Target artery selective marking method, device, electronic equipment and storage medium

Abstract

The present application relates to a target artery selective labeling method, device, electronic equipment and storage medium, comprising: acquiring a blood vessel positioning image of a target artery; determining a selective excitation pulse according to the blood vessel positioning image; and obtaining a perfusion map of the target artery by labeling according to the selective excitation pulse. The present application selects the position of the artery to be labeled, designs a selective excitation pulse targeting the artery based on a single-channel or multi-channel parallel emission platform, and applies the selective excitation pulse to the labeling section in the artery spin labeling sequence, thereby achieving the effect of selectively labeling a specific artery and obtaining a perfusion map of the specific artery.

Description

Technical Field

[0001] This invention relates to the field of arterial labeling technology, and particularly to a method, apparatus, electronic device, and storage medium for selective labeling of target arteries. Background Technology

[0002] Traditional pseudo-continuous arterial spin labeling imaging (pCASL) typically labels all arteries flowing through the labeling plane at once, thus the acquired perfusion map is a contribution from all arteries. In the clinical field, perfusion maps of single arteries have important clinical significance in areas such as stroke and arteriosclerosis assessment. Therefore, there is a need for an arterial spin labeling method that can selectively label single or multiple vessels.

[0003] In existing technologies, a common labeling method involves adding an additional horizontal gradient between adjacent labeling pulses on top of the original pCASL sequence. This additional gradient results in different phase differences at different horizontal spatial locations. By adjusting the phase of each labeling pulse to ensure it remains consistent with the phase of the target artery, specific labeling of the target artery is achieved. However, this method has limitations. Firstly, its labeling results are sensitive to the B0 field; inhomogeneities in the B0 field can cause deviations between the target location and the actual vessel location, thus reducing the labeling efficiency. Secondly, it cannot specifically adjust the diameter of the target vessel and its positional relationship with other vessels, thus failing to maximize the specific labeling of the target vessel and the inhibition of other vessels. Therefore, improving the accuracy and efficiency of target vessel labeling is a pressing issue. Summary of the Invention

[0004] In view of this, it is necessary to provide a method, device, electronic device and storage medium for selective labeling of target arteries to overcome the problem that the labeling of target blood vessels in the prior art is easily affected by various factors and thus has poor accuracy.

[0005] To address the aforementioned technical problems, the present invention provides a method for selective labeling of target arteries, comprising:

[0006] Obtain a vascular localization image of the target artery;

[0007] Based on the described vascular localization image, a selective excitation pulse is determined;

[0008] The perfusion map of the target artery is obtained by marking the selective excitation pulse.

[0009] Further, determining the selective excitation pulse based on the vascular localization image includes:

[0010] Based on the blood vessel localization image, a cost function is constructed;

[0011] The selective excitation pulse is determined by optimizing the cost function.

[0012] Further, the step of constructing a cost function based on the blood vessel localization image includes:

[0013] The value of the first generation is determined based on the unknown excitation pulse and the preset weight value;

[0014] The second-generation value is determined based on the unknown excitation pulse and the vascular localization image;

[0015] The cost function is constructed based on the sum of the first-generation value and the second-generation value.

[0016] Furthermore, determining the first-generation value based on the unknown excitation pulse and a preset weight value includes:

[0017] The first generation value is obtained by multiplying the square of the norm of the unknown excitation pulse and the preset weight value.

[0018] Further, determining the second-generation value based on the unknown excitation pulse and the vascular localization image includes:

[0019] Determine the system matrix based on the preset gradient;

[0020] Based on the blood vessel localization image, determine the target excitation region matrix;

[0021] The second-generation value is determined based on the system matrix, the target excitation region matrix, and the unknown excitation pulse.

[0022] Further, determining the target excitation region matrix based on the blood vessel localization image includes:

[0023] In the vascular localization image, the target region where the target artery is located is set as the first pixel value, and other regions other than the target region are set as the second pixel value to generate the target excitation region matrix.

[0024] Further, determining the second-generation value based on the system matrix, the target excitation region matrix, and the unknown excitation pulse includes:

[0025] The actual excitation region matrix is ​​obtained by multiplying the system matrix and the unknown excitation pulse.

[0026] The difference matrix is ​​obtained based on the difference between the actual excitation region matrix and the target excitation region matrix;

[0027] The second generation value is obtained based on the square of the norm of the difference matrix.

[0028] Further, determining the selective excitation pulse by optimizing the cost function includes:

[0029] Iterate over the unknown excitation pulses and optimize the cost function until the sum of the first generation value and the second generation value satisfies the iteration condition. The unknown excitation pulses that are solved when the iteration condition is satisfied are determined as the selective excitation pulses.

[0030] Furthermore, the method of acquiring the vascular localization image of the target artery further includes: acquiring the magnetic field map of the target artery, wherein the magnetic field map includes a B0 field map and / or a B1 field map; the method further includes:

[0031] The cost function is constructed based on the blood vessel localization image and the magnetic field map.

[0032] Furthermore, based on the magnetic field map and the preset gradient, the system matrix corresponding to the second-generation value in the cost function is determined.

[0033] The present invention also provides a target artery selective labeling device, comprising:

[0034] The acquisition unit is used to acquire the vascular localization image of the target artery;

[0035] The selection unit is used to determine a selective excitation pulse based on the blood vessel localization image;

[0036] A marking unit is used to mark the target artery according to the selective excitation pulse to obtain the perfusion map of the target artery.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the target artery selective labeling method as described above.

[0038] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the target artery selective labeling method as described above.

[0039] Compared with existing technologies, the beneficial effects of this invention include: First, effectively acquiring the vascular localization image of the target artery to determine its location information; then, based on the location information contained in the vascular localization image of the target artery, selecting a corresponding selective excitation pulse, that is, utilizing the characteristics of the feedback location information of the target artery to specifically determine a selective excitation pulse for the target artery, using the location information of the target artery to accurately select the excitation pulse, effectively suppressing the influence of other blood vessels and eliminating errors caused by magnetic field inhomogeneity; finally, using the determined selective excitation pulse for marking during the perfusion imaging process, eliminating the influence of other blood vessels, and obtaining the perfusion map corresponding to the target artery. In summary, this invention, by selecting the artery location to be marked, designs a selective excitation pulse for the target artery using the location information of the target artery, specifically constructs the selective excitation pulse, suppresses the influence of other blood vessels, and reduces errors caused by magnetic field inhomogeneity. Furthermore, based on a single-channel or multi-channel parallel transmission platform, it can design a selective excitation pulse targeting the target artery online and apply it to the marking segment in the arterial spin labeling sequence, achieving the effect of selective labeling of the target artery and obtaining the selected specific target artery perfusion map. Attached Figure Description

[0040] Figure 1 A schematic diagram illustrating a scenario of an application system for the target artery selective labeling method provided by the present invention;

[0041] Figure 2 This is a flowchart illustrating an embodiment of the target artery selective labeling method provided by the present invention;

[0042] Figure 3 Provided by the present invention Figure 2 A flowchart illustrating an embodiment of step S202;

[0043] Figure 4 Provided by the present invention Figure 3 A flowchart illustrating an embodiment of step S301;

[0044] Figure 5 Provided by the present invention Figure 4 A flowchart illustrating an embodiment of step S402;

[0045] Figure 6 Provided by the present invention Figure 5 A flowchart illustrating an embodiment of step S503;

[0046] Figure 7 Provided by the present invention Figure 4 A flowchart illustrating an embodiment of step S403;

[0047] Figure 8This is a schematic diagram of an embodiment of the target artery selective labeling device provided by the present invention. Detailed Implementation

[0048] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0049] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0050] In the description of this invention, reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the described embodiments can be combined with other embodiments.

[0051] This invention provides a method, apparatus, electronic device, and storage medium for selective labeling of target arteries. It designs selective excitation pulses for target arteries online and applies them to the labeling segment in an arterial spin labeling sequence, providing a new approach to further improve the accuracy of specific labeling of target arteries.

[0052] Before describing the specific embodiments, the technical terms involved are explained as follows:

[0053] pCASL sequence: pseudo-continuous ASL labeling. In arterial spin labeling perfusion (ASL) technology, carotid artery blood flow is labeled, and two signals are acquired. The unlabeled image is subtracted from the labeled image to obtain a brain perfusion map. Ideally, continuous perfusion imaging of the entire brain would be achieved by continuously emitting pulses in the lower labeled area to continuously label the entire inflowing blood. However, due to limitations, continuous pulse emission is not possible. Therefore, different labeled pulses need to be selected for perfusion imaging. ASL can be classified based on the labeled pulses used. The pCASL sequence uses pulse signals that are not continuous, yet achieve a similar effect, hence the term "pseudo-continuous." Using pCASL sequences for ASL labeling pulses offers advantages such as uniform perfusion, high SNR (signal-to-noise ratio), and low SAR (specifically, low SAR values), and is widely used in various medical scenarios.

[0054] B0 Field Diagram: Exciting atomic nuclei in an external magnetic field with radio frequency (RF) pulses of a specific frequency causes the spin axes of these nuclei to deviate from the positive or negative longitudinal axis, producing resonance—this is the phenomenon of magnetic resonance. After the spin axes of the excited atomic nuclei deviate from the positive or negative longitudinal axis, the nuclei acquire a transverse magnetization component. After the RF pulse emission stops, the excited atomic nuclei emit echo signals, gradually releasing the absorbed energy in the form of electromagnetic waves. Their phase and energy levels return to their pre-excitation state. The echo signals emitted by the atomic nuclei can be further processed, such as through spatial encoding, to reconstruct the image. In magnetic resonance imaging (MRI) devices, the external magnetic field is generated by a magnet and is generally referred to as the static field or simply the B0 field. The uniformity of the B0 field has a crucial impact on image quality. To measure the B0 field, a fast gradient echo sequence is typically used, acquiring two images with different echo times. The phase difference between these two images contains information about the B0 field.

[0055] B1 Field Map: During the transmission of high-frequency excitation pulses (commonly referred to as high-frequency pulses) via a high-frequency transmitting coil, a gradient field can be generated using a gradient coil device. The high-frequency field generated by the high-frequency pulses is typically called the B1 field. Inhomogeneities in both the B0 and B1 fields can pose challenges to MRI. For example, B1 field inhomogeneities can lead to degraded MR image quality, such as low signal-to-noise ratio (SNR) and image shading.

[0056] Using pCASL sequences as labeling pulses in perfusion imaging often results in the labeling of all arteries flowing through the labeling plane at once, failing to specifically label selected single or multiple arteries among numerous vessels. Current methods employ adding an additional horizontal gradient between two adjacent labeling pulses in the pCASL sequence to maintain phase alignment with the target artery's position, achieving specific labeling of the target artery. However, due to the inhomogeneity of the B0 field and the relative relationship between the target artery and other vessels, the labeling effect often lacks accuracy. Therefore, to address these issues, this invention provides a method, apparatus, electronic device, and storage medium for selective labeling of target arteries, which are described in detail below:

[0057] This invention provides an application system for a selective labeling method for target arteries. Figure 1 This is a schematic diagram illustrating a scenario of an application system for the target artery selective labeling method provided by the present invention. The system may include a server 100, which integrates a target artery selective labeling device, such as... Figure 1 The server in the middle.

[0058] In this embodiment of the invention, server 100 is mainly used for:

[0059] Obtain a vascular localization image of the target artery;

[0060] Based on the vascular localization image, the selective excitation pulse is determined;

[0061] The perfusion map of the target artery is obtained by marking the selective excitation pulse.

[0062] In this embodiment of the invention, the server 100 can be a standalone server, a server network, or a server cluster. For example, the server 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0063] It is understood that the terminal 200 used in this embodiment of the invention can be a device that includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the terminal 200 may be a desktop computer, a portable computer, a network server, a PDA (Personal Digital Assistant), a mobile phone, a tablet computer, a wireless terminal device, a communication device, an embedded device, etc. This embodiment does not limit the type of terminal 200.

[0064] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the present invention and does not constitute a limitation on the application scenarios of the present invention. Other application environments may include those that are more specific to the present invention. Figure 1 The number of more or fewer terminals shown, for example Figure 1 Only two terminals are shown in the diagram. It is understood that the application system of this target artery selective labeling method may also include one or more other terminals, which are not limited here.

[0065] In addition, such as Figure 1 As shown, the application system of this targeted artery selective labeling method may also include a memory 300 for storing data such as vascular localization images, B0 field maps, B1 field maps, selective excitation pulses, perfusion maps, etc.

[0066] It should be noted that, Figure 1 The schematic diagram of the application system of the selective labeling method for target arteries shown is merely an example. The application system and scenarios of the selective labeling method for target arteries described in the embodiments of the present invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. As those skilled in the art will know, with the evolution of the application system of the selective labeling method for target arteries and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0067] This invention provides a method for selective labeling of target arteries. Figure 2 This is a schematic flowchart of an embodiment of the target artery selective labeling method provided by the present invention, including steps S201 to S203, wherein:

[0068] In step S201, a vascular localization image of the target artery is acquired;

[0069] In step S202, a selective excitation pulse is determined based on the blood vessel localization image;

[0070] In step S203, the target artery is marked according to the selective excitation pulse to obtain the perfusion map.

[0071] In this embodiment of the invention, firstly, the vascular localization image of the target artery is effectively acquired to determine the localization information of the target artery; then, based on the localization information contained in the vascular localization image of the target artery, a corresponding selective excitation pulse is selected, that is, by utilizing the characteristics of the feedback localization information of the target artery, a specific selective excitation pulse for the target artery is determined in a targeted manner. By using the localization information of the target artery, a precise selection of excitation pulses is performed, effectively suppressing the influence of other blood vessels and eliminating errors caused by magnetic field inhomogeneity; finally, the determined selective excitation pulse is used for marking in the perfusion imaging process to eliminate the influence of other blood vessels and obtain the perfusion map corresponding to the target artery.

[0072] As a more specific embodiment, the vascular localization image includes T1-weighted structural images or time-of-flight angiography images. In this embodiment of the invention, the type of vascular localization image is not limited, as long as it can effectively acquire the regional location of the target artery and other blood vessels.

[0073] It should be noted that the target arteries of this invention include, but are not limited to, arteries corresponding to any of the organs of the human or animal, such as the brain, heart, liver, lungs, stomach, and kidneys. Any blood vessel in any of these organs (or other unlisted sites) can be selected as the target artery and then marked, making it applicable to a wide range of scenarios. In a specific embodiment of this invention, taking brain scanning as an example, selecting the target artery in the brain yields a perfusion map of the cerebral blood flow in the target artery.

[0074] As a preferred embodiment, combined with Figure 3 Let's take a look. Figure 3 Provided by the present invention Figure 2 A flowchart illustrating an embodiment of step S202, wherein step S202 includes steps S301 to S302, wherein:

[0075] In step S301, a cost function is constructed based on the blood vessel localization image;

[0076] In step S302, the selective excitation pulse is determined by optimizing the cost function.

[0077] In this embodiment of the invention, a corresponding cost function is constructed based on the positioning information contained in the vascular positioning image of the target artery; then, the corresponding selective excitation pulse is determined by iterative determination of the cost function, that is, by utilizing the positioning information features of the target artery fed back in the cost function, a specific selective excitation pulse for the target artery is determined in a targeted manner, effectively suppressing the influence of other blood vessels.

[0078] As a preferred embodiment, combined with Figure 4 Let's take a look. Figure 4 Provided by the present invention Figure 3 A flowchart illustrating an embodiment of step S301, wherein step S301 includes steps S401 to S403, wherein:

[0079] In step S401, the first generation value is determined based on the unknown excitation pulse and the preset weight value;

[0080] In step S402, the second-generation value is determined based on the unknown excitation pulse, the vascular localization image, and the B0 field map;

[0081] In step S403, a cost function is constructed based on the sum of the first-generation value and the second-generation value.

[0082] In this embodiment of the invention, a first-generation value is effectively constructed by using an unknown excitation pulse and a preset weight value; a second-generation value is constructed by combining the features of the vascular localization image and the B0 field map, that is, by using the localization information of the target artery and the B0 field information; and a cost function is constructed based on the first-generation value and the second-generation value.

[0083] As a more specific embodiment, the aforementioned preset weight value is a set weight for the combination of the first-generation value and the second-generation value. In this embodiment of the invention, the first-generation value and the second-generation value are added together using the preset weight value through a certain weight to construct a cost function.

[0084] In a preferred embodiment, step S401 specifically includes:

[0085] The first-generation value is obtained by multiplying the square of the norm of the unknown excitation pulse by a preset weight value.

[0086] In this embodiment of the invention, the first-generation value is effectively constructed by multiplying the square of the norm of the unknown excitation pulse with a preset weight value.

[0087] As a more specific example, the value of the first generation is expressed by the following formula:

[0088] K1=λ||x|| 2

[0089] Where K1 represents the first-generation value, λ represents the preset weight value, x is the unknown excitation pulse, and ||x|| represents the norm of the unknown excitation pulse.

[0090] It should be further explained that the first-generation value, used to limit the energy value (SAR) of the excitation pulse, is proportional to the square of the second norm of the unknown excitation pulse, ensuring a low SAR value and guaranteeing the labeling effect. The SAR index is used to evaluate the reconstructed image; it is also called the parabolic index or the stop-and-turn operation point index.

[0091] As a preferred embodiment, combined with Figure 5 Let's take a look. Figure 5 Provided by the present invention Figure 4 A flowchart illustrating an embodiment of step S402 includes steps S501 to S503, wherein:

[0092] In step S501, the system matrix is ​​determined according to the preset gradient;

[0093] In step S502, the target excitation region matrix is ​​determined based on the blood vessel localization image;

[0094] In step S503, the second-generation value is determined based on the system matrix, the target excitation region matrix, and the unknown excitation pulse.

[0095] In this embodiment of the invention, the target excitation region matrix is ​​used to feed back the location information of the target artery; the system matrix, the target excitation region matrix and the unknown excitation pulse are combined to construct a second-generation value, thereby combining multiple aspects of the target artery information.

[0096] As a more specific embodiment, in step S501, the system matrix is ​​calculated using the following formula:

[0097] A = (a mn )

[0098] Where m represents an integer between 1 and Ns, Ns represents the number of spatial locations, n represents an integer between 1 and Nt, and Nt represents the number of time points;

[0099] a mn =iγB1(m)M0e∧i(k(n).r(m)+ΔB0(m)(t n -T))

[0100] Where, k(n)=(k x (n),k y (n),k z (n)) is a pre-defined k-space trajectory, and r(m) = (x(m), y(m), z(m)) is the position vector of each point in space.

[0101] In a preferred embodiment, step S502 specifically includes:

[0102] In the vascular localization image, the target region where the target artery is located is set as the first pixel value, and other regions other than the target region are set as the second pixel value to generate a target excitation region matrix.

[0103] In this embodiment of the invention, by setting pixel values ​​in the blood vessel localization image, the target artery and other areas can be effectively distinguished, and interference from other blood vessels can be eliminated.

[0104] As a more specific embodiment, the first pixel value is preferably 1, and the second pixel value is preferably 0, so as to effectively distinguish the target artery from other regions.

[0105] As a preferred embodiment, combined with Figure 6 Let's take a look. Figure 6 Provided by the present invention Figure 5 A flowchart illustrating an embodiment of step S503 includes steps S601 to S602, wherein:

[0106] In step S601, the actual excitation region matrix is ​​obtained by multiplying the system matrix and the unknown excitation pulse.

[0107] In step S602, a difference matrix is ​​obtained based on the difference between the actual excitation region matrix and the target excitation region matrix;

[0108] In step S603, the second-generation value is obtained based on the square of the norm of the difference matrix.

[0109] In this embodiment of the invention, the second-generation value is effectively constructed by utilizing the system matrix, the unknown excitation pulse, and the target excitation region matrix.

[0110] As a more specific embodiment, the value of the second generation is expressed by the following formula:

[0111] K2=||Ax-b|| 2

[0112] Where K2 represents the second-generation value, A represents the system matrix, x is the unknown excitation pulse to be obtained, and b represents the target excitation region matrix.

[0113] It should be noted that the unknown excitation pulse can be understood as an iterative unknown, which can be iterated within a preset range or among a preset number of excitation pulses to find the unknown excitation pulse that minimizes the first sum.

[0114] As a preferred embodiment, combined with Figure 7 Let's take a look. Figure 7 Provided by the present invention Figure 4A flowchart illustrating an embodiment of step S403, wherein step S403 includes steps S701 to S702, wherein:

[0115] In step S701, the first sum value is obtained based on the sum of the first generation value and the second generation value;

[0116] In step S702, a cost function is constructed based on the first sum value.

[0117] In this embodiment of the invention, the cost function is effectively constructed by combining the first-generation value and the second-generation value, which facilitates subsequent iterative optimization.

[0118] In a preferred embodiment, step S203 specifically includes:

[0119] Iterate through unknown excitation pulses and optimize the cost function until the sum of the first-generation value and the second-generation value satisfies the iteration condition. The unknown excitation pulse that is solved when the iteration condition is satisfied is determined as the selective excitation pulse.

[0120] In this embodiment of the invention, the iteration condition is preferably that the sum of the first-generation value and the second-generation value (i.e., the first sum) is minimized. During the iteration process, the unknown excitation pulse that minimizes the first sum is determined, which is the selective excitation pulse that is finally used for the pCASL sequence, thus ensuring the specificity and accuracy of the labeling of the target artery.

[0121] As a more specific example, the cost function is expressed by the following formula:

[0122] X = arg min(||Ax-b|| 2 +λ||x|| 2 )

[0123] Where X represents the selective excitation pulse, A represents the system matrix, x is the unknown excitation pulse, and b represents the target excitation region matrix.

[0124] In a preferred embodiment, step S203 specifically includes:

[0125] Selective excitation pulses are used as labeling pulses in the pCASL sequence to label the target artery and determine the corresponding perfusion map.

[0126] In this embodiment of the invention, the selective excitation pulse determined by iteration is used as the labeling pulse in the pCASL sequence to label the target artery. This effectively constructs a selective excitation pulse based on the vascular location information of the target artery, allowing for more targeted labeling and eliminating possible interference from other blood vessels.

[0127] In a preferred embodiment, step S201 further includes: acquiring a magnetograph of the target artery, wherein the magnetograph includes a B0 field map and / or a B1 field map; the method further includes:

[0128] A cost function is constructed based on the vascular localization image and the magnetograph.

[0129] In this embodiment of the invention, by combining vascular localization images and magnetic field maps, that is, by combining the localization information and magnetic field information of the target artery, a cost function for the target blood vessel is constructed using more comprehensive and richer information, thereby improving the accuracy of the selective excitation pulse solution.

[0130] In a preferred embodiment, the system matrix corresponding to the second-generation value in the cost function is determined based on the magnetic field map and the preset gradient.

[0131] In this embodiment of the invention, the magnetic field diagram (B0 field diagram and / or B1 field diagram) is used to effectively determine the system matrix corresponding to the second-generation value, thereby enhancing its accuracy.

[0132] In a specific embodiment of the present invention, in conjunction with the formula for the second cost value described above, the system matrix A is determined by the magnetic field diagram (e.g., the B0 field diagram) and the preset gradient.

[0133] As a more specific embodiment, after step S203, the method further includes: converting the perfusion map into an angiography map, with the following specific steps:

[0134] Immediately after the marked, a corresponding perfusion map is acquired, and the corresponding angiography map is determined based on the interpolation of the perfusion map.

[0135] In this embodiment of the invention, the method for confirming the angiography image is completely consistent with the ASL principle and sequence, with only the PLD adjusted, to achieve the purpose of effectively determining the angiography image of the target artery.

[0136] The following specific application example will more clearly illustrate the technical solution of the present invention:

[0137] The first step is to obtain a vascular localization image of the target artery;

[0138] The second step is to generate a cost function based on the vascular location information of the target artery in the vascular localization image (that is, the system matrix A of the second-generation value in the cost function is determined according to the preset gradient).

[0139] The third step involves using optimization methods such as gradient descent to calculate the final selective excitation pulse based on the cost function. This selective excitation pulse is then applied to the labeling pulse in the pCASL sequence to label the target artery, thereby achieving perfusion imaging and obtaining the perfusion map of the target artery.

[0140] The technical solution of the present invention will be further illustrated below with another specific application example:

[0141] Step 1: Obtain the vascular localization image of the target artery;

[0142] Step 2: Acquire the B0 field map corresponding to the target artery;

[0143] Step 3: Based on the vascular location information of the target artery in the vascular localization image and the B0 field map, generate a cost function (that is, the system matrix A of the second-generation value in the cost function is determined according to the preset gradient and the B0 field map).

[0144] Step 4: Using optimization methods such as gradient descent, the final selective excitation pulse is calculated based on the cost function. The selective excitation pulse is then applied to the label pulse in the pCASL sequence to label the target artery, thereby achieving perfusion imaging and obtaining the perfusion map of the target artery.

[0145] This invention also provides a target artery selective labeling device, combined with Figure 8 Let's take a look. Figure 8 This is a schematic diagram of an embodiment of the target artery selective labeling device provided by the present invention. The target artery selective labeling device 800 includes:

[0146] Acquisition unit 801 is used to acquire a vascular localization image of the target artery;

[0147] Selection unit 802 is used to determine a selective excitation pulse based on the vascular localization image;

[0148] The labeling unit 803 is used to label the target artery based on the selective excitation pulse to obtain the perfusion map of the target artery.

[0149] More specific implementations of the various units of the target artery selective labeling device can be found in the description of the target artery selective labeling method described above, and it has similar beneficial effects, so they will not be repeated here.

[0150] This invention also provides a target artery selective labeling device, which stores a computer program. When the computer processor executes the program, it implements the target artery selective labeling method as described above.

[0151] More specific implementations of the various units of the target artery selective labeling device can be found in the description of the target artery selective labeling method described above, which has similar beneficial effects and will not be repeated here.

[0152] This invention also provides a medical system including the target artery selective labeling device described above.

[0153] For more specific implementation details of the various units of the medical system, please refer to the description of the above-mentioned selective labeling method for target arteries, which has similar beneficial effects and will not be elaborated here.

[0154] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the target artery selective labeling method as described above.

[0155] Generally, computer instructions for implementing the methods of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for signals themselves that are temporarily propagating.

[0156] Computer-readable storage media can be, for example—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0157] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks based on TensorFlow, PyTorch, etc., can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0158] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the target artery selective labeling method as described above.

[0159] The computer-readable storage medium and electronic device provided in the above embodiments of the present invention can be implemented with reference to the content specifically described in the present invention for implementing the target artery selective labeling method as described above, and have similar beneficial effects as the target artery selective labeling method as described above, which will not be repeated here.

[0160] This invention discloses a method, apparatus, electronic device, and storage medium for selective labeling of a target artery. First, a vascular localization image of the target artery is effectively acquired to determine its location information. Then, based on the location information contained in the vascular localization image, a corresponding selective excitation pulse is selected. This utilizes the characteristics of the feedback location information of the target artery to specifically determine a selective excitation pulse for that artery. By using the location information of the target artery, precise selection of the excitation pulse is achieved, effectively suppressing the influence of other blood vessels and eliminating errors caused by magnetic field inhomogeneity. Finally, the determined selective excitation pulse is used for labeling during the perfusion imaging process, eliminating the influence of other blood vessels, and obtaining the perfusion map corresponding to the target artery.

[0161] The technical solution of this invention selects the location of the artery to be labeled, uses the positioning information of the target artery to design a selective excitation pulse for the target artery, constructs a selective excitation pulse in a targeted manner, suppresses the influence of other blood vessels, and reduces the error caused by magnetic field inhomogeneity. Then, based on a single-channel or multi-channel parallel transmission platform, the selective excitation pulse with the target artery as the target can be designed online and applied to the labeling segment in the arterial spin labeling sequence to achieve the effect of selective labeling of the target artery and obtaining the perfusion map of the selected specific target artery.

[0162] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for selectively labeling a target artery, characterized in that, include: Obtain a vascular localization image of the target artery; Based on the described vascular localization image, a selective excitation pulse is determined; The perfusion map of the target artery is obtained by marking the selective excitation pulses. The step of determining the selective excitation pulse based on the vascular localization image includes: Based on the blood vessel localization image, a cost function is constructed; The selective excitation pulse is determined by optimizing the cost function; The method of acquiring the vascular localization image of the target artery further includes: acquiring the magnetic field map of the target artery, wherein the magnetic field map includes the B0 field map; The step of constructing a cost function based on the blood vessel localization image includes: The value of the first generation is determined based on the unknown excitation pulse and the preset weight value; The second-generation value is determined based on the unknown excitation pulse, the vascular localization image, and the B0 field map; The cost function is constructed based on the sum of the first-generation value and the second-generation value.

2. The method for selective labeling of target arteries according to claim 1, characterized in that, The process of determining the first-generation value based on the unknown excitation pulse and a preset weight value includes: The first generation value is obtained by multiplying the square of the norm of the unknown excitation pulse and the preset weight value.

3. The method for selectively labeling target arteries according to claim 1, characterized in that, The step of determining the second-generation value based on the unknown excitation pulse and the vascular localization image includes: Determine the system matrix based on the preset gradient; Based on the blood vessel localization image, determine the target excitation region matrix; The second-generation value is determined based on the system matrix, the target excitation region matrix, and the unknown excitation pulse.

4. The method for selective labeling of target arteries according to claim 3, characterized in that, The step of determining the target excitation region matrix based on the blood vessel localization image includes: In the vascular localization image, the target region where the target artery is located is set as the first pixel value, and other regions other than the target region are set as the second pixel value to generate the target excitation region matrix.

5. The method for selectively labeling target arteries according to claim 3, characterized in that, The step of determining the second-generation value based on the system matrix, the target excitation region matrix, and the unknown excitation pulse includes: The actual excitation region matrix is ​​obtained by multiplying the system matrix and the unknown excitation pulse. The difference matrix is ​​obtained based on the difference between the actual excitation region matrix and the target excitation region matrix; The second generation value is obtained based on the square of the norm of the difference matrix.

6. The method for selective labeling of target arteries according to claim 5, characterized in that, The step of determining the selective excitation pulse by optimizing the cost function includes: Iterate over the unknown excitation pulses and optimize the cost function until the sum of the first generation value and the second generation value satisfies the iteration condition. The unknown excitation pulses that are solved when the iteration condition is satisfied are determined as the selective excitation pulses.

7. The method for selective labeling of target arteries according to claim 1, characterized in that, Based on the magnetic field diagram and the preset gradient, the system matrix corresponding to the second-generation value in the cost function is determined.

8. A target artery selective labeling device, characterized in that, include: An acquisition unit is used to acquire a vascular localization image and a magnetic field map of a target artery, wherein the magnetic field map includes a B0 field map; The selection unit is used to determine a selective excitation pulse based on the blood vessel localization image; The step of determining the selective excitation pulse based on the blood vessel localization image includes: constructing a cost function based on the blood vessel localization image; and determining the selective excitation pulse by optimizing the cost function. The step of constructing a cost function based on the vascular localization image includes: determining a first-generation value based on an unknown excitation pulse and a preset weight value; determining a second-generation value based on the unknown excitation pulse, the vascular localization image, and the BO field map; and constructing the cost function based on the sum of the first-generation value and the second-generation value. A marking unit is used to mark the target artery according to the selective excitation pulse to obtain the perfusion map of the target artery.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for selective labeling of target arteries according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for selective labeling of target arteries according to any one of claims 1 to 7.

Images