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

Abstract

The present application provides a magnetic resonance imaging apparatus, an image analysis apparatus, and a fluid analysis method. Fluid parameters such as WSS and EL are accurately calculated using flow velocity information obtained by diffusion tensor imaging. A variance of a flow velocity distribution of a fluid is calculated using a diffusion tensor image obtained for an examination object including the fluid, a model for estimation is set for a distribution shape of the flow velocity within a voxel, and a differential value of the flow velocity is calculated using the model for estimation and the variance of the flow velocity distribution. Fluid parameters representing characteristics of flow of the fluid are calculated using the calculated differential value.

Description

Technical Field

[0001] This invention relates to magnetic resonance imaging (MRI) apparatus and techniques for analyzing measurement data obtained by MRI apparatus, particularly techniques for analyzing diffusion tensor images. Background Technology

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

[0003] One such method, disclosed in Patent Document 1, involves using the phase contrast (PC) method of MRI to analyze kinetic energy loss and other parameters from obtained measurement data. In the PC method, a tilted magnetic field pulse that causes phase dispersion of the NMR signal is combined with a tilted magnetic field pulse that causes phase convergence of the NMR signal, resulting in a difference in phase between signals from stationary regions and signals from flowing regions. This allows the calculation of the velocity of fluids such as blood flowing within a desired region. In the technique described in Patent Document 1, the difference information between adjacent vectors is calculated based on a graph of the fluid velocity vector values ​​obtained using the PC method (a blood flow vector image where velocity vector values ​​are set as pixel values), and this difference is used to calculate EL (energy loss) and other parameters.

[0004] In addition, as imaging methods for observing fluids, there are diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI). DTI is a method that uses the measurement data obtained by performing DWI with MPG pulses in at least 6 directions to show information characterizing the anisotropy of diffusion by using a diffusion tensor for each voxel. Non-Patent Literature 1 reports that it is effective in observing pseudo-random flow of cerebrospinal fluid, etc.

[0005] Existing technical documents

[0006] Patent documents

[0007] Patent Document 1: JP Patent No. 6467341 Specification

[0008] Non-patent literature

[0009] Non-patent literature 1: Magnetic Resonance in Medicine 2021; 86, 1369-1382

[0010] In the PC method, a convergent tilted magnetic field pulse needs to be applied at given time intervals to the divergent tilted magnetic field pulse that causes phase diffusion, and the pulse sequence for acquiring the signal needs to be repeated, resulting in relatively low spatial resolution of the image. When obtaining the difference between adjacent vectors 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 the error in the calculated blood flow parameters such as EL may increase. This error is particularly prone to amplification when the fluid velocity is slow. Summary of the Invention

[0011] The objective of this invention is to provide a technique for calculating fluid parameters with high accuracy using MRI. This is particularly important for improving the accuracy of fluid parameter calculations for slow-moving fluids.

[0012] To address the aforementioned issues, this invention uses velocity information obtained through diffusion tensor imaging and a model of velocity distribution to calculate the differential vector of the velocity, thereby calculating the fluid parameters.

[0013] That is, the MRI apparatus of the present invention includes: a measurement unit that executes a pulse sequence for diffusion-weighted imaging including MPG pulses and acquires measurement data; and a calculation unit that uses the measurement data acquired by the measurement unit to calculate a pseudo-diffusion tensor having information on the flow velocity and diffusion of fluid within the object of examination, the calculation unit including: a fluid parameter calculation unit that uses an estimated model of the voxel distribution of the pseudo-diffusion tensor and the flow velocity to calculate fluid parameters other than the pseudo-diffusion tensor.

[0014] Furthermore, the image analysis apparatus of the present invention is an independent analysis apparatus having the function of the calculation unit of the aforementioned MRI apparatus, comprising: a receiving unit that receives measurement data measured in the MRI apparatus or the value of the pseudo-diffusion tensor calculated based on the measurement data; and a calculation unit that uses the measurement data or the value of the pseudo-diffusion tensor received by the receiving unit to calculate fluid parameters, the calculation unit including: a fluid parameter calculation unit that uses a presumed model of the distribution of the pseudo-diffusion tensor and the flow velocity to calculate fluid parameters that are indicators representing the characteristics of the fluid flow.

[0015] Furthermore, the present invention provides a fluid analysis method that utilizes a diffusion tensor image obtained by a magnetic resonance imaging device for an examination object containing fluid. The fluid analysis method includes: calculating the variance of the fluid velocity using the diffusion tensor image; calculating the differential vector of the velocity using the variance and the distribution shape of the velocity within a voxel; and calculating fluid parameters representing the characteristics of the fluid flow using the differential vector.

[0016] The effects of the invention

[0017] This 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, which uses averaged flow velocity within a voxel and the difference between voxels, this invention utilizes the flow velocity distribution within the voxel and avoids calculation errors associated with low resolution, thus enabling accurate calculation of fluid parameters. Particularly for slow-velocity fluids, it suppresses the decrease in calculation accuracy associated with reduced spatial resolution. Attached Figure Description

[0018] Figure 1 This is a diagram showing an overall overview of an MRI device.

[0019] Figure 2 This is a functional block diagram of the arithmetic unit.

[0020] Figure 3 This is a flowchart illustrating the processing flow of the fluid parameter calculation unit.

[0021] Figure 4 This is a diagram illustrating an example of the pulse sequence used in diffusion tensor imaging.

[0022] Figure 5 It is a graph representing the relationship between various flows and their corresponding diffusion tensors (DTs).

[0023] Figure 6 This is a diagram representing an example of a brain image and its diffusion tensor image.

[0024] Figure 7 It is a diagram illustrating the relationship between linear flow and diffuse tilted magnetic field.

[0025] Figure 8 This is a diagram illustrating an example of a presumed model for velocity distribution.

[0026] Figure 9 It is a diagram that schematically represents the flow of linear flow and diffusion phase mixing.

[0027] Figure 10 This is a functional block diagram illustrating an example of the arithmetic unit in Embodiment 2.

[0028] Figure 11 This is a functional block diagram of the arithmetic unit in Embodiment 3.

[0029] Figure 12 (A) and (B) are diagrams representing examples of the GUI of Implementation Method 3.

[0030] Explanation of reference numerals in the attached figures

[0031] 1: MRI device; 2: Image processing device; 10: Measurement unit; 11: Static magnetic field generating unit (magnet); 12: Probe (RF transceiver coil); 13: Inclined magnetic field coil; 14: Receiver; 15: High-frequency magnetic field generator; 16: Inclined magnetic field power supply; 17: Sequence generator (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. Detailed Implementation

[0032] The embodiments of the MRI device of the present invention will be described below with reference to the accompanying drawings.

[0033] First, the structure of the MRI device using the present invention will be described. The structure of MRI device 1 is as follows: Figure 1 As shown, similar to the structure of a typical MRI device, it includes: a magnet 11 that generates a uniform static magnetic field in the examination space where the subject is placed; a tilting magnetic field coil 12 that imparts a magnetic field gradient to the static magnetic field generated by the magnet 11; a probe 13 that includes a transmitting coil that applies a pulsed high-frequency magnetic field to the subject and induces nuclear magnetic resonance in the atomic nuclei of the atoms constituting the subject's tissue, and a receiving coil that receives the nuclear magnetic resonance signal generated from the subject; a receiver 14 connected to the receiving coil; a high-frequency magnetic field generator 15 connected to the transmitting coil; a tilting magnetic field power supply 16 connected to the tilting magnetic field coil 12; a sequence generator 17 that controls the receiver 14, the high-frequency magnetic field generator 15, and the tilting magnetic field power supply 16 according to a given pulse sequence; and a computer 200. All of the above elements except the computer 200 are collectively referred to herein as the measurement unit 10.

[0034] The nuclear magnetic resonance signal received by the receiver 14 of the measuring unit 10 is digitized and transferred to the computer 200 as measurement data.

[0035] The structure and function of each part constituting the measuring unit 10 are the same as those of known MRI devices. Furthermore, since the present invention can be applied to various types of known MRI devices and elements, a detailed description of the measuring unit 10 is omitted here.

[0036] The computer 200 functions as both a control system for the overall MRI apparatus and a processing unit 20 that performs various calculations, including image generation, on the measurement data from the measurement unit 10. It can be a general-purpose computer or workstation equipped with a CPU and memory. Alternatively, the computer 200 can be a separate computer or workstation capable of receiving and transmitting data from the MRI apparatus. In this case, all or part of the functions of the processing unit 20 can be performed in a separate device (image analysis device). Such an image analysis device has a receiving unit that receives data from the MRI apparatus, achieving the same image analysis function as the computer 200 described below.

[0037] Furthermore, the processing of measurement data can also utilize hardware such as PLC (Programmable Logic Controller), ASIC, and FPGA, which are also included in the computing unit 20 in a broad sense.

[0038] The MRI apparatus of this embodiment is characterized in that the measurement unit 10 performs measurements using a pulse sequence employing diffusion-weighted imaging (DWI), and the calculation unit 20 uses the measurement data obtained through DWI to calculate fluid parameters for detecting abnormalities in the flow of fluids such as blood and cerebrospinal fluid within the body of the subject. Therefore, the computer 200 sends instructions to the sequence generator 17 (measurement control unit) to control it, causing 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, and the calculation unit 20 uses the measurement data to perform the aforementioned calculation for determining the fluid parameters. Furthermore, as needed, a parameter image with pixel values ​​representing the calculated parameters is generated and displayed on the display device 40, or stored in the storage medium 50 or forwarded to other storage devices such as a medical information database.

[0039] An example of a function block diagram of the arithmetic unit 20 that implements the above functions is shown below. Figure 2As shown, the computation unit 20 includes: a DT calculation unit 21, which processes DWI measurement data to calculate a pseudo-diffusion tensor (a tensor representing information obtained by mixing flow velocity and diffusion); a fluid parameter calculation unit 23, which calculates fluid parameters using fluid vectors; and an image generation unit 25. The fluid parameter calculation unit 23 includes: a variance calculation unit 231, which calculates the variance of the flow velocity per pixel (voxel of the DT image); a differential calculation unit 233, which calculates the differential vector of the flow velocity using the variance calculated by the variance calculation unit 231, and an estimated model of the distribution of the calculated variance and the flow velocity (the shape of the distribution within the voxel); and various parameter calculation units, which calculate each fluid parameter using the differential vectors. Among the fluid parameters, in addition to WSS and EL, there are PG (pressure gradient), OSI (oscillatory shear index), etc. Figure 2 In the example, only the WSS calculation unit 235 and the EL calculation unit 237, which calculate WSS and EL, are shown.

[0040] The DTI calculation unit 21, which calculates the pseudo-diffusion tensor from DWI measurement data, functions similarly to existing DTI methods and is widely known. However, the MRI apparatus of this embodiment is characterized in that the fluid parameter calculation unit 23 uses the pseudo-diffusion tensor obtained by DTI to calculate fluid parameters with high accuracy that cannot be obtained by the PC method. Specific details regarding the fluid parameter calculation will be described in the embodiments described later. Reference is made below. Figure 3 This will illustrate the general process common to all implementations.

[0041] First, under the control of the sequence generator 17, the measurement unit 10 executes the pulse sequence for DWI and collects measurement data (S1). The pulse sequence for DWI is generally an EPI sequence, but multi-emission EPI, SS (single-emission) FSE, and other pulse sequences can be used. Figure 4 The figure shows a typical DWI pulse sequence. In the figure, 401 and 403 are RF pulses, 402 and 404 are slice tilt magnetic fields (Gs), 405 is phase-encoded tilt magnetic field (Gp), 406 is derived tilt magnetic field (Gr), and 407 is the NMR signal.

[0042] As shown in the figure, the pulse sequence of DWI is characterized by applying tilted magnetic field pulses (MPG pulses) 408 and 409 that promote phase diffusion of the fluid before and after the application of a 180-degree RF pulse 403 that induces magnetization reversal by an excitation RF pulse 401. By varying the intensity of these MPG pulses 408 and 409, a phase variance corresponding to the velocity difference (velocity variance) of the fluid within the voxel is generated, resulting in a decrease in the signal intensity of the voxel. In other words, by reducing the signal intensity, information about the velocity variance (pseudo-diffusion) of the fluid within the voxel can be obtained. That is, when dealing with fluids, information about the velocity variance can be obtained through DWI. The intensity of the MPG pulse is referred to as the b-value, and a relatively low b-value is preferred when obtaining fluid parameters for fast-moving fluids. Multiple b-values ​​can be preset within a given range, and a specific b-value can also be set by the user.

[0043] Furthermore, by altering the axis of the applied MPG pulse, information related to the anisotropy of diffusion and pseudo-diffusion can be obtained. Figure 4 The example shown illustrates applying an MPG pulse along the derive direction (Gr axis), but in diffusion tensor imaging (DTI), to calculate DT, MPG pulses in more than six directions are required, for multiple pre-defined axes. Figure 4 The same measurements were performed.

[0044] In EPI, multiple NMR signals 407 constituting an image are obtained by applying a phase-encoded tilt magnetic field pulse 405 and a reversed derived tilt magnetic field pulse 406 after an initial excitation RF pulse. The NMR signals 407 are positioned in k-space according to the applied phase encoding amount and the derived tilt magnetic field, and are transferred to the computer 200 as digital data (measurement data), where the arithmetic unit 20 performs various calculations.

[0045] The arithmetic unit 20 first calculates the diffusion tensor (S2) of each voxel (each pixel) in the DT calculation unit 21.

[0046] The method for calculating the diffusion tensor DT is well known. To put it simply, the diffusion tensor DT can be determined by obtaining the slope of the corresponding straight line represented by the following equation (1) based on the signal strength of multiple DWIs captured by changing the b vector, and represented by a 3×3 matrix.

[0047]

Mathematical Formula 1

[0048] ln(Si / S0)=-bGi T ·DT·Gi (1)

[0049]

[0050] In equation (1), Si is the signal strength obtained given the b vector, b is the absolute value of the b vector, and Gi is the unit vector characterizing the direction of the tilted magnetic field applied to the b vector. In addition, there is a notation that sets the b value as a multi-dimensional numerical value, that is, a notation synonymous with the b vector.

[0051] By diagonalizing the diffusion tensor DT, intrinsic values ​​and intrinsic vectors are obtained. By transforming it into intrinsic values ​​and intrinsic vectors, the principal axes of the diffusion tensor in the voxel and the two axes orthogonal to it are determined. Furthermore, the apparent diffusion coefficient ADC can be calculated using the diagonal elements of the intrinsic vectors.

[0052] Diffusion tensor with Figure 5 The ellipsoidal model shown (bottom side) represents the flow within the voxel. In the case of (1) plug flow, where the flow within the voxel is at the same velocity as shown on the left side of the top side of the figure, it becomes a small spherical shape that only reflects molecular diffusion. In the case of laminar flow, as shown in the three in the center (2) and (3), it becomes an anisotropic ellipsoidal shape. In the case of isotropic random flow (4), it becomes a large spherical shape. DTI is obtained by... Figure 5 The method of obtaining the image shown on the lower side by taking the diffusion tensor from the signal value of the fluid as shown on the upper side, but the fluid parameters that represent the state of the flow cannot be directly calculated from the DTI.

[0053] The fluid parameter calculation unit 23 of this embodiment calculates the variance and velocity differential vector by setting assumptions about the velocity distribution within the voxel, and can calculate the fluid parameters using the velocity differential vector.

[0054] Therefore, the fluid parameter calculation unit 23 first uses the variance calculation unit 231 to calculate the variance of the 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 velocity is calculated using the assumed shape of the velocity distribution ρ and the calculated variance (S4). Next, the individual parameter calculation units (235, 237) use the differential value of the velocity to calculate WSS and EL (S5).

[0055] The image generation unit 25 generates a morphological image and a DTI (diffusion tensor image) obtained through DWI, and generates a display image representing the values ​​of the calculated parameters (S6). An example of the displayed DTI is shown in... Figure 6 As shown. Figure 6 The top of the image shows a sagittal section of the brain image, and the bottom shows the DTI (Distributed Tissue Image) of the area enclosed by the four corners of the brain image. As shown, by displaying the DTI in voxels, the wall of the cerebrospinal fluid can be depicted, and the direction of the normal to which the wall shear stress should be calculated can be indicated or determined.

[0056] As briefly described above, the MRI apparatus of this embodiment can calculate the differential value of the diffusion vector obtained by DWI by using the shape of the distribution of fluid vectors within the voxel, and can use the differential value to calculate fluid parameters with good accuracy.

[0057] The following describes a specific implementation of the processing performed by the arithmetic unit 20.

[0058] <Implementation Method 1>

[0059] In this embodiment, the fluid flow is assumed to be linear, and a rectangular distribution is used as the model for the velocity distribution.

[0060] Linear flow is a flow in which the distance a fluid travels is proportional to the time elapsed. If a pair of tilted magnetic fields are applied to this linear flow, causing phase diffusion of the fluid... Figure 4 MPG pulses 408 and 409: They are sandwiched by pulse 403, which is reversed by 180 degrees (therefore, the positive and negative are reversed), so it is like... Figure 7 As shown, with the pulse application time set to δ and the time from the first pulse to the second pulse set to Δ, the diffusion time τd is...

[0061] τd=Δ-δ / 3 (2)

[0062] To characterize.

[0063] The variance calculation unit 231 uses the diffusion time τd to calculate the variance Cov(ρ) of the velocity distribution ρ(v, xi) of each voxel. It can be seen that for the DT (3×3 matrix) calculated by the DT calculation unit 21 shown in equation (1), if the tensor that makes b close to 0 is set as P, then P has the following relationship with the variance Cov(ρ) of the velocity distribution ρ (Non-Patent Document 1: Equation (3)).

[0064]

Mathematical Expression 3

[0065]

[0066] Here, τd is the diffusion time, and D is the diffusion coefficient (the same applies below). If written as a matrix, it becomes the following formula.

[0067]

[0068] Furthermore, depending on the conditions, it can be simplified as follows.

[0069] (1) The case where Cov(ρ) is sufficiently large compared to D

[0070]

Mathematical Expression 31

[0071]

[0072] (2) Furthermore, when Δ>>δ, since τ can be performed d = an approximation of Δ, therefore

[0073]

Mathematical Expression 32

[0074]

[0075] The variance calculation unit 231 uses equation (3), (31), or (32) to calculate the variance Cov(ρ) of the velocity distribution ρ(v, xi) of each voxel. Furthermore, when using equation (3), the diffusion coefficient (tensor) D is subtracted from the pseudo-diffusion tensor P, but there are two methods: one using predictive information and the other using additional measurement data. In the method using predictive information, the diffusion coefficient of free water (e.g., the diffusion coefficient of free water at a biological temperature of 37°C is 3 × 10^(-9) m^2 / s) is subtracted from P as prior information. In the method using additional measurement data, there are two further methods. One method is to measure multiple P values ​​while changing τd, and simultaneously obtain Cov(ρ) and D by fitting them. Another method is to measure only D using an MPG consisting of a group of bipolar pulses that cancel out velocity components, and subtract it from P. The specific process for calculating the variance Cov(ρ) is as follows. For simplicity, it will be explained primarily in two dimensions.

[0076] exist Figure 5 In the case of laminar (parallel) flow shown in the upper (2) diagram, for the sake of generality, if it is considered to be laminar flow in the x-direction by means of appropriate rotational coordinate transformation, then the following holds true.

[0077] (a is a given value, Characterized by differentiating the velocity component of velocity V in the i-direction along the j-direction.

[0078] Cov(ρ x ρ x )=a^2,Cov(ρ x ρ y )=Cov(ρ y ρ x )=Cov(ρ y ρ y ) = 0.

[0079] Furthermore, in the three-dimensional case, since the y and z directions become uncertain, it is necessary to determine their ratio. For example, the rate of change of the nearby Cov is calculated, and values ​​are allocated in the y and z directions corresponding to this ratio. Additionally, when phase information is obtained through DTI, methods such as calculating the average velocity of each voxel based on the phase and allocating it with the square of the velocity difference from the nearby voxels can be used. The method for calculating the average velocity of each voxel based on the phase is the same as the PC method. Furthermore, when using the PC method described later, methods such as calculating the average velocity of each voxel and allocating it with the square of the velocity difference from the nearby voxels can also be used. Moreover, the sign of the velocity differential vector calculated from a standard DTI is unknown, but it can be assigned a sign by using the sign of the DTI or the associated PC method.

[0080] In addition, Figure 5 In the case of laminar flow (linear expansion) shown in the central (3) diagram, the flow is captured as a portion of the fluid gushing out from a point (radius r, angle θ) and transformed into a polar coordinate system. In incompressible liquids, the following equation holds according to the relationship between flow rate and cross-sectional area.

[0081] (Two-dimensional case)

[0082] (Three-dimensional case)

[0083]

[0084] Cov(ρ r ρ r ) = 1 / (4π 2 r 4 (Two-dimensional case)

[0085] Cov(ρ r ρ r ) = 1 / (4π 2 r 6 (Three-dimensional case)

[0086] Cov(ρ θ ρ θ ) = r 2 θ 2 ,Cov(ρ r ρ θ )=Cov(ρ θ ρ r ) = 0

[0087] In the three-dimensional case, as with laminar (parallel) flow, the y and z directions become uncertain, thus requiring a determination of their proportions. For example, to calculate the rate of change of the nearby Cov and allocate accordingly, when phase information is obtained through DTI, the average velocity of each voxel is calculated based on the phase and allocated using the square of the velocity difference with the vicinity. Furthermore, when using the PC method described later, methods such as calculating the average velocity of each voxel and allocating using the square of the velocity difference with the vicinity can also be employed.

[0088] Next, the differential calculation unit 233 establishes an assumption about the shape of the velocity distribution and uses the calculated variance Cov(ρ) to calculate the velocity differential vector. Here, it is assumed that the shape of the velocity distribution ρ is rectangular, i.e., as shown... Figure 8 As shown, assuming a linear distribution in the intrinsic space of voxels, the variance of the rectangular distribution and the velocity differential vector are... This becomes the relationship shown in equation (4), where the velocity differential vector is... It is represented by equation (5).

[0089]

[0090]

[0091] In equations (4) and (5), Δx is the voxel size.

[0092] The differential calculation unit 233 calculates the velocity differential vector using equation (5). Here, since the DT measured when b is sufficiently small can be regarded as P, if the DT obtained with a low b value is set as P in equation (3) and substituted into equation (5), then equation (5) becomes

[0093]

[0094] As shown in equation (31), when P is sufficiently large compared to D, D can be ignored. Although the calculated The symbol is unknown, but through the use of It can calculate fluid parameters that are independent of the sign.

[0095] The fluid parameter calculation unit 23 uses the velocity differential vector calculated in this way to calculate various fluid parameters.

[0096] For example, WSS calculation unit 235 first as follows Figure 6As shown, the wall through which the fluid flows is extracted. In wall extraction, methods such as setting a threshold for the mean diffusivity (MD) of DTI, and other methods that measure and emphasize the fluid image using MRA (MR angiography) and intensity T2W imaging, and setting a threshold for brightness, are considered. Next, the normal vector n for the wall is calculated, and the differential value of the flow velocity in the normal direction is calculated. WSS can use this differential value of the flow velocity in the normal direction. The following formula can be used to calculate it.

[0097]

Mathematical Expression 6

[0098]

[0099] In equation (6), W represents WSS, and w represents the differential value of the flow velocity. μ is the viscosity of the fluid (a fixed value) (for example, if it is blood, then μ = 0.004 Pa*s) (the same applies below).

[0100] In addition, the EL calculation unit 237 specifies the region V as the target and calculates the energy loss (EL) using the following formula (7).

[0101]

Mathematical Expression 7

[0102]

[0103] Furthermore, the EL calculation unit 237 can also use other methods that are mathematically equivalent to the above equation (7). That is, it can directly use the elements or intrinsic values ​​of DTI, and implicitly use the velocity differential vector. For example, an element obtained by expanding the above equation (7) after performing a suitable coordinate transformation for diagonalization. j^2 and a component Pi,j of DTI are equivalent except for being a constant multiple. Here, if a rectangular distribution, Gaussian distribution, etc., is assumed as the velocity distribution, then the constant multiple is uniquely determined. Thus, EL can be calculated without explicitly determining the velocity differential vector based on information obtained through DTI. Furthermore, in Figure 2 The text only illustrates the WSS and EL calculation units, but as fluid parameters, the PG (pressure gradient) related to the user-specified direction n can also be calculated. The method for calculating PG is... Characterization.

[0104] The fluid parameters calculated in this way are the same as Figure 6 The DTI (Discharge Time Indicator) is displayed together with the DTI on the display device 40, providing a prompt to the user. Thus, the user can obtain the DTI and indicators related to fluid anomalies.

[0105] According to this embodiment, the differential vector of the flow velocity can be obtained by using DTI data (pseudo-diffusion tensor) and an estimated model of the flow velocity distribution of the fluid, and the fluid parameters can be calculated using this vector. Here, the variance of the flow velocity distribution used to calculate the differential vector is less susceptible to spatial resolution issues, such as those encountered in the PC method, since it can be directly measured using DTI. Therefore, achieving the same spatial resolution as another method (e.g., the PC method) improves accuracy, and reducing spatial resolution shortens measurement time.

[0106] <Modification 1 of Implementation Method 1>

[0107] In the above-described embodiment 1, the case of using WSS and EL as fluid parameters was explained. However, when body motion is synchronously captured during DWI measurement to obtain information related to body motion, this information can be added to further calculate other fluid parameters.

[0108] In synchronized body movement imaging, as is widely known, there are heart-synchronized imaging or electrocardiogram-synchronized imaging that measures in sync with heartbeats or electrocardiograms, respiratory-synchronized imaging that measures in sync with respiratory movements, and further, synchronized imaging that uses both types of body movement at specific times; any of these methods can be used. Furthermore, as a method for synchronized imaging, there are methods that use a synchronization signal as a trigger for measurement, and retrospective analysis methods that use the synchronization signal to analyze continuously measured data afterward; however, there are no particular limitations as long as the synchronization signal information can be obtained.

[0109] As fluid parameters that utilize synchronization signals, such as TAWSS (Time Average WSS) and OSI (Vibration Shear Index), if the WSS calculated in each phase is set as W(t), then TAWSS can be calculated by the following equation (8).

[0110]

Mathematical Expression 8

[0111]

[0112] Furthermore, OSI can be calculated using the following formula (9).

[0113]

Mathematical Expression 9

[0114]

[0115] <Modification 2 of Implementation Method 1>

[0116] In Implementation 1, the estimated model for the distribution of flow velocity is set as a rectangular distribution. However, the estimated model is not limited to a rectangular distribution. It can be modified to take into account the shape and size of the organs through which the fluid flows. By changing the relationship representing the distribution of flow velocity, the fluid parameters can be calculated in the same way as in Implementation 1.

[0117] As an example, this illustrates the use of Figure 8 The case of a normal distribution as shown in (b). The velocity probability density function ρ(v) is approximated by truncating the normal distribution ±3σ. If it is assumed to be linearly distributed in the intrinsic space of voxels, then the variance of the same distribution and the velocity differential have the following relationship.

[0118]

[0119] Here, Δx is the voxel size.

[0120] Therefore, the above equation (5) becomes the following equation (52).

[0121]

[0122] Equation (52) is similar to the case of the rectangular distribution, using P (=b when DT is sufficiently small) measured by DTI, which becomes Equation (53).

[0123]

[0124] Able to calculate the differential value In this variation, D can be ignored when P is sufficiently larger than D.

[0125] Then, the calculated differential vector is used to calculate fluid parameters such as WSS and EL, which is the same as in Implementation Method 1.

[0126] <Modification 3 of Implementation Method 1>

[0127] In this variation, phase contrast (velocity-dependent phase shift) is obtained using the pulse sequence of diffusion-weighted imaging itself, and the flow velocity obtained from the phase contrast is used in conjunction with or as a substitute for the low b-value DTI.

[0128] That is, the measurement unit 10 performs a pulse sequence of DWI in the same manner as in Embodiment 1 to obtain complex-valued measurement data. The calculation unit 20 uses the phase information of the complex-valued measurement data to calculate the average flow velocity of the fluid in each voxel within the inspection object. The fluid parameter calculation unit 23 uses the pseudo-diffusion tensor calculated by the DT calculation unit 21, the flow velocity calculated from the phase contrast, and the estimated model of the fluid flow velocity distribution to calculate the flow velocity differential vector. There are two methods.

[0129] One approach is to determine the directional scale of the velocity differential vector, which becomes uncertain only in the pseudo-diffusion tensor, using the difference from the surroundings calculated based on the velocity of each voxel from the phase contrast. For example, the scale could be determined by the square of the velocity difference. Another approach is to use the velocity calculated from the phase contrast, setting the difference from the surrounding voxels as... Let the velocity differential vector obtained from the pseudo-diffusion tensor using the above method be denoted as Choose any one of the following: the average of the two, a weighted average, or a threshold for the flow velocity obtained using phase contrast. Furthermore, the latter method will be described in detail in Embodiment 3, which uses a measurement with phase contrast.

[0130] Then, the fluid parameters are calculated using the velocity differential vector, just like in Implementation 1.

[0131] <Implementation Method 2>

[0132] In Implementation 1, the differential vector of the flow velocity was calculated based on linear flow. However, in this implementation, the fluid parameters of the complex flow between linear flow and diffusion are calculated. The flow involving a mixture of linear flow and diffusion is as follows: Figure 9 As shown, for example, in cases where there are vortices within the voxel or where the flow is complex and bendable, the transport energy decreases, and the transport distance and time are not proportional.

[0133] In this embodiment, mimicking anomalous diffusion, the diffusion time (τd) raised to the power of α is calculated using the index α, which models the time variation, through the following formula (33). α (0≤α≤1) and variance Cov(ρ).

[0134] P=τd α / 2*(Cov(ρ))+D (33)

[0135] Here, α = 0 implies diffusion, and α = 1 implies linear flow.

[0136] The differential is calculated using the variance derived from equation (33). The fluid parameters are then calculated, and in the calculation of the differential, a rectangular shape or a Gaussian distribution shape can be used as the estimated shape of the velocity distribution, which is the same as in Embodiment 1 or its variations.

[0137] This section explains the measurement and calculation methods for the index α. A functional block diagram of the fluid parameter calculation unit for this situation is provided. Figure 10 As shown in the figure, in this embodiment, a diffusion index calculation unit 239 is added. Other elements are... Figure 2 Similarly, they are shown using the same reference numerals.

[0138] As a method for estimating the diffusion element α, multiple measurements (e.g., three times) are performed at different intervals of the diffusion tilting magnetic field (MPG). When the interval for the first measurement is set to Δ1, the interval for the second measurement is set to Δ2, the interval for the third measurement is set to Δ3, and so on, if the fluid velocity obtained for each measurement changes linearly with respect to the interval, it can be considered as a linear flow, and α can be calculated based on the deviation from linearity.

[0139] After that, except for replacing equation (3) with equation (33), everything else is the same as in embodiment 1. In addition, this description describes the case where the index α is obtained by measurement, but it can also be used as a biological model to obtain fluid parameters by fixing the index α. Typically, fixing α = 1 is a linear flow model, and fixing α = 0 is a diffusion-only model.

[0140] <Implementation Method 3>

[0141] It is known that general diffusion tensor imaging can obtain DTI (pseudo-diffusion tensor) values ​​with high accuracy for slow-flowing fluids, but in fast-flowing fluids, the accuracy decreases due to signal attenuation caused by diffusion. On the other hand, in slow-flowing conditions, the phase difference obtained by the PC method is small, thus reducing accuracy.

[0142] In this embodiment, the characteristics of both are effectively utilized, and two measurements are selected either individually or together, thereby improving the accuracy of fluid parameter calculations over a wide velocity range.

[0143] The function blocks of the arithmetic unit in this embodiment are in Figure 11 As shown. In Figure 11 In the middle, for those with Figure 2 Elements with the same function are shown with the same reference numerals, and repeated descriptions are omitted.

[0144] like Figure 11 As shown, the calculation unit 20 of this embodiment differs from the fluid parameter calculation unit 23 that uses DTI, and includes a second fluid parameter calculation unit 23B that calculates fluid parameters using measurement data obtained by the PC method. Alternatively, it may be configured to include a parameter merging unit 29 that combines the results of the two fluid parameter calculation units.

[0145] Although not illustrated, the second fluid parameter calculation unit 23B includes a velocity calculation unit 231B that calculates the flow velocity using measurement data from the PC method, a differential calculation unit 233B that calculates the differential vector of the flow velocity, etc. Using the differential vector of the flow velocity calculated by the differential calculation unit 233B, fluid parameters such as WSS and EL are calculated using the above-mentioned equations (6) and (7).

[0146] Whether to set the processing of the fluid parameter calculation unit 23 or the processing of the second fluid parameter calculation unit 23B can be preset based on a given threshold for the fluid velocity, or preset according to whether the fluid to be targeted is blood or cerebrospinal fluid, arterial flow or venous flow, etc., or can be selected by the user through the GUI or the like displayed on the display device 40, and the calculation unit 20 can be set.

[0147] An example of such a GUI is Figure 12 As shown in (A) and (B). Figure 12 In the example shown, the GUI displayed in screen 1200, which sets the camera conditions, is as follows: the default setting calculates the fluid parameters, or if a user instruction such as "need" is received to calculate the fluid parameters, the user can choose to accept information such as the location, type, and flow rate of the fluid calculated based on the PC method.

[0148] If a given flow rate is set via the GUI, the measurement unit 10 will perform PC-based imaging instead of DWI imaging if the flow rate is faster than the predetermined flow rate, and will perform DWI imaging if the set flow rate is slower than the predetermined flow rate.

[0149] When the measuring unit 10 performs PC-based imaging, the second fluid parameter calculation unit 23B calculates the fluid parameters using existing methods (e.g., techniques described in Patent Document 1, etc.). Furthermore, when the measuring unit 10 performs DWI imaging, similar to Embodiments 1 and 2, the DT calculation unit 21 is configured as a DTI, and the fluid parameter calculation unit 23 uses the DTI information to calculate the fluid parameters.

[0150] The above is an example of selectively performing DWI or PC imaging, but both can also be used together, with the results weighted and summed. In this case, the measurement unit 10 performs DWI and PC imaging respectively. The order of imaging is not limited. The calculation unit 20 transfers the data obtained from the two imaging operations to the DT calculation unit 21 and the second fluid parameter calculation unit 23B respectively, and performs the processing of the fluid parameter calculation unit 23 and the second fluid parameter calculation unit 23B in parallel.

[0151] In the calculation of fluid parameters, the sign of the velocity differential vector calculated from the usual DTI is unknown, but the sign of the PC is used. Therefore, fluid parameters for which a sign is required can also be calculated.

[0152] Then, the parameter merging unit 29 adds the processing results of the two fluid parameter calculation units 23 and 23B in a weighted manner as shown in the following formula.

[0153] WSS=ω1·WSS DW +ω2·WSS PC

[0154] In the formula, WSS DW The result is calculated by the fluid parameter calculation unit 23, WSS. PC This is the result calculated by the fluid parameter calculation unit 23B. An example of WSS is shown here, but the same applies to other fluid parameters.

[0155] The weights ω1 and ω2 can be set to constants such as 0.5 regardless of the type and speed of the fluid, or they can be predetermined according to the type and speed of the fluid, for example, using weights corresponding to the type and speed of the fluid input by the user through the GUI or the speed of the fluid calculated by the flow rate calculation unit 231B.

[0156] According to this embodiment, as a measurement method for calculating basic data of fluid parameters, two cameras with different characteristics are used. By combining the calculation results of each camera, fluid parameters with good accuracy can be calculated corresponding to a wide range of velocities and locations.

Claims

1. A magnetic resonance imaging device, characterized in that, have: The measurement unit executes a pulse sequence for diffusion-weighted imaging, including a diffusion tilt magnetic field (MPG) pulse, to acquire measurement data; and The computation unit uses the measurement data obtained by the measurement unit to calculate the pseudo-diffusion tensor of the fluid within the inspected object. The pseudo-diffusion tensor is a tensor that characterizes information obtained by mixing flow velocity and diffusion. The arithmetic unit includes: The fluid parameter calculation unit uses the pseudo-diffusion tensor and the estimated model of the intravoxel distribution of the flow velocity to calculate fluid parameters other than the pseudo-diffusion tensor. The fluid parameter calculation unit uses the pseudo-diffusion tensor to calculate the variance of the velocity distribution, uses the calculated variance of the velocity distribution and the estimation model to calculate the velocity differential vector, and calculates the fluid parameters based on this velocity differential vector. The fluid parameters calculated by the fluid parameter calculation unit include any one or more of the following: wall shear stress (WSS), kinetic energy (KE), energy loss (EL), vibration shear index (OSI), and pressure gradient (PG).

2. The magnetic resonance imaging device according to claim 1, characterized in that, The computing unit further includes an image generation unit that generates an image with pixel values ​​of the fluid parameters, and displays the image on a display device.

3. The magnetic resonance imaging device according to claim 1, characterized in that, The velocity distribution of the estimation model used by the fluid parameter calculation unit is a rectangular distribution.

4. The magnetic resonance imaging device according to claim 1, characterized in that, The velocity distribution of the estimation model used by the fluid parameter calculation unit is a Gaussian distribution.

5. The magnetic resonance imaging device according to claim 1, characterized in that, The estimation model includes indices representing deviations from linear flow in the fluid. The computation unit calculates the index based on the pseudo-diffusion tensor.

6. The magnetic resonance imaging apparatus according to claim 5, characterized in that, The computation unit calculates the index based on the pseudo-diffusion tensor calculated over multiple diffusion times.

7. The magnetic resonance imaging device according to claim 1, characterized in that, The measurement unit synchronizes the diffusion-weighted imaging with the periodic body movements of the object being examined to collect measurement data. The fluid parameter calculation unit calculates the fluid parameters by incorporating the information of the periodic body motion.

8. The magnetic resonance imaging device according to claim 1, characterized in that, The measurement unit executes the pulse sequence of the diffusion-weighted imaging to obtain complex-valued measurement data. The arithmetic unit uses the phase information of the complex numerical measurement data to calculate the flow velocity of the fluid within the object under inspection. The fluid parameter calculation unit uses the pseudo-diffusion tensor, the flow velocity, and the estimation model to calculate the fluid parameters.

9. The magnetic resonance imaging device according to claim 1, characterized in that, The measurement unit performs a first measurement using the pulse sequence obtained by diffusion-weighted imaging and a second measurement using the pulse sequence obtained by phase contrast method. The arithmetic unit also includes: The second fluid parameter calculation unit calculates the fluid parameters using the measurement data obtained in the second measurement.

10. The magnetic resonance imaging apparatus according to claim 9, characterized in that, The arithmetic unit also includes: The parameter merging unit merges the fluid parameters calculated by the fluid parameter calculation unit and the fluid parameters calculated by the second fluid parameter calculation unit.

11. The magnetic resonance imaging apparatus according to claim 9, characterized in that, The magnetic resonance imaging device also features: The input section receives conditions related to the fluid being measured. The measurement unit selectively performs the pulse sequence of the diffusion-weighted imaging and the pulse sequence of the phase contrast method, depending on the conditions accepted by the input unit.

12. An image analysis device, characterized in that, have: The receiving unit receives measurement data measured in a magnetic resonance imaging device or the value of a pseudo-diffusion tensor calculated based on the measurement data, wherein the pseudo-diffusion tensor is a tensor characterizing information obtained by mixing flow velocity and diffusion. and Arithmetic unit, Specifically, when the receiving unit receives measurement data, the calculation unit calculates the pseudo-diffusion tensor based on the measurement data. The arithmetic unit includes: The fluid parameter calculation unit uses the value of the pseudo-diffusion tensor received by the receiving unit, or the calculated pseudo-diffusion tensor, and a presumed model of the flow velocity distribution to calculate fluid parameters that represent the characteristics of the fluid flow. The fluid parameter calculation unit also includes: The variance calculation unit calculates the variance of the velocity distribution based on the value of the pseudo-diffusion tensor received by the receiving unit or the calculated pseudo-diffusion tensor; and The differential calculation unit uses the calculated variance of the velocity distribution and the estimated model to calculate the velocity differential vector. The fluid parameters calculated by the fluid parameter calculation unit include any one or more of the following: wall shear stress (WSS), kinetic energy (KE), energy loss (EL), vibration shear index (OSI), and pressure gradient (PG).

13. A method for analyzing fluids, utilizing a diffusion tensor image obtained by a magnetic resonance imaging device for an object containing fluid, the method being characterized by comprising: The step of using the diffusion tensor image to calculate the variance of the fluid velocity distribution; The step of calculating the differential value of the flow velocity using the variance and the distribution shape of the flow velocity within the voxel; and The step of using the differential value to calculate the fluid parameters that represent the characteristics of the fluid flow. In the step of calculating the fluid parameters, the fluid parameters include at least one of the following: wall shear stress (WSS), kinetic energy (KE), energy loss (EL), vibration shear index (OSI), and pressure gradient (PG).

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