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

Abstract

To execute anatomical normalization for matching a functional image such as a blood flow image acquired by using an MRI device with a standard form accurately.SOLUTION: A form image in which a form is shown and a function image in which a function is shown are captured in the same imaging region of a subject. An arithmetic processing part executes processing for deforming the form image using deformation parameters, and moves a position of one or more structures in the form image to a position of a structure of a predetermined standard form respectively, and then deforms the function image using a value of the deformation parameters used when deforming the form image, thereby causing a position of a region in the function image to coincide with a position of a corresponding region of the standard form. Alternatively, the arithmetic processing part deforms the standard form in a reverse direction using the value of the deformation parameters, and causes the position of the region of the structure in the standard form to coincide with the position of the corresponding region in the function image.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a magnetic resonance imaging (hereinafter referred to as "MRI") apparatus or an image display apparatus, and in particular to a technique for analyzing cerebral blood flow dynamics from blood flow images. [Background technology]

[0002] An MRI system measures NMR signals generated by the nuclear spins that make up the tissues of a subject, particularly a human body, and produces two-dimensional or three-dimensional images of the shape and function of the head, abdomen, limbs, etc. During imaging, the NMR signals are given different phase encodings by gradient magnetic fields and are frequency encoded, and are measured as time-series data. The measured NMR signals are reconstructed into an image by two-dimensional or three-dimensional Fourier transform.

[0003] By evaluating cerebral blood flow dynamics, imaging diagnosis is performed for brain diseases in which abnormalities in cerebral blood flow are observed, such as dementia, cerebral infarction, cerebrovascular disorders such as vascular stenosis, and epilepsy.

[0004] One method for imaging cerebral blood flow using MRI is the arterial spin labeling (ASL) method (see Non-Patent Document 1). In the ASL method, RF pulses are applied to a label surface set upstream of the blood flow from the imaging region, thereby inverting the spin of protons in the blood passing through the label surface (labeling). When this spin-inverted blood reaches the imaging region via the blood flow, it is exchanged in the capillaries with the spin of protons in the tissue of the imaging region, changing the T1 relaxation time of the tissue. A blood flow image can be obtained by calculating the difference between a labeled image of the imaging region in this state and a control image taken without inverting the blood protons. This method can label blood with RF pulses, making it possible to generate blood flow images noninvasively.

[0005] Meanwhile, a technique called anatomical standardization (normalization) is widely used to convert brain images of subjects obtained by PET (Positron Emission Tomography) or MRI into a standard brain coordinate system and align them to a standard brain. Anatomical standardization converts brain images with individual morphological differences into a standard brain, allowing for pixel-by-pixel comparison of blood flow and metabolic images between subjects and between subject groups. Known methods for anatomical standardization include diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) processing, which performs nonlinear transformations using multiple parameters.

[0006] Patent Document 1 also discloses a technology for obtaining cerebral blood flow images by SPECT (Single Photon Emission Computed Tomography), which involves administering a drug labeled with a radioisotope to a subject, detecting the emitted radiation to obtain projection data, and then performing image reconstruction. The technology in Patent Document 1 obtains a standard brain image by transforming the reconstructed image to fit a standard brain (anatomical normalization), and then quantifies the standard brain image to obtain various quantitative value images. It also proposes using the obtained quantitative value images to evaluate blood flow dynamics, thereby performing pathological analysis by quantitative evaluation. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Kimura, Kabasawa, Yonekura, et al, Cerebral perfusion measurements using continuous arterial spin labeling: accuracy and limits of a quantitative approach, International Congress Series, 1256: 236-247, 2004 [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-119022 Summary of the Invention [Problem to be solved by the invention]

[0009] The ASL method, which uses an MRI device to capture cerebral blood flow images, is a non-invasive method that does not require the use of drugs, and has significant advantages for subjects compared to SPECT. Furthermore, if the cerebral blood flow images obtained by the ASL method can be converted to the standard brain coordinate system, it will be possible to compare the blood flow of subjects and healthy subjects in the same coordinate system, which will be useful for diagnosis, etc.

[0010] However, since the cerebral blood flow images taken using the ASL method do not contain information about the shape of the brain, it is difficult to accurately convert the cerebral blood flow images into the standard brain coordinate system.

[0011] Patent Document 1 discloses the determination of deformation parameters for deforming a cerebral blood flow image obtained by SPECT to fit a standard brain, but because the cerebral blood flow image obtained by SPECT does not contain shape information, it is presumed that it is not easy to perform accurate coordinate transformation to fit a standard brain.

[0012] Furthermore, the technique of obtaining cerebral blood flow images by SPECT described in Patent Document 1 requires longer examination times and higher examination costs compared to imaging using an MRI device as in Non-Patent Document 1.

[0013] An object of the present invention is to perform anatomical normalization to accurately match functional images, such as blood flow images, acquired using an MRI apparatus to a standard form. [Means for solving the problem]

[0014] To achieve the above object, the present invention provides an imaging system including a static magnetic field generator that applies a static magnetic field to an imaging space in which a subject is placed, a gradient magnetic field generator that applies a gradient magnetic field to the imaging space, an irradiation coil that irradiates a radio frequency magnetic field to the subject in the imaging space, a receiving coil that receives nuclear magnetic resonance signals from the subject, a measurement controller that controls the gradient magnetic field generator, the irradiation coil, and the receiving coil to execute an imaging sequence and capture images, and a calculation processor. The measurement controller captures a morphological image showing the morphology and a functional image showing the function of the same imaging region of the subject. The calculation processor then deforms the morphological image using deformation parameters to move the positions of one or more structures in the morphological image to the positions of the structures in a predetermined standard form, and then deforms the functional image using the values ​​of the deformation parameters used to deform the morphological image so that the positions of the regions in the functional image coincide with the positions of the corresponding regions in the standard form, or deforms the standard form in the opposite direction using the values ​​of the deformation parameters so that the positions of the regions of the structures in the standard form coincide with the positions of the corresponding regions in the functional image. [Effects of the Invention]

[0015] According to the present invention, functional images such as blood flow images acquired using an MRI device can be anatomically normalized to accurately match a standard form, allowing for easy evaluation by comparing them with functional images of healthy individuals. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a block diagram showing an example of the overall configuration of an MRI apparatus according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a diagram for explaining an example of an imaging pulse sequence in the ASL method. [Figure 3] 4 is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus of the first embodiment. [Figure 4] FIG. 3 is a diagram showing an example of imaging conditions for the imaging pulse sequence of FIG. 2. [Figure 5](a-1) to (a-3) are explanatory diagrams showing the procedure for anatomical normalization of 3DT1-weighted images, and (b-1) to (b-2) are explanatory diagrams showing the procedure for anatomical normalization of CBF images and ATT images. [Figure 6] FIG. 2 is a diagram showing an example of a GUI that displays images generated by the arithmetic processing unit 8 of the MRI apparatus of the first embodiment, analysis results, and the like. [Figure 7] 10 is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus of the second embodiment. [Figure 8] 11 is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus of the third embodiment. [Figure 9] 10 is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus of the fourth embodiment. [Figure 10] 10 is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus of the fifth embodiment. [Figure 11] 13 is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus of the sixth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, preferred embodiments of the MRI apparatus of the present invention will be described in detail with reference to the accompanying drawings. In all drawings for explaining the embodiments of the invention, parts having the same functions are designated by the same reference numerals, and repeated explanations thereof will be omitted.

[0018] <Embodiment 1> First, an overview of the MRI apparatus of the first embodiment will be described.

[0019] The MRI apparatus of this embodiment 1 has a structure shown in FIG. 1 as an example, and includes a static magnetic field generating unit 2 that applies a static magnetic field to an imaging space in which a subject 1 is placed, a gradient magnetic field generating unit 3 that applies a gradient magnetic field to the imaging space, an irradiation coil 14a that irradiates a high-frequency magnetic field to the subject 1 in the imaging space, a receiving coil 14b that receives nuclear magnetic resonance signals from the subject 1, a measurement control unit (sequencer) 4 that controls the gradient magnetic field generating unit 3, the irradiation coil 14a, and the receiving coil 14b to execute an imaging sequence and capture an image, and an arithmetic processing unit 8.

[0020] The measurement control unit 4 captures a morphological image showing the morphology and a functional image showing the function for the same imaging region of the subject 1.

[0021] The arithmetic processing unit 8 deforms the anatomical image using the deformation parameters, and performs a process (anatomical normalization process) of moving the positions of one or more structures in the anatomical image to the positions of structures in a predetermined standard form using the deformation parameters. The arithmetic processing unit 8 deforms the functional image using the values ​​of the deformation parameters used when deforming the anatomical image, thereby matching the positions of areas in the functional image to the positions of corresponding areas in the standard form.

[0022] Alternatively, the calculation processing unit 8 uses the values ​​of the above-mentioned transformation parameters to transform the standard form in the reverse direction, thereby making the position of the region of the structure in the standard form coincide with the position of the corresponding region in the functional image.

[0023] This allows for anatomical normalization, in which the coordinates of functional images that do not show the brain's morphology, such as blood flow images, can be accurately matched to the coordinates of a standard morphology using an MRI device. Therefore, functional images such as blood flow images of the subject 1 and a healthy subject can be compared using the same coordinate system, which is useful for diagnosis, etc.

[0024] The morphological image referred to here is an image showing the morphology of the subject, and a T1-weighted image is used here. Note that the image is not limited to a T1-weighted image, and may be any image showing the morphology of the subject, such as an absolute value image, a T2-weighted image, or a proton density-weighted image.

[0025] A functional image is an image that represents the function of a subject. In the present embodiment, an example will be described in which the functional image is an arterial spin labeling (ASL) image of cerebral blood flow, particularly an arterial transit time (ATT) image calculated from a multi-PLD ASL image, and / or a cerebral blood flow (CBF) image. Note that, not limited to these images, any image that represents the function of the subject may be used. For example, images having various physical property values and quantitative values such as T2, T2*, diffusion coefficient, flow velocity, magnetic susceptibility, elastic modulus, and contrast agent concentration as pixel values, or a fluid attenuated IR (FLAIR) image in which the signal of water is suppressed can be used.

[0026] On the other hand, as the standard form, any form may be used as long as the coordinates of one or more structures are determined. For example, the standard brain of Talairach can be used.

[0027] As described above, in the present embodiment, instead of directly normalizing a functional image such as a cerebral blood flow image with respect to the standard form (standard brain, etc.), first, the morphological image captured for the same imaging region is deformed to match the coordinates of the structures in the standard form (standard brain, etc.) (normalized), and the functional image is deformed with the value of the deformation parameter used for the deformation. Thus, even when the functional image does not include morphological information, the functional image can be accurately normalized.

[0028] Note that the morphological image is preferably captured for the same imaging region continuously with the capture of the functional image. However, as long as it is captured for the same imaging region of the same subject, it may be captured before or after.

[0029] <Configuration of MRI apparatus> Hereinafter, the MRI apparatus of the present embodiment will be described in detail.

[0030] The structure of an MRI apparatus according to the present invention will be described in detail with reference to Fig. 1. Fig. 1 is a block diagram showing the overall configuration of one embodiment of an MRI apparatus according to the present invention. This MRI apparatus obtains tomographic images of a subject by utilizing the NMR phenomenon, and as shown in Fig. 1, the MRI apparatus is configured to include a static magnetic field generating system (static magnetic field generating unit) 2, a gradient magnetic field generating system (gradient magnetic field generating unit) 3, a transmitting system 5, a receiving system 6, a signal processing system 7, a sequencer (measurement control unit) 4, and a central processing unit (CPU) (arithmetic processing unit) 8.

[0031] The static magnetic field generating system 2 applies a static magnetic field to the imaging space in which the subject is placed. If the static magnetic field generating system 2 is a vertical magnetic field type, it generates a uniform static magnetic field in a direction perpendicular to the body axis of the subject 1, and if it is a horizontal magnetic field type, it generates a uniform static magnetic field in the direction of the body axis of the subject 1. A static magnetic field generating source of the permanent magnet type, normal conducting type, or superconducting type is arranged around the subject 1.

[0032] The gradient magnetic field generating system 3 applies a gradient magnetic field to the imaging space. The gradient magnetic field generating system 3 includes gradient magnetic field coils 9 that apply gradient magnetic fields in three directions, X, Y, and Z, which are the coordinate system (stationary coordinate system) of the MRI apparatus, and gradient magnetic field power supplies 10 that drive each gradient magnetic field coil. Gradient magnetic fields Gx, Gy, and Gz are applied in the three directions, X, Y, and Z, by driving the gradient magnetic field power supplies 10 of each coil in accordance with commands from a sequencer 4 (described later). During imaging, a slice selection gradient magnetic field pulse (Gs) is applied in a direction perpendicular to the slice plane (imaging cross section) to set the slice plane for the subject 1, and a phase encoding gradient magnetic field pulse (Gp) and a frequency encoding gradient magnetic field pulse (Gf) are applied in the remaining two directions that are perpendicular to the slice plane and perpendicular to each other, to encode position information in each direction into the echo signal.

[0033] The sequencer 4 controls the gradient magnetic field generating system 2, the transmitting system 5, and the receiving system 6 to execute an imaging sequence and capture an image. That is, the sequencer 4 is a control means that repeatedly applies radio frequency magnetic field pulses (hereinafter referred to as "RF pulses") and gradient magnetic field pulses in a predetermined pulse sequence, and operates under the control of the calculation processing unit 8, sending various commands to the transmitting system 5, the gradient magnetic field generating system 3, and the receiving system 6 that are required to collect data on tomographic images of the subject 1.

[0034] The transmission system 5 irradiates the subject 1 with an RF pulse (radio frequency magnetic field) to induce nuclear magnetic resonance in the nuclear spins of atoms constituting the biological tissue of the subject 1, and is composed of a radio frequency oscillator 11, a modulator 12, a radio frequency amplifier 13, and a radio frequency coil (transmitting coil) 14a on the transmitting side. The RF pulse output from the radio frequency oscillator 11 is amplitude-modulated by the modulator 12 at a timing according to a command from the sequencer 4, and this amplitude-modulated RF pulse is amplified by the radio frequency amplifier 13 and then supplied to the radio frequency coil 14a arranged close to the subject 1, whereby the RF pulse is irradiated onto the subject 1.

[0035] The receiving system 6 receives echo signals (NMR signals) emitted by nuclear magnetic resonance of atomic spins constituting the biological tissue of the subject 1, and is composed of a receiving radio frequency coil (receiving coil) 14b, a signal amplifier 15, a quadrature phase detector 16, and an A / D converter 17. The NMR signal of the response of the subject 1 induced by the electromagnetic waves irradiated from the transmitting radio frequency coil 14a is detected by the radio frequency coil 14b placed close to the subject 1, amplified by the signal amplifier 15, and then split into two orthogonal systems of signals by the quadrature phase detector 16 at a timing specified by the sequencer 4. Each signal is converted into a digital quantity by the A / D converter 17 and sent to the signal processing system 7. The signal processing system 7 processes various data and displays and stores the processed results, etc. The signal processing system 7 has external storage devices such as an optical disk 19 and a magnetic disk 18, and a display 20 such as a CRT.

[0036] A processing unit (CPU) 8 receives the data from the receiving system 6, performs predetermined signal processing, and then executes image reconstruction processing to generate a tomographic image of the subject 1. The generated image is displayed on a display 20 and is also recorded on an external storage device such as a magnetic disk 18.

[0037] The operation unit 25 is used to input various control information for the MRI apparatus and control information for the processing performed by the signal processing system 7, and is composed of a trackball or mouse 23 and a keyboard 24. This operation unit 25 is placed close to the display 20, and the operator can interactively control various processes of the MRI apparatus through the operation unit 25 while looking at the display 20.

[0038] 1, the radio frequency coil 14a on the transmitting side and the gradient magnetic field coil 9 are placed in the static magnetic field space of the static magnetic field generating system 2 into which the subject 1 is inserted, facing the subject 1 in the case of a vertical magnetic field system, or surrounding the subject 1 in the case of a horizontal magnetic field system. On the other hand, the radio frequency coil 14b on the receiving side is placed facing the subject 1 or surrounding it.

[0039] The nuclide currently commonly imaged by MRI systems in clinical practice is hydrogen nuclei (protons), which are the main constituent of the subject. By imaging information on the spatial distribution of proton density and the spatial distribution of the relaxation time of the excited state, the morphology and function of the human head, abdomen, limbs, etc. can be imaged in two or three dimensions.

[0040] <Capturing functional images> The measurement control unit (sequencer) 4 captures images of cerebral blood flow as functional images using the arterial spin labeling (ASL) method. An example of an imaging sequence is shown in Figure 2. ASL labeling methods include pulsed ASL and pseudo-continuous ASL (pASL). The sequence in Figure 2 is an example of a pASL sequence. The measurement control unit (sequencer) 4 executes the imaging sequence shown in Figure 2 by controlling the gradient magnetic field generator 3, the irradiation coil 14a, and the receiving coil 14b. The ASL method involves adjusting RF pulses and gradient magnetic fields to irradiate and label blood passing through a label surface set upstream of the blood flow from the imaging region with radio-frequency pulses that invert protons. After this, an image (label image) is captured after the post-labeling delay time (PLD) has elapsed, which is the time elapsed after the blood reaches the imaging region. Similarly, an image (control image) is captured of the same imaging region without inverting blood protons. Blood flow information is visualized by calculating the difference between the labeled image and the control image.

[0041] In the pASL sequence shown in Figure 2, after labeling the blood passing through the label surface, imaging is performed after a delay time PLD (post labeling delay time) has elapsed to wait for the labeled blood to flow into the region of interest.

[0042] Specifically, as shown in FIG. 2, the measurement control unit (sequencer) 4 executes a label imaging sequence for capturing a label image and a control imaging sequence for capturing a control image. Both of these sequences sequentially irradiate and apply the following RF pulses 51 to 58 and gradient magnetic field pulses. First, a pre-saturation pulse 51, which tilts the spins by 90 degrees, is irradiated to the imaging region, while a gradient magnetic field is applied to dephase the transverse magnetization. Next, an IR pulse 52, which is an inversion recovery pulse, is selectively or non-selectively irradiated to the imaging region. Next, in the label imaging sequence, an ASL pulse 53, which inverts blood protons by 180 degrees, is selectively irradiated to the label surface (for example, a region 1-2 cm from the bottom of the cerebellum). In the control imaging sequence, a pulse 53 that does not invert blood protons is similarly irradiated to the label surface. Next, IR pulses 54 and 55, which are inversion recovery pulses, are selectively irradiated to the imaging region. Furthermore, a fat suppression pulse 56 is irradiated onto the imaging region, followed by non-selective irradiation with a blood flow signal suppression pulse 57. After a predetermined delay time (PLD) has elapsed since the ASL pulse 53 was irradiated onto the imaging region, a readout process 58 is executed and an NMR signal is acquired from the imaging region. Specifically, in the readout process 58, an RF pulse is irradiated onto the imaging region, thereby exciting spins, and then a readout gradient magnetic field and a phase encoding gradient magnetic field are applied, and an NMR signal is acquired by the receive coil 14b.

[0043] As a result, in the label imaging sequence, NMR signals of the imaging region in a state where labeled blood has reached the imaging region are acquired, and the arithmetic processing unit 8 reconstructs a label image from the acquired NNR signals. Also, in the control imaging sequence, NMR signals are acquired for the same imaging region without labeling the blood, and the arithmetic processing unit 8 reconstructs a control image from the acquired NNR signals.

[0044] Furthermore, the measurement control unit (sequencer) 4 repeatedly executes the imaging sequence while changing the delay time (PLD) (the time from the end of application of the ASL pulse 53 to the start of the readout 58) (Multi-PLD ASL method). As a result, the calculation processing unit 8 reconstructs multiple label images with different delay times (PLD).

[0045] The calculation processing unit 8 calculates a cerebral blood flow (hereinafter referred to as CBF) image and an arterial transit time (hereinafter referred to as ATT) image from the obtained ASL image.

[0046] <Normalization of functional images> Next, the arithmetic processing unit 8 applies anatomical normalization to the ATT image and the CBF image. Furthermore, the arithmetic processing unit 8 performs ROI analysis, voxel analysis, and statistical analysis on the anatomically normalized ATT image and CBF image. As a result, the arithmetic processing unit 8 visualizes and displays abnormal blood flow based on the results of the statistical analysis.

[0047] <Control and arithmetic processing of the arithmetic processing unit 8> The control and calculation processing of the calculation processing unit 8 will be specifically described using the flowchart in Fig. 3. Fig. 4 is a diagram showing an example of imaging conditions, Fig. 5 shows images obtained in the steps of the flow of embodiment 1, and Fig. 6 shows an example of a screen displaying the imaging results of embodiment 1.

[0048] 3 of the arithmetic processing unit 8 is realized by software, in which a CPU in the arithmetic processing unit 8 reads and executes a program stored in advance on the magnetic disk 18 or the like. Note that it is also possible to realize part or all of the arithmetic processing unit 8 by hardware. For example, part or all of the arithmetic processing unit 8 may be configured using a custom IC such as an ASIC (Application Specific Integrated Circuit) or a programmable IC such as an FPGA (Field-Programmable Gate Array), and a circuit may be designed to realize the function.

[0049] First, the operator places the subject 1 on a bed in the imaging area.

[0050] (Step S201) The processing unit 8 receives settings of imaging conditions for imaging sequences of multi-PLD ASL images, 3D T1-weighted images, and proton density-weighted images from the user via the operation unit 25. For example, the settings of the imaging conditions shown in FIG. 4 are received. The imaging region is the entire brain region in all imaging sequences of multi-PLD ASL, TI-weighted images, and proton density-weighted images. In the multi-PLD ASL imaging sequence, seven delay times (PLD) are set, ranging from 500 ms to 3000 ms.

[0051] (Step S202) The calculation processing unit 8 instructs the sequencer (measurement control unit 4) 4 to execute the Multi-PLD ASL imaging sequence shown in Fig. 2 under the imaging conditions set in step S201. As a result, the sequencer 4 controls the gradient magnetic field generating unit 3, the irradiation coil 14a, the receiving coil 14b, etc., and executes the imaging sequence. The calculation processing unit (CPU) 8 receives data from the receiving system 6 and reconstructs an ASL image for each Multi-PLD delay time (PLD) of the imaging region.

[0052] Furthermore, the arithmetic processing unit 8 causes the sequencer (measurement control unit) 4 to execute, for example, a known gradient echo sequence before or after the Multi-PLD ASL imaging sequence. This allows the arithmetic processing unit (CPU) 8 to receive data from the receiving system 6 and reconstruct a 3D T1-weighted image of the imaging region. Furthermore, a proton density-weighted image of the imaging region is captured using the same known GRACE sequence as the ASL imaging sequence. The imaging region, FOV, and matrix of the proton density-weighted image are the same as those of the ASL. If they differ, registration of the ASL image and the proton density-weighted image may be performed after reconstruction.

[0053] The processing unit 8 stores the reconstructed image data on the magnetic disk 18. (Step S203) The calculation processing unit 8 receives a selection of an ASL image of a delay time (PLD) used for calculating CBF and ATT from the operator via the operation unit 25. Note that this selection of an ASL image may be performed automatically. (Step S204) The arithmetic processing unit 8 calculates the ATT and CBF for each pixel from the pixel values ​​of the multi-PLD ASL image selected in step S203, generating an ATT image and a CBF image. For example, the theoretical value of the ASL signal is calculated for each delay time (PLD) using a theoretical formula based on the two-compartment model of equations (1) and (2). The ATT and CBF are calculated for each pixel by fitting the change in the theoretical ASL signal value due to the delay time (PLD) to the actual measured value of the ASL signal for each PLD (i.e., the pixel value of the ASL image captured for each PLD) using curve fitting based on the nonlinear least squares method. From the ATT and CBF values ​​for each pixel, an ATT image displaying the ATT value as a pixel value and a CBF image displaying the CBF value can be generated. Note that the theoretical formulas (1) and (2) can be solved using the known method described in the aforementioned Non-Patent Document 1, and therefore a detailed description thereof will be omitted here.

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[0054] In equation (1), t is time, T1m is the apparent T1 value inside the blood vessel, T1e is the apparent T1 value outside the blood vessel, M0 is the magnetization (pixel value) of the proton density weighted image, and M m0 is the value in the capillary of the proton density weighted image, and M e0 is the extravascular value of the proton density bridge image. Other variables and counts are as shown in Table 1 below. [Table 1]

[0055] Here, ATT and CBF are calculated using equations (1) and (2) by the nonlinear least squares method based on the Two Compartment Model, but they may also be calculated using other known theoretical models or fitting techniques.

[0056] (Step S205) The calculation processing unit 8 performs processing (anatomical normalization) to deform the 3D T1-weighted image captured in step S202 to match it with a standard brain, and obtains a deformation field for matching the 3D T1-weighted image with the standard brain.

[0057] Specifically, as shown in FIGS. 5(a-1) to 5(a-3), the arithmetic processing unit 8 first performs a process of moving the positions and shapes of structures (gray matter, white matter, etc.) in the 3D T1-weighted image to the coordinates of a predetermined standard brain structure using a known method (anatomical normalization (standardization)). For example, normalization is performed using diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) processing, which performs nonlinear transformation using multiple parameters. In DARTEL processing, the target brain structure is deformed (anatomical normalization) according to a standard brain template created in advance.

[0058] Next, the calculation processing unit 8 saves the normalization parameters (a set of values ​​set as parameters of the DARTEL transformation) used when transforming the target brain as transformation parameters.

[0059] Here, the method for obtaining the anatomical normalization parameters is not limited to the DARTEL process, and other known normalization methods may also be used.

[0060] (Step S206) The calculation processing unit 8 registers the ATT image and CBF image generated in step S204 with the 3D T1-weighted image captured in step S202, as shown in Figures 5(b-1) and 5(b-2). For example, the ATT image and CBF image are registered with the 3D T1-weighted image by a known registration technique.

[0061] As a registration method, for example, the mutual information MI between the ATT image and the 3D T1 weighted image, and the mutual information MI between the CBF image and the 3D T1 weighted image are calculated using the following equations (3) to (6), and the positions are aligned (for example, by rotational and translational movement) so that the mutual information MI is maximized.

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[0062] Here, ai is the pixel value (grayscale) of a pixel in the ATT image or CBF image, and bj is the pixel value (grayscale) of the corresponding pixel in the 3D T1-weighted image. h(ai,bj) is a 2D histogram that counts the frequency of pixel value (grayscale) combinations for all corresponding pixel value combinations (ai,bj) and maps them. p(ai,bj) represents the probability that pixel values ​​ai and bj occur simultaneously (joint probability) and is calculated using equation (4). p(ai) and p(bj) represent the probability that pixel values ​​ai and bj occur, respectively (marginal probability), and are calculated using equations (5) and (6), respectively.

[0063] (Step S207) The calculation processing unit 8 uses the values ​​of the deformation parameters stored in step S205 to deform the ATT image and the CBF image after alignment in step S206.

[0064] This allows for anatomical normalization of ATT and CBF.

[0065] It is desirable to use the same processing as in step S205 for the transformation processing, and although DARTEL processing is used here, other known transformation processing may also be used.

[0066] (Step S208) The calculation processing unit 8 analyzes cerebral blood flow dynamics using the ATT image and CBF image anatomically normalized in step S207. For example, from a healthy subject database (a database of anatomically normalized ATT images and CBF images of, for example, 50 healthy subjects), it calculates the degree of deviation between the normalized ATT image and CBF image of the subject calculated in step 207. For example, a z-score is calculated for each voxel, and the degree of deviation (deviation) of the normalized ATT image and CBF image of the subject from the healthy image is calculated as a numerical value (z-score).

[0067] (Step S209) The calculation processing unit 8 displays the discrepancy (Z-score) of the blood flow rate (CBF image) and / or the blood flow arrival time (ATT image) calculated in step 208 on the display 20. As a display screen, for example, as shown in FIG. 6, each cross-sectional image of the CBF image and the ATT image and a 3D image are displayed, and the z-score is displayed in grayscale on top of them. The ATT image and / or CBF image to be displayed may be a normalized image or an image before normalization. Note that in this embodiment, the discrepancy (Z-score) of the blood flow rate (CBF image) and / or the blood flow arrival time (ATT image) is displayed in grayscale in FIG. 6, but it is of course also possible to display it in color.

[0068] The above is the description of the processing flow of the arithmetic processing unit 8 of the first embodiment.

[0069] As described above, in the first embodiment of the present invention, the calculation processing unit 8 can perform anatomical normalization after calculating functional images (ATT images and CBF images) that do not include brain shape information. This allows voxel analysis to be performed on the anatomically normalized functional images (ATT images and CBF images), and statistical analysis, etc. Therefore, from the results of the statistical analysis, abnormal blood flow that deviates from images of healthy individuals can be visualized and displayed, making it possible to evaluate cerebral blood flow in dementia, cerebrovascular disease, epilepsy, and other conditions in which reduced cerebral blood flow is observed, using only an MRI device.

[0070] In the above-mentioned step 203, the selection of the images (Multi-PLD ASL images) to be used for calculating the functional images (ATT image and CBF image) is accepted from the user, but the calculation processing unit 8 may automatically select the images according to predetermined criteria.

[0071] It is also very effective to store the selected image on the magnetic disk 18 or the like, and perform step 204 and subsequent steps at a later date.

[0072] <<Embodiment 2>> An MRI apparatus according to a second embodiment of the present invention will be described.

[0073] The MRI apparatus of the second embodiment calculates an ATT image and a CBF image, and then applies anatomical normalization to the ATT image and the CBF image, as in the first embodiment. By performing ROI analysis and statistical analysis on the anatomically normalized ATT image and the CBF image, abnormal blood flow is visualized and displayed.

[0074] Fig. 7 shows the processing flow of the arithmetic processing unit 8 of embodiment 2. The processing of the arithmetic processing unit 78 will be described below with reference to Fig. 7. Note that in the flow of Fig. 7, the same steps as those in the flow of Fig. 3 of embodiment 1 are assigned the same step reference numerals, and their description will be omitted.

[0075] (Steps S201 to S207) The calculation processing unit 8 performs steps S201 to S207 similar to steps S201 to S207 in the first embodiment to generate anatomically normalized ATT images and CBF images.

[0076] (Step S301) The processor 8 sets one or more ROIs (regions of interest) on the anatomically normalized ATT image and CBF image in step S207. The ROIs may have the shape of standard brain structures or may have a desired shape drawn by the user. For example, an ROI from a known brain atlas such as the AAL (Automated Anatomical Labeling) atlas may be selected, or the user may draw an ROI on the image.

[0077] The calculation processor 8 analyzes the cerebral blood flow dynamics within the ROIs of the anatomically normalized ATT image and CBF image. For example, the degree of deviation of the ATT image and CBF image from the healthy subject database is calculated as a z-score for each ROI, and the degree of deviation of the blood flow arrival time and blood flow rate from those of healthy subjects is analyzed. Specifically, for example, a z-score is calculated indicating how much the blood flow arrival time or blood flow rate of the subject's ROI deviates from the average blood flow arrival time or blood flow rate of the ROIs of 50 subjects in the healthy subject database. This allows a value (z-score) indicating the degree of deviation to be calculated for each ROI when there are multiple regions (ROIs).

[0078] (Step S209) As in step S209 in the first embodiment, the calculation processing unit 8 displays the ATT image and / or CBF image and the z-score calculated for the ROI on the display 20. For example, the z-score is displayed superimposed on the position of the ROI on the ATT image or CBF image.

[0079] As described above, in the second embodiment of the present invention, a z-score is calculated from the results of statistical analysis and can be displayed together with an ATT image and / or a CBF image, so that ROIs with large z-scores, i.e., ROIs that deviate from the image of a healthy subject, can be visualized and displayed. As a result, similar to the first embodiment described above, it becomes possible to evaluate cerebral blood flow in dementia, cerebrovascular disorder, epilepsy, and other conditions in which reduced cerebral blood flow is observed, using only MRI.

[0080] Furthermore, in the second embodiment, an ROI is set and the inside of the ROI is analyzed, so it is possible to determine easily and with a small amount of calculation whether the blood flow in the ROI is lower than that of a healthy person.

[0081] In this embodiment, an example has been described in which a known ROI atlas such as an AAL atlas or an ROI drawn by a user is set as the ROI in step S301, but the ROI may also be set by the arithmetic processing unit 8. For example, the arithmetic processing unit 8 may extract in advance a region where there is a significant difference in blood flow between the healthy database and the disease database, define it as the ROI, and set it as the ROI in step S301.

[0082] Furthermore, the calculation processing unit 8 may compare the ATT image and / or CBF image generated in step S207 with images in the healthy subject database, extract a region with a significant difference, and set it as the ROI in step S301. <<Embodiment 3>> An MRI apparatus according to a third embodiment will be described.

[0083] The MRI apparatus of the third embodiment has a function of calculating the probability that a subject has a disease using an ATT image and a CBF image.

[0084] 8 is a flowchart showing the processing flow of the arithmetic processing unit 8 of embodiment 3. Each step in FIG. 8 will be described below.

[0085] (Steps S201 to S208) As in the first or second embodiment, the calculation processing unit 8 generates anatomically normalized ATT images and CBF images.

[0086] (Step S401) The arithmetic processing unit 8 preliminarily sets predetermined ROIs (regions of interest) on the ATT images and CBF images in the healthy database (a database containing anatomically normalized ATT images and CBF images of healthy individuals) and the disease database (a database containing anatomically normalized ATT images and CBF images of individuals with a disease) using, for example, a brain atlas, and calculates feature values ​​for each ROI. For example, the average pixel value (ATT value or CBF value) can be used as the feature value. The arithmetic processing unit 8 preliminarily calculates the boundary (discriminant plane) between the distribution of feature values ​​of the ATT images and CBF images of multiple healthy individuals and the distribution of feature values ​​of multiple individuals with a disease. A known method (e.g., a machine learning algorithm such as a support vector machine) is used to calculate the discriminant plane.

[0087] The calculation processing unit 8 sets the above-mentioned predetermined ROIs on the normalized ATT image and CBF image of the subject obtained in step 208, calculates feature amounts, and calculates whether the calculated feature amounts are on the healthy subject side or the diseased subject side of the discrimination plane, as well as the distance from the discrimination plane. Based on the calculated distance, the probability that the subject belongs to the diseased subject is calculated by a known method.

[0088] (Step S402) The calculation processing unit 8 displays the normalized ATT image and CBF image, and the result calculated in step S401 (the probability that the subject belongs to a person with a disease). At this time, the distance calculated in step S401 may be displayed together with the probability.

[0089] The above is the description of the processing flow of the arithmetic processing unit 8 of the third embodiment.

[0090] According to the MRI apparatus of the third embodiment described above, the probability that a subject has a disease can be calculated from the ATT image and the CBF image, thereby assisting doctors in making diagnoses.

[0091] The method for calculating the discriminant plane (boundary) is not limited to machine learning algorithms such as Support Vector Machine, and any method that can distinguish between the similarity of the ATT images and CBF images of a diseased person and a healthy person may be used. For example, a deep learning method other than Support Vector Machine, clustering, statistical analysis, etc. may also be used.

[0092] <<Embodiment 4>> An MRI apparatus according to a fourth embodiment will be described.

[0093] In the fourth embodiment, blood flow analysis is performed using a single PLD image (a blood flow image) instead of a multi-PLD ASL. That is, a CBF image is calculated from a single PLD image, and anatomical normalization and statistical analysis are performed as in the first and second embodiments, and the results are displayed.

[0094] Fig. 9 is a flowchart showing the processing flow of the arithmetic processing unit 8 according to the fourth embodiment of the present invention. Each step in Fig. 9 will be described below. Note that when the same processing as that in the step in Fig. 3 is performed in the step in Fig. 9, the description will be omitted.

[0095] (Steps S901 to S903) Similar to steps 201 and 202 in FIG. 3, the calculation processing unit 8 receives settings of imaging conditions from the user via the operation unit 25, and then executes the imaging sequence of the Multi-PLD ASL in FIG. 2 of the first embodiment with a single PLD (one delay time PLD), and captures a label image and a control image.

[0096] Furthermore, the arithmetic processing unit 8 captures a 3DT1 weighted image and a proton density weighted image.

[0097] The calculation processing unit 8 selects a label image and a control image captured with a single PLD (one delay time PLD) as images to be used in calculating CBF.

[0098] (Step S904) The calculation processing unit 8 calculates a CBF image from the image of the single PLD and the proton density. For example, the calculation method uses equation (7), and calculates the CBF (f in equation (7)) by inputting the difference ΔM in pixel values ​​between the label image and the control image, the delay time PLD, and the pixel values ​​of PD to equation (7). The calculation processing unit 8 generates a CBF image by calculating the CBF value for each pixel value.

[0099]

number

[0100] (Steps S205 to S209) The arithmetic processing unit 8 performs anatomical normalization to match the 3DT1-weighted image with a standard brain, similar to steps 205 to 209 in Fig. 3 of the first embodiment, and anatomically normalizes the CBF image using the transformation parameters at that time. Then, it calculates the degree of deviation (z-score) that indicates how much the normalized CBF image deviates from the CBF image in the healthy subject database. The calculated degree of deviation is displayed together with the CBF image.

[0101] The above is the description of the processing flow of the arithmetic processing unit 8 of the fourth embodiment.

[0102] As described above, in the fourth embodiment of the present invention, hemodynamics (CBF) can be analyzed using the CBF of a single PLD. Therefore, even when it is difficult to obtain multi-PLD ASL images, the fourth embodiment makes it possible to analyze hemodynamics using the ASL image of a single PLD.

[0103] <<Embodiment 5>> An MRI apparatus according to a fifth embodiment will be described.

[0104] In the fifth embodiment, analysis is performed in the individual brain space by deforming the ROI shape of the brain atlas so that it matches the brain morphology of an individual (subject). That is, unlike the first to fourth embodiments, in which the ATT image and CBF image are deformed to match a standard brain using deformation parameters, the brain atlas is deformed to fit the individual brain space of the subject by applying the deformation parameters in the reverse direction to the brain atlas, and the ATT image and CBF image are analyzed using the ROI of the deformed brain atlas.

[0105] 10 is a flowchart showing the processing flow of the arithmetic processing unit 8 according to the third embodiment of the present invention. Each step in FIG. 10 will be described in detail below.

[0106] (Steps S201 to S205) The calculation processing unit 8 performs steps S201 to S205 in the same manner as in the first embodiment.

[0107] (Step S601) The calculation processing unit 8 uses the deformation parameters saved in step S205 to deform the brain atlas in the reverse direction, thereby deforming the brain atlas into a shape that matches the 3DT1-weighted image of the subject before normalization.

[0108] Furthermore, the processing unit 8 registers the inversely transformed brain atlas to the ATT image and the CBF image. The transformation method used is, for example, DARTEL.

[0109] (Step S602) The calculation processing unit 8 calculates the degree of discrepancy from a healthy subject, in the same manner as in step S208, using the brain atlas that matches the brain space of the subject calculated in step S601, the ATT image, and the CBF image.

[0110] (Step S209) The calculation processing unit 8 displays the degree of discrepancy and the ATT image and CBF image, similarly to step S209 in the first embodiment.

[0111] The above is the description of the processing flow of the arithmetic processing unit 8 of the fifth embodiment.

[0112] As described above, in the fifth embodiment of the present invention, cerebral blood flow dynamics are analyzed by inversely transforming the atlas. This makes it possible to avoid changes in CBF and ATT values ​​due to standard brain transformation, enabling more accurate blood flow evaluation.

[0113] <<Embodiment 6>> An MRI apparatus according to a sixth embodiment will be described.

[0114] In the sixth embodiment, in addition to the ATT image, the CBF image, and the 3D T1 weighted image, the QSM (Quantitative Susceptibility Mapping) image is also used to perform statistical analysis.

[0115] 11 is a flowchart showing the processing flow of the arithmetic processing unit 8 according to the third embodiment of the present invention. Each step in FIG. 11 will be described in detail below.

[0116] (Steps S1101 to S1102) As in steps S201 to S202 in the first embodiment, the calculation processing unit 8 accepts settings of imaging conditions for Multi-PLD ASL images, 3D T1-weighted images, and proton density-weighted images, and performs imaging. However, in this embodiment, in addition to these, it also accepts settings of imaging conditions for QSM images, and performs imaging.

[0117] (Steps S203 to S205) The calculation processing unit 8 performs steps S203 to S205 of the first embodiment to generate a CBF image and an ATT image, and also obtains deformation parameters for matching the 3D T1 weighted image to the standard brain.

[0118] (Step S1106) The calculation processing unit 8 performs QSM analysis. For example, using a known method, a global magnetic field map is created by removing phase wrapping from the collected phase images, and a local magnetic field map is obtained by removing the background magnetic field. Then, a magnetic susceptibility map (QSM) is obtained by estimating the dipole magnetic field. (Steps S1107 to S1109) As in steps S206 to S208 of embodiment 1, the calculation processing unit 8 aligns the CBF image, ATT image, and QSM image with the 3DT1-weighted image, and then anatomically normalizes them by deforming them using the deformation parameters saved in step S205.

[0119] Furthermore, the calculation processing unit 8 performs statistical analysis using the anatomically normalized ATT image, CBF image, QSM image, and 3DT1 weighted image. For example, similar to the first embodiment, the degree of deviation from the image of a healthy subject is calculated.

[0120] The calculation processing unit 8 can perform analysis using multiple image types, such as ATT images and CBF images, as well as QSM images and 3DT1 weighted images, and can simultaneously calculate the discrepancy in blood flow, magnetic susceptibility, and brain volume in the same region.

[0121] (Step S209) The calculation processing unit 8 displays the analysis results as an image.

[0122] The above is the description of the processing flow of the arithmetic processing unit 8 of the sixth embodiment.

[0123] As described above, in the sixth embodiment of the present invention, by performing analysis using multiple image types, it becomes possible to simultaneously calculate the discrepancy of blood flow, magnetic susceptibility, and brain volume in the same region. By simultaneously analyzing blood flow, magnetic susceptibility, and brain volume, diagnosis can be made easier.

[0124] Although the first to sixth embodiments of the present invention have been described above, the present invention is not limited to these embodiments. [Explanation of symbols]

[0125] 1: subject, 2: static magnetic field generating system, 3: gradient magnetic field generating system, 4: sequencer, 5: transmitting system, 6: receiving system, 7: signal processing system, 8: arithmetic processing unit (CPU), 9: gradient magnetic field coil, 10: gradient magnetic field power supply, 11: high frequency oscillator, 12: modulator, 13: high frequency amplifier, 14a: high frequency coil (transmitting coil), 14b: high frequency coil (receiving coil), 15: signal amplifier, 16: quadrature phase detector, 17: A / D converter, 18: magnetic disk, 19: optical disk, 20: display, 21: ROM, 22: RAM, 23: trackball or mouse, 24: keyboard

Claims

1. a static magnetic field generating unit that applies a static magnetic field to an imaging space in which a subject is placed, a gradient magnetic field generating unit that applies a gradient magnetic field to the imaging space, a transmitting coil that transmits a radio frequency magnetic field to the subject in the imaging space, a receiving coil that receives a nuclear magnetic resonance signal from the subject, a measurement control unit that controls the gradient magnetic field generating unit, the transmitting coil, and the receiving coil to execute an imaging sequence and capture an image, and a calculation processing unit, the measurement control unit captures, for the brain of the same subject, a morphological image showing the morphology of brain structures and a cerebral blood flow image showing cerebral blood flow; the measurement control unit captures the cerebral blood flow image by an arterial spin labeling (ASL) method, The arithmetic processing unit performing a predetermined process on the anatomical image, which uses a plurality of deformation parameters to deform the shape of the brain structure in the brain image so as to conform to a predetermined standard brain, thereby conforming the brain structure in the anatomical image to the standard brain, and determining values ​​of the plurality of deformation parameters used when conforming the anatomical image to the standard brain; A magnetic resonance imaging apparatus characterized in that the cerebral blood flow image is deformed using the calculated values ​​of the deformation parameters, or the standard brain is deformed in the inverse direction using the values ​​of the deformation parameters to generate a deformed standard brain.

2. 2. The magnetic resonance imaging apparatus according to claim 1, wherein the process of conforming the brain structure to a predetermined standard brain by nonlinearly deforming the brain structure using a plurality of predetermined deformation parameters is DARTEL (diffeomorphic anatomical registration through exponentiated Lie algebra) processing.

3. 2. The magnetic resonance imaging apparatus according to claim 1, wherein the morphological image is a T1 weighted image.

4. 2. The magnetic resonance imaging apparatus according to claim 1, wherein the brain structures include gray matter and white matter.

5. 2. The magnetic resonance imaging apparatus according to claim 1, wherein the ASL method is a Multi-PLD ASL (arterial spin labeling) method in which a plurality of times from blood labeling to imaging are set and imaging is performed for each of the plurality of times.

6. 6. The magnetic resonance imaging apparatus according to claim 5, wherein the cerebral blood flow image is an image of arterial transit time (ATT) and / or an image of cerebral blood flow (CBF).

7. 2. The magnetic resonance imaging apparatus according to claim 1, wherein the ASL method is a sequence that realizes a Single PLD ASL (arterial spin labeling) method in which imaging is performed by setting one type of time from labeling blood to imaging, A magnetic resonance imaging apparatus characterized in that the cerebral blood flow image is an image of cerebral blood flow (CBF).

8. a processing unit that receives a morphological image showing the morphology of brain structures imaged by a magnetic resonance imaging device and a cerebral blood flow image showing cerebral blood flow imaged by the magnetic resonance imaging device using an ASL (arterial spin labeling) method for the brain of the same subject, The arithmetic processing unit performing a predetermined process on the anatomical image, which uses a plurality of deformation parameters to deform the shape of the brain structure in the brain image so as to conform to a predetermined standard brain, thereby conforming the brain structure in the anatomical image to the standard brain, and determining values ​​of the plurality of deformation parameters used when conforming the anatomical image to the standard brain; An image processing device characterized by using the calculated values ​​of the deformation parameters to deform the cerebral blood flow image, or by using the values ​​of the deformation parameters to deform the standard brain in the opposite direction to generate a deformed standard brain.

9. receiving a morphological image showing the morphology of brain structures imaged by a magnetic resonance imaging device and a cerebral blood flow image showing cerebral blood flow imaged by the magnetic resonance imaging device using an ASL (arterial spin labeling) method for the brain of the same subject; performing a predetermined process on the anatomical image, which uses a plurality of deformation parameters to deform the shape of the brain structure in the brain image so as to conform to a predetermined standard brain, thereby conforming the brain structure in the anatomical image to the standard brain, and determining values ​​of the plurality of deformation parameters used when conforming the anatomical image to the standard brain; The calculated values ​​of the deformation parameters are used to deform the cerebral blood flow image, or the values ​​of the deformation parameters are used to deform the standard brain in the reverse direction to generate a deformed standard brain. An image processing method comprising:

Images