Magnetic resonance imaging apparatus, image analysis apparatus, and fluid analysis method

Abstract

To accurately calculate a fluid parameter such as WSS and EL by using flow rate information obtained by diffusion tensor imaging.SOLUTION: A magnetic resonance imaging apparatus calculates a scatter of the flow rate distribution of fluid by using a diffusion tensor image obtained for an inspection object including the fluid, sets an estimation model for the distribution shape of the flow rate in a voxel, calculates a differential value of the flow rate by using the estimation model and the scatter of the flow rate distribution, and calculates a fluid parameter indicating the characteristics of the flow of the fluid by using the calculated differential value.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a magnetic resonance imaging (hereinafter abbreviated as MRI) apparatus and a technique for analyzing measurement data acquired by the MRI apparatus, and in particular to a technique for analyzing a diffusion tensor image. [Background technology]

[0002] By analyzing the flow of fluids in the human brain and body, we can obtain indices that are effective for diagnosing various disorders. For example, fluid parameters such as wall shear stress (WSS), kinetic energy (KE) or kinetic energy loss (EL) are effective indices for detecting abnormalities in the flow of blood or cerebrospinal fluid, and various techniques for measuring and analyzing them have been proposed.

[0003] As one of them, Patent Document 1 discloses a method of analyzing kinetic energy loss and the like from measurement data obtained using the phase contrast (PC) method of MRI. In the PC method, a gradient magnetic field pulse that dephasing and a gradient magnetic field pulse that rephasing the phase of an NMR signal are combined to make the phase of a signal from a stationary part different from that of a flowing part, thereby calculating the velocity of a fluid such as blood flowing in a desired region. In the technology described in Patent Document 1, difference information of adjacent vectors is calculated from a map of the velocity vector value of the fluid obtained by the PC method (a blood flow vector image with the velocity vector value as the pixel value), and EL and the like are calculated using this difference.

[0004] Diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) are MRI imaging methods that can observe fluids. DTI is a method that uses diffusion tensors to show information that represents the anisotropy of diffusion for each voxel from measurement data obtained by performing DWI using MPG pulses in at least six directions, and it has been reported in Non-Patent Document 1 that DTI is effective for observing pseudorandom flows of cerebrospinal fluid. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 6467341 specification [Non-patent literature]

[0006] [Non-Patent Document 1] Magnetic Resonance in Medicine 2021;86, 1369―1382 DISCLOSURE OF THEINVENTION [Problem to be solved by the invention]

[0007] In the PC method, a pulse sequence is required to repeatedly apply a rephase gradient magnetic field pulse at a specified time interval to a dephase gradient magnetic field pulse that spreads the phase, and acquire signals, resulting in a relatively low spatial resolution of the image. When the difference between adjacent vectors is taken in a blood flow vector image with low spatial resolution, the differential value of the flow velocity is calculated to be lower than the actual differential value, and there is a risk that the error of the blood flow parameter value such as EL calculated based on that will also increase. The error is particularly likely to increase when the fluid velocity is slow.

[0008] An object of the present invention is to provide a technique for calculating fluid parameters with high accuracy using MRI, and in particular to improve the accuracy of calculating fluid parameters for a fluid with a slow velocity. [Means for solving the problem]

[0009] In order to solve the above problems, the present invention uses flow velocity information obtained by diffusion tensor imaging and a model of flow velocity distribution to calculate a differential vector of the flow velocity and calculate fluid parameters.

[0010] That is, the MRI apparatus of the present invention comprises a measurement unit that executes a pulse sequence of diffusion weighted imaging including an MPG pulse to acquire measurement data, and a calculation unit that calculates a pseudo diffusion tensor having information on the flow velocity and diffusion of fluid within the subject to be examined using the measurement data acquired by the measurement unit, and the calculation unit includes a fluid parameter calculation unit that calculates fluid parameters other than the pseudo diffusion tensor using the pseudo diffusion tensor and an estimated model of the intra-voxel distribution of flow velocity.

[0011] In addition, the image analysis device of the present invention is an independent analysis device that has the functions of the calculation unit of the above-mentioned MRI device, and is equipped with a reception unit that receives measurement data measured in the MRI device or a pseudo diffusion tensor value calculated from the measurement data, and a calculation unit that calculates fluid parameters using the measurement data or the pseudo diffusion tensor value received by the reception unit, and the calculation unit includes a fluid parameter calculation unit that calculates fluid parameters that are indicators of the characteristics of the fluid flow using the pseudo diffusion tensor and an estimated model of the flow velocity distribution.

[0012] The present invention further provides a method for analyzing a fluid using a diffusion tensor image acquired by a magnetic resonance imaging device for an examination object containing a fluid, the method including the steps of: calculating a variance of the fluid flow velocity using the diffusion tensor image; calculating a differential vector of the flow velocity using the variance and a distribution shape of the flow velocity in a voxel; and calculating fluid parameters indicating characteristics of the fluid flow using the differential vector. Effect of the Invention

[0013] The present invention provides a novel method for calculating fluid parameters from DTI by using an estimated distribution of fluid within a voxel. Compared to the PC method that uses the average flow velocity within a voxel and the difference between the average flow velocity and the difference between the voxels, the present invention uses the flow velocity distribution within a voxel and can avoid calculation errors associated with low resolution, so that fluid parameters can be calculated with high accuracy. In particular, for fluids with low speeds, the decrease in calculation accuracy associated with a decrease in spatial resolution can be suppressed. [Brief description of the drawings]

[0014] [Figure 1] A diagram showing an overall overview of an MRI device. [Diagram 2] FIG. [Diagram 3] 4 is a flow chart showing a process flow of a fluid parameter calculation unit. [Figure 4] FIG. 2 is a diagram showing an example of a pulse sequence used in diffusion tensor imaging. [Diagram 5] Diagram showing the relationship between various flows and their corresponding diffusion tensors (DT). [Figure 6] 1A and 1B are diagrams showing examples of a brain image and its diffusion tensor image. [Figure 7] FIG. 2 is a schematic diagram showing the relationship between a linear flow and a diffusion gradient magnetic field. [Figure 8] FIG. 13 is a diagram showing an example of an estimation model for flow velocity distribution. [Figure 9] Schematic diagram of a mixture of linear flow and diffusion. [Figure 10] FIG. 11 is a functional block diagram showing an example of a calculation unit according to a second embodiment. [Figure 11] FIG. 11 is a functional block diagram of a calculation unit according to a third embodiment. [Figure 12] 13A and 13B are diagrams showing examples of a GUI according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of an MRI apparatus of the present invention will be described with reference to the drawings.

[0016] First, the configuration of the MRI apparatus to which the present invention is applied will be described. As shown in Fig. 1, the configuration of the MRI apparatus 1 is the same as that of a general MRI apparatus, and includes a magnet 11 that generates a uniform static magnetic field in an examination space in which a subject is placed, a gradient coil 12 that gives a magnetic field gradient to the static magnetic field generated by the magnet 11, a probe 13 equipped with a transmitter coil that applies a pulsed high-frequency magnetic field to the subject to cause nuclear magnetic resonance in the nuclei of atoms constituting the subject's tissue and a receiver coil that receives a nuclear magnetic resonance signal generated from the subject, a receiver 14 connected to the receiver coil, a high-frequency magnetic field generator 15 to which the transmitter coil is connected, a gradient magnetic field power supply 16 to which the gradient coil 12 is connected, a sequencer 17 that controls the receiver 14, the high-frequency magnetic field generator 15, and the gradient magnetic field power supply 16 according to a predetermined pulse sequence, and a computer 200. Here, the above-mentioned elements except the computer 200 are collectively referred to as a measurement unit 10.

[0017] The nuclear magnetic resonance signals received by the receiver 14 of the measurement unit 10 are digitized and passed to the computer 200 as measurement data.

[0018] The structure, functions, etc. of each component of the measurement unit 10 are similar to those of known MRI devices, and the present invention can be applied to various types of known MRI devices and elements, so a detailed description of the measurement unit 10 will be omitted here.

[0019] The computer 200 functions as a control system that controls the entire MRI apparatus and a calculation unit 20 that performs various calculations including image generation on the measurement data of the measurement unit 10, and can be configured as a general-purpose computer or workstation equipped with a CPU and memory. The computer 200 may also be a computer or workstation independent of the MRI apparatus that can send and receive data to and from the MRI apparatus, in which case all or part of the functions of the calculation unit 20 can be performed by an independent device (image analysis device). Such an image analysis device has a receiving unit that receives data from the MRI apparatus, and realizes image analysis functions similar to those of the computer 200 described below.

[0020] In addition, hardware such as a programmable logic controller (PLC), ASIC, or FPGA can also be used to process the measurement data, and these are also included in the calculation unit 20 in a broad sense.

[0021] The MRI apparatus of this embodiment is characterized in that the measurement unit 10 performs measurement using a pulse sequence of diffusion weighted imaging (DWI), and the calculation unit 20 uses the measurement data obtained by DWI to calculate fluid parameters for detecting abnormalities in the flow of fluids such as blood and cerebrospinal fluid flowing through the body of the subject, and therefore the computer 200 sends a command to the sequencer 17 (measurement control unit) to control the measurement unit 10 to execute the DWI sequence under imaging conditions (imaging parameters) specified by the user via the input device (input unit) 30, etc., and the calculation unit 20 performs calculations to calculate the above-mentioned fluid parameters using the measurement data. If necessary, a parameter image with the calculated parameter value as the pixel value is generated, and is displayed on the display device 40, stored in the storage medium 50, or transferred to another storage device such as a medical information database.

[0022] An example of a functional block diagram of the calculation unit 20 that realizes the above functions is shown in Fig. 2. As shown in the figure, the calculation unit 20 includes a DT calculation unit 21 that processes measurement data of DWI and calculates a pseudo diffusion tensor (a tensor that represents mixed information of flow velocity and diffusion) and the like, a fluid parameter calculation unit 23 that calculates fluid parameters using a fluid vector, and an image generation unit 25. The fluid parameter calculation unit 23 includes a variance calculation unit 231 that calculates the variance of the flow velocity for each pixel (voxel of the DT image), a differential calculation unit 233 that calculates a differential vector of the flow velocity using the variance calculated by the variance calculation unit 231 and an estimated model of the calculated variance and the flow velocity distribution (shape of distribution within a voxel), and various parameter calculation units that calculate individual fluid parameters using the differential vector. Fluid parameters include, for example, WSS, EL, PG (pressure gradient), OSI (oscillatory shear index), etc., but FIG. 2 shows only a WSS calculation unit 235 and an EL calculation unit 237 that calculate WSS and EL as representative examples.

[0023] The function of the DTI calculation unit 21 that calculates the pseudo diffusion tensor from the measurement data of the DWI is similar to that of the conventional DTI and is well known, but the MRI apparatus of this embodiment is characterized in that the fluid parameter calculation unit 23 uses the pseudo diffusion tensor obtained by the DTI to calculate highly accurate fluid parameters that cannot be obtained by the PC method. Specific fluid parameter calculation will be described in the following embodiment, and the outline of the process common to each embodiment will be described below with reference to FIG. 3.

[0024] First, under the control of the sequencer 17, the measurement unit 10 executes a DWI pulse sequence and collects measurement data (S1). An EPI sequence is generally used as the DWI pulse sequence, but a multi-shot EPI or SS (single shot) FSE pulse sequence may also be used. A typical DWI pulse sequence is shown in FIG. 4. In the figure, 401 and 403 are RF pulses, 402 and 404 are slice gradient magnetic fields (Gs), 405 is a phase encoding gradient magnetic field (Gp), 406 is a readout gradient magnetic field (Gr), and 407 is an NMR signal.

[0025] As shown in the figure, the pulse sequence of DWI is characterized by applying gradient magnetic field pulses (MPG pulses) 408 and 409 that promote phase diffusion of the fluid before and after application of a 180-degree RF pulse 403 that inverts the magnetization excited by the excitation RF pulse 401. By making the intensities of these MPG pulses 408 and 409 different, phase dispersion according to the difference in the velocity of the fluid in the voxel (velocity dispersion) is generated, and as a result, the signal intensity of the voxel is reduced. In other words, this reduction in signal intensity makes it possible to obtain information on the velocity dispersion (pseudo-diffusion) of the fluid in the voxel. That is, when the target is a fluid, information on the flow velocity dispersion can be obtained by DWI. An index of the intensity of the MPG pulse is called a b-value, and when obtaining fluid parameters for a fluid with a high velocity, it is preferable to set the b-value to a relatively low value. The b-value may be set to a plurality of b-values ​​within a predetermined range in advance, or a specific b-value may be set by the user.

[0026] Information on the anisotropy of diffusion and pseudodiffusion can also be obtained by varying the axis along which the MPG pulse is applied. Figure 4 shows an example of applying the MPG pulse in the readout direction (Gr axis), but in diffusion tensor imaging (DTI), the application of MPG in six or more directions is required to calculate DT, and measurements are performed in the same manner as in Figure 4 for multiple axes that have been set in advance.

[0027] In EPI, after one excitation RF pulse, a phase encoding gradient magnetic field pulse 405 and an inverted readout gradient magnetic field pulse 406 are applied, thereby obtaining a plurality of NMR signals 407 constituting one image. The NMR signals 407 are arranged at positions in the k-space determined by the applied phase encoding amount and the readout gradient magnetic field, and are passed to the computer 200 as digital data (measurement data), where the calculation unit 20 performs various calculations.

[0028] First, the calculation unit 20 causes the DT calculation unit 21 to calculate a diffusion tensor for each voxel (each pixel) (S2).

[0029] Although the method for calculating the diffusion tensor DT is well known, in simple terms, the diffusion tensor DT can be determined from the signal intensities of multiple DWI images taken while changing the b vector by calculating the gradient of the corresponding line expressed by the following equation (1), and is expressed as a 3 × 3 matrix.

[0030]

number

[0031] By diagonalizing this diffusion tensor DT, eigenvalues ​​and eigenvectors are obtained. By converting to eigenvalues ​​and eigenvectors, the principal axis of the diffusion tensor in the voxel and two axes perpendicular to it can be obtained. In addition, the apparent diffusion coefficient ADC etc. can be calculated using the diagonal elements of the eigenvector.

[0032] The diffusion tensor is represented by an ellipsoid model as shown in Fig. 5 (bottom), and in the case of (1) plug flow where the flow in the voxel flows at the same speed as shown in the upper left corner of the figure, it becomes a small perfect sphere reflecting only molecular diffusion, in the case of (2) and (3) laminar flow as shown in the three in the center, it becomes an anisotropic ellipsoid, and in the case of (4) where there is isotropic random flow, it becomes a large perfect sphere. DTI obtains the diffusion tensor from the signal values ​​of the fluid as shown in the upper part of Fig. 5 to obtain an image as shown in the lower part, but it is not possible to directly calculate fluid parameters, which are indicators of the state of the flow, from DTI.

[0033] The fluid parameter calculation unit 23 of this embodiment makes assumptions about the distribution of flow velocity within a voxel, calculates its variance and flow velocity differential vector, and enables calculation of fluid parameters using the flow velocity differential vector.

[0034] For this reason, in the fluid parameter calculation unit 23, first, the variance calculation unit 231 calculates the variance of the flow velocity distribution ρ using the diffusion tensor DT of each voxel calculated by the DT calculation unit 21 (S3). Next, the differential value of the flow velocity is calculated using the assumed shape of the flow velocity distribution ρ and the calculated variance (S4). Next, individual parameter calculation units (235, 237) calculate WSS and EL using the differential value of the flow velocity (S5).

[0035] The image generating unit 25 generates a display image showing the calculated parameter values ​​together with the morphological image and DTI (diffusion tensor image) obtained by DWI (S6). An example of the displayed DTI is shown in FIG. 6. The upper side of FIG. 6 is a diagram showing a sagittal section of the brain image, and the lower side is a diagram showing the DTI of a part enclosed in a square in the brain image. As shown in the figure, by displaying the DTI in voxel units, the wall surface through which the cerebrospinal fluid flows is depicted, and the normal direction in which the wall shear stress should be calculated can be presented or understood.

[0036] As outlined above, the MRI apparatus of this embodiment can calculate vector differential values ​​from diffusion vectors obtained by DWI measurement by using an estimated shape for the shape of the distribution of fluid vectors within a voxel, and can use this differential value to accurately calculate fluid parameters.

[0037] A specific embodiment of the processing by the calculation unit 20 will be described below.

[0038] <Embodiment 1> In this embodiment, it is assumed that the flow of the fluid is a linear flow, and a rectangular distribution is used as a model of the distribution of the flow velocity.

[0039] A linear flow is a flow in which the transport distance of a fluid is proportional to the passage of time. When a pair of gradient magnetic fields (MPG pulses 408 and 409 in FIG. 4: these are inverted positive and negative because they sandwich an inversion of 180 degrees 403) that diffuses the phase of the fluid are applied to this linear flow, the diffusion time τd is, as shown in FIG. 7, where the application time of the pulse is δ and the time from the first pulse to the second pulse is Δ, τd=Δ-δ / 3 (2) It is expressed as:

[0040] The variance calculation unit 231 calculates the variance Cov(ρ) of the flow velocity distribution ρ(v, xi) of each voxel using this diffusion time τd. If the tensor P is the limiting value of DT (3×3 matrix) calculated by the DT calculation unit 21 shown in formula (1) when b approaches 0, it is known that P has the following relationship with the variance Cov(ρ) of the flow velocity distribution ρ (Non-Patent Document 1: Formula (3)).

[0041]

number

[0042] Depending on the conditions, it can be further simplified as follows: (1) When Cov(ρ) is sufficiently larger than D

number

[0043] (2) Furthermore, when Δ>>δ, τ d = Δ can be approximated

number

[0044] The variance calculation unit 231 calculates the variance Cov(ρ) of the flow velocity distribution ρ(v, xi) of each voxel using this formula (3), (31), or (32). When formula (3) is used, a process of subtracting the diffusion coefficient (tensor) D from the pseudo diffusion tensor P is performed, and there are two ways to do this: a method using prior information and a method using additional measurement data. In the method using prior information, the diffusion coefficient of free water, which is information known in advance (for example, the diffusion coefficient of free water at a body temperature of 37°C, 3×10^(-9) m^2 / s), is subtracted from P. There are two more ways to use additional measurement data. One is a method of measuring multiple Ps while changing τd and fitting them to simultaneously obtain Cov(ρ) and D. The other is a method of measuring only D by using an MPG consisting of a set of bipolar pulses that cancels out the velocity component, and subtracting it from P. The specific flow of the calculation of the variance Cov(ρ) is as follows. Here, for simplicity, it will be mainly explained in two dimensions.

[0045] In the case of laminar flow (parallel) shown in the upper part of Figure 5 (2), in order to give generality, if we consider it as laminar flow in the x direction by appropriate rotation coordinate transformation, ∇Vxx = ∇Vyx = ∇Vyy = 0,∇Vxy = a (a is a given value, ∇Vij represents the value obtained by differentiating the i-direction flow velocity component of the flow velocity V with respect to the j-direction) and Cov(ρ x ρ x ) = a^2, Cov(ρ x ρ y ) = Cov(ρ y ρ x ) = Cov(ρ y ρ y ) = 0 holds true.

[0046] In the case of three dimensions, the y and z directions are indefinite, so the ratio must be determined. For example, the rate of change of Cov in the vicinity is calculated, and values ​​are assigned to the y and z directions according to that ratio. If phase information is obtained by DTI, the average velocity of each voxel can be calculated from the phase, and assigned by the square of the velocity difference with the neighbors. The method of calculating the average velocity of each voxel from the phase is the same as the PC method. In addition, even when the PC method described below is used in combination, the average velocity of each voxel can be calculated and assigned by the square of the velocity difference with the neighbors. In addition, the positive and negative signs are not known in the flow velocity differential vector calculated from normal DTI, but it is possible to assign positive and negative signs by applying the sign of DTI or the PC used in combination.

[0047] In the case of laminar flow (linear expansion) shown in the center of Figure 5 (3), the flow is considered as a part of the fluid (radius r, angle θ) gushing out from a certain point, and transformed into a polar coordinate system. For incompressible liquids, the following equation holds based on the relationship between flow rate and cross-sectional area. ∇Vrr = (1 / (2πr))' = -1 / (2πr 2 ) (2D case) ∇Vrr = (1 / (4πr 2 )' = - 1 / (2πr 3 ) (3D case) ∇Vrθ = ∇Vθr = ∇Vθθ = 0 Cov(ρ r ρ r ) = 1 / (4π 2 r 4 ) (2D case) Cov(ρ r ρ r ) = 1 / (4π 2 r 6 ) (3D case) Cov(ρ θ ρ θ ) = r 2 θ 2 , Cov(ρ r ρ θ ) = Cov(ρ θ ρr ) = 0

[0048] In the case of three dimensions, as in the case of laminar (parallel) flow, the y and z directions are indefinite, so their ratios are determined. For example, the rate of change of Cov in the vicinity is calculated and allocated according to that ratio, or if phase information is obtained by DTI, the average velocity of each voxel is calculated from the phase and allocated by the square of the velocity difference with the neighbors. Also, when the PC method described below is used in combination, a method such as calculating the average velocity of each voxel and allocating by the square of the velocity difference with the neighbors can be used.

[0049] Next, the differential calculation unit 233 estimates the shape of the flow velocity distribution and calculates the flow velocity differential vector using the calculated variance Cov(ρ). If it is assumed that the shape of the flow velocity distribution ρ is a rectangular distribution, that is, a spatially linear distribution within a voxel as shown in Fig. 8, the variance of the rectangular distribution and the flow velocity differential vector ∇V have a relationship shown in the following formula (4), and the flow velocity differential vector ∇V is expressed by formula (5).

[0050] Cov(ρ) = (vmax-vmin) 2 / 12 = (∇V Δx) 2 / 12 (4) ∇V = √(12 Cov(ρ)) / Δx (5) In equations (4) and (5), Δx is the voxel size.

[0051] The differential calculation unit 233 calculates the flow velocity differential vector by the formula (5). Here, DT measured when b is sufficiently small can be regarded as P. Therefore, when DT obtained at a low b value is substituted into the formula (5) as P in the formula (3), the formula (5) becomes ∇V = √(24 (PD) / τd) / Δx (51) As shown in equation (31), when P is sufficiently larger than D, D can be ignored. Although the sign of the calculated ∇V is unknown, it is possible to calculate the sign-independent fluid parameters by using ∇V.

[0052] The fluid parameter calculation unit 23 calculates various fluid parameters using the flow velocity differential vector thus calculated.

[0053] For example, the WSS calculation unit 235 first extracts the wall surface through which the fluid flows, as shown in FIG. 6. The wall surface can be extracted by setting a threshold value for the MD (mean diffusivity) of DTI, or by separately measuring an image that emphasizes the liquid, such as MRA (MR angiography) or high-intensity T2W imaging, and setting a threshold value for the brightness. Next, the normal vector n to the wall surface is calculated, and the flow velocity differential value in the normal direction is calculated. The WSS can be calculated by the following equation using this flow velocity differential value ∇V·n in the normal direction.

[0054]

number

[0055] Moreover, the EL calculation unit 237 designates a target region V, and calculates the energy loss (EL) by the following formula (7).

number

[0056] The EL calculation unit 237 can also use another method that is mathematically equivalent to the above formula (7). That is, it is possible to directly use each element or eigenvalue of the DTI and implicitly use the flow velocity differential vector. For example, one element ∇Vi,j^2 obtained by performing an appropriate coordinate transformation to diagonalize the above formula (7) and then expanding it is equivalent to one element Pi,j of the DTI except for a constant multiplication. Here, the constant multiplication is uniquely determined by assuming a rectangular distribution or a Gaussian distribution as the velocity distribution. In this way, EL may be calculated from the information obtained by the DTI without explicitly obtaining the flow velocity differential vector. Also, in FIG. 2, only the WSS calculation unit and the EL calculation unit are illustrated, but the PG (pressure gradient) in the direction n specified by the user may be calculated as a fluid parameter. The calculation method of PG is expressed as ∇V·n.

[0057] The fluid parameters calculated in this manner are presented to the user, for example by displaying them on the display device 40 together with the DTI shown in Fig. 6. This allows the user to obtain an index relating to abnormalities in the fluid along with the DTI.

[0058] According to this embodiment, by using DTI data (pseudo diffusion tensor) and an estimation model for the flow velocity distribution of the fluid, a differential vector of the flow velocity can be obtained, and the fluid parameters can be calculated using the differential vector. Here, the variance of the flow velocity distribution for calculating the differential vector can be directly measured by DTI, so it is not easily affected by the spatial resolution of the PC method. Therefore, if the spatial resolution is the same as the other method (for example, the PC method), the accuracy can be improved, and the measurement time can be shortened because the spatial resolution can be lowered.

[0059] <Modification 1 of the First Embodiment> In the above-mentioned first embodiment, the case where WSS and EL are used as fluid parameters has been described. However, if motion-synchronous imaging is performed during DWI measurement and information regarding motion is obtained, it is also possible to calculate further fluid parameters by adding this information.

[0060] As is well known, the body motion synchronized imaging includes heartbeat synchronized imaging or electrocardiogram synchronized imaging, which performs measurements in synchronization with the heartbeat or electrocardiogram, respiratory synchronized imaging, which is synchronized with respiratory motion, and further synchronized imaging using the timing of both body motions, and can be used for all of these. In addition, the synchronized imaging method includes a method of performing measurements using a synchronization signal as a trigger, and a retrospective analysis method of analyzing continuously measured data after the fact using a synchronization signal, but there is no particular limitation as long as information on the synchronization signal can be obtained.

[0061] Fluid parameters using synchronized signals include, for example, TAWSS (time-averaged WSS) and OSI (oscillary shear index). TAWSS can be calculated by the following equation (8), where W(t) is the WSS calculated at each time phase.

number

[0062] Moreover, OSI can be calculated by the following formula (9).

number

[0063] <Modification 2 of the First Embodiment> In the first embodiment, the estimation model for the flow velocity distribution is a rectangular distribution, but the estimation model is not limited to a rectangular distribution and can be modified taking into account the shape and size of the organ through which the fluid flows, and by changing the relational equation showing the flow velocity distribution, the fluid parameters can be calculated in the same way as in the first embodiment.

[0064] As an example, we will explain the case where a normal distribution as shown in Figure 8(b) is used. If we approximate the flow velocity probability density function ρ(v) by a normal distribution (truncated at ±3σ) and assume that it is spatially linearly distributed within a voxel, the variance of the distribution and the flow velocity derivative have the following relationship: Cov(ρ) = σ 2 = (∇V Δx) 2 / 6 (10) where Δx is the voxel size.

[0065] Therefore, the above-mentioned equation (5) becomes the following equation (52). ∇V = √(6 Cov(ρ)) / Δx (52) As in the case of rectangular distribution, equation (52) is expressed as equation (53) below using P (= DT when b is sufficiently small) measured by DTI. ∇V = √(12 (PD) / τd) / Δx (53) It is possible to calculate the differential value ∇V. In this modification, too, when P is sufficiently larger than D, D may be ignored.

[0066] Thereafter, the calculated differential vector is used to calculate fluid parameters such as WSS and EL, similarly to the first embodiment.

[0067] <Modification 3 of the First Embodiment> In this modification, the diffusion-weighted imaging pulse sequence itself is used to obtain phase contrast (velocity-dependent phase shift), and the flow velocity calculated from the phase contrast is used in conjunction with or as a replacement for low b-value DTI.

[0068] That is, the measurement unit 10 executes a DWI pulse sequence to acquire complex-valued measurement data in the same manner as in the first embodiment. The calculation unit 20 calculates the average flow velocity of the fluid in each voxel in the test object using phase information of the complex-valued measurement data. The fluid parameter calculation unit 23 calculates a flow velocity differential vector ∇V using the pseudo diffusion tensor calculated by the DT calculation unit 21, the flow velocity calculated from the phase contrast, and an estimated model of the flow velocity distribution of the fluid. Specifically, there are two methods.

[0069] One method is to determine the directional ratio of the flow velocity differential vector, which is indefinite only with the pseudo diffusion tensor as described above, by the difference with the surroundings calculated from the flow velocity of each voxel calculated from the phase contrast. For example, the ratio is determined by the square of the flow velocity difference. The other method is to use the flow velocity calculated from the phase contrast, to set the difference with the surrounding voxels as ∇VP, to set the flow velocity differential vector obtained from the pseudo diffusion tensor by the above-mentioned method as ∇VD, and to select either one by the average or weighted average of the two, or by the threshold value of the flow velocity obtained by the phase contrast. The latter method will be described in detail in the third embodiment in which a measurement for phase contrast is also used.

[0070] Thereafter, the flow velocity differential vector is used to calculate the fluid parameters, similarly to the first embodiment.

[0071] <Embodiment 2> In the first embodiment, the differential vector of the flow velocity is calculated on the assumption of a linear flow, but in the present embodiment, the fluid parameters of a complex flow that is intermediate between a linear flow and diffusion are calculated. A flow that is a mixture of linear flow and diffusion is a case where, for example, there is an eddy in a voxel or the flow is bent in a complex manner, and the transport ability is reduced and the transport distance is not proportional to the time, as shown in Fig. 9.

[0072] In this embodiment, following the example of anomalous diffusion, an index α that models the time change is used to calculate the diffusion time (τd) to the power α, α " (0≦α≦1) and calculate the variance Cov(ρ). P = τd α / 2*(Cov(ρ))+D (33)

[0073] Here, α=0 means diffusion and α=1 means linear flow.

[0074] The variance calculated based on equation (33) is used to calculate the derivative ∇V, and the fluid parameters are further calculated. In addition, a rectangular or Gaussian distribution shape may be used as the estimated shape of the flow velocity distribution in calculating the derivative, as in the first embodiment or its modified examples.

[0075] A method for measuring and calculating the index α will now be described. A functional block diagram of the fluid parameter calculation unit in this case is shown in Fig. 10. As shown in the figure, in this embodiment, a diffusion index calculation unit 239 is added. The other elements are similar to those in Fig. 2 and are indicated with the same reference numerals.

[0076] One method for estimating the diffusion element α is to perform multiple measurements (e.g., three times) with different intervals between the diffusion gradient magnetic field (MPG). If the first interval is Δ1, the second interval is Δ2, the third interval is Δ3..., and the fluid flow velocity obtained for each interval changes linearly with respect to the interval, it can be considered a linear flow, and α is calculated based on the deviation from linearity.

[0077] After that, the procedure is the same as in the first embodiment, except that equation (3) is replaced with equation (33). In this explanation, the index α is calculated by measurement, but the index α may be fixed as a biological model to calculate the fluid parameters. Typically, the case where α is fixed at 1 is a linear flow model, and the case where α is fixed at 0 is a diffusion-only model.

[0078] <Embodiment 3> It is generally known that diffusion tensor imaging can obtain DTI values ​​(pseudo diffusion tensors) with high accuracy for fluids with slow flow velocities, but the accuracy decreases for fluids with fast flow velocities because the signal is completely attenuated by diffusion. On the other hand, the accuracy of the PC method decreases when the flow velocity is slow because the phase difference obtained by measurement is small.

[0079] In this embodiment, the characteristics of both are utilized, and the two measurements are performed alternatively or in combination to improve the accuracy of fluid parameter calculation over a wide velocity range.

[0080] A functional block diagram of the calculation unit of this embodiment is shown in Fig. 11. In Fig. 11, elements having the same functions as those shown in Fig. 2 are indicated by the same reference numerals, and duplicated explanations will be omitted.

[0081] 11, the calculation unit 20 of this embodiment includes a second fluid parameter calculation unit 23B that calculates fluid parameters using measurement data obtained by the PC method, in addition to the fluid parameter calculation unit 23 that uses DTI. Also, the calculation unit 20 may include a parameter integration unit 29 that integrates the results of the two fluid parameter calculation units.

[0082] The second fluid parameter calculation unit 23B, although not shown, includes a flow velocity calculation unit 231B that calculates the flow velocity using measurement data obtained by the PC method, a differential calculation unit 233B that calculates a differential vector of the flow velocity, and the like, and calculates fluid parameters such as WSS and EL using the differential vector of the flow velocity calculated by the differential calculation unit 233B, using the above-mentioned equations (6), (7), etc.

[0083] Whether to use the processing of the fluid parameter calculation unit 23 or the processing of the second fluid parameter calculation unit 23B may be determined in advance by setting a predetermined threshold value for the fluid velocity in advance, or may be determined in advance depending on whether the fluid in question is blood or cerebrospinal fluid, arterial flow or venous flow, etc., or the user may select via a GUI displayed on the display device 40 and set it in the calculation unit 20.

[0084] Examples of such a GUI are shown in Figures 12(A) and (B) . In the example shown in Figure 12, on a screen 1200 for setting imaging conditions, calculation of fluid parameters is set as a default, or when a user instruction that calculation of fluid parameters is "required" is received, a GUI is displayed that alternatively receives information such as the part, type, and flow rate of the fluid for which fluid parameters are to be calculated using the PC method.

[0085] When, for example, a predetermined flow velocity is set using this GUI, the measurement unit 10 performs PC imaging instead of DWI imaging if the flow velocity is faster than the predetermined flow velocity, and performs DWI imaging if the set flow velocity is slower than the predetermined flow velocity.

[0086] When the measurement unit 10 performs imaging using the PC method, the second fluid parameter calculation unit 23B calculates the fluid parameters using a conventional method (for example, the technique described in Patent Document 1, etc.). When the measurement unit 10 performs DWI imaging, the DT calculation unit 21 creates a DTI, and the fluid parameter calculation unit 23 calculates the fluid parameters using the information on the DTI, as in the first and second embodiments.

[0087] The above is an example in which DWI imaging or PC imaging is performed alternatively, but both may be used in combination and the results weighted and added. In that case, the measurement unit 10 performs DWI imaging and PC imaging, respectively. The order of imaging is not limited. The calculation unit 20 passes the data obtained by both imaging to the DT calculation unit 21 and the second fluid parameter calculation unit 23B, respectively, and performs processing by the fluid parameter calculation unit 23 and processing by the second fluid parameter calculation unit 23B in parallel.

[0088] In the calculation of fluid parameters, the positive or negative sign is unknown in the flow velocity differential vector calculated from normal DTI, but the sign of the PC is applied. This makes it possible to calculate fluid parameters that require a positive or negative sign for calculation.

[0089] Thereafter, the parameter integration unit 29 weights and adds the processing results of both the fluid parameter calculation units 23 and 23B as follows: WSS = ω1 WSS DW +ω2·WSS PC During the ceremony, WSS DW is the calculation result of the fluid parameter calculation unit 23, WSS PC are the calculation results of the fluid parameter calculation unit 23 B. Here, an example of WSS is shown, but the same applies to other fluid parameters.

[0090] The weights ω1 and ω2 may be constants such as 0.5 and 0.5 regardless of the type and speed of the fluid, but a predetermined weight may be determined in advance depending on the type and speed of the fluid, and a weight corresponding to the type and speed of the fluid input by the user via the GUI, or the speed of the fluid calculated by the flow velocity calculation unit 231B, may be used.

[0091] According to this embodiment, two types of imaging with different characteristics are adopted as a method for measuring basic data for calculating fluid parameters, and by combining the respective calculation results, it is possible to calculate accurate fluid parameters that correspond to a wide range of speeds and parts of the body. [Explanation of symbols]

[0092] 1: MRI device, 2: image processing device, 10: measurement unit, 11: static magnetic field generator (magnet), 12: probe (transmit / receive RF coil), 13: gradient magnetic field coil, 14: receiver, 15: high frequency magnetic field generator, 16: gradient magnetic field power supply, 17: sequencer (measurement control unit), 20: calculation unit, 21: DT calculation unit, 23: fluid parameter calculation unit, 23B: second fluid parameter calculation unit, 25: image generation unit, 30: input device (input unit), 40: display device, 50: storage medium, 200: computer, 231: variance calculation unit, 233: differential calculation unit, 235: WSS calculation unit, 237: EL calculation unit, 239: diffusion index calculation unit

Claims

1. a measurement unit that executes a pulse sequence of diffusion weighted imaging including an MPG pulse and acquires measurement data; a calculation unit that calculates a pseudo diffusion tensor of a fluid in the test object using the measurement data acquired by the measurement unit, the calculation unit includes a fluid parameter calculation unit that calculates, using the pseudo diffusion tensor and an estimated model of intra-voxel distribution of flow velocity, fluid parameters including one or more of wall shear stress (WSS), kinetic energy (KE) or energy loss (EL), oscillatory shear index (OSI), and pressure gradient (PG) as fluid parameters other than the pseudo diffusion tensor; The fluid parameter calculation unit calculates the variance of the flow velocity distribution using the pseudo diffusion tensor, and uses an estimation model in which the flow velocity distribution in a voxel is assumed to be a rectangular distribution or a Gaussian distribution as the estimation model, calculates a flow velocity differential vector using the calculated variance of the flow velocity distribution and the estimation model, and calculates the fluid parameters based on the flow velocity differential vector.

2. 2. The magnetic resonance imaging apparatus according to claim 1, The magnetic resonance imaging apparatus according to claim 1, further comprising an image generating unit configured to generate an image having the values ​​of the fluid parameters as pixel values, and displaying the image on a display device.

3. 2. The magnetic resonance imaging apparatus according to claim 1, the estimation model includes an indicator of deviation from linear flow in the fluid; The magnetic resonance imaging apparatus is characterized in that the calculation unit calculates the index from the pseudo diffusion tensor.

4. 4. The magnetic resonance imaging apparatus according to claim 3, The magnetic resonance imaging apparatus according to claim 1, wherein the calculation unit calculates the index from the pseudo diffusion tensor calculated for a plurality of diffusion times.

5. 2. The magnetic resonance imaging apparatus according to claim 1, the measurement unit synchronizes the diffusion weighted imaging with a periodic body movement of the test subject to collect measurement data; A magnetic resonance imaging apparatus, characterized in that the fluid parameter calculation unit calculates the fluid parameters by adding information about the periodic body movement.

6. 2. The magnetic resonance imaging apparatus according to claim 1, the measurement unit executes a pulse sequence of the diffusion weighted imaging to obtain complex valued measurement data; The calculation unit calculates a flow velocity of a fluid within the test object using phase information of the complex value measurement data, The magnetic resonance imaging apparatus according to claim 1, wherein the fluid parameter calculation unit calculates the fluid parameters by using the pseudo diffusion tensor, the flow velocity, and the estimation model.

7. 2. The magnetic resonance imaging apparatus according to claim 1, the measurement unit performs a first measurement using a pulse sequence of the diffusion weighted imaging and a second measurement using a pulse sequence of a phase contrast method; The magnetic resonance imaging apparatus according to claim 1, wherein the calculation unit further comprises a second fluid parameter calculation unit that calculates the fluid parameter by using measurement data obtained in the second measurement.

8. 8. The magnetic resonance imaging apparatus according to claim 7, A magnetic resonance imaging apparatus characterized in that the calculation unit further includes a parameter integration unit that integrates the fluid parameters calculated by the fluid parameter calculation unit and the fluid parameters calculated by the second fluid parameter calculation unit.

9. 8. The magnetic resonance imaging apparatus according to claim 7, An input unit for receiving conditions related to the fluid to be measured is further provided, a measurement unit that selectively performs a pulse sequence of the diffusion-weighted imaging method or a pulse sequence of the phase contrast method according to a condition received by the input unit;

10. a reception unit that receives measurement data measured by a magnetic resonance imaging apparatus or a pseudo diffusion tensor value calculated from the measurement data; a calculation unit that calculates a fluid parameter by using the measurement data or the pseudo diffusion tensor value received by the reception unit, the calculation unit includes a fluid parameter calculation unit that calculates, using the pseudo diffusion tensor and an estimated model of intra-voxel distribution of flow velocity, fluid parameters including one or more of wall shear stress (WSS), kinetic energy (KE) or energy loss (EL), oscillatory shear index (OSI), and pressure gradient (PG) as fluid parameters other than the pseudo diffusion tensor; The image analysis device is characterized in that the fluid parameter calculation unit calculates the variance of the flow velocity distribution using the pseudo diffusion tensor, and uses an estimation model that assumes that the flow velocity distribution in a voxel is a rectangular distribution or a Gaussian distribution as the estimation model, calculates a flow velocity differential vector using the calculated variance of the flow velocity distribution and the estimation model, and calculates the fluid parameters based on the flow velocity differential vector.

11. The image analysis device according to claim 10, The image analysis device characterized in that the fluid parameter calculation unit further includes a variance calculation unit that calculates the variance of the flow velocity distribution from the pseudo diffusion tensor, and a differential calculation unit that calculates a flow velocity differential vector using the calculated variance of the flow velocity distribution and the estimation model.

12. A method for analyzing a fluid using a diffusion tensor image acquired by a magnetic resonance imaging apparatus for an examination target including the fluid, comprising: calculating a variance of a flow velocity distribution of a fluid using the diffusion tensor image; Calculating a differential value of the flow velocity using the variance and a distribution shape of the flow velocity in the voxel; calculating a fluid parameter characteristic of the fluid flow using the differential value; Including, the step of calculating a differential value of the flow velocity uses a rectangular distribution or a Gaussian distribution as a distribution shape of the flow velocity in the voxel; The method for analyzing a fluid, wherein the step of calculating the fluid parameters includes calculating one or more of the fluid parameters: wall shear stress (WSS), kinetic energy (KE) or energy loss (EL), oscillatory shear index (OSI), and pressure gradient (PG).

Images