Magnetic Resonance Imaging Device

Abstract

The present invention provides a magnetic resonance imaging apparatus, an image processing apparatus, and an image processing method for performing anatomical normalization that enables a functional image such as a blood flow image obtained using an MRI apparatus to be accurately aligned with a standard form with excellent precision. A morphological image showing a form and a functional image showing a function are captured for the same imaging region of a subject. After the arithmetic processing unit performs processing to deform the morphological image using deformation parameters and move the positions of one or more structures in the morphological image to the positions of the structures in a predetermined standard form respectively, the arithmetic processing unit deforms the functional image using the values of the deformation parameters used when deforming the morphological image, thereby aligning the positions of the regions in the functional image with the positions of the corresponding regions in the standard form. Alternatively, the arithmetic processing unit deforms the standard form in the opposite direction using the values of the deformation parameters, thereby aligning the positions of the regions of the structures in the standard form with the positions of the corresponding regions in the functional image.

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 more particularly, to a technique for analyzing cerebral blood flow dynamics from blood flow images. Background Art

[0002] An MRI apparatus measures NMR signals generated by nuclear spins of tissues constituting a subject, particularly a human body, and two-dimensionally or three-dimensionally images the morphology and function of the head, abdomen, limbs, etc. During imaging, different phase encodings are imparted to the NMR signals by a gradient magnetic field, and frequency encoding is performed, and the measurement is performed as time-series data. The measured NMR signals are reconstructed into images by performing two-dimensional or three-dimensional Fourier transform.

[0003] By evaluating cerebral blood flow dynamics, image diagnosis of cerebrovascular disorders such as dementia, cerebral infarction, and vascular stenosis, and brain diseases with abnormal cerebral blood flow confirmation such as epilepsy is performed.

[0004] As a method for imaging cerebral blood flow using MRI, there is an arterial spin labeling (ASL) method (see Non-Patent Document 1). In the ASL method, an RF pulse is irradiated to a label plane set upstream of the blood flow compared to the imaging region, and the spins of the protons in the blood passing through the label plane are inverted (labeled). When the blood with the inverted spins reaches the imaging region through the blood flow, the spins of the protons in the tissue in the capillary are exchanged with the spins of the protons in the tissue in the imaging region, and thus the T1 relaxation time of the tissue changes. By obtaining the difference between the label image of the imaging region in this state and the control image obtained without inverting the spins of the blood protons, a blood flow image can be obtained. Since this method can label blood with an RF pulse, a blood flow image can be generated non-invasively.

[0005] On the other hand, a method called anatomical standardization (normalization) that transforms the brain images of a subject obtained by PET (Positron Emission Tomography), MRI, etc. into a standard brain coordinate system and matches them with the standard brain is widely used. By this anatomical standardization, brain images with individual morphological differences are transformed into a standard brain, and thus it is possible to compare blood flow and metabolic images for each pixel between subjects, or to compare between groups of subjects. As a method of anatomical standardization, there is known a diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) process that performs non-linear transformation using a plurality of parameters.

[0006] In addition, Patent Document 1 discloses a SPECT (Single Photon Emission Computed Tomography) technique in which a medicament labeled with a radioactive isotope is administered to a subject, the emitted radiation is detected to obtain projection data, and image reconstruction is performed to obtain a cerebral blood flow image. In the technique of Patent Document 1, the reconstructed image is deformed to match the standard brain, thereby obtaining a standard brain image (anatomical normalization), and the standard brain image is quantified to obtain various quantitative value images. In addition, it has been proposed to evaluate blood flow dynamics by using the obtained quantitative value images and perform pathological analysis based on quantitative evaluation, etc.

[0007] Prior Art Documents

[0008] Non-Patent Documents

[0009] 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 (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)

[0010] Patent Documents

[0011] Patent Document 1: Japanese Patent Application Laid-Open No. 2006-119022 Summary of the Invention

[0012] Problems to be Solved by the Invention

[0013] When the ASL method for photographing a cerebral blood flow image using an MRI device is compared with SPECT, it is possible to perform non-invasive photographing without using a medicament, which is highly advantageous for the subject. In addition, if the cerebral blood flow image obtained by the ASL method can be transformed into the standard brain coordinate system, it is possible to compare the blood flow of the subject and a healthy person in the same coordinate system, which is useful for diagnosis and the like.

[0014] However, the cerebral blood flow images taken by the ASL method do not contain the shape information of the brain, so it is difficult to transform the cerebral blood flow images with excellent accuracy into the standard brain coordinate system.

[0015] Patent Document 1 discloses obtaining deformation parameters for deforming the cerebral blood flow image obtained by SPECT to match the standard brain. However, the cerebral blood flow image obtained by SPECT also does not contain shape information, so it is presumed that it is actually not easy to perform coordinate transformation with excellent accuracy to match the standard brain.

[0016] In addition, when comparing the technique of obtaining cerebral blood flow images by SPECT described in Patent Document 1 with the case of taking images with an MRI device as in Non-Patent Document 1, the examination time is long and the examination cost also becomes high.

[0017] An object of the present invention is to perform anatomical normalization in which functional images such as blood flow images obtained using an MRI device are made to coincide with the standard form with excellent accuracy.

[0018] Technical means for solving the problem

[0019] To achieve the above object, according to the present invention, there is provided: a static magnetic field generation unit that applies a static magnetic field to the imaging space in which the subject is disposed; an inclined magnetic field generation unit that applies an inclined magnetic field to the imaging space; an irradiation coil that irradiates the subject in the imaging space with a high-frequency magnetic field; a reception coil that receives nuclear magnetic resonance signals from the subject; a measurement control unit that controls the inclined magnetic field generation unit, the irradiation coil, and the reception coil to execute an imaging sequence and capture an image; and an arithmetic processing unit. The measurement control unit captures a morphological image showing the morphology and a functional image showing the function for the same imaging region of the subject. After the arithmetic processing unit performs processing of deforming the morphological image using deformation parameters and moving the positions of one or more structures in the morphological image to the positions of the structures in a predetermined standard form respectively, the arithmetic processing unit deforms the functional image using the values of the deformation parameters used when deforming the morphological image, thereby making 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, thereby making the positions of the regions of the structures in the standard form coincide with the positions of the corresponding regions in the functional image.

[0020] Advantages of the invention

[0021] According to the present invention, it is possible to perform anatomical normalization in which functional images such as blood flow images obtained using an MRI device are made to coincide with the standard form with excellent accuracy. Therefore, it is possible to easily perform evaluation by comparing with the functional images of healthy subjects. Description of the drawings

[0022] Figure 1It is a block diagram showing an example of the overall structure of the MRI apparatus according to Embodiment 1 of the present invention.

[0023] Figure 2 It is a diagram showing an example of the imaging pulse sequence of the ASL method.

[0024] Figure 3 It is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus according to Embodiment 1.

[0025] Figure 4 It shows Figure 2 An example of the imaging conditions of the imaging pulse sequence.

[0026] Figure 5 Of (a-1) to Figure 5 Of (a-3) is an explanatory diagram showing the process of anatomical normalization of the 3D T1-weighted image, Figure 5 Of (b-1) to Figure 5 Of (b-2) is an explanatory diagram showing the process of anatomical normalization of the CBF image and the ATT image.

[0027] Figure 6 It is a diagram showing an example of the GUI representing the image and analysis results generated by the arithmetic processing unit 8 of the MRI apparatus according to Embodiment 1.

[0028] Figure 7 It is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus according to Embodiment 2.

[0029] Figure 8 It is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus according to Embodiment 3.

[0030] Figure 9 It is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus according to Embodiment 4.

[0031] Figure 10 It is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus according to Embodiment 5.

[0032] Figure 11 It is a flowchart showing the processing flow of the arithmetic processing unit 8 of the MRI apparatus according to Embodiment 6.

[0033] Explanation of reference numerals

[0034] 1: Object to be detected, 2: Static magnetic field generation system, 3: Tilted magnetic field generation system, 4: Sequencer, 5: Transmission system, 6: Reception system, 7: Signal processing system, 8: Arithmetic processing unit (CPU), 9: Tilted magnetic field coil, 10: Tilted magnetic field power supply, 11: High-frequency oscillator, 12: Modulator, 13: High-frequency amplifier, 14a: High-frequency coil (transmission coil), 14b: High-frequency coil (reception 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 Detailed implementation mode

[0035] Hereinafter, a preferred implementation mode of the MRI apparatus of the present invention will be described in detail with reference to the accompanying drawings. In all the drawings for explaining the implementation mode of the invention, elements having the same function are denoted by the same reference numerals, and repeated explanations thereof are omitted.

[0036] <Embodiment 1>

[0037] First, an outline of the MRI apparatus according to Embodiment 1 of the present invention will be described.

[0038] As an example, the MRI apparatus according to Embodiment 1 of the present invention is Figure 1 configured as shown, and includes: a static magnetic field generation unit 2 that applies a static magnetic field to an imaging space in which an object to be detected 1 is arranged; a tilted magnetic field generation unit 3 that applies a tilted magnetic field to the imaging space; an irradiation coil 14a that irradiates a high-frequency magnetic field to the object to be detected 1 in the imaging space; a reception coil 14b that receives a nuclear magnetic resonance signal from the object to be detected 1; a measurement control unit (sequencer) 4 that controls the tilted magnetic field generation unit 3, the irradiation coil 14a, and the reception coil 14b to execute an imaging sequence and capture an image; and an arithmetic processing unit 8.

[0039] The measurement control unit 4 captures a morphological image showing a form and a functional image showing a function for the same imaging region of the object to be detected 1.

[0040] The arithmetic processing unit 8 performs the following processing (anatomical normalization processing), that is, deforms the morphological image using a deformation parameter, and moves the positions of one or more structures in the morphological image to the positions of structures in a predetermined standard form respectively using the deformation parameter. The arithmetic processing unit 8 deforms the functional image by using the value of the deformation parameter used when deforming the morphological image, so that the positions of regions in the functional image coincide with the positions of corresponding regions in the standard form.

[0041] Alternatively, the arithmetic processing unit 8 deforms the standard form in the reverse direction by using the value of the above-described deformation parameter, so that the position of the region of the structure in the standard form coincides with the position of the corresponding region in the functional image.

[0042] Thereby, it is possible to perform anatomical normalization in which the coordinates of the functional image that does not show the morphology of the brain coincide with the coordinates of the standard form with excellent accuracy, such as a blood flow image, using an MRI device. Therefore, it is possible to compare the functional images such as the blood flow image of the subject 1 and a healthy subject in the same coordinate system, which is useful for diagnosis and the like.

[0043] The morphological image here is an image showing the morphology of the subject. Here, a T1-weighted image is used. In addition, it is not limited to the T1-weighted image, and any image that shows the morphology of the subject may be used, and other images such as an absolute value image, a T2-weighted image, and a proton density-weighted image may also be used.

[0044] The functional image is an image showing the function of the subject. In the present embodiment, an example in which the functional image is an arterial transit time (ATT) image and / or a cerebral blood flow (CBF) image calculated from an ASL (arterial spin labeling) image, which is a cerebral blood flow image, particularly a multi-PLD ASL image, will be described. In addition, it is not limited to these images, and any image that shows the function of the subject may be used. For example, an image using 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, a fluid-attenuated IR (FLAIR) image in which the signal of water is suppressed, and the like can be used.

[0045] 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.

[0046] Thus, in the present embodiment, instead of directly normalizing a functional image such as a cerebral blood flow image with respect to a standard form (standard brain, etc.), first, the morphological image taken for the same imaging region is deformed to coincide with the coordinates of the structure of the standard form (standard brain, etc.) (normalized), and the functional image is deformed using the value of the deformation parameter used in the deformation. Thereby, even when the functional image does not contain morphological information, it is possible to normalize the functional image with excellent accuracy.

[0047] In addition, regarding the morphological image, it is preferable to capture the same imaging area continuously with the imaging of the functional image, but it may be captured beforehand or afterwards as long as the same imaging area of ​​the same subject is captured.

[0048] <MRI装置的结构>

[0049] Hereinafter, the MRI apparatus according to the present embodiment will be described in detail.

[0050] according to Figure 1 The structure of the MRI apparatus according to the present invention will be described in detail. Figure 1 1 is a block diagram showing the overall structure of an embodiment of the MRI apparatus according to the present invention. The MRI apparatus uses the NMR phenomenon to obtain a tomographic image of a subject. Figure 1 As shown, 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) (calculation processing unit) 8.

[0051] The static magnetic field generating system 2 applies a static magnetic field to the imaging space where the subject is arranged. If the vertical magnetic field method is adopted, the static magnetic field generating system 2 generates a uniform static magnetic field in a direction orthogonal to the body axis of the subject 1, and if the horizontal magnetic field method is adopted, a uniform static magnetic field is generated in the body axis direction of the subject 1. A static magnetic field generating source of a permanent magnet method, a normal conductor method or a superconducting method is arranged around the subject 1.

[0052] The gradient magnetic field generating system 3 applies a gradient magnetic field to the imaging space. The gradient magnetic field generating system 3 includes a gradient magnetic field coil 9 for applying a gradient magnetic field in the three-axis directions of X, Y, and Z, which are the coordinate system (stationary coordinate system) of the MRI device, and a gradient magnetic field power supply 10 for driving each gradient magnetic field coil. By driving the gradient magnetic field power supply 10 of each coil according to a command from a sequencer 4 described later, gradient magnetic fields Gx, Gy, and Gz are applied in the three-axis directions of X, Y, and Z. During imaging, a slice selection gradient magnetic field pulse (Gs) is applied in a direction orthogonal to the slice plane (imaging section) to set the slice plane for the object 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 orthogonal to the slice plane and orthogonal to each other, and the position information in each direction is encoded in the echo signal.

[0053] Sequencer 4 controls the tilt magnetic field generation system 3, the transmission system 5, and the reception system 6 to execute an imaging sequence and capture an image. That is, sequencer 4 is a control unit that repeatedly applies high-frequency magnetic field pulses (hereinafter referred to as "RF pulses") and tilt magnetic field pulses in a given pulse sequence, operates under the control of the arithmetic processing unit 8, and sends various commands required for collecting data of tomographic images of the subject 1 to the transmission system 5, the tilt magnetic field generation system 3, and the reception system 6.

[0054] The transmission system 5 irradiates the subject 1 with RF pulses (high-frequency magnetic fields) to cause nuclear magnetic resonance of the atomic nuclei of the biological tissues constituting the subject 1, and includes a high-frequency oscillator 11, a modulator 12, a high-frequency amplifier 13, and a high-frequency coil (transmission coil) 14a on the transmission side. At a timing based on an instruction from sequencer 4, the RF pulse output from the high-frequency oscillator 11 is amplitude-modulated by the modulator 12, and after the amplitude-modulated RF pulse is amplified by the high-frequency amplifier 13, it is supplied to the high-frequency coil 14a disposed close to the subject 1, thereby irradiating the subject 1 with the RF pulse.

[0055] The reception system 6 receives the echo signal (NMR signal) released by the nuclear magnetic resonance of the atomic nuclei of the biological tissues constituting the subject 1, and includes a high-frequency coil (reception coil) 14b, a signal amplifier 15, a quadrature phase detector 16, and an A / D converter 17 on the reception side. The NMR signal of the response of the subject 1 induced by the electromagnetic wave irradiated from the high-frequency coil 14a on the transmission side is detected by the high-frequency coil 14b disposed close to the subject 1, amplified by the signal amplifier 15, and at a timing based on an instruction from sequencer 4, is divided into signals of two orthogonal systems by the quadrature phase detector 16, and each is converted into a digital quantity by the A / D converter 17 and sent to the signal processing system 7.

[0056] The signal processing system 7 performs various data processing, displays and stores the processing results, etc. The signal processing system 7 has external storage devices such as an optical disc 19 and a magnetic disk 18, and a display 20 including a CRT, etc.

[0057] The arithmetic processing unit (CPU) 8 receives the data from the reception system 6, performs given signal processing, and then executes image reconstruction processing, thereby generating a tomographic image of the subject 1. The generated image is displayed on the display 20 and recorded in the external storage device, i.e., the magnetic disk 18, etc.

[0058] The operation unit 25 inputs various control information of the MRI apparatus and control information of the processing performed by the signal processing system 7, and includes a trackball or a mouse 23 and a keyboard 24. The operation unit 25 is disposed close to the display 20, and the operator interactively controls various processes of the MRI apparatus while watching the display 20 through the operation unit 25.

[0059] In addition, in Figure 1 , the transmitting-side high-frequency coil 14a and the gradient magnetic field coil 9 are disposed opposite to the subject 1 in the static magnetic field space of the static magnetic field generation system 2 into which the subject 1 is inserted in the case of a vertical magnetic field method, or are disposed so as to surround the subject 1 in the case of a horizontal magnetic field method. On the other hand, the receiving-side high-frequency coil 14b is disposed opposite to the subject 1 or disposed so as to surround the subject 1.

[0060] Currently, regarding the nuclide of the imaging object of the MRI apparatus, as the nuclide that has been clinically popularized, it is the hydrogen nucleus (proton) that is the main structural substance of the subject. By imaging the information related to the spatial distribution of the proton density and the spatial distribution of the relaxation time of the excitation state, the morphology or function of the human head, abdomen, limbs, etc. is photographed two-dimensionally or three-dimensionally.

[0061] <Imaging of Functional Images>

[0062] The measurement control unit (sequencer) 4 photographs an image of cerebral blood flow as a functional image by the ASL (arterial spin labeling) method. An example of the imaging sequence is shown in Figure 2 . The labeling method of ASL includes a pulsed ASL method and a pseudo-continuous ASL (pASL) method, and Figure 2 the sequence is an example of the sequence of the pASL method. The measurement control unit (sequencer) 4 controls the gradient magnetic field generation unit 3, the irradiation coil 14a, the receiving coil 14b, etc. to execute Figure 2 the imaging sequence. In the ASL method, after irradiating a high-frequency pulse that reverses protons to the blood in the label plane set upstream of the blood flow compared to the imaging region by adjusting the RF pulse and the gradient magnetic field and performing labeling, an image (label image) is photographed at the timing of the delay time PLD (post labeling delay time: time elapsed after labeling) when the blood reaches the imaging region. Similarly, an image (control image) is photographed for the same imaging region without reversing the blood protons. By obtaining the difference between the label image and the control image, the blood flow information is imaged.

[0063] In Figure 2In the sequence of the pASL method, after labeling the blood passing through the labeling plane, in order to wait for the labeled blood to flow into the region of interest, an image is taken after waiting for the post-labeling delay time (PLD).

[0064] Specifically, the measurement control unit (sequencer) 4, as Figure 2 described, executes a labeling imaging sequence for imaging a labeling image and a control imaging sequence for imaging a control image. These are both sequences in which the following RF pulses 51 to 58 and gradient magnetic field pulses are irradiated and applied in sequence. First, a presaturation pulse 51 that tilts the spins by 90 degrees is irradiated to the imaging region, and a gradient magnetic field is applied to dephase the transverse magnetization. Next, an IR pulse 52 as an inversion recovery pulse is selectively or non-selectively irradiated to the imaging region. Next, in the labeling imaging sequence, an ASL pulse 53 that inverts the blood protons by 180 degrees is selectively irradiated to the labeling plane (for example, a region 1 - 2 cm from the lower end of the cerebellum). In the control imaging sequence, a pulse 53 that does not invert the blood protons is similarly irradiated to the labeling plane. Next, IR pulses 54 and 55 as inversion recovery pulses are selectively irradiated to the imaging region. Further, a fat suppression pulse 56 is irradiated to the imaging region, and then a blood flow signal suppression pulse 57 is non-selectively irradiated. An ASL pulse 53 is irradiated to the imaging region, and then at a timing after a predetermined delay time (PLD), a read-out process 58 is executed to obtain an NMR signal from the imaging region. Regarding the read-out process 58, specifically, an RF pulse is irradiated to the imaging region, whereby after exciting the spins, while applying a read-out gradient magnetic field and a phase encoding gradient magnetic field, an NMR signal is obtained through the receiving coil 14b.

[0065] Thus, in the labeling imaging sequence, an NMR signal of the imaging region in a state where the labeled blood has reached the imaging region is obtained, and the image processing unit 8 reconstructs a labeling image based on the obtained NNR signal. In addition, in the control imaging sequence, an NMR signal of the same imaging region is obtained without labeling the blood, and the image processing unit 8 reconstructs a control image based on the obtained NNR signal.

[0066] In addition, the measurement control unit (sequencer) 4 repeatedly executes the imaging sequence (multi-PLD ASL method) while changing the delay time (PLD) (the time from the end of the application of the ASL pulse 53 to the start of the read-out 58). Thus, the image processing unit 8 reconstructs a plurality of labeling images with different delay times (PLD).

[0067] The arithmetic processing unit 8 calculates a cerebral blood flow (CBF) image and an arterial transit time (ATT) image based on the obtained ASL image. (Cerebral blood flow is hereinafter referred to as CBF, and arterial transit time is hereinafter referred to as ATT.)

[0068] <Normalization of functional images>

[0069] Next, the arithmetic processing unit 8 applies anatomical normalization to the ATT image and the CBF image. Further, the arithmetic processing unit 8 performs ROI analysis and voxel analysis on the anatomically normalized ATT image and CBF image, and performs statistical analysis. Thus, the arithmetic processing unit 8 visually displays abnormal blood flow based on the results of the statistical analysis.

[0070] <Control and arithmetic processing of the arithmetic processing unit 8>

[0071] Use Figure 3 The flowchart of is used to specifically describe the control and arithmetic processing of the arithmetic processing unit 8. Figure 4 is a diagram showing an example of imaging conditions, Figure 5 shows the images obtained in the process of the flow of Embodiment 1, Figure 6 shows an example of a screen displaying the imaging results of Embodiment 1.

[0072] Regarding the Figure 3 control of the process and the operation of the arithmetic processing unit 8 are realized by software here, by the CPU in the arithmetic processing unit 8 reading and executing a program pre-stored in the disk 18 or the like. In addition, part or all of the arithmetic processing unit 8 can also be realized by hardware. For example, as long as a custom IC such as an ASIC (Application Specific Integrated Circuit) or a programmable IC such as an FPGA (Field-Programmable Gate Array) is used to form part or all of the arithmetic processing unit 8 and circuit design is performed to realize its function.

[0073] First, the operator places the subject 1 on the bed in the imaging area.

[0074] (Step S201)

[0075] The arithmetic processing unit 8 receives the setting of the imaging conditions of the imaging sequence of the multi-PLD ASL image and the 3D T1-weighted image and proton density-weighted image from the user via the operation unit 25. For example, it receives Figure 4Setting of the imaging conditions shown. The imaging region is the whole brain region in any one of the imaging sequences of multi-PLD ASL, TI-weighted image, and proton density-weighted image. In the imaging sequence of multi-PLD ASL, the delay time (PLD) is set here to seven kinds of times from 500 ms to 3000 ms.

[0076] (Step S202)

[0077] The arithmetic processing unit 8 instructs the sequencer (measurement control unit 4) 4 to execute under the imaging conditions set in step S201 Figure 2 the multi-PLD ASL imaging sequence shown. Thus, the sequencer 4 controls the gradient magnetic field generation unit 3, the irradiation coil 14a, the reception coil 14b, etc., and executes the imaging sequence. The arithmetic processing unit (CPU) 8 receives data from the reception system 6 and reconstructs the ASL images for each delay time (PLD) of the multi-PLD in the above imaging region.

[0078] In addition, before or after the multi-PLD ASL imaging sequence, the arithmetic processing unit 8 causes the sequencer (measurement control unit) 4 to execute, for example, a known gradient echo sequence. Thus, the arithmetic processing unit (CPU) 8 receives data from the reception system 6 and reconstructs the 3D T1-weighted image of the above imaging region. Furthermore, using a known GRACE sequence or the like that is the same as the ASL imaging sequence, the proton density-weighted image of the above imaging region is taken. Also, make the imaging region, field of view (FOV), and matrix (Matrix) of the proton density-weighted image the same as those of ASL. In different cases, registration, etc. of the ASL image and the proton density-weighted image can also be performed after reconstruction.

[0079] The arithmetic processing unit 8 stores the reconstructed respective image data in the disk 18.

[0080] (Step S203)

[0081] The arithmetic processing unit 8 receives the selection of the ASL image of the delay time (PLD) used in the calculation of CBF and ATT from the operator via the operation unit 25. In addition, the selection of this ASL image can also be performed automatically.

[0082] (Step S204)

[0083] The operation processing unit 8 calculates the ATT and CBF for each pixel based on the pixel values of the multi-PLD ASL image selected in step S203, and generates an ATT image and a CBF image. For example, using the theoretical formulas of the two-compartment model based on equations (1) and (2), the theoretical value of the ASL signal is calculated for each delay time (PLD). By curve fitting based on the non-linear least squares method, etc., the change of the theoretical value of the ASL signal based on the delay time (PLD) is fitted to the measured value of the ASL signal for each PLD (i.e., the pixel value of the ASL image taken for each PLD), thereby calculating the ATT and CBF for each pixel. Based on the ATT value and CBF value of each pixel, an ATT image showing the ATT value as the pixel value and a CBF image showing the CBF value can be generated. In addition, for the solution method of the theoretical formulas of equations (1) and (2), the well-known method described in the above-mentioned non-patent document 1 can be used, so the detailed description is omitted here.

[0084] [Equation 1]

[0085]

[0086] [Equation 2]

[0087]

[0088] In equation (1), t is time, and T 1m is the apparent T1 value in the blood vessel. T 1e is the apparent T1 value outside the blood vessel. M0 uses the magnetization (pixel value) of the proton density weighted image, M m0 is the value inside the capillary of the proton density weighted image, and M e0 is the value outside the blood vessel of the proton density bridging image. Other variables and notations are shown in Table 1 below.

[0089] [Table 1]

[0090]

[0091] In addition, here, the ATT and CBF are calculated by the non-linear least squares method based on the two-compartment model according to equations (1) and (2), but other well-known theoretical formula models and fitting methods can also be used for calculation.

[0092] (Step S205)

[0093] The arithmetic processing unit 8 performs processing (anatomical normalization) to deform the 3D T1-weighted image captured in step S202 to match the standard brain, and obtains a deformation field for making the 3D T1-weighted image match the standard brain.

[0094] Specifically, first, the arithmetic processing unit 8 performs the following processing (anatomical normalization (standardization)), that is, as Figure 5 shown in (a-1) to (a-3), using a known method, the positions and shapes of the structures (gray matter, white matter, etc.) in the 3D T1-weighted image are respectively moved to the coordinates of the structures of a predetermined standard brain. For example, normalization is performed using diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) processing, where this processing uses multiple parameters for non-linear transformation. In DARTEL processing, according to a pre-made standard brain template, the structures of the object brain are deformed (anatomical normalization is performed).

[0095] Next, the arithmetic processing unit 8 saves the normalization parameters (a set of values set as the parameters of the DARTEL deformation formula) used when deforming the object brain as deformation parameters.

[0096] Here, the method for obtaining the anatomical normalization parameters can be not only DARTEL processing, but also other known normalization methods.

[0097] (Step S206)

[0098] As Figure 5 shown in (b-1) and (b-2), the arithmetic processing unit 8 matches the positions of the ATT image and the CBF image generated in step S204 with the 3D T1-weighted image captured in step S202. For example, through a known registration technique, the ATT image and the CBF image are matched in position with the 3D T1-weighted image.

[0099] As a position matching method, for example, the following method is used, that is, 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 respectively calculated by the following mathematical formulas (3) to (6), and position matching is performed to maximize the mutual information MI (for example, rotation movement and translation movement are performed).

[0100] [Mathematical formula 3]

[0101]

[0102] [Equation 4]

[0103]

[0104] [Equation 5]

[0105]

[0106] [Equation 6]

[0107]

[0108] where a i is the pixel value (gray level) of a certain pixel in the ATT image or the CBF image, and b j is the pixel value (gray level) of the corresponding pixel in the 3D T1-weighted image. h(a i , b j ) is a two-dimensional histogram that counts the frequency of combinations of pixel values (a i , b j ) for all corresponding pixels and maps them. p(a i , b j ) represents the probability of the simultaneous occurrence of pixel values a i and b j (simultaneous probability), which is calculated by Equation (4). p(a i ), p(b j ) represent the probabilities of the occurrence of pixel values a i and pixel value b j respectively (peripheral probabilities), which are calculated by Equations (5) and (6) respectively.

[0109] (Step S207)

[0110] The arithmetic processing unit 8 uses the values of the deformation parameters saved in Step S205 to deform the ATT image and the CBF image after position matching in Step S206 respectively.

[0111] Thereby, anatomical normalization of ATT and CBF can be performed.

[0112] In addition, it is preferable to use the same processing as in Step S205 in the deformation processing. Here, DARTEL processing is used, but other known deformation processes can also be used.

[0113] (Step S208)

[0114] The arithmetic processing unit 8 analyzes the cerebral blood flow dynamics using the ATT image and the CBF image that have undergone anatomical normalization in step S207. For example, based on a healthy database (a database that has collected images of ATT images and CBF images of healthy people that have undergone anatomical normalization for, for example, 50 people), it calculates how much the normalized ATT image and CBF image of the subject calculated in step S207 deviate. For example, it calculates for each voxel as a z-score, and calculates the degree of deviation (deviation degree) of the normalized ATT image and CBF image of the subject from the healthy image as a numerical value (z-score).

[0115] (Step S209)

[0116] The arithmetic processing unit 8 displays the degree of deviation (Z-score) of the blood flow volume (CBF image) and / or the blood flow arrival time (ATT image) calculated in step S208 on the display 20. As a display screen, for example, as Figure 6 shown, it displays the sectional images and 3D images of the CBF image and the ATT image, and displays the z-score in gray scale thereon. The ATT image and / or CBF image to be displayed can be a normalized image or an image before normalization. In addition, in the present embodiment, Figure 6 the degree of deviation (Z-score) of the blood flow volume (CBF image) and / or the blood flow arrival time (ATT image) is displayed in gray scale, but of course it can also be displayed in color.

[0117] The above is the description of the processing flow of the arithmetic processing unit 8 in Embodiment 1.

[0118] As described above, in Embodiment 1 of the present invention, after the arithmetic processing unit 8 calculates the functional images (ATT image and CBF image) that do not include the shape information of the brain, it can perform anatomical normalization. Thereby, voxel analysis and statistical analysis can be performed on the functional images (ATT image, CBF image) that have undergone anatomical normalization. Therefore, based on the results of the statistical analysis, it is possible to visually display the blood flow that deviates abnormally from the images of healthy people, and it is possible to evaluate the cerebral blood flow volume in dementia, cerebrovascular disorders, epilepsy, etc. such as confirming a decrease in cerebral blood flow only by an MRI device.

[0119] In addition, in the above step S203, the structure is to receive the selection of the image (multi-PLD ASL image) used in the calculation of the functional images (ATT image and CBF image) from the user, but the arithmetic processing unit 8 can also automatically select according to a predetermined criterion.

[0120] In addition, it may also be the following structure, that is, the selected image is stored in a disk 18 or the like, and the operations after step S204 are performed at a later time.

[0121] <<Embodiment 2>>

[0122] The MRI apparatus according to Embodiment 2 of the present invention will be described.

[0123] Similar to Embodiment 1, the MRI apparatus according to Embodiment 2 applies anatomical normalization to the ATT image and the CBF image after calculating the ATT image and the CBF image. By performing ROI analysis and statistical analysis on the anatomically normalized ATT image and CBF image, abnormal blood flow is visually displayed.

[0124] Figure 7 This is the processing flow of the arithmetic processing unit 8 according to Embodiment 2. Hereinafter, Figure 7 the processing of the arithmetic processing unit 8 will be described. In addition, in Figure 7 this flow, the same step numbers as those in the flow of Figure 3 Embodiment 1 are assigned to the same steps, and the description thereof is omitted.

[0125] (Steps S201 to S207)

[0126] The arithmetic processing unit 8 performs the same steps S201 to S207 as those in steps S201 to S207 of Embodiment 1, and generates an anatomically normalized ATT image and a CBF image.

[0127] (Step S301)

[0128] The arithmetic processing unit 8 sets one or more ROIs (regions of interest) for the anatomically normalized ATT image and CBF image in step S207. The ROI may be the shape of a structure of a standard brain, or may be an ROI of a desired shape drawn by a user. For example, an ROI of a known brain atlas such as an AAL (Automated Anatomical Labeling) atlas can be selected, or a user can draw an ROI on the image.

[0129] The arithmetic processing unit 8 analyzes the cerebral blood flow dynamics within the ROI of the ATT image and the CBF image that have undergone anatomical normalization. For example, for each ROI, the degree of deviation of the ATT image and the CBF image from the healthy database is calculated as a z-score, and the deviation degrees of the blood flow arrival time and the blood flow volume from those of healthy individuals are analyzed. Specifically, for example, a z-score is obtained that represents how much the blood flow arrival time or the blood flow volume of the ROI of the subject being examined deviates from the average of the blood flow arrival times or the blood flow volumes of the ROIs of 50 people in the healthy database. Thus, in the case where there are multiple regions (ROIs), a value (z-score) representing the degree of deviation can be calculated for each ROI.

[0130] (Step S209)

[0131] The arithmetic processing unit 8 displays the ATT image and / or the CBF image and the z-score calculated for the ROI on the display 20 in the same manner as in step S209 of Embodiment 1. For example, the z-score is displayed by overlapping it at the position of the ROI of the ATT image or the CBF image.

[0132] As described above, in Embodiment 2 of the present invention, a z-score is calculated based on the results of statistical analysis and can be displayed together with the ATT image and / or the CBF image. Therefore, an ROI with a large z-score, that is, an ROI that deviates from the image of a healthy individual, can be visually displayed. Thus, similar to the aforementioned Embodiment 1, it is possible to evaluate, using only MRI, the cerebral blood flow volume in dementia, cerebrovascular disorders, epilepsy, etc., such as when a decrease in cerebral blood flow is confirmed.

[0133] In addition, in Embodiment 2, since an ROI is set and the analysis is performed within the ROI, it is possible to simply and with a small amount of calculation determine whether the blood flow of the ROI has decreased compared to that of a healthy individual.

[0134] In addition, in the present embodiment, an example is described in which a known ROI map such as the AAL atlas or an ROI drawn by the user is set as the ROI in step S301, but it can also be set by the arithmetic processing unit 8. For example, the arithmetic processing unit 8 can also extract regions with significant differences in blood flow in the healthy database and the disease database in advance and define them as ROIs, and set them as ROIs in step S301.

[0135] In addition, the arithmetic processing unit 8 can also compare the ATT image and / or the CBF image generated in step S207 with the images in the healthy database, extract regions with significant differences, and set them as ROIs in step S301.

[0136] <<Embodiment 3>>

[0137] The MRI apparatus according to Embodiment 3 will be described.

[0138] The MRI apparatus according to Embodiment 3 has a function of calculating the probability that the subject has a disease by using the ATT image and the CBF image.

[0139] Figure 8 FIG. is a flowchart showing the processing flow of the arithmetic processing unit 8 according to Embodiment 3. Hereinafter, Figure 8 each step will be described.

[0140] (Steps S201 to S208)

[0141] Similar to Embodiment 1 or Embodiment 2, the arithmetic processing unit 8 generates an anatomically normalized ATT image and a CBF image.

[0142] (Step S401)

[0143] The arithmetic processing unit 8 sets a given ROI, for example, by using a brain atlas or the like, for the ATT image and the CBF image in the healthy database (a database that has collected images obtained by anatomically normalizing the ATT image and the CBF image of healthy subjects) and the disease database (a database that has collected images obtained by anatomically normalizing the ATT image and the CBF image of subjects with diseases), and obtains feature amounts for each ROI. For example, as the feature amount, the average value of the pixel values (ATT value or CBF value) can be used. The arithmetic processing unit 8 previously obtains the boundary (discriminant surface) between the distributions of the feature amounts of the ATT images and the CBF images of a plurality of healthy subjects and the distributions of the feature amounts of a plurality of subjects with diseases. As a method for obtaining the discriminant surface, a known method (for example, a machine learning algorithm such as Support Vector Machine) is used.

[0144] The arithmetic processing unit 8 sets the above-mentioned given ROI for the normalized ATT image and the CBF image of the subject obtained in Step S208, calculates the feature amount, and calculates whether the calculated feature amount is on the healthy subject side or on the side of the subject with a disease with respect to the discriminant surface, and the distance from the discriminant surface. Based on the calculated distance, the probability that the subject belongs to a subject with a disease is calculated by a known method.

[0145] (Step S402)

[0146] The arithmetic processing unit 8 displays the normalized ATT image and the CBF image, and the result calculated in Step S401 (the probability that the subject belongs to a subject with a disease). At this time, the distance calculated in Step S401 may also be displayed together with the probability.

[0147] The above is the description of the processing flow of the arithmetic processing unit 8 in Embodiment 3.

[0148] According to the MRI apparatus of the above-described Embodiment 3, the probability that the subject has a disease can be calculated based on the ATT image and the CBF image, and thus the diagnosis by a doctor can be assisted.

[0149] In addition, the calculation method of the above discriminant surface (boundary) is not limited to machine learning algorithms such as Support Vector Machine, and any method can be used as long as it can discriminate whether the ATT image and the CBF image of a person with a disease and a healthy person are similar. For example, it can be a deep learning method other than Support Vector Machine, clustering, statistical analysis method, etc.

[0150] <<Embodiment 4>>

[0151] The MRI apparatus of Embodiment 4 will be described.

[0152] In Embodiment 4, instead of using multi-PLD ASL, an image (blood flow image) of a single PLD is used for blood flow analysis. That is, the CBF image is calculated based on the image of a single PLD, and anatomical normalization and statistical analysis are performed in the same manner as in Embodiments 1 and 2, and the results are displayed.

[0153] Figure 9 It is a flowchart showing the processing flow of the arithmetic processing unit 8 of Embodiment 4 of the present invention. Hereinafter, Figure 9 each step will be described. In addition, in Figure 9 the step, when the same processing as in Figure 3 the step is performed, the description will be omitted.

[0154] (Steps S901 to S903)

[0155] The arithmetic processing unit 8, Figure 3 in the same manner as steps S201 to S202, after receiving the setting of imaging conditions from the user via the operation unit 25, causes the imaging sequence of the multi-PLD ASL of Figure 2 Embodiment 1 to be executed with a single PLD (one delay time PLD), and captures a label image and a control image.

[0156] In addition, the arithmetic processing unit 8 captures a 3D T1-weighted image and a proton density-weighted image.

[0157] The arithmetic processing unit 8 selects the label image and the control image captured by a single PLD (one delay time PLD) as the images used in the calculation of CBF.

[0158] (Step S904)

[0159] The arithmetic processing unit 8 calculates a CBF image based on the image of a single PLD and the proton density. For example, the calculation method uses Equation (7), assigns the difference ΔM in pixel values between the label image and the control image, the delay time PLD, and the pixel value of PD to Equation (7), and calculates CBF (f in Equation (7)). The arithmetic processing unit 8 generates a CBF image by calculating the CBF value for each pixel value.

[0160] [Equation 7]

[0161]

[0162] ΔM: the change in signal between the label image and control image

[0163] M0: the proton density image

[0164] α: the efficiency of inversion

[0165] f: CBF

[0166] T 1a : the arterial blood water relaxation time

[0167] λ: the tissue blood partition coefficient of water

[0168] τ: the duration of labeling

[0169] PLD: the post labeling delay

[0170] δ α: the arterial transit time

[0171] (Steps S205 - S209)

[0172] The arithmetic processing unit 8 is the same as that in Embodiment 1 Figure 3 In Steps S205 - S209, perform anatomical normalization to make the 3D T1 - weighted image consistent with the standard brain, and use the deformation parameters at this time to perform anatomical normalization on the CBF image. Then, calculate the degree of deviation (z - score) indicating how much the normalized CBF image deviates from the CBF image in the healthy database. Display the calculated degree of deviation together with the CBF image.

[0173] The above is the description of the processing flow of the arithmetic processing unit 8 in Embodiment 4.

[0174] As described above, in Embodiment 4 of the present invention, it is possible to analyze the blood flow dynamics (CBF) using the CBF of a single PLD. Therefore, even in the case where it is difficult to capture images of multi - PLD ASL, according to Embodiment 4, it is possible to analyze the blood flow dynamics using the ASL image of a single PLD.

[0175] <<Embodiment 5>>

[0176] Describe the MRI apparatus of Embodiment 5.

[0177] In Embodiment 5, by deforming the ROI shape of the brain atlas to be consistent with the brain morphology of an individual (the subject), analysis in the individual brain space is performed. That is, instead of using the deformation parameters to deform the ATT image and CBF image to be consistent with the standard brain as in Embodiments 1 - 4, the deformation parameters are applied to the brain atlas in the opposite direction, thereby deforming the brain atlas to be consistent with the individual brain space of the subject, and using the ROI of the deformed brain atlas to analyze the ATT image and CBF image.

[0178] Figure 10 is a flowchart showing the processing flow of the arithmetic processing unit 8 in Embodiment 5 of the present invention. Hereinafter, for Figure 10 each step will be described in detail.

[0179] (Steps S201 - S205)

[0180] The arithmetic processing unit 8 performs Steps S201 - S205 in the same manner as in Embodiment 1.

[0181] (Step S601)

[0182] The arithmetic processing unit 8 deforms the brain atlas in the reverse direction using the deformation parameters saved in step S205. As a result, the brain atlas is deformed into a shape that matches the 3D T1-weighted image of the subject before normalization.

[0183] Furthermore, the arithmetic processing unit 8 registers the reversely deformed brain atlas with the ATT image and the CBF image. As a deformation method, for example, a method such as DARTEL is used.

[0184] (Step S602)

[0185] The arithmetic processing unit 8 calculates the deviation from a healthy person in the same manner as in step S208 using the brain atlas, the ATT image, and the CBF image that match the brain space of the subject calculated in step S601.

[0186] (Step S209)

[0187] The arithmetic processing unit 8 displays the deviation degree, the ATT image, and the CBF image in the same manner as in step S209 of Embodiment 1.

[0188] The above is the description of the processing flow of the arithmetic processing unit 8 in Embodiment 5.

[0189] As described above, in Embodiment 5 of the invention, the dynamic cerebral blood flow is analyzed by deforming the atlas in the reverse direction. It is possible to avoid the change in values caused by deforming CBF and ATT into a standard brain, and more accurate blood flow evaluation can be performed.

[0190] <<Embodiment 6>>

[0191] An MRI apparatus according to Embodiment 6 will be described.

[0192] In Embodiment 6, in addition to the ATT image, the CBF image, and the 3D T1-weighted image, a QSM (Quantitative Susceptibility Mapping) image is also used for statistical analysis.

[0193] Figure 11 is a flowchart showing the processing flow of the arithmetic processing unit 8 according to Embodiment 6 of the present invention. Hereinafter, each Figure 11 step will be described in detail.

[0194] (Steps S1101 to S1102)

[0195] The operation processing unit 8, in the same manner as steps S201 to S202 of Embodiment 1, accepts the setting of the 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 the setting of the imaging conditions for QSM images and performs imaging.

[0196] (Steps S203 to S205)

[0197] The operation processing unit 8 performs steps S203 to S205 of Embodiment 1, generates a CBF image and an ATT image, and obtains deformation parameters for aligning the 3D T1-weighted image with the standard brain.

[0198] (Step S1106)

[0199] The operation processing unit 8 performs QSM analysis. For example, using a known method, an overall magnetic field map with phase wrapping removed from the collected phase image is created, and further a local magnetic field map with the background magnetic field removed is obtained. In contrast, a susceptibility map (QSM) is obtained by estimating the dipole magnetic field.

[0200] (Steps S1107 to S1109)

[0201] The operation processing unit 8, in the same manner as steps S206 to S208 of Embodiment 1, in addition to the CBF image and the ATT image, matches the QSM image with the 3D T1-weighted image in position and then deforms it using the deformation parameters saved in step S205, thereby performing anatomical normalization.

[0202] Furthermore, the operation processing unit 8 performs statistical analysis using the anatomically normalized ATT image, CBF image, QSM image, and 3D T1-weighted image. For example, in the same manner as Embodiment 1, the degree of deviation from the images of healthy subjects is calculated.

[0203] The operation processing unit 8 can also perform analysis using multiple image types such as the QSM image and the 3D T1-weighted image in addition to the ATT image and the CBF image, and simultaneously calculate the degree of deviation of blood flow, the degree of deviation of magnetic susceptibility, and the degree of deviation of brain volume in the same region.

[0204] (Step S209)

[0205] The operation processing unit 8 displays the analysis results as images.

[0206] The above is the description of the processing flow of the operation processing unit 8 in Embodiment 6.

[0207] As described above, in Embodiment 6 of the present invention, multiple image types are used for analysis, whereby it is possible to simultaneously calculate the deviation of blood flow, the deviation of magnetic susceptibility, and the deviation of brain volume in the same region. By simultaneously analyzing blood flow, magnetic susceptibility, and brain volume, diagnosis can be easily performed.

[0208] The above describes Embodiments 1 to 6 of the present invention, but the present invention is not limited to these embodiments.

Claims

1. A magnetic resonance imaging apparatus, characterized in that, it has: a static magnetic field generating unit that applies a static magnetic field to an imaging space in which a subject is disposed; a gradient magnetic field generating unit that applies a gradient magnetic field to the imaging space; a transmitting coil that transmits a high-frequency magnetic field to the subject in the imaging space; a receiving coil that receives nuclear magnetic resonance signals 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 an arithmetic processing unit, the measurement control unit captures a morphological image showing a form and a functional image showing a function for the same imaging region of the subject, after the arithmetic processing unit performs a process of deforming the morphological image using a deformation parameter and moving the positions of one or more structures in the morphological image to the positions of structures in a predetermined standard form respectively, the arithmetic processing unit deforms the functional image using the value of the deformation parameter used when deforming the morphological image, thereby making the position of the region in the functional image coincide with the position of the corresponding region in the standard form, or deforms the standard form in the opposite direction using the value of the deformation parameter, 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, the arithmetic processing unit calculates the degree of deviation of the deformed functional image of the subject with respect to a healthy functional image obtained by deforming a functional image previously obtained for a healthy person so that the position of the corresponding region coincides with the standard form.

2. The magnetic resonance imaging apparatus according to claim 1, characterized in that, the arithmetic processing unit sets a region of interest for the deformed functional image of the subject, calculates the degree of deviation between the region of interest in the functional image and the corresponding region of the healthy functional image, and displays it to the user.

3. A magnetic resonance imaging apparatus, characterized in that, it has: a static magnetic field generating unit that applies a static magnetic field to an imaging space in which a subject is disposed; a gradient magnetic field generating unit that applies a gradient magnetic field to the imaging space; a transmitting coil that transmits a high-frequency magnetic field to the subject in the imaging space; a receiving coil that receives nuclear magnetic resonance signals 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 an arithmetic processing unit, the measurement control unit captures a morphological image showing a form and a functional image showing a function for the same imaging region of the subject, After performing processing to deform the morphological image using the deformation parameters and move the positions of one or more structures within the morphological image to the positions of the structures in a predetermined standard form respectively, the arithmetic processing unit deforms the functional image using the values of the deformation parameters used when deforming the morphological image, thereby making the positions of the regions within 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, thereby making the positions of the regions of the structures in the standard form coincide with the positions of the corresponding regions within the functional image. The arithmetic processing unit sets a region of interest for the deformed functional image, calculates a predetermined feature quantity for the functional image within the region of interest, and calculates on which side of the boundary between the distributions of the feature quantities of a plurality of healthy persons and the distributions of the feature quantities of a plurality of diseased persons the calculated feature quantity is located, and the distance from the boundary.

4. A magnetic resonance imaging apparatus, characterized in that it has: a static magnetic field generation unit that applies a static magnetic field to the imaging space where the subject is disposed; an inclined magnetic field generation unit that applies an inclined magnetic field to the imaging space; a transmission coil that transmits a high-frequency magnetic field to the subject in the imaging space; a reception coil that receives nuclear magnetic resonance signals from the subject; a measurement control unit that controls the inclined magnetic field generation unit, the transmission coil, and the reception coil to execute an imaging sequence and capture an image; and an arithmetic processing unit. The measurement control unit captures a morphological image showing a form and a functional image showing a function for the same imaging region of the subject. After performing processing to deform the morphological image using the deformation parameters and move the positions of one or more structures within the morphological image to the positions of the structures in a predetermined standard form respectively, the arithmetic processing unit deforms the functional image using the values of the deformation parameters used when deforming the morphological image, thereby making the positions of the regions within 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, thereby making the positions of the regions of the structures in the standard form coincide with the positions of the corresponding regions within the functional image. The functional image includes a cerebral blood flow image and a quantitative susceptibility mapping image, and the morphological image is a T1-weighted image. The arithmetic processing unit calculates the deviation degree of the images within the corresponding regions of the cerebral blood flow image, the quantitative susceptibility mapping image, and the T1-weighted image of the subject from the images within the corresponding regions of the cerebral blood flow image, the quantitative susceptibility mapping image, and the T1-weighted image of healthy persons.

Images