Magnetic resonance imaging apparatus, image processing apparatus, and signal separation method

Abstract

To accurately separate signals from a plurality of metabolites in an MRI image.SOLUTION: A signal separation unit generates a plurality of signal patterns that have changed the value of variables of each material on the basis of prior information as a dictionary, and executes signal separation of each material by matching the dictionary with measurement signals. In this case, the order of signal strength of each material is used as prior information, and while the dictionary used for the matching is changed according to the order, selection of the best matched dictionary is repeated for each material.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a magnetic resonance imaging apparatus (MRI apparatus), and more particularly to a technique for separating signals of a plurality of metabolic substances contained in a subject in an image (MR image) acquired by an MRI apparatus. [Background technology]

[0002] The nuclear species that MRI devices target are mainly hydrogen nuclei (protons), and images obtained by MRI devices are reconstructed using signals generated from protons by nuclear magnetic resonance, such as the density of protons in the tissue to be examined. Since hydrogen exists mainly in molecules that make up water and fat in tissues, when imaging tissues that are mainly water, such as blood flow, it is necessary to separate signals from fat (hereinafter referred to as fat signals) that are mixed with signals from protons in water (hereinafter referred to as water signals). In addition, major tissues that can be imaged by MRI include subcutaneous fat, skeletal muscle, and bone marrow, and these tissues contain a lot of fat, so it is essential to separate water signals and fat signals in order to obtain high-contrast images, and improving the accuracy of separation is important to improve the quality of separated images.

[0003] In MRI, one of the techniques for separating water signals from separated signals is the Dixon method, which utilizes the difference in resonance frequency between water signals and fat signals, i.e., chemical shift. Dixon uses the fact that the phase difference between water signals and fat signals changes when the echo time TE is changed due to the difference in resonance frequency, to obtain multiple echo signals with different TE. Images are calculated from the echo signals with different TEs, a signal model based on the Bloch equation is defined, and the water signals and fat signals contained in the images are separated using the nonlinear least squares method.

[0004] In the Dixon method, the variables to be estimated are the signal intensity of water, the signal intensity of each peak (e.g., six peaks) of the fat signal, which has different peaks depending on the metabolite, and the apparent transverse magnetization relaxation rate R2 of water and fat. * (T2 *A total of 10 variables must be estimated: TE, the reciprocal of TE, and static magnetic field inhomogeneity. Therefore, to calculate all variables, at least 10 images of each echo with different TE are required. Since images are generally affected by noise, more echo images are required to improve the accuracy of variable estimation, which leads to problems such as extended imaging time and associated reduced accuracy.

[0005] To address this issue, particularly the issue of extended imaging time, several methods have been proposed for fitting with fewer variables. For example, the technology described in Patent Document 1 uses a signal model incorporating the resonance frequency and relative signal intensity of each metabolite instead of a signal model based on the Bloch equation, thereby reducing the number of variables and the number of echo images to be acquired. In addition, the technology described in Patent Document 2 uses an apparent transverse magnetization relaxation rate (R2 * ) is applied to the signal model. This method uses individual R2 * This improves the accuracy of the water / fat image compared to when [Prior art documents] [Patent documents]

[0006] [Patent Document 1] U.S. Patent No. 7,202,665 [Patent Document 2] U.S. Patent No. 7,468,605 Summary of the Invention [Problem to be solved by the invention]

[0007] By separating the signals of each metabolic substance categorized as water and fat, it is possible to quantify each metabolic substance, which contributes to diagnostic imaging. For example, chronic liver disease often leads to liver cancer through destruction and regeneration due to inflammation and the progression of fibrosis, and therefore it is becoming increasingly important to accurately measure the fat and iron accumulated in the liver (liver quantification MRI technology) in order to understand the pathology of chronic liver disease. In order to accurately calculate the amount of fat (Fat Fraction: FF), it is important to improve the accuracy of signal separation. Furthermore, as chronic liver disease progresses, iron deposition occurs in addition to fat. Iron deposition is measured on MRI as an apparent transverse magnetization relaxation rate R2 * This appears as an increase in R2 * Accurate understanding of this will improve diagnostic capabilities.

[0008] To address these issues, the method of Patent Document 1 improves the accuracy of the calculated fat mass compared to a signal model that treats fat as one peak, but has the problem that the accuracy of the fat mass decreases depending on the tissue because the relative signal intensity of the peak differs depending on the fat tissue, such as subcutaneous fat, skeletal muscle, bone marrow, etc. Also, it is not possible to calculate the signal intensity of each peak.

[0009] The method of Patent Document 2 uses the apparent transverse magnetization relaxation rate R2 common to water and fat. * Since we use a signal model that applies R2 * However, in pixels where water and fat are mixed, the accuracy of the water-fat image is improved compared to when the R2 * The accuracy of the measurement decreases.

[0010] The present invention provides a new signal separation technology that solves the problems of the conventional Dixon method improvement technology, that is, it calculates the peak intensity of each fat multi-peak individually while reducing the variables to be estimated, and * The objective is to accurately calculate the above. [Means for solving the problem]

[0011] In order to solve the above problem, the MRI apparatus of the present invention does not use one signal model, but generates a dictionary of multiple signal patterns in which the variable values ​​of each substance are changed based on prior information, and performs signal separation of each substance by matching the measured signal with the dictionary. At this time, the order of the signal intensity of each substance is used as prior information, and the dictionary used for matching is changed according to the order, and the selection of the dictionary that best matches each substance is repeated.

[0012] That is, the MRI apparatus of the present invention comprises a measurement unit that measures nuclear magnetic resonance signals generated from a subject, and a calculation unit that performs calculations including image reconstruction using the nuclear magnetic resonance signals, and the calculation unit comprises a signal separation unit that separates a plurality of metabolites contained in the subject into signals for each metabolite using a plurality of images created from nuclear magnetic resonance signals collected by the measurement unit at different echo times. The signal separation unit comprises a dictionary generation unit that creates a dictionary of a plurality of signal patterns created by changing the values ​​of a plurality of variables for each metabolite using prior information, and a matching unit that performs pattern matching between the dictionary for each metabolite created by the dictionary generation unit and the signal measured by the measurement unit, and selects the dictionary that best matches. The present invention also includes an image processing device having the functions of the above-mentioned calculation unit.

[0013] The prior information includes, for example, information on the order of resonance frequencies and signal intensities of multiple metabolites, and the signal separation unit sequentially selects a dictionary for each metabolite and performs matching using the selected dictionary according to the prior information to separate the signals of each metabolite. The "signal pattern" created by changing the values ​​of multiple variables is a signal intensity pattern created by changing the physical property values ​​of metabolites (e.g., proton density, R2*, resonance frequency, B0 heterogeneity = f0) and imaging conditions (e.g., TE, ΔTE).

[0014] Furthermore, the signal separation method of the present invention is a signal separation method that processes a plurality of images created from NMR signals (echo signals) measured at different echo times by MRI, and separates a plurality of metabolic substances contained in a subject that generate echo signals into signals for each metabolic substance, and includes a dictionary creation step in which information regarding the resonance frequencies and signal intensity order of the plurality of metabolic substances is input as prior information, and a dictionary of a plurality of signal patterns with different values ​​for each metabolic substance is created, and a matching step in which pattern matching is performed between the created plurality of signal patterns and the measured signal, and the most matching signal pattern is selected, and the dictionary selection and matching between the signal pattern included in the selected dictionary and the measured signal are sequentially performed according to the order of signal intensity of the plurality of metabolic substances, to separate the signals of each metabolic substance.

[0015] Furthermore, the present invention includes a program for causing a computer to execute each step of the above-described signal separation method. Effect of the Invention

[0016] According to the present invention, by using a dictionary of multiple signal patterns with different variable values ​​and repeating the estimation (signal separation) of the signal pattern for each substance while changing the applied dictionary, it is possible to estimate individual metabolic substances, for example, individual fat components. In addition, the transverse magnetization relaxation rate R2 * R2 * This makes it possible to improve the accuracy of the calculation of metabolic substances. This makes it possible to quantify metabolic substances and understand changes in metabolic substances accompanying changes in tissue. For example, this can provide information that is useful for diagnosing liver diseases, such as distinguishing fatty liver from liver cancer and estimating the amount of iron deposition in the liver. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a diagram showing the overall configuration of an MRI apparatus to which the present invention is applied. [Diagram 2] Functional block diagram of the computer installed in the MRI device [Diagram 3] Diagram showing the flow of signal separation processing [Figure 4]Figure showing multi-peaks of fat [Diagram 5] Figure showing the flow of the process of Embodiment 1 [Figure 6] Figure showing an example of a dictionary of water signals [Figure 7] Figure showing an example of a dictionary of fat multi-peaks [Figure 8] (A) is a diagram showing the details of the signal separation flow, and (B) is a diagram showing the details of step S41 in (A). [Figure 9] Figure showing an example of an image created after signal separation [Figure 10] Figure showing the flow of the process of Embodiment 2 [Figure 11] Functional block diagram of the computer in Embodiment 2

Mode for Carrying Out the Invention

[0018] Hereinafter, embodiments of the MRI apparatus and signal method of the present invention will be described with reference to the drawings. First, the overall configuration of the MRI apparatus 1 to which the present invention is applied will be described.

[0019] <Functional Configuration of MRI Apparatus> As shown in FIG. 1, the MRI apparatus 1 of the present embodiment includes a static magnetic field generation unit such as a static magnetic field coil 11 that generates a static magnetic field in a space where a subject is placed, a transmission high-frequency coil 12 (hereinafter simply referred to as a transmission coil) and a transmitter 16 that transmits a high-frequency magnetic field pulse to a measurement region of the subject, a reception high-frequency coil 13 (hereinafter simply referred to as a reception coil) and a receiver 17 that receive a nuclear magnetic resonance signal generated from the subject, a gradient magnetic field coil 14 that applies a magnetic field gradient to the static magnetic field generated by the static magnetic field coil 11 and a gradient magnetic field power supply 15 that is its driving power source, a sequence control device 18, and a computer 20 (computation unit). The respective parts of the MRI apparatus 1 excluding the computer 20 are collectively referred to as a measurement unit 10.

[0020] The MRI apparatus 1 may be of a vertical magnetic field type or a horizontal magnetic field type depending on the direction of the static magnetic field generated, and various types of static magnetic field coils 11 are adopted depending on the type. The gradient magnetic field coil 14 is made up of a combination of multiple coils that generate gradient magnetic fields in three mutually orthogonal axial directions (x-direction, y-direction, and z-direction), and each is driven by a gradient magnetic field power supply 15. By applying a gradient magnetic field, position information can be added to the nuclear magnetic resonance signal generated from the subject.

[0021] In the illustrated example, the transmitting coil 12 and the receiving coil 13 are separate, but a single coil may be used that functions as both the transmitting coil 12 and the receiving coil 13. The high frequency magnetic field emitted by the transmitting coil 12 is generated by a transmitter 16. The nuclear magnetic resonance signal detected by the receiving coil 13 is sent to a computer 20 via a receiver 17.

[0022] The sequence control device 18 controls the operation of the gradient magnetic field power supply 15, the transmitter 16, and the receiver 17, controls the application of the gradient magnetic field and the radio frequency magnetic field, and controls the timing of receiving the nuclear magnetic resonance signal, and executes the measurement. The control time chart is called an imaging sequence, which is set in advance according to the measurement, and is stored in a storage device or the like provided in the computer 20 described later.

[0023] The computer 20 is an information processing device equipped with a CPU, a memory, a storage device, etc., and controls the operation of each part of the MRI apparatus via the sequence control device 18, and performs arithmetic processing on the received echo signals to obtain an image of a predetermined imaging area. The functions realized by the computer 20 will be described later, but the functions may be realized as the computer 20 included in the MRI apparatus 1, or may be realized by a computer or a workstation independent of the MRI apparatus. In other words, the computer 20 may be an image processing device including some or all of the functions of the computer 20.

[0024] A display device 30, an input device 40, an external storage device 50, and the like are connected to the computer 20. The display device 30 is an interface that displays results obtained by arithmetic processing to an operator. The input device 40 is an interface that allows an operator to input conditions, parameters, and the like required for the measurement and arithmetic processing performed in this embodiment. A user can input measurement parameters, such as the number of echoes to be measured, the echo time TE, and the echo interval, via the input device 40. The external storage device 50, together with a storage device inside the computer 20, holds data used in various arithmetic processing executed by the computer 20, data obtained by the arithmetic processing, input conditions, parameters, and the like.

[0025] As described above, the computer 20 controls the measurement unit 10 of the MRI apparatus and processes the signals measured by the measurement unit 10. The nuclear magnetic resonance signal measured by the measurement unit 10 is obtained as the sum of signals from various substances (particularly substances containing hydrogen) contained in the tissue, and the computer 20 of this embodiment has a function of separating the signals of each substance as part of the signal processing.

[0026] The configuration of the computer 20 that realizes such a function is shown in FIG. 2. As shown in the figure, the computer 20 of this embodiment performs calculations such as Fourier transform on the measurement data for each echo time TE collected by the measurement unit 10. Go, The apparatus includes an image reconstruction unit 21 that generates an image for each echo time (echo image), a signal separation unit 23 that uses the echo image generated by the image reconstruction unit 21 to separate signals of each substance mixed in the image, and a measurement control unit 25 that controls the measurement unit 10. The signal separation unit 23 includes a dictionary generation unit 232 and a matching unit 233, and may further include a prior information calculation unit 231. The signal separation unit 23 may further include an image generation unit 234 that generates a calculated image using signals and variables separated by the signal separation unit 23, and a quantitative value calculation unit 235 that calculates the amount, ratio, etc. of a metabolic substance in a predetermined tissue using the value of the variable obtained for each metabolic substance.

[0027] The dictionary generating unit 232 substitutes the resonance frequency (known information) of each metabolite obtained as prior information into a signal model based on the Bloch equation to calculate a signal pattern, and creates a dictionary for each metabolite.

[0028] The prior information calculation unit 231 calculates the values ​​of predetermined variables that can be calculated from measurement data, etc., among the variables included in the signal model used by the dictionary generation unit 232, and generates the values ​​as prior information (second prior information) used by the dictionary generation unit 232. The variables calculated by the prior information calculation unit 231 are not limited, but may be, for example, the apparent transverse magnetization relaxation rate R2 * , static magnetic field distribution f0, and either one of them or both may be used. In the dictionary creation, the variables calculated by the prior information calculation unit 231 do not necessarily have to be calculated as predetermined values, and in that case, the prior information calculation unit 231 can be omitted.

[0029] When the prior information calculation unit 231 calculates the values ​​of the variables as the prior information, the dictionary generation unit 232 sets the values ​​as reference values ​​of the variables and creates multiple signal patterns by substituting values ​​within a predetermined range based on the calculated values ​​into the signal model. The dictionary generation unit 232 registers the multiple signal patterns created for the metabolites in a memory or the like in the computer as a dictionary for each metabolite.

[0030] The signal patterns created as the dictionary may be time-series signal patterns (measurement signals) that represent signal values ​​relative to the time axis, or may be spectrum signal patterns obtained by Fourier transforming the time-series signal patterns. In this case, the measurement data also uses spectrum signals that have been Fourier transformed.

[0031] The matching unit 233 performs matching between the signal pattern and the measurement data using a dictionary corresponding to the order of the magnitude of the signal intensity of each metabolite (peak) registered as prior information (first prior information) from the registered signal pattern, and separates the signal of each metabolite by repeating the matching while sequentially removing the matched signals. In the signal separation, information on the magnitude of the apparent transverse magnetization relaxation rate may be used as prior information.

[0032] 3 shows a flow of processing by each unit of the above-mentioned signal separation unit 23. First, the echo image generated by the image reconstruction unit 21 is read (S1), and in one embodiment, the prior information calculation unit 231 calculates prior information that can be calculated from the echo image (S2).

[0033] Next, the dictionary generating unit 232 takes in the prior information calculated by the prior information calculating unit 231, and creates a dictionary of signal patterns for each substance (S3). Furthermore, the dictionary generating unit 232 creates a plurality of signal patterns in each dictionary, in which the values ​​of variables (values ​​calculated as prior information) are varied within a predetermined range. Note that in an embodiment in which the prior information calculating unit 231 or calculation of a reference value by it is omitted, a dictionary is created by substituting a plurality of discrete values ​​(fixed values) for a predetermined variable. The size of a single dictionary created for each substance depends on the number of variables that are to be varied.

[0034] Thereafter, the matching unit 233 performs matching between the measurement data (each signal value of each echo image) and a plurality of signal patterns included in the dictionary created by the dictionary generating unit 232 for each metabolic substance, and determines the best matching signal pattern (S4). The signal intensity of the determined signal pattern is set as the signal intensity of the metabolic substance (S4).

[0035] The matching unit 233 repeats the above-mentioned process (S4) using different dictionaries. In this repetition, the order of the dictionaries to be used is determined by referring to prior information. The prior information used here is the order of the magnitude of the signal intensity of each substance. For example, it is known which of the water signal and fat signal has a stronger signal intensity depending on the tissue, and the order of the magnitude of the peak values ​​of each fat component is also known, and such known information is used as the prior information. In addition, the apparent transverse magnetization relaxation rate R2 of water is known. * w and apparent transverse magnetization relaxation rate R2 of fat * Relationship with f, i.e. R2 * w <R2 *The matching unit 233 performs matching between the dictionary and the measurement signal, and at that time, a process of removing a signal estimated to be a signal of a predetermined metabolite from the measurement signal before processing is repeated, thereby finally separating the signals of each individual metabolite. Once the signal pattern for each metabolite is determined, the R2 ratio for each metabolite is calculated from the signal intensity for each metabolite as well as the variable values ​​set for that signal pattern. * , and the static magnetic field inhomogeneity f0 can be obtained.

[0036] Thereafter, the image generating unit 234 may generate a display image using the signals obtained for each metabolic substance, and the quantitative value calculating unit 235 may calculate the amount, ratio, etc. of the metabolic substance in a specified tissue.

[0037] As described above, the signal separation unit 23 uses a number of dictionaries (dictionaries consisting of a plurality of signal patterns) corresponding to each metabolic substance, selects a predetermined dictionary in accordance with the order of magnitude of the signal intensity of each substance, and performs matching between the signal pattern and the measurement data. This makes it possible to accurately obtain the signal intensity and R2 of each metabolic substance with a small number of measurement data, i.e., a small number of echo images. * can be calculated, and the accuracy of quantification of metabolic substances can be improved.

[0038] The present invention is applicable to various metabolic substances with different chemical shifts, such as water, fat, choline and their metabolites, and is particularly suitable for separating water and fat components. Hereinafter, a specific embodiment of the processing of the signal separation unit 23 will be described using separation of water and fat components as an example.

[0039] <Embodiment 1> In this embodiment, the water signal contained in the tissue is separated from the signals of each component of fat. The fat signal is separated into CH 2 Peak of CH group 3 Peak of CH group 2 CH=CHCH 2It is a multi-peak signal having six peaks such as the peak of the base. The resonance frequency of each peak is known, but the intensity of each peak varies depending on the lipid composition, and by estimating the intensity of each individual peak, it may be possible to discriminate the type of lipid. Also, although the intensity of each peak varies depending on the composition, the peak with the maximum peak intensity is CH 2 It is the peak of the group, and the relative relationship of the peak intensities, that is, the order of the magnitudes of the peak intensities is generally the same.

[0040] In the present embodiment, a signal pattern for each resonance frequency of each peak is created as a dictionary, and the signal intensity of each peak (i.e., each metabolite) is estimated by matching the measurement signal with the signal pattern. When creating the dictionary, provisional effective R2 * and the value of the static magnetic field inhomogeneity f0 are calculated, and based on that, Effective R2 * and the value of static magnetic field inhomogeneity f0 A plurality of signal patterns are created by varying them within a predetermined range. The matching between the signal pattern and the measurement data is sequentially performed for each peak (for each component having a different resonance frequency), the signal pattern of each peak is determined, and signal separation is performed. When sequentially performing this signal separation for each peak, the order of the peak intensities is used as prior information, and the matching is performed in the order of decreasing signal intensity.

[0041] Hereinafter, the processing flow by the signal separation unit 23 will be described with reference to FIG. 5. In FIG. 5, redundant explanations for the same processing as in FIG. 3 will be omitted.

[0042] <S1: Measurement and Image Capture> First, on the premise that the measurement control unit 25 controls the measurement unit 10 via the sequence control device 18 to collect a plurality of measurement data with different TEs. The pulse sequence for the measurement unit 10 to collect measurement data is not particularly limited as long as it can collect the number of echo signals necessary for reconstructing one image for each TE. The number of measurement data may be the combined number of water and fat components, which here may be 7 or more, and may be less than 10 required by the conventional Dixon method. The image reconstruction unit 21 reconstructs each of the plurality of measurement data collected by the measurement unit 10 to obtain a plurality (N sheets) of echo images. The signal separation unit 23 reads these images.

[0043] <S2: Calculation of prior information> The signal separation unit 23 calculates prior information (reference values of variables) necessary for creating a dictionary used for signal separation. The dictionary (signal pattern) used in this embodiment can be represented by a general formula (1) representing signal values and includes a plurality of variables.

[0044] [Number] In the formula, D is the signal value of the pixel, m 0 is the reference signal intensity, and R2 * eff is the R2 of water * w and the apparent transverse magnetization relaxation rate (effective R2 * ) that has not separated the R2 * f of fat, tn is the nth TE, and fm is the static magnetic field inhomogeneity (offset frequency). Fk is the resonance frequency of metabolite k (each component of water and fat). Assuming Fk = 0 for water and representing the offset frequency with respect to water for each component of fat (the same hereinafter). Among these, tn and Fk are known, and m 0 is m 0 = 1 and is a fixed value, then the effective R2 * and fm are variables, and fm can be expressed as fm = f0 + Δf0m when the reference value is f0.

[0045] The prior information calculation unit 231, in this formula (1), the effective R2 * (R2 *Calculate the effective value (eff) and the static magnetic field distribution f0 as prior information. The calculation method will be described below.

[0046] <<S21: Effective R2 * Map calculation >> The prior information calculation unit 231 calculates the absolute value of each echo image and solves the signal equation of Equation (2) by the least squares method to obtain the effective intensity M0 and the effective R2 * (R2 * (eff).

[0047]

Equation

[0048] The method of solving the signal equation of Equation (1) is well-known. For example, a method of calculating by the linear least squares method after taking the natural logarithm of both sides (Solution 1), a method of calculating by a non-linear least squares method such as the Gauss-Newton method or the Levenberg-Marquardt method (Solution 2), etc. can be adopted.

[0049] <<S22: f0 map calculation >> The prior information calculation unit 231 calculates the f0 map using each echo image or each echo signal (complex signal). A well-known method can also be adopted for the calculation of the f0 map. For example, in the method using an echo image (Solution 1), after calculating the phase of each pixel value of each echo image, the folding of -π to +π is removed for each pixel, and the slope of the phase is calculated. From the slope of the phase, the frequency difference is calculated by the following formula. A frequency difference map is obtained by calculating the frequency difference for all pixels. [Frequency difference] = [Slope of phase] / 2π · ΔTE

[0050] Next, set the water pixel as the seed point on the frequency map, and use the RegionGrowing method to remove the frequency offset between water and fat to obtain the f0 map. As the seed point of the water pixel, for example, the pixel with the minimum error during the calculation of the effective R2 * The pixel with the minimum error can be set as the seed point.

[0051] As another method for calculating the f0 map, each echo signal may be used to employ the method (solution method 2) disclosed in Japanese Patent No. 6014266. In this method, the complex signal of each echo is transformed into the discrete Fourier space to calculate each pixel spectrum. For each pixel, the frequency corresponding to the maximum value of the spectrum absolute value is calculated. This is performed for all pixels. Similar to solution method 1, water pixels are set as seed points, and the Region Growing method is used to remove the frequency offsets of water and fat.

[0052] <S3: Creation of dictionary> The dictionary generation unit 232 creates a signal pattern as a dictionary using the effective apparent transverse magnetization relaxation rate R2 * and f0 calculated by the prior information calculation unit 231 through the above-described processing (S21, S22).

[0053] The signal pattern is represented by Equation (3) and is created for each of the water and fat multi-peaks (resonance frequencies). This Equation (3) substitutes the values of R2 * and f0 in the signal pattern of the basic form Equation (1) with various changed values calculated in advance as the reference effective R2 * and f0 values respectively, and is created for each pixel.

[0054]

Equation

[0055] Therefore, for one (the k-th) substance, for each pixel, a dictionary consisting of a signal pattern of a total number of L × M × N is created. Here R2 * l = effective R2 * + ΔR2 * l fm = f0 + Δf0m As represented by, the range of fm to be changed and R2 * The range of l can be set to a limited range based on f0 and effective R2 calculated as prior information, so the dictionary size can be reduced. * Examples of the dictionaries created for each substance are shown in FIGS. 6 and 7. FIG. 6 is a dictionary for each peak of water, and FIG. 7 is a dictionary for fat. In the graph representing each dictionary, the vertical axis is the signal intensity and the horizontal axis is the echo number (corresponding to TE). This example is an example in which R2

[0056] is changed to 0, 20, 200 [1 / sec], and f0 is changed to -10, 5, 10 [Hz] respectively, and is created for each of the real part and the imaginary part of the complex signal. *

[0057] <S4: Signal separation (variable estimation) process> In this process, the matching unit 233 sequentially performs estimation processing using the dictionary based on the order of the signal intensities of the respective metabolic components obtained as prior information. The flow of the process is shown in FIGS. 8(A) and 8(B).

[0058] <<S41: Separation of water and fat main peaks>> First, the separation of water (k = 1) with the largest signal intensity and the next largest fat main peak (k = 2) is performed. For this purpose, as the dictionary Dk in Equation (1), [D1, D2] (the combination of D1 and D2) is set (S411). The size of these dictionaries is (L × M × 2) × (number of echoes N).

[0059] Next, estimation is performed by pattern matching using the dictionary Dk (S412). For example, taking the water dictionary shown in Fig. 6 as an example, if the dictionary shown on the right side of Fig. 6 is set for the real and imaginary parts of the water signal, and the measurement signal (real and imaginary part signals) shown on the left side of Fig. 6 are obtained as N pieces of measurement data with different echo times, a signal pattern that best matches the measurement signal is searched for among the multiple signal patterns that make up the dictionary. Dk can be expressed as a vector with N × 1 elements, and pattern matching is performed by calculating the inner product of the dictionary Dk, which is a vector, and the measurement signal sn, and selecting the j-th signal pattern Dk(j) that maximizes the inner product.

[0060] Next, the inverse matrix invDk(j) of Dk(j) is obtained, and the signal intensity Aj of the selected signal pattern Dk(j) is calculated using the measurement signal sn according to the following equation (4). Aj = invDk(j) * sn (4)

[0061] Next, a judgment is made as to whether the signal intensity Aj thus calculated is a water signal or a fat main peak signal, and the value of the water signal or fat main peak is determined (S413). This judgment is made based on the index (here, j) of the matched dictionary Dk. Specifically, If 1 ≦ j < L × M, it is determined to be water, and the water signal value W = Aj. If L × M + 1 ≦ j < L × M × 2, it is determined to be fat, and the fat main peak signal F1 = Aj.

[0062] The signal of the estimated substance is removed from the measurement signal sn by equation (5) (S414). sn' = sn-D(j)*Aj (5) Using sn' after removal, the process proceeds to separation processing of the water-fat component whose signal intensity has not been determined (for example, fat) (S415). For example, if the estimated substance is water, fat multi-peak signal separation is performed. Fat separation is similar to the separation of water and fat main peaks described above, and S411 and S412 are repeated. At this time, in dictionary selection (S411), dictionary D2 for fat main peak is used to perform pattern matching between the measurement signal after water signal removal and the dictionary (its signal pattern) (S412). Thereafter, water / fat determination (S413) is not performed, and the signal value determined by the signal pattern selected by pattern matching is set as the intensity of the fat main peak. Note that dictionary selection (S411) is not performed, and dictionary D is used to perform pattern matching between the measurement signal after water signal removal and the dictionary (its signal pattern) (S412). Thereafter, water / fat determination (S413) may be performed.

[0063] The determined fat signal is subtracted from the signal sn' before processing (S414: similar to formula (5)), and fat signals other than the main peak are separated based on the result (S42, S43). If the estimated substance is fat in S143, the same process is performed using the dictionary D1 for water on the measured signal after removing the estimated substance signal.

[0064] Separation of fat signals other than the main peak is similar to the above-mentioned signal separation of the water and fat main peaks. First, the dictionary of the k-th fat peak is set to Dk. Here, the k-th fat peak is in the order of intensity obtained as prior information about the intensity of the fat multi-peaks. Specifically, if the main peak with the highest intensity among the six peaks shown in FIG. 4 is k=1, the next highest intensity peak is k=2, the third highest intensity peak is k=3, and so on. In either case, the dictionary size is (L×M)×(number of echoes).

[0065] Next, for the measurement signal sn″ after removing the water signal and the main fat signal, pattern matching is performed using the dictionary Dk, and the dictionary Dk(j) with the maximum inner product is selected.

[0066] The inverse matrix invDk(j) of the selected dictionary Dk(j) is obtained, and the signal intensity Ak of ​​the dictionary Dk(j) is calculated by equation (4'), which is similar to equation (4), using the measurement signal sn''. Ak = invDk(j) * sn” (4') This signal intensity Ak is the estimated signal intensity for the k-th fat peak.

[0067] Thereafter, the measurement signal of the k-th fat peak component is removed from the measurement signal sn'' to be processed (S44). The measurement signal after removal is treated as a new signal to be processed, and the above-mentioned steps S43 to S44 are repeated until the processing of the fat peak with the smallest signal intensity is completed (S42).

[0068] Finally, the signal intensities of the water and fat components are separated, and the respective signal intensities can be obtained. * (R2 * w, R2 * For f), and f0, the values ​​set in the dictionary D(j) selected by pattern matching can be used as the values.

[0069] The image generating unit 234 calculates the signal intensity value and the R2 of water and fat obtained for each component by the signal separation process. * Various images can be generated using the static magnetic field inhomogeneity map f0. Examples of images generated by the image generating unit 234 include a water image, a fat image, a FatFraction image, and a water R2 image. * Map, Fat R2 * map, f0 map, pseudo in-phase image, or pseudo out-of-phase image.

[0070] The fat image may be a total peak image obtained by adding up the signals separated for each peak, or may be a fat image for each peak. * For the map, R2 for each fat component * Maps can also be generated.

[0071] The FatFraction image is an image of the fat mass ratio (FF) to the total amount of water and fat calculated for each pixel by the quantitative value calculation unit 235. The FatFraction (FF) may be calculated using a complex signal as shown in equation (6-1), or may be calculated using the total value obtained by finding the absolute value from the complex signal as shown in equation (6-2). FF=|Fat ÷ (Fat+Water)|×100 (6-1) FF={|Fat|÷(|Fat|+|Water|)}×100 (6-2)

[0072] The FatFraction image may also be an image of the entire fat, or an image of each component.

[0073] A pseudo in-phase image is an image when the phases of water signals and fat signals are aligned (in-phase), and a pseudo out-of-phase image is an image when the phases of water signals and fat signals are inverted (out-of-phase). Although the signals of each TE actually measured are not necessarily in-phase or out-of-phase, it is possible to obtain a pseudo image of the desired TE by substituting each estimated quantity into equation (3).

[0074] An example of an image (abdominal AX plane) generated by the image generating unit 234 is shown in FIG. 9. The upper side of the image shows, from the left, a water image, a fat image, and a total FF image, and the lower side shows FF images of each fat peak. As shown in the figure, according to this embodiment, in addition to a fat image and a total fat FF image showing the distribution of the entire fat, an image and an FF image for each peak can be created and displayed. This makes it possible to provide information useful for diagnosis of tissues in which the amount and type of fat deposited changes with the progression of diseases such as the liver.

[0075] Although not shown in FIG. 9, the R2 of water and fat (total fat and each component) * Since it is possible to generate a map of R2 * Changes in iron deposition in chronic hepatitis can be understood by observing changes over time.

[0076] <Embodiment 2> In the first embodiment, the effective R2 is calculated from the measurement data as prior information. * The map and static magnetic field inhomogeneity f0 were calculated, but they contain errors. In the second embodiment, as shown in FIG. * The present embodiment is characterized in that a step (S23) of precisely estimating the magnetic field inhomogeneity map f0 is added. The other processing steps are the same as those in the first embodiment shown in Fig. 5, and the following description will focus on the differences from the first embodiment.

[0077] In this embodiment, as shown in Fig. 11, a reference value estimation unit 236 is added to the prior information calculation unit 231. The reference value estimation unit 236 estimates f0 and R2, which are the reference values ​​calculated by the prior information calculation unit 231 in steps S21 and S22. * is substituted into the signal model of the water or fat main peak, and the reference value is calculated again. The following formula (6) is an example of a formula using the fat signal model, where R * "The R2 of one component * + fixed offset" and f0 is "reference f0 + offset frequency f fat = fat main peak resonance frequency".

[0078]

number

[0079] In this signal model, the unknowns are ρw, ρf, and f 0 , and R2* By using five or more measurement data, it can be calculated by the nonlinear least squares method. 0 , and R2 * The reference values ​​f0 and R2 for creating the fat dictionary (signal pattern) * Let us assume that. Instead of equation (6), the signal model described in Patent Document 1 or Patent Document 2 may be used. The refined f0 and R2 * As in the first embodiment, water / fat separation (each step in FIG. 8) is performed using the above as a reference value.

[0080] Since f0 may take a value in a range wider than -1000 to 1000 Hz, the size of the dictionary D, which tries to cover all of them, becomes large. However, in this embodiment, by increasing the precision of f0 set as the reference value, the range of f0 changed in the dictionary can be limited to a narrow range such as ±10 Hz, and the dictionary size can be significantly reduced. In addition, since the interval of the changed f0 can be made small, the signal strength of each component can be estimated more accurately, and the accuracy of signal separation can be improved. R2 * The same applies to. [Explanation of symbols]

[0081] 10: measurement unit, 11: static magnetic field coil, 12: transmitting coil, 13: receiving coil, 14: gradient magnetic field coil, 15: gradient magnetic field power supply, 16: transmitter, 17: receiver, 18: sequence control device, 20: computer, 21: image reconstruction unit, 23: signal separation unit, 25: measurement control unit, 30: display device, 40: input device, 50: external storage device, 231: prior information calculation unit, 232: dictionary generation unit, 233: matching unit, 234: image generation unit, 235: quantitative value calculation unit, 236: reference value estimation unit.

Claims

1. A measurement unit that measures a nuclear magnetic resonance signal generated from a subject, and a calculation unit that performs calculations including image reconstruction using the nuclear magnetic resonance signal, the calculation unit includes a signal separation unit that separates a plurality of metabolic substances contained in the subject into signals for each metabolic substance by using a plurality of images created from nuclear magnetic resonance signals collected by the measurement unit at different echo times; The plurality of metabolic substances includes fat containing a plurality of metabolic substances having different resonant frequencies; The signal separation unit includes: a dictionary generating unit that generates a dictionary of a plurality of signal patterns by changing values ​​of a plurality of variables for each metabolite using the prior information; a matching unit that performs pattern matching between the dictionary for each metabolite created by the dictionary creation unit and the signal measured by the measurement unit, and selects the dictionary that best matches.

2. 2. The magnetic resonance imaging apparatus according to claim 1, The prior information includes information regarding the order of resonance frequencies and signal intensities of the plurality of metabolites; The magnetic resonance imaging apparatus according to claim 1, wherein the signal separation unit sequentially selects a dictionary for each metabolite and performs matching using the selected dictionary in accordance with the prior information, thereby separating the signals of each metabolite.

3. 3. The magnetic resonance imaging apparatus according to claim 2, the signal separation unit further includes a prior information calculation unit that calculates, as the prior information, a reference value of a variable of the signal pattern; The magnetic resonance imaging apparatus according to claim 1, wherein the dictionary generating unit generates a plurality of signal patterns by changing the values ​​of the variables based on the reference values.

4. 4. The magnetic resonance imaging apparatus according to claim 3, 4. A magnetic resonance imaging apparatus, comprising: a first set of information for measuring a transverse magnetization relaxation time of water and a second set of information for measuring a transverse magnetization relaxation time of fat;

5. 4. The magnetic resonance imaging apparatus according to claim 3, The magnetic resonance imaging apparatus according to claim 1, wherein the prior information calculation unit calculates a reference value f0 of a frequency distribution caused by static magnetic field inhomogeneity using a plurality of images having different echo times.

6. 4. The magnetic resonance imaging apparatus according to claim 3, The prior information calculation unit calculates an effective apparent transverse magnetization relaxation rate (R ＊ ) is calculated.

7. 4. The magnetic resonance imaging apparatus according to claim 3, The prior information calculation unit calculates the apparent transverse magnetization relaxation rates (R ＊ ) and a frequency distribution f0 using a signal model, and setting the estimation result as a reference value of the dictionary.

8. 2. The magnetic resonance imaging apparatus according to claim 1, 4. A magnetic resonance imaging apparatus, comprising: a dictionary generating unit generating a dictionary of signal patterns each of which is a time-series signal pattern or a spectral signal pattern.

9. 2. The magnetic resonance imaging apparatus according to claim 1, The magnetic resonance imaging apparatus according to claim 1, wherein the plurality of metabolic substances further includes at least one of water and choline.

10. 2. The magnetic resonance imaging apparatus according to claim 1, 4. A magnetic resonance imaging apparatus comprising: an image generating unit configured to generate an image showing a characteristic of each metabolic substance by using the signal for each metabolic substance separated by the signal separating unit.

11. 11. The magnetic resonance imaging apparatus according to claim 10, The image created by the image creation unit may be an image consisting of one or a type of signal value of a metabolic substance, an image consisting of signal values ​​of each of a plurality of substances contained in a metabolic substance, an image showing the ratio of one or a type of metabolic substance, an image showing the transverse magnetization relaxation rate (R ＊ ) an f0 map, or a pseudo image of an echo time in which two metabolic substances are in phase or out of phase with each other.

12. An image processing device for processing a plurality of images created from nuclear magnetic resonance signals collected at different echo times by magnetic resonance imaging, the image processing device comprising: a signal separation unit for separating a plurality of metabolic substances contained in a subject generating nuclear magnetic resonance signals into signals for each metabolic substance; The plurality of metabolic substances includes fat containing a plurality of metabolic substances having different resonant frequencies; The signal separation unit includes: a dictionary generating unit that generates a dictionary of a plurality of signal patterns by changing values ​​of a plurality of variables for each metabolite using the prior information; an image processing device comprising: a matching unit that performs pattern matching between the dictionary for each metabolite created by the dictionary creation unit and a measured signal, and selects the dictionary that best matches the metabolite.

13. A signal separation method for processing a plurality of images created from nuclear magnetic resonance signals measured at different echo times by magnetic resonance imaging, and separating a plurality of metabolic substances contained in a subject generating nuclear magnetic resonance signals into metabolic substance signals, comprising: The plurality of metabolic substances includes fat containing a plurality of metabolic substances having different resonant frequencies; a dictionary creation step of inputting information on the order of resonance frequencies and signal intensities of the plurality of metabolic substances as prior information and creating a dictionary of a plurality of signal patterns having different values ​​for each metabolic substance; A matching step of performing pattern matching between the plurality of generated signal patterns and the measured signal and selecting the most matching signal pattern, A signal separation method comprising the steps of: selecting a dictionary and matching the measured signal with a signal pattern contained in the selected dictionary in accordance with the order of signal intensities of the multiple metabolic substances, thereby separating the signals of each metabolic substance.

14. 14. A signal separation method according to claim 13, comprising the steps of: The dictionary creation step includes: A frequency distribution calculation step of calculating a frequency distribution f0 caused by static magnetic field inhomogeneity using a plurality of images with different echo times, and an effective apparent transverse magnetization relaxation rate (R2 ＊ ) to calculate the effective R2 ＊ A map calculation step, The frequency distribution f0 and the effective R2 ＊ The signal separation method according to claim 1, further comprising the step of: creating a dictionary for each of the metabolites using the above-mentioned information as one of the pieces of prior information.

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