阅读说明：本技术 梯度信号检测方法、系统、装置、计算机设备和存储介质 (Gradient signal detection method, system, apparatus, computer device and storage medium ) 是由 李建华 于 2021-08-13 设计创作，主要内容包括：本申请涉及一种梯度信号检测方法、系统、装置、计算机设备和存储介质,该方法通过获取待测梯度信号的梯度数据；根据梯度数据,计算待测梯度信号的检测参数；根据检测参数与预设参数范围,判断待测梯度信号是否正常。本申请提供的梯度信号检测方法通过对待测梯度信号的检测参数与预设参数范围进行匹配,判断待测梯度信号是否正常。这样可以避免人工对待测梯度信号进行检测时会造成的误判,从而能够提高检测待测梯度信号是否正常的准确度。(The application relates to a gradient signal detection method, a system, a device, a computer device and a storage medium, wherein the method comprises the steps of obtaining gradient data of a gradient signal to be detected; calculating the detection parameters of the gradient signals to be detected according to the gradient data; and judging whether the gradient signal to be detected is normal or not according to the detection parameter and the preset parameter range. The gradient signal detection method provided by the application judges whether the gradient signal to be detected is normal or not by matching the detection parameter of the gradient signal to be detected with a preset parameter range. Therefore, misjudgment caused by manual detection of the gradient signal to be detected can be avoided, and the accuracy of detecting whether the gradient signal to be detected is normal can be improved.)

1. A gradient signal detection method, comprising:

acquiring gradient data of a gradient signal to be detected; the gradient data is obtained by sampling the gradient signal to be detected;

calculating the detection parameters of the gradient signal to be detected according to the gradient data;

and judging whether the gradient signal to be detected is normal or not according to the detection parameter and a preset parameter range.

2. The method according to claim 1, wherein the calculating the detection parameters of the gradient signal to be detected according to the gradient data comprises:

analyzing the gradient data to obtain sampling points of the gradient signal to be detected, wherein the sampling points comprise sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time;

and calculating the detection parameters according to the sampling points.

3. The gradient signal detecting method according to claim 2, wherein said calculating the detection parameter based on the sampling points includes:

calculating the sub-climbing rate of each rising edge in the gradient signal to be detected and the number of the rising edges according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time, and calculating the sub-descending rate of each falling edge in the gradient signal to be detected and the number of the falling edges;

calculating the average value of all the sub climbing rates in the gradient signal to be detected according to the sub climbing rates and the number of the rising edges to obtain the climbing rate;

and/or the presence of a gas in the gas,

and calculating the average value of all the sub-descending rates in the gradient signal to be detected according to the sub-descending rates and the number of the descending edges to obtain the descending rate.

4. The gradient signal detecting method according to claim 2, wherein said calculating the detection parameter based on the sampling points further comprises:

determining each sub-platform region of the gradient signal to be detected, the maximum value of the amplitudes of all the sub-platform regions and the minimum value of the amplitudes of all the sub-platform regions according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time;

calculating the average value of the amplitudes of all the sub-platform areas to obtain the average amplitude of the platform area; and/or the presence of a gas in the gas,

and calculating the difference value between the maximum value and the minimum value to obtain the amplitude of the platform area.

5. The method for detecting a gradient signal according to claim 1, wherein the determining whether the gradient signal to be detected is normal according to the detection parameter and a preset parameter range comprises:

judging whether the climbing rate of the gradient signal to be detected is within a preset climbing rate range, whether the descending rate of the gradient signal to be detected is within a preset descending rate range, whether the average amplitude of the platform area of the gradient signal to be detected is within a preset average amplitude range, and whether the amplitude of the platform area of the gradient signal to be detected is within a preset amplitude range.

6. The method according to claim 1, wherein the acquiring gradient data of the gradient signal to be detected comprises:

reading gradient data to be analyzed from a storage device;

and analyzing the gradient data to be analyzed, and determining the gradient data belonging to the gradient signal to be analyzed.

7. A gradient signal detection system, comprising: the magnetic resonance equipment comprises a transmitting board card and a gradient board card;

the computer equipment is used for sending control information to the gradient board card through the transmitting board card;

the gradient board card is used for receiving the control information and generating a gradient signal according to the control signal;

the computer device is further configured to receive the gradient signal, and store gradient data corresponding to the gradient signal according to the gradient signal to perform the steps of the gradient signal detection method according to any one of claims 1 to 6.

8. A gradient signal detecting apparatus, comprising:

the acquisition module is used for acquiring gradient data of a gradient signal to be detected; the gradient data is obtained by sampling the gradient signal to be detected;

the calculation module is used for calculating the detection parameters of the gradient signals to be detected according to the gradient data;

and the determining module is used for judging whether the gradient signal to be detected is normal or not according to the detection parameters and the preset parameter range.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.

Technical Field

The present application relates to the field of magnetic resonance system technology, and in particular, to a method, a system, an apparatus, a computer device, and a storage medium for gradient signal detection.

Background

In the magnetic resonance equipment, a gradient signal is generated through a gradient board card, but the signal emission interval of the gradient board card is deviated due to the problems of production technology and the like of the gradient board card, so that the gradient signal sent by the gradient board card is abnormal. The safety of a patient can be affected when the signal emission interval of the gradient board card deviates, and therefore whether the signal emission interval of the gradient board card deviates under a fixed instruction needs to be detected.

In the conventional technology, a method for detecting whether a signal emission interval of a gradient board card is deviated includes that a magnetic resonance system executes normal protocol scanning to obtain gradient data, the gradient data is converted into a gradient image, and whether burrs appear on gradient waveforms in the gradient image is checked through naked eyes to determine whether the gradient signal is normal, so that whether the signal emission interval of the gradient board card is deviated is determined.

However, when the gradient signal is detected in the conventional technology, the detection accuracy is low.

Disclosure of Invention

In view of the above, it is necessary to provide a gradient signal detection method, system, apparatus, computer device and storage medium for solving the above technical problems.

In a first aspect, an embodiment of the present application provides a gradient signal detection method, including:

acquiring gradient data of a gradient signal to be detected; the gradient data is obtained by sampling a gradient signal to be detected;

calculating the detection parameters of the gradient signals to be detected according to the gradient data;

and judging whether the gradient signal to be detected is normal or not according to the detection parameter and the preset parameter range.

In one embodiment, calculating the detection parameters of the gradient signal to be detected according to the gradient data comprises:

analyzing the gradient data to obtain sampling points of the gradient signal to be detected, wherein the sampling points comprise sampling time and amplitude values of the gradient signal to be detected corresponding to the sampling time;

and calculating detection parameters according to the sampling points.

In one embodiment, calculating the detection parameters according to the sampling points comprises:

calculating the sub-climbing rate and the number of rising edges of each rising edge in the gradient signal to be detected and the sub-falling rate and the number of falling edges of each falling edge in the gradient signal to be detected according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time;

calculating the average value of all the sub-climbing rates in the gradient signal to be detected according to the sub-climbing rates and the number of the rising edges to obtain the climbing rate;

and/or the presence of a gas in the gas,

and calculating the average value of all the sub-descending rates in the gradient signal to be detected according to the sub-descending rates and the number of the descending edges to obtain the descending rate.

In one embodiment, calculating the detection parameters according to the sampling points further includes:

determining each sub-platform region of the gradient signal to be detected, the maximum value of the amplitudes of all the sub-platform regions and the minimum value of the amplitudes of all the sub-platform regions according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time;

calculating the average value of the amplitudes of all the sub-platform areas to obtain the average amplitude of the platform area;

and/or the presence of a gas in the gas,

and calculating the difference between the maximum value and the minimum value to obtain the amplitude of the platform area.

In one embodiment, the determining whether the gradient signal to be detected is normal according to the detection parameter and the preset parameter range includes:

judging whether the climbing rate of the gradient signal to be detected is within a preset climbing rate range, whether the descending rate of the gradient signal to be detected is within a preset descending rate range, whether the average amplitude of the platform area of the gradient signal to be detected is within a preset average amplitude range, and whether the amplitude of the platform area of the gradient signal to be detected is within a preset amplitude range.

In one embodiment, acquiring gradient data of a gradient signal to be measured includes:

reading gradient data to be analyzed from a storage device;

and analyzing the gradient data to be analyzed, and determining the gradient data belonging to the gradient signal to be analyzed.

In a second aspect, an embodiment of the present application provides a gradient signal detection system, including: the magnetic resonance equipment comprises an emission board card and a gradient board card;

the computer equipment is used for sending control information to the gradient board card through the transmitting board card;

the gradient board card is used for receiving the control information and generating a gradient signal according to the control signal;

the computer device is further configured to receive the gradient signal, and store gradient data corresponding to the gradient signal according to the gradient signal to perform the steps of the gradient signal detection method provided in the above embodiment.

In a third aspect, an embodiment of the present application provides a gradient signal detection apparatus, including:

the acquisition module is used for acquiring gradient data of a gradient signal to be detected; the gradient data is obtained by sampling a gradient signal to be detected;

the calculation module is used for calculating the detection parameters of the gradient signals to be detected according to the gradient data;

and the determining module is used for judging whether the gradient signal to be detected is normal or not according to the detection parameter and the preset parameter range.

In a fourth aspect, an embodiment of the present application provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method provided in the foregoing embodiment when executing the computer program.

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

The embodiment of the application provides a method, a system, a device, computer equipment and a storage medium for detecting a gradient signal, wherein the method comprises the steps of obtaining gradient data of a gradient signal to be detected; calculating the detection parameters of the gradient signals to be detected according to the gradient data; and judging whether the gradient signal to be detected is normal or not according to the detection parameter and a preset parameter range. The gradient signal detection method provided by the application detects whether the gradient signal to be detected is normal or not through the detection parameter and the preset parameter range of the gradient signal to be detected. Therefore, the misjudgment condition that the gradient signal to be detected can be judged normally or not manually can be avoided, the accuracy of detecting whether the gradient signal to be detected is normal or not can be improved, and the accuracy of detecting whether the signal emission interval of the gradient board card deviates or not can be improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the description of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic flowchart illustrating steps of a gradient signal detection method according to an embodiment of the present application;

FIG. 2 is a schematic flow chart illustrating steps of a gradient signal detection method according to another embodiment of the present application;

FIG. 3 is a schematic flowchart illustrating steps of a gradient signal detection method according to another embodiment of the present application;

FIG. 4 is a schematic diagram of a gradient signal provided by an embodiment of the present application;

FIG. 5 is a schematic flowchart illustrating steps of a gradient signal detection method according to another embodiment of the present application;

FIG. 6 is a schematic flowchart illustrating steps of a gradient signal detection method according to another embodiment of the present application;

fig. 7 is a schematic structural diagram of a gradient signal detection system according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a gradient signal detection apparatus according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of embodiments in many different forms than those described herein and that modifications may be made by one skilled in the art without departing from the spirit and scope of the application and it is therefore not intended to be limited to the specific embodiments disclosed below.

When magnetic resonance imaging is carried out by using magnetic resonance equipment, a gradient signal needs to be generated through a gradient board card, but the signal emission interval of the gradient board card deviates due to the problems of production technology and the like of the gradient board card, so that the gradient signal sent by the gradient board card is abnormal. The safety of a patient is affected when the signal transmission interval of the gradient board card deviates, and therefore whether the signal transmission interval under the fixed instruction of the gradient board card deviates or not needs to be detected.

In the conventional technology, the method for detecting whether the signal transmission interval of the gradient board card deviates mainly comprises the following steps: the method comprises the steps of executing a normal scanning protocol through scanning software of a magnetic resonance system to obtain gradient data, converting the gradient data into a gradient image through a data image conversion tool, judging whether a gradient signal is normal or not through manually judging whether burrs appear on a gradient waveform in the gradient image, and if the burrs appear on the gradient waveform, indicating that the gradient waveform is unqualified, judging that the gradient signal is abnormal. However, the manual judgment may have misjudgment due to artificial fatigue or unclear visual image, which may result in low accuracy for detecting whether the gradient signal is normal. In this regard, the present application proposes a gradient signal detection method.

The gradient signal detection method provided by the application can be realized by computer equipment. Computer devices include, but are not limited to, control chips, personal computers, laptops, smartphones, tablets, and portable wearable devices. The method provided by the application can be realized through JAVA software and can also be applied to other software.

The following describes the technical solutions of the present application and how to solve the technical problems with the technical solutions of the present application in detail with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Referring to fig. 1, an embodiment of the present application provides a method for detecting a gradient signal, and the embodiment specifically describes the method for detecting a gradient signal by using a computer device as an execution subject, and the method includes:

step 100, obtaining gradient data of a gradient signal to be detected; the gradient data is obtained by sampling the gradient signal to be measured.

The gradient signal to be detected is a gradient signal sent by the gradient board card to be detected. The gradient board card to be tested is sent through different channels when transmitting the gradient signals. The gradient signal to be detected may be a gradient signal sent by one channel of the gradient board card to be detected, or may be a gradient signal sent by a plurality of channels of the gradient board card to be detected respectively. The gradient signal to be measured sent by each channel has corresponding gradient data. The gradient data refers to that after the gradient board card to be detected sends a gradient signal, the gradient signal is sampled to obtain the gradient data. And the computer equipment acquires gradient data of the gradient signal to be detected, which is obtained by sampling. The embodiment does not limit the specific method for acquiring gradient data by the computer equipment, and only can realize the functions of the method.

And 110, calculating the detection parameters of the gradient signal to be detected according to the gradient data.

The detection parameters refer to parameters related to the gradient signal to be detected, and whether the gradient signal to be detected is normal or not can be judged through the parameters. After the computer equipment obtains the gradient data, calculating and judging the detection parameters required by the normal condition of the gradient signal to be detected according to the gradient data. In this embodiment, the detection parameters may include any one or more of a climb rate, a descent rate, an average amplitude of the platform region, and an amplitude of the platform region. Wherein, the climbing rate refers to the rising speed of the rising edge in the gradient waveform corresponding to the gradient signal to be detected; the falling rate refers to the falling speed of a falling edge in a gradient waveform corresponding to the gradient signal to be detected; the platform area refers to an area where a waveform tending to be gentle in a gradient waveform corresponding to a gradient signal to be detected is located. In an ideal state, the amplitude values of the platform regions of the gradient waveforms are the same, that is, the waveforms of the platform regions are a horizontal straight line, but in an actual scene, due to the influence of various factors, the waveforms of the platform regions may be represented as a curve, and in this embodiment, whether the waveforms of the platform regions of the gradient signals to be detected are normal is determined by the average amplitude value of the platform regions and the amplitude of the platform regions. The present embodiment does not limit the specific method of calculating the detection parameters from the gradient data as long as the function thereof can be achieved.

And step 120, judging whether the gradient signal to be detected is normal or not according to the detection parameter and the preset parameter range.

And after the computer equipment obtains the calculated detection parameters, comparing the detection parameters with a preset parameter range, and judging whether the gradient signal to be detected is normal or not. The preset parameter ranges correspond to the detection parameters one to one, and the preset parameter ranges may include any one or more of a preset climbing rate range, a preset descending rate range, a preset average amplitude range and a preset amplitude range. The climbing rate in the detection parameters corresponds to a preset climbing rate range in a preset parameter range, and the preset climbing rate range refers to a range between the minimum value and the maximum value of the preset climbing rate; the descending rate in the detection parameters corresponds to a preset descending rate range in a preset parameter range, and the preset descending rate range refers to a range between the minimum value and the maximum value of the preset descending rate; detecting the average amplitude of the platform area in the parameters, wherein the average amplitude corresponds to a preset average amplitude in a preset parameter range, and the preset average amplitude refers to the range between the minimum value and the maximum value of the preset average amplitude; the amplitude of the platform region in the detection parameters corresponds to a preset amplitude range within a preset parameter range, and the preset amplitude range refers to a range between a minimum value and a maximum value of a preset amplitude. The present embodiment does not limit the configuration method of the preset parameter range, as long as the function thereof can be realized.

In an alternative embodiment, the preset parameter range may be configured by using a normal gradient board card (a gradient board card with no deviation in the signal emission interval) to send a gradient signal by a worker, sampling the gradient signal to obtain gradient data, and calculating a detection parameter according to the gradient data.

The gradient signal detection method provided by the embodiment of the application obtains the gradient data of the gradient signal to be detected; calculating the detection parameters of the gradient signals to be detected according to the gradient data; and judging whether the gradient signal to be detected is normal or not according to the detection parameter and a preset parameter range. The gradient signal detection method provided by the application detects whether the gradient signal to be detected is normal or not through the detection parameter and the preset parameter range of the gradient signal to be detected. Therefore, the misjudgment condition that the gradient signal to be detected can be judged normally or not manually can be avoided, the accuracy of detecting whether the gradient signal to be detected is normal or not can be improved, and the accuracy of detecting whether the signal emission interval of the gradient board card deviates or not can be improved. In addition, when the gradient signal detection method provided by the application is used for detecting the gradient signal, a detection result can be obtained by directly using computer equipment to execute the gradient signal detection method provided by the application without the need of professional technical background of detection personnel. Meanwhile, the gradient signal detection method provided by the application can be applied to different scenes and has strong practicability. For example, warehouse management personnel of the gradient board card can use the gradient signal detection method provided by the application to detect the gradient board card put in storage, so that the reject ratio is reduced; the gradient signal detection method provided by the application can be used by a worker to detect the gradient board card used in the production integration process.

Referring to fig. 2, in an embodiment, a possible implementation manner of calculating a detection parameter of a gradient signal to be detected according to gradient data is provided, and the specific steps include:

step 200, analyzing the gradient data to obtain sampling points of the gradient signal to be detected, wherein the sampling points comprise sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time.

The gradient data is obtained by sampling the gradient signal to be detected, and the computer equipment analyzes the gradient data after obtaining the gradient data, so that the sampling point corresponding to the gradient signal to be detected can be obtained. The sampling point comprises sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time. That is, when sampling the gradient signal to be measured, sampling is performed according to time points, that is, the amplitude values of the gradient signal corresponding to all the time points are obtained through sampling.

And step 210, calculating detection parameters according to the sampling points.

And after the computer equipment obtains the sampling point, calculating the detection parameter of the gradient signal to be detected according to the sampling time in the sampling point and the amplitude of the gradient signal corresponding to the sampling time. For different detection parameters, methods for calculating the amplitude of the gradient signal using the sampling time and the sampling time are different, and this embodiment is not limited thereto as long as the function thereof can be realized.

Referring to fig. 3, when the detection parameter includes a climbing rate and/or a descending rate, in one embodiment, a possible implementation manner of calculating the detection parameter according to the sampling point is provided, and the specific steps include:

and step 300, calculating the sub climbing rate and the number of the rising edges of each rising edge in the gradient signal to be detected and the sub falling rate and the number of the falling edges of each falling edge in the gradient signal to be detected according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time.

The computer equipment can determine the rising edges, the number of the rising edges, the falling edges and the number of the falling edges of the gradient signals to be detected according to the sampling time and the amplitude of the gradient signals to be detected corresponding to the sampling time. According to the sampling time corresponding to each rising edge in the gradient signal to be detected and the amplitude of the gradient signal to be detected corresponding to the sampling time, the climbing rate of each rising edge, namely the sub-climbing rate, can be calculated. Similarly, the falling rate of each falling edge can be calculated 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.

Step 310, calculating the average value of all sub-climbing rates in the gradient signal to be detected according to the sub-climbing rates and the number of rising edges to obtain the climbing rate;

and/or step 320, calculating the average value of all the sub-falling rates in the gradient signal to be detected according to the sub-falling rates and the number of the falling edges to obtain the falling rate.

After the computer device calculates and obtains the sub-climbing rate of each rising edge and the number of all rising edges, the ratio of the sum of the sub-climbing rates corresponding to all rising edges to the number of the rising edges is calculated, and the climbing rate of the gradient signal to be measured can be obtained. Meanwhile, the computer equipment can also calculate the ratio of the sum of the sub-falling rates corresponding to all falling edges to the number of the falling edges according to the obtained sub-falling rate of each falling edge and the number of all the falling edges, so that the falling rate of the gradient signal to be detected can be obtained. The computer device may calculate a rate of climb and a rate of descent; it is also possible to calculate only the rate of climb, or only the rate of decline.

With continued reference to fig. 3, when the detection parameter includes the average amplitude of the plateau region and/or the amplitude of the plateau region, in one embodiment, the step of calculating the detection parameter according to the sampling points further includes:

step 330, determining each sub-platform region of the gradient signal to be detected, the maximum value of the amplitudes of all sub-platform regions and the minimum value of the amplitudes of all sub-platform regions according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time.

The computer equipment can determine each sub-platform area existing in the trapezoidal waveform of the gradient signal to be detected by analyzing the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time. And acquiring the maximum value of the amplitudes of all the sub-platform regions and the minimum value of the amplitudes of all the sub-platform regions according to the amplitudes of all the sub-platform regions. That is, the amplitudes of all the sub-plateau regions are obtained and compared to determine the maximum and minimum values of all the amplitudes.

Step 340, calculating the average value of the amplitudes of all the sub-platform areas to obtain the average amplitude of the platform area.

The computer device can obtain the average amplitude of the platform region corresponding to the gradient signal to be measured by calculating the ratio of the amplitudes of all the sub-platform regions to the number of sampling points corresponding to the amplitudes of all the sub-platform regions.

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

The computer device can obtain the amplitude of the platform region corresponding to the gradient signal to be detected through calculating the difference between the maximum value of the amplitudes of all the sub-platform regions and the minimum value of the amplitudes of all the sub-platform regions. If the obtained maximum and minimum values of the computer device are negative, then the difference between the maximum and minimum values is calculated as the difference between the absolute and minimum values. The computer device may calculate the average amplitude of the land area and the amplitude of the land area, may calculate only the average amplitude of the land area, or may calculate only the average amplitude of the land area.

In a specific embodiment, as shown in fig. 4, it is assumed that there are two rising edges L1 and L2, two sub-platform regions L3 and L4, and two falling edges L5 and L6 in the gradient signal to be measured, the x axis represents sampling time, and the y axis represents the amplitude of the gradient signal to be measured corresponding to the sampling time. E (x)₁，y₁) The first sample point, A (x), for the first rising edge L1₂，y₂) The sub-climb rate for the first rising edge, which is the last sample point of L1 for the first rising edge, can be expressed as:G(x₅，y₅) The first sample point, C (x), for the second rising edge L2₆，y₆) For the last sampling point of the second rising edge L2, the sub-climb rate of the second rising edge can be expressed as:the rate of climb of the gradient signal under test can be expressed as:B(x₃，y₃) The first sample point, F (x), for the first falling edge L5₄，y₄) The sub-falling rate of the first falling edge L5, which is the last sampling point of the first falling edge L5, can be expressed asD(x₇，y₇) The first sample point, H (x), for the second falling edge L6₈，y₈) The sub-falling rate of the second falling edge L6 can be expressed as the last sampling point of the second falling edge L6The rate of fall of the gradient signal under test can be expressed as:A(x₂，y₂) Also the first sample point of the first land area L3, B (x)₃，y₃) Also the last sample point, C (x), of the first land area L3₆，y₆) Is also the first sample point, D (x), of the second plateau region L4₇，y₇) Which is also the last sample point of the second land area L4. The average amplitude of the plateau region is calculated as sample point A (x)₂，y₂) And sample point B (x)₃，y₃) Amplitude corresponding to all sample points and sample point C (x)₆，y₆) And sample point D (x)₇，y₇) The mean of the corresponding amplitudes of all the samples in between. The magnitude of the plateau region is the calculated sample point A (x)₂，y₂) And sample point B (x)₃，y₃) The corresponding amplitude of all the sampling points between, and the sampling point C (x)₆，y₆) And sample point D (x)₇，y₇) The difference between the maximum value of the amplitude values and the minimum value of the amplitude values in the amplitude values corresponding to all the sampling points.

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

Referring to fig. 5, in an embodiment, a possible implementation manner for determining whether a gradient signal to be detected is normal according to a detection parameter and a preset parameter range is provided, and the specific steps include:

step 500, judging whether the climbing rate is in a preset climbing rate range, whether the descending rate is in a preset descending rate range, whether the average amplitude of the platform area is in a preset average amplitude range, and whether the amplitude of the platform area is in a preset amplitude range.

After obtaining the climbing rate, the descending rate, the average amplitude of the platform area and the amplitude of the platform area in the detection parameters through calculation, the computer device compares the climbing rate, the descending rate, the average amplitude and the preset amplitude in the corresponding preset parameter range with a preset climbing rate range, a preset descending rate range, a preset average amplitude range and a preset amplitude range, and judges whether the climbing rate is in the preset climbing rate range (the range of the minimum value and the maximum value of the preset climbing rate), whether the descending rate is in the preset descending rate range (the range of the minimum value and the maximum value of the preset descending rate), whether the average amplitude of the platform area is in the preset average amplitude range (the range of the minimum value and the maximum value of the preset average amplitude), and whether the amplitude of the platform area is in the preset amplitude range (the range of the minimum value and the maximum value of the preset amplitude).

And 510, if the climbing rate is within a preset climbing range, the descending rate is within a preset descending range, the average amplitude of the platform area is within a preset average amplitude range, and the amplitude of the platform area is within a preset amplitude range, determining that the gradient signal to be detected is normal.

And 520, if the climbing rate is not in the preset climbing range, or the descending rate is not in the preset descending range, or the average amplitude of the platform area is not in the preset average amplitude range, or the amplitude of the platform area is not in the preset amplitude range, determining that the gradient signal to be detected is abnormal.

If the computer equipment is compared, it is determined that the climbing rate is within a preset climbing range, the descending rate is within a preset descending range, the average amplitude of the platform region is within a preset average amplitude range, and the amplitude of the platform region is within a preset amplitude range, that is, when the climbing rate, the descending rate, the average amplitude of the platform region and the amplitude of the platform region are within corresponding ranges, it can be determined that the gradient signal to be detected is normal. If the computer equipment determines that the climbing rate is not within the preset climbing range, or the descending rate is not within the preset descending range, or the average amplitude of the platform region is not within the preset average amplitude range, or the amplitude of the platform region is not within the preset amplitude range, that is, the gradient signal to be measured can be determined to be abnormal when only one of the climbing rate, the descending rate, the average amplitude of the platform region and the amplitude of the platform region is not within the corresponding range.

Referring to fig. 6, in an embodiment, a possible implementation manner of obtaining gradient data of a gradient signal to be measured is provided, which includes the specific steps of:

step 600, reading gradient data to be analyzed from a storage device.

And 610, analyzing the gradient data to be analyzed, and determining the gradient data belonging to the gradient signal to be analyzed.

The storage device may refer to a storage device in the computer device, or may be an independent external storage device. The storage device stores gradient data obtained by sampling the gradient signals, wherein the gradient signals comprise gradient signals to be detected, and the gradient data comprise gradient data corresponding to the gradient signals to be detected. When the computer device needs to acquire the gradient signal to be measured, the computer device needs to read the gradient data from the storage device. After the gradient data are read from the storage device, the computer device takes the gradient data as gradient data to be analyzed and analyzes the gradient data to obtain gradient data corresponding to the gradient information to be analyzed from the gradient data to be analyzed. The embodiment does not limit the specific method for analyzing the gradient data to be analyzed, as long as the gradient data of the gradient signal to be detected can be determined from the gradient data to be solved.

In this embodiment, the method of acquiring gradient data is simple and convenient.

In an alternative embodiment, the gradient data is stored in the storage device in a "row" manner, the computer device reads row data when reading the gradient data to be resolved from the storage device, and a specific field exists in each row data, and the specific field may indicate that the row data is a certain line in the gradient waveform corresponding to the gradient signal transmitted by a certain channel belonging to the gradient board. The computer device can obtain a specific field in each row of data by analyzing the read gradient data to be analyzed, so that the gradient data corresponding to the gradient signal to be detected in the gradient data to be analyzed can be determined, and the gradient data can be obtained by sampling the gradient signal sent by a certain channel of the gradient board card to be detected corresponding to the gradient signal to be detected.

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

channel name	Preset parameter range	Detecting parameters	Comparison results
				CH3 channel	310±20	298	By passing
CH4 channel	485±20	510	By passing
				CH5 channel	600±20	579	Do not pass through

TABLE 1 climbing Rate comparison Table

Channel name	Preset parameter range	Detecting parameters	Comparison results
				CH3 channel	-310±20	-301	By passing
CH4 channel	-485±20	-491	By passing
				CH5 channel	-600±20	-599	By passing

TABLE 2 Depression Rate comparison Table

TABLE 3 average amplitude and amplitude contrast table for plateau region

In the table, the comparison result shows that the detection parameter is in the preset parameter range, the comparison result shows that the detection parameter is not in the preset parameter range, and the comparison result shows that the detection parameter is not in the preset parameter range. It can be seen from the table that if the comparison result of the climbing rates of the gradient signals transmitted by the CH5 channels of the gradient signals to be detected is failed, it indicates that the signal transmission area of the gradient board card corresponding to the gradient signals to be detected has a deviation.

It should be understood that, although the steps in the flowcharts in the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

Referring to fig. 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 configured to send control information to the gradient board 14 via the transmitting board 13. The gradient board card 14 is used for receiving the control information and generating a gradient signal according to the control signal; the computer device 11 is further configured to receive the gradient signal, and store gradient data corresponding to the gradient signal according to the gradient signal to perform the steps of the gradient signal detection method provided in the above-mentioned embodiment.

The transmitting board card 13 is a board card to be detected, and when the transmitting board card 12 needs to be detected, the computer device 11 sends a control signal to the transmitting board card 13 to control the transmitting board card 13 to send control information to the gradient board card 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 executes the steps of the gradient signal detection method provided in the above embodiment, so as to detect whether the gradient signal sent by the gradient board 14 is normal, thereby detecting whether the signal emission interval of the gradient board 14 is deviated.

In an alternative embodiment, after receiving the gradient signal, the computer device 11 samples the gradient signal to obtain gradient data, and stores the gradient data in a hard disk, so that the computer device 11 reads the gradient data from the hard disk when executing the gradient signal detection method.

In an alternative embodiment, the gradient signal detection system 10 further comprises a security monitoring board, which is connected between the gradient board 13 and the computer device 11. The safety monitoring board card is used for receiving the gradient signal, sampling the gradient signal to obtain gradient data, and sending the gradient data to the computer device 11.

The gradient signal detection system 10 provided in this application executes the steps of the gradient signal detection method provided in the foregoing embodiment, and the gradient signal detection system 10 has all the beneficial effects of the gradient signal detection method, and is not described herein again.

Referring to fig. 8, an embodiment of the present application provides a gradient signal detection apparatus 20, which includes an obtaining module 21, a calculating module 22, and a determining module 23. Wherein the content of the first and second substances,

the obtaining module 21 is configured to obtain gradient data of a gradient signal to be detected; the gradient data is obtained by sampling a gradient signal to be detected;

the calculation module 22 is configured to calculate a detection parameter of the gradient signal to be detected according to the gradient data; the detection parameters comprise climbing rate, descending rate, average amplitude of the platform area and amplitude of the platform area;

the determining module 23 is configured to determine whether the gradient signal to be detected is normal according to the detection parameter and a preset parameter range; the preset parameter range comprises a preset climbing rate range, a preset descending rate range, a preset average amplitude range and a preset amplitude range.

In one embodiment, the calculation module 22 includes an analysis unit and a calculation unit, where the analysis unit is configured to analyze the gradient data to obtain a sampling point of the gradient signal to be detected, and the sampling point includes a sampling time and an amplitude of the gradient signal to be detected corresponding to the sampling time; the calculating unit is used for calculating the detection parameters according to the sampling points.

In an embodiment, the calculating unit is specifically configured to calculate, according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time, a sub-climbing rate and a number of rising edges of each rising edge in the gradient signal to be detected, and a sub-falling rate and a number of falling edges of each falling edge in the gradient signal to be detected; calculating the average value of all the sub-climbing rates in the gradient signal to be detected according to the sub-climbing rates and the number of the rising edges to obtain the climbing rate; and calculating the average value of all the sub-descending rates in the gradient signal to be detected according to the sub-descending rates and the number of the descending edges to obtain the descending rate.

In an embodiment, the calculating unit is further specifically configured to determine each sub-platform region of the gradient signal to be detected, and a maximum value of the amplitudes of all the sub-platform regions and a minimum value of the amplitudes of all the sub-platform regions according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time; calculating the average value of the amplitudes of all the 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.

In one embodiment, the determination module 23 includes a judgment unit and a determination unit. The judging unit is used for judging whether the climbing rate is in a preset climbing rate range, whether the descending rate is in a preset descending rate range, whether the average amplitude of the platform area is in a preset average amplitude range and whether the amplitude of the platform area is in a preset amplitude range; the determining unit is used for determining that the gradient signal to be detected is normal if the climbing rate is within a preset climbing range, the descending rate is within a preset descending range, the average amplitude of the platform area is within a preset average amplitude range, and the amplitude of the platform area is within a preset amplitude range.

In an embodiment, the obtaining module 21 is specifically configured to read gradient data to be resolved from a storage device; and analyzing the gradient data to be analyzed, and determining the gradient data belonging to the gradient signal to be analyzed.

For the specific limitations of the gradient signal detection apparatus 20, reference may be made to the limitations of the gradient signal detection method described above, which are not described herein again. The various modules in the gradient signal detection apparatus 20 may be implemented in whole or in part by software, hardware, and combinations thereof. The above devices, modules or units may be embedded in hardware or independent from a processor in a computer device, or may be stored in a memory in the computer device in software, so that the processor can call and execute operations corresponding to the above devices or modules.

Referring to fig. 9, in one embodiment, a computer device is provided, and the computer device may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing gradient data, preset parameter ranges and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer device is executed by a processor to implement a gradient signal detection method.

Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, there is provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program:

acquiring gradient data of a gradient signal to be detected; the gradient data is obtained by sampling a gradient signal to be detected;

calculating the detection parameters of the gradient signals to be detected according to the gradient data; the detection parameters comprise climbing rate, descending rate, average amplitude of the platform area and amplitude of the platform area;

judging whether the gradient signal to be detected is normal or not according to the detection parameter and a preset parameter range; the preset parameter range comprises a preset climbing rate range, a preset descending rate range, a preset average amplitude range and a preset amplitude range.

In one embodiment, the processor, when executing the computer program, further performs the steps of: analyzing the gradient data to obtain sampling points of the gradient signal to be detected, wherein the sampling points comprise sampling time and amplitude values of the gradient signal to be detected corresponding to the sampling time; and calculating detection parameters according to the sampling points.

In one embodiment, the processor, when executing the computer program, further performs the steps of: calculating the sub-climbing rate and the number of rising edges of each rising edge in the gradient signal to be detected and the sub-falling rate and the number of falling edges of each falling edge in the gradient signal to be detected according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time; calculating the average value of all the sub-climbing rates in the gradient signal to be detected according to the sub-climbing rates and the number of the rising edges to obtain the climbing rate; and calculating the average value of all the sub-descending rates in the gradient signal to be detected according to the sub-descending rates and the number of the descending edges to obtain the descending rate.

In one embodiment, the processor, when executing the computer program, further performs the steps of: determining each sub-platform region of the gradient signal to be detected, the maximum value of the amplitudes of all the sub-platform regions and the minimum value of the amplitudes of all the sub-platform regions according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time; calculating the average value of the amplitudes of all the 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.

In one embodiment, the processor, when executing the computer program, further performs the steps of: judging whether the climbing rate is in a preset climbing rate range, whether the descending rate is in a preset descending rate range, whether the average amplitude of the platform area is in a preset average amplitude range and whether the amplitude of the platform area is in a preset amplitude range; and if the climbing rate is within a preset climbing range, the descending rate is within a preset descending range, the average amplitude of the platform area is within a preset average amplitude range, and the amplitude of the platform area is within a preset amplitude range, determining that the gradient signal to be detected is normal.

In one embodiment, the processor, when executing the computer program, further performs the steps of: reading gradient data to be analyzed from a storage device; and analyzing the gradient data to be analyzed, and determining the gradient data belonging to the gradient signal to be analyzed.

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:

acquiring gradient data of a gradient signal to be detected; the gradient data is obtained by sampling a gradient signal to be detected;

calculating the detection parameters of the gradient signals to be detected according to the gradient data; the detection parameters comprise climbing rate, descending rate, average amplitude of the platform area and amplitude of the platform area;

judging whether the gradient signal to be detected is normal or not according to the detection parameter and a preset parameter range; the preset parameter range comprises a preset climbing rate range, a preset descending rate range, a preset average amplitude range and a preset amplitude range.

In one embodiment, the computer program when executed by the processor further performs the steps of: analyzing the gradient data to obtain sampling points of the gradient signal to be detected, wherein the sampling points comprise sampling time and amplitude values of the gradient signal to be detected corresponding to the sampling time; and calculating detection parameters according to the sampling points.

In one embodiment, the computer program when executed by the processor further performs the steps of: calculating the sub-climbing rate and the number of rising edges of each rising edge in the gradient signal to be detected and the sub-falling rate and the number of falling edges of each falling edge in the gradient signal to be detected according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time; calculating the average value of all the sub-climbing rates in the gradient signal to be detected according to the sub-climbing rates and the number of the rising edges to obtain the climbing rate; and calculating the average value of all the sub-descending rates in the gradient signal to be detected according to the sub-descending rates and the number of the descending edges to obtain the descending rate.

In one embodiment, the computer program when executed by the processor further performs the steps of: determining each sub-platform region of the gradient signal to be detected, the maximum value of the amplitudes of all the sub-platform regions and the minimum value of the amplitudes of all the sub-platform regions according to the sampling time and the amplitude of the gradient signal to be detected corresponding to the sampling time; calculating the average value of the amplitudes of all the 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.

In one embodiment, the computer program when executed by the processor further performs the steps of: judging whether the climbing rate is in a preset climbing rate range, whether the descending rate is in a preset descending rate range, whether the average amplitude of the platform area is in a preset average amplitude range and whether the amplitude of the platform area is in a preset amplitude range; and if the climbing rate is within a preset climbing range, the descending rate is within a preset descending range, the average amplitude of the platform area is within a preset average amplitude range, and the amplitude of the platform area is within a preset amplitude range, determining that the gradient signal to be detected is normal.

In one embodiment, the computer program when executed by the processor further performs the steps of: reading gradient data to be analyzed from a storage device; and analyzing the gradient data to be analyzed, and determining the gradient data belonging to the gradient signal to be analyzed.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. 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. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.