Magnetic resonance data processing method and device, computer equipment, medium and product

Abstract

The invention relates to a magnetic resonance data processing method and device, computer equipment, a medium and a product. The method comprises the following steps: acquiring first data acquired by a coil channel in a magnetic resonance imaging process; according to a plurality of signal values of voxels in the first data at different acquisition times, calculating to obtain signal characteristics of each voxel; determining the weight of a coil channel corresponding to the voxel according to the signal characteristics; and adjusting a signal value corresponding to the coil channel according to the weight to obtain second data of the voxel. By adopting the method, the time signal-to-noise ratio of the magnetic resonance image can be improved while the magnetic resonance data processing efficiency is ensured.

Description

Technical Field

[0001] The present application relates to the field of magnetic resonance data processing, and in particular to magnetic resonance data processing methods, devices, computer equipment, media and products. Background Art

[0002] Currently, most MRI (Magnetic Resonance Imaging) signals are collected by an array of several small surface coils, and the signal of each coil is usually received by an independent hardware channel. Collecting MRI signals based on multi-channel technology can improve the signal-to-noise ratio and expand the acquisition field of view, but the signals received by different channels are also different. The local signal will be better near the coil and worse far away from the coil. In order to obtain a high-quality image in the final combined image, the signals from each channel are generally combined with a weighting factor derived from its local signal strength.

[0003] In the traditional method, the signal amplitude itself is used as a weighting factor to weight the coil signal, and the magnetic resonance data is obtained based on the weighted sum of squares (SOS) combination method. However, MRI data is often interfered by thermal noise and artifacts, which in turn affects the accuracy of the image data. This method of simply weighting and adding data will add the areas that are severely contaminated by artifacts with the same weight, resulting in the signal-to-noise ratio of the final image being lowered.

[0004] Another traditional method is adaptive weighted averaging, which gives a target and continuously optimizes the difference between the current value and the target value, constantly approaches the target, and adjusts the weights of different coil channels to achieve the maximum signal-to-noise ratio. The problem with this method is that the algorithm is complex and takes a long time to calculate.

[0005] There is no effective solution to the problem that the magnetic resonance images obtained by the multi-channel data acquisition method in the related art cannot take into account both high temporal signal-to-noise ratio and high data processing efficiency. Summary of the invention

[0006] Based on this, it is necessary to provide a magnetic resonance data processing method, device, computer equipment, medium and product that can take into account both magnetic resonance image quality and data processing efficiency in order to address the above technical problems.

[0007] In a first aspect, a method for processing magnetic resonance data is provided in this embodiment, the method comprising:

[0008] Acquire first data collected by a coil channel during magnetic resonance imaging;

[0009] Calculating the signal characteristics of each voxel according to a plurality of signal values ​​of the voxels in the first data at different acquisition times;

[0010] Determining the weight of the coil channel corresponding to the voxel according to the signal characteristics;

[0011] The signal values ​​corresponding to the coil channels are adjusted respectively according to the weights to obtain second data of the voxels.

[0012] In some embodiments, one or more of the following signal characteristics are calculated based on a plurality of signal values ​​of a voxel in the first data at different acquisition times: a temporal signal-to-noise ratio, a skewness, and a kurtosis of the voxel.

[0013] In some of the embodiments, determining the weight of the corresponding coil channel according to the signal feature includes:

[0014] The value of the signal feature is multiplied by the corresponding preset coefficient to obtain the weight of the coil channel corresponding to the voxel.

[0015] In some of the embodiments, the preset coefficient is obtained based on the value of the signal feature, wherein the higher the image quality corresponding to the multiple signal values ​​determined based on the value of the signal feature, the larger the preset coefficient corresponding to the signal feature.

[0016] In some embodiments, adjusting the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel includes:

[0017] The signal values ​​of the plurality of coil channels are multiplied by corresponding weights respectively to obtain the second data of the voxel.

[0018] In some embodiments, after adjusting the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel, the method further includes:

[0019] The second data of a plurality of voxels are superimposed to obtain a magnetic resonance image.

[0020] In a second aspect, a magnetic resonance data processing device is provided in this embodiment, the device comprising:

[0021] An acquisition module, used for acquiring first data acquired by the coil channel during magnetic resonance imaging;

[0022] A first calculation module, configured to calculate a signal feature of each voxel according to a plurality of signal values ​​of the voxels in the first data at different acquisition times;

[0023] A second calculation module, used to determine the weight of the coil channel corresponding to the voxel according to the signal feature;

[0024] An adjustment module is used to adjust the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel.

[0025] In a third aspect, a computer device is provided in this embodiment, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the magnetic resonance data processing method described in the first aspect when executing the computer program.

[0026] In a fourth aspect, in this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the magnetic resonance data processing method described in the first aspect is implemented.

[0027] In a fifth aspect, a computer program product is provided in this embodiment, comprising a computer program, which, when executed by a processor, implements the magnetic resonance data processing method described in the first aspect.

[0028] The above-mentioned magnetic resonance data processing method obtains the signal characteristics of the voxel in the time domain according to the signal values ​​of each voxel at different time points in the first data, determines the weight of the coil channel corresponding to the voxel based on the signal characteristics, and adjusts the signal value according to the weight to obtain the second data with less artifact contamination, thereby improving the image time signal-to-noise ratio while ensuring high data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a diagram of an application environment of a magnetic resonance data processing method in one embodiment;

[0030] Figure 2 is a schematic flow chart of a magnetic resonance data processing method in one embodiment;

[0031] Figure 3 is a schematic diagram of fMRI data in one embodiment;

[0032] Figure 4 is a schematic diagram of signal characteristics in one embodiment;

[0033] Figure 5 is a structural block diagram of a magnetic resonance data processing device in one embodiment;

[0034] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] The magnetic resonance data processing method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Based on the terminal, the server, or a system including the terminal and the server, the first data collected during the magnetic resonance imaging process is processed, and the first data, the second data or the processing process can be stored in the data storage system. Among them, the terminal 102 can be but not limited to various personal computers, laptops, tablet computers, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0037] In one embodiment, Figure 2 As shown, a magnetic resonance data processing method is provided, and the method is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate, and the following steps are included:

[0038] Step 202: Acquire first data collected by a coil channel during magnetic resonance imaging.

[0039] The first data is magnetic resonance data to be processed. Optionally, during magnetic resonance imaging, electromagnetic wave signals are captured based on a coil array, and the corresponding hardware channel receives the signal captured by each coil, so that the terminal can obtain the first data based on the coil channel composed of the coil and the hardware channel.

[0040] Step 204 : Calculate the signal characteristics of each voxel according to the multiple signal values ​​of the voxels in the first data at different acquisition times.

[0041] The first data includes a plurality of voxels. The signal feature is used to indicate the statistical characteristics of the signal in the time dimension, for example: the time signal-to-noise ratio used to indicate the signal quality; the kurtosis, skewness, variance, etc. used to indicate the distribution characteristics of the signal. Optionally, the imaging object is repeatedly scanned during the magnetic resonance imaging process to obtain the signal values ​​of the plurality of voxels at different times. Based on the signal value of a voxel at different times, one or more signal features of the signal value of the voxel in the time dimension can be calculated.

[0042] Step 206: Determine the weight of the coil channel corresponding to the voxel according to the signal characteristics.

[0043] Among them, if the signal feature indicates that the time consistency of the signal value is poor, the image obtained based on the signal value of the voxel will have problems with noise and artifacts. A smaller weight is configured for the coil channel corresponding to the voxel to prevent the signal value from affecting the imaging quality of the magnetic resonance image; conversely, a larger weight is configured for the coil channel corresponding to the voxel. If step 204 only calculates one signal feature, the corresponding weight is directly assigned to the coil channel according to the signal feature. If step 204 calculates multiple signal features, multiple weights corresponding to the same coil channel can be obtained according to each signal feature, and the multiple weights are combined to obtain the final weight of the coil channel. Optionally, the combination of multiple weights is achieved by summing or averaging multiple weights.

[0044] Step 208: Adjust the signal values ​​corresponding to the coil channels according to the weights to obtain second data of the voxels.

[0045] Optionally, after adjusting the signal value according to the weight, the adjusted signal values ​​of multiple coil channels are added to obtain second data of the voxel, so as to adjust the influence of the signal value collected by each channel on the magnetic resonance image.

[0046] In the above-mentioned magnetic resonance data processing method, the weight of the coil channel corresponding to the voxel is determined according to the signal characteristics associated with the time dimension of each voxel in the first data, and the signal value of the coil channel is adjusted according to the weight, so as to assign corresponding weights to different coil channels, and reduce the influence of the signal value seriously contaminated by artifacts on the magnetic resonance image based on a simple algorithm. The new magnetic resonance image can be reconstructed according to the adjusted second data, which reduces the influence of artifacts and noise on the magnetic resonance image and improves the signal-to-noise ratio of the magnetic resonance image in the time dimension; at the same time, there is no need to adjust the parameters in the magnetic resonance imaging process, avoiding the sacrifice of time resolution and spatial resolution by adjusting the magnetic resonance imaging parameters, making the overall signal of the image more stable, and solving the problem that the magnetic resonance image obtained by the multi-channel data acquisition method cannot take into account both the time signal-to-noise ratio and data processing efficiency.

[0047] In one embodiment, one or more of the following signal features are calculated based on a plurality of signal values ​​of voxels in the first data at different acquisition times: a temporal signal-to-noise ratio, a skewness, and a kurtosis of the voxels.

[0048] The temporal signal-to-noise ratio of a voxel is an indicator that measures the relative size of the signal value and the noise in the time domain. A high temporal signal-to-noise ratio means that the signal value of the voxel is stronger than the noise; a low temporal signal-to-noise ratio means that the signal value of the voxel is overwhelmed by the noise. The magnetic resonance image obtained based on the voxel with a low temporal signal-to-noise ratio has the problem of low quality.

[0049] The skewness of the voxel is used to count the asymmetry of the signal values ​​at different acquisition times. The closer the skewness of the voxel is to 0, the lower the asymmetry of the signal values ​​at different acquisition times, and the higher the stability of the signal values ​​in the time domain; conversely, the higher the asymmetry of the signal values ​​at different acquisition times, the greater the possibility that the signal values ​​are interfered by noise, and the magnetic resonance images obtained based on voxels with high skewness have low quality.

[0050] The kurtosis of a voxel is the characteristic number of the peak height at the average value of the probability density distribution curve composed of multiple signal values. The closer the kurtosis of a voxel is to 0, the closer the probability density distribution curve is to the normal distribution, and the stability of the signal value in the time domain is high; on the contrary, if the probability density distribution curve is close to a peak state or a flat state, the signal value is likely to be interfered by noise, and the magnetic resonance image obtained based on the voxel with high skewness has the problem of low quality.

[0051] In this embodiment, the data quality of the voxel is determined by calculating one or more signal characteristics of the voxel's temporal signal-to-noise ratio, skewness, and kurtosis, thereby obtaining the weight of the coil channel, which can improve the signal-to-noise ratio and stability of the image in the temporal dimension without losing spatial resolution.

[0052] Based on the data quality indicated by the signal feature, a corresponding preset coefficient can be obtained to determine the weight of the voxel. In one embodiment, determining the weight of the coil channel corresponding to the voxel according to the signal feature includes: multiplying the value of the signal feature by the corresponding preset coefficient to obtain the weight of the coil channel corresponding to the voxel. Wherein, if multiple signal features are included, each signal feature is multiplied by the corresponding preset coefficient respectively, and the result of multiplying the multiple signal features is combined to obtain the weight of the coil channel corresponding to the voxel.

[0053] Optionally, in one embodiment, the preset coefficient is obtained based on the value of the signal feature, wherein the higher the image quality corresponding to the multiple signal values ​​determined based on the value of the signal feature, the larger the preset coefficient corresponding to the signal feature. Conversely, the lower the image quality corresponding to the multiple signal values ​​determined based on the value of the signal feature, the smaller the preset coefficient corresponding to the signal feature.

[0054] Among them, in the first data collected by the coil channel determined based on the value of the signal feature, if the multiple signal values ​​corresponding to the voxel are greatly affected by artifact contamination, the multiple signal values ​​have poor data stability and poor time consistency in the time dimension, and the quality of the image data obtained based on the multiple signal values ​​is correspondingly low. At this time, the value of the signal feature can be multiplied by a lower preset coefficient to reduce the influence of the coil channel on the image imaging quality; conversely, the value of the signal feature can be multiplied by a higher preset coefficient.

[0055] Optionally, taking the signal feature including one or more of the time signal-to-noise ratio, skewness and kurtosis as an example, the less the contamination of artifacts on the multiple signal values ​​of the same voxel at different acquisition times of the coil channel is, the higher the corresponding image quality is, and the signal feature may have one or more of the following situations: the time signal-to-noise ratio is large, the absolute value of the skewness is low, and the absolute value of the kurtosis is low. Therefore, it can be set as follows: the preset coefficient corresponding to a high time signal-to-noise ratio is high, and the preset coefficient corresponding to a low time signal-to-noise ratio is low; the preset coefficient corresponding to a skewness with a high absolute value is low, and the preset coefficient corresponding to a skewness with a low absolute value is high; the preset coefficient corresponding to a kurtosis with a high absolute value is low, and the preset coefficient corresponding to a kurtosis with a low absolute value is high. For ease of understanding, in the case where the signal feature includes the time signal-to-noise ratio, skewness and kurtosis, the preset coefficient of the time signal-to-noise ratio is set to W1, the preset coefficient of the skewness is set to W2, and the preset coefficient of the kurtosis is set to W3. The size of the preset coefficient is set according to the values ​​of the time signal-to-noise ratio, skewness and kurtosis. Among them, when the preset coefficient is a constant, the default setting may be W1=1, W2=W3=0.5. On the basis of the default setting, the values ​​of W1, W2, and W3 are adjusted positively or negatively according to the values ​​of the time signal-to-noise ratio, skewness, and kurtosis. Taking the time signal-to-noise ratio as an example, when it is determined that the time signal-to-noise ratio is high, the value of W1 may be increased to obtain a magnetic resonance image with a high time signal-to-noise ratio characteristic. The preset coefficient may be set to other default values, and the adjustment of the skewness and kurtosis is similar, which will not be described in detail here.

[0056] Furthermore, before multiplying the value of the signal feature by the corresponding preset coefficient, the value of the signal feature can also be processed based on a preset function. In the case of multiple signal features, you can choose to process the values ​​of some of the signal features based on a preset function, or you can choose to process the values ​​of all the signal features. Taking the processing of skewness based on the preset function tanh(x) as an example, tanh(Skewness) is calculated, and the processed signal feature tanh(Skewness) is multiplied by the corresponding preset coefficient. At this time, when the skewness is large, tanh(Skewness) is low, and even if the preset coefficient is not adjusted, the effect of adjusting the coil channel weight can be achieved. The preset function can also be There is no restriction on other functions.

[0057] In order to improve the signal-to-noise ratio of the magnetic resonance data in the time dimension, the weight of the voxel can be obtained by combining the above-mentioned multiple signal features. Optionally, after multiplying the value of the signal feature by the corresponding preset coefficient, the multiple products are added together to obtain the weight of the coil channel.

[0058] In this embodiment, the weight of the coil channel corresponding to each voxel is calculated according to the signal characteristics, and the influence of the voxels with local signal difference on the image quality can be reduced based on the weight, thereby achieving the effect of improving the quality of magnetic resonance imaging.

[0059] In one embodiment, acquiring first data acquired by a coil channel during magnetic resonance imaging includes: rearranging four-dimensional data acquired by the coil channel into a three-dimensional matrix to obtain the first data, wherein index values ​​of a first axis and a second axis in the first data are used to indicate an acquisition position of each voxel in the four-dimensional data, and an index value of a third axis in the first data is used to indicate an acquisition time of each voxel in the four-dimensional data.

[0060] Optionally, the object to be imaged is repeatedly acquired based on the coil channel to obtain four-dimensional data of the object to be imaged at different times. Among them, the first axis, the second axis and the third axis in the three-dimensional matrix correspond to the three axes of the three-dimensional matrix that are perpendicular to each other. For example, the first axis and the second axis can be set to the x-axis and the y-axis of the three-dimensional matrix, and the third axis can be set to the z-axis of the three-dimensional matrix. Based on the same principle, the corresponding relationship between the axis and the x-axis, y-axis, and z-axis of the three-dimensional matrix can be changed, which will not be repeated here.

[0061] In this embodiment, four-dimensional data is sorted according to the acquisition time and position of the signal value of each voxel to obtain a three-dimensional matrix. The signal value of the same voxel at each time can be quickly obtained based on the index value of the three-dimensional matrix, thereby improving the efficiency of magnetic resonance data processing.

[0062] In one embodiment, adjusting the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel includes: multiplying the signal values ​​of multiple coil channels by the corresponding weights to obtain the second data of the voxel. Among them, the higher the weight corresponding to the coil channel, the greater the impact of the signal value of the coil channel on the image quality during imaging. The signals received by different coil channels are also different. The local signal received in the area close to the coil is better, and the local signal received in the area far from the coil is poor. Multiplying the signal values ​​of each coil channel corresponding to the voxel in the first data by the corresponding weight can reduce the impact of data with poor signal quality on the imaging quality.

[0063] Furthermore, in one embodiment, after adjusting the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel, the method further includes: superimposing the second data of multiple voxels to obtain a magnetic resonance image. Optionally, the following steps are performed on multiple voxels respectively: calculating the signal characteristics of the voxel according to multiple signal values ​​of the voxel at different acquisition times; determining the weight of the coil channel corresponding to the voxel according to the signal characteristics to obtain the second data of the multiple voxels. The second data of the multiple voxels are superimposed to obtain a high-quality magnetic resonance image.

[0064] In one embodiment, another magnetic resonance data processing method is provided, including: arranging each voxel of four-dimensional data of different channels of fMRI (functional magnetic resonance imaging) into a three-dimensional matrix according to the acquisition time and the voxel position. The column of the matrix represents the signal change of the voxel at that position on the time axis, which is recorded as S x,y,z (t). Where (x, y, z) is the voxel position obtained based on the coil acquisition result, and t is the acquisition time corresponding to the signal value of the voxel. Figure 3 is a schematic diagram of fMRI data, such as Figure 3 As shown, the four-dimensional data of each coil channel are arranged into a three-dimensional matrix; a column of data in the three-dimensional matrix corresponds to the signal of the same voxel at different times; different columns of data in the three-dimensional matrix correspond to voxels at different positions.

[0065] According to S x,y,z (t) Three signal features are calculated respectively, namely, time signal-to-noise ratio tSNR, skewness and kurtosis.

[0066] tSNR x，y，z =Mean(S x，y，z (t)) / STD(S x，y，z (t))

[0067]

[0068]

[0069] Wherein, t is the number of data acquisitions during the magnetic resonance imaging process, and i is the number of acquisitions of the current data.

[0070] These three signal features have different meanings, and the value of the signal features can reflect the image quality of the channel in the time dimension. Among them, the higher the tSNR, the better the data quality; conversely, the data quality is poor. Skewness and Kurtosis can be used to quantify the noise distribution. Non-Gaussian noise, such as eddy current artifacts, can be determined by Skewness and Kurtosis. When there are obvious artifacts, the absolute values ​​of Skewness and Kurtosis are very high. If the calculated Skewness and Kurtosis values ​​are close to 0, it means that no obvious artifacts appear. Figure 4 is a schematic diagram of the signal characteristics, such as Figure 4 As shown: Figure 4 (a) is a schematic diagram of the temporal signal-to-noise ratio tSNR; Figure 4 (b) is a schematic diagram of skewness Skewnes; Figure 4(c) is a schematic diagram of Kurtosis. The higher the tSNR value, the better the temporal signal-to-noise ratio of the area. The larger the absolute values ​​of Skewness and Kurtosis, the more artifacts there are in the area. Figure 4 (b) and Figure 4 The area indicated by the arrow in (c) is the area where artifacts exist.

[0071] Optionally, if the value of tSNR is high, a higher weight is assigned to the corresponding coil channel; if the value of tSNR is low, a lower weight is assigned to the corresponding coil channel. Alternatively, if the absolute value of Skewness is high, a lower weight is assigned to the corresponding coil channel; if the value of Skewness is high, a lower weight is assigned to the corresponding coil channel. Alternatively, if the absolute value of Kurtosis is high, a lower weight is assigned to the signal value; if the value of Kurtosis is high, a lower weight is assigned to the signal value.

[0072] Optionally, the values ​​of one or more signal features of the temporal signal-to-noise ratio tSNR, skewness and kurtosis can be processed based on a preset function, and the voxel index can be obtained by combining the processed signal features and the corresponding preset coefficients. Take the combination of the above three signal features to obtain the voxel index as an example: According to the calculated values ​​of the three signal features and the corresponding three preset coefficients W1, W2, W3, the voxel index W is obtained comprehensively:

[0073]

[0074] Optionally, the method for calculating the index W of the voxel also includes:

[0075] W=W1tSNR+W2tanh(Skewness)+W3tanh(Kurtosis)

[0076] in,

[0077]

[0078] The preset coefficients W1, W2, and W3 may be preset values ​​or may be modified according to application requirements. For example, if a higher tSNR is preferred for magnetic resonance images, the value of the preset coefficient W1 may be set larger; if a smaller artifact is preferred for magnetic resonance images, the values ​​of W2 and W3 may be set larger. The default settings may be W1=1, W2=W3=0.5.

[0079] After calculating the index W of the voxel, the weight of the coil channel corresponding to the voxel is calculated according to the index W, and then multiplied by the signal value of the channel and added together to obtain the final data S(t), that is, the second data of the voxel:

[0080]

[0081] Wherein, i is the number of coil channels in the magnetic resonance imaging process, and i is a positive integer.

[0082] This embodiment obtains the data acquisition results of the coil array at different time points, and calculates the index of the voxel based on the signal value of the same voxel at different time points, and determines the weight of the corresponding coil channel based on the index of the voxel. The signal value corresponding to each coil channel is weighted based on the weight to complete the screening and combination of the data, and imaging is performed based on the processed magnetic resonance data, thereby achieving the effect of improving the signal-to-noise ratio in the time dimension and reducing artifacts. Compared with the traditional method of weighted averaging and summing the signals of each channel, the method of this embodiment is simple and convenient, and can improve the signal-to-noise ratio in the time dimension without sacrificing the time resolution and spatial resolution, so that the overall signal is more stable.

[0083] Based on the same inventive concept, the embodiment of the present application also provides a magnetic resonance data processing device for implementing the magnetic resonance data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more magnetic resonance data processing device embodiments provided below can refer to the limitations of the magnetic resonance data processing method above, and will not be repeated here.

[0084] In one embodiment, Figure 5 As shown, a magnetic resonance data processing device is provided, comprising: an acquisition module, a first calculation module, a second calculation module and an adjustment module, wherein:

[0085] An acquisition module, used for acquiring first data acquired by the coil channel during magnetic resonance imaging;

[0086] A first calculation module, configured to calculate a signal feature of each voxel according to a plurality of signal values ​​of the voxels in the first data at different acquisition times;

[0087] A second calculation module is used to determine the weight of the coil channel corresponding to the voxel according to the signal characteristics;

[0088] The adjustment module is used to adjust the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel.

[0089] In one embodiment, the first calculation module calculates one or more of the following signal characteristics based on multiple signal values ​​of voxels in the first data at different acquisition times: temporal signal-to-noise ratio, skewness and kurtosis of the voxels.

[0090] In one embodiment, the second calculation module determines the weight of the corresponding coil channel according to the signal feature, including: multiplying the value of the signal feature by the corresponding preset coefficient to obtain the weight of the coil channel corresponding to the voxel. Further, the preset coefficient is obtained based on the value of the signal feature, wherein the higher the image quality corresponding to the multiple signal values ​​determined based on the value of the signal feature, the larger the preset coefficient corresponding to the signal feature.

[0091] In one embodiment, the adjustment module adjusts the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel, including: multiplying the signal values ​​of multiple coil channels by the corresponding weights respectively to obtain the second data of the voxel.

[0092] Optionally, after the adjustment module adjusts the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel, the method further includes: superimposing the second data of multiple voxels to obtain a magnetic resonance image.

[0093] Each module in the above-mentioned magnetic resonance data processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0094] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a magnetic resonance data processing method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0095] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0096] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0097] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0098] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0099] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0100] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for processing magnetic resonance data, characterized in that: The method comprises: Acquire first data collected by a coil channel during magnetic resonance imaging; Calculating the signal characteristics of each voxel according to a plurality of signal values ​​of the voxels in the first data at different acquisition times; Determining the weight of the coil channel corresponding to the voxel according to the signal characteristics; The signal value corresponding to the coil channel is adjusted according to the weight to obtain second data of the voxel.

2. The method according to claim 1, characterized in that According to a plurality of signal values ​​of voxels in the first data at different acquisition times, one or more of the following signal characteristics are calculated: a time signal-to-noise ratio, a skewness, and a kurtosis of the voxels.

3. The method according to claim 1, characterized in that Determining the weight of the corresponding coil channel according to the signal feature includes: The value of the signal feature is multiplied by the corresponding preset coefficient to obtain the weight of the coil channel corresponding to the voxel.

4. The method according to claim 3, characterized in that The preset coefficient is obtained based on the value of the signal feature, wherein the higher the image quality corresponding to the multiple signal values ​​determined based on the value of the signal feature, the larger the preset coefficient corresponding to the signal feature.

5. The method according to claim 1, characterized in that Adjusting the signal value corresponding to the coil channel according to the weight to obtain second data of the voxel includes: The signal values ​​of the plurality of coil channels are multiplied by corresponding weights respectively to obtain the second data of the voxel.

6. The method according to claim 1 or 5, characterized in that: After adjusting the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel, the method further includes: The second data of a plurality of voxels are superimposed to obtain a magnetic resonance image.

7. A magnetic resonance data device, characterized in that: The device comprises: An acquisition module, used for acquiring first data acquired by the coil channel during magnetic resonance imaging; A first calculation module, configured to calculate a signal feature of each voxel according to a plurality of signal values ​​of the voxels in the first data at different acquisition times; A second calculation module, used to determine the weight of the coil channel corresponding to the voxel according to the signal feature; An adjustment module is used to adjust the signal value corresponding to the coil channel according to the weight to obtain the second data of the voxel.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images