A method and system for optimizing low-to-medium field strength susceptibility weighted imaging

Abstract

The application discloses a kind of low field intensity magnetic susceptibility weighted imaging optimization method and system, it is related to magnetic susceptibility weighted imaging field, comprising: obtaining the K space original data obtained by scanning target object;According to K space original data reconstruction image field data, extract the amplitude data and phase data of image field data;Phase data is preliminary unwrapping processing, and unwrapping phase data is obtained;Unwrapping phase data is separated from background field, and local phase diagram is obtained;Local phase diagram and amplitude data are operated based on complex domain phase separation high-pass filter, and high-frequency phase data is obtained;According to high-frequency phase data and amplitude data, the reconstruction of magnetic susceptibility weighted image is carried out, and the magnetic susceptibility weighted image of target object is obtained.The application can significantly reduce the phase artifact of SWI image, and improve the definition of image detail.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of magnetic susceptibility weighted imaging, and particularly relates to a method and system for optimizing low-field magnetic susceptibility weighted imaging. BACKGROUND

[0002] Susceptibility Weighted Imaging (SWI) is a technique that generates high-contrast images using the differences in magnetic susceptibility between tissues. It combines the amplitude information and phase data from complex images acquired by Magnetic Resonance Imaging (MRI), and enhances the sensitivity to local susceptibility changes through unique post-processing methods such as phase mask and minimum intensity projection. The differences in magnetic susceptibility of human tissues (such as deoxyhemoglobin in venous blood, iron deposition or calcification) can significantly affect the phase data, and SWI can clearly display small blood vessels, hemorrhage and calcification by highlighting these differences. Since the distribution of these substances is closely related to various pathological processes (such as cerebrovascular diseases and neurodegenerative diseases), SWI is widely used in the clinical diagnosis and evaluation of brain diseases.

[0003] The classic magnetic susceptibility weighted imaging and reconstruction usually adopts 3DGRE sequence to acquire data, reconstructs the image and obtains the original data containing amplitude and phase, generates a phase mask after unwrapping and high-pass filtering the original phase, and applies the mask to the amplitude multiple times; finally, the minimum intensity projection technique is used to project the processed three-dimensional data.

[0004] This method uses high-pass filtering for phase separation to remove low-frequency background interference, and must first perform a time-consuming phase unwrapping step to process the wrapped phase. This not only significantly prolongs the reconstruction time and increases the memory consumption, but also the unwrapping process is susceptible to noise and complex phase structures, which may cause error propagation and introduce artifacts. In addition, the zero padding operation is often required for frequency domain high-pass filtering, which may introduce strip artifacts and ringing effects. Finally, in order to ensure that the phase range is within [-π, π] for subsequent phase mask calculation, phase unwrapping processing is also required after filtering, which is a very lengthy process.

[0005] Moreover, since the signal-to-noise ratio increases with the increase of field strength, there is a natural disadvantage in the effect of low-field SWI. When removing the low-frequency background field, the setting of the cutoff frequency of high-pass filtering will indiscriminately filter out all spatial frequency components below the threshold. Although the magnetic field changes generated by local tissues are mainly high frequency, they still contain low frequency components related to the size of the object. When the filtering cutoff frequency is too high or the object size is large, these effective low-frequency signals that belong to the local field will be mistakenly removed, resulting in the attenuation or even disappearance of the local magnetic susceptibility signal, which manifests as a decrease in tissue contrast or the missed detection of small lesions. SUMMARY

[0006] To solve the technical problems in the background art, the present application provides a low-field MRI optimization method and system.

[0007] In a first aspect, the present application provides a low-field MRI optimization method and system, which comprises:

[0008] Obtaining K-space raw data obtained by scanning a target object;

[0009] Reconstructing image domain data from the K-space raw data, and extracting amplitude data and phase data of the image domain data;

[0010] Performing preliminary unwrapping processing on the phase data to obtain unwrapped phase data;

[0011] Separating the unwrapped phase data from the background field to obtain a local phase map;

[0012] Performing a high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain high-frequency phase data;

[0013] Reconstructing a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object.

[0014] Preferably, the scanning mode is parallel scanning.

[0015] Preferably, the parallel scanning mode is a GRAPPA parallel scanning mode.

[0016] Preferably, the unwrapped phase data is separated from the background field to obtain a local phase map, which specifically comprises:

[0017] The unwrapped phase data is separated from the background field using a dipole field projection method to obtain a local phase map.

[0018] Preferably, the unwrapped phase data is separated from the background field using a dipole field projection method to obtain a local phase map, which specifically comprises:

[0019] Generating a binary mask from the unwrapped phase data, and marking a local field region in the binary mask;

[0020] Removing the background field from the local field region to obtain a local phase map.

[0021] Preferably, the high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain high-frequency phase data specifically comprises:

[0022] Performing complex reconstruction on the local phase map and the amplitude data to obtain complex data;

[0023] extracting real and imaginary parts of the complex data;

[0024] performing multiple 3D space smoothing operations on the real and imaginary parts of the complex data respectively to obtain real low-frequency data and imaginary low-frequency data;

[0025] combining the real low-frequency data and the imaginary low-frequency data to obtain low-frequency complex data;

[0026] performing unitary conjugate operation on the low-frequency complex data;

[0027] multiplying the low-frequency complex data processed by the unitary conjugate operation with the complex data to obtain high-frequency data;

[0028] performing phase extraction on the high-frequency data to obtain high-frequency phase data.

[0029] Preferably, the reconstruction of the susceptibility weighted image according to the high-frequency phase data and the amplitude data obtains the susceptibility weighted image of the target object, and specifically comprises:

[0030] generating a phase mask according to the high-frequency phase data;

[0031] multiplying the phase mask with the amplitude data multiple times iteratively pixel by pixel to obtain an original susceptibility weighted image;

[0032] performing minimum density projection on the original susceptibility weighted image to obtain the susceptibility weighted image of the target object.

[0033] In a second aspect, the present application further provides a low-field susceptibility weighted imaging optimization system, comprising:

[0034] an acquisition module configured to acquire K-space raw data obtained by scanning a target object;

[0035] a processing module configured to reconstruct image domain data according to the K-space raw data, extract amplitude data and phase data of the image domain data, perform preliminary unwrapping processing on the phase data to obtain unwrapped phase data, perform background field separation on the unwrapped phase data to obtain a local phase map, perform high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain high-frequency phase data, and reconstruct a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object.

[0036] Preferably, in the process of performing background field separation, the dipole field projection method is used to perform background field separation on the unwrapped phase data.

[0037] Preferably, the high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain the high-frequency phase data specifically comprises:

[0038] reconstructing the local phase map and the amplitude data into complex data;

[0039] extracting real and imaginary parts of the complex data;

[0040] performing multiple 3D spatial smoothing operations on the real and imaginary parts of the complex data respectively to obtain real low-frequency data and imaginary low-frequency data;

[0041] combining the real low-frequency data and the imaginary low-frequency data to obtain low-frequency complex data;

[0042] performing a unitary conjugate operation on the low-frequency complex data;

[0043] multiplying the low-frequency complex data processed by the unitary conjugate operation with the complex data to obtain high-frequency data;

[0044] performing phase extraction on the high-frequency data to obtain high-frequency phase data.

[0045] Preferably, the scanning mode is parallel scanning.

[0046] Preferably, the parallel scanning mode is a GRAPPA parallel scanning mode.

[0047] In the present application, the proposed low-field strength susceptibility weighted imaging optimization method and system acquires K-space raw data by scanning the target object, reconstructs image domain data according to the K-space raw data, extracts amplitude data and phase data of the image domain data, performs preliminary unwrapping processing on the phase data to obtain unwrapped phase data, performs background field separation on the unwrapped phase data to obtain a local phase map, performs high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain high-frequency phase data, and reconstructs a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object, which can significantly reduce phase artifacts of the SWI image and improve image details. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The flowchart of the low-field strength susceptibility weighted imaging optimization method in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0050] In a first aspect, with reference to Figure 1 The low-field strength susceptibility weighted imaging optimization method proposed in the present application comprises:

[0051] acquire K-space raw data obtained by scanning the target object;

[0052] reconstruct image domain data according to the K-space raw data, extract amplitude data and phase data of the image domain data;

[0053] perform preliminary unwrapping processing on the phase data to obtain unwrapped phase data;

[0054] perform background field separation on the unwrapped phase data to obtain a local phase map;

[0055] perform high-pass filtering operation on the local phase map and the amplitude data to obtain high-frequency phase data;

[0056] reconstruct a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object.

[0057] Since the SWI imaging is characterized by highlighting pathology by using the difference in magnetic susceptibility, the difference in magnetic susceptibility is directly related to the phase data. The processing of the phase data is directly related to the quality of the image and affects observation and diagnosis. The original phase data contains wrapped phase, low-frequency global background phase, which will affect the performance of the key local high-frequency phase data on the susceptibility weighted image.

[0058] It should be understood that the SWI phase data is superimposed by a local field and a background field. If high-pass filtering is directly performed on the amplitude data and the phase data of the image domain data to remove the low-frequency background, although this operation can remove the low-frequency background field, it will inevitably cause signal loss of the local field information. Especially since the background phase amplitude is huge, any filtering or denoising operation prior to background removal will hinder the preservation of the low-amplitude local field information content.

[0059] The present application acquires K-space raw data by scanning the target object, reconstructs image domain data according to the K-space raw data, extracts amplitude data and phase data of the image domain data, performs preliminary unwrapping processing on the phase data, performs background field separation on the obtained unwrapped phase data to obtain a local phase map, performs high-pass filtering operation on the local phase map and the amplitude data to obtain high-frequency phase data, and reconstructs a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object, which can significantly reduce the phase artifacts of the SWI image and improve the image details.

[0060] In the present embodiment, the scanning mode of the target object is parallel scanning to speed up the scanning.

[0061] In one specific embodiment, the parallel scanning mode is the GRAPPA parallel scanning mode.

[0062] The K-space raw data in the embodiment is collected by GRAPPA parallel scanning to speed up the scanning and reduce motion artifacts.

[0063] It should be understood that GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions) is a parallel imaging technique widely used in magnetic resonance imaging, which uses the spatial sensitivity information of multiple receiving coils to reconstruct the missing K-space lines by collecting partial data (lower than the Nyquist sampling rate) in K-space and using the correlation between the signals collected by adjacent coils. The core is to use the self-calibration signal (usually collect a small amount of ACS line in the center of K-space), directly calculate the reconstruction weight between coils from the actual scanning data, without separate pre-scanning, thereby significantly speeding up the scanning or reducing motion artifacts.

[0064] K-space data is actually a representation of frequency space, which contains the raw data collected in magnetic resonance scanning. In magnetic resonance scanning, the magnetic field gradient excites the magnetic resonance signal by radio frequency pulse to form a series of spatial domain data, which contains frequency and phase data stored in K-space. By repeating the process of radio frequency pulse and gradient pulse in different gradient fields, a series of data points can be collected in K-space. After K-space data collection is completed, K-space is converted to image space by Fourier transform, so as to obtain the final magnetic resonance image.

[0065] It should be understood that the local phase map in the embodiment is a phase map containing only local phase.

[0066] In one specific embodiment, the background field separation of the unwrapped phase data is performed to obtain the local phase map, which specifically includes:

[0067] The dipole field projection (PDF) method is used to separate the background field of the unwrapped phase data to obtain the local phase map.

[0068] It should be understood that the PDF in the embodiment is Projection onto Dipole Fields, which is an algorithm for phase unwrapping and background field removal specially used in quantitative susceptibility imaging research. It is based on a physical model that assumes that the macroscopic background phase can be described by a dipole field model. The dipole field is a typical representation of the inhomogeneity of the macroscopic magnetic field in a uniform magnetic field. By iterative optimization, the original phase data is projected onto the dipole field model, so as to separate the background phase (low frequency, global) and the local phase (high frequency, local).

[0069] In SWI, the local magnetic field generated by the target tissue is superimposed with the background magnetic field originating from the imperfect device shimming and the difference in magnetic susceptibility of the surrounding tissue, which interferes with the clear display of local details. When the PDF technology simplifies the magnetic source into a magnetic dipole model, the magnetic field generated by the dipole source inside the local field is approximately orthogonal to the magnetic field generated by the dipole source outside the local field. By using the projection theorem of Hilbert space, the PDF decomposes and projects the background field in the local field onto the magnetic field space composed of the external dipole source to separate the target local field.

[0070] The dipole field projection method is used in the embodiment to remove the background from the phase data in the strong magnetic susceptibility weighted imaging, which can reduce the signal loss of the local field, enhance the details of the magnetic susceptibility weighted image, effectively separate the target local field, and significantly improve the quality of the magnetic susceptibility weighted image, especially at a low field.

[0071] The dipole field projection method is used in the embodiment to remove the background from the phase data in the strong magnetic susceptibility weighted imaging, which can reduce the signal loss of the local field, enhance the details of the magnetic susceptibility weighted image, effectively separate the target local field, and significantly improve the quality of the magnetic susceptibility weighted image, especially at a low field.

[0072] A binary mask is generated according to the unwrapped phase data, and the local field region in the binary mask is marked.

[0073] The background field is removed from the local field region to obtain a local phase map.

[0074] In the embodiment, the high-pass filtering operation is a high-pass filtering operation based on complex domain phase separation.

[0075] It should be understood that the high-pass filtering based on complex domain phase separation is a technology for separating low-frequency information and achieving high-pass filtering effect in MRI. The original phase extracted in the MRI imaging process contains low-frequency and high-frequency information, and there is a phase wrapping problem. The commonly used method is to first unwrap and then use the window function high-pass filtering method. However, by separating the phase of the complex data, using multiple spatial smoothing filters to separate the low-frequency information, and then multiplying the normalized conjugate of the original complex data to extract the high-frequency phase information, the low-frequency information is removed to achieve high-pass filtering, which can eliminate the unwrapping step.

[0076] In the embodiment, the high-pass filtering operation based on complex domain phase separation first combines the local phase map and the amplitude data into complex data, then extracts the real part and the imaginary part of the complex data for multiple spatial smoothing filtering, and then combines the filtered real part and the imaginary part to form low-frequency complex data. By constructing the unitized conjugate of the low-frequency complex signal and multiplying it with the original complex data, the high-frequency phase information is extracted, achieving the effect of removing the low-frequency background phase.

[0077] Therefore, in the embodiment, the high-pass filtering operation based on complex domain phase separation is performed on the local phase map and the amplitude data to obtain high-frequency phase data, specifically including:

[0078] performing complex reconstruction on the local phase map and the amplitude data to obtain complex data;

[0079] extracting real and imaginary parts of the complex data;

[0080] performing multiple 3D spatial smoothing operations on the real and imaginary parts of the complex data respectively to obtain real low-frequency data and imaginary low-frequency data;

[0081] merging the real low-frequency data and the imaginary low-frequency data to obtain low-frequency complex data;

[0082] performing unitary conjugate operation on the low-frequency complex data;

[0083] multiplying the low-frequency complex data processed by the unitary conjugate operation with the complex data to obtain high-frequency data;

[0084] performing phase extraction on the high-frequency data to obtain high-frequency phase data.

[0085] The embodiment adopts high-pass filtering operation based on phase separation in the complex domain, avoids the 2π jump problem caused by phase map subtraction operation, and keeps the output phase between [-π, π], which is convenient for phase mask calculation. Moreover, the spatial smoothing filter adopts the boundary replication method, which can introduce less strip artifacts and reduce ringing effect compared with the zero padding operation of frequency domain filtering. Finally, the method of directly processing the complex domain can naturally process the wrapped phase, avoiding the necessary unwrapping step before traditional high-pass filtering, reducing reconstruction time and memory consumption.

[0086] It should be understood that the improved high-pass filtering operation in the embodiment can remove the wrapped artifacts while ensuring the details of the tissue compared with the traditional homodyne filtering, and the operation speed is fast, which can ensure the accuracy of the separated low-frequency phase data. The dipole field projection method belongs to high-order processing, and in the embodiment, it is used for low-to-medium field SWI. By solving the dipole inverse problem, the large-scale background phase is robustly separated from the total phase to improve the image quality. Moreover, the dipole field projection method is applied before the high-pass filtering operation, which maximizes the preservation of fine vein structure details, avoids the loss of key low-amplitude local phase information, ensures that high-frequency information is not removed too much, and thus can ensure the extraction of high-frequency information and avoid the loss of high-frequency information.

[0087] In another specific embodiment, the unwrapped phase data is subjected to background field separation to obtain a local phase map, specifically comprising:

[0088] performing background field separation on the unwrapped phase data by using the V-SHARP method to obtain a local phase map.

[0089] The V-SHARP (Variable-kernel Sophisticated Harmonic Artefact Reduction for Phase data) method in the embodiment is a variable kernel ball mean method.

[0090] The reconstruction of the susceptibility weighted image according to the high-frequency phase data and the amplitude data obtains the susceptibility weighted image of the target object, and specifically includes:

[0091] A phase mask is generated according to the high-frequency phase data;

[0092] The phase mask is multiplied with the amplitude data pixel by pixel for multiple iterations to obtain an original susceptibility weighted image;

[0093] The original susceptibility weighted image is subjected to minimum intensity projection to obtain the susceptibility weighted image of the target object.

[0094] In specific implementation, a phase mask with a value in the interval [0, 1) is calculated and generated for a phase map in the high-frequency phase data with a range of [-π, π]; the phase mask is designed for the phase characteristics of a specific target tissue (such as venous blood containing paramagnetic substances); the phase mask is iteratively applied to the amplitude data, which can selectively significantly attenuate the signal intensity of the target tissue region, thereby enhancing the contrast of the target tissue with the surrounding tissue in the amplitude data; the amplitude maps of multiple adjacent layers in the original susceptibility weighted image are subjected to projection processing in a direction perpendicular to the layers by using minimum intensity projection, and the pixel value with the lowest signal intensity on the projection path is selected as the projection result, further strengthening the continuity of the target tissue structure (such as small veins) in the three-dimensional space and highlighting the low signal feature.

[0095] It should be understood that minimum intensity projection (MinIP) is a medical image post-processing method that generates a two-dimensional image by taking the minimum intensity value of each projection line of a three-dimensional image in a specific direction (such as coronal, sagittal or any viewing angle).

[0096] In the embodiment, minimum intensity projection is used in susceptibility weighted imaging (SWI) to highlight structures with extremely low signals and assist clinicians in improving diagnostic efficiency.

[0097] In a second aspect, the present application further provides a low-field strength susceptibility weighted imaging optimization system, comprising:

[0098] An acquisition module configured to acquire K-space raw data obtained by scanning a target object;

[0099] The processing module is configured to reconstruct image domain data from the K-space raw data, extract amplitude data and phase data of the image domain data, perform preliminary unwrapping processing on the phase data to obtain unwrapped phase data, perform background field separation on the unwrapped phase data to obtain a local phase map, perform high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain high-frequency phase data, and reconstruct a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object.

[0100] The scanning manner in the embodiment is parallel scanning.

[0101] In one specific embodiment, the parallel scanning manner is a GRAPPA parallel scanning manner.

[0102] The processing module in the embodiment includes:

[0103] The reconstruction unit is configured to reconstruct image domain data from the K-space raw data, extract amplitude data and phase data of the image domain data, perform preliminary unwrapping processing on the phase data to obtain unwrapped phase data, and perform background field separation on the unwrapped phase data to obtain a local phase map.

[0104] The background field separation unit is configured to perform background field separation on the unwrapped phase data to obtain a local phase map.

[0105] The high-pass filtering unit is configured to perform high-pass filtering operation on the local phase map and the amplitude data to obtain high-frequency phase data.

[0106] The susceptibility weighted imaging and reconstruction unit is configured to reconstruct a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object.

[0107] In one specific embodiment, the background field separation unit is a dipole field projection unit configured to perform background field separation on the unwrapped phase data by using a dipole field projection method to obtain a local phase map.

[0108] In one specific embodiment, the background field separation unit is a VSHARP unit configured to perform background field separation on the unwrapped phase data by using a VSHARP method to obtain a local phase map.

[0109] In one specific embodiment, the high-pass filtering operation is a high-pass filtering operation based on complex domain phase separation. That is, the high-pass filtering unit in the embodiment performs high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain high-frequency phase data.

[0110] The application will be described in detail below with reference to specific embodiments.

[0111] Embodiment 1

[0112] The embodiment provides a low-field-strength magnetic susceptibility weighted imaging optimization method, which comprises the following steps:

[0113] K-space raw data obtained by scanning the target object is acquired;

[0114] Image domain data is reconstructed according to the K-space raw data, and amplitude data and phase data of the image domain data are extracted;

[0115] Preliminary unwrapping processing is performed on the phase data to obtain unwrapped phase data;

[0116] A dipole field projection method is used to separate the background field of the unwrapped phase data to obtain a local phase map;

[0117] High-pass filtering operation based on complex domain phase separation is performed on the local phase map and the amplitude data to obtain high-frequency phase data;

[0118] Magnetic susceptibility weighted image reconstruction is performed according to the high-frequency phase data and the amplitude data, and the magnetic susceptibility weighted image of the target object is obtained.

[0119] Embodiment 2

[0120] The application further provides a low-field-strength magnetic susceptibility weighted imaging optimization system, which comprises the following steps:

[0121] An acquisition module is configured to acquire K-space raw data obtained by performing GRAPPA parallel scanning on a target object;

[0122] A processing module comprises the following steps:

[0123] A reconstruction unit is configured to reconstruct image domain data according to the K-space raw data, extract amplitude data and phase data of the image domain data, and perform preliminary unwrapping processing on the phase data to obtain unwrapped phase data;

[0124] A dipole field projection unit is configured to separate the background field of the unwrapped phase data by using a dipole field projection method to obtain a local phase map;

[0125] A high-pass filtering unit is configured to perform high-pass filtering operation based on complex domain phase separation on the local phase map and the amplitude data to obtain high-frequency phase data;

[0126] A magnetic susceptibility weighted imaging and reconstruction unit is configured to perform magnetic susceptibility weighted image reconstruction according to the high-frequency phase data and the amplitude data, and obtain a magnetic susceptibility weighted image of the target object.

[0127] In specific implementation, the acquisition module acquires K-space raw data obtained by performing GRAPPA parallel scanning on a target object, and delivers the K-space raw data to the reconstruction unit;

[0128] The reconstruction unit reconstructs image domain data from the K-space raw data, extracts amplitude data and phase data of the image domain data, performs preliminary unwrapping processing on the phase data to obtain unwrapped phase data, and transmits the unwrapped phase data to the PDF unit and the amplitude data to the high-pass filtering unit;

[0129] The PDF unit separates the unwrapped phase data from the background field by using a dipole field projection method to obtain a local phase map, and transmits the local phase map to the high-pass filtering unit;

[0130] The high-pass filtering unit performs high-pass filtering operation on the local phase map and the amplitude data based on complex domain phase separation to obtain high-frequency phase data, and transmits the high-frequency phase data to the susceptibility weighted imaging and reconstruction unit;

[0131] The susceptibility weighted imaging and reconstruction unit reconstructs a susceptibility weighted image according to the high-frequency phase data and the amplitude data to obtain a susceptibility weighted image of the target object.

[0132] The above is only the preferred specific implementation of the present application, but the protection scope of the present application is not limited to this, any skilled person in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A method for optimizing magnetically sensitive weighted imaging in medium and low field strength, characterized in that, include: Obtain the raw K-space data obtained by scanning the target object; Reconstruct image domain data from raw K-space data, and extract amplitude and phase data from the image domain data; The phase data is initially unwound to obtain unwound phase data; Background field separation is performed on the unwound phase data to obtain a local phase map; Complex data is obtained by reconstructing the local phase map and amplitude data; the real and imaginary parts of the complex data are extracted; multiple 3D spatial smoothing operations are performed on the real and imaginary parts of the complex data to obtain real low-frequency data and imaginary low-frequency data; the real and imaginary low-frequency data are merged to obtain low-frequency complex data. Perform a normalization-conjugation operation on the low-frequency complex data; multiply the normalized-conjugation-operated low-frequency complex data with the complex data to obtain high-frequency data; extract the phase from the high-frequency data to obtain high-frequency phase data; The magnetic susceptibility weighted image of the target object is obtained by reconstructing the magnetic susceptibility weighted image based on the high-frequency phase data and amplitude data.

2. The method for optimizing magnetically sensitive imaging in low and medium field strengths according to claim 1, characterized in that, The scanning method is parallel scanning.

3. The method for optimizing magnetically sensitive imaging in low and medium field strengths according to claim 2, characterized in that, The parallel scanning method is the GRAPPA parallel scanning method.

4. The method for optimizing magnetically sensitive imaging in medium and low field strength according to claim 1, characterized in that, Background field separation is performed on the unwound phase data to obtain a local phase map, specifically including: The background field of the unwound phase data is separated by the dipole field projection method to obtain a local phase map.

5. The method for optimizing magnetically sensitive imaging in medium and low field strengths according to claim 4, characterized in that, The background field of the unwound phase data is separated using the dipole field projection method to obtain a local phase map, specifically including: A binary mask is generated based on the unwound phase data, and local field regions in the binary mask are marked. Background field removal is performed on local field regions to obtain local phase maps.

6. The method for optimizing magnetically sensitive imaging in medium and low field strength according to claim 1, characterized in that, Based on high-frequency phase and amplitude data, a magnetic susceptibility-weighted image is reconstructed to obtain the magnetic susceptibility-weighted image of the target object, specifically including: Generate a phase mask based on high-frequency phase data; The phase mask is iterated and multiplied pixel by pixel with the amplitude data multiple times to obtain the original magnetic susceptibility weighted image; The original magnetic susceptibility weighted image is subjected to minimum density projection to obtain the magnetic susceptibility weighted image of the target object.

7. A medium-to-low field strength magnetically sensitive weighted imaging optimization system, characterized in that, include: The acquisition module is used to acquire the raw K-space data obtained by scanning the target object; The processing module is used to reconstruct image domain data from the original K-space data, extract amplitude and phase data from the image domain data; perform preliminary unwinding processing on the phase data to obtain unwound phase data; perform background field separation on the unwound phase data to obtain a local phase map; perform complex reconstruction on the local phase map and amplitude data to obtain complex data; extract the real and imaginary parts of the complex data; perform multiple 3D spatial smoothing operations on the real and imaginary parts of the complex data respectively to obtain low-frequency real part data and low-frequency imaginary part data; and merge the low-frequency real part data and low-frequency imaginary part data to obtain low-frequency complex data. The low-frequency complex data is normalized and conjugated; the normalized and conjugated low-frequency complex data is multiplied by the complex data to obtain high-frequency data; the high-frequency data is phase extracted to obtain high-frequency phase data; and the magnetic susceptibility weighted image is reconstructed based on the high-frequency phase data and amplitude data to obtain the magnetic susceptibility weighted image of the target object.

8. The low-to-medium field strength magnetic sensitivity weighted imaging optimization system according to claim 7, characterized in that, In the process of background field separation, the dipole field projection method is used to separate the background field of the unwound phase data.

Images