Magnetic resonance simulation device, magnetic resonance simulation method, and magnetic resonance imaging device

Abstract

To reduce a calculation amount in magnetic resonance simulation.SOLUTION: A magnetic resonance simulation device according to an embodiment comprises an acquisition unit, a calculation unit, and an output unit. The acquisition unit acquires phantom information about a phantom that includes a set of positions and physical values for a plurality of isochromats, and group information that classifies the plurality of isochromats for each isochromat in which physical properties of magnetization become identical when preset conditions are satisfied according to a pulse sequence with respect to the phantom information. The calculation unit collectively executes the magnetic resonance simulation for the isochromats classified into the same group in the plurality of isochromats, using the phantom information and the group information. The output unit outputs the simulation results obtained from the magnetic resonance simulation.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to a magnetic resonance simulation apparatus, a magnetic resonance simulation method, and a magnetic resonance imaging apparatus.

Background Art

[0002] Conventionally, in magnetic resonance simulation using the Bloch equation, it has been required to achieve both high accuracy and high speed. For example, in magnetic resonance simulation that emphasizes high speed, a matrix (also referred to as a combined transition matrix) obtained by multiplying a transition matrix (also referred to as a transformation matrix) indicating the conversion (state transition) of the state of particles corresponding to an RF pulse over the application period of the RF pulse is calculated before performing the magnetic resonance simulation. For example, using "isochromats", the isochromats are sorted by the value of the magnetic field in the Z direction after applying the gradient magnetic field, and for each position where the magnetic field in the Z direction has the same value, the state transition of the particles is calculated once. At this time, since sorting is required for each waveform (pattern of RF and gradient magnetic field) in the pulse sequence, there is a problem that the calculation of sorting takes time.

[0003] In addition, when there is no overlap in the magnetic field in the Z direction due to the sorting, the calculation of the sorting becomes wasted. In particular, in high-precision magnetic resonance simulation, such overlap may not exist. In addition, when the magnetic field in the Z direction is changed even slightly in magnetic resonance simulation, the calculation of the combined transition matrix has to be redone, so there is a problem that the pre-calculated combined transition matrix cannot be reused.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Non-Patent Documents

[0005]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to reduce the amount of calculation in magnetic resonance simulation. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the effects of each configuration shown in the embodiments described later can also be regarded as other problems.

Means for Solving the Problems

[0007] The magnetic resonance simulation apparatus according to the embodiment includes an acquisition unit, a calculation unit, and an output unit. The acquisition unit acquires phantom information regarding a phantom having a set of positions and physical values for a plurality of isochromats, and group information obtained by classifying the plurality of isochromats for each isochromat having the same physical characteristics of magnetization when satisfying preset conditions according to a pulse sequence with respect to the phantom information. The calculation unit uses the group information and the phantom information to collectively execute magnetic resonance simulation for the isochromats classified into the same group among the plurality of isochromats. The output unit outputs the simulation result obtained by the magnetic resonance simulation.

Brief Description of the Drawings

[0008]

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MODE FOR CARRYING OUT THE INVENTION

[0009] Hereinafter, with reference to the drawings, embodiments of a magnetic resonance (hereinafter referred to as MR (Magnetic Resonance)) simulation apparatus, an MR simulation method, and a magnetic resonance imaging (hereinafter referred to as MRI (Magnetic Resonance Imaging)) apparatus will be described in detail. FIG. 1 is a block diagram showing an example of the MR simulation apparatus 1.

[0010] (First Embodiment) Hereinafter, for the sake of specific description, the MR simulation apparatus 1 will be described as calculating (simulating) the magnetic resonance phenomenon using the Bloch equation that phenomenologically describes magnetic resonance according to classical mechanics. The MR simulation apparatus 1 performs numerical calculation of magnetic resonance by applying, for example, the Rodriguez rotation formula. Note that the numerical calculation of magnetic resonance by the MR simulation apparatus 1 is not limited to applying the Rodriguez rotation formula, and may be realized by other methods such as the Runge-Kutta method or the variable time Runge-Kutta method. Note that the method of simulating the magnetic resonance phenomenon is not limited to the application of the Bloch equation, and for example, the Bloch-Torrey equation, the Bloch-McConnell equation, etc. may be applied.

[0011] In the MR simulation for numerically calculating the magnetic resonance phenomenon, in each of a plurality of voxels in the phantom, a virtual molecule is set at the center of the voxel. Each of the plurality of voxels may correspond to, for example, an isochromat. At this time, the phantom has a set of positions and physical values for a plurality of isochromats. The physical values are, for example, the longitudinal relaxation time (T 1 ), the transverse relaxation time (T 2 ), and the external magnetic field inhomogeneity (ΔB 0 ). The external magnetic field inhomogeneity may have a static magnetic field inhomogeneity and a chemical shift. The phantom information regarding the phantom having a set of positions and physical values for a plurality of isochromats is stored in the memory 15.

[0012] The time evolution of magnetization in an isochromat (the time-series state transition of magnetization) is given by the Bloch equation. The time-series state transition is preset by the pulse sequence at each time point in the MR simulation. The pulse sequence is, for example, information (sequence information) that defines a procedure for imaging.

[0013] In the pulse sequence, for example, the strength of the current supplied by the gradient magnetic field power supply to the gradient magnetic field coil and the timing of supplying the current in a magnetic resonance imaging apparatus (hereinafter referred to as an MRI (Magnetic Resonance Imaging) apparatus), the intensity of the RF pulse supplied by the transmission circuit to the transmission coil and the timing of applying the RF pulse in the MRI apparatus, the timing of detecting the MR signal by the reception circuit in the MRI apparatus, etc. are defined.

[0014] The magnetic resonance simulation apparatus 1 includes, for example, an input interface 11, an output interface 13, a memory 15, and a processing circuit 17. Note that the magnetic resonance simulation apparatus 1 may include an external storage device (for example, various storages, various memories, etc.) that stores programs for realizing various functions executed in the processing circuit 17 and / or output results by the output function 177.

[0015] The external storage device may be, for example, a drive device that reads and writes various information between semiconductor memory elements such as HDD (Hard disk Drive), SSD (Solid State Drive), RAM (Random Access Memory), flash memory, optical discs such as CD (Compact Disc) and DVD (Digital Versatile Disc), portable storage media, etc.

[0016] The input interface 11 is electrically connected to, for example, the pulse sequence input function 2 in the MRI apparatus. At this time, the input interface 11 is connected to the output terminal of the pulse sequence input function 2 of the MRI apparatus.

[0017] Note that the input interface 11 is not limited to being connected to the MRI apparatus, and may be connected to, for example, an external device (e.g., a sequence generation device) that can generate and output data of a pulse sequence related to MR imaging. Also, the connection between the input interface 11 and various devices as the input source of the pulse sequence may be made via a network.

[0018] Further, the input interface 11 may have an input device that receives various instructions and information inputs from the user. At this time, the input interface 11 corresponds to, for example, a pointing device such as a mouse or a trackball, or an input device such as a keyboard. For example, the input interface 11 inputs a pulse sequence to be the subject of MR simulation according to a user's instruction.

[0019] Also, the input interface 11 may input an output instruction regarding the result of the MR simulation by the MR simulation apparatus 1 according to a user's operation. The output instruction is, for example, an instruction to output the result of the MR simulation to various external devices and / or a display at the output destination of the output interface 13. The input interface 11 corresponds to an input unit.

[0020] FIG. 2 is a diagram showing an example of a pulse sequence in the spin echo method. In FIG. 2, Tx indicates the magnitude of the transmitted RF pulse. Gz indicates the slice selection gradient magnetic field. Gy indicates the phase encoding gradient magnetic field. Gx indicates the readout gradient magnetic field. In the pulse sequence shown in FIG. 2, after the elapse of the echo time (TE) from the application of the 90° RF pulse, an MR signal is collected together with the application of the readout gradient magnetic field.

[0021] The MR signal is converted into a digital signal by an analog-to-digital converter (ADC). Although not shown in FIG. 2, for example, various pulses such as a preparation pulse may be appropriately applied according to the type of pulse sequence.

[0022] As shown in FIG. 2, the pulse sequence is represented by a combination of the application of a gradient magnetic field (Gradient), the application of a gradient magnetic field and an RF pulse (RF&Gradient), the application of a gradient magnetic field and analog-to-digital conversion (Gradient&ADC), and the time without the application of a gradient magnetic field (No-Gradient). For example, in the case of the spin echo method shown in FIG. 2, a combination of Gradient, RF&Gradient including the application of an RF pulse, Gradient&ADC related to the acquisition of the MR signal, and No-Gradient is repeated over the number of phase encoding steps. For each phase encoding, the Gradient and No-Gradient shown in FIG. 2 are calculated once (relaxation calculation), and Gradient&ADC is repeated the number of times of the ADC (number of samplings). The calculations of Gradient and No-Gradient are realized with fewer calculation times compared to, for example, RF&Gradient.

[0023] The pulse sequence input function 2 inputs Gradient such as a preparation pulse, RF&Gradient, Gradient such as a crusher, RF&Gradient, Gradient&ADC, and No-Gradient to the input interface 11 in time series. The input interface 11 outputs Gradient, RF&Gradient, RF&Gradient, Gradient&ADC, and No-Gradient to the processing circuit 17 in time series.

[0024] Note that the pulse sequence related to this embodiment is not limited to the pulse sequence shown in FIG. 2. In a general pulse sequence, combinations of RF&Gradient, Gradient, Gradient&ADC, and No-Gradient are appropriately repeated. Also, the input interface 11 may be connected, for example, to the output end of the transmission circuit and the output end of the gradient magnetic field power supply in an MRI apparatus. At this time, signals corresponding to the current and voltage corresponding to the intensity of the gradient magnetic field, the application timing of the gradient magnetic field, the current supplied to the transmission coil and the application timing of the RF pulse, the detection timing of the MR signal, etc. are input from the MRI apparatus to the input interface 11.

[0025] The output interface 13 is connected, for example, to the sampling data output function 3 of the sequence control circuit in an MRI apparatus. The output interface 13 outputs the signal value calculated by the output function 217 to the sampling data output function 3 under the control of the control function 171. Note that the connection destination of the output interface 13 is not limited to the sequence control circuit, and may be various devices (for example, various displays, various analysis devices, various image generation devices, etc.) that can use the output value for display, analysis, processing, etc.

[0026] Also, the connection between the output interface 13 and various devices that are the output destinations of the output values may be made via a network. Further, the output interface 13 may have a display for displaying the output value or the like under the control of the control function 171. At this time, the display is a known display device such as a liquid crystal display.

[0027] The memory 15 is, for example, a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or an integrated circuit memory device that stores various information. In addition to HDDs, SSDs, etc., the memory 15 may also be a drive device that reads and writes various information to and from portable storage media such as CDs (Compact Discs), DVDs (Digital Versatile Discs), flash memories, and semiconductor memory elements such as RAMs (Random Access Memories).

[0028] The memory 15 stores various programs related to the execution of, for example, a control function 171, an acquisition function 173, a calculation function 175, an output function 177, etc. Further, the memory 15 stores various data generated by the execution of, for example, the control function 171, the acquisition function 173, the calculation function 175, the output function 177, etc. The memory 15 corresponds to the storage unit.

[0029] For example, the memory 15 stores the pulse sequence input via the input interface 11. Further, the memory 15 stores the correspondence table generated at the time of phantom design or at the time of phantom setting to the MR simulation device 1. The correspondence table may be generated at the time of the first waveform processing in the pulse sequence. The correspondence table is generated, for example, by dividing the pulse sequence into four types of gradient magnetic fields and classifying a plurality of isochromats. The correspondence table has group information based on the classification for a plurality of isochromats. The group information is T 1 (longitudinal relaxation time), and T 2 (transverse relaxation time), and ΔB 0Information indicating that, among isochromats having the same (non-uniform external magnetic field) (hereinafter referred to as physically identical isochromats), the intensity of the gradient magnetic field applied to the physically identical isochromats belongs to the same group. Among the physically identical isochromats, the isochromats to which the intensity of the gradient magnetic field applied to the physically identical isochromats is the same are isochromats having the same physical characteristics of magnetization (hereinafter referred to as physically identical isochromats).

[0030] Specifically, the group information is information obtained by classifying a plurality of isochromats for each isochromat having the same physical characteristics when a preset condition (hereinafter referred to as a predetermined condition) is satisfied according to a pulse sequence. The predetermined condition includes that no gradient magnetic field is applied along at least one direction in the pulse sequence. More specifically, the group information is based on the physical values and the application positions of the gradient magnetic fields in a plurality of isochromats. Hereinafter, for the sake of specific explanation, the predetermined condition will be described as including that no gradient magnetic field is applied in each of two directions orthogonal to each other. One group corresponds to a set of isochromats having the same magnetization state transition among a plurality of isochromats. The correspondence table is stored in the memory 15 in association with phantom information regarding the phantom that is the source of the correspondence table.

[0031] Specifically, during the application period of the RF pulse (RF&Gradient: hereinafter referred to as the RF application period) and the collection period of the MR signal (Gradient&ADC: hereinafter referred to as the collection period) in the pulse sequence, for each direction (Gx, Gy, Gz) of the gradient magnetic field, a plurality of isochromats are grouped into isochromats having the same characteristics. This grouping is performed for each of a plurality of phantoms. Note that the period targeted by the present invention is not limited to these two periods, and for example, this grouping can be performed for Gradient or No-Gradient, and the same processing as RF&Gradient can be performed (assuming that RF is always 0).

[0032] For example, in this grouping, first, corresponding to setting at least one of Gx, Gy, and Gz to zero among the positions (x, y, z) of each of the plurality of isochromats in one phantom, at least one of the coordinates of the positions x, y, and z is set to zero. As a result, for one isochromat, for example, seven combinations are generated with respect to physical characteristics (position and physical value). Next, for each of the plurality of isochromats, a lookup table generation process is executed such that the isochromats for which each of the seven combinations is the same are classified into the same group, and the isochromats for which this is not the case (each of the seven combinations is not the same) are not classified into the same group. Thereby, a lookup table having a plurality of entries corresponding to the plurality of isochromats and the combination in which the gradient magnetic field becomes zero is generated. This process can also be speeded up, for example, by defining a hash function for which this reduction of collisions can be expected and performing classification according to the hash key (hash value). Alternatively, it is also possible to perform collision determination by defining a comparison function corresponding to the combination and using sorting using the comparison function. Regarding this sorting process, unlike using the magnetic field strength in the Z direction itself in each isochromat as described in Japanese Patent Laid-Open No. 9-47442 for the comparison function, it is clearly different from the known technique in that sorting is performed using physical characteristics that do not depend on the strength of the gradient magnetic field in a direction in which the gradient magnetic field in the Z direction is not zero (and the strength of those gradient magnetic fields can be arbitrarily changed). If the magnetic field strength in the Z direction itself is used for the comparison function for sorting as described in Japanese Patent Laid-Open No. 9-47442, the sorting process has to be redone every time the gradient magnetic field strength changes.

[0033] For example, when there is no applied gradient magnetic field (No-Gradient), it is assumed that a lookup table #1 (which may also be referred to as an entry number) corresponding to No-Gradient is used. At this time, the position of the isochromat is arbitrary, and the physical values (T 1 , T 2 , ΔB 0A plurality of isochromats with the same [[ID=]] are grouped as characteristic-identical isochromats. For the plurality of characteristic-identical isochromats, a first index (index within the look-up table) is assigned according to the difference in physical values. Thereby, the plurality of first indices and the plurality of characteristic-identical isochromats are associated with each other and registered as look-up table #1. At this time, a collision process for checking whether the same isochromat has already been registered is performed. If it is desired to speed up this collision process, it is also possible to use the hash method when generating the look-up table. The plurality of first indices are assigned so as to be different for isochromats with different physical values. As described above, in the case where no gradient magnetic field is applied (No-Gradient), grouping is realized such that the magnetization state transitions are the same for the plurality of isochromats.

[0034] Also, when only the gradient magnetic field Gx is applied, let the look-up table be #2. At this time, for the isochromats at the same position x, a second index (index within the look-up table) is assigned to the plurality of characteristic-identical isochromats according to the difference in physical values. The plurality of second indices and the plurality of characteristic-identical isochromats are associated with each other and registered as look-up table #2 after the collision process. In other words, the plurality of second indices are assigned so as to be different for isochromats with different physical values and different x coordinates of the isochromats. Thereby, in the case where only the gradient magnetic field Gx is applied, grouping is realized such that the magnetization state transitions are the same for the plurality of isochromats.

[0035] Also, when only the gradient magnetic field Gy is applied, assume that the look-up table is #3. At this time, for isochromats at the same position y, a plurality of isochromats with the same characteristics are assigned a third index (index within the look-up table) according to the difference in physical values. The plurality of third indices and the plurality of isochromats with the same characteristics are associated with each other and registered as look-up table #3 after collision processing. In other words, the plurality of third indices are assigned differently to isochromats with different physical values and y coordinates of the isochromats. Thereby, when only the gradient magnetic field Gy is applied, grouping in which the state transition of magnetization is the same is realized for a plurality of isochromats.

[0036] Also, when only the gradient magnetic field Gz is applied, assume that the look-up table is #4. At this time, for isochromats at the same position z, a plurality of isochromats with the same characteristics are assigned a fourth index (index within the look-up table) according to the difference in physical values. The plurality of fourth indices and the plurality of isochromats with the same characteristics are associated with each other and registered as look-up table #4 after collision processing. In other words, the plurality of fourth indices are assigned differently to isochromats with different physical values and z coordinates of the isochromats. Thereby, when only the gradient magnetic field Gz is applied, grouping in which the state transition of magnetization is the same is realized for a plurality of isochromats.

[0037] The above grouping, i.e., the generation of the correspondence table, is executed by, for example, the control function 171 for each of a plurality of phantoms before the execution of the MR simulation (such as at the time of phantom setting or at the time of the first waveform processing in the pulse sequence). Note that the grouping may be executed by a grouping function (not shown) instead of the control function 171. Further, the grouping process is an example, and is not limited to performing grouping on the given data. For example, phantoms arranged as pre-aligned data may be used. Alternatively, since it is often difficult to group phantoms that do not consider the present invention with a small number of groups, a known clustering algorithm (for example, the K-Means method or the MeanShift method) may be appropriately used with such phantoms as input, and the generated cluster group may be used as a look-up table as a characteristic-identical isochromat group. By these, the group information is stored in the memory 15 as a correspondence table (look-up table) that holds, for example, which isochromat belongs to which group.

[0038] FIG. 3 is a diagram showing an example of an outline of grouping (classification) of isochromats using a hash function. As shown in FIG. 3, the position and physical value of each of a plurality of isochromats are input to the hash function along with a position set to zero according to the gradient magnetic field regarding the duplication of the magnetic field strength. Thereby, the hash function outputs hash keys #1 to #4. The hash keys #1 to #4 are associated with the first to fourth indexes. Thereby, hash tables #1 to 4 are generated. The position and physical value of the isochromat corresponding to the hash key are separately recorded, and a collision that could not be avoided using the hash key is resolved using the separately recorded position and physical value (a collision in which different positions and physical values are regarded as the same by the same hash key). Thereby, each of the plurality of isochromats is associated with which group it is included in by the index. That is, the index corresponds to a number for identifying a plurality of groups with one hash key. Note that the hash table described above corresponds to a correspondence table regarding isochromats with the same characteristics and will be described as a look-up table with the same characteristics in order to avoid limitations due to hash processing.

[0039] FIG. 4 is a diagram showing an example of criteria for group information. As shown in FIG. 4, the group information includes physical values (T 1 , T 2 , ΔB 0) and is based on the position where the gradient magnetic field is applied (the isochromat position). For example, when the predetermined condition is no application of the gradient magnetic field (No gradients), for a plurality of isochromats, under the characteristic-identical look-up table #1, for characteristic-identical isochromats having the same physical value regardless of the isochromat position, the first index is assigned. Also, when the predetermined condition is that only the Gx gradient magnetic field is applied, for a plurality of isochromats, under the characteristic-identical look-up table #2, for characteristic-identical isochromats having the same x in the isochromat position and the same physical value, the second index is assigned. Also, when the predetermined condition is that only the Gy gradient magnetic field is applied, for a plurality of isochromats, under the characteristic-identical look-up table #3, for characteristic-identical isochromats having the same y in the isochromat position and the same physical value, the third index is assigned. Also, when the predetermined condition is that only the Gz gradient magnetic field is applied, for a plurality of isochromats, under the characteristic-identical look-up table #4, for characteristic-identical isochromats having the same z in the isochromat position and the same physical value, the fourth index is assigned. The table shown in FIG. 4 is generated for each phantom and stored in the memory 15.

[0040] The processing circuit 17 controls the overall operation of the MR simulation device 1 in response to the electrical signal of the input operation output from the input interface 11. For example, as hardware resources, the processing circuit 17 has processors such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), and a GPU (Graphics Processing Unit), and memories such as a ROM (Read Only Memory) and a RAM.

[0041] Further, the processing circuit 17 may be implemented by an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), other Complex Programmable Logic Devices (CPLDs), Simple Programmable Logic Devices (SPLDs), or the like.

[0042] The processing circuit 17 has, for example, a control function 171, an acquisition function 173, a calculation function 175, and an output function 177. In the control function 171, the acquisition function 173, the calculation function 175, and the output function 177, each function is stored in the memory 15 in the form of a program executable by a computer. The processing circuit 17 executes the control function 171, the acquisition function 173, the calculation function 175, and the output function 177 by a processor that executes the program developed in the memory.

[0043] That is, the processing circuit 17 corresponds to a processor that realizes the functions corresponding to the respective programs by reading and executing the programs from the memory 15. In other words, the processing circuit 17 in the state where each program is read has the functions corresponding to the read programs. Note that the functions in the control function 171, the acquisition function 173, the calculation function 175, and the output function 177 are not limited to being realized by a single processing circuit. It is also possible to configure the processing circuit by combining a plurality of independent processors, and each processor executes a program to realize the functions in the control function 171, the acquisition function 173, the calculation function 175, and the output function 177. The processing circuit 17 that realizes the control function 171, the acquisition function 173, the calculation function 175, and the output function 177 is an example of a control unit, an acquisition unit, a calculation unit, and an output unit.

[0044] The processing circuit 17 controls each function of the processing circuit 17 by the control function 171. Specifically, the processing circuit 17 reads out the control program stored in the memory 15, expands it onto the memory in the processing circuit 17, and controls each part of the MR simulation apparatus 1 according to the expanded control program. Regarding the acquisition function 173, calculation function 175, and output function 177 realized by the processing circuit 17, they will be described later along the procedure of executing the process of MR simulation (hereinafter referred to as MR simulation process).

[0045] The overall configuration of the MR simulation apparatus 1 has been described above. Hereinafter, the procedure of the MR simulation process will be described. FIG. 5 is a flowchart showing an example of the procedure of the MR simulation process.

[0046] (MR simulation process) (Step S501) The processing circuit 17 acquires phantom information used in the MR simulation process by the acquisition function 173. Specifically, the acquisition function 173 acquires the phantom information from the memory 15. Note that the phantom information is not limited to being acquired from the memory 15, and may be acquired from an external device such as a sequence generation device via the input interface 11.

[0047] (Step S502) The processing circuit 17 acquires group information for the acquired phantom information by the acquisition function 173. Specifically, the acquisition function 173 acquires the group information related to the phantom information used in the MR simulation process from the memory 15. Note that the group information is not limited to being acquired from the memory 15, and when the phantom information is acquired from an external device such as a sequence generation device, it may be acquired from the external device via the input interface 11.

[0048] (Step S503) The processing circuit 17 acquires a pulse sequence related to MR imaging by means of the acquisition function 173. Note that the pulse sequence may be generated according to a user's instruction via the input interface 11. The acquisition function 173 stores the acquired pulse sequence in the memory 15. Note that the acquisition of the pulse sequence can be carried out at any timing, such as before the processing of step S501 or before the processing of step S502.

[0049] FIG. 6 is a diagram showing an example of the acquired pulse sequence and the type of gradient magnetic field in a part of the pulse sequence. The alphabets A, B, and C shown in FIG. 6 indicate the processing content. Further, the groups shown in FIG. 6 indicate an example of group information applied in MR simulation during the RF application period and the acquisition period. As shown in FIG. 6, the pulse sequence to be processed is divided into an RF pulse (with-RF), an ADC sampling (with-ADC), and three other parts. In the present embodiment, the parts with RF and with ADC and the other parts are classified according to the gradient magnetic field.

[0050] (Step S504) The processing circuit 17 selects group information in the RF application period based on the pulse sequence by means of the calculation function 175. Further, the calculation function 175 selects group information in the acquisition period based on the pulse sequence. Thereby, the calculation function 175 groups a plurality of isochromats into a plurality of groups based on the group information for the application period (RF application period) of the RF pulse in the pulse sequence. At this time, an index is used as an index for identifying the groups in the plurality of groups. Hereinafter, as an example, the determination of the group information in the pulse sequence shown in FIG. 6 will be described. Note that for the parts of the pulse sequence (other parts) that cannot be covered by the group information, known other acceleration methods may be applied each time.

[0051] FIG. 7 is a diagram showing an example of group information selected for the type of gradient magnetic field during the RF application period in the pulse sequence shown in FIG. 6. As shown in FIG. 6, during the RF application period, a gradient magnetic field Gz along the z direction is applied together with the RF pulse. Therefore, as shown in FIG. 7, the computing function 175 determines #4 as the group information during the RF application period.

[0052] FIG. 8 is a diagram showing an example of group information selected for the type of gradient magnetic field during the acquisition period in the pulse sequence shown in FIG. 6. As shown in FIG. 6, during the acquisition period, a gradient magnetic field Gx along the x direction is applied. Therefore, as shown in FIG. 8, the computing function 175 selects #2 as the group information during the acquisition period.

[0053] (Step S505) The processing circuit 17, by the computing function 175, for each of the plurality of indexes in the selected group information (#4 in FIG. 6), that is, for each of the plurality of characteristic-identical isochromats, integrates a matrix (transition matrix) showing the state transition of magnetization over the number of samples of the RF pulse to calculate the product of the matrices by this integration (hereinafter referred to as the combined transition matrix). That is, the computing function 175 calculates a combined transition matrix showing the state transition of the magnetization of the isochromat over the RF application period for each of the plurality of groups over the number of samples of the RF pulse.

[0054] Thereby, the computing function 175 calculates a combined transition matrix for each of the plurality of indexes (elements) in the group information #3. The calculated combined transition matrix corresponds to a matrix that transitions the state of magnetization in an isochromat (characteristic-identical isochromat) at the same magnetic field strength over the RF application period. The computing function 175 stores a plurality of combined transition matrices corresponding to the plurality of elements included in the selected group information in the memory 15.

[0055] (Step S506) The processing circuit 17 starts the calculation of the MR simulation according to the pulse sequence by the computing function 175. Specifically, as shown in FIG. 6, during the period before the application of RF, the computing function 175 calculates the time evolution of magnetization for a plurality of isochromats according to the processing content A. Hereinafter, the processing content A regarding the calculation of Gradient and No-Gradient will be described.

[0056] FIG. 9 is a diagram showing an example of the outline of the processing content A. Each of the left circles in FIG. 9 indicates each ISC of a plurality of isochromats of the BTE before the time evolution. Also, each of the right circles in FIG. 9 indicates each ISC of a plurality of isochromats of the ATE after the time evolution. The magnetization of each ISC of the plurality of isochromats is calculated (in one step) without iterative calculation by applying physical values and the position of the isochromat to the analytical solution of the Bloch equation. The magnetization calculated for each ISC of the plurality of isochromats is stored in the memory 15.

[0057] Next, as shown in FIG. 6, the processing circuit 17 applies the combined transition matrix calculated according to the processing content B for each of the plurality of groups to each of the grouped plurality of isochromats according to the processing content B during the RF application period by the computing function 175, and calculates the state transition (time evolution) of the magnetization of the plurality of isochromats. FIG. 10 is a diagram showing an example of the outline of the processing content B. Hereinafter, the processing content B regarding the calculation of RF&Gradient will be described.

[0058] The left circle in FIG. 10 shows each of the plurality of isochromats ISC of BARF immediately before the application of the RF pulse. Also, the rectangle enclosing the four isochromats indicates a plurality of grouped isochromats, that is, characteristic-identical isochromats. The arrow CTM in FIG. 10 indicates that the combined transition matrix is calculated for each group (for each group made unique by the look-up table). Also, the right circle in FIG. 10 shows each of the plurality of isochromats ISC of AARF after the end of the RF application period. The combined transition matrix is calculated for each group (in units of entries in the look-up table) by integrating the transition matrix over the number of applications of the RF pulse in step S505. Note that the calculation for generating the combined transition matrix may be executed before the execution of the processing content B in this step, as shown in FIG. 10.

[0059] In this step, the magnetization of each of the plurality of isochromats ISC is calculated in one step without repeated calculation for each of the plurality of isochromats ISC, as shown in FIG. 10, by applying the combined transition matrix for each group (in units of entries in the look-up table) to the magnetization immediately before the RF application period for each group, based on the physical value and the position of the isochromat. Thus, the computing function 175 executes magnetic resonance simulations in a batch for the isochromats classified into the same group among the previous isochromats, using the group information and the phantom information. The computing function 175 stores the magnetization calculated for each of the plurality of isochromats ISC in the memory 15.

[0060] Subsequently, as shown in FIG. 6, the processing circuit 17 successively executes processing content A and processing content B by the computing function 175. Thereafter, the computing function 175 executes the processing content A again. In addition, during the collection period (Readout), the computing function 175 calculates the sampling of the MR signal by the processing content C using a plurality of magnetizations corresponding to a plurality of isochromats immediately before the sampling pulse of the MR signal. As a result, the ADC of the MR signal during the collection period (Readout) is performed.

[0061] FIG. 11 is a diagram showing an example of the outline of the processing content C. Hereinafter, the processing content C regarding the calculation of Gradient & ADC will be described. Prior to the collection of the MR signal, the processing circuit 17 calculates the sum of the magnetizations (transverse magnetization M xy and longitudinal magnetization M z ) included in each of the plurality of groups in the group information #2 by the computing function 175. That is, the computing function 175 calculates the sum of the magnetizations in a plurality of isochromats for each of the plurality of groups for the collection period of the MR signal in the pulse sequence. As a result, the computing function 175 calculates the sum (hereinafter referred to as group magnetization) M (g)xy of a plurality of magnetizations (specifically, transverse magnetization) corresponding to each of the plurality of groups as shown in FIG. 11 for each characteristic-identical isochromat (group).

[0062] Next, the processing circuit 17 calculates the transverse relaxation for the group magnetization M 0 from the start point t (g)xy of the collection period to the sampling point t for each of the plurality of groups g using the following formula (1) to which physical values for each group are applied. The calculation of the transverse relaxation for the group magnetization M (g)xy (t 0 ) corresponds to the processing content A.

[0063]

Equation

[0064] According to Equation (1), the computing function 175 calculates the group magnetization M (g)xy (t) at any sampling point t. B on the right side of Equation (1) z is G for each characteristic-identical isochromat x (t)×x + G y (t)×y + G z (t)×z + ΔB 0 represented by (B z = G x (t)×x + G y (t)×y + G z (t)×z + ΔB 0 ). Note that isochromats satisfying the condition of being the same at each time in the time series can be grouped even if they move. In this case, (as in Equation (6) of [arXiv:2009.02789] A Beginner’s Guide to Bloch Equation Simulations of Magnetic Resonance Imaging Sequences) by using the higher-order moment of Bz, it is also possible to apply Equation (1). Next, as shown in Equation (2) below, the computing function 175 calculates the ADC value (ADCs(t)) at any sampling point t by taking the sum of the group magnetization M (g)xy (t) over the number of groups g.

[0065]

Number

[0066] Note that when the total number of MR signal samplings is N as shown in FIG. 11 ADC the ADC value at the n-th (1 ≤ n ≤ N ADC ) sampling of the MR signal can be obtained by substituting the elapsed time from t 0 into the n-th sampling time t for Equations (1) and (2).

[0067] As described above, the computing function 175 calculates the sum of the magnetizations in a plurality of isochromats for each of a plurality of groups with respect to the acquisition period of the MR signal in the pulse sequence. Next, the computing function 175 calculates the relaxation of the sum calculated using the physical values for each sampling interval of the MR signal and for each group. Subsequently, the computing function 175 calculates the MR signal (ADC value (ADCs(t))) collected for each sampling interval by adding the calculated relaxations across the groups. The computing function 175 stores the calculated ADC value (ADCs(t)) in the memory 15. The process described in this step is repeated for the total number of phase encodings.

[0068] (Step S507) When the MR simulation for the pulse sequence is completed, the processing circuit 17 outputs the simulation result (calculation result) obtained by the MR simulation by the output function 177. For example, the output function 177 outputs the ADC value to an external device such as an MRI device or an analysis device and / or to a display.

[0069] The MR simulation device 1 according to the first embodiment described above acquires phantom information regarding a phantom having a set of positions and physical values for a plurality of isochromats, and group information obtained by classifying, for each isochromat for which the physical characteristics of magnetization are the same when a preset condition is satisfied according to a pulse sequence, with respect to the phantom information. Using the group information and the phantom information, magnetic resonance simulation is collectively performed on the isochromats classified into the same group among the plurality of isochromats, and the simulation results obtained by the magnetic resonance simulation are output. Further, in the MR simulation device 1 according to the first embodiment, the physical values include a longitudinal relaxation time, a transverse relaxation time, and an external magnetic field inhomogeneity, the condition includes that no gradient magnetic field is applied along at least one direction, and the group information is based on the physical values and the application positions of the gradient magnetic fields in the plurality of isochromats. Further, in the MR simulation device 1 according to the first embodiment, the condition includes that no gradient magnetic field is applied in each of two mutually orthogonal directions.

[0070] Also, the MR simulation device 1 according to the first embodiment groups a plurality of isochromats into a plurality of groups based on the group information for the application period of the RF pulse in the pulse sequence, calculates a transition matrix indicating the state transition of the magnetization of the isochromats for each of the plurality of groups over the number of samples of the RF pulse, applies the transition matrix corresponding to each of the plurality of groups to each of the grouped plurality of isochromats to calculate the state transition of the magnetization in each of the plurality of isochromats, calculates the sum of the magnetizations in the plurality of isochromats for each of the plurality of groups for the collection period of the magnetic resonance signal in the pulse sequence, calculates the relaxation of the calculated sum using the physical values for each sampling interval of the magnetic resonance signal and for each group, and adds the calculated relaxations over the groups to calculate the magnetic resonance signal collected for each sampling interval.

[0071] According to the MR simulation apparatus 1 according to the first embodiment, from these facts, during periods with a high effect of speeding up calculations, such as when there is no gradient magnetic field or when the gradient magnetic field is uniaxial, a table can be created that avoids duplicate calculations at positions with the same magnetic field strength in terms of the phantom unit, and by using an appropriate table based on the pulse sequence during the MR simulation, calculations during the application of the RF pulse and calculations during the collection (ADC) of the MR signal can be executed without performing duplicate calculations. Furthermore, according to the MR simulation apparatus 1 according to the first embodiment, since the frequency of updating the phantom is often overwhelmingly less than the frequency of updating the pulse sequence, for the same phantom, the table created in terms of the phantom unit can be diverted according to the pulse sequence.

[0072] Therefore, according to the MR simulation apparatus 1 according to the first embodiment, since calculations during the RF application period and the collection period can be executed in terms of groups according to the phantom, the amount of calculation in the MR simulation, that is, the calculation cost in the Bloch simulation, can be reduced. Thereby, according to the MR simulation apparatus 1 according to the first embodiment, the calculation of the MR simulation can be speeded up.

[0073] (First Application Example) In the group information in the first embodiment, grouping was performed for the case where two of the three-axis gradient magnetic fields are zero, but in the first application example, grouping is also performed for the case where one of the three-axis gradient magnetic fields is zero. In this case, as shown in FIG. 12, the group information shown in FIG. 4 is generated for each phantom.

[0074] FIG. 12 is a diagram showing an example of group information according to the first application example. As shown in FIG. 12, the group information has group information #5 to #6 in addition to the group information #1 to #4 shown in FIG. 4. When a gradient magnetic field of only Gx and Gy is applied under a predetermined condition, for a plurality of isochromats, under the look-up table #5, for isochromats having the same x and the same y in terms of the position of the isochromat and the same physical value, the same fifth index is assigned to the characteristic-identical isochromats. In other words, the plurality of fifth indices are assigned differently to isochromats having different physical values and different x and y coordinates of the isochromat.

[0075] Also, when a gradient magnetic field of only Gy and Gz is applied under a predetermined condition, for a plurality of isochromats, under the look-up table #6, for isochromats having the same y and the same z in terms of the position of the isochromat and the same physical value, the same sixth index is assigned to the characteristic-identical isochromats. In other words, the plurality of sixth indices are assigned differently to isochromats having different physical values and different y and z coordinates of the isochromat. Further, when a gradient magnetic field of only Gz and Gx is applied under a predetermined condition, for a plurality of isochromats, under the look-up table #7, for isochromats having the same z and the same x in terms of the position of the isochromat and the same physical value, the same seventh index is assigned to the characteristic-identical isochromats. In other words, the plurality of seventh indices are assigned differently to isochromats having different physical values and different z and x coordinates of the isochromat. The table shown in FIG. 12 is generated for each phantom and stored in the memory 15.

[0076] Since the procedure of the MR simulation process in this first application example is the same as that in FIG. 5, the description thereof is omitted. Further, according to the MR simulation apparatus 1 according to the first application example, if there is a portion corresponding to any of the group information #5 to #7 shown in FIG. 12 in the pulse sequence, the calculation can be executed in units of groups without performing duplicate calculations. Therefore, according to the MR simulation apparatus 1 according to the first application example, the amount of calculation in the MR simulation can be further reduced. Other effects are the same as those in the first embodiment, and thus the description thereof is omitted.

[0077] (Second Application Example) This application example is to calculate the ADC value in consideration of the sensitivity of the receiving coil. For example, in this application example, the processing circuit 17, by means of the calculation function 175, calculates the sensitivity of the receiving coil according to the position of the isochromat, that is, using weighted addition, to calculate the group magnetization M (g)xy (t). The subsequent calculation process is the same as that in the first embodiment, and thus the description thereof is omitted.

[0078] Note that the consideration of the sensitivity of the receiving coil is not limited to the above method. For example, by calculating in advance the sensitivity regarding the group according to the position of the isochromat, the calculation function 175 can calculate the sum on the right side shown in Equation (4) as the sum of products of the sensitivity according to the group and the group magnetization M (g)xy (t), that is, the weighted addition of the group magnetization M (g)xy (t) by the sensitivity. Since known methods can be appropriately used for the consideration of the sensitivity of the receiving coil, the description thereof is omitted.

[0079] (Third Application Example) This application example generates a physical value map (hereinafter referred to as a smoothed map) with smoothed physical value changes (data) by applying a clustering process to a map showing the distribution of physical values generated from a phantom (hereinafter referred to as a physical value map). Next, this application example executes an MR simulation process using the smoothed map to obtain a map of nuclear magnetization M 0 along the direction of the static magnetic field (M 0to generate a (map) with higher image quality. The nuclear magnetization M along the static magnetic field direction 0 is the nuclear magnetization in the equilibrium state in the static magnetic field (hereinafter referred to as equilibrium magnetization).

[0080] The input interface 11 inputs a user's instruction regarding whether to execute the clustering process. Prior to the execution of the MR simulation process, for example, the display in the output interface 13 displays a user interface for specifying whether to execute the clustering process and the number of clusters in the clustering process. Based on the user's instruction via the input interface 11, the user determines whether to execute the clustering process. When the clustering process is executed, an instruction for the number of clusters is input based on the user's instruction via the input interface 11. Note that the instruction for the number of clusters is not limited to the input of the value of the number of clusters, and may be, for example, an operation of selecting the number of clusters between 16 and 128. As described above, the input interface 11 inputs whether to execute the clustering process and the number of clusters in the clustering process for the map of physical values based on the phantom.

[0081] The processing circuit 17, by means of the computing function 175, executes a clustering process so as to realize the number of clusters for the physical value map prior to the execution of the magnetic resonance simulation. Note that any method may be used for the above clustering process, and for example, the k-means method or the MeanShift method can be used. Thereby, the computing function 175 generates a smooth characteristic map. The smooth characteristic map corresponds to, for example, a map of physical values that is clustered into 16 to 128 with respect to the map of physical values and in which the change (data) of the physical values is smoothed. Various programs related to the clustering process are stored in the memory 15 in advance. The computing function 175 stores the smooth characteristic map in the memory 15.

[0082] The processing circuit 17 executes magnetic resonance simulation using a map of clustered physical values (smoothed map) by the computing function 175. In the magnetic resonance simulation, the smoothed map is used, for example, for generation of group information shown in FIG. 4, selection of group information, and the like. By the computing function 175, as a simulation result, a map M of nuclear magnetization along the static magnetic field direction 0 is calculated. The output function 177 outputs, as a simulation result, a map M of nuclear magnetization along the static magnetic field direction 0 .

[0083] FIG. 13 is a diagram showing an example of an outline of a process in which a map M is generated with clustering processing 0 . As shown in FIG. 13, prior to the MR simulation process MRSP, clustering processing is executed on the physical value map PPM based on the phantom. By the MR simulation process MRSP using the smoothed map SPM generated by the clustering processing, a map M 0 is generated.

[0084] The MR simulation apparatus 1 according to this application example inputs the possibility of executing clustering processing on the map of physical values based on the phantom and the number of clusters in the clustering processing, and prior to execution of magnetic resonance simulation, executes clustering processing so as to realize the number of clusters for the map of physical values, and executes magnetic resonance simulation using the clustered map of physical values. Thereby, according to the MR simulation apparatus 1 according to this application example, a map M of nuclear magnetization along the static magnetic field direction 0 can be generated with higher accuracy. Since other effects are the same as those in the embodiment, the description is omitted.

[0085] (Second Embodiment) The second embodiment is to implement the MR simulation process by various functions realized by the MR simulation device 1 described in the first embodiment using an MRI device. The MRI device according to the second embodiment has various components related to the execution of the MR simulation process. FIG. 14 is a diagram showing an example of the configuration of the MRI device 100 according to the second embodiment.

[0086] As shown in FIG. 14, the MRI device 100 includes a static magnetic field magnet 101, a static magnetic field power supply 102, a gradient magnetic field coil 103, a gradient magnetic field power supply 104, a bed 105, a bed control circuit 106, a transmission coil 107, a transmission circuit 108, a reception coil 109, a reception circuit 110, a sequence control circuit 120, and a computer 130 (also referred to as an image processing device, an information processing device, etc.). Note that the MRI device 100 does not include a subject P (for example, a human body). Also, the configuration shown in FIG. 14 is merely an example. For example, each part in the sequence control circuit 120 and the computer 130 may be appropriately integrated or separated. The computer 130 is mounted on, for example, a console.

[0087] The static magnetic field magnet 101 is a magnet formed in a hollow substantially cylindrical shape and generates a static magnetic field in the internal space. The static magnetic field magnet 101 is, for example, a superconducting magnet or the like and is excited by receiving current supply from the static magnetic field power supply 102. The static magnetic field power supply 102 supplies current to the static magnetic field magnet 101. Note that the static magnetic field magnet 101 may be a permanent magnet, and in this case, the MRI device 100 may not include the static magnetic field power supply 102. Also, the static magnetic field power supply 102 may be provided separately from the MRI device 100.

[0088] The gradient magnetic field coil 103 is a coil formed in a hollow substantially cylindrical shape and is disposed inside the static magnetic field magnet 101. The gradient magnetic field coil 103 is formed by combining three coils corresponding to the X, Y, and Z axes orthogonal to each other. These three coils are individually supplied with current from the gradient magnetic field power supply 104 to generate a gradient magnetic field whose magnetic field strength changes along the X, Y, and Z axes. The gradient magnetic fields of the X, Y, and Z axes generated by the gradient magnetic field coil 103 are, for example, the slice gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr. The gradient magnetic field power supply 104 supplies current to the gradient magnetic field coil 103.

[0089] The bed 105 includes a top plate 105a on which the subject P is placed. Under the control of the bed control circuit 106, the top plate 105a is inserted into the cavity (imaging opening) of the gradient magnetic field coil 103 with the subject P placed thereon. Usually, the bed 105 is installed such that its longitudinal direction is parallel to the central axis of the static magnetic field magnet 101. The bed control circuit 106 drives the bed 105 to move the top plate 105a in the longitudinal direction and the vertical direction under the control of the computer 130.

[0090] The transmission coil 107 is disposed inside the gradient magnetic field coil 103 and generates a high-frequency magnetic field upon receiving an RF pulse from the transmission circuit 108. The transmission circuit 108 supplies an RF pulse corresponding to the Larmor frequency determined by the type of the target atom and the magnetic field strength to the transmission coil 107.

[0091] The reception coil 109 is disposed inside the gradient magnetic field coil 103 and receives an MR signal emitted from the subject P due to the influence of the high-frequency magnetic field. When the reception coil 109 receives the MR signal, it outputs the received MR signal to the reception circuit 110.

[0092] Note that the above-described transmission coil 107 and reception coil 109 are merely examples. The transmission coil 107 and reception coil 109 may be configured by combining one or more of a coil having only a transmission function, a coil having only a reception function, or a coil having both transmission and reception functions.

[0093] The reception circuit 110 detects the MR signal output from the reception coil 109 and generates MR data based on the detected MR signal. Specifically, the reception circuit 110 generates MR data by digitally converting the MR signal output from the reception coil 109. Further, the reception circuit 110 transmits the generated MR data to the sequence control circuit 120. Note that the reception circuit 110 may be provided on the gantry device side including the static magnetic field magnet 101, the gradient magnetic field coil 103, and the like.

[0094] The sequence control circuit 120 performs imaging of the subject P by driving the gradient magnetic field power supply 104, the transmission circuit 108, and the reception circuit 110 based on the sequence information transmitted from the computer 130. Hereinafter, for the sake of specificity in the description, the sequence information will be described as a pulse sequence regarding the subject P. The pulse sequence is generated in advance or generated according to a user's instruction via the input device 141 and stored in the storage circuit 132.

[0095] The sequence control circuit 120 executes the pulse sequence to collect MR data from the subject P. The sequence control circuit 120 stores the collected MR data in the storage circuit 132.

[0096] The sequence control circuit 120 drives the gradient magnetic field power supply 104, the transmission circuit 108, and the reception circuit 110 to image the subject P, and receives MR data from the reception circuit 110. The sequence control circuit 120 transfers the received MR data to the computer 130. The sequence control circuit 120 is realized by, for example, an integrated circuit such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), an electronic circuit such as a CPU or an MPU. The sequence control circuit 120 corresponds to a sequence control unit.

[0097] The computer 130 performs overall control of the MRI apparatus 100 and generation of images, etc. The computer 130 includes a storage circuit 132, an input device 141, a display 143, and a processing circuit 150. The processing circuit 150 includes an interface function 131, a control function 133, a generation function 134, an acquisition function 173, a calculation function 175, and an output function 177.

[0098] Each processing function performed by the interface function 131, the control function 133, the generation function 134, the acquisition function 173, the calculation function 175, and the output function 177 is stored in the storage circuit 132 in the form of a program executable by the computer 130. The processing circuit 150 is a processor that reads out and executes the program from the storage circuit 132 to realize the functions corresponding to the respective programs. In other words, the processing circuit 150 in the state of having read out each program has each function shown in the processing circuit 150 of FIG. 14.

[0099] In FIG. 14, although the processing functions performed by the interface function 131, control function 133, generation function 134, acquisition function 173, calculation function 175, and output function 177 are described as being realized by a single processing circuit 150, it is also possible to configure the processing circuit 150 by combining a plurality of independent processors, and each processor realizes the functions by executing a program. In other words, each of the above-described functions may be configured as a program, and even when one processing circuit 150 executes each program, or when a specific function is implemented in a dedicated independent program execution circuit, it may be the case.

[0100] The term "processor" used in the above description means, for example, a CPU, GPU, or a circuit such as an application-specific integrated circuit, programmable logic device (e.g., simple programmable logic device (SPLD), complex programmable logic device (CPLD), and field programmable gate array (FPGA)). The processor realizes the functions by reading and executing the program stored in the storage circuit 132.

[0101] Note that instead of storing the program in the storage circuit 132, it may be configured to directly incorporate the program into the circuit of the processor. In this case, the processor realizes the functions by reading and executing the program incorporated in the circuit. Note that the bed control circuit 106, transmission circuit 108, reception circuit 110, sequence control circuit 120, etc. are also similarly constituted by the above-described electronic circuits such as the processor.

[0102] The storage circuit 132 stores MR data received by the processing circuit 150 having the interface function 131, various data acquired by the acquisition function 173, various image data generated by the generation function 134, a program related to the calculation process used in the calculation function 175, the calculation result calculated by the calculation process, a program related to the output process used in the output function 177, the output result output by the output function 177, and the like.

[0103] For example, the memory circuit 132 stores the phantom information and group information acquired by the acquisition function 173. The memory circuit 132 stores the ADC value (ADCs(t)) calculated by the calculation function 175. The memory circuit 132 stores the simulation result output by the output function 177.

[0104] Also, the memory circuit 132 stores the MR data (also referred to as k-space data) arranged in the k-space by the control function 133. For example, the memory circuit 132 is realized by a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, or the like. The memory circuit 132 may be referred to as a memory.

[0105] The input device 141 receives various instructions and information inputs from the user. The input device 141 is realized, for example, by a trackball, a switch button, a mouse, a keyboard, a touch pad that performs an input operation by touching an operation surface, a touch screen in which a display screen and a touch pad are integrated, a non-contact input circuit using an optical sensor, a voice input circuit, and the like. The input device 141 is electrically connected to the processing circuit 150, converts the input operation received from the user into an electrical signal, and outputs it to the processing circuit 150. The input device 141 corresponds to the input unit.

[0106] Note that in this specification, the input device 141 is not limited to only those equipped with physical operation components (input interfaces) such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the MRI device 100 and outputs this electrical signal to the control circuit is also included in the example of the input device 141. The input device 141 corresponds to the input unit and may be referred to as an input interface, an operating device, or the like.

[0107] The display 143 receives inputs such as imaging conditions through a GUI (Graphical User Interface) under the control of a processing circuit 150 having a control function 133, and displays images and the like generated by the processing circuit 150 having a generation function 134. Further, the display 143 displays the analysis results described later by an analysis function 137. The display 143 is realized by, for example, a CRT display, a liquid crystal display, an organic EL display, an LED display, a plasma display, or any other display device such as a monitor known in the art. The display 143 corresponds to a display unit.

[0108] The processing circuit 150 transmits sequence information to the sequence control circuit 120 through an interface function 131 and receives MR data from the sequence control circuit 120. Further, when receiving the MR data, the processing circuit 150 having the interface function 131 stores the received MR data in a storage circuit 132. The processing circuit 150 realizing the interface function 131 corresponds to an interface unit.

[0109] The processing circuit 150 performs overall control of the MRI apparatus 100 through a control function 133, and controls imaging, image generation, image display, and the like. For example, the processing circuit 150 having the control function 133 receives an input of imaging conditions (imaging parameters, etc.) on the GUI, and generates sequence information according to the conditions of the saturation pulse set according to the received imaging conditions. Further, the processing circuit 150 having the control function 133 transmits the generated sequence information to the sequence control circuit 120. The processing circuit 150 realizing the control function 133 corresponds to a control unit.

[0110] The processing circuit 150 generates an image by the generation function 134 reading k-space data from the storage circuit 132 and performing a reconstruction process such as a Fourier transform on the read k-space data. For example, the generation function 134 generates an MR image of the subject P based on the MR data. The generation function 134 stores the generated MR image in the storage circuit 132. Since the generation of the MR image can appropriately utilize known methods, the description is omitted. The processing circuit 150 that realizes the generation function 134 corresponds to the generation unit.

[0111] Further, the generation function 134 may generate an image (hereinafter referred to as a simulation image) by arranging the ADC value (ADCs(t)), which is the simulation result calculated by the calculation function 175, in k-space and performing a reconstruction process such as a Fourier transform. At this time, the generation function 134 stores the simulation image in the storage circuit 132. Since the generation of the simulation image is the same as the generation of the MR image, the description is omitted.

[0112] The processing circuit 150 acquires, by the acquisition function 173, the pulse sequence executed in the sequence control circuit 120 from the storage circuit 132. Further, the acquisition function 173 acquires phantom information and group information. Since these acquisition processes are the same as those in the first embodiment, the description is omitted.

[0113] When the pulse sequence executed in the sequence control circuit 120 satisfies a predetermined condition, the processing circuit 150 collectively executes magnetic resonance simulations for the isochromats related to each group belonging to the group information corresponding to the condition by the calculation function 175. Since the magnetic resonance simulation executed by the calculation function 175 is the same as that in the first embodiment, the description is omitted.

[0114] The processing circuit 150 outputs, via the output function 177, the simulation results obtained from the magnetic resonance simulation calculated by the calculation function 175 to, for example, the storage circuit 132. Further, the output function 177 outputs the simulation image generated by the generation function 134 to the display 143. Since the output processing by the output function 177 is the same as that in the first embodiment, the description thereof is omitted.

[0115] The MR simulation process executed by the MRI apparatus 100 of the present embodiment configured as described above is the same as that in the first embodiment, and thus the description thereof is omitted. In the magnetic resonance simulation process in the present embodiment, an ADC value (ADCs(t)) calculated by the calculation function 175 may be arranged in the k-space and subjected to reconstruction processing such as Fourier transform to generate a simulation image. At this time, the output function 177 stores the generated simulation image in the storage circuit 132 and / or displays it on the display 143.

[0116] The MRI apparatus 100 according to the second embodiment described above acquires phantom information regarding a phantom having a set of positions and physical values for a plurality of isochromats, and group information obtained by classifying the plurality of isochromats for each isochromat whose physical characteristics of magnetization are the same when satisfying preset conditions according to a pulse sequence with respect to the phantom information. Using the group information and the phantom information, magnetic resonance simulation is collectively executed for the isochromats classified into the same group among the plurality of isochromats, and the simulation results obtained from the magnetic resonance simulation are output. The effects of the MRI apparatus 100 according to the second embodiment are the same as those in the first embodiment, and thus the description thereof is omitted.

[0117] When realizing the technical idea in the embodiment by a nuclear magnetic resonance simulation method, the nuclear magnetic resonance simulation method includes: phantom information regarding a phantom having a set of positions and physical values for a plurality of isochromats, and for the phantom information, group information obtained by classifying the plurality of isochromats into groups such that the physical characteristics of magnetization are the same for each isochromat that satisfies preset conditions according to a pulse sequence. Using the group information and the phantom information, nuclear magnetic resonance simulation is collectively performed on the isochromats classified into the same group among the plurality of isochromats, and the simulation result obtained by the nuclear magnetic resonance simulation is output. Since the procedure and effect of the MR simulation process are the same as those in the first embodiment, the description thereof is omitted.

[0118] As a modification of the first embodiment, the nuclear magnetic resonance simulation apparatus may include: a display unit that displays a user interface for inputting whether or not to execute clustering processing and / or the number of clusters in the clustering processing with respect to a map of the physical values based on the phantom information regarding a phantom having a set of positions and physical values for a plurality of isochromats; an input unit that receives an input to the user interface; and a calculation unit that performs clustering processing on the map of the physical values based on the input result to the input unit, and uses the map of the physical values after the clustering processing to perform nuclear magnetic resonance simulation on the plurality of isochromats, and an output unit that outputs the simulation result obtained by the nuclear magnetic resonance simulation. Since the display unit, the input unit, the calculation unit, the output unit, the functions executed by these units, the procedure and effect of the MR simulation process are the same as those in the first embodiment and the second embodiment, etc., the description thereof is omitted.

[0119] According to at least one of the embodiments, application examples, etc. described above, in nuclear magnetic resonance simulation, the amount of calculation can be reduced, that is, the calculation cost can be reduced.

[0120] Although some embodiments have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, changes, and combinations of embodiments can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, as well as in the invention described in the claims and the equivalent scope thereof.

Explanation of Reference Numerals

[0121] 1 Magnetic resonance simulation device 2 Pulse sequence input function 3 Sampling data output function 11 Input interface 13 Output interface 15 Memory 17 Processing circuit 100 Magnetic resonance imaging device 101 Static magnetic field magnet 102 Static magnetic field power supply 103 Gradient magnetic field coil 104 Gradient magnetic field power supply 105 Bed 105a Top plate 106 Bed control circuit 107 Transmitting coil 108 Transmitting circuit 109 Receiving coil 120 Sequence control circuit 130 Computer 131 Interface function 132 Memory circuit 133 Control function 141 Input device 143 Display 150 Processing circuit 171 Control function 173 Acquisition function 175 Calculation function 177 Output function

Claims

1. an acquisition unit that acquires phantom information regarding a phantom having a set of positions and physical values ​​for a plurality of isochromats, and group information in which the plurality of isochromats are classified according to isochromats that have the same physical properties of magnetization when a preset condition is satisfied according to a pulse sequence, for the phantom information; a calculation unit that collectively performs a magnetic resonance simulation on the isochromats classified into the same group among the plurality of isochromats by using the group information and the phantom information; an output unit that outputs a simulation result obtained by the magnetic resonance simulation; A magnetic resonance simulation device comprising:

2. The physical values ​​include a longitudinal relaxation time, a transverse relaxation time, and an external magnetic field inhomogeneity; the condition includes no gradient magnetic field being applied along at least one direction; The group information is based on the physical value and the application position of the gradient magnetic field.

2. The magnetic resonance simulation apparatus according to claim 1.

3. The condition includes that no gradient magnetic field is applied in each of two directions perpendicular to each other.

3. The magnetic resonance simulation apparatus according to claim 2.

4. The calculation unit is grouping a plurality of isochromats into a plurality of groups based on the group information for an application period of an RF pulse in the pulse sequence; Calculating a transition matrix indicating a state transition of the magnetization of the isochromat for each of the plurality of groups over the number of samples of the RF pulse; applying the transition matrix calculated for each of the plurality of groups to each of the plurality of grouped isochromats to calculate the magnetization state transition in each of the plurality of isochromats; calculating a sum of magnetizations in the plurality of isochromats for each of the plurality of groups during an acquisition period of a magnetic resonance signal in the pulse sequence; calculating a relaxation of the sum using the physical values ​​for each sampling interval of the magnetic resonance signals and for each group; calculating a magnetic resonance signal acquired for each sampling interval by summing the relaxations over the groups; 4. The magnetic resonance simulation device according to claim 1.

5. an input unit for inputting whether or not a clustering process is to be performed on the map of physical values ​​based on the phantom and the number of clusters to be used in the clustering process, The calculation unit is Prior to the execution of the magnetic resonance simulation, the clustering process is performed on the map of physical values ​​to realize the number of clusters; performing the magnetic resonance simulation using the map of physical values ​​on which the clustering process has been performed; 2. The magnetic resonance simulation apparatus according to claim 1.

6. Acquire phantom information on a phantom having a set of positions and physical values ​​for a plurality of isochromats, and group information in which the plurality of isochromats are classified for each isochromat that has the same physical property of magnetization when a preset condition is satisfied according to a pulse sequence for the phantom information; Using the group information and the phantom information, a magnetic resonance simulation is performed collectively for the isochromats classified into the same group among the plurality of isochromats; outputting a simulation result obtained by the magnetic resonance simulation; A magnetic resonance simulation method comprising:

7. an acquisition unit that acquires phantom information regarding a phantom having a set of positions and physical values ​​for a plurality of isochromats, and group information in which the plurality of isochromats are classified according to isochromats that have the same physical properties of magnetization when a preset condition is satisfied according to a pulse sequence, for the phantom information; a calculation unit that collectively performs a magnetic resonance simulation on the isochromats classified into the same group among the plurality of isochromats by using the group information and the phantom information; an output unit that outputs a simulation result obtained by the magnetic resonance simulation; A magnetic resonance imaging apparatus comprising:

8. a display unit that displays a user interface for inputting whether or not to execute clustering processing and / or the number of clusters in the clustering processing for a map of physical values ​​based on phantom information regarding a phantom having positions and a set of physical values ​​for a plurality of isochromats; an input unit that accepts input to the user interface; a calculation unit that performs a clustering process on the map of physical values ​​based on the input result to the input unit, and executes a magnetic resonance simulation for the plurality of isochromats using the map of physical values ​​after the clustering process; an output unit that outputs a simulation result obtained by the magnetic resonance simulation; A magnetic resonance simulation device comprising:

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