Spatial resolution detection method and device for magnetic resonance image, and electronic equipment

Abstract

The invention relates to the field of magnetic resonance imaging, in particular to a magnetic resonance image spatial resolution detection method and device and electronic equipment. The method comprises the following steps: acquiring a rectangular magnetic resonance image comprising a target line segment; and performing edge detection to obtain an edge point set and fitting the edge point set into an edge line. Determining the directed distance from each pixel to the edge line in the rectangular magnetic resonance image, and fitting the relation between the pixel value of each pixel and the directed distance to obtain an edge spread function; determining the change rate of the pixel value of each pixel in the edge spread function along with the change of the position to obtain a change rate function, extracting features from the change rate function and normalizing the features to obtain a line spread function, and converting the line spread function to a frequency domain and normalizing the line spread function to obtain a modulation transfer function; and determining a corresponding spatial frequency when the modulation transfer function value is a threshold value as a spatial resolution score of the rectangular magnetic resonance image. The method is used for improving the accuracy of spatial resolution evaluation of the magnetic resonance image and widening the evaluation range of the magnetic resonance spatial resolution.

Description

Technical Field

[0001] The present application relates to the field of magnetic resonance imaging, and in particular to a method, device and electronic equipment for detecting the spatial resolution of magnetic resonance images. Background Art

[0002] Magnetic Resonance Imaging (MRI) is an advanced medical imaging technology that features no ionizing radiation, high soft tissue contrast, and diverse imaging modalities. MRI can reconstruct multiple tomographic images, clearly displaying organ or tissue structures, and has great advantages in disease diagnosis. However, long-term, high-load use of MRI can lead to performance degradation, so regular quality control is required. Key parameters such as high-contrast resolution (spatial resolution), low-contrast resolution, signal-to-noise ratio, uniformity, geometric distortion, layer thickness, etc. are tested through phantoms to objectively evaluate image quality and overall device performance. Among them, high-contrast resolution (abbreviated as high resolution) is an important indicator for evaluating MRI imaging quality, reflecting the ability of MRI to display subtle anatomical structures under high contrast conditions.

[0003] MRI high-score evaluation results are affected by the subjective judgment of quality control personnel, resulting in increased measurement uncertainty. Differences in professional experience among different quality control personnel may lead to deviations in the evaluation results. For example, when the high-score evaluation result is between two groups of line pair values, it is impossible to accurately calculate the specific intermediate value and can only be roughly classified into a certain distinguishable line pair group. With the advancement of MRI technology, the voxel size has gradually decreased, and the high-score performance has continued to improve. The existing line pair evaluation standard can no longer meet the evaluation requirements of higher high-score performance. How to improve the accuracy of the spatial resolution evaluation of magnetic resonance images and broaden the scope of spatial resolution evaluation has become an issue worthy of discussion. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, and electronic device for detecting the spatial resolution of a magnetic resonance image, which are used to improve the accuracy of the spatial resolution evaluation of a magnetic resonance image and broaden the range of spatial resolution evaluation.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting the spatial resolution of a magnetic resonance image, the method comprising:

[0006] acquiring a rectangular magnetic resonance image including a target line segment, wherein the target line segment is an inclined line passing through a set of opposite sides of the rectangular magnetic resonance image;

[0007] Performing edge detection on the rectangular magnetic resonance image to obtain an edge point set, fitting the edge points in the edge point set to obtain an edge line, the edge line representing the position and shape of the target line segment in the rectangular magnetic resonance image;

[0008] Determine the directed distance from each pixel to the edge line in the rectangular magnetic resonance image, fit the relationship between the pixel value of each pixel and the directed distance, and obtain an edge spread function for describing the change of pixels near the edge line;

[0009] The rate of change of the pixel value of each pixel in the edge spread function as the position changes is calculated to obtain a rate of change function, features related to the line spread function are extracted from the rate of change function and normalized to obtain a line spread function, the line spread function is converted from the spatial domain to the frequency domain, and the line spread function converted to the frequency domain is normalized to obtain a modulation transfer function, the line spread function is used to describe the edge line extension, and the modulation transfer function is used to describe the change of contrast in the image with spatial frequency;

[0010] The spatial frequency corresponding to the threshold value of the modulation transfer function is determined, and the spatial frequency is used as the spatial resolution fraction of the rectangular magnetic resonance image. The threshold value is used to determine whether the line pair in the magnetic resonance image is visible, and the spatial resolution fraction is used to represent the spatial resolution of the magnetic resonance image.

[0011] Optionally, the above-mentioned rectangular magnetic resonance image is obtained by scanning a magnetic resonance imaging performance detection phantom, and the magnetic resonance imaging performance detection phantom includes at least a rectangular line pair group and a diagonal line element, the diagonal line element intersects with at least one line pair in the rectangular line pair group, the rectangular line pair group is arranged horizontally or vertically, and the rectangular line pair group includes multiple line pairs with increasing line widths, and the target line segment is obtained by scanning the diagonal line element.

[0012] Optionally, the inclination angle corresponding to the slope of the target line segment is within a preset angle range.

[0013] Optionally, performing edge detection on the rectangular magnetic resonance image to obtain an edge point set, and fitting the edge points in the edge point set to obtain edge lines, specifically includes:

[0014] Performing image preprocessing on the rectangular magnetic resonance image to obtain a normalized grayscale image;

[0015] The Canny edge detection algorithm is used to perform edge detection on the normalized grayscale image to obtain the edge point set;

[0016] The edge points in the edge point set are fitted using the least square method to obtain the coefficients of the fitting polynomial;

[0017] Based on the fitting polynomial and the rectangular magnetic resonance image, edge lines in the rectangular magnetic resonance image are determined.

[0018] Optionally, determining the directed distance from each pixel to the edge line in the rectangular magnetic resonance image, fitting the relationship between the pixel value of each pixel and the directed distance, and obtaining an edge spread function for describing pixel changes near the edge line specifically includes:

[0019] traversing each pixel in the rectangular magnetic resonance image, determining a directed distance from each pixel to the edge line, wherein the direction of the directed distance is determined based on the position of the pixel relative to the edge line;

[0020] Use the directed distance of each pixel as the x value and the pixel value as the y value to construct a scatter plot;

[0021] Use the cubic spline interpolation algorithm to fit the points in the scatter plot to obtain the initial edge spread function;

[0022] Calculating a truncation length according to a preset truncation factor and the total length of the initial edge spread function;

[0023] The edge extension function is obtained by deleting the head truncation length and the tail truncation length in the initial edge extension function.

[0024] Optionally, determining the rate of change of the pixel value of each pixel in the edge spread function as the position changes to obtain a rate of change function, extracting features related to the line spread function from the rate of change function and performing normalization processing to obtain the line spread function, converting the line spread function from the spatial domain to the frequency domain, and normalizing the line spread function converted to the frequency domain to obtain the modulation transfer function, specifically includes:

[0025] The edge extension function is differentially derived to determine the rate of change of the pixel value of each pixel in the edge extension function as the position changes to obtain a rate of change function;

[0026] Extracting features related to the line spread function from the rate of change function and performing normalization processing to obtain the line spread function;

[0027] The line spread function is converted from the spatial domain to the frequency domain by discrete Fourier transform;

[0028] The line spread function converted to the frequency domain is normalized and interpolated to obtain the modulation transfer function.

[0029] In a second aspect, an embodiment of the present application provides a device for detecting the spatial resolution of a magnetic resonance image, the device comprising:

[0030] a transceiver module for acquiring a rectangular magnetic resonance image including a target line segment, wherein the target line segment is an inclined line passing through a set of opposite sides of the rectangular magnetic resonance image;

[0031] a processing module, configured to perform edge detection on the rectangular magnetic resonance image to obtain an edge point set, and fit the edge points in the edge point set to obtain an edge line, wherein the edge line represents the position and shape of the target line segment in the rectangular magnetic resonance image;

[0032] The processing module is further configured to determine a directed distance from each pixel in the rectangular magnetic resonance image to the edge line, fit the relationship between the pixel value of each pixel and the directed distance, and obtain an edge spread function for describing pixel changes near the edge line;

[0033] The processing module is further configured to determine a rate of change of a pixel value of each pixel in the edge spread function as its position changes to obtain a rate of change function, extract features related to the line spread function from the rate of change function and perform normalization processing to obtain a line spread function, convert the line spread function from the spatial domain to the frequency domain, and normalize the line spread function converted to the frequency domain to obtain a modulation transfer function, wherein the line spread function is used to describe edge line extension, and the modulation transfer function is used to describe how contrast in an image changes with spatial frequency;

[0034] The processing module is further configured to determine a spatial frequency corresponding to a threshold value of the modulation transfer function, and use the spatial frequency as a spatial resolution fraction of the rectangular magnetic resonance image. The threshold value is used to determine whether a line pair in the magnetic resonance image is visible, and the spatial resolution fraction is used to represent the spatial resolution of the magnetic resonance image.

[0035] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the processor implements any one of the methods in the first aspect above.

[0036] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, any one of the methods in the first aspect is implemented.

[0037] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising a computer program, which is executed by a processor to implement any one of the methods in the first aspect above.

[0038] The technical effects brought about by any implementation method in the second to fifth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a method for detecting the spatial resolution of a magnetic resonance image provided in an embodiment of the present application;

[0040] Figure 2 A schematic top view of a magnetic resonance imaging performance testing phantom provided in an embodiment of the present application;

[0041] Figure 3A schematic top view of another magnetic resonance imaging performance testing phantom provided in an embodiment of the present application;

[0042] Figure 4 A schematic top view of another magnetic resonance imaging performance testing phantom provided in an embodiment of the present application;

[0043] Figure 5a A schematic diagram of a rectangular magnetic resonance image provided in an embodiment of the present application;

[0044] Figure 5b A schematic diagram of another rectangular magnetic resonance image provided in an embodiment of the present application;

[0045] Figure 6 A schematic diagram of an inclination angle provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of an initial value edge spread function provided in an embodiment of the present application;

[0047] Figure 8 A schematic diagram of a line spread function provided in an embodiment of the present application;

[0048] Figure 9 A schematic diagram of a modulation transfer function provided in an embodiment of the present application;

[0049] Figure 10 A schematic structural diagram of a spatial resolution device for magnetic resonance images provided in an embodiment of the present application;

[0050] Figure 11 This is a schematic diagram of the structure of a control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] Below, some terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0052] 1. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0053] 2. The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar items and are not necessarily used to describe a particular order or sequence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments described herein can be practiced in an order other than that illustrated or described herein.

[0054] 3. Magnetic Resonance Imaging Performance Testing: A phantom is a container filled with a specific solution that simulates how human tissue appears during an MRI scan. In this application, the phantom is filled with a copper sulfate solution, which helps produce clear image contrast during MRI scans.

[0055] 4. Superconducting MRI is a device for performing MRI scans. It has a superconducting magnet that can generate a strong magnetic field for imaging.

[0056] 5. The head and neck combined coil is a specially designed coil for MRI scans of the head and neck, which can provide high-quality images.

[0057] 6. Line pairs is a term used exclusively in cinematography, but it is also used in medical imaging technologies such as MRI and CT to describe the unit of resolution. Specifically, it refers to the number of line pairs that the instrument can distinguish within one millimeter. These lines are usually a pair of equal-sized light and dark stripes or regularly spaced light and dark stripes. In MRI equipment, by adjusting the image display window width and window level, a minimum set of line pairs can be visually distinguished, representing the high-resolution capability of the MRI equipment. The more line pairs that can be distinguished, the better the resolution.

[0058] 7. Window Width (WW) refers to the grayscale range selected for image display, that is, the density range seen in an image. The size of the window width directly affects image contrast and clarity. In MRI image display, adjusting the window width can change the grayscale range displayed on the image. When the window width is adjusted to the minimum value, the displayed grayscale range becomes smaller, image contrast is enhanced, and it is helpful for observing tissue structures with similar densities.

[0059] 9. Window Level (WL). In MRI, the window level refers to the center position of the image's grayscale. It is primarily used to adjust image display, particularly when observing tissues of varying densities. In MRI images, different tissues exhibit varying grayscale values due to their physical properties (such as density or relaxation time). The window level, along with the window width, determines which grayscale values are displayed in the image and how they are displayed.

[0060] 10. Number of excitations refers to the number of signal averages (NSA) or the number of excitations (NEX). This refers to the number of times the same data is repeatedly acquired to increase the signal-to-noise ratio.

[0061] 11. Echo Time (TE) refers to the time between the transmission of the RF pulse and the receipt of the echo signal, usually measured in milliseconds (ms).

[0062] 12. Repetition Time (TR) refers to the time interval between two consecutive RF pulses, usually in milliseconds (ms).

[0063] 13. Modulation Transfer Function (MTF) is a key metric used to quantify the ability of an optical system or photosensitive material to transmit spatial frequency signals. It describes the imaging system's ability to transmit signals of varying spatial frequencies. Spatial frequency refers to the frequency of light and dark variations in an image, typically measured in line pairs per millimeter (lp / mm). MTF values range from 0 to 1, with higher values indicating a greater ability to reproduce detail.

[0064] To make the objectives, technical solutions, and advantages of this application more clear, this application will be further described in detail below with reference to the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0065] The application scenarios described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Persons skilled in the art will appreciate that, as new application scenarios emerge, the technical solutions provided by the embodiments of this application are equally applicable to similar technical problems. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0066] Magnetic Resonance Imaging (MRI) is an advanced medical imaging technology that features no ionizing radiation, high soft tissue contrast, and diverse imaging modalities. MRI can reconstruct multiple tomographic images, clearly displaying organ or tissue structures, and has great advantages in disease diagnosis. However, long-term, high-load use of MRI can lead to performance degradation, so regular quality control is required. Key parameters such as high-contrast resolution (spatial resolution), low-contrast resolution, signal-to-noise ratio, uniformity, geometric distortion, layer thickness, etc. are tested through phantoms to objectively evaluate image quality and overall device performance. Among them, high-contrast resolution (abbreviated as high resolution) is an important indicator for evaluating MRI imaging quality, reflecting the ability of MRI to display subtle anatomical structures under high contrast conditions.

[0067] MRI high-score evaluation results are affected by the subjective judgment of quality control personnel, resulting in increased measurement uncertainty. Differences in professional experience among different quality control personnel may lead to deviations in the evaluation results. For example, when the high-score evaluation result is between two groups of line pair values, it is impossible to accurately calculate the specific intermediate value and can only be roughly classified into a certain distinguishable line pair group. With the advancement of MRI technology, the voxel size has gradually decreased, and the high-score performance has continued to improve. The existing line pair evaluation standard can no longer meet the evaluation requirements of higher high-score performance. How to improve the accuracy of the spatial resolution evaluation of magnetic resonance images and broaden the scope of spatial resolution evaluation has become an issue worthy of discussion.

[0068] To address the above-mentioned issues, the present application provides a method for detecting the spatial resolution of magnetic resonance images. The method comprises: acquiring a rectangular magnetic resonance image including a target line segment, where the target line segment is an inclined line passing through a set of opposite sides of the rectangular magnetic resonance image. Edge detection is performed on the rectangular magnetic resonance image to obtain a set of edge points, and the edge points in the set of edge points are fitted to obtain an edge line. The edge line represents the position and shape of the target line segment in the rectangular magnetic resonance image. A directed distance is determined from each pixel in the rectangular magnetic resonance image to the edge line, and a relationship is fitted between the pixel value of each pixel and the directed distance to obtain an edge spread function that describes the variation of pixels near the edge line. The rate of change of the pixel value of each pixel in the edge spread function with position is determined to obtain a rate of change function. Features related to the line spread function are extracted from the rate of change function and normalized to obtain a line spread function. The line spread function is converted from the spatial domain to the frequency domain, and the converted line spread function is normalized to obtain a modulation transfer function. The line spread function describes the spread of edge lines. The modulation transfer function describes the variation of contrast in an image with spatial frequency. The spatial frequency corresponding to the threshold value of the modulation transfer function is determined, and the spatial frequency is used as the spatial resolution fraction of the rectangular magnetic resonance image. The threshold value is used to determine whether the line pair in the magnetic resonance image is visible, and the spatial resolution fraction is used to represent the spatial resolution of the magnetic resonance image.

[0069] In the above method, edge detection obtains a set of edge points, and fitting the edge points in the set of edge points yields edge lines. This method accurately extracts the position and shape of the target line segment in the MRI image, namely the edge line. This facilitates accurate understanding and quantification of edge features in subsequent analysis. By determining the directed distance from each pixel to the edge line and fitting the relationship between the pixel value and the directed distance, an edge spread function can be derived. The line spread function, obtained through differential derivation and normalization, is then subjected to a discrete Fourier transform, normalization, and interpolation to obtain the modulation transfer function (MTF) used for image quality assessment. The spatial frequency corresponding to a certain threshold value of the MTF is determined and used as the spatial resolution score of the rectangular MRI image. This method provides a quantitative basis for objectively evaluating the spatial resolution of MRI images, facilitating more accurate assessments in medical imaging diagnosis, scientific research, and other fields. It also helps improve the accuracy of MRI spatial resolution assessment and broaden the scope of MRI spatial resolution assessment.

[0070] like Figure 1 FIG. 1 is a flow chart of a method for detecting the spatial resolution of a magnetic resonance image provided by an embodiment of the present application, which may specifically include the following steps.

[0071] Step S101 : Acquire a rectangular magnetic resonance image including a target line segment, where the target line segment is an inclined line passing through a set of opposite sides of the rectangular magnetic resonance image.

[0072] In an optional embodiment, a rectangular magnetic resonance image is obtained by scanning a magnetic resonance imaging performance test phantom. The magnetic resonance imaging performance test phantom includes at least a rectangular line pair group and a diagonal line element. The diagonal line element intersects at least one line pair in the rectangular line pair group, and the rectangular line pair group is arranged horizontally or vertically. The rectangular line pair group includes multiple line pairs with increasing line widths. The diagonal line element is scanned to obtain a target line segment.

[0073] For example, when using an MRI performance test phantom, it can be placed in an MRI machine and scanned using a head-neck combined coil. The scanning sequence is a T1 spin echo (T1_SE) sequence, with a field of view (FOV) of 250 mm × 250 mm, one slice with a slice thickness of 5 mm, a repetition time (TR) of 500 ms, an echo time (TE) of 30 ms, two excitations, and the same reconstruction matrix as the scan matrix. To improve the accuracy of the spatial resolution assessment of MRI images, the MRI performance test phantom can be scanned using multiple different MRI machines. For example, the MRI performance test phantom was scanned using three MRI machines from two different manufacturers. The three MRI machines were coded as 1.5 T_A, 3.0 T_A, and 3.0 T_B. The scan matrix was set in increasing order from 192 × 192 to 1024 × 1024 to evaluate the high-resolution performance of MRI devices at different resolutions. After scanning, rectangular MRI images were obtained from each MRI device.

[0074] like Figure 2 As shown, an embodiment of the present application provides a schematic top view of a magnetic resonance imaging performance detection phantom. Figure 2 The invention comprises a vertically arranged rectangular line pair group and a right-angled triangular hole. The hypotenuse of the right-angled triangular hole is a slant line element 21. The extension line of the slant line element 21 intersects the extension line of the vertical short side of the rectangular line pair group.

[0075] like Figure 3 As shown, an embodiment of the present application provides a schematic top view of another magnetic resonance imaging performance testing phantom. Figure 3 The invention comprises a horizontally arranged rectangular line pair group and a trapezoidal hole. The waist 31 of the trapezoidal hole is a diagonal element. The extension line of the diagonal element 31 intersects the extension line of the horizontal long side of the rectangular line pair group.

[0076] like Figure 4 As shown, an embodiment of the present application provides a schematic top view of another magnetic resonance imaging performance detection phantom. 41 is a hole for fixing the magnetic resonance imaging performance detection phantom. 46 is a positioning hole for correcting the magnetic resonance image. 43 is a horizontally arranged rectangular line pair group. 42 is a first right-angled triangle hole. The hypotenuse of the first right-angled triangle hole is a first oblique line element. The extension line of the first oblique line element intersects with the extension line of the horizontal long side direction of the horizontally arranged rectangular line pair group. 44 is a vertically arranged rectangular line pair group. 45 is a second right-angled triangle hole. The hypotenuse of the second right-angled triangle hole is a second oblique line element. The extension line of the second oblique line element intersects with the extension line of the vertical short side direction of the vertically arranged rectangular line pair group.

[0077] like Figure 5a As shown, an embodiment of the present application provides a schematic diagram of a rectangular magnetic resonance image. Figure 5a The location of the dotted line box is the rectangular magnetic resonance image. Figure 5b As shown, an embodiment of the present application provides a schematic diagram of another rectangular magnetic resonance image. Figure 5b The center is a rectangular magnetic resonance image. b is a target line segment. b is an inclined line passing through a pair of opposite sides of the rectangular magnetic resonance image.

[0078] Optionally, the inclination angle corresponding to the slope of the target line segment is within a preset angle range. For example, the preset inclination angle range may be 2° to 10°. The inclination angle corresponding to the slope of the target line segment may be 5.711°.

[0079] In the above method, by setting the inclination angle corresponding to the slope of the target line segment within a preset angle range, calculation deviation caused by an angle that is too large or too small can be reduced.

[0080] like Figure 6 As shown in FIG, an embodiment of the present application provides a schematic diagram of an inclination angle. Figure 6 In the example, the x-axis is parallel to the horizontal long side of the rectangular line pair, and the y-axis is parallel to the vertical short side of the rectangular line pair. Taking the y-axis as the reference, the angle a (a∈[0,π) and a≠π / 2) between line l and the positive y-axis is called the inclination angle of line l.

[0081] Step S102: performing edge detection on the rectangular magnetic resonance image to obtain an edge point set, and fitting the edge points in the edge point set to obtain edge lines.

[0082] The edge line represents the position and shape of the target segment in the rectangular magnetic resonance image.

[0083] In an optional embodiment, image preprocessing can be performed on the rectangular magnetic resonance image to obtain a normalized grayscale image. Edge detection can be performed on the normalized grayscale image using a Canny edge detection algorithm to obtain a set of edge points. The edge points in the set of edge points are fitted using a least squares method to obtain coefficients of a fitting polynomial. Based on the fitting polynomial and the rectangular magnetic resonance image, edge lines in the rectangular magnetic resonance image are determined.

[0084] For example, the original rectangular magnetic resonance image is preprocessed to obtain a normalized grayscale image. Preprocessing may include operations such as denoising, contrast enhancement, and normalization to improve the accuracy of subsequent processing. The normalized grayscale image is processed using the Canny edge detection algorithm to identify multiple edge points from the normalized grayscale image. Multiple edge points constitute an edge point set. Among them, the edge point usually corresponds to the position where the grayscale value changes most dramatically in the image, that is, the boundary of the object. The edge points in the edge point set are fitted using the least squares method to obtain the coefficients of the fitting polynomial. Among them, the fitting polynomial can describe the distribution of the edge points. Based on the fitted polynomial coefficients and the original rectangular magnetic resonance image, the edge line in the image can be determined.

[0085] For example, as mentioned above Figure 5b As shown, Figure 5b The target line segment b is the edge line in the image.

[0086] In the above method, the normalization operation in image preprocessing can eliminate grayscale value differences in magnetic resonance images caused by factors such as equipment and acquisition conditions, so that the image has a more consistent grayscale distribution in subsequent processing. The Canny edge detection algorithm can accurately identify edge points in normalized grayscale images and generate detailed and continuous edge contours. In edge line fitting, the least squares method can use the data points in the edge point set to fit a polynomial line that is closest to these points. Based on the fitted polynomial and the original rectangular magnetic resonance image, the edge lines in the image can be determined. Accurately extracting edge lines helps improve the accuracy and efficiency of subsequent spatial resolution detection of magnetic resonance images.

[0087] Step S103: determining the directed distance from each pixel in the rectangular magnetic resonance image to the edge line, fitting the relationship between the pixel value of each pixel and the directed distance, and obtaining an edge extension function for describing pixel changes near the edge line.

[0088] In an optional embodiment, each pixel in a rectangular magnetic resonance image is traversed to determine a directed distance from each pixel to the edge line, where the direction of the directed distance is determined based on the position of the pixel relative to the edge line. For example, if the pixel is to the left of the edge line, the distance is positive; if the pixel is to the right of the edge line, the distance is negative. A scatter plot is constructed using the directed distance of each pixel as the x-value and the pixel value as the y-value. A cubic spline interpolation algorithm is used to fit the points in the scatter plot to obtain an initial edge extension function. A truncation length is calculated based on a preset truncation factor and the total length of the initial edge extension function. The head truncation length and the tail truncation length in the initial edge extension function are deleted to obtain the edge extension function.

[0089] It is understood that the truncation factor can be adjusted according to actual needs. For example, the truncation factor is 0.15.

[0090] like Figure 7 As shown in FIG, an embodiment of the present application provides a schematic diagram of an initial value edge expansion function. Figure 7 In FIG, the positions marked 71 indicate the start and end points of the head truncation length calculated based on the preset truncation factor and the total length of the initial edge extension function. The positions marked 72 indicate the start and end points of the tail truncation length.

[0091] like Figure 8 As shown, an embodiment of the present application provides a schematic diagram of a line spread function.

[0092] In this method, by precisely calculating the directed distance from each pixel to the edge line and constructing a scatter plot for fitting, a more accurate edge extension function can be obtained. This helps to more accurately reflect the changes in pixel values near the edge in subsequent analysis. Using a cubic spline interpolation algorithm for fitting generates a smooth and continuous curve, avoiding jumps or abrupt changes caused by discrete data points. By presetting the truncation factor and calculating the truncation length, noise can be removed from the initial edge extension function. This helps to reduce noise interference in subsequent analysis, improving the accuracy and reliability of the analysis.

[0093] Step S104: determine the rate of change of the pixel value of each pixel in the edge spread function as the position changes to obtain a rate of change function, extract features related to the line spread function from the rate of change function and normalize them to obtain a line spread function, convert the line spread function from the spatial domain to the frequency domain, and normalize the line spread function converted to the frequency domain to obtain a modulation transfer function.

[0094] The line spread function is used to describe the edge line expansion, and the modulation transfer function is used to describe the change of image contrast with spatial frequency.

[0095] For example, the edge spread function can be differentially derived to determine the rate of change of the pixel value of each pixel in the edge spread function as the position changes, thereby obtaining a rate of change function. Each element represents the rate of change of the pixel value at the corresponding position. Features related to the line spread function are extracted from the rate of change function and normalized to obtain the line spread function. For example, the features related to the line spread function may include edge width, brightness distribution, gradient change, etc. The line spread function is converted from the spatial domain to the frequency domain through a discrete Fourier transform. The line spread function converted to the frequency domain is normalized and interpolated to obtain the modulation transfer function.

[0096] In the above method, by calculating the modulation transfer function, the image quality can be more accurately assessed and potential blur or distortion issues can be identified, thereby improving the accuracy of the spatial resolution detection of magnetic resonance images.

[0097] Step 105 : Determine the spatial frequency corresponding to when the modulation transfer function value is a threshold, and use the spatial frequency as the spatial resolution fraction of the rectangular magnetic resonance image.

[0098] The threshold is used to determine whether a line pair is visible in the magnetic resonance image, and the spatial resolution score is used to represent the spatial resolution of the magnetic resonance image.

[0099] It is understood that the threshold can be adjusted according to actual needs. For example, the cutoff factor is 0.56.

[0100] In this method, the spatial resolution of an MRI image can be quantitatively assessed by using the spatial frequency corresponding to the modulation transfer function value as the spatial resolution fraction. This facilitates subsequent comparison of MRI image quality obtained under different imaging conditions or with different equipment, and allows for optimization of imaging parameters to improve MRI image quality.

[0101] like Figure 9 As shown in FIG, an embodiment of the present application provides a schematic diagram of a modulation transfer function. Assume that the threshold is 0.56. Figure 9 The spatial frequency corresponding to the modulation transfer function threshold of 0.56 is determined to be 9.934. Therefore, the spatial resolution fraction of the rectangular magnetic resonance image is 9.934.

[0102] Below Figure 1 Let's take an example:

[0103] For example, scanning as above Figure 4 The MRI performance test phantom shown above produces a rectangular MRI image. Table 1 shows the subjective evaluation results of the quality control personnel at different resolutions, the test results using the MRI spatial resolution test method of the present application, and the objective evaluation results using existing techniques.

[0104] Table 1 3.0T_A subjective and objective high score evaluation results

[0105]

[0106] Table 2-1 1.5T_A subjective and objective high score evaluation results

[0107]

[0108]

[0109] Table 2-2 1.5T_A subjective and objective high score evaluation results (continued from Table 2-1 above)

[0110]

[0111] Table 3. Subjective and objective high-score evaluation results of 3.0T_B

[0112]

[0113]

[0114] As can be seen from Tables 1, 2-1, 2-2, and 3 above, the test results obtained using the spatial resolution detection method for magnetic resonance images of the present application have a small difference rate with the subjective evaluation results, and are nearly consistent. This indicates that the spatial resolution detection method of the present application has high accuracy and reliability, can automatically process and analyze magnetic resonance images, and reduces the manual operation and time consumption of quality control personnel. This not only improves work efficiency but also reduces the risk of human error, and can obtain relatively objective and consistent test results, enhance the stability and credibility of the results, and broaden the scope of spatial resolution evaluation.

[0115] Figure 10 This is a schematic diagram of the structure of a magnetic resonance image spatial resolution detection device provided in an embodiment of the present application. Figure 10 As shown, the device includes: a transceiver module 1001 and a processing module 1002.

[0116] The transceiver module 1001 is configured to acquire a rectangular magnetic resonance image including a target line segment, where the target line segment is an inclined line passing through a set of opposite sides of the rectangular magnetic resonance image;

[0117] A processing module 1002 is configured to perform edge detection on a rectangular magnetic resonance image to obtain an edge point set, and fit the edge points in the edge point set to obtain an edge line, where the edge line represents the position and shape of a target line segment in the rectangular magnetic resonance image.

[0118] The processing module 1002 is further configured to determine a directed distance from each pixel in the rectangular magnetic resonance image to the edge line, fit the relationship between the pixel value of each pixel and the directed distance, and obtain an edge spread function for describing pixel changes near the edge line;

[0119] The processing module 1002 is further configured to determine a rate of change of a pixel value of each pixel in the edge spread function as its position changes to obtain a rate of change function, extract features related to the line spread function from the rate of change function and perform normalization processing on the features to obtain a line spread function, convert the line spread function from the spatial domain to the frequency domain, and perform normalization processing on the line spread function converted to the frequency domain to obtain a modulation transfer function, where the line spread function is used to describe edge line spread, and the modulation transfer function is used to describe how contrast in an image changes with spatial frequency;

[0120] The processing module 1002 is further configured to determine a spatial frequency corresponding to a threshold value of the modulation transfer function, and use the spatial frequency as a spatial resolution fraction of the rectangular magnetic resonance image. The threshold value is used to determine whether a line pair in the magnetic resonance image is visible, and the spatial resolution fraction is used to represent the spatial resolution of the magnetic resonance image.

[0121] Optionally, the rectangular magnetic resonance image is obtained by scanning a magnetic resonance imaging performance detection phantom, the magnetic resonance imaging performance detection phantom includes at least a rectangular line pair group and a diagonal line element, the diagonal line element intersects with at least one line pair in the rectangular line pair group, the rectangular line pair group is arranged horizontally or vertically, the rectangular line pair group includes multiple line pairs with increasing line widths, and the magnetic resonance scanning diagonal line element obtains the target line segment.

[0122] Optionally, the inclination angle corresponding to the slope of the target line segment is within a preset angle range.

[0123] Optionally, edge detection is performed on the rectangular magnetic resonance image to obtain an edge point set, and the edge points in the edge point set are fitted to obtain edge lines. The processing module 1002 is specifically configured to:

[0124] Performing image preprocessing on the rectangular magnetic resonance image to obtain a normalized grayscale image;

[0125] The Canny edge detection algorithm is used to perform edge detection on the normalized grayscale image to obtain the edge point set;

[0126] The edge points in the edge point set are fitted using the least square method to obtain the coefficients of the fitting polynomial;

[0127] Based on the fitting polynomial and the rectangular magnetic resonance image, edge lines in the rectangular magnetic resonance image are determined.

[0128] Optionally, a directed distance from each pixel in the rectangular magnetic resonance image to the edge line is determined, and a relationship between the pixel value of each pixel and the directed distance is fitted to obtain an edge extension function for describing pixel changes near the edge line. The processing module 1002 is specifically configured to:

[0129] traversing each pixel in the rectangular magnetic resonance image, determining a directed distance from each pixel to the edge line, wherein the direction of the directed distance is determined based on the position of the pixel relative to the edge line;

[0130] Use the directed distance of each pixel as the x value and the pixel value as the y value to construct a scatter plot;

[0131] Use the cubic spline interpolation algorithm to fit the points in the scatter plot to obtain the initial edge spread function;

[0132] Calculating a truncation length according to a preset truncation factor and the total length of the initial edge spread function;

[0133] The edge extension function is obtained by deleting the head truncation length and the tail truncation length in the initial edge extension function.

[0134] Optionally, the rate of change of the pixel value of each pixel in the edge spread function as the position changes is determined to obtain a rate of change function, features related to the line spread function are extracted from the rate of change function and normalized to obtain a line spread function, the line spread function is converted from the spatial domain to the frequency domain, and the line spread function converted to the frequency domain is normalized to obtain a modulation transfer function. The processing module 1002 is specifically configured to:

[0135] The edge extension function is differentially derived to determine the rate of change of the pixel value of each pixel in the edge extension function as the position changes to obtain a rate of change function;

[0136] Extracting features related to the line spread function from the rate of change function and performing normalization processing to obtain the line spread function;

[0137] The line spread function is converted from the spatial domain to the frequency domain by discrete Fourier transform;

[0138] The line spread function converted to the frequency domain is normalized and interpolated to obtain the modulation transfer function.

[0139] Figure 11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0140] At least one processor 1101, and a memory 1102 connected to the at least one processor 1101. The specific connection medium between the processor 1101 and the memory 1102 is not limited in the embodiment of the present application. Figure 11 In the example, the processor 1101 and the memory 1102 are connected via the bus 1100. Figure 11 The bus 1100 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 11 The diagram is represented by only one thick line, but this does not mean that there is only one bus or one type of bus. Alternatively, the processor 1101 may also be referred to as a controller, without limitation to the name.

[0141] In the embodiment of the present application, the memory 1102 stores instructions that can be executed by at least one processor 1101. The at least one processor 1101 can execute the spatial resolution detection method of a magnetic resonance image discussed above by executing the instructions stored in the memory 1102. The processor 1101 can implement Figure 11 The functions of each module in the device shown.

[0142] Among them, the processor 1101 is the control center of the device, which can use various interfaces and lines to connect the various parts of the entire control device, and monitor the device as a whole by running or executing instructions stored in the memory 1102 and calling data stored in the memory 1102, the various functions of the device and processing data.

[0143] In one possible design, processor 1101 may include one or more processing units. Processor 1101 may integrate an application processor and a modem processor. The application processor primarily processes the operating system, driver interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 1101. In some embodiments, processor 1101 and memory 1102 may be implemented on the same chip. In some embodiments, they may also be implemented on separate chips.

[0144] The processor 1101 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method for detecting the spatial resolution of a magnetic resonance image disclosed in the embodiments of this application can be directly implemented as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0145] Memory 1102 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. Memory 1102 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. Memory 1102 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 1102 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0146] By designing and programming the processor 1101, the code corresponding to the method for detecting the spatial resolution of a magnetic resonance image described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1 The embodiment shown is a method for detecting the spatial resolution of a magnetic resonance image. How to design and program the processor 1101 is well known to those skilled in the art and will not be described in detail here.

[0147] It should be noted here that the above-mentioned electronic device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment and can achieve the same technical effects. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0148] An embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to enable a computer to execute a method for detecting spatial resolution of a magnetic resonance image in the above embodiment.

[0149] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 Process or processes and / or boxes Figure 1 A device that performs the functions specified in a box or multiple boxes.

[0151] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Process or processes and / or boxes Figure 1 The function specified in the box or boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 Process or processes and / or boxes Figure 1 Steps to perform the functions specified in a box or boxes.

[0153] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for detecting the spatial resolution of a magnetic resonance image, characterized in that: The method comprises: acquiring a rectangular magnetic resonance image including a target line segment, wherein the target line segment is an inclined line passing through a set of opposite sides of the rectangular magnetic resonance image; Performing edge detection on the rectangular magnetic resonance image to obtain an edge point set, fitting the edge points in the edge point set to obtain an edge line, wherein the edge line represents the position and shape of the target line segment in the rectangular magnetic resonance image; determining a directed distance from each pixel in the rectangular magnetic resonance image to the edge line, fitting a relationship between the pixel value of each pixel and the directed distance, and obtaining an edge spread function for describing pixel changes near the edge line; Determining a rate of change of a pixel value of each pixel in the edge spread function as its position changes to obtain a rate of change function, extracting features related to a line spread function from the rate of change function and performing normalization processing to obtain the line spread function, converting the line spread function from a spatial domain to a frequency domain, and normalizing the line spread function converted to the frequency domain to obtain a modulation transfer function, wherein the line spread function is used to describe the edge line extension, and the modulation transfer function is used to describe the change of contrast in an image as a function of spatial frequency; A spatial frequency corresponding to a threshold value of the modulation transfer function value is determined, and the spatial frequency is used as a spatial resolution fraction of the rectangular magnetic resonance image. The threshold value is used to determine whether a line pair in the magnetic resonance image is visible, and the spatial resolution fraction is used to represent the spatial resolution of the magnetic resonance image.

2. The method according to claim 1, characterized in that The rectangular magnetic resonance image is obtained by scanning a magnetic resonance imaging performance detection phantom, wherein the magnetic resonance imaging performance detection phantom includes at least a rectangular line pair group and a diagonal line element, wherein the diagonal line element intersects with at least one line pair in the rectangular line pair group, and the rectangular line pair group is arranged horizontally or vertically, and includes a plurality of line pairs with increasing line widths. The target line segment is obtained by scanning the diagonal line element.

3. The method according to claim 1, characterized in that The inclination angle corresponding to the slope of the target line segment is within a preset angle range.

4. The method according to claim 1, wherein The performing edge detection on the rectangular magnetic resonance image to obtain an edge point set, and fitting the edge points in the edge point set to obtain edge lines specifically includes: performing image preprocessing on the rectangular magnetic resonance image to obtain a normalized grayscale image; Performing edge detection on the normalized grayscale image using a Canny edge detection algorithm to obtain the edge point set; Fitting edge points in the edge point set using a least squares method to obtain coefficients of a fitting polynomial; Based on the fitting polynomial and the rectangular magnetic resonance image, edge lines in the rectangular magnetic resonance image are determined.

5. The method according to claim 1, wherein Determining the directed distance from each pixel in the rectangular magnetic resonance image to the edge line, fitting the relationship between the pixel value of each pixel and the directed distance, and obtaining an edge spread function for describing pixel changes near the edge line specifically includes: traversing each pixel in the rectangular magnetic resonance image, and determining a directed distance from each pixel to the edge line, wherein a direction of the directed distance is determined based on a position of the pixel relative to the edge line; Use the directed distance of each pixel as the x value and the pixel value as the y value to construct a scatter plot; Fitting the points in the scatter plot using a cubic spline interpolation algorithm to obtain an initial edge spread function; Calculating a truncation length according to a preset truncation factor and a total length of the initial edge spread function; The edge extension function is obtained by deleting the head truncation length and the tail truncation length in the initial edge extension function.

6. The method according to claim 1, characterized in that The method further comprises: determining a rate of change of a pixel value of each pixel in the edge spread function as the position changes to obtain a rate of change function; extracting features related to a line spread function from the rate of change function and performing normalization processing to obtain the line spread function; converting the line spread function from a spatial domain to a frequency domain; and normalizing the line spread function converted to the frequency domain to obtain a modulation transfer function. performing differential derivation on the edge extension function to determine a rate of change of a pixel value of each pixel in the edge extension function as the position changes to obtain a rate of change function; Extracting features related to the line spread function from the rate of change function and performing normalization processing to obtain the line spread function; Converting the line spread function from the spatial domain to the frequency domain by discrete Fourier transform; The line spread function converted into the frequency domain is normalized and interpolated to obtain the modulation transfer function.

7. A device for detecting the spatial resolution of a magnetic resonance image, characterized in that: The device comprises: a transceiver module, configured to acquire a rectangular magnetic resonance image including a target line segment, wherein the target line segment is an inclined line passing through a set of opposite sides of the rectangular magnetic resonance image; a processing module, configured to perform edge detection on the rectangular magnetic resonance image to obtain an edge point set, and fit the edge points in the edge point set to obtain an edge line, wherein the edge line represents the position and shape of the target line segment in the rectangular magnetic resonance image; The processing module is further configured to determine a directed distance from each pixel in the rectangular magnetic resonance image to the edge line, and to fit a relationship between a pixel value of each pixel and the directed distance to obtain an edge spread function for describing pixel changes near the edge line; The processing module is further configured to determine a rate of change of a pixel value of each pixel in the edge spread function as its position changes to obtain a rate of change function, extract features related to a line spread function from the rate of change function and perform normalization processing to obtain the line spread function, convert the line spread function from a spatial domain to a frequency domain, and perform normalization processing on the line spread function converted to the frequency domain to obtain a modulation transfer function, wherein the line spread function is used to describe the edge line extension, and the modulation transfer function is used to describe the change of contrast in an image as a function of spatial frequency; The processing module is further configured to determine a spatial frequency corresponding to a threshold value of the modulation transfer function, and use the spatial frequency as a spatial resolution fraction of the rectangular magnetic resonance image. The threshold value is used to determine whether a line pair in the magnetic resonance image is visible, and the spatial resolution fraction is used to represent the spatial resolution of the magnetic resonance image.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is configured to cause the computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product is called by a computer, the computer is caused to execute the method according to any one of claims 1 to 6.

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