Methods, apparatus, devices, and storage media of determining relaxation time

Abstract

The application provides a method, device, equipment and storage medium for determining relaxation time, wherein the method comprises: acquiring multiple original images corresponding to different sampling parameters; setting a simplified objective function containing only unknown relaxation time; the simplified objective function is determined by taking partial derivatives of unknown amplitude and intercept in the original objective function, and removing the amplitude and intercept in the original objective function under the constraint that the partial derivative of the amplitude and the partial derivative of the intercept are both zero; and the relaxation time is determined by minimizing the simplified objective function. The method, device, equipment and storage medium for determining relaxation time provided by the embodiment of the application simplify the objective function to have only one unknown quantity, and the minimum value of the simplified objective function can be determined conveniently and quickly by using a one-dimensional search algorithm; and the process of converting the original objective function into the simplified objective function is applicable to various types of relaxation time, and the method is applicable to quantitative determination of various types of relaxation time.

Description

Technical Field

[0001] This invention relates to the field of magnetic resonance imaging technology, and more specifically, to a method, apparatus, device, and storage medium for determining relaxation time. Background Technology

[0002] In magnetic resonance imaging, signals excited by radio frequency pulses gradually decay and eventually return to an equilibrium state; this phenomenon is called relaxation. The rate at which the signal decays over time can be described by the relaxation time. Based on different generation mechanisms, common relaxation times can be categorized as longitudinal relaxation time T1 (also known as spin-lattice relaxation time), transverse relaxation time T2 (also known as spin-spin relaxation time), and apparent transverse relaxation time T2 in inhomogeneous magnetic fields. * And the longitudinal relaxation time T in the rotating coordinate system 1ρ Although the relaxation time of magnetic resonance imaging (MRI) is related to the strength of the magnetic field of the device, it is more of an intrinsic property of the sample being tested. Studies have found that, under normal circumstances, there are significant differences in the relaxation times T1 and T2 between normal human tissues and lesion sites; the relaxation time T1 of cartilage tissue in acute injury or degenerative diseases is significantly different. 1ρ T2 was significantly higher than that of normal cartilage tissue; while the relaxation time T2 * A significant decrease in relaxation time indicates a high content of ferromagnetic substances in the tissue. This demonstrates the crucial role of relaxation time in clinical research and disease diagnosis during magnetic resonance imaging (MRI).

[0003] Relaxation time can be represented using quantitative images of relaxation time obtained from magnetic resonance imaging (MRI). These images cannot be directly acquired through MRI scanning and require post-processing: The same scanned area is scanned with different sampling parameters x, resulting in a set of raw images. The relaxation time is calculated by observing the changes in pixel intensity with respect to the sampling parameter x, ultimately generating the quantitative relaxation time image. This image visually displays the relaxation time at various spatial locations within the scanned area and is typically represented using grayscale or pseudocolor. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, device, and storage medium for determining relaxation time, which can quickly and accurately determine relaxation time.

[0005] In a first aspect, embodiments of the present invention provide a method for determining relaxation time, comprising:

[0006] Multiple original images are obtained by performing magnetic resonance scanning on the object under test with different sampling parameters; each sampling parameter corresponds one-to-one with the original image.

[0007] A simplified objective function is set up, which contains only the relaxation time as an unknown quantity. The simplified objective function is determined by taking the partial derivatives of the unknown magnitude and intercept in the original objective function and removing the magnitude and intercept from the original objective function under the constraint that the partial derivatives of the magnitude and the intercept are both zero. The original objective function is used to represent the sum of squares of the fitting error.

[0008] The relaxation time at the corresponding position is determined by minimizing the simplified objective function.

[0009] In one possible implementation, the simplified objective function is the objective function obtained by transforming the original objective function, which contains only the relaxation time function, as a whole; the introduced function satisfies:

[0010]

[0011] Where x represents the sampling parameter, b represents the relaxation time, and λ and μ are coefficients determined according to the type of relaxation time to be measured and the type of sampling parameter; This refers to the introduced function.

[0012] In one possible implementation, the simplified objective function g(b) satisfies:

[0013]

[0014] Alternatively, neglecting the influence of the average noise level, the simplified objective function g(b) satisfies:

[0015]

[0016] Among them, y n This represents the true pixel intensity in the nth original image. x n This represents the nth sampling parameter, and N represents the number of sampling parameters.

[0017] In one possible implementation, after determining the relaxation time at the corresponding position by minimizing the simplified objective function, the method further includes:

[0018] The amplitude is determined based on the determined relaxation time and the relationship between the amplitude and the relaxation time;

[0019] And / or, the intercept is determined based on the determined relaxation time and the relationship between the intercept and the relaxation time;

[0020] The relationship between the amplitude and the relaxation time, and / or the relationship between the intercept and the relaxation time, is determined under the constraint that the partial derivatives of the amplitude and the partial derivatives of the intercept are both zero.

[0021] In one possible implementation, the relationship between the amplitude and the relaxation time satisfies:

[0022]

[0023] Alternatively, neglecting the influence of the average noise level, the relationship between the amplitude and the relaxation time satisfies:

[0024]

[0025] Where 'a' represents the amplitude.

[0026] In one possible implementation, determining the relaxation time at the corresponding position by minimizing the simplified objective function includes:

[0027] The minimum value of the simplified objective function is found by a one-dimensional search algorithm, and the relaxation time corresponding to the minimum value of the simplified objective function is determined.

[0028] In one possible implementation, the method further includes:

[0029] Determine the relaxation time at all locations and generate corresponding quantitative relaxation time images.

[0030] Secondly, embodiments of the present invention also provide an apparatus for determining relaxation time, comprising:

[0031] The acquisition module is used to acquire multiple original images obtained by performing magnetic resonance scanning on the object under test with different sampling parameters; the sampling parameters correspond one-to-one with the original images.

[0032] The setting module is used to set a simplified objective function whose unknowns only include relaxation time. The simplified objective function is determined by taking the partial derivatives of the unknown magnitude and intercept in the original objective function, and removing the magnitude and intercept from the original objective function under the constraint that the partial derivatives of the magnitude and the intercept are both zero. The original objective function is used to represent the sum of squares of the fitting error.

[0033] The processing module is used to determine the relaxation time at the corresponding position by minimizing the simplified objective function.

[0034] Thirdly, embodiments of the present invention provide an apparatus for determining relaxation time, including a processor and a memory, wherein the memory stores a computer program, characterized in that the processor executes the computer program stored in the memory, and the computer program, when executed by the processor, implements the method for determining relaxation time described in the first aspect.

[0035] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for determining relaxation time described in the first aspect.

[0036] Fifthly, this application also provides a computer program product, including a computer program that, when executed, can implement the method for determining relaxation time as described in the first aspect or any possible design of the first aspect.

[0037] The method, apparatus, device, and storage medium for determining relaxation time provided in this invention employ an objective function representing pixel intensity loss as the sum of squared fitting errors. Under the constraint that the partial derivatives of the amplitude and intercept are both zero, this original objective function can be easily converted into a simplified objective function where the unknown quantity only includes the relaxation time. Theoretically, minimizing the original objective function and minimizing the simplified objective function are equivalent. Therefore, minimizing the simplified objective function allows for a relatively accurate determination of the relaxation time. Furthermore, since the simplified objective function has only one unknown, its minimum value can be easily and quickly determined, thus enabling rapid determination of the relaxation time. Moreover, the process of converting the original objective function into a simplified objective function is applicable to various types of relaxation times, making this method suitable for quantifying multiple types of relaxation times and having a wide range of applications. Utilizing a one-dimensional search algorithm, optimization can be achieved through a small number of iterations, determining the minimum value of the simplified objective function and the corresponding relaxation time. Furthermore, it significantly improves computational efficiency while ensuring the objective function remains substantially unchanged, and avoids the difficulty of initial value selection in traditional nonlinear optimization methods. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0039] Figure 1 A flowchart of a method for determining relaxation time provided by an embodiment of the present invention is shown;

[0040] Figure 2 This shows a quantitative image of the actual relaxation time used in the simulation process provided in the embodiments of the present invention;

[0041] Figure 3 Six original images obtained through simulation provided in this embodiment of the invention are shown;

[0042] Figure 4 This invention provides a quantitative image of relaxation time determined by processing six original images using three different methods, as shown in an embodiment of the invention.

[0043] Figure 5 A schematic diagram of the structure of a device for determining relaxation time provided in an embodiment of the present invention is shown;

[0044] Figure 6 A schematic diagram of a device for determining relaxation time provided by an embodiment of the present invention is shown. Detailed Implementation

[0045] The pixel intensity y of the raw image obtained by magnetic resonance scanning has a certain functional relationship with the sampling parameter x, which is related to the relaxation time; moreover, this functional relationship is generally different for different relaxation times. The sampling parameter x is a parameter related to the scan time, and the required sampling parameter x may also be different when determining different relaxation times or when determining the same relaxation time using different methods.

[0046] If the inversion recovery method is used to determine the longitudinal relaxation time T1 and achieve quantitative T1, then the sampling parameter x is the inversion time (TI), and the pixel intensity y at a certain position in the original image satisfies the following equation (1.1):

[0047] y(x)=a(1-2e -xb )+c(1.1)

[0048] Where a, b, and c are the unknowns to be solved, representing the amplitude, relaxation time, and intercept, respectively; generally, the intercept c represents the average noise level.

[0049] If the saturation recovery method is used to determine the longitudinal relaxation time T1 and achieve quantitative T1, then the sampling parameter x is the saturation time (TS), and the pixel intensity y at a certain position in the original image satisfies the following equation (1.2) with respect to the sampling parameter x:

[0050] y(x)=a(1-e -xb )+c(1.2)

[0051] If we consider the transverse relaxation time T2, and the apparent transverse relaxation time T2 in the non-uniform magnetic field... * Quantitatively, the sampling parameter x is the echo time (TE); if the longitudinal relaxation time T in the rotating coordinate system is... 1ρQuantitatively, the sampling parameter x is the spin lock time (TSL). Furthermore, for T2, T2... * And T 1ρ In quantitative analysis, the pixel intensity y at a certain location in the original image satisfies the following equation (1.3) with respect to the sampling parameter x:

[0052] y(x)=ae -xb +c(1.3)

[0053] The original image can represent the situation at multiple locations within the scanned area. For example, each pixel in the original image corresponds to a location within the scanned area, and each pixel has a corresponding pixel intensity y. The aforementioned functional relationship can represent the relationship between the pixel intensity y at a certain location and the relaxation time b at that location, and the functional relationship is the same at different locations. Through the aforementioned functional relationship, the relaxation time at each location can be calculated, thereby generating the corresponding quantitative relaxation time image.

[0054] Currently, the actual solution for relaxation time is mainly achieved through least-squares fitting, and its objective function is represented by the fitting error between the true and calculated values. One such objective function is f(a,b,c):

[0055]

[0056] Where N is the number of sampling parameters x, which is also the number of original images (there is a one-to-one correspondence between sampling parameters and original images; one sampling parameter x can generate one original image), and n represents its index; y n y(x) represents the true pixel intensity (true value) in the nth original image. n ) represents the nth sampling parameter x n The calculated pixel intensity (calculated value). The objective function above represents the sum of squared fitting errors. By minimizing this objective function, the specific values ​​of amplitude a, relaxation time b, and intercept c can be finally solved.

[0057] Because the unknown variable b is in an exponential position in the functional relationship between pixel intensity y and sampling parameter x, the unknown variable b in the objective function is also in an exponential position. Therefore, a linear algorithm cannot directly solve the objective function; a nonlinear optimization algorithm is needed to continuously reduce the objective function value through multiple iterations until convergence. The nonlinear optimization process first requires providing suitable initial values ​​for each unknown variable (including a, b, and c), and the algorithm must start optimization from these initial values. In each iteration, in addition to calculating the objective function value, it is also necessary to calculate the gradient vector of the variables, the first derivative matrix, the second derivative matrix, and the inverse of the second derivative matrix, etc., resulting in a large computational load, and the convergence speed is easily affected by the algorithm parameters and the deviation between the initial value and the optimal solution. With good initial values, satisfactory optimization results can be obtained within a finite number of iterations.

[0058] Nonlinear optimization methods provide relatively accurate results and are generally considered the gold standard for quantitative relaxation time imaging. However, for magnetic resonance imaging (MRI) images with large datasets, the computation of nonlinear optimization methods is extremely time-consuming.

[0059] There are other methods that can significantly improve computational efficiency while sacrificing some computational accuracy. For example, in relaxation times T2, T2... * And T 1ρ In the quantitative analysis, the unknown quantity c is not considered, that is, the influence of the average noise level is not considered, and the average noise level is forcibly assumed to be zero. At this time, the above equation (1.3) becomes the following equation (3):

[0060] y(x)=ae -xb (3)

[0061] Taking the logarithm of both sides of the above equation transforms the original nonlinear mathematical model into a linear model, where the least squares problem has an analytical solution. Therefore, the relaxation time can be directly calculated using the above linear logarithmic transformation operation, without providing initial values ​​or iterative calculations.

[0062] As mentioned above, nonlinear optimization methods can minimize the objective function and obtain the most accurate solution, but they have certain requirements for the initial values ​​of unknowns, and the computation is large and time-consuming, which poses certain difficulties for generating quantitative images of relaxation time in real time.

[0063] The simplified linear logarithmic transformation method is computationally efficient and time-saving. However, the logarithmic operation does not consider the variable 'c', resulting in varying weightings for noise in the data. This leverage effect of data noise can easily cause the solution to deviate from the optimal solution, reducing the accuracy of relaxation time quantification. Furthermore, since image data is modulo-valued data, the noise follows a Rayleigh distribution with a non-zero (greater than zero) mean and should generally not be ignored. Therefore, when the signal-to-noise ratio of the data is not high enough, the relaxation time obtained using the linear logarithmic transformation method has low accuracy and reliability. Moreover, the linear logarithmic transformation method is generally not applicable to T1 quantification.

[0064] In addition, there are other solution methods, such as using the integral area of ​​the data decay curve to approximate the relaxation time. Although this integral area approximation method is slightly more accurate than linear logarithmic calculation, it is still inferior to nonlinear optimization and cannot consider noise levels in its fitted model. Furthermore, this method has stringent requirements on sampling parameters, requiring linearly and equally spaced echo times (TE), which limits the effective measurement range and prevents further optimization of the sampling mode. Moreover, this method cannot be applied to T1 quantification, resulting in insufficient functional completeness.

[0065] To address the problems of high computational cost of nonlinear optimization methods and low accuracy and limited applicability of other simplified methods, this invention provides a method for determining relaxation time. This method can quickly and accurately determine the relaxation time and is applicable to various types of relaxation time quantification, thus having a wide range of applications. The embodiments of this invention are described below with reference to the accompanying drawings.

[0066] Figure 1 A flowchart illustrating a method for determining relaxation time provided by an embodiment of the present invention is shown. Figure 1 As shown, the method includes:

[0067] Step 101: Obtain multiple original images of the object under test by performing magnetic resonance scanning with different sampling parameters; the sampling parameters correspond one-to-one with the original images.

[0068] In this embodiment of the invention, the object to be tested is the object requiring magnetic resonance scanning, such as a part of the human body. When it is necessary to determine the corresponding relaxation time, magnetic resonance scanning is performed on the object to be tested with different sampling parameters, thereby obtaining multiple raw images. As mentioned above, the sampling parameter is a parameter related to the scanning time. When determining different types of relaxation times or using different methods, the type of sampling parameter needs to be determined based on the actual situation. The sampling parameter can be inversion time, saturation time, echo time, spin-lock time, etc.

[0069] In this embodiment, the sampling parameters correspond one-to-one with the original images; that is, one sampling parameter corresponds to one original image. For example, if the sampling parameter is the inversion time (TI), and scanning is performed with echo times of 100ms, 200ms, and 300ms respectively, three original images can be obtained. Multiple original images can be obtained using existing mature technologies, which will not be detailed in this embodiment.

[0070] Step 102: Set a simplified objective function that includes only relaxation time as the unknown quantity; the simplified objective function is determined by taking the partial derivatives of the unknown magnitude and intercept in the original objective function and removing the magnitude and intercept from the original objective function under the constraint that the partial derivatives of the magnitude and intercept are both zero; the original objective function is used to represent the sum of squares of the fitting error.

[0071] This invention defines the unknowns in a minimizing objective function. The unknowns in a traditional objective function include the magnitude *a*, relaxation time *b*, and intercept *c*. For ease of description, this traditional objective function is referred to as the original objective function. When the original objective function is at its minimum, the partial derivatives of all three unknowns should be zero. Therefore, in this invention, the partial derivatives of the magnitude *a* and the intercept *c* are constrained to zero. Under this constraint, the original objective function can be simplified, so that the unknowns in the simplified original objective function only include the relaxation time *b*. For ease of description, this simplified original objective function is referred to as the "simplified objective function".

[0072] In this embodiment of the invention, the original objective function represents the sum of squares of the fitting error. The original objective function is an objective function related to the sum of squares of the fitting error, where the fitting error is the true pixel intensity y in the original image. n With the calculated pixel intensity y(x) n The error between the two. For example, the original objective function can be Equation (2) above. Taking the partial derivative of the sum of squares of the fitting error, the partial derivatives of the amplitude a and the intercept c can be determined relatively easily. Furthermore, under the constraint that the partial derivatives of the amplitude a and the partial derivatives of the intercept c are both zero, the relationship between the amplitude a and the relaxation time b can be determined, that is, the amplitude a can be represented by the relaxation time b; similarly, the relationship between the intercept c and the relaxation time b can be determined, that is, the intercept c can be represented by the relaxation time b. In this case, for the amplitude a and the intercept c at the position in the original objective function, they can be uniformly represented by the unknown relaxation time b, so that the original objective function can be converted into an objective function containing only the one unknown quantity of relaxation time b, that is, a simplified objective function.

[0073] Under the constraint that the partial derivatives of amplitude a and intercept c are both zero, the original objective function is transformed into a simplified objective function. This process is independent of the functional relationship between pixel intensity and sampling parameters. That is, this transformation process is applicable to the functional relationships shown in equations (1.1), (1.2), and (1.3) above, and can calculate relaxation times for various types of functions.

[0074] Step 103: Determine the relaxation time at the corresponding location by minimizing the simplified objective function.

[0075] In this embodiment of the invention, since the simplified objective function contains only one unknown quantity, the relaxation time *b*, minimizing the simplified objective function can be achieved relatively easily, thereby determining the minimum value of the simplified objective function; the relaxation time corresponding to the simplified objective function reaching its minimum value is the calculated relaxation time. Optionally, in this embodiment of the invention, since the simplified objective function contains only one unknown quantity to be solved, namely the relaxation time, a one-dimensional search algorithm can be applied to the quantitative solution of the relaxation time. Specifically, the minimum value of the simplified objective function is found through a one-dimensional search algorithm, and the relaxation time corresponding to the minimum of the simplified objective function is determined. For example, the one-dimensional search algorithm can be the golden section method; this algorithm is suitable for one-dimensional convex optimization (least squares problems belong to convex optimization), does not require differentiation of the simplified objective function, has a small computational load, stable convergence speed, and is easy to apply. Using the one-dimensional search algorithm, optimization can be achieved through a small number of iterations, determining the minimum value of the simplified objective function and the corresponding relaxation time; and, while ensuring that the objective function remains substantially unchanged, computational efficiency is greatly improved, and the difficulty of initial value selection in traditional nonlinear optimization methods is avoided.

[0076] Specifically, by performing steps 102 and 103 on a location in the original image, the relaxation time at that location can be determined. By performing the above process of determining the relaxation time on all locations in the original image, the relaxation time at all locations can be determined, thereby generating a corresponding quantitative relaxation time image. This quantitative relaxation time image uses corresponding grayscale or pseudocolor to represent the magnitude of the relaxation time at each location.

[0077] The method for determining relaxation time provided in this invention uses an objective function representing pixel intensity loss as the sum of squared fitting errors as the original objective function. Under the constraint that the partial derivatives of the amplitude and intercept are both zero, this original objective function can be easily converted into a simplified objective function whose unknown quantity only includes the relaxation time. Theoretically, minimizing the original objective function and minimizing the simplified objective function are equivalent. Therefore, minimizing the simplified objective function can accurately determine the relaxation time. Furthermore, since the simplified objective function has only one unknown quantity, its minimum value can be easily and quickly determined, thus enabling rapid determination of the relaxation time. Moreover, the process of converting the original objective function into a simplified objective function is applicable to various types of relaxation times, making this method suitable for quantifying various types of relaxation times and having a wide range of applications.

[0078] When quantifying different types of relaxation time, that is, when determining different types of relaxation time, as shown in equations (1.1), (1.2), and (1.3) above, the pixel intensity y and the sampling parameter x satisfy different functional relationships; however, these functional relationships have commonalities. In this embodiment of the invention, these functional relationships are unified and expressed as equation (4) below.

[0079] y(x)=a(λ+μe -xb )+c (4)

[0080] Where a, b, and c still represent the amplitude, relaxation time, and intercept (generally the average noise level), respectively, and λ and μ are preset coefficients. The coefficients λ and μ are determined according to the type of relaxation time to be measured and the pulse sequence (i.e., sampling parameters) used. If the conventional inversion recovery method is used, then λ = 1, μ = –2, and the above equation (4) is the same as the above equation (1.1); if the conventional saturation recovery method is used, then λ = 1, μ = –1, and the above equation (4) is the same as the above equation (1.2); at T2, T2 * And T 1ρ In quantitative analysis, λ = 0 and μ = 1, and the above formula (4) is the same as the above formula (1.3).

[0081] Accordingly, based on the nth sampling parameter x n The calculated pixel intensity y(x) n This can be represented as: y(x) nSubstituting into equation (2) above yields the original objective function applicable to various types of relaxation times. While it's possible to convert the original objective function into a simplified objective function containing only the relaxation time b when both the partial derivatives of amplitude a and intercept c are zero, the simplified objective function is more complex. In this embodiment, during the conversion of the original objective function into a simplified objective function, the part of equation (4) that is only related to the relaxation time b is treated as a whole, i.e., λ+μe -xb As a whole, the original objective function is transformed into a simplified objective function. As a whole, the relationship between the magnitude *a*, intercept *c*, and relaxation time *b* can be expressed more concisely. Furthermore, based on the derivation process provided later in this embodiment, a more concise simplified objective function can be determined, facilitating the rapid determination of its minimum value.

[0082] In this embodiment of the invention, the unknown quantity in the original objective function that only contains a portion of the relaxation time is treated as a whole, that is, λ+μe -xb As a whole, and with This is referred to as the "introduced function" in this embodiment of the invention, that is, the function introduced in this embodiment. Specifically, firstly, let the introduced function be defined. Then, the relationship between the pixel intensity of the original image and the sampling parameters, as expressed in equation (4) above, is: Accordingly, the original objective function represented by equation (2) above becomes:

[0083]

[0084] Among them, y n This represents the true pixel intensity in the nth original image; Right now x n This represents the nth sampling parameter.

[0085] Expanding equation (5) above, we get:

[0086]

[0087] Taking the partial derivatives of the original objective function in equation (6) with respect to the magnitude a and the intercept c, we get:

[0088]

[0089]

[0090] Since the objective function needs to be minimized during the solution process, when the objective function is at its minimum, the partial derivatives of the magnitude 'a' and the intercept 'c' should both be zero, that is, the values ​​of equations (7.1) and (7.2) above should be zero. Combining equations (7.1) and (7.2) above, we can obtain:

[0091]

[0092] Among them, the first equation in the above equation (8) is the relationship between the amplitude a and the relaxation time b; the second equation in the above equation (8) is the relationship between the intercept c and the relaxation time b, and the intercept c is expressed in terms of the amplitude a.

[0093] In this embodiment of the invention, substituting the two partial derivatives shown in equations (7.1) and (7.2) into equation (6), the original objective function can be rewritten as:

[0094]

[0095] In this equation (9), the two terms on the right-hand side containing partial derivatives should also be zero; and, substituting the expressions for a and c in equation (8) into equation (9), the original objective function is:

[0096]

[0097] Wherein, ω(y) includes the second and third terms on the right side of the first row of equation (10), that is... At this point, the objective function of the relaxation time quantitative least squares becomes dependent only on x, y, and b, with the only unknown being the relaxation time b. The minimum value of the objective function can be found by changing the value of b. In this process, since ω(y) is a function that depends only on the pixel intensity y in the original image, and ω(y) remains a fixed constant, it does not contribute to finding the minimum value. Therefore, the original objective function f(a,b,c) can be replaced by an objective function g(b) with ω(y) removed. This objective function g(b) satisfies:

[0098]

[0099] The objective function g(b) can be used as the simplified objective function in the embodiments of the present invention.

[0100] If the signal-to-noise ratio of the original image is high, the influence of the intercept c can be ignored, i.e., the influence of the average noise level can be neglected, and the variable c is assumed to be zero. In this case, following the same derivation process as above, it can be deduced that the variables a and the objective function g(b) satisfy the following:

[0101]

[0102]

[0103] In this embodiment of the invention, when determining the relaxation time b of any type, N original images can be determined, and the objective function shown in equation (11) above can be used as a simplified objective function. The simplified objective function only has one unknown quantity, the relaxation time b, which can quickly determine the size of the relaxation time. For example, a one-dimensional search algorithm can be used to achieve a fast solution. The solution process does not require the use of traditional nonlinear optimization methods, nor does it require providing initial values. The calculation speed is fast and the efficiency is high. Moreover, when minimizing the objective function, the simplified objective function is equivalent to the original objective function used by the nonlinear optimization method, that is, the objective function shown in equation (2) above. Compared with the nonlinear optimization method, which is the gold standard, this embodiment of the invention retains the original least squares evaluation standard, and at the same time adopts an improved objective function to reduce the amount of calculation, which greatly improves the calculation efficiency and can ensure the accuracy and reliability of the quantitative result of the relaxation time. Compared with other approximation methods such as linear logarithmic transformation, the method provided by this embodiment of the invention basically maintains a high calculation efficiency and improves the accuracy of the solution result. In addition, unlike approximation methods, it can handle T2, T2* and T 1ρ This method can handle all types of relaxation time quantitative data, even when quantitative data or sampling parameters are limited.

[0104] Optionally, the method provided in this embodiment of the invention can also determine the amplitude a and the intercept c. Specifically, after step 103 "determining the relaxation time at the corresponding position by minimizing the simplified objective function" above, the method further includes step A1 or step A2:

[0105] Step A1: Determine the amplitude based on the determined relaxation time and the relationship between the amplitude and the relaxation time.

[0106] Step A2: Determine the intercept based on the determined relaxation time and the relationship between the intercept and the relaxation time.

[0107] The relationship between amplitude and relaxation time, and / or the relationship between intercept and relaxation time, are determined under the constraint that the partial derivatives of amplitude and intercept are both zero.

[0108] In this embodiment of the invention, when the partial derivatives of amplitude a and intercept c are both zero, the relationship between amplitude a and relaxation time b, and the relationship between intercept c and relaxation time b, can be determined. For example, equation (8) above shows the relationship between amplitude a and relaxation time b, and the relationship between intercept c and relaxation time b.

[0109] Furthermore, the method provided in this embodiment of the invention can also optionally consider the average noise level. Specifically, as shown above, under normal circumstances, i.e., when considering the average noise level, the objective function shown in equation (11) above can be used as a simplified objective function; correspondingly, the amplitude a and the intercept c satisfy equation (8) above. When not considering the average noise level, the objective function shown in equation (13) above can be used as a simplified objective function; correspondingly, the amplitude a satisfies equation (12) above, and the intercept c is zero.

[0110] The following is a detailed description of the method for determining the relaxation time through an embodiment, and a comparison with traditional nonlinear optimization methods and linear logarithmic methods. This embodiment quantifies the transverse relaxation time T2, and correspondingly, the sampling parameter x is the echo time. For ease of comparison with traditional methods, this embodiment is a simulation-based example.

[0111] In this embodiment of the invention, the object under test is a simulated object comprising nine circular regions, each corresponding to a different lateral relaxation time. The quantitative image of the actual relaxation time of the object under test is shown below. Figure 2 As shown, the resolution of this quantitative relaxation time image is 256×256. The nine circular regions on the image correspond to different T2 values, from left to right and top to bottom: 100, 200, 300, 400, 500, 600, 700, 800, and 900 milliseconds. There is no signal outside the circular regions, which is used to simulate the background of a magnetic resonance imaging (MRI) image. Based on this real quantitative relaxation time image, original images at different echo times can be simulated, and these original images are also simulated images. Those skilled in the art will understand that in practice, calculating the quantitative relaxation time image of the object under test is necessary, and such images do not exist in reality. Figure 2 The quantitative image shown, Figure 2 The quantitative images shown are mainly used for simulation to generate the corresponding original images.

[0112] In this embodiment of the invention, six simulated relaxation data matrices of size 256×256 are generated using echo times of 50, 100, 200, 400, 800, and 1600 milliseconds. Each data matrix represents an original image, and N=6. Figure 2 The original image directly generated from the quantitative image shown is free of noise contamination. Nonlinear optimization methods, linear logarithmic methods, and the method provided in this embodiment can all accurately solve for the T2 value from the data. However, relaxed data in the real world is inevitably accompanied by noise. To simulate real-world conditions, this embodiment adds Rayleigh distributed random noise with a noise level of 1% to the original image. Figure 3 Six original images with a 1% increase in noise level are shown. Figure 3 The vertical axis (i.e., the color bar) represents the pixel intensity; for example... Figure 3 As shown, as the echo time increases, the pixel intensity of different circular regions gradually becomes zero, that is, close to the background, and the smaller the relaxation time T2, the faster the change rate.

[0113] Processed using the method provided in the embodiments of the present invention Figure 3 The noisy original image shown can be used to determine the relaxation time T2 at each location. This embodiment of the invention uses the golden section algorithm to optimize the improved simplified objective function g(b) to determine its minimum value, thereby determining the corresponding relaxation time and calculating a quantitative image of the relaxation time. The specific implementation steps are as follows:

[0114] (1) Parameter initialization

[0115] Given the initial search interval [b] for relaxation time b LB ,b UB The initial search range is defined as ε, where ε is the minimum search range and ε is the minimum search range, both in milliseconds. In practice, the relaxation time of magnetic resonance imaging (MRI) is generally no more than 3,000 milliseconds. To be on the safe side, the initial search range can be set from 0 to 10,000 milliseconds. Currently, the relaxation time quantification accuracy of mainstream international MRI equipment manufacturers is generally integer. Therefore, in this embodiment, the minimum search range is set to 0.1 milliseconds to ensure the accuracy of the calculation results.

[0116] (2) Iterative optimization of the golden ratio

[0117] Step I: Set the golden ratio Calculate b1 = b UB –κ(b UB -b LB b2 = b LB +κ(b UB -b LB The values ​​of objective functions g(b1) and g(b2) are calculated according to equation (11), and g1 = g(b1) and g2 = g(b2). Among them, without considering the average noise level, the values ​​of objective functions g(b1) and g(b2) can be calculated according to equation (13).

[0118] Step II: Compare the sizes of g1 and g2. If g1 > g2, then perform the following operations in sequence: Let b LB =b1, b1=b2, g1=g2, b2=b LB +κ(b UB -b LB ), calculate g2 = g(b2); if g1 ≤ g2, then perform the following operations in sequence: let b UB =b2, b2=b1, g2=g1, b1=b UB –κ(b UB -b LB), calculate g1=g(b1).

[0119] Step III: Determine if the exit condition is met. If b UB -b LB If b < ε, then the calculation is complete and the iteration exits; if b UB -b LB If ≥ε, then return to step II and continue iterating until the exit condition is met.

[0120] (3) Output the final result

[0121] Let the final solution of the relaxation time be b = (b LB +b UB ) / 2. In addition, substituting b into equation (9) or (12) can calculate the amplitude a and the intercept c.

[0122] The embodiments of the present invention also employ traditional nonlinear optimization methods and linear logarithmic methods to process the data. Figure 2 The original image containing noise is shown, and the T2 quantification maps obtained using three methods are as follows: Figure 4 As shown; where, Figure 2 and Figure 4 The vertical axis represents the relaxation time, in milliseconds.

[0123] Through quantitative images of actual relaxation time ( Figure 2 A comparison reveals that the results of the nonlinear optimization method and the method provided in this embodiment are closer to the true values, while the linear logarithmic method yields unsatisfactory results when the relaxation time T2 is small. In this example, the quantitative error of T2 is large when the echo time is 100 and 200 milliseconds. The reason for the large quantitative error of the linear logarithmic method is that the tail noise of the relaxation curve generated by smaller T2 values ​​is more easily amplified by logarithmic transformation, causing a leverage effect in data fitting. At a 1% noise level, the absolute deviations of the quantitative results of the three methods from the true values ​​are 15.3 ± 18.1 milliseconds (nonlinear optimization method), 62.9 ± 86.3 milliseconds (linear logarithmic method), and 15.2 ± 17.7 milliseconds (the method provided in this embodiment), respectively. The reason why the accuracy of the nonlinear optimization method is slightly lower than that of the method provided in this embodiment is that the initial values ​​of some data optimization processes are not ideal, causing the algorithm to fail to converge effectively to the optimal solution.

[0124] Regarding computational speed, the three methods took 33.9 seconds (nonlinear optimization method), 1.2 seconds (linear logarithmic method), and 2.4 seconds (the method provided in this embodiment) to process the 256×256×6 simulation data in this example, respectively. It is evident that the computational efficiency of the linear logarithmic method and the method provided in this embodiment is significantly higher than that of the nonlinear optimization method. Although the computational speed of the method provided in this embodiment is slightly slower than that of the linear logarithmic method, the overall time consumption is still within an acceptable range, enabling real-time quantification of relaxation time. Furthermore, the accuracy of the quantitative results obtained by the method provided in this embodiment is no less than that of the gold standard nonlinear optimization method and is significantly higher than that of the linear logarithmic method.

[0125] Numerical simulation comparisons reveal that the beneficial effects of the method provided in this embodiment of the invention give it superior overall performance compared to existing methods, as well as the ability and feasibility to replace existing methods.

[0126] The method for determining relaxation time provided by the embodiments of the present invention has been described in detail above. This method can also be implemented by a corresponding device. The device for determining relaxation time provided by the embodiments of the present invention will be described in detail below.

[0127] Figure 5 A schematic diagram of a device for determining relaxation time provided in an embodiment of the present invention is shown. Figure 5 As shown, the apparatus for determining the relaxation time includes:

[0128] The acquisition module 51 is used to acquire multiple original images obtained by performing magnetic resonance scanning on the object under test with different sampling parameters; the sampling parameters correspond one-to-one with the original images;

[0129] Setting module 52 is used to set a simplified objective function whose unknowns only include relaxation time; the simplified objective function is determined by taking the partial derivatives of the unknown magnitude and intercept in the original objective function, and removing the magnitude and intercept from the original objective function under the constraint that the partial derivatives of the magnitude and the intercept are both zero; the original objective function is used to represent the sum of squares of the fitting error;

[0130] Processing module 53 is used to determine the relaxation time at the corresponding position by minimizing the simplified objective function.

[0131] In one possible implementation, the simplified objective function is the objective function obtained by transforming the original objective function, which contains only the relaxation time function, as a whole; the introduced function satisfies:

[0132]

[0133] Where x represents the sampling parameter, b represents the relaxation time, and λ and μ are coefficients determined according to the type of relaxation time to be measured and the type of sampling parameter; This refers to the introduced function.

[0134] In one possible implementation, the simplified objective function g(b) satisfies:

[0135]

[0136] Alternatively, neglecting the influence of the average noise level, the simplified objective function g(b) satisfies:

[0137] Among them, y n This represents the true pixel intensity in the nth original image. x n This represents the nth sampling parameter, and N represents the number of sampling parameters.

[0138] In one possible implementation, after determining the relaxation time at the corresponding position by minimizing the simplified objective function, the processing module 53 is further configured to:

[0139] The amplitude is determined based on the determined relaxation time and the relationship between the amplitude and the relaxation time;

[0140] And / or, the intercept is determined based on the determined relaxation time and the relationship between the intercept and the relaxation time;

[0141] The relationship between the amplitude and the relaxation time, and / or the relationship between the intercept and the relaxation time, is determined under the constraint that the partial derivatives of the amplitude and the partial derivatives of the intercept are both zero.

[0142] In one possible implementation, the relationship between the amplitude and the relaxation time satisfies:

[0143]

[0144] Alternatively, neglecting the influence of the average noise level, the relationship between the amplitude and the relaxation time satisfies:

[0145]

[0146] Where 'a' represents the amplitude.

[0147] In one possible implementation, the processing module 53 determines the relaxation time at the corresponding position by minimizing the simplified objective function, including:

[0148] The minimum value of the simplified objective function is found by a one-dimensional search algorithm, and the relaxation time corresponding to the minimum value of the simplified objective function is determined.

[0149] In one possible implementation, the device further includes:

[0150] The image generation module is used to determine the relaxation time at all locations and generate corresponding quantitative relaxation time images.

[0151] It should be noted that the apparatus for determining relaxation time provided in the above embodiments is only illustrated by the division of the above functional modules when implementing the corresponding functions. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus for determining relaxation time and the method for determining relaxation time provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0152] According to one aspect of this application, embodiments of the present invention also provide a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component. When the computer program is executed by a processor, the method for determining relaxation time provided in embodiments of this application is performed.

[0153] Furthermore, embodiments of the present invention also provide an apparatus for determining relaxation time. The apparatus includes a processor and a memory. The memory stores a computer program, and the processor is capable of executing the computer program stored in the memory. When the computer program is executed by the processor, it can implement the method for determining relaxation time provided in any of the above embodiments.

[0154] For example, Figure 6 An embodiment of the present invention provides a device for determining relaxation time, the device including a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150 and a user interface 1160.

[0155] In this embodiment of the invention, the device further includes a computer program stored in a memory 1150 and executable on a processor 1120, which, when executed by the processor 1120, implements the various processes of the above-described method embodiment for determining relaxation time.

[0156] Transceiver 1130 is used to receive and send data under the control of processor 1120.

[0157] In this embodiment of the invention, a bus architecture (represented by bus 1110) is used. Bus 1110 may include any number of interconnected buses and bridges. Bus 1110 connects various circuits, including one or more processors represented by processor 1120 and memory represented by memory 1150.

[0158] Bus 1110 represents one or more of several types of bus architectures, including memory buses and memory controllers, peripheral buses, Accelerated Graphics Port (AGP), processors, or local buses using any bus architecture from various bus architectures. As an example and not a limitation, such architectures include: Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MCA) buses, Enhanced ISA (EISA) buses, Video Electronics Standards Association (VESA) buses, and Peripheral Component Interconnect (PCI) buses.

[0159] The processor 1120 can be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The processors mentioned above include: general-purpose processors, central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), programmable logic arrays (PLAs), microcontroller units (MCUs) or other programmable logic devices, discrete gates, transistor logic devices, and discrete hardware components. They can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. For example, the processor can be a single-core processor or a multi-core processor, and the processor can be integrated on a single chip or located on multiple different chips.

[0160] Processor 1120 can be a microprocessor or any conventional processor. The method steps disclosed in the embodiments of the present invention can be directly executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in readable storage media known in the art, such as Random Access Memory (RAM), Flash Memory, Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), registers, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0161] Bus 1110 can also connect various other circuits, such as peripheral devices, voltage regulators, or power management circuits. Bus interface 1140 provides an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Therefore, embodiments of the present invention will not be described further.

[0162] Transceiver 1130 can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. For example, transceiver 1130 receives external data from other devices, and transceiver 1130 is used to send data processed by processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as a touchscreen, physical keyboard, monitor, mouse, speaker, microphone, trackball, joystick, or stylus.

[0163] It should be understood that, in embodiments of the present invention, memory 1150 may further include memory remotely configured relative to processor 1120, and such remotely configured memory can be connected to a server via a network. One or more portions of the aforementioned network may be an ad hoc network, intranet, extranet, virtual private network (VPN), local area network (LAN), wireless local area network (WLAN), wide area network (WAN), wireless wide area network (WWAN), metropolitan area network (MAN), Internet, public switched telephone network (PSTN), ordinary old-style telephone service (POTS), cellular telephone network, wireless network, Wi-Fi network, and combinations of two or more of the aforementioned networks. For example, cellular telephone networks and wireless networks can be Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), WiMAX, General Packet Radio Service (GPRS), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Advanced Long Term Evolution (LTE-A), Universal Mobile Telecommunications System (UMTS), Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), Ultra Reliable Low Latency Communications (uRLLC), etc.

[0164] It should be understood that the memory 1150 in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. Non-volatile memory includes: read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0165] Volatile memory includes random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1150 described in this embodiment includes, but is not limited to, the above and any other suitable types of memory.

[0166] In this embodiment of the invention, the memory 1150 stores the following elements of the operating system 1151 and the application 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0167] Specifically, the operating system 1151 includes various system programs, such as a framework layer, a core library layer, and a driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 1152 includes various applications, such as a media player and a browser, used to implement various application functions. Programs implementing the methods of this embodiment of the invention can be included in the application program 1152. The application program 1152 includes applets, objects, components, logic, data structures, and other computer system executable instructions that perform specific tasks or implement specific abstract data types.

[0168] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the various processes of the above-described method embodiments for determining relaxation time and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0169] Computer-readable storage media include: permanent and non-permanent, removable and non-removable media, which are tangible devices capable of retaining and storing instructions for use by an instruction execution device. Computer-readable storage media include: electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, and any suitable combination thereof. Computer-readable storage media include: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape storage, magnetic disk storage or other magnetic storage devices, memory sticks, mechanical encoding devices (e.g., punched cards or raised structures in grooves on which instructions are recorded), or any other non-transfer medium that can be used to store information accessible by a computing device. As defined in the embodiments of the present invention, computer-readable storage media do not include temporary signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0170] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, devices, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0171] 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; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to solve the problems addressed by the embodiments of the present invention, depending on actual needs.

[0172] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0173] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (including: a personal computer, a server, a data center, or other network device) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media listed above that can store program code.

[0174] In the description of the embodiments of the present invention, those skilled in the art should understand that the embodiments of the present invention can be implemented as methods, apparatuses, devices, and storage media. Therefore, the embodiments of the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, the embodiments of the present invention can also be implemented as a computer program product in one or more computer-readable storage media, the computer-readable storage media containing computer program code.

[0175] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any combination thereof. In embodiments of the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0176] The computer program code contained in the aforementioned computer-readable storage medium may be transmitted using any suitable medium, including wireless, wire, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0177] Computer program code for performing the operations of the embodiments of the present invention can be written in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or in one or more programming languages ​​or combinations thereof. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The computer 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 or an external computer via any type of network, including a local area network (LAN) or a wide area network (WAN).

[0178] The embodiments of the present invention describe the provided methods, apparatus, and devices through flowcharts and / or block diagrams.

[0179] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0180] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction apparatus product that includes the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0181] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable data processing apparatus provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0182] The above description is merely a specific implementation of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention should be determined by the protection scope of the claims.

Claims

1. A method of determining a relaxation time, characterized by, The method comprises: obtaining a plurality of original images obtained by performing magnetic resonance scanning on a to-be-tested object respectively with different sampling parameters; the sampling parameters correspond to the original images one by one; setting a simplified objective function in which unknown quantities only include a relaxation time; the simplified objective function is an objective function determined by removing an amplitude and an intercept in an original objective function in a constraint that partial derivatives of the amplitude and the intercept are both zero; the original objective function is used to represent a sum of squares of fitting errors; determining the relaxation time at a corresponding position by minimizing the simplified objective function.

2. The method of claim 1, wherein, The simplified objective function is an objective function converted from the original objective function in a case that an introduction function including only the relaxation time in the original objective function is taken as a whole; the introduction function satisfies: Wherein, x represents the sampling parameter, b represents the relaxation time; λ, μ are coefficients determined according to the type of the relaxation time to be measured and the kind of the sampling parameter; represents the introduction function.

3. The method of claim 2, wherein, The simplified objective function g(b) satisfies: Or, in a case that an average noise level is ignored, the simplified objective function g(b) satisfies: wherein y n represents the real pixel intensity in the nth original image, x n represents the nth sampling parameter, and N represents the number of the sampling parameters.

4. The method of claim 2, wherein, After the relaxation time at the corresponding position is determined by minimizing the simplified objective function, the method further comprises: determining the amplitude according to the determined relaxation time and a relationship between the amplitude and the relaxation time; and / or determining the intercept according to the determined relaxation time and a relationship between the intercept and the relaxation time; The relationship between the amplitude and the relaxation time, and / or the relationship between the intercept and the relaxation time is determined in the constraint that the partial derivatives of the amplitude and the intercept are both zero. The relationship between the amplitude and the relaxation time satisfies:

5. The method of claim 4, wherein, Or, in a case that an average noise level is ignored, the relationship between the amplitude and the relaxation time satisfies: Wherein, a represents the amplitude. The determining the relaxation time at the corresponding position by minimizing the simplified objective function comprises:

6. The method of claim 1, wherein, finding a minimum value of the simplified objective function by using a one-dimensional search algorithm, and determining the relaxation time corresponding to the minimum value of the simplified objective function. The method further comprises:

7. The method of claim 1, wherein, determining the relaxation time at all positions, and generating a corresponding quantitative relaxation time image. The method comprises:

8. An apparatus for determining a relaxation time, characterized by an acquisition module, configured to acquire a plurality of original images obtained by performing magnetic resonance scanning on a to-be-tested object respectively with different sampling parameters; the sampling parameters correspond to the original images one by one; a setting module, configured to set a simplified objective function in which unknown quantities only include a relaxation time; the simplified objective function is an objective function determined by removing an amplitude and an intercept in an original objective function in a constraint that partial derivatives of the amplitude and the intercept are both zero; the original objective function is used to represent a sum of squares of fitting errors; a processing module, configured to determine the relaxation time at a corresponding position by minimizing the simplified objective function. The processor executes the computer program stored in the memory to implement the method for determining the relaxation time according to any one of claims 1 to 7.

9. An apparatus for determining a relaxation time, comprising a processor and a memory, the memory storing a computer program, characterized in that, The computer program is executed by the processor to implement the method for determining the relaxation time according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, ​

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