Method and apparatus for high temporal resolution magnetic resonance thermography

Abstract

This application discloses a high temporal resolution magnetic resonance temperature imaging method and apparatus, belonging to the field of magnetic resonance temperature imaging technology. The high temporal resolution magnetic resonance temperature imaging method includes: scanning the region to be diagnosed using a radial sequence based on magnetic resonance coils to acquire k-space data corresponding to the region; grouping the k-space data, the spokes of the radial sequence, and the weights of the radial sequence based on time frames to acquire at least two groups of grouped data; reconstructing the at least two groups of grouped data based on the sensitivity map of the magnetic resonance coils to acquire multiple target time frame images; ensuring that the multiple target time frame images are free of undersampling artifacts; and acquiring a target temperature change image based on the temperature difference between any two adjacent target time frame images. This high temporal resolution magnetic resonance temperature imaging method can acquire the continuously changing temperature process of the region to be diagnosed, improving the temporal resolution of the temperature change process.

Description

Technical Field

[0001] This application belongs to the field of magnetic resonance temperature imaging technology, and particularly relates to a high temporal resolution magnetic resonance temperature imaging method and apparatus. Background Technology

[0002] During ablation therapy, temperature monitoring of the treatment area is necessary to adjust the position of the ablation needle. In related techniques, the area to be diagnosed is scanned twice using a GRE sequence before and after the temperature change process. The temperature difference before and after the start of the temperature change is then calculated using the PRF method. However, commonly used magnetic resonance imaging (MRI) methods, when requiring detailed temperature change data, require a limited increase in the number of scans within the temperature change timeframe. This results in cumbersome scanning procedures and data processing, and the inability to obtain continuously changing temperature data, leading to low temporal resolution. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a high temporal resolution magnetic resonance temperature imaging method and apparatus, which can acquire the continuously changing temperature process of the area to be diagnosed, thereby improving the temporal resolution of the temperature change process.

[0004] In a first aspect, this application provides a high temporal resolution magnetic resonance temperature imaging method, the method comprising:

[0005] A radial sequence is used to scan the region to be diagnosed based on magnetic resonance coils to obtain k-space data corresponding to the region to be diagnosed.

[0006] The k-space data, the spokes of the radial sequence, and the weights of the radial sequence are grouped based on time frames to obtain at least two groups of grouped data; the number of time frames is determined based on the number of spokes in each frame of the acquired image;

[0007] Based on the sensitivity map of the magnetic resonance coil, the at least two groups of grouped data are reconstructed to obtain multiple target time frame images; there are no undersampling artifacts in the multiple target time frame images;

[0008] Based on the temperature difference between any two adjacent target time frame images, obtain the target temperature change image.

[0009] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, the region to be diagnosed is scanned based on a radial sampling sequence. Then, the k-space data obtained from the scan is grouped, and multi-channel undersampling reconstruction is performed on each spoke group. Then, multiple frames of target time frame images without undersampling artifacts are reconstructed based on a compressed sensing algorithm. Finally, the target temperature change image is obtained based on the temperature difference between any two adjacent target time frame images. This method can obtain the continuous temperature change process of the region to be diagnosed, thus improving the temporal resolution of the temperature change process.

[0010] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, wherein acquiring a target temperature change image based on the temperature difference between any two adjacent target time frame images includes:

[0011] Based on each pair of adjacent target time frame images, obtain the temperature difference corresponding to each pair of adjacent target time frame images;

[0012] The target temperature change image is determined based on the temperature difference between two adjacent target time frame images.

[0013] According to an embodiment of the present application, a high temporal resolution magnetic resonance temperature imaging method is used to obtain the temperature difference between two adjacent target time frame images. Then, based on the temperature difference between multiple adjacent target time frame images, a target temperature change image is determined. In practical applications, the target temperature change image can also be organized into an animated image and output to the user, so that the user can more intuitively observe the continuous temperature change process of the area to be diagnosed.

[0014] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, wherein obtaining the temperature difference corresponding to each pair of adjacent target time frame images based on each pair of adjacent target time frame images includes:

[0015] The proton resonance frequency method is used to process the two adjacent target time frame images to obtain the temperature difference corresponding to the two adjacent target time frame images.

[0016] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, wherein the method employs the proton resonance frequency method to process adjacent two target time frame images to obtain the temperature difference corresponding to the adjacent two target time frame images, including:

[0017] Based on the following formula:

[0018]

[0019] The temperature difference is defined as follows: ΔT is the temperature difference; ΔΦ is the phase difference between two adjacent target time frame images; γ is the gyromagnetic ratio; α is the thermal coefficient of the proton resonance frequency; B0 is the main magnetic field strength; and TE is the echo time.

[0020] According to an embodiment of the present application, a high temporal resolution magnetic resonance temperature imaging method obtains the temperature difference between two adjacent target time frame images by using the proton resonance frequency method. It can calculate the inter-frame temperature difference frame by frame and obtain the target temperature change image based on the temperature difference between multiple adjacent target time frame images. The calculation accuracy and precision are high, which makes it easy to accurately observe the continuous temperature change process of the area to be diagnosed in practical applications.

[0021] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, wherein the method reconstructs at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images, including:

[0022] Undersampled reconstruction is performed on the at least two groups of grouped data to obtain multi-frame undersampled time frame images with undersampled artifacts;

[0023] The multi-frame undersampled time frame images are iteratively reconstructed using a compressed sensing algorithm to obtain the multi-frame target time frame images.

[0024] According to an embodiment of the present application, a high temporal resolution magnetic resonance temperature imaging method obtains multi-frame undersampled time frame images with undersampled artifacts by undersampling reconstruction of at least two groups of grouped data. Then, iterative reconstruction of the multi-frame undersampled time frame images is performed based on a compressed sensing algorithm to obtain multi-frame target time frame images. This method can recover artifact-free images from highly undersampled data, eliminate aliasing artifacts in the final image used to calculate the temperature difference, and in practical applications, even when the number of spokes is small and the undersampling rate is high, it can still reconstruct high-quality images, thus improving the image quality of temperature imaging.

[0025] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, wherein a radial sequence is used to perform magnetic resonance coil-based scanning of the region to be diagnosed to obtain k-space data corresponding to the region to be diagnosed, comprising:

[0026] The target scan duration is determined based on the duration of temperature changes covering the area to be diagnosed;

[0027] The region to be diagnosed is scanned based on the target scanning duration to obtain k-space data corresponding to the region to be diagnosed.

[0028] According to an embodiment of the high temporal resolution magnetic resonance temperature imaging method of this application, the duration of temperature change covering the area to be diagnosed is determined as the target scan duration, and then the area to be diagnosed is scanned based on the target scan duration to obtain k-space data corresponding to the area to be diagnosed. This ensures that the entire temperature change process of the area to be diagnosed is scanned, improves the accuracy and precision of the obtained k-space data, and thus improves the precision of the final obtained temperature change image.

[0029] One embodiment of the high temporal resolution magnetic resonance temperature imaging method of this application further includes, before reconstructing the at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images:

[0030] Based on the scan data corresponding to the magnetic resonance coil, the sensitivity map of the magnetic resonance coil is determined.

[0031] According to one embodiment of the present application, a high temporal resolution magnetic resonance temperature imaging method determines the sensitivity map of the magnetic resonance coil based on the scanning data corresponding to the magnetic resonance coil. In subsequent applications, an image without undersampling artifacts can be reconstructed based on the sensitivity map, thereby achieving rapid imaging. Moreover, the sensitivity map of the same coil only needs to be calculated once, and the steps are simple and easy to implement.

[0032] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, wherein the size of the radial sequence is determined based on the number of spokes and the number of sampling points in the radial sequence.

[0033] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, wherein the size of the k-space data is determined based on the number of spokes of the radial sequence, the number of sampling points, and the number of channels of the magnetic resonance coil.

[0034] One embodiment of this application provides a high temporal resolution magnetic resonance temperature imaging method, which, by employing a radial sequence to perform magnetic resonance coil-based scanning of the region to be diagnosed and acquiring k-space data corresponding to the region to be diagnosed, further includes:

[0035] The region to be diagnosed is scanned based on the golden angle radial sequence to obtain the k-space data corresponding to the region to be diagnosed.

[0036] According to one embodiment of the present application, a high temporal resolution magnetic resonance temperature imaging method scans the area to be diagnosed based on a golden angle radial sequence to obtain k-space data. For any number of spokes, the k-space can be uniformly covered. In application, even with fewer spokes, an image with less undersampling artifacts can be reconstructed. At the same time, more time frames can be obtained, thus improving the temporal resolution of temperature imaging.

[0037] Secondly, this application provides a high temporal resolution magnetic resonance temperature imaging device, the device comprising:

[0038] The first processing module is used to perform a magnetic resonance coil-based scan of the region to be diagnosed using a radial sequence to obtain k-space data corresponding to the region to be diagnosed.

[0039] The second processing module is used to group the k-space data, the spokes of the radial sequence, and the weights of the radial sequence based on time frames to obtain at least two groups of grouped data; the number of time frames is determined based on the number of spokes in each frame of the acquired image;

[0040] The third processing module is used to reconstruct the at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images; the multiple target time frame images are free of undersampling artifacts;

[0041] The fourth processing module is used to obtain a target temperature change image based on the temperature difference between any two adjacent target time frame images.

[0042] According to the high temporal resolution magnetic resonance temperature imaging device provided in the embodiments of this application, the region to be diagnosed is scanned based on a radial sampling sequence. Then, the k-space data obtained from the scan is grouped, and multi-channel undersampling reconstruction is performed on each spoke group. Then, multiple frames of target time frame images without undersampling artifacts are reconstructed based on a compressed sensing algorithm. Finally, the target temperature change image is obtained based on the temperature difference between any two adjacent target time frame images. This device can obtain the continuous temperature change process of the region to be diagnosed, thus improving the temporal resolution of the temperature change process.

[0043] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the high temporal resolution magnetic resonance temperature imaging method as described in the first aspect above.

[0044] Fourthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the high temporal resolution magnetic resonance temperature imaging method as described in the first aspect above.

[0045] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the high temporal resolution magnetic resonance temperature imaging method as described in the first aspect above.

[0046] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects:

[0047] The system scans the area to be diagnosed based on radial sampling sequences, then groups the k-space data obtained from the scan, and then performs multi-channel undersampling reconstruction on each spoke group. Then, it reconstructs multiple target time frame images without undersampling artifacts based on compressed sensing algorithms. Finally, it obtains the target temperature change image based on the temperature difference between any two adjacent target time frame images. This method can obtain the continuous temperature change process of the area to be diagnosed, thus improving the temporal resolution of the temperature change process.

[0048] Furthermore, by undersampling and reconstructing at least two groups of grouped data, multi-frame undersampled time frame images with undersampled artifacts are obtained. Then, based on the compressed sensing algorithm, the multi-frame undersampled time frame images are iteratively reconstructed to obtain multi-frame target time frame images. This can recover artifact-free images from highly undersampled data, eliminating aliasing artifacts in the final image used to calculate the temperature difference. Moreover, in practical applications, even with a small number of spokes and a high undersampled rate, high-quality images can be reconstructed, thus improving the image quality of temperature imaging.

[0049] Furthermore, by obtaining the temperature difference between two adjacent target time frame images using the proton resonance frequency method, the inter-frame temperature difference can be calculated frame by frame. Based on the temperature difference between multiple adjacent target time frame images, the target temperature change image can be obtained. The calculation accuracy and precision are high, making it easy to accurately observe the continuous temperature change process of the area to be diagnosed in practical applications.

[0050] Furthermore, the duration of temperature change covering the area to be diagnosed is determined as the target scan duration. Then, the area to be diagnosed is scanned based on the target scan duration to obtain the k-space data corresponding to the area to be diagnosed. This ensures that the entire temperature change process of the area to be diagnosed is scanned, improving the accuracy and precision of the obtained k-space data, and thus improving the precision of the final temperature change image.

[0051] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0052] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0053] Figure 1 This is one of the flowcharts of the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application;

[0054] Figure 2 This is one of the schematic diagrams illustrating the principle of the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application;

[0055] Figure 3 This is the second schematic diagram of the principle of the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application;

[0056] Figure 4 This is a second schematic flowchart of the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application;

[0057] Figure 5 This is the third schematic diagram illustrating the principle of the high temporal resolution magnetic resonance temperature imaging method provided in this application embodiment;

[0058] Figure 6 This is a schematic diagram of the structure of the high temporal resolution magnetic resonance thermal imaging device provided in the embodiments of this application;

[0059] Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0061] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0062] The following is combined with Figures 1 to 5 This application describes a high temporal resolution magnetic resonance temperature imaging method according to embodiments of the present application.

[0063] It should be noted that the execution entity of the high temporal resolution magnetic resonance thermal imaging method can be a server, a high temporal resolution magnetic resonance thermal imaging device, or a user's terminal, including but not limited to mobile terminals and non-mobile terminals.

[0064] For example, mobile terminals include, but are not limited to, mobile phones, PDA smart terminals, tablets, and in-vehicle smart terminals; non-mobile terminals include, but are not limited to, PCs.

[0065] like Figure 1 As shown, the high temporal resolution magnetic resonance temperature imaging method includes steps 110, 120, 130 and 140.

[0066] Step 110: Use a radial sequence to scan the region to be diagnosed based on magnetic resonance coils to obtain k-space data corresponding to the region to be diagnosed.

[0067] In this step, the radial sequence is the sequence used for radial sampling, such as... Figure 2 As shown.

[0068] In some embodiments, the size of the radial sequence can be determined based on the number of spokes and the number of sampling points in the radial sequence.

[0069] In this embodiment, the size of the radial sequence can be represented based on the product of the number of spokes and the number of sampling points of the radial sequence. For example, the size of the radial sequence can be represented as noRadials*noSamples, where noRadials is the number of spokes and noSamples is the number of sampling points.

[0070] Radial sampling involves traversing the entire k-space in a spoke-like manner by changing the direction of the frequency encoding.

[0071] In some embodiments, step 110 may include:

[0072] The region to be diagnosed is scanned using a golden angle radial sequence to obtain k-space data corresponding to the region to be diagnosed.

[0073] In this embodiment, radial sampling can be based on traversing the k-space based on the golden angle, for example, the golden angle can be 111.25°.

[0074] During the research and development process, the inventors discovered that there are methods in related technologies that perform radial sampling based on ordinary angles, such as... Figure 3 As shown in (a), this method cannot uniformly cover the k-space.

[0075] In this application, the region to be diagnosed is scanned based on a golden angle radial sequence. For any number of spokes, a k-space coverage with high temporal incoherence and uniformity can be obtained for every 111.25° increase in the angle between adjacent radial lines in the radial sampling. Figure 3 (b) shows the radial sampling sequence of the golden angle of 20 spokes.

[0076] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, the region to be diagnosed is scanned based on the golden angle radial sequence to obtain k-space data. For any number of spokes, the k-space can be uniformly covered. In application, even with fewer spokes, an image with less undersampling artifacts can be reconstructed. At the same time, more time frames can be obtained, which improves the temporal resolution of temperature imaging.

[0077] The area to be diagnosed is the area where temperature changes are to be observed. For example, in the process of treating a patient based on thermal ablation, the area to be diagnosed can be the treatment area.

[0078] Magnetic resonance coils can be multi-channel coils. Magnetic resonance coils are used to scan the area to be diagnosed in order to obtain the corresponding data of the area to be diagnosed.

[0079] k-space data includes the spatial frequency and phase information of all pixels.

[0080] k-space is the discretized storage space for the raw data of magnetic resonance imaging signals.

[0081] In some embodiments, the size of the k-space data can be determined based on the number of spokes, the number of sampling points, and the number of channels of the magnetic resonance coil.

[0082] In this embodiment, the size of the k-space data can be represented based on the product of the number of spokes, the number of sampling points, and the number of channels of the magnetic resonance coil.

[0083] For example, the size of k-space data can be expressed as noRadials*noSamples*noChannels, where noRadials is the number of spokes, noSamples is the number of sampling points, and noChannels is the number of channels.

[0084] In some embodiments, step 110 may include:

[0085] The target scan duration is determined based on the duration of temperature changes covering the area to be diagnosed.

[0086] The region to be diagnosed is scanned based on the target scan duration to obtain k-space data corresponding to the region to be diagnosed.

[0087] In this embodiment, the target scan duration is the total scan duration of the area to be diagnosed based on the magnetic resonance coil.

[0088] The target scan duration can cover the total duration of temperature changes in the area to be diagnosed.

[0089] In actual execution, based on the target scan duration, a radial sequence can be used to scan the area to be diagnosed during the temperature change process of the area to be diagnosed, thereby obtaining the k-space data corresponding to the area to be diagnosed.

[0090] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, the duration of temperature change covering the area to be diagnosed is determined as the target scan duration. Then, the area to be diagnosed is scanned based on the target scan duration to obtain k-space data corresponding to the area to be diagnosed. This ensures that the entire temperature change process of the area to be diagnosed is scanned, improving the accuracy and precision of the obtained k-space data, and thus improving the precision of the final obtained temperature change image.

[0091] Step 120: Group the k-space data, the spokes of the radial sequence, and the weights of the radial sequence based on the time frame to obtain at least two groups of grouped data; the number of time frames is determined based on the number of spokes in each frame of the acquired image.

[0092] In this step, the number of time frames can be determined based on the number of spokes in each captured image frame.

[0093] The number of spokes in each frame of an image can be determined based on the Fibonacci sequence (1, 1, 2, 3, 5, 8, 13, 21...).

[0094] The spokes of the radial sequence all pass through the center of the k-space, such as Figure 2 The spokes are shown in the diagram.

[0095] The weights of the radial sequence are used to characterize the importance of the radial sequence relative to the temperature of the region to be diagnosed.

[0096] The grouped data is obtained by grouping the k-space data, the spokes of the radial sequence, and the weights of the radial sequence.

[0097] In actual execution, the number of spokes (nspokes) per frame can be input. Based on the number of spokes in the time dimension, the k-space data, the spokes of the radial sequence, and the weights of the radial sequence are grouped. At least two groups of grouped data are then obtained. The size of the k-space data in the grouped data is nspokes * noSamples * noChannels, where nspokes is the number of spokes per frame, noSamples is the number of sampling points, and noChannels is the number of channels. The size of the spokes of the radial sequence in the grouped data is nspokes * noSamples, where nspokes is the number of spokes per frame, and noSamples is the number of sampling points. The weights of the radial sequence in the grouped data are nspokes * noSamples, where nspokes is the number of spokes per frame, and noSamples is the number of sampling points.

[0098] You can set each group to have nspokes spokes, then there will be a total of noRadial / nspokes grouped data after grouping.

[0099] Step 130: Reconstruct at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images; there are no undersampling artifacts in the multiple target time frame images.

[0100] In this step, the sensitivity map is used to characterize the sensitivity of the magnetic resonance coil to magnetic resonance signals at different spatial locations.

[0101] The sensitivity map can be determined based on the following method:

[0102] In some embodiments, prior to step 130, the high temporal resolution magnetic resonance temperature imaging method may further include:

[0103] Based on the scanning data corresponding to the magnetic resonance coil, the sensitivity map of the magnetic resonance coil is determined.

[0104] In this embodiment, the scan data is the raw data obtained after scanning the area to be diagnosed using a magnetic resonance coil.

[0105] Based on the scan data corresponding to the magnetic resonance coil, a low-resolution sensitivity map can be calculated.

[0106] The size of the low-resolution sensitivity map can be n*n, where n can be 100 or 200, etc., and can be user-defined; this application does not impose any restrictions.

[0107] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, the sensitivity map of the magnetic resonance coil is determined based on the scanning data corresponding to the magnetic resonance coil. In subsequent applications, an image without undersampling artifacts can be reconstructed based on the sensitivity map, thereby achieving rapid imaging. Moreover, the sensitivity map of the same coil only needs to be calculated once, and the steps are simple and easy to implement.

[0108] The target time frame image is obtained by reconstructing the grouped data based on the sensitivity map.

[0109] By reconstructing at least two groups of grouped data, multiple target time frame images can be obtained.

[0110] There are no undersampling artifacts in the target time frame image.

[0111] Artifacts are various forms of images that exist in the scanned image but do not exist in the scanned area.

[0112] like Figure 4 As shown, in some embodiments, step 130 may include:

[0113] Undersampled reconstruction is performed on at least two groups of grouped data to obtain multi-frame undersampled time frame images with undersampled artifacts;

[0114] The compressed sensing algorithm is used to iteratively reconstruct multiple undersampled time frame images to obtain multiple target time frame images.

[0115] In this embodiment, undersampling reconstruction is performed on the grouped k-space data, the spokes of the radial sequence, and the weights of the radial sequence to obtain multi-frame undersampling time frame images with undersampling artifacts.

[0116] The undersampled time frame image is obtained after undersampling and reconstructing the grouped data.

[0117] The size of the undersampled aliased frame sequence corresponding to multiple undersampled time frame images can be expressed as: in, This represents the number of frames in the undersampled time frame image.

[0118] Compressed sensing (CS) algorithms can compress data during the sampling process.

[0119] Compressed sensing algorithms can create incoherent aliasing artifacts based on irregular undersampling schemes and enhance sparsity in a suitable transform domain based on nonlinear reconstruction.

[0120] The target time frame image is obtained by iterative reconstruction of the undersampled time frame image.

[0121] The size of the frame sequence without undersampling artifacts corresponding to the target time frame image can be expressed as:

[0122] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, by undersampling reconstruction of at least two groups of grouped data, multi-frame undersampled time frame images with undersampling artifacts are obtained. Then, based on the compressed sensing algorithm, the multi-frame undersampled time frame images are iteratively reconstructed to obtain multi-frame target time frame images. This method can recover artifact-free images from highly undersampled data, eliminate aliasing artifacts in the final image used to calculate the temperature difference, and in practical applications, even when the number of spokes is small and the undersampling rate is high, it can still reconstruct high-quality images, thus improving the image quality of temperature imaging.

[0123] Step 140: Based on the temperature difference between any two adjacent target time frame images, obtain the target temperature change image.

[0124] In this step, the target temperature change image is used to characterize the temperature change process in the area to be diagnosed.

[0125] Based on the temperature difference between any two adjacent target time frame images, the target temperature change image can be obtained.

[0126] In actual implementation, such as Figure 4 As shown, the sensitivity map of the magnetic resonance coil can be calculated first. Based on the target scan duration, the area to be diagnosed is scanned based on the radial sequence during the temperature change process to obtain the k-space data corresponding to the area to be diagnosed.

[0127] Then, based on the time frame, the k-space data kdata, the weight w of the radial sequence, and the spoke trajectory k of the radial sequence are grouped to obtain the noRadial / nspokes group data;

[0128] Then, undersampling reconstruction is performed on the noRadial / nspokes group data to obtain multi-frame undersampling time frame images with undersampling artifacts. Finally, the target time frame image without artifacts is reconstructed from the undersampling data based on the compressed sensing algorithm.

[0129] Finally, based on the temperature difference between any two adjacent target time frame images, the target temperature change image is obtained.

[0130] During the research and development process, the inventors discovered that in related technologies, the area to be diagnosed is scanned twice based on the GRE sequence before and after the temperature change process, and then the temperature difference before and after the temperature change process is calculated based on the PRF method. When a more detailed temperature change process is needed, the number of sequence scans needs to be increased within a limited time during the temperature change process. The scanning steps and data are cumbersome, and the continuous temperature change process cannot be obtained, resulting in low temporal resolution.

[0131] In this application, the region to be diagnosed is scanned based on a radial sampling sequence. Then, the k-space data obtained from the scan is grouped, and multi-channel undersampling reconstruction is performed on each spoke group. Then, multiple frames of target time frame images without undersampling artifacts are reconstructed based on a compressed sensing algorithm. Finally, the target temperature change image is obtained based on the temperature difference between any two adjacent target time frame images. This can obtain the continuous temperature change process of the region to be diagnosed, thus improving the temporal resolution of the temperature change process.

[0132] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, the region to be diagnosed is scanned based on a radial sampling sequence. Then, the k-space data obtained from the scan is grouped, and multi-channel undersampling reconstruction is performed on each spoke group. Then, multiple frames of target time frame images without undersampling artifacts are reconstructed based on a compressed sensing algorithm. Finally, the target temperature change image is obtained based on the temperature difference between any two adjacent target time frame images. This method can obtain the continuous temperature change process of the region to be diagnosed, thus improving the temporal resolution of the temperature change process.

[0133] In some embodiments, step 130 may include:

[0134] Based on each pair of adjacent target time frame images, obtain the temperature difference corresponding to each pair of adjacent target time frame images;

[0135] The target temperature change image is determined based on the temperature difference between multiple adjacent target time frame images.

[0136] In this embodiment, the temperature difference between two adjacent target time frame images can be obtained frame by frame, and then the target temperature change image can be determined.

[0137] In some embodiments, obtaining the temperature difference between two adjacent target time frame images based on two adjacent target time frame images may include:

[0138] The proton resonance frequency method is used to process each pair of adjacent target time frame images to obtain the temperature difference between each pair of adjacent target time frame images.

[0139] like Figure 5 As shown, in this embodiment, the proton resonance frequency (PRF) method can obtain the phase map corresponding to each target time frame image, and then obtain the temperature difference corresponding to the two adjacent target time frame images based on the phase difference between the phase maps corresponding to the two adjacent target time frame images.

[0140] In some embodiments, processing adjacent target time frame images using the proton resonance frequency method to obtain the temperature difference between adjacent target time frame images may include:

[0141] Based on the following formula:

[0142]

[0143] The temperature difference is defined as follows: ΔT is the temperature difference, ΔΦ is the phase difference between two adjacent target time frame images, γ is the gyromagnetic ratio, α is the thermal coefficient of the proton resonance frequency, B0 is the main magnetic field strength, and TE is the echo time.

[0144] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, the temperature difference between two adjacent target time frame images is obtained by the proton resonance frequency method. The inter-frame temperature difference can be calculated frame by frame, so as to obtain the target temperature change image based on the temperature difference between multiple adjacent target time frame images. The calculation accuracy and precision are high, which makes it easy to accurately observe the continuous temperature change process of the area to be diagnosed in practical applications.

[0145] In actual execution, the temperature difference between two adjacent target time frame images can be obtained. Then, based on the temperature difference between multiple adjacent target time frame images, the target temperature change image is determined, and the temperature change image is organized into an animated image and output to the user.

[0146] According to the high temporal resolution magnetic resonance temperature imaging method provided in the embodiments of this application, the temperature difference between two adjacent target time frame images is obtained, and then the target temperature change image is determined based on the temperature difference between multiple adjacent target time frame images. In practical applications, the target temperature change image can also be organized into an animated image and output to the user, so that the user can more intuitively observe the continuous temperature change process of the area to be diagnosed.

[0147] The high temporal resolution magnetic resonance thermal imaging device provided in this application will be described below. The high temporal resolution magnetic resonance thermal imaging device described below can be referred to in correspondence with the high temporal resolution magnetic resonance thermal imaging method described above.

[0148] The high temporal resolution magnetic resonance thermal imaging method provided in this application can be executed by a high temporal resolution magnetic resonance thermal imaging device. This application uses an example of a high temporal resolution magnetic resonance thermal imaging device executing the high temporal resolution magnetic resonance thermal imaging method to illustrate the high temporal resolution magnetic resonance thermal imaging device provided in this application.

[0149] This application also provides a high temporal resolution magnetic resonance temperature imaging device.

[0150] like Figure 6 As shown, the high temporal resolution magnetic resonance temperature imaging device includes: a first processing module 610, a second processing module 620, a third processing module 630 and a fourth processing module 640.

[0151] The first processing module 610 is used to perform a magnetic resonance coil-based scan of the region to be diagnosed using a radial sequence to obtain k-space data corresponding to the region to be diagnosed.

[0152] The second processing module 620 is used to group the k-space data, the spokes of the radial sequence, and the weights of the radial sequence based on time frames, and obtain at least two groups of grouped data; the number of time frames is determined based on the number of spokes in each frame of the acquired image;

[0153] The third processing module 630 is used to reconstruct at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images; there are no undersampling artifacts in the multiple target time frame images;

[0154] The fourth processing module 640 is used to obtain a target temperature change image based on the temperature difference between any two adjacent target time frame images.

[0155] According to the high temporal resolution magnetic resonance temperature imaging device provided in the embodiments of this application, the region to be diagnosed is scanned based on a radial sampling sequence. Then, the k-space data obtained from the scan is grouped, and multi-channel undersampling reconstruction is performed on each spoke group. Then, multiple frames of target time frame images without undersampling artifacts are reconstructed based on a compressed sensing algorithm. Finally, the target temperature change image is obtained based on the temperature difference between any two adjacent target time frame images. This device can obtain the continuous temperature change process of the region to be diagnosed, thus improving the temporal resolution of the temperature change process.

[0156] In some embodiments, the high temporal resolution magnetic resonance thermal imaging device may further include:

[0157] The fifth processing module is used to obtain the temperature difference between two adjacent target time frame images based on each two adjacent target time frame images;

[0158] The sixth processing module is used to determine the target temperature change image based on the temperature difference between two adjacent target time frame images.

[0159] According to the high temporal resolution magnetic resonance temperature imaging device provided in the embodiments of this application, the temperature difference between two adjacent target time frame images is obtained, and then the target temperature change image is determined based on the temperature difference between multiple adjacent target time frame images. In practical applications, the target temperature change image can also be organized into an animated image and output to the user, so that the user can more intuitively observe the continuous temperature change process of the area to be diagnosed.

[0160] In some embodiments, the high temporal resolution magnetic resonance thermal imaging apparatus may further include:

[0161] The seventh processing module is used to process two adjacent target time frame images using the proton resonance frequency method to obtain the temperature difference between the two adjacent target time frame images.

[0162] In some embodiments, the seventh processing module can also be used for:

[0163] Based on the following formula:

[0164]

[0165] The temperature difference is defined as follows: ΔT is the temperature difference, ΔΦ is the phase difference between two adjacent target time frame images, γ is the gyromagnetic ratio, α is the thermal coefficient of the proton resonance frequency, B0 is the main magnetic field strength, and TE is the echo time.

[0166] According to the high temporal resolution magnetic resonance temperature imaging device provided in the embodiments of this application, the temperature difference between two adjacent target time frame images is obtained by the proton resonance frequency method. The inter-frame temperature difference can be calculated frame by frame to obtain the target temperature change image based on the temperature difference between multiple adjacent target time frame images. The calculation accuracy and precision are high, which makes it easy to accurately observe the continuous temperature change process of the area to be diagnosed in practical applications.

[0167] In some embodiments, the third processing module 630 can also be used for:

[0168] Undersampled reconstruction is performed on at least two groups of grouped data to obtain multi-frame undersampled time frame images with undersampled artifacts;

[0169] The compressed sensing algorithm is used to iteratively reconstruct multiple undersampled time frame images to obtain multiple target time frame images.

[0170] According to the high temporal resolution magnetic resonance temperature imaging device provided in the embodiments of this application, by undersampling reconstruction of at least two groups of grouped data, a multi-frame undersampled time frame image with undersampling artifacts is obtained. Then, based on the compressed sensing algorithm, the multi-frame undersampled time frame image is iteratively reconstructed to obtain a multi-frame target time frame image. It can recover an artifact-free image from highly undersampled data, eliminate aliasing artifacts in the final image used to calculate the temperature difference, and in practical applications, even when the number of spokes is small and the undersampling rate is high, a high-quality image can still be reconstructed, thus improving the image quality of temperature imaging.

[0171] In some embodiments, the first processing module 610 may also be used for:

[0172] The target scan duration is determined based on the duration of temperature changes covering the area to be diagnosed.

[0173] The region to be diagnosed is scanned based on the target scan duration to obtain k-space data corresponding to the region to be diagnosed.

[0174] According to the high temporal resolution magnetic resonance temperature imaging device provided in the embodiments of this application, the duration of temperature change covering the area to be diagnosed is determined as the target scan duration. Then, the area to be diagnosed is scanned based on the target scan duration to obtain k-space data corresponding to the area to be diagnosed. This ensures that the entire temperature change process of the area to be diagnosed is scanned, improving the accuracy and precision of the obtained k-space data, and thus improving the precision of the final obtained temperature change image.

[0175] In some embodiments, the high temporal resolution magnetic resonance thermal imaging apparatus may further include:

[0176] The eighth processing module is used to determine the sensitivity map of the magnetic resonance coil based on the scanning data corresponding to the magnetic resonance coil before reconstructing at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil and acquiring multiple target time frame images.

[0177] According to the high temporal resolution magnetic resonance temperature imaging device provided in the embodiments of this application, the sensitivity map of the magnetic resonance coil is determined based on the scanning data corresponding to the magnetic resonance coil. In subsequent applications, an image without undersampling artifacts can be reconstructed based on the sensitivity map, thereby achieving rapid imaging. Moreover, the sensitivity map of the same coil only needs to be calculated once, and the steps are simple and easy to implement.

[0178] The high temporal resolution magnetic resonance temperature imaging device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television set (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0179] The high temporal resolution magnetic resonance temperature imaging device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0180] The high temporal resolution magnetic resonance thermal imaging device provided in this application embodiment can achieve Figures 1 to 5 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0181] In some embodiments, such as Figure 7As shown, this application embodiment also provides an electronic device 700, including a processor 701, a memory 702, and a computer program stored in the memory 702 and executable on the processor 701. When the program is executed by the processor 701, it implements the various processes of the above-described high temporal resolution magnetic resonance temperature imaging method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0182] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0183] On the other hand, this application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the various processes of the above-described high temporal resolution magnetic resonance temperature imaging method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0184] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the various processes of the above-described high temporal resolution magnetic resonance temperature imaging method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0185] In another aspect, this application embodiment provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described high temporal resolution magnetic resonance temperature imaging method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0186] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0187] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0188] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method of high temporal resolution magnetic resonance thermography, characterized in that, include: A radial sequence is used to scan the region to be diagnosed based on magnetic resonance coils to obtain k-space data corresponding to the region to be diagnosed. The k-space data, the spokes of the radial sequence, and the weights of the radial sequence are grouped based on time frames to obtain at least two groups of grouped data; the number of time frames is determined based on the number of spokes in each frame of the acquired image; Based on the sensitivity map of the magnetic resonance coil, the at least two groups of grouped data are reconstructed to obtain multiple target time frame images; there are no undersampling artifacts in the multiple target time frame images; Based on the temperature difference between any two adjacent target time frame images, obtain the target temperature change image.

2. The high temporal resolution magnetic resonance temperature imaging method according to claim 1, characterized in that, The step of obtaining a target temperature change image based on the temperature difference between any two adjacent target time frame images includes: Based on each pair of adjacent target time frame images, obtain the temperature difference corresponding to each pair of adjacent target time frame images; The target temperature change image is determined based on the temperature difference between two adjacent target time frame images.

3. The high temporal resolution magnetic resonance thermography method of claim 2, wherein, The step of obtaining the temperature difference corresponding to each pair of adjacent target time frame images based on each pair of adjacent target time frame images includes: The proton resonance frequency method is used to process the two adjacent target time frame images to obtain the temperature difference corresponding to the two adjacent target time frame images.

4. The high temporal resolution magnetic resonance thermography method of claim 3, wherein, The process of processing adjacent target time frame images using the proton resonance frequency method to obtain the temperature difference corresponding to each adjacent target time frame image includes: Based on the following formula: The temperature difference is defined as follows: ΔT is the temperature difference; ΔΦ is the phase difference between two adjacent target time frame images; γ is the gyromagnetic ratio; α is the thermal coefficient of the proton resonance frequency; B0 is the main magnetic field strength; and TE is the echo time.

5. The high temporal resolution magnetic resonance temperature imaging method according to any one of claims 1-4, characterized in that, The process of reconstructing the at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images includes: Undersampled reconstruction is performed on the at least two groups of grouped data to obtain multi-frame undersampled time frame images with undersampled artifacts; The multi-frame undersampled time frame images are iteratively reconstructed using a compressed sensing algorithm to obtain the multi-frame target time frame images.

6. The high temporal resolution magnetic resonance temperature imaging method according to any one of claims 1-4, characterized in that, The step of using a radial sequence to scan the region to be diagnosed based on magnetic resonance coils to obtain k-space data corresponding to the region to be diagnosed includes: The target scan duration is determined based on the duration of temperature changes covering the area to be diagnosed; The region to be diagnosed is scanned based on the target scanning duration to obtain k-space data corresponding to the region to be diagnosed.

7. The high temporal resolution magnetic resonance thermography method according to any one of claims 1 to 4, characterized in that, Before reconstructing the at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images, the method further includes: Based on the scan data corresponding to the magnetic resonance coil, the sensitivity map of the magnetic resonance coil is determined.

8. The high temporal resolution magnetic resonance thermography method according to any one of claims 1 to 4, characterized in that, The size of the radial sequence is determined based on the number of spokes and the number of sampling points in the radial sequence.

9. The high temporal resolution magnetic resonance thermography method according to any one of claims 1 to 4, characterized in that, The size of the k-space data is determined based on the number of spokes, the number of sampling points, and the number of channels of the magnetic resonance coil in the radial sequence.

10. The high temporal resolution magnetic resonance thermography method according to any one of claims 1 to 4, characterized in that, The step of using a radial sequence to perform magnetic resonance coil-based scanning of the region to be diagnosed, and acquiring k-space data corresponding to the region to be diagnosed, further includes: The region to be diagnosed is scanned based on the golden angle radial sequence to obtain the k-space data corresponding to the region to be diagnosed.

11. A high temporal resolution magnetic resonance temperature imaging device, characterized in that, include: The first processing module is used to perform a magnetic resonance coil-based scan of the region to be diagnosed using a radial sequence to obtain k-space data corresponding to the region to be diagnosed. The second processing module is used to group the k-space data, the spokes of the radial sequence, and the weights of the radial sequence based on time frames to obtain at least two groups of grouped data; the number of time frames is determined based on the number of spokes in each frame of the acquired image; The third processing module is used to reconstruct the at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images; the multiple target time frame images are free of undersampling artifacts; The fourth processing module is used to obtain a target temperature change image based on the temperature difference between any two adjacent target time frame images.

12. The high temporal resolution magnetic resonance thermography apparatus of claim 11, wherein, The device further includes: The fifth processing module is used to obtain the temperature difference between each pair of adjacent target time frame images based on each pair of adjacent target time frame images; The sixth processing module is used to determine the target temperature change image based on the temperature difference between two adjacent target time frame images.

13. The high temporal resolution magnetic resonance thermography apparatus of claim 12, wherein, The device further includes: The seventh processing module is used to process the two adjacent target time frame images using the proton resonance frequency method to obtain the temperature difference corresponding to the two adjacent target time frame images.

14. The high temporal resolution magnetic resonance thermography apparatus of claim 13, wherein, The seventh processing module is also used for: Based on the following formula: The temperature difference is defined as follows: ΔT is the temperature difference; ΔΦ is the phase difference between two adjacent target time frame images; γ is the gyromagnetic ratio; α is the thermal coefficient of the proton resonance frequency; B0 is the main magnetic field strength; and TE is the echo time.

15. The high temporal resolution magnetic resonance thermal imaging apparatus according to any one of claims 11-14, characterized in that, The third processing module is also used for: Undersampled reconstruction is performed on the at least two groups of grouped data to obtain multi-frame undersampled time frame images with undersampled artifacts; The multi-frame undersampled time frame images are iteratively reconstructed using a compressed sensing algorithm to obtain the multi-frame target time frame images.

16. A high temporal resolution magnetic resonance thermography apparatus as claimed in any of the claims 11-14, characterized in that, The first processing module is further configured to: The target scan duration is determined based on the duration of temperature changes covering the area to be diagnosed; The region to be diagnosed is scanned based on the target scanning duration to obtain k-space data corresponding to the region to be diagnosed.

17. A high temporal resolution magnetic resonance thermography apparatus as claimed in any of claims 11-14, characterized in that, The device further includes: The eighth processing module is used to determine the sensitivity map of the magnetic resonance coil based on the scanning data corresponding to the magnetic resonance coil before reconstructing the at least two groups of grouped data based on the sensitivity map of the magnetic resonance coil to obtain multiple target time frame images.

18. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the high temporal resolution magnetic resonance temperature imaging method as described in any one of claims 1-10. 19.A non-transitory computer-readable storage medium having stored thereon a computer program, wherein, When executed by a processor, the computer program implements the high temporal resolution magnetic resonance temperature imaging method as described in any one of claims 1-10.

20. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the high temporal resolution magnetic resonance temperature imaging method as described in any one of claims 1-10.

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