Gradient signal detection method, system, device, computer equipment and storage medium

Abstract

The present application relates to a gradient signal detection method, system, device, computer equipment and storage medium, the method obtains the gradient data of the gradient signal to be measured; calculates the detection parameters of the gradient signal to be measured according to the gradient data; and judges whether the gradient signal to be measured is normal according to the detection parameters and the preset parameter range. The gradient signal detection method provided by the present application judges whether the gradient signal to be measured is normal by matching the detection parameters of the gradient signal to be measured with the preset parameter range. In this way, the misjudgment caused by manual detection of the gradient signal to be measured can be avoided, thereby improving the accuracy of detecting whether the gradient signal to be measured is normal.

Description

Technical Field

[0001] The present application relates to the technical field of magnetic resonance systems, and in particular to a gradient signal detection method, system, device, computer equipment and storage medium. Background Art

[0002] In magnetic resonance imaging equipment, gradient signals are generated by gradient plates. However, due to problems such as the production process of the gradient plates, the signal emission interval of the gradient plates may deviate, which may cause the gradient signals sent by the gradient plates to be abnormal. When the signal emission interval of the gradient plates deviates, it will affect the safety of the patients. Therefore, it is necessary to detect whether the signal emission interval of the gradient plates under fixed instructions deviates.

[0003] In the conventional technology, the method for detecting whether the signal transmission interval of the gradient board card is deviated is to obtain gradient data by performing normal protocol scanning through the magnetic resonance system, convert the gradient data into a gradient image, and use the naked eye to check whether there are burrs in the gradient waveform in the gradient image to determine whether the gradient signal is normal, thereby determining whether the signal transmission interval of the gradient board card is deviated.

[0004] However, when detecting gradient signals in conventional technologies, there is a problem of low detection accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a gradient signal detection method, system, device, computer equipment and storage medium to address the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides a gradient signal detection method, comprising:

[0007] Acquiring gradient data of the gradient signal to be measured; the gradient data is obtained by sampling the gradient signal to be measured;

[0008] Calculating detection parameters of the gradient signal to be measured according to the gradient data;

[0009] According to the detection parameters and the preset parameter range, it is determined whether the gradient signal to be tested is normal.

[0010] In one embodiment, calculating the detection parameters of the gradient signal to be measured according to the gradient data includes:

[0011] Analyze the gradient data to obtain sampling points of the gradient signal to be measured, where the sampling points include sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time;

[0012] According to the sampling points, the detection parameters are calculated.

[0013] In one embodiment, the detection parameters are calculated according to the sampling points, including:

[0014] Calculate the sub-climbing rate of each rising edge and the number of rising edges in the gradient signal to be measured, and the sub-falling rate of each falling edge and the number of falling edges in the gradient signal to be measured according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time;

[0015] According to the sub-climbing rates and the number of rising edges, the average of all sub-climbing rates in the gradient signal to be measured is calculated to obtain the climbing rate;

[0016] and / or,

[0017] According to the sub-decline rates and the number of falling edges, the average of all sub-decline rates in the gradient signal to be measured is calculated to obtain the decline rate.

[0018] In one embodiment, calculating the detection parameters according to the sampling points also includes:

[0019] Determine each sub-platform region of the gradient signal to be measured, the maximum amplitude value of all sub-platform regions, and the minimum amplitude value of all sub-platform regions according to the sampling time and the amplitude value of the gradient signal to be measured corresponding to the sampling time;

[0020] Calculate the mean of the amplitudes of all sub-platform areas to obtain the average amplitude of the platform area;

[0021] and / or,

[0022] The difference between the maximum and minimum values ​​is calculated to obtain the amplitude of the plateau area.

[0023] In one embodiment, judging whether the gradient signal to be tested is normal according to the detection parameter and the preset parameter range includes:

[0024] Determine whether the climbing rate of the gradient signal to be measured is within a preset climbing rate range, whether the falling rate of the gradient signal to be measured is within a preset falling rate range, whether the average amplitude of the platform area of ​​the gradient signal to be measured is within a preset average amplitude range, and whether the amplitude of the platform area of ​​the gradient signal to be measured is within a preset amplitude range.

[0025] In one embodiment, obtaining gradient data of a gradient signal to be measured includes:

[0026] Read the gradient data to be parsed from the storage device;

[0027] The gradient data to be analyzed are analyzed to determine the gradient data belonging to the gradient signal to be measured.

[0028] In a second aspect, an embodiment of the present application provides a gradient signal detection system, comprising: a computer device and a magnetic resonance device, the magnetic resonance device comprising a transmitting board and a gradient board;

[0029] A computer device for sending control information to the gradient board via the transmitter board;

[0030] A gradient board card is used to receive control information and generate a gradient signal according to the control signal;

[0031] The computer device is also used to receive the gradient signal and store the gradient data corresponding to the gradient signal according to the gradient signal to execute the steps of the gradient signal detection method provided in the above embodiment.

[0032] In a third aspect, an embodiment of the present application provides a gradient signal detection device, comprising:

[0033] An acquisition module is used to acquire gradient data of the gradient signal to be measured; the gradient data is obtained by sampling the gradient signal to be measured;

[0034] A calculation module, used for calculating the detection parameters of the gradient signal to be measured according to the gradient data;

[0035] The determination module is used to determine whether the gradient signal to be tested is normal according to the detection parameters and the preset parameter range.

[0036] In a fourth aspect, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method provided in the above embodiment are implemented.

[0037] In a fifth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method provided in the above embodiment when the computer program is executed by a processor.

[0038] The embodiment of the present application provides a gradient signal detection method, system, device, computer equipment and storage medium, the method obtains the gradient data of the gradient signal to be measured; calculates the detection parameters of the gradient signal to be measured according to the gradient data; and judges whether the gradient signal to be measured is normal according to the detection parameters and the preset parameter range. The gradient signal detection method provided by the present application detects whether the gradient signal to be measured is normal through the detection parameters of the gradient signal to be measured and the preset parameter range. In this way, it can avoid the misjudgment that may exist when manually judging whether the gradient signal to be measured is normal, thereby improving the accuracy of detecting whether the gradient signal to be measured is normal, and further improving the accuracy of detecting whether the signal transmission interval of the gradient board card is deviated. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For different technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A schematic diagram of the steps of a gradient signal detection method provided in one embodiment of the present application;

[0041] Figure 2 A schematic flow chart of the steps of a gradient signal detection method provided in another embodiment of the present application;

[0042] Figure 3 A schematic flow chart of the steps of a gradient signal detection method provided in another embodiment of the present application;

[0043] Figure 4 A schematic diagram of a gradient signal provided for one embodiment of the present application;

[0044] Figure 5 A schematic diagram of a step flow chart of a gradient signal detection method provided in yet another embodiment of the present application;

[0045] Figure 6 A schematic diagram of a step flow chart of a gradient signal detection method provided in yet another embodiment of the present application;

[0046] Figure 7 A schematic diagram of the structure of a gradient signal detection system provided in one embodiment of the present application;

[0047] Figure 8 A schematic diagram of the structure of a gradient signal detection device provided in one embodiment of the present application;

[0048] Fig. 9 A schematic diagram of the structure of a computer device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0050] When using a magnetic resonance device for magnetic resonance imaging, a gradient signal needs to be generated by a gradient board. However, due to problems such as the production process of the gradient board, the signal emission interval of the gradient board may deviate, which may cause the gradient signal sent by the gradient board to be abnormal. When the signal emission interval of the gradient board deviates, it will affect the safety of the patient. Therefore, it is necessary to detect whether the signal emission interval under the fixed instruction of the gradient board deviates.

[0051] In the traditional technology, the method for detecting whether the signal emission interval of the gradient plate card is deviated is mainly: the normal scanning protocol is executed by the scanning software of the magnetic resonance system to obtain gradient data, the gradient data is converted into a gradient image by a data image conversion tool, and then the gradient waveform in the gradient image is manually judged to determine whether the gradient signal is normal. If the gradient waveform has burrs, it means that the gradient waveform is unqualified, and the gradient signal is abnormal. However, manual judgment may be misjudged due to manual fatigue or unclear image viewing with the naked eye, which will result in low accuracy in detecting whether the gradient signal is normal. In this regard, the present application proposes a gradient signal detection method.

[0052] The gradient signal detection method provided in the present application can be implemented by a computer device. The computer device includes but is not limited to a control chip, a personal computer, a laptop computer, a smart phone, a tablet computer, and a portable wearable device. The method provided in the present application can be implemented by JAVA software and can also be applied to other software.

[0053] The technical solution of the present application and how the technical solution of the present application solves the technical problem are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0054] See also Figure 1 An embodiment of the present application provides a gradient signal detection method. This embodiment uses a computer device as an execution subject to specifically illustrate the gradient signal detection method. The steps of the method include:

[0055] Step 100: Obtain gradient data of the gradient signal to be measured; the gradient data is obtained by sampling the gradient signal to be measured.

[0056] The gradient signal to be tested refers to the gradient signal sent by the gradient board to be tested. The gradient board to be tested sends the gradient signal through different channels when transmitting the gradient signal. The gradient signal to be tested can be a gradient signal sent by one channel of the gradient board to be tested, or it can be a gradient signal sent by multiple channels of the gradient board to be tested. The gradient signal to be tested sent by each channel has corresponding gradient data. Gradient data refers to the gradient data obtained by sampling the gradient signal after the gradient board to be tested sends the gradient signal. The computer device obtains the gradient data of the sampled gradient signal to be tested. This embodiment does not impose any restrictions on the specific method for the computer device to obtain the gradient data, as long as it can achieve its function.

[0057] Step 110: Calculate detection parameters of the gradient signal to be measured according to the gradient data.

[0058] Detection parameters refer to parameters related to the gradient signal to be measured, and these parameters can be used to determine whether the gradient signal to be measured is normal. After obtaining the gradient data, the computer device calculates the detection parameters required to determine whether the gradient signal to be measured is normal according to the gradient data. In this embodiment, the detection parameters may include any one or more of the climbing rate, the descent rate, the average amplitude of the platform area, and the amplitude of the platform area. Among them, the climbing rate refers to the rising speed of the rising edge in the gradient waveform corresponding to the gradient signal to be measured; the descent rate refers to the descent speed of the falling edge in the gradient waveform corresponding to the gradient signal to be measured; the platform area refers to the area where the waveform tends to be flat in the gradient waveform corresponding to the gradient signal to be measured. Ideally, the amplitude of the platform area of ​​the gradient waveform is the same, that is, the waveform of the platform area is a horizontal straight line, but in actual scenarios, due to the influence of various factors, the waveform of the platform area will appear as a curve. In this embodiment, the average amplitude of the platform area and the amplitude of the platform area are used to determine whether the waveform of the platform area of ​​the gradient signal to be measured is normal. This embodiment does not limit the specific method of calculating the detection parameters according to the gradient data, as long as its function can be achieved.

[0059] Step 120: Determine whether the gradient signal to be tested is normal according to the detection parameters and the preset parameter range.

[0060] After obtaining the calculated detection parameters, the computer device compares them with the preset parameter range to determine whether the gradient signal to be tested is normal. The preset parameter range corresponds to the detection parameter one-to-one, and the preset parameter range may include any one or more of the preset climbing rate range, the preset descent rate range, the preset average amplitude range, and the preset amplitude range. The climbing rate in the detection parameter corresponds to the preset climbing rate range in the preset parameter range, and the preset climbing rate range refers to the range between the preset minimum and maximum values ​​of the climbing rate; the descent rate in the detection parameter corresponds to the preset descent rate range in the preset parameter range, and the preset descent rate range refers to the range between the preset minimum and maximum values ​​of the descent rate; the average amplitude of the platform area in the detection parameter corresponds to the preset average amplitude in the preset parameter range, and the preset average amplitude refers to the range between the preset minimum and maximum values ​​of the average amplitude; the amplitude of the platform area in the detection parameter corresponds to the preset amplitude range in the preset parameter range, and the preset amplitude range refers to the range between the preset minimum and maximum values ​​of the amplitude. This embodiment does not impose any restrictions on the configuration method of the preset parameter range, as long as its function can be achieved.

[0061] In an optional embodiment, the preset parameter range can be configured by a staff member using a normal gradient board card (a gradient board card with no deviation in the signal transmission interval) to send a gradient signal, sampling the gradient signal to obtain gradient data, and calculating the detection parameters based on the gradient data.

[0062] The gradient signal detection method provided in the embodiment of the present application obtains the gradient data of the gradient signal to be tested; according to the gradient data, calculates the detection parameters of the gradient signal to be tested; and judges whether the gradient signal to be tested is normal according to the detection parameters and the preset parameter range. The gradient signal detection method provided in the present application detects whether the gradient signal to be tested is normal through the detection parameters of the gradient signal to be tested and the preset parameter range. In this way, it can avoid the misjudgment that may exist when manually judging whether the gradient signal to be tested is normal, thereby improving the accuracy of detecting whether the gradient signal to be tested is normal, and then improving the accuracy of detecting whether the signal emission interval of the gradient board card has a deviation. In addition, when the gradient signal detection method provided in the present application is used to detect the gradient signal, it is not necessary for the detection personnel to have a professional technical background, and the detection result can be obtained by directly using a computer device to execute the gradient signal detection method provided in the present application. At the same time, the gradient signal detection method provided in the present application can be applied in different scenarios and has strong practicality. For example, warehouse managers of gradient boards and cards can use the gradient signal detection method provided in this application to detect the gradient boards and cards entering the warehouse to reduce the defective rate; tooling personnel can use the gradient signal detection method provided in this application to detect the gradient boards and cards used in the production integration process.

[0063] See also Figure 2 In one embodiment, a possible implementation method for calculating detection parameters of a gradient signal to be measured based on gradient data is provided, and the specific steps include:

[0064] Step 200: parse the gradient data to obtain sampling points of the gradient signal to be measured, where the sampling points include the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time.

[0065] The gradient data is obtained by sampling the gradient signal to be measured. After obtaining the gradient data, the computer device analyzes it to obtain the sampling point corresponding to the gradient signal to be measured. The sampling point includes the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time. In other words, when sampling the gradient signal to be measured, the sampling is performed based on the time point, that is, the amplitude of the gradient signal corresponding to all time points is obtained by sampling.

[0066] Step 210: Calculate detection parameters according to the sampling points.

[0067] After obtaining the sampling points, the computer device calculates the detection parameters of the gradient signal to be measured according to the sampling time in the sampling points and the amplitude of the gradient signal corresponding to the sampling time. For different detection parameters, the method of calculating the amplitude of the gradient signal using the sampling time and the sampling time is different, and this embodiment does not limit this, as long as its function can be achieved.

[0068] See also Figure 3 When the detection parameter includes a climbing rate and / or a descending rate, in one embodiment, a possible implementation method for calculating the detection parameter according to the sampling point is provided, and the specific steps include:

[0069] Step 300: Calculate the sub-climbing rate of each rising edge and the number of rising edges in the gradient signal to be measured, and the sub-falling rate of each falling edge and the number of falling edges in the gradient signal to be measured according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time.

[0070] The computer device can determine the rising edge, the number of rising edges, the falling edge, and the number of falling edges of the gradient signal to be measured according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time. According to the sampling time corresponding to each rising edge in the gradient signal to be measured and the amplitude of the gradient signal to be measured corresponding to the sampling time, the climbing rate of each rising edge, that is, the sub-climbing rate, can be calculated. Similarly, according to the sampling time corresponding to each falling edge in the gradient signal to be measured and the amplitude of the gradient signal to be measured corresponding to the sampling time, the falling rate of each falling edge can be calculated.

[0071] Step 310: Calculate the average of all sub-climbing rates in the gradient signal to be measured according to the sub-climbing rates and the number of rising edges to obtain the climbing rate;

[0072] And / or, step 320, calculating the average of all sub-decline rates in the gradient signal to be measured according to the sub-decline rates and the number of falling edges to obtain the decline rate.

[0073] After calculating the sub-climbing rate of each rising edge and the number of all rising edges, the computer device calculates the ratio of the sum of the sub-climbing rates corresponding to all rising edges to the number of rising edges, and the climbing rate of the gradient signal to be measured can be obtained. At the same time, the computer device can also calculate the ratio of the sum of the sub-decline rates corresponding to all falling edges to the number of falling edges based on the obtained sub-decline rate of each falling edge and the number of all falling edges, and the decline rate of the gradient signal to be measured can be obtained. The computer device can calculate the climbing rate and the decline rate; it can also calculate only the climbing rate, or only the decline rate.

[0074] Please continue to see Figure 3 When the detection parameter includes the average amplitude of the platform area and / or the amplitude of the platform area, in one embodiment, the method for calculating the detection parameter according to the sampling point further includes:

[0075] Step 330: Determine each sub-platform region of the gradient signal to be measured, the maximum amplitude value of all sub-platform regions, and the minimum amplitude value of all sub-platform regions according to the sampling time and the amplitude value of the gradient signal to be measured corresponding to the sampling time.

[0076] The computer device can determine each sub-platform area in the trapezoidal waveform of the gradient signal to be measured by analyzing the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time. The maximum amplitude of all sub-platform areas and the minimum amplitude of all sub-platform areas can be obtained according to the amplitudes of all sub-platform areas. In other words, the amplitudes of all sub-platform areas are obtained, and all amplitudes are compared to determine the maximum and minimum values ​​of all amplitudes.

[0077] Step 340: Calculate the mean of the amplitudes of all sub-platform areas to obtain the average amplitude of the platform area.

[0078] The computer device can obtain the average amplitude of the platform area corresponding to the gradient signal to be measured by calculating the ratio between the sum of the amplitudes of all sub-platform areas and the number of sampling points corresponding to the amplitudes of all sub-platform areas.

[0079] And / or, step 350, calculating the difference between the maximum value and the minimum value to obtain the amplitude of the platform area.

[0080] The computer device can obtain the amplitude of the platform area corresponding to the gradient signal to be measured by calculating the difference between the maximum amplitude of all sub-platform areas and the minimum amplitude of all sub-platform areas. If the maximum and minimum values ​​obtained by the computer device are negative numbers, when calculating the difference between the maximum and the minimum, the difference between the absolute value of the maximum and the absolute value of the minimum is calculated. The computer device can calculate the average amplitude of the platform area and the amplitude of the platform area, or only calculate the average amplitude of the platform area, or only calculate the average amplitude of the platform area.

[0081] In a specific embodiment, Figure 4 As shown, assuming that there are two rising edges L1 and L2, two sub-platform areas L3 and L4, and two falling edges L5 and L6 in the gradient signal to be measured, the x-axis represents the sampling time, and the y-axis represents the amplitude of the gradient signal to be measured corresponding to the sampling time. 1 ,y 1 ) is the first sampling point of the first rising edge L1, A(x 2 ,y 2 ) is the last sampling point of L1 of the first rising edge, and the sub-climbing rate of the first rising edge can be expressed as: G(x 5 ,y 5 ) is the first sampling point of the second rising edge L2, C(x 6 ,y 6 ) is the last sampling point of the second rising edge L2, and the sub-climbing rate of the second rising edge can be expressed as: The climbing rate of the gradient signal to be measured can be expressed as: B(x 3 ,y 3 ) is the first sampling point of the first falling edge L5, F(x 4 ,y 4 ) is the last sampling point of the first falling edge L5, and the sub-fall rate of the first falling edge L5 can be expressed as D(x 7 ,y 7 ) is the first sampling point of the second falling edge L6, H(x 8 ,y 8 ) is the last sampling point of the second falling edge L6, and the sub-fall rate of the second falling edge L6 can be expressed as The decreasing rate of the gradient signal to be measured can be expressed as: A(x 2 ,y 2 ) is also the first sampling point of the first platform area L3, B(x 3 ,y 3 ) is also the last sampling point of the first platform area L3, C(x6 ,y 6 ) is also the first sampling point of the second platform area L4, D(x 7 ,y 7 ) is also the last sampling point of the second platform area L4. The average amplitude of the platform area is calculated by sampling point A(x 2 ,y 2 ) and sampling point B(x 3 ,y 3 ) and the amplitude corresponding to all sampling points between the sampling points C(x 6 ,y 6 ) and sampling point D(x 7 ,y 7 ) is the average of the amplitudes corresponding to all sampling points between . The amplitude of the platform area is calculated by sampling point A(x 2 ,y 2 ) and sampling point B(x 3 ,y 3 ) and the amplitudes corresponding to all sampling points between , and the sampling point C(x 6 ,y 6 ) and sampling point D(x 7 ,y 7 ) is the difference between the maximum amplitude and the minimum amplitude among the amplitudes corresponding to all sampling points between .

[0082] In this embodiment, the method for calculating the detection parameters of the gradient signal to be measured is simple and easy to understand.

[0083] See also Figure 5 In one embodiment, a possible implementation method is provided for determining whether the gradient signal to be tested is normal according to the detection parameters and the preset parameter range, and the specific steps include:

[0084] Step 500: Determine whether the climbing rate is within a preset climbing rate range, whether the descent rate is within a preset descent rate range, whether the average amplitude of the platform area is within a preset average amplitude range, and whether the amplitude of the platform area is within a preset amplitude range.

[0085] After obtaining the climbing rate, descent rate, average amplitude of the platform area and amplitude of the platform area in the detection parameters through calculation, the computer device compares them with the preset climbing rate range, preset descent rate range, preset average amplitude range and preset amplitude range in the corresponding preset parameter range, and determines whether the climbing rate is within the preset climbing rate range (the range of the preset minimum and maximum values ​​of the climbing rate), whether the descent rate is within the preset descent rate range (the range of the preset minimum and maximum values ​​of the descent rate), whether the average amplitude of the platform area is within the preset average amplitude range (the range of the preset minimum and maximum values ​​of the average amplitude), and whether the amplitude of the platform area is within the preset amplitude range (the range of the preset minimum and maximum values ​​of the amplitude).

[0086] Step 510: If the climbing rate is within the preset climbing range, the descending rate is within the preset descending range, the average amplitude of the platform area is within the preset average amplitude range, and the amplitude of the platform area is within the preset amplitude range, it is determined that the gradient signal to be measured is normal.

[0087] Step 520: If the climbing rate is not within the preset climbing range, or the descent rate is not within the preset descent range, or the average amplitude of the platform area is not within the preset average amplitude range, or the amplitude of the platform area is not within the preset amplitude range, it is determined that the gradient signal to be measured is abnormal.

[0088] If the computer device determines through comparison that the climbing rate is within the preset climbing range, the descent rate is within the preset descent range, the average amplitude of the platform area is within the preset average amplitude range, and the amplitude of the platform area is within the preset amplitude range, that is, when the climbing rate, the descent rate, the average amplitude of the platform area, and the amplitude of the platform area are all within the corresponding ranges, it can be determined that the gradient signal to be tested is normal. If the computer device determines through comparison that the climbing rate is not within the preset climbing range, or the descent rate is not within the preset descent range, or the average amplitude of the platform area is not within the preset average amplitude range, or the amplitude of the platform area is not within the preset amplitude range, that is, when any one of the climbing rate, the descent rate, the average amplitude of the platform area, and the amplitude of the platform area is not within the corresponding range, it can be determined that the gradient signal to be tested is abnormal.

[0089] See also Figure 6 In one embodiment, a possible implementation method for obtaining gradient data of a gradient signal to be measured is provided, and the specific steps include:

[0090] Step 600: Read the gradient data to be analyzed from the storage device.

[0091] Step 610: Analyze the gradient data to be analyzed to determine the gradient data belonging to the gradient signal to be measured.

[0092] The storage device may refer to a storage device in a computer device, or may be an independent external storage device. The storage device stores gradient data obtained by sampling a gradient signal, and the gradient signal includes a gradient signal to be measured, and the gradient data includes gradient data corresponding to the gradient signal to be measured. When the computer device needs to obtain the gradient signal to be measured, it needs to read the gradient data from the storage device. After reading the gradient data from the storage device, the computer device uses it as the gradient data to be analyzed, and analyzes it to obtain the gradient data corresponding to the gradient information to be measured from the gradient data to be analyzed. This embodiment does not impose any restrictions on the specific method for analyzing the gradient data to be analyzed, as long as the gradient data of the gradient signal to be measured can be determined from the gradient data to be solved.

[0093] In this embodiment, the method for obtaining gradient data is simple and convenient.

[0094] In an optional embodiment, the gradient data is stored in a storage device in a "row" manner. When the computer device reads the gradient data to be analyzed from the storage device, it reads the row data. There is a specific field in each row of data, and the specific field can indicate that the row of data is a certain line in the gradient waveform corresponding to the gradient signal emitted by a certain channel of the gradient board. The computer device can obtain the specific field in each row of data by analyzing the read gradient data to be analyzed, so as to determine the gradient data corresponding to the gradient signal to be measured in the gradient data to be analyzed, and determine that the gradient data is obtained by sampling the gradient signal sent by a certain channel of the gradient board to be measured corresponding to the gradient signal to be measured.

[0095] In a specific embodiment, the gradient signal to be tested is a signal sent by three channels of the gradient board to be tested. The results of the gradient signal detection method provided in the embodiment of the present application after detecting the gradient signal to be tested are as follows:

[0096] Channel Name Preset parameter range Detection parameters Comparison results CH3 channel 310±20 298 pass CH4 channel 485±20 510 pass CH5 channel 600±20 579 Not passed

[0097] Table 1 Comparison of climb rates

[0098] Channel Name Preset parameter range Detection parameters Comparison results CH3 channel -310±20 -301 pass CH4 channel -485±20 -491 pass CH5 channel -600±20 -599 pass

[0099] Table 2 Comparison of drop rates

[0100]

[0101] Table 3 Comparison of average amplitude and amplitude in platform area

[0102] The comparison result in the table is passed, indicating that the detection parameters are within the preset parameter range, and the comparison result is failed, indicating that the detection parameters are not within the preset parameter range. It can be seen from the table that the comparison result of the climbing rate of the gradient signal emitted by the CH5 channel of the gradient signal to be tested is failed, indicating that there is a deviation in the signal emission area of ​​the gradient board corresponding to the gradient signal to be tested.

[0103] It should be understood that, although the various steps in the flowchart in the figure are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0104] See also Figure 7 An embodiment of the present application provides a gradient signal detection system 10, which includes a computer device 11 and a magnetic resonance device 12, wherein the magnetic resonance device 12 includes a transmitting board 13 and a gradient board 14. The computer device 11 is used to send control information to the gradient board 14 through the transmitting board 13. The gradient board 14 is used to receive the control information and generate a gradient signal according to the control signal; the computer device 11 is also used to receive the gradient signal, store the gradient data corresponding to the gradient signal according to the gradient signal, so as to perform the steps of the gradient signal detection method provided in the above embodiment.

[0105] The transmitting board 13 is a board to be detected. When the transmitting board 12 needs to be detected, the computer device 11 sends a control signal to the transmitting board 13 to control the transmitting board 13 to send control information to the gradient board 14. After receiving the control information, the gradient board 14 generates a gradient signal according to the information. After receiving the gradient signal sent by the gradient board 14, the computer device 11 performs the steps of the gradient signal detection method provided in the above embodiment to detect whether the gradient signal sent by the gradient board 14 is normal, thereby detecting whether the signal transmission interval of the gradient board 14 is deviated.

[0106] In an optional embodiment, after receiving the gradient signal, the computer device 11 samples it to obtain gradient data, and stores the gradient data in a hard disk, so that the computer device 11 can read the gradient data from the hard disk when executing the gradient signal detection method.

[0107] In an optional embodiment, the gradient signal detection system 10 further includes a safety monitoring board connected between the gradient board 13 and the computer device 11. The safety monitoring board is used to receive the gradient signal, sample it to obtain gradient data, and send the gradient data to the computer device 11.

[0108] The gradient signal detection system 10 provided in the present application executes the steps of the gradient signal detection method provided in the above embodiment, and the gradient signal detection system 10 has all the beneficial effects of the gradient signal detection method, which will not be described in detail here.

[0109] See also Figure 8 An embodiment of the present application provides a gradient signal detection device 20, which includes an acquisition module 21, a calculation module 22 and a determination module 23.

[0110] The acquisition module 21 is used to acquire the gradient data of the gradient signal to be measured; the gradient data is obtained by sampling the gradient signal to be measured;

[0111] The calculation module 22 is used to calculate the detection parameters of the gradient signal to be measured according to the gradient data; the detection parameters include the climbing rate, the descending rate, the average amplitude of the platform area and the amplitude of the platform area;

[0112] The determination module 23 is used to determine whether the gradient signal to be measured is normal according to the detection parameters and the preset parameter range; the preset parameter range includes a preset climbing rate range, a preset descent rate range, a preset average amplitude range and a preset amplitude range.

[0113] In one embodiment, the calculation module 22 includes an analysis unit and a calculation unit. The analysis unit is used to analyze the gradient data to obtain sampling points of the gradient signal to be measured. The sampling points include the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time; the calculation unit is used to calculate the detection parameters according to the sampling points.

[0114] In one embodiment, the calculation unit is specifically used to calculate the sub-climbing rate of each rising edge and the number of rising edges in the gradient signal to be measured, and the sub-decline rate of each falling edge and the number of falling edges in the gradient signal to be measured according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time; calculate the average of all sub-climbing rates in the gradient signal to be measured according to the sub-climbing rate and the number of rising edges to obtain the climbing rate; calculate the average of all sub-decline rates in the gradient signal to be measured according to the sub-decline rate and the number of falling edges to obtain the decline rate.

[0115] In one embodiment, the calculation unit is further used to determine each sub-platform area of ​​the gradient signal to be measured, as well as the maximum amplitude of all sub-platform areas and the minimum amplitude of all sub-platform areas according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time; calculate the mean of the amplitudes of all sub-platform areas to obtain the average amplitude of the platform area; calculate the difference between the maximum value and the minimum value to obtain the amplitude of the platform area.

[0116] In one embodiment, the determination module 23 includes a judgment unit and a determination unit. The judgment unit is used to judge whether the climbing rate is within a preset climbing rate range, whether the descent rate is within a preset descent rate range, whether the average amplitude of the platform area is within a preset average amplitude range, and whether the amplitude of the platform area is within a preset amplitude range; the determination unit is used to determine that the gradient signal to be measured is normal if the climbing rate is within the preset climbing range, the descent rate is within the preset descent range, the average amplitude of the platform area is within the preset average amplitude range, and the amplitude of the platform area is within the preset amplitude range.

[0117] In one embodiment, the acquisition module 21 is specifically used to read the gradient data to be analyzed from the storage device; analyze the gradient data to be analyzed, and determine the gradient data belonging to the gradient signal to be measured.

[0118] For the specific definition of the above-mentioned gradient signal detection device 20, please refer to the definition of the gradient signal detection method above, which will not be repeated here. Each module in the gradient signal detection device 20 can be implemented in whole or in part by software, hardware, or a combination thereof. The above-mentioned devices, modules, or units can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned devices or modules.

[0119] See also Fig. 9 In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store gradient data and preset parameter ranges, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer device is executed by the processor, a gradient signal detection method is implemented.

[0120] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0121] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0122] Acquiring gradient data of the gradient signal to be measured; the gradient data is obtained by sampling the gradient signal to be measured;

[0123] Calculate the detection parameters of the gradient signal to be measured according to the gradient data; the detection parameters include the climbing rate, the descending rate, the average amplitude of the platform area and the amplitude of the platform area;

[0124] Whether the gradient signal to be tested is normal is determined based on the detection parameters and the preset parameter range; the preset parameter range includes a preset climbing rate range, a preset descent rate range, a preset average amplitude range and a preset amplitude range.

[0125] In one embodiment, when the processor executes the computer program, the following steps are further implemented: parsing the gradient data to obtain sampling points of the gradient signal to be measured, the sampling points including the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time; and calculating the detection parameters according to the sampling points.

[0126] In one embodiment, when the processor executes the computer program, the following steps are further implemented: according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time, the sub-climbing rate of each rising edge and the number of rising edges in the gradient signal to be measured, as well as the sub-decline rate of each falling edge and the number of falling edges in the gradient signal to be measured are calculated; according to the sub-climbing rate and the number of rising edges, the average of all sub-climbing rates in the gradient signal to be measured is calculated to obtain the climbing rate; according to the sub-decline rate and the number of falling edges, the average of all sub-decline rates in the gradient signal to be measured is calculated to obtain the decline rate.

[0127] In one embodiment, when the processor executes the computer program, the following steps are further implemented: determining each sub-platform area of ​​the gradient signal to be measured, as well as the maximum amplitude of all sub-platform areas and the minimum amplitude of all sub-platform areas according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time; calculating the mean of the amplitudes of all sub-platform areas to obtain the average amplitude of the platform area; and calculating the difference between the maximum value and the minimum value to obtain the amplitude of the platform area.

[0128] In one embodiment, when the processor executes the computer program, the following steps are also implemented: determining whether the climbing rate is within a preset climbing rate range, whether the descent rate is within a preset descent rate range, whether the average amplitude of the platform area is within a preset average amplitude range, and whether the amplitude of the platform area is within a preset amplitude range; if the climbing rate is within the preset climbing range, the descent rate is within the preset descent range, the average amplitude of the platform area is within the preset average amplitude range, and the amplitude of the platform area is within the preset amplitude range, then determining that the gradient signal to be measured is normal.

[0129] In one embodiment, when the processor executes the computer program, the following steps are further implemented: reading the gradient data to be analyzed from the storage device; analyzing the gradient data to be analyzed to determine the gradient data belonging to the gradient signal to be measured.

[0130] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0131] Acquiring gradient data of the gradient signal to be measured; the gradient data is obtained by sampling the gradient signal to be measured;

[0132] Calculate the detection parameters of the gradient signal to be measured according to the gradient data; the detection parameters include the climbing rate, the descending rate, the average amplitude of the platform area and the amplitude of the platform area;

[0133] Whether the gradient signal to be tested is normal is determined based on the detection parameters and the preset parameter range; the preset parameter range includes a preset climbing rate range, a preset descent rate range, a preset average amplitude range and a preset amplitude range.

[0134] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: parsing the gradient data to obtain sampling points of the gradient signal to be measured, the sampling points including the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time; and calculating the detection parameters according to the sampling points.

[0135] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time, the sub-climbing rate of each rising edge and the number of rising edges in the gradient signal to be measured, as well as the sub-decline rate of each falling edge and the number of falling edges in the gradient signal to be measured are calculated; according to the sub-climbing rate and the number of rising edges, the average of all the sub-climbing rates in the gradient signal to be measured is calculated to obtain the climbing rate; according to the sub-decline rate and the number of falling edges, the average of all the sub-decline rates in the gradient signal to be measured is calculated to obtain the decline rate.

[0136] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining each sub-platform area of ​​the gradient signal to be measured, as well as the maximum amplitude of all sub-platform areas and the minimum amplitude of all sub-platform areas according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time; calculating the mean of the amplitudes of all sub-platform areas to obtain the average amplitude of the platform area; and calculating the difference between the maximum value and the minimum value to obtain the amplitude of the platform area.

[0137] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining whether the climbing rate is within a preset climbing rate range, whether the descent rate is within a preset descent rate range, whether the average amplitude of the platform area is within a preset average amplitude range, and whether the amplitude of the platform area is within a preset amplitude range; if the climbing rate is within the preset climbing range, the descent rate is within the preset descent range, the average amplitude of the platform area is within the preset average amplitude range, and the amplitude of the platform area is within the preset amplitude range, then determining that the gradient signal to be measured is normal.

[0138] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: reading the gradient data to be analyzed from the storage device; analyzing the gradient data to be analyzed to determine the gradient data belonging to the gradient signal to be measured.

[0139] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0140] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A gradient plate detection system, characterized in that: It includes a computer device and a magnetic resonance device, wherein the magnetic resonance device includes a transmitting board and a gradient board to be tested; The computer device is used to send control information to the gradient board to be tested through the transmitting board; The gradient board to be tested is used to receive the control information and generate a gradient signal to be tested according to the control information; The computer device is further used to receive the gradient signal to be measured, and store gradient data corresponding to the gradient signal to be measured according to the gradient signal to be measured; the gradient data is obtained by sampling the gradient signal to be measured; Calculate detection parameters of the gradient signal to be measured according to the gradient data; the detection parameters include a climbing rate, a descending rate, an average amplitude of a platform area, and an amplitude of a platform area, wherein the climbing rate refers to a rising speed of a rising edge in a gradient waveform corresponding to the gradient signal to be measured, the descending rate refers to a descending speed of a falling edge in a gradient waveform corresponding to the gradient signal to be measured, and the platform area refers to an area where a waveform tends to be flat in a gradient waveform corresponding to the gradient signal to be measured is located; According to the detection parameters and the preset parameter range, judging whether the gradient signal to be tested is normal, so as to judge whether the signal transmission interval of the gradient board to be tested deviates; The computer device is specifically used to read the gradient data to be analyzed from the storage device; analyze the gradient data to be analyzed, and determine the gradient data belonging to the gradient signal to be measured.

2. The detection system according to claim 1, characterized in that: The computer device is also specifically used for: Parsing the gradient data to obtain sampling points of the gradient signal to be measured, wherein the sampling points include a sampling time and an amplitude of the gradient signal to be measured corresponding to the sampling time; The detection parameters are calculated according to the sampling points.

3. The detection system according to claim 2, characterized in that: The computer device is also specifically used for: Calculating, according to the sampling time and the amplitude of the gradient signal to be measured corresponding to the sampling time, a sub-climbing rate of each rising edge in the gradient signal to be measured and the number of the rising edges, and a sub-falling rate of each falling edge in the gradient signal to be measured and the number of the falling edges; According to the sub-climbing rates and the number of rising edges, calculating the average of all sub-climbing rates in the gradient signal to be measured to obtain the climbing rate; According to the sub-decline rates and the number of the falling edges, the average of all sub-decline rates in the gradient signal to be measured is calculated to obtain the decline rate.

4. The detection system according to claim 2, characterized in that: The computer device is also specifically used for: Determine each sub-platform region of the gradient signal to be measured, as well as the maximum amplitude value of all the sub-platform regions and the minimum amplitude value of all the sub-platform regions according to the sampling time and the amplitude value of the gradient signal to be measured corresponding to the sampling time; Calculating the mean of the amplitudes of all the sub-platform regions to obtain the average amplitude of the platform region; The difference between the maximum value and the minimum value is calculated to obtain the amplitude of the platform area.

5. The detection system according to claim 1, characterized in that: The computer device is also specifically used for: Determine whether the climbing rate of the gradient signal to be measured is within a preset climbing rate range, whether the falling rate of the gradient signal to be measured is within a preset falling rate range, whether the average amplitude of the platform area of ​​the gradient signal to be measured is within a preset average amplitude range, and whether the amplitude of the platform area of ​​the gradient signal to be measured is within a preset amplitude range.

6. The detection system according to claim 5, characterized in that: The computer device is also specifically used for: If the climbing rate is within the preset climbing rate range, the descending rate is within the preset descending rate range, the average amplitude of the platform area is within the preset average amplitude range, and the amplitude of the platform area is within the preset amplitude range, then it is determined that the gradient signal to be tested is normal; If the climbing rate is not within the preset climbing rate range, or the descent rate is not within the preset descent rate range, or the average amplitude of the platform area is not within the preset average amplitude range, or the amplitude of the platform area is not within the preset amplitude range, it is determined that the gradient signal to be measured is abnormal.

7. The detection system according to claim 1, characterized in that: The computer device is also specifically used for: Reading row data of the to-be-analyzed gradient data from the storage device; Parsing the gradient data to be parsed to obtain a specific field in each row of data; the specific field is used to indicate that the row of data belongs to a waveform line in a trapezoidal waveform corresponding to a transmission channel; Gradient data belonging to the gradient signal to be measured is determined according to the specific field.

8. The detection system according to claim 1, characterized in that: The preset parameter range is configured based on normal detection parameters calculated based on normal gradient data obtained by sampling the gradient signal sent by the normal gradient board.

9. The detection system according to claim 1, characterized in that: The detection system further comprises a safety monitoring board connected between the gradient board to be tested and the computer device; The safety monitoring board is used to receive the gradient signal to be measured, sample the gradient signal to be measured to obtain the gradient data, and send the gradient data to the computer device.

10. The detection system according to claim 1, characterized in that: The computer device is further specifically used to determine that the gradient data is acquired by collecting the gradient signal sent by the preset channel of the gradient board to be tested corresponding to the gradient signal to be tested.

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