A remote monitoring system and method for a magnetic resonance imaging device

Abstract

This invention provides a remote monitoring system and method for an MRI scanner. The monitoring system includes an MRI scanner located in the scanning room, a local controller, a cloud server, and terminal devices. The local controller has an image reconstruction module, and the cloud server has an image reconstruction unit. The image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively reconstruct images from the detection data and send the reconstructed images to the image comparison module of the local controller. The image reconstruction methods of the local controller and the cloud server are different. The image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal. When the image comparison is normal, it displays two reconstructed images simultaneously for medical staff to choose from. This provides multiple reconstructed images for medical staff to choose from and can promptly detect image reconstruction anomalies.

Description

Technical Field

[0001] This invention belongs to the medical field, specifically relating to a remote monitoring system and method for magnetic resonance imaging equipment. Background Technology

[0002] Magnetic resonance imaging (MRI) is a relatively new medical imaging technology, officially used clinically internationally since 1982. It uses static and radiofrequency magnetic fields to image human tissues, obtaining high-contrast, clear images without the need for ionizing radiation or contrast agents. It can reveal organ dysfunction and early lesions from within the human body at the molecular level. In many ways, it is superior to X-ray CT.

[0003] Nuclear magnetic resonance imaging (NMRI), also known as spin imaging or magnetic resonance imaging, utilizes the principle of nuclear magnetic resonance (NMR). Based on the varying attenuation of emitted energy in different structural environments within a substance, and by detecting the emitted electromagnetic waves through an applied gradient magnetic field, the location and type of atomic nuclei that make up the object can be determined, allowing for the creation of an image of the object's internal structure.

[0004] Currently, there are many methods for reconstructing MRI images, each with its own advantages and disadvantages. However, an MRI machine typically uses only one fixed image reconstruction method, which cannot provide multiple reconstructed images for medical staff to choose from, and it also cannot detect abnormalities in image reconstruction in a timely manner. Summary of the Invention

[0005] To address the above problems, this invention provides a remote monitoring system and method for magnetic resonance imaging (MRI) equipment. The monitoring system includes an MRI machine located in the scanning room, a local controller, a cloud server, and terminal devices. The local controller has an image reconstruction module, and the cloud server has an image reconstruction unit. The image reconstruction modules of the local controller and the cloud server respectively reconstruct images from the detection data and send the reconstructed images to the image comparison module of the local controller. The image reconstruction methods of the local controller and the cloud server are different. The image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal. When the image comparison is normal, it simultaneously displays two reconstructed images for medical staff to choose from. This provides multiple reconstructed images for medical staff to select from and can promptly detect image reconstruction anomalies.

[0006] This invention relates to a remote monitoring system for magnetic resonance imaging (MRI) equipment. The system comprises an MRI machine located in the scanning room, a local controller, a cloud server, and terminal devices. Each MRI machine corresponds to one local controller, and multiple local controllers are communicatively connected to the cloud server. The MRI machine scans the patient, uploads the scan data to the local controller and the cloud server, and is controlled by the local controller. Users can also remotely access the local controller or cloud server via a mobile terminal to obtain scan data and reconstructed images, and remotely control the MRI machine. The local controller includes a processor and an image reconstruction module and an image comparison module connected to the processor. The system includes a block, database, and data transmission module; the cloud server has an image reconstruction unit; the processor of the local controller receives the detection data from the MRI equipment through the data transmission module and sends it to its own image reconstruction module and the cloud server; the image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively reconstruct the images from the detection data and send the reconstructed images to the image comparison module of the local controller; the image reconstruction methods of the local controller and the cloud server are different; the image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal; when the image comparison is normal, the two reconstructed images are displayed simultaneously for medical staff to choose the best one.

[0007] Furthermore, the image comparison module determines the size difference between two reconstructed images based on one or more dimensions in certain directions. When the difference between the two reconstructed images exceeds a threshold, an alarm is issued, prompting medical staff to select one of the reconstructed images for storage. After the medical staff makes the selection, the selected reconstructed image is saved to the database of the local controller and the cloud server, while the other reconstructed image is discarded. Medical staff can also discard both reconstructed images and send control commands to the MRI machine to control it to re-scan and detect. If the processor does not receive any command input from the medical staff within a preset time, it saves both reconstructed images and sends a prompt message to the bound mobile terminal device.

[0008] Furthermore, the MRI equipment has a data acquisition function, and its data acquisition method is as follows: the phase encoding direction is sampled, and the sampled data in the phase encoding direction is W. W is divided into five parts on average. Then, for the first part W1 and the fifth part W5, one-quarter of them are randomly selected to form sets W1' and W5' respectively. For the second part W2 and the fourth part W4, half of them are randomly selected to form sets W2' and W4' respectively. For the third part W3, all data are selected to form set W3'. Each element in sets W1', W2', W3', W4' and W5' together constitutes a sampling matrix. Data acquisition is performed according to the sampling matrix.

[0009] Furthermore, the MRI equipment also has a data compression function. Before transmitting the detection data to the local controller, it is compressed. Data compression includes decompressing the acquired data, and the decomposition method is as follows:

[0010] A=C∑D T

[0011] Where A is an m*n dimensional sampling data matrix, C is an m*n orthogonal matrix, and D... T Let be an n*n orthogonal square matrix, and ∑ be an m*n singular diagonal matrix.

[0012] Furthermore, after receiving the detection data, the local controller decompresses it and then performs noise reduction on the decompressed detection data. The noise reduction method is as follows:

[0013] Let the detected data be f(x, y), and the noisy data be g(x, y). Then the noise function is as follows:

[0014]

[0015] Where S is the set of regions centered at (x, y), and M is the total number of coordinate points within S.

[0016] Furthermore, the image reconstruction method of the local controller is as follows: set the number of iterations, use interpolation to reconstruct the image from the detection data, use the result as input, and continue to perform interpolation reconstruction in a loop until the upper limit of the number of iterations is reached; the cloud server uses an artificial neural network for image reconstruction.

[0017] The present invention also relates to a remote control method for a magnetic resonance imaging device, the method comprising the following steps:

[0018] Step 1: The MRI machine scans the patient and collects data;

[0019] Step 2: Compress the collected detection data;

[0020] Step 3: Send the compressed detection data to the local controller;

[0021] Step 4: Decompress the received detection data;

[0022] Step 5: Denoise the decompressed detection data;

[0023] Step 6: The image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively perform image reconstruction on the detection data;

[0024] Step 7: The image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal; when the image comparison is normal, it displays the two reconstructed images simultaneously for medical staff to choose the best one.

[0025] Furthermore, the method for collecting detection data is as follows: Sampling is performed on the phase encoding direction, and the sampled data in the phase encoding direction is W. W is divided into five parts on average. Then, one-quarter of the first part W1 and the fifth part W5 are randomly selected to form sets W1' and W5' respectively. Half of the second part W2 and the fourth part W4 are randomly selected to form sets W2' and W4' respectively. All data of the third part W3 are selected to form set W3'. Each element in sets W1', W2', W3', W4' and W5' together constitutes a sampling matrix. Data is collected based on the sampling matrix.

[0026] Furthermore, data compression includes decompressing the collected data, and the decomposition method is as follows:

[0027] A=C∑D T

[0028] Where A is an m*n dimensional sampling data matrix, C is an m*n orthogonal matrix, and D... T Let be an n*n orthogonal square matrix, and ∑ be an m*n singular diagonal matrix.

[0029] Furthermore, the noise reduction methods are as follows:

[0030] Let the detected data be f(x, y), and the noisy data be g(x, y). Then the noise function is as follows:

[0031]

[0032] Where S is the set of regions centered at (x, y), and M is the total number of coordinate points within S.

[0033] The present invention has the following technical effects:

[0034] The local controller of this invention has an image reconstruction module, and the cloud server has an image reconstruction unit. The image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively reconstruct images from the detection data and send the reconstructed images to the image comparison module of the local controller. The image reconstruction methods of the local controller and the cloud server are different. The image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal. It can provide multiple reconstructed images for medical staff to choose from and can also detect abnormal image reconstruction in a timely manner. Attached Figure Description

[0035] Figure 1 A schematic diagram of the structure of a remote monitoring system for a magnetic resonance imaging device according to one embodiment of the present invention;

[0036] Figure 2 A flowchart of a remote control method for a magnetic resonance imaging device in one embodiment of the invention. Detailed Implementation

[0037] Embodiments of the invention will now be described more fully below with reference to the accompanying drawings, which illustrate examples of the invention. However, the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Throughout the text, the same numerals denote the same elements.

[0038] It will be understood that although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element without departing from the scope of the invention, and similarly, a second element may be referred to as a first element. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0039] It will be understood that when an element, such as a layer, region, or substrate, is referred to as being "on" or "extending" over another element, it may be directly on or extending over the other element, or an intermediate element may also be the other element. Conversely, when an element is referred to as being "directly on" or "extending directly to" another element, there is no intermediate element. It should also be understood that when an element is referred to as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or an intermediate element may exist. Conversely, when an element is referred to as being "directly connected" or "directly coupled" to another element, there is no intermediate element.

[0040] In this document, relative terms such as “below” or “above”, “above” or “below”, “horizontal” or “vertical” may be used to describe the relationship of one element, layer or region to another. As illustrated, it will be understood that these terms are intended to cover different orientations of the device in addition to those depicted in the figures.

[0041] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will be further understood that, when used herein, the terms “comprising,” “including,” “including,” and / or “comprising” specify the presence of the said feature, integer, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0042] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It will be further understood that, unless expressly defined herein, the terms used herein shall be interpreted as having the meaning consistent with their meaning in the context of this specification and the relevant field, and shall not be interpreted in an idealized or overly formal sense.

[0043] The invention is described below with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It will be understood that some blocks of the flowchart illustrations and / or block diagrams, as well as combinations of some blocks of the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be stored or implemented in microcontrollers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), state machines, programmable logic controllers (PLCs) or other processing circuits, general-purpose computers, or special-purpose computers. The instructions are used in computers or other programmable data processing devices (e.g., production machines) to create means or block diagram blocks for implementing the functions / actions specified in the flowcharts and / or block diagrams, which are executed by the processor of the computer or other programmable data processing device.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can instruct a computer or other programmable data processing device to operate in a particular manner, thereby causing the instructions stored in the computer-readable storage medium to produce an article of art including instruction means. This implements the functions / actions specified in the flowcharts and / or block diagrams.

[0045] Computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby making the instructions executable on the computer or other programmable apparatus possible. Other programmable devices provide steps for implementing the functions / actions specified in the flowcharts and / or block diagrams. It should be understood that the functions / actions indicated in the blocks may not occur in the order shown in the operation diagram. For example, depending on the functions / actions involved, two consecutively shown blocks may actually be performed substantially simultaneously, or sometimes they may be performed in reverse order. Although some diagrams include arrows on the communication path to indicate the main direction of communication, it should be understood that communication may occur in the direction opposite to the depicted arrows.

[0046] The specific implementation process of this invention is as follows:

[0047] like Figure 1 As shown, a remote monitoring system for an MRI scanner is characterized by comprising an MRI scanner located in the scanning room, a local controller, a cloud server, and terminal devices; each MRI scanner corresponds to one local controller, and multiple local controllers are communicatively connected to the cloud server; the MRI scanner scans the patient, uploads the detection data to the local controller and the cloud server, and is controlled by the local controller; users can also remotely access the local controller or the cloud server via a mobile terminal to obtain detection data and reconstructed images, and remotely control the MRI scanner; the local controller includes a processor and an image reconstruction module and an image comparison module respectively connected to the processor. The system includes a database and data transmission module; the cloud server has an image reconstruction unit; the processor of the local controller receives the detection data from the MRI equipment through the data transmission module and sends it to its own image reconstruction module and the cloud server; the image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively reconstruct the images from the detection data and send the reconstructed images to the image comparison module of the local controller; the image reconstruction methods of the local controller and the cloud server are different; the image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal; when the image comparison is normal, the two reconstructed images are displayed simultaneously for medical staff to choose the best one.

[0048] Magnetic resonance imaging (MRI) equipment can be connected to a local controller wirelessly or via wired connection, and the local controller can then wirelessly connect to a cloud server. Multiple terminal devices are possible, including mobile phones, tablets, laptops, and computers.

[0049] When the comparison results are abnormal, an alarm should be issued. This can be done through various means such as voice, signal lights, or buzzers, and there are no restrictions on the specific methods used.

[0050] The image comparison module determines the size difference between two reconstructed images based on one or more dimensions in certain directions. When the difference between the two reconstructed images exceeds a threshold, an alarm is issued, prompting medical staff to select one of the reconstructed images for storage. After the medical staff makes the selection, the selected reconstructed image is saved to the database of the local controller and the cloud server, while the other reconstructed image is discarded. Medical staff can also discard both reconstructed images and send control commands to the MRI machine to control it to re-scan and detect. If the processor does not receive any command input from the medical staff within a preset time, it saves both reconstructed images and sends a prompt message to the bound mobile terminal device.

[0051] Magnetic resonance imaging (MRI) equipment has a data acquisition function, and its data acquisition method is as follows: Sampling is performed along the phase encoding direction, and the sampled data along the phase encoding direction is denoted as W. W is divided into five equal parts. Then, for the first part W1 and the fifth part W5, one-quarter of each part is randomly selected to form sets W1' and W5', respectively. For the second part W2 and the fourth part W4, half of each part is randomly selected to form sets W2' and W4', respectively. For the third part W3, all data is selected to form set W3'. Each element in sets W1', W2', W3', W4', and W5' together constitutes a sampling matrix. Data acquisition is performed based on the sampling matrix.

[0052] For example, if the sampled data in the phase encoding direction is 600, it is equivalent to 600 rows of collected k-space data, i.e., W = 600. Divide the 600 into five equal parts, each with 120 rows. Randomly select one-quarter of the first and fifth parts (i.e., the 120 rows at the two ends), i.e., select 30 rows from each part to form two sets W1' and W5'. For the second and fourth parts (i.e., rows 121-240 from the two ends), randomly select one-half of the second and fourth parts, i.e., select 60 rows from each part to form sets W2' and W4' respectively. For the third part (i.e., the 120 rows in the middle), select all the data to form set W3'. Each element in sets W1', W2', W3', W4', and W5' (a total of 300 rows) together constitutes the sampling matrix.

[0053] Magnetic resonance imaging (MRI) equipment also has a data compression function. Before transmitting the detection data to the local controller, it is compressed. Data compression includes decompressing the acquired data. The decomposition method is as follows:

[0054] A=C∑D T

[0055] Where A is an m*n dimensional sampling data matrix, C is an m*n orthogonal matrix, and D... TLet be an n*n orthogonal square matrix, and ∑ be an m*n singular diagonal matrix.

[0056] After receiving the detection data, the local controller decompresses it and then performs noise reduction on the decompressed detection data. The noise reduction method is as follows:

[0057] Let the detected data be f(x, y), and the noisy data be g(x, y). Then the noise function is as follows:

[0058]

[0059] Where S is the set of regions centered at (x, y), and M is the total number of coordinate points within S.

[0060] The image reconstruction method of the local controller is as follows: set the number of iterations, use interpolation to reconstruct the image from the detection data, use the result as input, and continue to perform interpolation reconstruction in a loop until the upper limit of the number of iterations is reached; the cloud server uses an artificial neural network for image reconstruction.

[0061] like Figure 2 As shown, the present invention also relates to a remote control method for a magnetic resonance imaging (MRI) device, the method comprising the following steps:

[0062] Step 1: The MRI machine scans the patient and collects data;

[0063] Step 2: Compress the collected detection data;

[0064] Step 3: Send the compressed detection data to the local controller;

[0065] Step 4: Decompress the received detection data;

[0066] Step 5: Denoise the decompressed detection data;

[0067] Step 6: The image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively perform image reconstruction on the detection data;

[0068] Step 7: The image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal; when the image comparison is normal, it displays the two reconstructed images simultaneously for medical staff to choose the best one.

[0069] The method for collecting detection data is as follows: Sampling is performed on the phase encoding direction, and the sampled data in the phase encoding direction is W. W is divided into five parts on average. Then, one-quarter of the first part W1 and the fifth part W5 are randomly selected to form sets W1' and W5' respectively. Half of the second part W2 and the fourth part W4 are randomly selected to form sets W2' and W4' respectively. All data of the third part W3 are selected to form set W3'. Each element in sets W1', W2', W3', W4' and W5' together constitutes the sampling matrix. Data is collected according to the sampling matrix.

[0070] Data compression includes decompressing the collected data. The decomposition methods are as follows:

[0071] A=C∑D T

[0072] Where A is an m*n dimensional sampling data matrix, C is an m*n orthogonal matrix, and D... T Let be an n*n orthogonal square matrix, and ∑ be an m*n singular diagonal matrix.

[0073] The noise reduction methods are as follows:

[0074] Let the detected data be f(x, y), and the noisy data be g(x, y). Then the noise function is as follows:

[0075]

[0076] Where S is the set of regions centered at (x, y), and M is the total number of coordinate points within S.

[0077] In summary, the local controller of this invention has an image reconstruction module, and the cloud server has an image reconstruction unit. The image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively reconstruct images from the detection data and send the reconstructed images to the image comparison module of the local controller. The image reconstruction methods of the local controller and the cloud server are different. The image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal. It can provide multiple reconstructed images for medical staff to choose from and can also detect abnormal image reconstruction in a timely manner.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Any modifications or changes made to the present invention by those skilled in the art after reading this application and referring to the above embodiments are within the scope of protection claimed in the pending claims of this application.

Claims

1. A remote monitoring system for a nuclear magnetic resonance imaging (MRI) device, characterized in that, The system includes an MRI scanner located in the scanning room, a local controller, a cloud server, and terminal devices. Each MRI scanner corresponds to one local controller, and multiple local controllers communicate with the cloud server. The MRI scanner scans the patient, uploads the scan data to the local controller and the cloud server, and is controlled by the local controller. Users can also remotely access the local controller or cloud server via mobile terminals to obtain scan data and reconstructed images, and remotely control the MRI scanner. The local controller includes a processor and image reconstruction module, image comparison module, database, and data transmission module connected to the processor. The cloud server has an image reconstruction unit. The processor of the local controller receives the scan data from the MRI scanner through the data transmission module and sends it to its own image reconstruction module and the cloud server. The image reconstruction module of the local controller and the image reconstruction unit of the cloud server reconstruct images from the scan data and send the reconstructed images back to their respective terminals. The image comparison module of the local controller differs from that of the cloud server in its image reconstruction methods. The module compares and analyzes two received reconstructed images and issues an alarm when the comparison result is abnormal. When the image comparison is normal, both reconstructed images are displayed simultaneously for medical personnel to choose from. The image comparison module determines the reconstructed images based on size differences in one or more directions. When the difference between the two reconstructed images exceeds a threshold, an alarm is issued, prompting the medical personnel to select one image for storage. After selection, the selected image is saved to the local controller's database and the cloud server, while the unselected image is discarded. Alternatively, the medical personnel can discard both images and send control commands to the MRI machine to re-scan. If the processor does not receive any command input from the medical personnel within a preset time, both reconstructed images are saved, and a prompt message is sent to the bound mobile terminal device.

2. The remote monitoring system according to claim 1, characterized in that, Magnetic resonance imaging (MRI) equipment has a data acquisition function, and its data acquisition method is as follows: Sampling is performed along the phase encoding direction, and the sampled data along the phase encoding direction is denoted as W. W is divided into five equal parts. Then, for the first part W1 and the fifth part W5, one-quarter of each part is randomly selected to form sets W1' and W5', respectively. For the second part W2 and the fourth part W4, half of each part is randomly selected to form sets W2' and W4', respectively. For the third part W3, all data is selected to form set W3'. Each element in sets W1', W2', W3', W4', and W5' together constitutes a sampling matrix. Data acquisition is performed based on the sampling matrix.

3. The remote monitoring system according to claim 2, characterized in that, Magnetic resonance imaging (MRI) equipment also has a data compression function. Before transmitting the detection data to the local controller, it is compressed. Data compression includes decompressing the acquired data. The decomposition method is as follows: Where A is A dimensional sampled data matrix, C is an orthogonal square matrix for an orthogonal square matrix for A strange diagonal matrix.

4. The remote monitoring system according to claim 3, characterized in that, After receiving the detection data, the local controller decompresses it and then performs noise reduction on the decompressed detection data. The noise reduction method is as follows: Let the detection data be The data after noise reduction is The noise function is then defined as follows: Where S is the S is the set of centers, and M is the total number of coordinate points within S.

5. The remote monitoring system according to claim 4, characterized in that, The image reconstruction method of the local controller is as follows: set the number of iterations, use interpolation to reconstruct the image from the detection data, use the result as input, and continue to perform interpolation reconstruction in a loop until the upper limit of the number of iterations is reached; the cloud server uses an artificial neural network for image reconstruction.

6. A remote control method for a magnetic resonance imaging (MRI) device employing a remote monitoring system for an MRI device according to any one of claims 1-5, characterized in that, The method includes the following steps: Step 1: The MRI machine scans the patient and collects data; Step 2: Compress the collected detection data; Step 3: Send the compressed detection data to the local controller; Step 4: Decompress the received detection data; Step 5: Denoise the decompressed detection data; Step 6: The image reconstruction module of the local controller and the image reconstruction unit of the cloud server respectively perform image reconstruction on the detection data; Step 7: The image comparison module compares and analyzes the two received reconstructed images and issues an alarm when the comparison result is abnormal; when the image comparison is normal, it displays the two reconstructed images simultaneously for medical staff to choose the best one.

7. The method according to claim 6, characterized in that, The method for collecting detection data is as follows: Sampling is performed on the phase encoding direction, and the sampled data in the phase encoding direction is W. W is divided into five parts on average. Then, one-quarter of the first part W1 and the fifth part W5 are randomly selected to form sets W1' and W5' respectively. Half of the second part W2 and the fourth part W4 are randomly selected to form sets W2' and W4' respectively. All data of the third part W3 are selected to form set W3'. Each element in sets W1', W2', W3', W4' and W5' together constitutes the sampling matrix. Data is collected according to the sampling matrix.

8. The method according to claim 7, characterized in that, Data compression includes decompressing the collected data. The decomposition methods are as follows: Where A is A dimensional sampled data matrix, C is an orthogonal square matrix for an orthogonal square matrix for A strange diagonal matrix.

9. The method according to claim 8, characterized in that, The noise reduction methods are as follows: Let the detection data be The data after noise reduction is The noise function is then defined as follows: Where S is the S is the set of centers, and M is the total number of coordinate points within S.

Images