Pre-scanning discrimination method and device and magnetic resonance imaging system
By calculating the consistency of the MR data set in the MRI system, it is automatically determined whether prescans are needed, which solves the image artifact problem caused by changes in the prescan data, and improves image quality and patient experience.
Patent Information
- Application Number
- CN202410016102.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-03
- Publication Date
- 2025-07-04
AI Technical Summary
In the existing MRI technology, the failure to update the changes in pre-scan data in time leads to artifacts and abnormal shearing in image reconstruction, which increases the workload of MRI operators and imaging scanning time, and reduces the patient experience.
After each imaging scan, the imaging target is scanned by using a pre-scan to determine the consistency of the MR data set, and determine whether pre-scan is needed, including calculating indexes such as amplitude consistency, cosine similarity, Minkowsky distance and Pearson correlation coefficient, and automatically determine whether a re-scan is needed.
It realizes automatic, fast and accurate judgment of pre-scans, improves image reconstruction quality, reduces labor costs, and improves patient experience.
Smart Images
Figure CN120259693A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of MRI (Magnetic Resonance Imaging), and particularly to a pre-scan discrimination method, apparatus, and MRI system. Background Art
[0002] In MRI, before a formal imaging scan, a pre-scan needs to be performed first to complete the preparatory work before the imaging scan, such as frequency adjustment, B0 mapping, and coil sensitivity measurement. The duration required for a complete pre-scan is about several tens of seconds. To save time, after a complete pre-scan is executed, the obtained adjustment data will be stored in the system for subsequent imaging scans. Only when the patient's examination table position or coil selection changes will a new pre-scan be performed.
[0003] However, during an imaging scan or between two imaging scans, unexpected changes in the adjustment data are very common. For example, the movement of the patient's body, the movement of a non-fixed coil, or signal drift or glitches in the RF (Radio Frequency) transmitter-to-receiver link, etc., which will cause problems in the image reconstruction that requires the adjustment data.
[0004] Figure 1The following is an example diagram showing that during breast MRI of a patient, the patient's body movement causes artifacts in the reconstructed image and abnormal shearing. In this example, before the formal imaging scan of the patient's breast, a scout scan was first performed, and the scout image as shown in 11 was obtained. It can be seen from this scout image that the patient's body position is incorrect. After adjusting the patient's body position, a scout scan was performed again, and the scout image as shown in 12 was obtained. It can be seen that a complete breast image can be obtained at the current scan position. However, since the bed position, the selected coil and its position, and the scan field of view have not changed, the prescan was not redone, and the coil sensitivity information stored in the system was still obtained during the prescan before the patient's body position was adjusted. Then, an imaging scan of the patient's breast was performed, and the diffusion-weighted image as shown in 13 was obtained. It can be seen that worm-like artifacts as shown in 141 in 14 appear in this image. Further, a noise mask image filter was used to filter the diffusion-weighted image shown in 13, and the reconstructed image as shown in 15 was obtained. It can be seen from 15 that the uppermost part of the breast was abnormally sheared. If the system can trigger a redo of the prescan before the imaging scan, the latest coil sensitivity information can be obtained, and the artifacts shown in 141 will not appear. It should be noted that there are some white characters in 13, 14, and 15. These characters are automatically generated during the picture generation process. Since they cover the pictures, forcibly erasing them will also erase some image details, and the existence of these characters does not affect the understanding of this prior art. Therefore, these characters are retained.
[0005] Currently, only when the MRI operator discovers image abnormalities from the images obtained through the imaging scan can it be determined that a prescan needs to be redone. This not only increases the workload of the MRI operator but also increases the total duration of the imaging scan and reduces the patient experience. Summary of the Invention
[0006] In view of this, on the one hand, an embodiment of the present invention proposes a prescan discrimination method and device to achieve automatic discrimination of whether a prescan is required; on the other hand, an MRI system is proposed to achieve automatic discrimination of whether a prescan is required.
[0007] A prescan discrimination method, the method includes:
[0008] After each magnetic resonance (MR) imaging scan ends and before the next MR imaging scan starts, scan the imaging target using a prescan discrimination sequence, and collect the corresponding MR data set according to the position and number of the K-space lines to be collected preset.
[0009] Calculate the consistency between the MR data set collected this time and the MR data set collected using the prescan discrimination sequence last time.
[0010] According to the consistency between the two MR data sets, it is determined whether a prescan is required before the start of the next MR imaging scan.
[0011] The prescan discrimination sequence is: an MR sequence for obtaining a coil sensitivity map.
[0012] The acquisition of the corresponding MR data sets according to the positions and numbers of the K-space lines to be acquired preset includes:
[0013] Acquire a preset number of K-space lines located at the center of K-space.
[0014] The calculation of the consistency between the two MR data sets includes one or any combination of the following:
[0015] Calculate the consistency between the amplitudes of the two MR data sets;
[0016] Calculate the cosine similarity between the two MR data sets;
[0017] Calculate the Minkowski distance between the two MR data sets;
[0018] Calculate the Pearson correlation coefficient between the two MR data sets.
[0019] The calculation of the consistency between the amplitudes of the two MR data sets includes:
[0020] For each MR data set, find the data point with the largest amplitude on each K-space line of the MR data set, calculate the average amplitude of the data points with the largest amplitude on each K-space line of the MR data set, and calculate the ratio of the corresponding average amplitudes of the two MR data sets.
[0021] When the calculation of the consistency between the two MR data sets includes: calculating the consistency between the amplitudes of the two MR data sets,
[0022] The determination of whether a prescan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets includes:
[0023] Determine whether the ratio is within a preset range. If so, no prescan is required before the start of the next MR imaging scan; otherwise, a prescan is required before the start of the next MR imaging scan.
[0024] The calculation of the cosine similarity between the two MR data sets includes:
[0025] Multiply the vectors corresponding to the two MR data sets to obtain a vector product;
[0026] Calculate the norms of the vectors corresponding to the two MR data sets respectively, and multiply the two norms to obtain a norm product;
[0027] Calculate the ratio of the vector product and the product of the magnitudes to obtain the cosine similarity between the two MR data sets.
[0028] When calculating the consistency between the two MR data sets, including: when calculating the cosine similarity between the two MR data sets,
[0029] According to the consistency between the two MR data sets, determining whether a pre-scan is required before the start of the next MR imaging scan includes:
[0030] Determine whether the cosine similarity between the two MR data sets is greater than a preset first threshold. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0031] Calculating the Minkowski distance between the two MR data sets includes:
[0032]
[0033] where A and B are the vectors corresponding to the two MR data sets respectively, dist(A, B) is the Minkowski distance between the two MR data sets, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, and p is a variable integer, p ≥ 1.
[0034] When calculating the consistency between the two MR data sets, including: when calculating the Minkowski distance between the two MR data sets,
[0035] According to the consistency between the two MR data sets, determining whether a pre-scan is required before the start of the next MR imaging scan includes:
[0036] Determine whether the Minkowski distance between the two MR data sets is less than a preset second threshold. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0037] Calculating the Pearson correlation coefficient between the two MR data sets includes:
[0038]
[0039] where A and B are the vectors corresponding to the two MR data sets respectively, r is the Pearson correlation coefficient between the two MR data sets, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, is the mean of all data points in A, is the mean of all data points in B.
[0040] When calculating the consistency between the two MR data sets, including: when calculating the Pearson correlation coefficient between the two MR data sets,
[0041] Judging whether a prescan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets, including:
[0042] Judging whether the Pearson correlation coefficient between the two MR data sets is greater than a preset third threshold. If so, no prescan is required before the start of the next MR imaging scan; otherwise, a prescan is required before the start of the next MR imaging scan.
[0043] A prescan discrimination device, the device includes:
[0044] An acquisition module, configured to scan and image a target using a prescan discrimination sequence after each magnetic resonance (MR) imaging scan and before the start of the next MR imaging scan, and acquire corresponding MR data sets according to the positions and numbers of the K-space lines to be acquired preset;
[0045] A calculation module, configured to calculate the consistency between the two MR data sets according to the MR data set acquired this time and the MR data set acquired by using the prescan discrimination sequence last time;
[0046] A discrimination module, configured to judge whether a prescan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets.
[0047] The acquisition module acquires corresponding MR data sets according to the positions and numbers of the K-space lines to be acquired preset, including:
[0048] Acquire a preset number of K-space lines located at the center of the K-space.
[0049] The calculation module calculates the consistency between the two MR data sets, including one of the following or any combination:
[0050] Calculate the consistency between the amplitudes of the two MR data sets;
[0051] Calculate the cosine similarity between the two MR data sets;
[0052] Calculate the Minkowski distance between the two MR data sets;
[0053] Calculate the Pearson correlation coefficient between the two MR data sets.
[0054] The calculation module calculates the consistency between the amplitudes of the two MR data sets, including:
[0055] For each MR data set, find the data point with the largest amplitude on each K-space line of the MR data set respectively, calculate the average amplitude of the data points with the largest amplitudes on the K-space lines of the MR data set, and calculate the ratio of the average amplitudes corresponding to the two MR data sets;
[0056] Moreover, when the calculation module calculates the consistency between the two MR data sets, including calculating the consistency between the amplitudes of the two MR data sets, the discrimination module determines whether a pre-scan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets, including:
[0057] Determine whether the ratio is within a preset range. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0058] The calculation module calculates the cosine similarity between the two MR data sets, including:
[0059] Multiply the vectors corresponding to the two MR data sets to obtain a vector product; calculate the norms of the vectors corresponding to the two MR data sets respectively, multiply the two norms to obtain a norm product; calculate the ratio of the vector product and the norm product to obtain the cosine similarity between the two MR data sets;
[0060] Moreover, when the calculation module calculates the consistency between the two MR data sets, including calculating the cosine similarity between the two MR data sets, the discrimination module determines whether a pre-scan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets, including:
[0061] Determine whether the cosine similarity between the two MR data sets is greater than a preset first threshold. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0062] The calculation module calculates the Minkowski distance between the two MR data sets, including:
[0063]
[0064] where A and B are the vectors corresponding to the two MR data sets respectively, dist(A, B) is the Minkowski distance between the two MR data sets, a i and b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, and p is a variable integer, p ≥ 1;
[0065] Moreover, when the calculation module calculates the consistency between the two MR data sets, including calculating the Minkowski distance between the two MR data sets, the discrimination module determines whether a pre-scan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets, including:
[0066] Determine whether the Minkowski distance between the two MR data sets is less than a preset second threshold. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0067] The calculation module calculates the Pearson correlation coefficient between the two MR data sets, including:
[0068]
[0069] where A and B are the vectors corresponding to the two MR data sets respectively, r is the Pearson correlation coefficient between the two MR data sets, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, is the mean of all data points in A, is the mean of all data points in B;
[0070] Moreover, when the calculation module calculates the consistency between the two MR data sets, including calculating the Pearson correlation coefficient between the two MR data sets, the discrimination module determines whether a pre-scan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets, including:
[0071] Determine whether the Pearson correlation coefficient between the two MR data sets is greater than a preset third threshold. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0072] A magnetic resonance imaging (MRI) system, the MRI system includes the pre-scan discrimination device as described in any one of the above.
[0073] In the embodiments of the present invention, between every two imaging scans, a pre-scan discrimination sequence is used to scan the imaging target, and the consistency between the collected MR (Magnetic Resonance) data set and the MR data set collected by scanning the imaging target with the pre-scan discrimination sequence in the previous time is calculated. According to the calculated correlation, it is determined whether a pre-scan is required before the next MR imaging scan starts, thereby realizing automatic, fast, and accurate discrimination of whether a pre-scan is required, improving the quality of the reconstructed image, reducing the labor cost, and enhancing the patient experience. Description of the Drawings
[0074] The following will, by referring to the accompanying drawings, describe in detail the preferred embodiments of the present invention to make those of ordinary skill in the art more clearly understand the above and other features and advantages of the present invention. In the drawings:
[0075] Figure 1 It is an example diagram of artifacts and abnormal shearing in the reconstructed image caused by the patient's body movement during breast MRI of a patient in the existing technology;
[0076] Figure 2 It is a flowchart of the pre-scan discrimination method provided by the embodiments of the present invention;
[0077] Figure 3 It is an example diagram of the positions of 4 K-space lines collected in an application example of the present invention;
[0078] Figure 4 It is a schematic structural diagram of the pre-scan discrimination device provided by the embodiments of the present invention.
[0079] Among them, the reference numerals are as follows:
[0080]
[0081] Detailed Embodiments
[0082] To make the objectives, technical solutions, and advantages of the present invention clearer, the following examples are given to further elaborate on the present invention in detail.
[0083] Figure 2 It is a flowchart of the pre-scan discrimination method provided by the embodiments of the present invention, and its specific steps are as follows:
[0084] Step 201: After each MR imaging scan ends and before the next MR imaging scan starts, use a pre-scan discrimination sequence to scan the imaging target, and collect the corresponding MR data set according to the positions and numbers of the K-space lines to be collected preset.
[0085] In an alternative embodiment, the pre-scanning discrimination sequence is an MR sequence for obtaining a coil sensitivity map, such as a 3D FLASH (Fast Low Angle SHot) sequence. In practical applications, other sequences can also be used based on the results of multiple experiments, as long as the sequence satisfies the following two points: short acquisition time and the ability to significantly characterize the differences in MR images caused by the differences in the positions of the imaging targets and the selection of receiving coils.
[0086] In an alternative embodiment, according to the preset positions and numbers of the K-space lines to be acquired, the corresponding MR data sets are acquired, including: acquiring a preset number of K-space lines located at the center of the K-space.
[0087] The number of K-space lines to be acquired can be determined based on the acceptable time interval between two imaging scans and the fact that the more K-space lines are acquired, the more reliable the pre-scanning discrimination result. For example: when the pre-scanning discrimination sequence is an MR sequence for obtaining a coil sensitivity map, according to multiple experiments, it is known that when 4 K-space lines are acquired at the center of the K-space, the pre-scanning discrimination result is very reliable, and the acquisition duration of the MR data is less than 10 ms, and the patient can hardly perceive this acquisition process, which fully meets the acceptable time interval between two imaging scans.
[0088] Figure 3 This is the position of 4 K-space lines acquired in an application example of the present invention, where the positions of the 4 K-space lines are the positions where the four "×" are located.
[0089] Step 202: Calculate the consistency between the MR data set acquired this time and the MR data set acquired using the pre-scanning discrimination sequence last time.
[0090] The MR data set acquired using the pre-scanning discrimination sequence last time is the MR data set acquired by scanning the imaging target using the pre-scanning discrimination sequence after the end of the last imaging scan and before the start of the current imaging scan.
[0091] In an alternative embodiment, calculating the consistency between the two MR data sets includes one or any combination of the following: a1) calculating the consistency between the amplitudes of the two MR data sets; a2) calculating the cosine similarity between the two MR data sets; a3) calculating the Minkowski distance between the two MR data sets; a4) calculating the Pearson correlation coefficient between the two MR data sets.
[0092] In an alternative embodiment, calculating the degree of consistency between the amplitudes of the two MR data sets includes: calculating, for each MR data set, finding the data point with the largest amplitude on each K-space line of the MR data set, calculating the average amplitude of the data points with the largest amplitudes on the K-space lines of the MR data set, and calculating the ratio of the average amplitudes corresponding to the two MR data sets.
[0093] In an alternative embodiment, calculating the cosine similarity between the two MR data sets includes: multiplying the vectors corresponding to the two MR data sets to obtain a vector product; respectively calculating the magnitudes of the vectors corresponding to the two MR data sets, multiplying the two magnitudes to obtain a magnitude product; calculating the ratio of the vector product and the magnitude product to obtain the cosine similarity between the two MR data sets. That is,
[0094]
[0095] where A and B are the vectors corresponding to the two MR data sets respectively, cosθ is the cosine similarity between A and B, and |A| and |B| represent the magnitudes of A and B respectively. When cosθ = 1, the cosine similarity between A and B is the largest, and when cosθ = -1, the cosine similarity between A and B is the smallest. The sizes of vectors A and B are m * n * k, where m is the number of K-space lines, n is the number of data points on each K-space line, and k is the number of channels, i.e., the number of receive coils (such as local coils). In actual calculation, A and B can be one-dimensional vectors of size m * n * k.
[0096] In an alternative embodiment, calculating the Minkowski distance between the two MR data sets includes:
[0097]
[0098] where A and B are the vectors corresponding to the two MR data sets respectively, dist(A, B) is the Minkowski distance between A and B, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, p is a variable integer, p ≥ 1, and the specific value of p can be determined according to multiple experiments.
[0099] In an alternative embodiment, calculating the Pearson correlation coefficient between the two MR data sets includes:
[0100]
[0101] where A and B are the vectors corresponding to the two MR data sets respectively, r is the Pearson correlation coefficient between A and B, a i , b iThey are the i-th data points in A and B respectively, and n is the total number of data points in A and B. is the mean value of all data points in A. is the mean value of all data points in B.
[0102] Step 203: Determine whether a pre-scan is required before the start of the next MR imaging scan according to the consistency between the two MR data sets.
[0103] When in Step 202, calculating the consistency between the two MR data sets only includes: a1) calculating the consistency between the amplitudes of the two MR data sets, Step 203 includes: determining whether the ratio of the average amplitudes corresponding to the two MR data sets is within a preset range. If so, a pre-scan is not required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan. Among them, the closer the ratio of the average amplitudes corresponding to the two MR data sets is to 1, the closer the current position of the imaging target and the receiving coil is to the previous position; the specific value of the preset range can be determined according to multiple experiments.
[0104] When in Step 202, calculating the consistency between the two MR data sets only includes: a2) calculating the cosine similarity between the two MR data sets, Step 203 includes: determining whether the cosine similarity between the two MR data sets is greater than a preset first threshold. If so, a pre-scan is not required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan. Among them, the value range of the cosine similarity between the two MR data sets is [-1, 1], and the closer the cosine similarity between the two MR data sets is to 1, the closer the current position of the imaging target and the receiving coil is to the previous position; the specific value of the preset first threshold can be determined according to multiple experiments.
[0105] When in Step 202, calculating the consistency between the two MR data sets only includes: a3) calculating the Minkowski distance between the two MR data sets, Step 203 includes: determining whether the Minkowski distance between the two MR data sets is less than a preset second threshold. If so, a pre-scan is not required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan. Among them, the smaller the Minkowski distance between the two MR data sets, the closer the current position of the imaging target and the receiving coil is to the previous position; the specific value of the preset second threshold can be determined according to multiple experiments.
[0106] When in step 202, calculating the consistency between the two MR data sets only includes: a4) calculating the Pearson correlation coefficient between the two MR data sets, step 203 includes: determining whether the Pearson correlation coefficient between the two MR data sets is greater than a preset third threshold. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan. Wherein, the value range of the Pearson correlation coefficient is [-1, 1]. The closer the Pearson correlation coefficient between the two MR data sets is to 1, the closer the current position of the imaging target and the receiving coil is to the previous position; the specific value of the preset third threshold can be determined according to multiple experiments.
[0107] When in step 202, calculating the consistency between the two MR data sets includes multiple ones among a1)-a4), then in step 203, multiple corresponding conditions need to be determined. If all the multiple conditions are met, no pre-scan is required before the start of the next MR imaging scan. For example: If calculating the consistency between the two MR data sets includes a1) and a2), then step 203 includes: determining whether it is satisfied that the ratio of the amplitude averages corresponding to the two MR data sets is within a preset range, and the cosine similarity between the two MR data sets is less than a preset first threshold. If so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0108] In the above embodiments, between every two imaging scans, a pre-scan discrimination sequence is used to scan the imaging target, and the consistency between the collected MR data set and the MR data set collected by scanning the imaging target with the pre-scan discrimination sequence last time is calculated. According to the calculated correlation, it is determined whether a pre-scan is required before the start of the next MR imaging scan, thereby realizing the automatic, rapid and accurate discrimination of whether a pre-scan is required, improving the quality of the reconstructed image, reducing the labor cost, and improving the patient experience.
[0109] The following gives an application example of the present invention:
[0110] In this example, between every two imaging scans, that is, after each imaging scan ends and before the next imaging scan starts, an MR sequence for obtaining a coil sensitivity map is used to scan the abdomen of a volunteer, and MR data is collected at three positions through a body coil array and a spinal coil array respectively, and MR data is collected twice at each position. For the 6 MR data sets collected at the three positions, the Pearson correlation coefficient between each MR data set is calculated to verify the feasibility of the embodiments of the present invention. Among them:
[0111] P1-1 and P1-2 correspond to MR data sets collected twice at the first position, where the first position is: the volunteer's hand is placed beside the volunteer's body;
[0112] P2-1 and P2-2 correspond to MR data sets collected twice at the second position, where the second position is: the volunteer's hand is placed on top of the body coil array;
[0113] P3-1 and P3-2 correspond to MR data sets collected twice at the third position, where the third position is: the position of the volunteer's hand is the same as the first position, that is, placed beside the volunteer's body, but the body coil array is moved downward about 3 cm (centimeters) in the direction of the volunteer's feet.
[0114] The Pearson correlation coefficients between the calculated MR data sets are shown in Table 1:
[0115] P1-1 P1-2 P2-1 P2-2 P3-1 P3-2 P1-1 1 0.9954 0.8558 0.8599 0.853 0.8619 P1-2 1 0.8533 0.8573 0.8489 0.8633 P2-1 1 0.9985 0.7501 0.7567 P2-2 1 0.749 0.756 P3-1 1 0.9952 P3-2 1
[0116] Table 1
[0117] As can be seen from Table 1, the Pearson correlation coefficients between the two MR data sets collected at the same position are all greater than 0.995. For example, the Pearson correlation coefficient between P1-1 and P1-2 is 0.9954, the Pearson correlation coefficient between P2-1 and P2-2 is 0.9985, and the Pearson correlation coefficient between P3-1 and P3-2 is 0.99; while the Pearson correlation coefficients between the two MR data sets collected at different positions are all less than 0.9. For example, the Pearson correlation coefficient between P1-1 and P2-1 is 0.8558, and the Pearson correlation coefficient between P2-2 and P3-1 is 0.749.
[0118] Figure 4 The structural schematic diagram of the pre-scanning discrimination device 40 provided by the embodiment of the present invention. The device 40 mainly includes: a collection module 41, a calculation module 42, and a discrimination module 43, where:
[0119] The collection module 41 is used to scan and image the imaging target using a pre-scanning discrimination sequence after each magnetic resonance MR imaging scan and before the next MR imaging scan starts, and collect the corresponding MR data set according to the position and number of the K-space lines to be collected preset.
[0120] The calculation module 42 is used to calculate the consistency between the MR data set collected by the collection module 41 this time and the MR data set collected by using the pre-scanning discrimination sequence last time, and send the calculated consistency between the two MR data sets to the discrimination module 43.
[0121] A discrimination module 43 is configured to receive the consistency degree between the two MR data sets sent by the calculation module 42, and determine whether a pre-scan is required before the start of the next MR imaging scan according to the consistency degree between the two MR data sets.
[0122] In an optional embodiment, the acquisition module 41 acquires corresponding MR data sets according to the positions and numbers of the K-space lines to be acquired preset, including: acquiring a preset number of K-space lines located at the center of the K-space.
[0123] In an optional embodiment, the calculation module 42 calculates the consistency degree between the two MR data sets, including one or any combination of the following: calculating the consistency degree between the amplitudes of the two MR data sets; calculating the cosine similarity between the two MR data sets; calculating the Minkowski distance between the two MR data sets; calculating the Pearson correlation coefficient between the two MR data sets.
[0124] In an optional embodiment, the calculation module 42 calculates the consistency degree between the amplitudes of the two MR data sets, including: for each MR data set, finding the data point with the largest amplitude on each K-space line of the MR data set, calculating the average amplitude of the data points with the largest amplitudes on the K-space lines of the MR data set, and calculating the ratio of the average amplitudes corresponding to the two MR data sets;
[0125] Moreover, when the calculation module 42 calculates the consistency degree between the two MR data sets, including calculating the consistency degree between the amplitudes of the two MR data sets, the discrimination module 43 determines whether a pre-scan is required before the start of the next MR imaging scan according to the consistency degree between the two MR data sets, including: determining whether the ratio is within a preset range, if so, no pre-scan is required before the start of the next MR imaging scan; otherwise, a pre-scan is required before the start of the next MR imaging scan.
[0126] In an optional embodiment, the calculation module 42 calculates the cosine similarity between the two MR data sets, including: multiplying the vectors corresponding to the two MR data sets to obtain a vector product; respectively calculating the norms of the vectors corresponding to the two MR data sets, multiplying the two norms to obtain a norm product; calculating the ratio of the vector product and the norm product to obtain the cosine similarity between the two MR data sets;
[0127] Moreover, when the calculation module 42 calculates the consistency between the two MR data sets, including calculating the cosine similarity between the two MR data sets, the discrimination module 43 determines whether a pre-scan is required before the next MR imaging scan according to the consistency between the two MR data sets, including: determining whether the cosine similarity between the two MR data sets is greater than a preset first threshold. If so, no pre-scan is required before the next MR imaging scan; otherwise, a pre-scan is required before the next MR imaging scan.
[0128] In an alternative embodiment, the calculation module 42 calculates the Minkowski distance between the two MR data sets, including:
[0129]
[0130] where A and B are the vectors corresponding to the two MR data sets respectively, dist(A, B) is the Minkowski distance between the two MR data sets, a i and b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, and p is a variable integer, p ≥ 1;
[0131] Moreover, when the calculation module 42 calculates the consistency between the two MR data sets, including calculating the Minkowski distance between the two MR data sets, the discrimination module 43 determines whether a pre-scan is required before the next MR imaging scan according to the consistency between the two MR data sets, including: determining whether the Minkowski distance between the two MR data sets is less than a preset second threshold. If so, no pre-scan is required before the next MR imaging scan; otherwise, a pre-scan is required before the next MR imaging scan.
[0132] In an alternative embodiment, the calculation module 42 calculates the Pearson correlation coefficient between the two MR data sets, including:
[0133]
[0134] where A and B are the vectors corresponding to the two MR data sets respectively, r is the Pearson correlation coefficient between the two MR data sets, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, is the mean of all data points in A, is the mean of all data points in B;
[0135] Moreover, when the calculation module 42 calculates the consistency between the two MR data sets, including calculating the Pearson correlation coefficient between the two MR data sets, the discrimination module 43 determines whether a pre-scan is required before the next MR imaging scan according to the consistency between the two MR data sets, including: determining whether the Pearson correlation coefficient between the two MR data sets is greater than a preset third threshold. If so, no pre-scan is required before the next MR imaging scan; otherwise, a pre-scan is required before the next MR imaging scan.
[0136] An embodiment of the present invention further provides an MRI system, including the pre-scan discrimination device 40 described in any of the above embodiments.
[0137] Those skilled in the art can understand that the features described in the various embodiments and / or claims of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, without departing from the spirit and teachings of this application, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, and all such combinations and / or combinations fall within the scope of the disclosure of this application.
[0138] Specific embodiments are used herein to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application, and is not used to limit the present application. For those skilled in the art, changes can be made in the specific implementation manners and application scopes according to the ideas, spirits, and principles of the present application. Any modifications, equivalent replacements, improvements, etc. made by them shall be included within the scope of protection of the present application.
Claims
1. A pre-scanning discrimination method, characterized in that, The method includes: After each magnetic resonance (MR) imaging scan is completed and before the next MR imaging scan starts, a pre-scan discrimination sequence is used to scan the imaging target, and according to the position and number of the K-space lines to be acquired as preset, the corresponding MR data set is acquired; According to the MR data set acquired this time and the MR data set acquired by using the pre-scan discrimination sequence last time, the consistency between the two MR data sets is calculated; According to the consistency between the two MR data sets, it is determined whether a pre-scan needs to be performed before the next MR imaging scan starts.
2. The method according to claim 1, wherein The pre-scan discrimination sequence is: an MR sequence for obtaining a coil sensitivity map.
3. The method according to claim 1, characterized in that The acquiring the corresponding MR data set according to the position and number of the K-space lines to be acquired as preset includes: Acquiring a preset number of K-space lines located at the center of the K-space.
4. The method according to claim 1, characterized in that, The calculating the consistency between the two MR data sets includes one or any combination of the following: Calculating the consistency between the amplitudes of the two MR data sets; Calculating the cosine similarity between the two MR data sets; Calculating the Minkowski distance between the two MR data sets; Calculating the Pearson correlation coefficient between the two MR data sets.
5. The method according to claim 4, characterized in that, The calculating the consistency between the amplitudes of the two MR data sets includes: For each MR data set, finding the data point with the largest amplitude on each K-space line of the MR data set respectively, calculating the average amplitude of the data points with the largest amplitudes on the K-space lines of the MR data set, and calculating the ratio of the average amplitudes corresponding to the two MR data sets.
6. The method according to claim 5, wherein When the calculating the consistency between the two MR data sets includes: calculating the consistency between the amplitudes of the two MR data sets, The determining whether a pre-scan needs to be performed before the next MR imaging scan starts according to the consistency between the two MR data sets includes: Determining whether the ratio is within a preset range. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
7. The method according to claim 4, wherein The calculating the cosine similarity between the two MR data sets includes: Multiplying the vectors corresponding to the two MR data sets to obtain a vector product; Calculating the norms of the vectors corresponding to the two MR data sets respectively, and multiplying the two norms to obtain a norm product; Calculating the ratio of the vector product to the norm product to obtain the cosine similarity between the two MR data sets.
8. The method according to claim 7, wherein When the calculating the consistency between the two MR data sets includes: calculating the cosine similarity between the two MR data sets, The determining whether a pre-scan needs to be performed before the next MR imaging scan starts according to the consistency between the two MR data sets includes: Determining whether the cosine similarity between the two MR data sets is greater than a preset first threshold. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
9. The method according to claim 4, wherein The calculating the Minkowski distance between the two MR data sets includes: Wherein, A and B are vectors corresponding to the two MR data sets respectively, dist(A, B) is the Minkowski distance between the two MR data sets, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, and p is a variable integer, p≥1.
10. The method according to claim 9, characterized in that When the calculating the consistency between the two MR data sets includes: calculating the Minkowski distance between the two MR data sets, Judging whether a pre-scan is required before the next MR imaging scan starts according to the consistency degree between the two MR data sets, includes: Judging whether the Minkowski distance between the two MR data sets is less than a preset second threshold. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
11. The method according to claim 4, wherein Calculating the Pearson correlation coefficient between the two MR data sets, includes: Wherein, A and B are respectively the vectors corresponding to the two MR data sets, r is the Pearson correlation coefficient between the two MR data sets, a i , b i are respectively the i-th data points in A and B, n is the total number of data points in A and B, is the mean value of all data points in A, is the mean value of all data points in B.
12. The method according to claim 11, wherein When calculating the consistency degree between the two MR data sets, including calculating the Pearson correlation coefficient between the two MR data sets, Judging whether a pre-scan is required before the next MR imaging scan starts according to the consistency degree between the two MR data sets, includes: Judging whether the Pearson correlation coefficient between the two MR data sets is greater than a preset third threshold. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
13. A pre-scanning discrimination device (40), characterized in that, The device (40) includes: An acquisition module (41), configured to, after each magnetic resonance (MR) imaging scan ends and before the next MR imaging scan starts, scan an imaging target using a pre-scan discrimination sequence, and acquire corresponding MR data sets according to the positions and numbers of the K-space lines to be acquired preset; A calculation module (42), configured to calculate the consistency degree between the two MR data sets according to the MR data set acquired this time and the MR data set acquired last time using the pre-scan discrimination sequence; A discrimination module (43), configured to judge whether a pre-scan is required before the next MR imaging scan starts according to the consistency degree between the two MR data sets.
14. The device (40) according to claim 13, characterized in that, The acquisition module (41) acquires corresponding MR data sets according to the positions and numbers of the K-space lines to be acquired preset, includes: Acquiring a preset number of K-space lines located at the center of the K-space.
15. The device (40) according to claim 13, characterized in that, The calculation module (42) calculates the consistency degree between the two MR data sets, including one of the following or any combination: Calculating the consistency degree between the amplitudes of the two MR data sets; Calculating the cosine similarity between the two MR data sets; Calculating the Minkowski distance between the two MR data sets; Calculating the Pearson correlation coefficient between the two MR data sets.
16. The device (40) according to claim 15, characterized in that, The calculation module (42) calculates the consistency degree between the amplitudes of the two MR data sets, includes: Calculating, for each MR data set, respectively finding the data point with the largest amplitude on each K-space line of the MR data set, calculating the average amplitude of the data points with the largest amplitude on each K-space line of the MR data set, and calculating the ratio of the corresponding average amplitudes of the two MR data sets; And, when the calculation module (42) calculates the consistency degree between the two MR data sets, including calculating the consistency degree between the amplitudes of the two MR data sets, the discrimination module (43) judges whether a pre-scan is required before the next MR imaging scan starts according to the consistency degree between the two MR data sets, includes: Determine whether the ratio is within a preset range. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
17. The device (40) according to claim 15, characterized in that, The calculation module (42) calculates the cosine similarity between the two MR data sets, including: Multiply the vectors corresponding to the two MR data sets to obtain a vector product; calculate the norms of the vectors corresponding to the two MR data sets respectively, and multiply the two norms to obtain a norm product; calculate the ratio of the vector product to the norm product to obtain the cosine similarity between the two MR data sets; Moreover, when the calculation module (42) calculates the consistency between the two MR data sets, including calculating the cosine similarity between the two MR data sets, the discrimination module (43) determines whether a pre-scan is required before the next MR imaging scan starts according to the consistency between the two MR data sets, including: Determine whether the cosine similarity between the two MR data sets is greater than a preset first threshold. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
18. The device (40) according to claim 15, characterized in that, The calculation module (42) calculates the Minkowski distance between the two MR data sets, including: where A and B are the vectors corresponding to the two MR data sets respectively, dist(A, B) is the Minkowski distance between the two MR data sets, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, and p is a variable integer, p ≥ 1; Moreover, when the calculation module (42) calculates the consistency between the two MR data sets, including calculating the Minkowski distance between the two MR data sets, the discrimination module (43) determines whether a pre-scan is required before the next MR imaging scan starts according to the consistency between the two MR data sets, including: Determine whether the Minkowski distance between the two MR data sets is less than a preset second threshold. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
19. The device (40) according to claim 15, characterized in that, The calculation module (42) calculates the Pearson correlation coefficient between the two MR data sets, including: where A and B are the vectors corresponding to the two MR data sets respectively, r is the Pearson correlation coefficient between the two MR data sets, a i , b i are the i-th data points in A and B respectively, n is the total number of data points in A and B, is the mean of all data points in A, is the mean of all data points in B; Moreover, when the calculation module (42) calculates the consistency between the two MR data sets, including calculating the Pearson correlation coefficient between the two MR data sets, the discrimination module (43) determines whether a pre-scan is required before the next MR imaging scan starts according to the consistency between the two MR data sets, including: Determine whether the Pearson correlation coefficient between the two MR data sets is greater than a preset third threshold. If so, no pre-scan is required before the next MR imaging scan starts; otherwise, a pre-scan is required before the next MR imaging scan starts.
20. A magnetic resonance imaging (MRI) system, characterized in that, The MRI system includes the pre-scan discrimination device (40) according to any one of claims 13 to 19.