Brain white matter fiber bundle tracing analysis system based on magnetic resonance imaging

Abstract

The invention discloses a white matter fiber bundle tracing analysis system based on magnetic resonance imaging, which comprises a magnetic resonance image acquisition end, an image adjustment analysis end and a feature information processing end, relates to the technical field of magnetic resonance imaging, and solves the problem that the magnetic resonance image cannot be subjected to fuzzy recognition due to the influence of different factors. According to the technical scheme of the invention, the magnetic resonance image is identified and analyzed, whether the current magnetic resonance image needs to be adjusted and analyzed is judged by analyzing the shadow existing in the magnetic resonance image, and matching and screening are carried out according to the characteristics of the magnetic resonance image for the condition needing to be adjusted and analyzed. The current parameters corresponding to the current magnetic resonance image are analyzed in combination with historical records, and the current parameters are reasonably adjusted according to the magnetic resonance image under the normal condition, so that the scanning parameters can be adjusted in combination with different characteristics, the magnetic resonance image is ensured to be clear, and the identification error is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of magnetic resonance imaging, in particular to a brain white matter fiber bundle tracing and analysis system based on magnetic resonance imaging. Background Art

[0002] Fiber tract tracing analysis is a computational reconstruction method using diffusion-weighted magnetic resonance imaging (DWI). It can reveal the trajectory of white matter pathways in the human brain and infer the underlying structural connections of the brain. The core of fiber tract tracing analysis lies in diffusion tensor imaging (DTI), which is a magnetic resonance imaging technology that infers the direction and organizational structure of fiber bundles by measuring the diffusion of water molecules in the white matter of the brain.

[0003] According to the patent application number CN202010029910.8, a method and system for tracing and analyzing white matter fiber bundles based on magnetic resonance imaging, the specific method is: preprocessing the DTI images of each subject to obtain the fiber bundle direction distribution probability in each voxel in the whole brain of the corresponding subject, and transforming the seed area located in the standard space to the DTI space of each subject as the seed ROI; using multiple streamlines to start from each voxel of the seed ROI, and extending according to the fiber bundle direction distribution probability in the voxel, to obtain a probabilistic connection map reflecting the seed area to the whole brain; then retaining the voxels whose connection probability with the seed ROI is greater than the set threshold, transforming the white matter fiber bundles located in the DTI space of each subject to the standard MNI space, and according to the white matter fiber bundles of each subject in the standard MNI space, obtaining the standardized fiber bundles that are significantly present in the DTI images of each subject.

[0004] When some existing methods use magnetic resonance images for brain protein fiber bundle tracing analysis, due to the influence of various factors, the final magnetic resonance images may be blurred and unrecognizable, which will further affect subsequent judgments and may even cause misjudgment in severe cases. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a brain white matter fiber bundle tracing and analysis system based on magnetic resonance imaging, which solves the problem that the influence of different factors may cause the magnetic resonance image to be blurred and unrecognizable, thus affecting the overall judgment.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a brain white matter fiber bundle tracing and analysis system based on magnetic resonance imaging, comprising: The magnetic resonance image acquisition end is used to acquire the current magnetic resonance image and transmit the acquired current magnetic resonance image to the image adjustment and analysis end.

[0007] The image adjustment analysis terminal is used to identify the acquired current magnetic resonance image and determine whether the current scanning parameters need to be adjusted. The magnetic resonance scanning parameters mainly include the following: Repetition time (TR): The time interval between two adjacent pulse sequences. TR is one of the factors determining the signal intensity. The longer the TR, the stronger the signal, the higher the signal-to-noise ratio of the image, but the scanning time will also be extended. Conversely, the shorter the TR, the weaker the signal, the lower the signal-to-noise ratio of the image, but the scanning time is shortened.

[0008] Echo time (TE): The time from the excitation of the radiofrequency pulse to the formation of the maximum echo. It is one of the most important scanning parameters affecting the image contrast. The echo time is inversely correlated with the signal intensity. When the TE is extended, the T2 contrast is stronger and the signal-to-noise ratio is reduced; when the TE is shortened, the signal-to-noise ratio increases, the T1 weight increases, and the T2 contrast is reduced.

[0009] Number of slices: The number of image slices generated during the scanning process.

[0010] Slice thickness: The thickness of each image slice.

[0011] Slice gap: The distance between adjacent image slices.

[0012] Scanning time: The time required to complete the entire magnetic resonance scan.

[0013] In addition, there are also parameters such as inversion time (TI), flip angle (FA), and number of excitations (NEX).

[0014] And the specific judgment method is as follows: Acquire the current magnetic resonance image, and at the same time identify the number of fiber bundles in the current magnetic resonance image and label them as i, where i = 1, 2,..., j. Then, screen the fiber bundle i with shadows and denote it as the fiber bundle to be analyzed a, where a = 1, 2,..., b. At the same time, calculate the shadow area of the fiber bundle to be analyzed a and denote it as Sa. Then, sum up the shadow areas Sa of all the fiber bundles to be analyzed a to obtain the total shadow area and denote it as Sz; Compare the total shadow area Sz with the threshold Sy. The specific value of the threshold Sy is set by the operator. When Sz ≥ Sy, it means that the total shadow area exceeds the threshold, and an image adjustment signal is generated. Conversely, when Sz < Sy, it means that the total shadow area does not exceed the threshold, and an image output signal is generated. Then, directly transmit the image output signal to the adjustment information output terminal, and transmit the image adjustment signal to the feature information processing terminal. Here, the adjustment information output terminal will directly display the magnetic resonance image to the operator.

[0015] The characteristic information processing end is used to obtain the current magnetic resonance image corresponding to the image adjustment signal, and obtain the historical adjustment record transmitted by the adjustment record storage end, and adjust the current scanning parameters of the magnetic resonance image according to the historical adjustment record, and the specific adjustment method is as follows: The features of the current magnetic resonance image are extracted and recorded as target features, and at the same time, the historical magnetic resonance images in the historical adjustment records are acquired and the image features of the historical magnetic resonance images are extracted and recorded as comparison features, and then the target features and the comparison features are matched and analyzed; When the target feature matches the comparison feature, a feature matching signal is generated, which specifically indicates that there is a magnetic resonance image with the same features as the current magnetic resonance image in the historical adjustment record. Conversely, when the target feature does not match the comparison feature, a feature mismatch signal is generated, and the two are analyzed separately.

[0016] The specific analysis method when generating a feature matching signal is as follows: A historical magnetic resonance image with the same characteristics as the target in the historical adjustment record is obtained and recorded as k, where k=1, 2, ..., o, and the historical scanning parameters of the historical magnetic resonance image are obtained. The historical scanning parameters are then compared with the current scanning parameters to determine whether there are identical situations. If there are identical situations, a parameter identical signal is generated, otherwise, if there are no identical situations, a parameter different signal is generated; specifically, the identical parameters are indicated as the scanning parameters of the current magnetic resonance image are the same as the scanning parameters of the historical magnetic resonance image, which means different magnetic resonance images are generated under the same numerical values.

[0017] When a signal with the same parameters is generated, historical magnetic resonance images corresponding to the same parameters are obtained, and the historical magnetic resonance images are identified and judged. When the historical magnetic resonance images can be identified, it means that the historical scanning parameters are normal, and the current magnetic resonance image cannot be identified due to external interference, and the current scanning parameters need to be further adjusted. When the historical magnetic resonance images cannot be identified, it means that the historical scanning parameters are abnormal, and the current scanning parameters need to be further adjusted.

[0018] Then, a normal magnetic resonance image and its corresponding normal scanning parameters are obtained, and the difference between the two is compared, and the difference value is used as the adjustment parameter to generate the adjustment parameter information, and the adjustment parameter information is transmitted to the adjustment information output terminal. For example, a normal magnetic resonance image that can be clearly identified is obtained, and its corresponding parameters are recorded as normal scanning parameters, and the normal scanning parameters are further used as the adjustment standard for adjustment analysis, and then the current scanning parameters are compared with the normal scanning parameters, and the parameters with differences are adjusted, and the corresponding difference is calculated, and the difference is output as the adjustment parameter standard.

[0019] When a signal with different parameters is generated, a normal magnetic resonance image corresponding to the same feature is obtained, and normal scanning parameters corresponding to the normal magnetic resonance image are obtained, and then the difference between the normal scanning parameters and the current scanning parameters is calculated, and adjustment parameter information is generated at the same time.

[0020] The feature information processing end analyzes the generated feature mismatch signal.

[0021] And the specific method for processing the feature mismatch signal is: The target features and current scanning parameters of the current magnetic resonance image are obtained, and then the scanning parameters of the current magnetic resonance image are compared with the normal scanning parameters of the normal magnetic resonance image, and it is determined whether the current scanning parameters need to be increased or decreased, and at the same time, the manual adjustment record transmitted by the adjustment storage record end is obtained, and the maximum scanning parameter value and the minimum scanning parameter value corresponding to the normal magnetic resonance image are obtained according to the manual adjustment record, and the adjustment range is determined by the maximum scanning parameter value and the minimum scanning parameter value; At the same time, a normal magnetic resonance image matching the target feature is obtained, and the corresponding normal scanning parameters are obtained. At the same time, the normal adjustment range is determined based on the obtained normal magnetic resonance image. Then, the normal adjustment range and the adjustment range are subjected to interval intersection processing to obtain a specific adjustment range. At the same time, specific adjustment parameter information is generated and transmitted to the adjustment information output terminal.

[0022] The adjustment information output terminal is used to display the acquired specific adjustment information to the operator.

[0023] The present invention provides a brain white matter fiber bundle tracing and analysis system based on magnetic resonance imaging. Compared with the prior art, it has the following beneficial effects: The present invention identifies and analyzes magnetic resonance images, and determines whether it is necessary to adjust and analyze the current magnetic resonance image by analyzing the shadows existing in the magnetic resonance image. In the case where adjustment and analysis are required, matching and screening are performed according to the characteristics of the magnetic resonance image, and current parameters corresponding to the current magnetic resonance image are analyzed in combination with historical records. The current parameters are reasonably adjusted according to the magnetic resonance image under normal conditions, so that scanning parameters can be adjusted in combination with different characteristics, thereby ensuring the clarity of the magnetic resonance image and reducing recognition errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is the principle diagram of the system of the present invention; Figure 2 This is a schematic diagram of interval value selection in the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1 and Figure 2 The present application provides a brain white matter fiber bundle tracing and analysis system based on magnetic resonance imaging, including: a magnetic resonance image acquisition end, an image adjustment analysis end, a feature information processing end, an adjustment record storage end and an adjustment information output end. Embodiment 1

[0027] The magnetic resonance image acquisition end is used to acquire the current magnetic resonance image and transmit the acquired current magnetic resonance image to the image adjustment and analysis end.

[0028] The image adjustment analysis terminal is used to identify the current magnetic resonance image obtained and determine whether the current scanning parameters need to be adjusted. The magnetic resonance scanning parameters mainly include the following: Repetition time (TR): The time interval between two adjacent pulse sequences. TR is one of the factors that determine signal strength. The longer the TR, the stronger the signal and the higher the image signal-to-noise ratio, but the scanning time will also be longer. Conversely, the shorter the TR, the weaker the signal and the lower the image signal-to-noise ratio, but the scanning time will be shortened.

[0029] Echo time (TE): The time from the excitation of the radio frequency pulse to the formation of the maximum echo. It is one of the most important scanning parameters affecting image contrast. The echo time is inversely correlated with the signal intensity. When the TE is prolonged, the T2 contrast is stronger and the signal-to-noise ratio is reduced; when the TE is shortened, the signal-to-noise ratio increases, the T1 weight increases, and the T2 contrast decreases.

[0030] Number of slices: The number of image slices produced during the scanning process.

[0031] Slice Thickness: The thickness of each image slice.

[0032] Slice spacing: The distance between adjacent image slices.

[0033] Scan time: The time required to complete the entire MRI scan.

[0034] In addition, there are parameters such as inversion time (TI), flip angle (FA) and number of excitations (NEX).

[0035] The specific judgment method is as follows: The current magnetic resonance image is obtained. Meanwhile, the number of fiber bundles in the current magnetic resonance image is identified and numbered as i, where i = 1, 2, …, j. Then, the fiber bundle i with a shadow is screened and denoted as the fiber bundle a to be analyzed, where a = 1, 2, …, b. At the same time, the shadow area of the fiber bundle a to be analyzed is calculated and denoted as Sa. Then, the shadow areas Sa of all the fiber bundles a to be analyzed are summed up to obtain the total shadow area, denoted as Sz. The total shadow area Sz is compared with the threshold Sy, and the specific value of the threshold Sy is set by the operator. When Sz ≥ Sy, it means that the total shadow area exceeds the threshold, and an image adjustment signal is generated. On the contrary, when Sz < Sy, it means that the total shadow area does not exceed the threshold, and an image output signal is generated. Then, the image output signal is directly transmitted to the adjustment information output end, and the image adjustment signal is transmitted to the feature information processing end. Here, the adjustment information output end will directly display the magnetic resonance image to the operator.

[0036] The feature information processing end is used to obtain the current magnetic resonance image corresponding to the image adjustment signal, obtain the historical adjustment record transmitted from the adjustment record storage end, and adjust the current scanning parameters of the magnetic resonance image according to the historical adjustment record. The specific adjustment method is as follows: The features of the current magnetic resonance image are extracted and denoted as the target features. At the same time, the historical magnetic resonance images in the historical adjustment record are obtained, and the image features of the historical magnetic resonance images are extracted and denoted as the comparison features. Then, the target features and the comparison features are matched and analyzed. When the target features and the comparison features match, a feature matching signal is generated, specifically indicating that there is a magnetic resonance image with the same features as the current magnetic resonance image in the historical adjustment record. On the contrary, when the target features and the comparison features do not match, a feature mismatch signal is generated, and both are analyzed separately.

[0037] The specific analysis method when the feature matching signal is generated is as follows: The historical magnetic resonance image with the same target features as in the historical adjustment record is obtained and denoted as k, where k = 1, 2, …, o, and the historical scanning parameters of the historical magnetic resonance image are obtained. Then, the historical scanning parameters are compared with the current scanning parameters to determine whether there is the same situation. If there is the same situation, a parameter same signal is generated. On the contrary, if there is no same situation, a parameter different signal is generated. Specifically, the same parameters mean that the scanning parameters of the current magnetic resonance image are the same as those of the historical magnetic resonance image, which means that different magnetic resonance images are generated under the same numerical values.

[0038] When a signal with the same parameters is generated, historical magnetic resonance images corresponding to the same parameters are obtained, and the historical magnetic resonance images are identified and judged. When the historical magnetic resonance images can be identified, it means that the historical scanning parameters are normal, and the current magnetic resonance image cannot be identified due to external interference, and the current scanning parameters need to be further adjusted. When the historical magnetic resonance images cannot be identified, it means that the historical scanning parameters are abnormal, and the current scanning parameters need to be further adjusted.

[0039] Then, a normal magnetic resonance image and its corresponding normal scanning parameters are obtained, and the difference between the two is compared, and the difference value is used as the adjustment parameter to generate the adjustment parameter information, and the adjustment parameter information is transmitted to the adjustment information output terminal. For example, a normal magnetic resonance image that can be clearly identified is obtained, and its corresponding parameters are recorded as normal scanning parameters, and the normal scanning parameters are further used as the adjustment standard for adjustment analysis, and then the current scanning parameters are compared with the normal scanning parameters, and the parameters with differences are adjusted, and the corresponding difference is calculated, and the difference is output as the adjustment parameter standard.

[0040] When a signal with different parameters is generated, a normal magnetic resonance image corresponding to the same feature is obtained, and normal scanning parameters corresponding to the normal magnetic resonance image are obtained, and then the difference between the normal scanning parameters and the current scanning parameters is calculated, and adjustment parameter information is generated at the same time.

[0041] For example, the repetition time corresponding to the normal parameters is 0.1s, and the repetition time corresponding to the current scanning parameters is 0.04s. Therefore, the time difference between the two is calculated to be 0.06s. Therefore, when making subsequent adjustments, the standard value of the adjustment is 0.06s.

[0042] The adjustment information output terminal is used to display the acquired adjustment parameter information to the operator. Embodiment 2

[0043] As the second embodiment of the present invention, it is implemented on the basis of the first embodiment, and the difference from the first embodiment is that the feature information processing end analyzes the generated feature mismatch signal.

[0044] And the specific method for processing the feature mismatch signal is: The target features and current scanning parameters of the current magnetic resonance image are obtained, and then the scanning parameters of the current magnetic resonance image are compared with the normal scanning parameters of the normal magnetic resonance image, and it is determined whether the current scanning parameters need to be increased or decreased, and at the same time, the manual adjustment record transmitted by the adjustment storage record end is obtained, and the maximum scanning parameter value and the minimum scanning parameter value corresponding to the normal magnetic resonance image are obtained according to the manual adjustment record, and the adjustment range is determined by the maximum scanning parameter value and the minimum scanning parameter value; At the same time, a normal magnetic resonance image matching the target feature is obtained, and the corresponding normal scanning parameters are obtained. At the same time, the normal adjustment range is determined based on the obtained normal magnetic resonance image. Then, the normal adjustment range and the adjustment range are subjected to interval intersection processing to obtain a specific adjustment range. At the same time, specific adjustment parameter information is generated and transmitted to the adjustment information output terminal.

[0045] Combined with actual analysis, for example, the interval of the adjustment range is [A, B], and the normal adjustment range interval obtained after analysis is [C, D]. Therefore, to obtain the value of the intersection of the two intervals, it is necessary to compare the values ​​of A, B, C and D. When the value of C is greater than A, the minimum value of the two intervals is A, and when D is less than B, the maximum value is D. Therefore, the generated new interval range is [A, D].

[0046] The adjustment information output terminal is used to display the acquired specific adjustment information to the operator. Embodiment 3

[0047] As the third embodiment of the present invention, the focus is on combining the implementation processes of the first embodiment and the second embodiment.

[0048] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0049] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging, characterized in that: Including: A magnetic resonance image acquisition terminal for transmitting the acquired current magnetic resonance image to an image adjustment and analysis terminal; An image adjustment and analysis terminal for identifying the current magnetic resonance image, judging whether the current magnetic resonance image needs to adjust the current scanning parameters by comparing the shadow area of the current magnetic resonance image with a threshold, generating an image adjustment signal or an image output signal, and then transmitting the image adjustment signal to a feature information processing terminal; A feature information processing terminal for performing feature analysis on the current magnetic resonance image in combination with the historical adjustment record transmitted by the adjustment record storage terminal, and generating a feature matching signal or a feature non-matching signal; For the feature matching signal, judging whether the current scanning parameters corresponding to the current magnetic resonance image are the same as the historical scanning parameters corresponding to the historical magnetic resonance image in combination with the historical adjustment record, generating a parameter same signal or a parameter different signal, and respectively analyzing the two to generate adjustment parameter information; For the feature non-matching signal, comparing the current scanning parameters of the current magnetic resonance image with the normal scanning parameters of the normal magnetic resonance image, determining the adjustment range in combination with the manual adjustment record, generating a normal adjustment range according to the normal magnetic resonance image matching the target feature, then combining the determined adjustment range with the normal adjustment range to generate adjustment parameter information, and transmitting it to the adjustment information output terminal.

2. The brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging according to claim 1, characterized in that: The specific manner in which the image adjustment and analysis terminal identifies the current magnetic resonance image is as follows: A1: Obtain the current magnetic resonance image. At the same time, count the number of fiber bundles in the current magnetic resonance image and label them as i, where i = 1, 2, …, j. Then, screen the fiber bundle i with shadows and denote it as the fiber bundle to be analyzed a, where a = 1, 2, …, b. At the same time, calculate the shadow area of the fiber bundle to be analyzed a and denote it as Sa. Then, sum up the shadow areas Sa of all the fiber bundles to be analyzed a to obtain the total shadow area and denote it as Sz; A2: Compare the total shadow area Sz with the threshold Sy. When Sz ≥ Sy, generate an image adjustment signal. On the contrary, when Sz < Sy, generate an image output signal.

3. The brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging according to claim 1, characterized in that: The specific manner in which the feature information processing terminal analyzes the image adjustment signal is as follows: P1: Obtain the historical adjustment record transmitted by the adjustment record storage terminal, extract the features of the current magnetic resonance image and denote them as target features. At the same time, extract the image features corresponding to the historical magnetic resonance image in the historical adjustment record and denote them as comparison features, and compare the target features with the comparison features; P2: When the target features match the comparison features, generate a feature matching signal. On the contrary, when the target features do not match the comparison features, generate a feature non-matching signal.

4. The brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging according to claim 3, characterized in that: The specific manner in which the feature information processing terminal analyzes the feature matching signal is: Obtain the historical magnetic resonance image k that is the same as the target feature in the historical adjustment record, where k = 1, 2, …, o, obtain the historical scanning parameters of the historical magnetic resonance image, and compare the historical scanning parameters with the current scanning parameters to generate a parameter same signal or a parameter different signal; When a signal with different parameters is generated, a normal magnetic resonance image corresponding to the same feature is obtained, and normal scanning parameters corresponding to the normal magnetic resonance image are obtained, and then the difference between the normal scanning parameters and the current scanning parameters is calculated, and adjustment parameter information is generated at the same time.

5. The brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging according to claim 4, characterized in that: The specific method of analyzing the signal with the same parameters by the characteristic information processing end is: Obtaining historical magnetic resonance images corresponding to the same parameters, and identifying and judging the historical magnetic resonance images. When the historical magnetic resonance images can be identified, it means that the historical scanning parameters are normal. Conversely, when the historical magnetic resonance images can be identified, it means that the historical scanning parameters are abnormal. Then, a normal magnetic resonance image and its corresponding normal scanning parameters are obtained, and the difference between the two is compared, and the difference value is used as an adjustment parameter to generate adjustment parameter information.

6. The brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging according to claim 1, characterized in that: The specific method in which the feature information processing end analyzes the feature mismatch signal is: Obtaining the target features and current scanning parameters of the current magnetic resonance image, then comparing the scanning parameters of the current magnetic resonance image with normal scanning parameters of a normal magnetic resonance image, and simultaneously obtaining the manual adjustment record transmitted by the adjustment storage and recording end; And obtain the maximum scanning parameter value and the minimum scanning parameter value according to the manual adjustment record, and determine the adjustment range with the maximum scanning parameter value and the minimum scanning parameter value; A normal magnetic resonance image matching the target feature is obtained, and the corresponding normal scanning parameters are obtained. At the same time, a normal adjustment range is determined according to the obtained normal magnetic resonance image. Then, the determined adjustment range is processed with the normal adjustment range by performing interval intersection processing to obtain an adjustment range, and adjustment parameter information is generated at the same time.

7. The brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging according to claim 1, characterized in that: The adjustment record storage end is used to transmit historical adjustment records and manual adjustment records to the feature information processing end.

8. The brain white matter fiber tract tracing and analysis system based on magnetic resonance imaging according to claim 1, characterized in that: The adjustment information output terminal is used to display the adjustment parameter information to the operator.

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