fitting on the four detector thresholds to obtain an expression between energy and the thresholds;
s110: and selecting an optimal threshold value for the photon counting detector according to an expression between the energy and the threshold value, and correcting the counting rate of each pixel of the detector according to the optimal threshold value.
2. The method for correcting the pixel count rate of the CdZnTe photon counting detector according to claim 1, wherein the threshold corresponding to the maximum energy of the detector under each tube voltage is retrieved according to a differential graph of each energy spectrum, specifically:
searching in a differential graph of the energy spectrum to obtain a corresponding energy peak, and marking the energy peak as MaxC; wherein the energy peak is the maximum count value;
searching in a differential graph of the energy spectrum to obtain A, B points with the count of 0.2 times of MaxC and 0.1 times of MaxC;
the A, B points are connected and prolonged, and the horizontal axis is intersected with the C point, and the horizontal axis of the C point is the threshold value corresponding to the maximum energy of the detector under the current tube voltage.
3. A CdZnTe photon counting detector pixel count rate correction method according to claim 1, wherein the expression between the energy and threshold is: t=k×e+v; wherein K, V is two coefficient matrixes in the primary term expression respectively; t represents a threshold value; e represents energy; the threshold value corresponding to each detector pixel under unified energy can be calculated through the expression between the energy and the threshold value.
4. A method for correcting the pixel count rate of a CdZnTe photon counting detector according to claim 1, characterized by selecting an optimum threshold for the photon counting detector according to the expression between the energy and the threshold, in particular:
Because of the inconsistency among the detector pixels, the energy corresponding to the detector pixels in different thresholds is calculated according to the expression between the energy and the thresholds, the average energy is obtained, and finally the average energy is substituted into the expression between the energy and the thresholds to obtain the optimal threshold corresponding to each detector pixel.
5. The method for correcting the pixel count rate of a CdZnTe photon counting detector according to claim 1, wherein the full-threshold scanning is performed when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, and an integral graph of the energy spectrum under each tube voltage condition is obtained, specifically:
setting the threshold voltage of the detector to a preset value;
setting the tube voltages to 50kVp, 60kVp, 70kVp, and 80kVp in sequence, and at each tube voltage condition, ensuring that energy data for the radiation event is acquired within a full threshold range and delivering the acquired energy data into a database;
after the collection is finished, classifying the energy data belonging to different radiation events in the database to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube;
and carrying out accumulated summation on energy data corresponding to different radiation events under each tube voltage condition to draw an integral graph of energy spectrum under each tube voltage condition.
6. The method for correcting the pixel count rate of the CdZnTe photon counting detector according to claim 5, wherein the energy data belonging to different radiation events in the database are classified to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube, specifically:
s202: acquiring each energy data in a database, and performing dimension reduction processing on each energy data so that each energy data is represented in a data vector form; constructing a coordinate system, mapping the data vector into the coordinate system, so that each energy data is represented in the coordinate system in the form of data points;
s204: regarding each data point as an independent cluster, acquiring a coordinate value of each cluster, calculating Manhattan distance between clusters according to the coordinate value, and establishing a distance matrix according to the Manhattan distance between the clusters;
s206: screening out two clusters with the Manhattan distance nearest to the distance matrix, and combining the two clusters with the Manhattan distance nearest to the distance matrix into one cluster; after the combination is finished, updating the distance matrix and recalculating the Manhattan distance between the aggregation; stopping iteration until the aggregation is equal to the preset aggregation quantity, and generating an iteration result;
S208: and visualizing the iteration result into a tree diagram, and acquiring energy data corresponding to different radiation events by intercepting the tree diagram.
7. A method of correcting the pixel count rate of a CdZnTe photon counting detector according to claim 1, wherein the step after obtaining an integral map of the energy spectrum under each tube voltage condition further comprises the steps of:
singular value decomposition is carried out on the integral graph, so that an orthogonal matrix formed by a left singular vector and a right singular vector and a diagonal matrix arranged from large to small according to singular values are obtained;
selecting any limit vector in the orthogonal matrix and the diagonal matrix as a construction datum point, and establishing a two-dimensional coordinate system according to the datum point;
the orthogonal matrix formed by the left singular vector and the right singular vector and the diagonal matrix arranged from large to small according to singular values are imported into the two-dimensional coordinate system for feature transformation, and feature vectors of the orthogonal matrix and the diagonal matrix are generated;
and acquiring coordinate information of the feature vector in the two-dimensional coordinate system, generating a new coordinate number set according to the coordinate information of the feature vector, acquiring a limit coordinate point number set of the new coordinate number set, and importing the limit coordinate point number set into a world coordinate system for recombination to obtain an integration map after redundancy reduction.
8. The system for correcting the pixel count rate of the CdZnTe photon count detector is characterized by comprising a memory and a processor, wherein a pixel count rate correction method program is stored in the memory, and when the pixel count rate correction method program is executed by the processor, the following steps are realized:
presetting a counting rate linear interval of each pixel of the detector, and ensuring that the X-ray dose detected by each pixel in the imaging process is in the linear interval; prefabricating an ideal counting rate curve, and defining an interval between the ideal counting rate curve and 20% of the interval deviated from the ideal counting rate curve as an effective linear area of each pixel;
performing full-threshold scanning when the tube voltages are respectively 50kVp, 60kVp, 70kVp and 80kVp, obtaining an integral graph of energy spectrums under the condition of each tube voltage, and subtracting the count rate under the former threshold from the count rate under the latter threshold based on the integral graph to obtain a differential graph of each energy spectrum;
according to the differential graph of each energy spectrum, searching to obtain a threshold value corresponding to the maximum energy of the detector under each tube voltage;
obtaining detector thresholds corresponding to the tube voltages of 50kVp, 60kVp, 70kVp and 80kVp respectively according to the step S106, and performing least square straight line fitting on the four detector thresholds to obtain an expression between energy and the thresholds;
And selecting an optimal threshold value for the photon counting detector according to an expression between the energy and the threshold value, and correcting the counting rate of each pixel of the detector according to the optimal threshold value.
9. A CdZnTe photon counting detector pixel count rate correction system according to claim 8, wherein full threshold scanning is performed at tube voltages of 50kVp, 60kVp, 70kVp and 80kVp, respectively, resulting in an integral map of the energy spectrum under each tube voltage condition, in particular:
setting the threshold voltage of the detector to a preset value;
setting the tube voltages to 50kVp, 60kVp, 70kVp, and 80kVp in sequence, and at each tube voltage condition, ensuring that energy data for the radiation event is acquired within a full threshold range and delivering the acquired energy data into a database;
after the collection is finished, classifying the energy data belonging to different radiation events in the database to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube;
and carrying out accumulated summation on energy data corresponding to different radiation events under each tube voltage condition to draw an integral graph of energy spectrum under each tube voltage condition.
10. The system for correcting the pixel count rate of the CdZnTe photon counting detector according to claim 9, wherein the energy data belonging to different radiation events in the database are classified to obtain the energy data corresponding to the different radiation events under the voltage condition of each tube, specifically:
Acquiring each energy data in a database, and performing dimension reduction processing on each energy data so that each energy data is represented in a data vector form; constructing a coordinate system, mapping the data vector into the coordinate system, so that each energy data is represented in the coordinate system in the form of data points;
regarding each data point as an independent cluster, acquiring a coordinate value of each cluster, calculating Manhattan distance between clusters according to the coordinate value, and establishing a distance matrix according to the Manhattan distance between the clusters;
screening out two clusters with the Manhattan distance nearest to the distance matrix, and combining the two clusters with the Manhattan distance nearest to the distance matrix into one cluster; after the combination is finished, updating the distance matrix and recalculating the Manhattan distance between the aggregation; stopping iteration until the aggregation is equal to the preset aggregation quantity, and generating an iteration result;
and visualizing the iteration result into a tree diagram, and acquiring energy data corresponding to different radiation events by intercepting the tree diagram.
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Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002181947A (en) * | 2000-12-19 | 2002-06-26 | Aloka Co Ltd | Radiation measuring device |
| CN101297221A (en) * | 2005-10-28 | 2008-10-29 | 皇家飞利浦电子股份有限公司 | Method and apparatus for spectral computed tomography |
| WO2009043003A1 (en) * | 2007-09-28 | 2009-04-02 | Ev Products, Inc. | Correction method for photon counting imaging system |
| JP2011085479A (en) * | 2009-10-15 | 2011-04-28 | Tele Systems:Kk | Calibration device for photon counting type radiation detector and calibration method thereof |
| US20110311022A1 (en) * | 2010-06-22 | 2011-12-22 | Siemens Aktiengesellschaft | Counting Detector And Computed Tomography System |
| US20140185781A1 (en) * | 2013-01-02 | 2014-07-03 | Silke Reitz | Method for Calibrating a Counting Digital X-Ray Detector, X-Ray System for Performing Such a Method and Method for Acquiring an X-Ray Image |
| CN203824901U (en) * | 2014-05-18 | 2014-09-10 | 西北工业大学 | Device for detecting built-in electric field of telluride semiconductor detector |
| US20140270073A1 (en) * | 2013-03-12 | 2014-09-18 | Martin Spahn | Method and System for Acquiring an X-Ray Image |
| CN104076050A (en) * | 2013-03-25 | 2014-10-01 | 株式会社理学 | X-ray analyzing apparatus |
| US20150323687A1 (en) * | 2014-05-12 | 2015-11-12 | Purdue Research Foundation | Linear fitting of multi-threshold counting data |
| US20160195623A1 (en) * | 2015-01-07 | 2016-07-07 | Kabushiki Kaisha Toshiba | Apparatus and method for computing detector response of a photon-counting detector |
| CN106989835A (en) * | 2017-04-12 | 2017-07-28 | 东北大学 | Photon counting X-ray energy spectrum detection device and imaging system based on compressed sensing |
| US20210113178A1 (en) * | 2019-10-18 | 2021-04-22 | Canon Medical Systems Corporation | Method and apparatus for computed tomography (ct) and material decomposition with count-rate dependent pileup correction |
| CN114089409A (en) * | 2021-11-11 | 2022-02-25 | 上海联影医疗科技股份有限公司 | Detector correction method and system |
| CN114515160A (en) * | 2021-12-10 | 2022-05-20 | 兰州大学 | Cadmium zinc telluride positron emission tomography system and signal correction algorithm |
| US20220233162A1 (en) * | 2021-01-22 | 2022-07-28 | Canon Medical Systems Corporation | Counting response and beam hardening calibration method for a full size photon-counting ct system |
| CN115844432A (en) * | 2023-03-02 | 2023-03-28 | 武汉联影生命科学仪器有限公司 | Scanning method for CT device, photon counting detector and energy spectrum CT system |
| CN116088031A (en) * | 2022-12-30 | 2023-05-09 | 陕西翱翔辐射探测科技有限公司 | CdZnTe detector counting rate test method and device based on photocurrent response |
-
2023
- 2023-10-31 CN CN202311423917.8A patent/CN117148416B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002181947A (en) * | 2000-12-19 | 2002-06-26 | Aloka Co Ltd | Radiation measuring device |
| CN101297221A (en) * | 2005-10-28 | 2008-10-29 | 皇家飞利浦电子股份有限公司 | Method and apparatus for spectral computed tomography |
| WO2009043003A1 (en) * | 2007-09-28 | 2009-04-02 | Ev Products, Inc. | Correction method for photon counting imaging system |
| JP2011085479A (en) * | 2009-10-15 | 2011-04-28 | Tele Systems:Kk | Calibration device for photon counting type radiation detector and calibration method thereof |
| US20110311022A1 (en) * | 2010-06-22 | 2011-12-22 | Siemens Aktiengesellschaft | Counting Detector And Computed Tomography System |
| US20140185781A1 (en) * | 2013-01-02 | 2014-07-03 | Silke Reitz | Method for Calibrating a Counting Digital X-Ray Detector, X-Ray System for Performing Such a Method and Method for Acquiring an X-Ray Image |
| US20140270073A1 (en) * | 2013-03-12 | 2014-09-18 | Martin Spahn | Method and System for Acquiring an X-Ray Image |
| CN104076050A (en) * | 2013-03-25 | 2014-10-01 | 株式会社理学 | X-ray analyzing apparatus |
| US20150323687A1 (en) * | 2014-05-12 | 2015-11-12 | Purdue Research Foundation | Linear fitting of multi-threshold counting data |
| CN203824901U (en) * | 2014-05-18 | 2014-09-10 | 西北工业大学 | Device for detecting built-in electric field of telluride semiconductor detector |
| US20160195623A1 (en) * | 2015-01-07 | 2016-07-07 | Kabushiki Kaisha Toshiba | Apparatus and method for computing detector response of a photon-counting detector |
| CN106989835A (en) * | 2017-04-12 | 2017-07-28 | 东北大学 | Photon counting X-ray energy spectrum detection device and imaging system based on compressed sensing |
| US20210113178A1 (en) * | 2019-10-18 | 2021-04-22 | Canon Medical Systems Corporation | Method and apparatus for computed tomography (ct) and material decomposition with count-rate dependent pileup correction |
| US20220233162A1 (en) * | 2021-01-22 | 2022-07-28 | Canon Medical Systems Corporation | Counting response and beam hardening calibration method for a full size photon-counting ct system |
| CN114089409A (en) * | 2021-11-11 | 2022-02-25 | 上海联影医疗科技股份有限公司 | Detector correction method and system |
| CN114515160A (en) * | 2021-12-10 | 2022-05-20 | 兰州大学 | Cadmium zinc telluride positron emission tomography system and signal correction algorithm |
| CN116088031A (en) * | 2022-12-30 | 2023-05-09 | 陕西翱翔辐射探测科技有限公司 | CdZnTe detector counting rate test method and device based on photocurrent response |
| CN115844432A (en) * | 2023-03-02 | 2023-03-28 | 武汉联影生命科学仪器有限公司 | Scanning method for CT device, photon counting detector and energy spectrum CT system |
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