JP7237612B2 - Magnetic resonance imaging device and image processing device - Google Patents

Description

TECHNICAL FIELD Embodiments of the present invention relate to a magnetic resonance imaging apparatus and an image processing apparatus.

2. Description of the Related Art In recent years, as a technology related to magnetic resonance imaging apparatuses, expectations are rising for a pixel mapping method that displays a color map in which colors representing tissue quantitative values such as T1, T2, and T2* are assigned to pixels. As such a technique, for example, a T1 mapping method for displaying a T1 map image in which a color representing a T1 value is assigned to each pixel is known. However, clinically, it is often difficult to diagnose tissue properties using such conventional map images.

JP 2014-036855 A JP 2014-208286 A JP 2016-097241 A

The problem to be solved by the present invention is to provide information for assisting diagnosis of tissue characterization.

A magnetic resonance imaging apparatus according to an embodiment includes a storage unit, an acquisition unit, and an identification unit. The storage unit stores longitudinal magnetization and transverse magnetization of the tissue during a period before reaching a steady state when a pulse sequence is executed to bring the longitudinal magnetization and transverse magnetization of the tissue to a steady state by continuously irradiating RF pulses. Accumulate and store reference information based on magnetic resonance signals that reflect behavior. The acquisition unit acquires data based on the magnetic resonance signals in a period before reaching the steady state by executing the pulse sequence. The identifying unit compares the data collected by the collecting unit with reference information stored in the storage unit, and identifies reference information having a predetermined relationship.

FIG. 1 is a diagram showing a configuration example of a magnetic resonance imaging apparatus according to a first embodiment. FIG. 2 is a diagram illustrating an example of a balanced SSFP sequence according to the first embodiment; 3 is a diagram illustrating an example of reference information stored by a storage circuit according to the first embodiment; FIG. 4 is a diagram illustrating an example of reference information stored by a storage circuit according to the first embodiment; FIG. FIG. 5 is a diagram showing a first example of image generation by the generation function according to the first embodiment. FIG. 6 is a diagram showing a first example of image generation by the generation function according to the first embodiment. FIG. 7 is a diagram illustrating a second example of image generation by the generation function according to the first embodiment. FIG. 8 is a diagram illustrating a second example of image generation by the generation function according to the first embodiment. FIG. 9 is a diagram illustrating an example of setting a region of interest by a specific function according to the first embodiment; FIG. 10 is a diagram illustrating an example of derivation of approximated curves by the specific function according to the first embodiment. FIG. 11 is a diagram illustrating an example of identification of reference information by the identification function according to the first embodiment. FIG. 12 is a diagram showing an example of information display by the discrimination function according to the first embodiment. FIG. 13 is a flow chart showing the procedure of processing performed by the MRI apparatus according to the first embodiment. FIG. 14 is a diagram showing a configuration example of an image processing apparatus according to the second embodiment. FIG. 15 is a diagram showing a configuration example of an MRI apparatus according to the third embodiment. FIG. 16 is a flow chart showing the procedure of processing performed by the MRI apparatus according to the third embodiment. FIG. 17 is a diagram showing the relationship between the excitation angle, tissue quantification value, and off-resonance by the RF pulse according to the third embodiment. FIG. 18 is a diagram showing an example of measurement and calculation of MR signals according to the third embodiment.

Hereinafter, embodiments of a magnetic resonance imaging apparatus and an image processing apparatus will be described in detail with reference to the drawings.

(First embodiment)
FIG. 1 is a diagram showing a configuration example of a magnetic resonance imaging apparatus according to a first embodiment.

For example, as shown in FIG. 1, a magnetic resonance imaging (MRI) apparatus 100 according to the present embodiment includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power supply 3, a whole body (WB) Coil 4, transmitting circuit 5, local coil 6, receiving circuit 7, RF (Radio Frequency) shield 8, pedestal 9, bed 10, ECG (Electrocardiogram) sensor 11, ECG circuit 12, interface 13, display 14, memory circuit 15, Processing circuits 16-19 are provided.

The static magnetic field magnet 1 generates a static magnetic field in an imaging space in which the subject S is arranged. Specifically, the static magnetic field magnet 1 is formed in a hollow, substantially cylindrical shape (including one having an elliptical cross section perpendicular to the central axis of the cylinder), and a static magnetic field is applied to the space inside the cylinder. generate. For example, the static magnetic field magnet 1 has a substantially cylindrical cooling container and a magnet such as a superconducting magnet immersed in a coolant (for example, liquid helium) filled in the cooling container. Here, for example, the static magnetic field magnet 1 may use a permanent magnet to generate a static magnetic field.

The gradient magnetic field coil 2 is arranged inside the static magnetic field magnet 1 and applies a gradient magnetic field to the imaging space in which the subject S is arranged. Specifically, the gradient magnetic field coil 2 is formed in a hollow, substantially cylindrical shape (including one having an elliptical cross-sectional shape perpendicular to the central axis of the cylinder), and the space inside the cylinder is perpendicular to each other. A gradient magnetic field is generated along each of the X-, Y-, and Z-axes. Here, the X-axis, Y-axis, and Z-axis constitute an apparatus coordinate system unique to the MRI apparatus 100 . For example, the Z axis coincides with the axis of the cylinder of the gradient coil 2 and is set along the magnetic flux of the static magnetic field generated by the static magnetic field magnet 1 . The X-axis is set along the horizontal direction perpendicular to the Z-axis, and the Y-axis is set along the vertical direction perpendicular to the Z-axis.

The gradient magnetic field power supply 3 supplies current to the gradient magnetic field coil 2 to generate gradient magnetic fields along the X, Y, and Z axes in the space inside the gradient magnetic field coil 2 . In this way, the gradient magnetic field power supply 3 generates gradient magnetic fields along the X-axis, Y-axis, and Z-axis, respectively, thereby generating gradient magnetic fields along the readout direction, the phase-encode direction, and the slice direction, respectively. can be done. Axes along the readout direction, the phase encoding direction, and the slice direction constitute a logical coordinate system for defining a slice region or volume region to be imaged. Hereinafter, the gradient magnetic field along the readout direction is called the readout gradient magnetic field, the gradient magnetic field along the phase encode direction is called the phase encode gradient magnetic field, and the gradient magnetic field along the slice direction is called the slice gradient magnetic field. .

These gradient magnetic fields are superimposed on the static magnetic field generated by the static magnetic field magnet 1 and used to give spatial position information to magnetic resonance (MR) signals. Specifically, the readout gradient magnetic field changes the frequency of the MR signal according to the position in the readout direction, thereby imparting positional information along the readout direction to the MR signal. Also, the phase-encoding gradient magnetic field changes the phase of the MR signal along the phase-encoding direction, thereby imparting positional information in the phase-encoding direction to the MR signal. When the imaging region is a slice region, the slice gradient magnetic field is used to determine the direction, thickness, and number of slice regions. By changing the phase of the MR signal using the MR signal, positional information along the slice direction is added to the MR signal.

The WB coil 4 is arranged inside the gradient magnetic field coil 2, irradiates an RF pulse (high-frequency magnetic field pulse) to an imaging space in which the subject S is placed, and generates from the subject S under the influence of the RF pulse. RF coil for receiving MR signals. Specifically, the WB coil 4 is formed in a hollow, substantially cylindrical shape (including one having an elliptical cross section perpendicular to the central axis of the cylinder), and the RF pulse supplied from the transmission circuit 5 is Based on the signal, the space within the cylinder is irradiated with an RF pulse. The WB coil 4 also receives MR signals generated from the subject S under the influence of RF pulses, and outputs the received MR signals to the receiving circuit 7 .

A transmission circuit 5 outputs an RF pulse signal corresponding to the Larmor frequency to the WB coil 4 . Specifically, the transmission circuit 5 includes an oscillator, a phase selector, a frequency converter, an amplitude modulator, and an RF amplifier. The oscillator generates an RF wave (radio frequency) signal at a resonance frequency (Larmor frequency) unique to the nuclei of interest placed in a static magnetic field. A phase selector selects the phase of the RF wave signal. A frequency converter converts the frequency of the RF wave signal output from the phase selector. The amplitude modulator generates an RF pulse signal by modulating the amplitude of the RF wave signal output from the frequency converter with, for example, a sinc function waveform. The RF amplifier power-amplifies the RF pulse signal output from the amplitude modulator and outputs it to the WB coil 4 .

The local coil 6 is an RF coil that receives MR signals generated from the subject S. Specifically, the local coil 6 is attached to the subject S placed inside the WB coil 4, receives the MR signal generated from the subject S under the influence of the RF pulse irradiated by the WB coil 4, It outputs the received MR signal to the receiving circuit 7 . For example, the local coils 6 are receiving coils prepared for each region to be imaged, such as a receiving coil for the head, a receiving coil for the neck, a receiving coil for the shoulder, a receiving coil for the chest, and a receiving coil for the abdomen. These include receiving coils, receiving coils for the lower extremities, receiving coils for the spine, and the like. Note that the local coil 6 may further have a transmission function for emitting RF pulses. In that case, the local coil 6 is connected to the transmission circuit 5 and irradiates the subject S with RF pulses based on the RF pulse signal supplied from the transmission circuit 5 .

The receiving circuit 7 generates MR signal data based on the MR signal output from the WB coil 4 or the local coil 6 and outputs the generated MR signal data to the processing circuit 17 . For example, the receiving circuit 7 includes a selector, a preamplifier, a phase detector, and an A/D (Analog/Digital) converter. The selector selectively inputs the MR signal output from the WB coil 4 or the local coil 6 . The preamplifier power-amplifies the MR signal output from the selector. The phase detector detects the phase of the MR signal output from the preamplifier. The A/D converter converts the analog signal output from the phase detector into a digital signal to generate MR signal data, and outputs the generated MR signal data to the processing circuit 17 .

An RF shield 8 is arranged between the gradient coil 2 and the WB coil 4 to shield the gradient coil 2 from RF pulses generated by the WB coil 4 . Specifically, the RF shield 8 is formed in a hollow, substantially cylindrical shape (including one having an elliptical cross-sectional shape perpendicular to the central axis of the cylinder), and the inner peripheral side of the gradient magnetic field coil 2 It is arranged in the space so as to cover the outer peripheral surface of the WB coil 4 .

A pedestal 9 accommodates the static magnetic field magnet 1 , the gradient magnetic field coil 2 , the WB coil 4 and the RF shield 8 . Specifically, the frame 9 has a cylindrical hollow bore B, and the static magnetic field magnet 1, the gradient magnetic field coil 2, the WB coil 4, and the RF shield 8 are arranged so as to surround the bore B. Each of them is housed in the same state. Here, the space inside the bore B of the gantry 9 serves as an imaging space in which the subject S is arranged when the subject S is imaged.

Here, an example in which the MRI apparatus 100 has a so-called tunnel configuration in which the static magnetic field magnet 1, the gradient magnetic field coil 2, and the WB coil 4 are each formed in a substantially cylindrical shape will be described. is not limited to this. For example, the MRI apparatus 100 has a so-called open configuration in which a pair of static magnetic field magnets, a pair of gradient magnetic field coils, and a pair of RF coils are arranged to face each other across an imaging space in which the subject S is arranged. You may have

The bed 10 has a tabletop 10a on which the subject S is placed. For example, the bed 10 is installed so that its longitudinal direction is parallel to the central axis of the static magnetic field magnet 1 .

The ECG sensor 11 is attached to the body surface of the subject S and detects an electrocardiographic signal of the subject S. FIG. The ECG sensor 11 then outputs the detected electrocardiogram signal to the ECG circuit 12 .

The ECG circuit 12 detects a predetermined electrocardiogram waveform based on the electrocardiogram signal output from the ECG sensor 11 . For example, the ECG circuit 12 detects an R wave as a predetermined electrocardiographic waveform. Then, the ECG circuit 12 generates a trigger signal at the timing when a predetermined electrocardiographic waveform is detected, and outputs the generated trigger signal to the processing circuit 17 .

The interface 13 receives various instructions and various information input operations from the operator. Specifically, the interface 13 is connected to the processing circuit 19 , converts an input operation received from the operator into an electric signal, and outputs the electric signal to the processing circuit 19 . For example, the interface 13 includes a trackball, a switch button, a mouse, a keyboard, a touch pad for performing input operations by touching the operation surface, and a display screen for setting imaging conditions and regions of interest (ROI). It is realized by a touch screen integrated with a touch pad, a non-contact input circuit using an optical sensor, an audio input circuit, and the like. In this specification, the interface 13 is not limited to having physical operation parts such as a mouse and a keyboard. For example, the interface 13 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the control circuit. Note that the interface 13 is an example of means for implementing the input unit.

The display 14 displays various information and various images. Specifically, the display 14 is connected to the processing circuit 19 and converts various information and image data sent from the processing circuit 19 into electrical signals for display and outputs the electrical signals. For example, the display 14 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like. Note that the display 14 is an example of means for realizing the display unit.

The storage circuit 15 stores various data. Specifically, the memory circuit 15 stores MR signal data and image data. For example, the storage circuit 15 is implemented by a semiconductor memory device such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, or the like. Note that the memory circuit 15 is an example of means for implementing the memory unit.

The processing circuit 16 has a bed control function 16a. The bed control function 16 a is connected to the bed 10 and outputs control electric signals to the bed 10 to control the operation of the bed 10 . For example, the bed control function 16a receives an instruction from the operator via the interface 13 to move the tabletop 10a in the longitudinal direction, the vertical direction, or the horizontal direction, and controls the bed so as to move the tabletop 10a according to the received instruction. The drive mechanism of the top plate 10a of 10 is operated.

The processing circuit 17 has an acquisition function 17a. The acquisition function 17a acquires data of the subject S by executing various pulse sequences. Specifically, the acquisition function 17 a executes various pulse sequences by driving the gradient magnetic field power supply 3 , the transmission circuit 5 and the reception circuit 7 based on the sequence execution data output from the processing circuit 19 .

Here, the sequence execution data is information defining a pulse sequence indicating a procedure for acquiring MR signal data. Specifically, the sequence execution data includes the timing and strength of the current supplied by the gradient magnetic field power supply 3 to the gradient magnetic field coil 2, the strength of the RF pulse signal supplied to the WB coil 4 by the transmission circuit 5, and the supply voltage. This information defines the timing, the detection timing at which the receiving circuit 7 detects the MR signal, and the like.

Then, the acquisition function 17 a receives MR signal data from the receiving circuit 7 as a result of executing the pulse sequence, and stores the received MR signal data in the storage circuit 15 . At this time, the acquisition function 17a measures the synchronization information indicating the cardiac time phase based on the trigger signal output from the ECG circuit 12, and stores it in association with the MR signal data. The set of MR signal data received by the acquisition function 17a is arranged two-dimensionally or three-dimensionally according to the positional information given by the readout magnetic field gradient, the phase-encoding magnetic field gradient, and the slice magnetic field gradient. Thus, it is stored in the storage circuit 15 as data forming the k-space.

The processing circuit 18 has a generating function 18a. The generating function 18 a generates an image based on the MR signal data stored in the storage circuit 15 . Specifically, the generation function 18a reads the MR signal data stored in the storage circuit 15 by the acquisition function 17a, and performs post-processing, that is, reconstruction processing such as Fourier transform on the read MR signal data to obtain an image. to generate The generation function 18a also causes the storage circuit 15 to store the image data of the generated image.

The processing circuit 19 has a main control function 19a, a creation function 19b, a correction function 19c, a specific function 19d, an estimation function 19e, and a determination function 19f. The main control function 19 a performs overall control of the MRI apparatus 100 by controlling each component of the MRI apparatus 100 . For example, the main control function 19a receives input of imaging conditions from the operator via the interface 13 . Then, the main control function 19a generates sequence execution data based on the accepted imaging conditions, and transmits the sequence execution data to the processing circuit 17 to execute various pulse sequences. Further, for example, the main control function 19a reads image data from the storage circuit 15 and outputs it to the display 14 in response to a request from the operator. The creating function 19b, the correcting function 19c, the specifying function 19d, the estimating function 19e, and the determining function 19f will be described later in detail.

For example, the processing circuits 16-19 described above are implemented by a processor. In this case, each processing function of the processing circuits 16 to 19 is stored in the memory circuit 15 in the form of a computer-executable program. Each processing circuit reads out each program from the storage circuit 15 and executes it, thereby realizing a function corresponding to each program. In other words, each processing circuit with each program read has each function shown in each processing circuit in FIG. In the example shown in FIG. 1, it is assumed that each processing function is realized by a plurality of processors. I don't mind if it's a thing. Also, the processing functions of each processing circuit may be appropriately distributed or integrated in a single or a plurality of processing circuits. In the example shown in FIG. 1, the single memory circuit 15 stores the programs corresponding to the respective processing functions. A configuration in which the corresponding program is read out from the circuit may be used.

The overall configuration of the MRI apparatus 100 according to this embodiment has been described above. With such a configuration, the MRI apparatus 100 according to the present embodiment uses, as a typical high-speed imaging method, a pulse sequence in which the longitudinal magnetization and transverse magnetization of the tissue are brought into a steady state by continuously irradiating RF pulses. has the function of executing

In this way, a pulse sequence in which longitudinal magnetization and transverse magnetization of a tissue are brought to a steady state by continuously applying RF pulses is called an SSFP (Steady-State Free Precession) sequence. In the SSFP sequence, the tissue to be measured is irradiated with an RF pulse with a short repetition time TR (repetition time) of about several ms to excite nuclear spins (hereinafter abbreviated as spins). The excited spins are in the process of recovering to a thermal equilibrium state due to longitudinal relaxation and transverse relaxation, and the MR signal is measured until the next excitation. By repeating this process, the spins converge to a steady state in which excitation and relaxation are balanced after a period of about the longitudinal relaxation time (T1) has passed. Further, the state during which the spin reaches the steady state from the thermal equilibrium state is called a transient response period (transient state) or a steady state transition period.

In this embodiment, as an example of the SSFP sequence, an example in which a balanced SSFP sequence (also called true SSFP) is used will be described.

FIG. 2 is a diagram illustrating an example of a balanced SSFP sequence according to the first embodiment;

Here, "RF" shown in FIG. 2 indicates the timing at which the RF pulse is emitted. Further, "Gs" indicates the timing at which the gradient magnetic field in the slice direction is applied, "Gp" indicates the timing at which the gradient magnetic field in the phase encoding direction is applied, and "Gr" indicates the readout. It shows the timing at which the gradient magnetic field in the direction is applied. "Signal" indicates the timing at which the MR signal is generated. Note that FIG. 2 collectively shows a plurality of gradient magnetic fields with respect to the gradient magnetic field in the phase encoding direction.

For example, as shown in FIG. 2, a balanced SSFP (hereinafter referred to as bSSFP) sequence is an SSFP sequence in which the gradient magnetic field is positive and negative symmetrical within one TR (Repetition Time). In the bSSFP sequence, the subject S is continuously irradiated with RF pulses at intervals of TR (Repetition Time), and MR signals are emitted from the subject S at the timing when TE (Echo Time) has passed since each RF pulse was irradiated. occurs. In the bSSFP sequence, a gradient magnetic field in the slice direction, a gradient magnetic field in the phase encode direction, and a gradient magnetic field in the readout direction are applied so that the gradient magnetic field in one TR is positive and negative symmetrical. Here, the gradient magnetic field in the phase encoding direction is applied while changing the strength stepwise for each TR.

According to such a bSSFP sequence, as described above, by continuously irradiating RF pulses at short TR intervals, the longitudinal magnetization and transverse magnetization of the tissue are changed to Converge to steady state. Here, the behavior of the longitudinal magnetization and transverse magnetization in the time series during the transient response period depends on tissue quantitative values such as the T1 value, the T2 value, and the T2* value, and is considered to significantly represent the state of the tissue. .

Therefore, the MRI apparatus 100 according to the present embodiment is configured so as to be able to estimate the tissue quantitative value of the tissue to be diagnosed using the data collected during the transient response period of the bSSFP sequence. According to such a configuration, it is possible to provide information for assisting diagnosis of tissue characterization.

The MRI apparatus 100 having such a configuration will be described in detail below. In this embodiment, an example in which the tissue to be diagnosed is myocardium will be described. Also, in this embodiment, an example of estimating the T1 value will be described as an example of the tissue quantitative value.

Specifically, in the present embodiment, the memory circuit 15 shown in FIG. Accumulate and store reference information based on For example, the memory circuit 15 stores reference information based on the relationship between time-series data based on MR signals that reflect the behavior of longitudinal magnetization and transverse magnetization of the myocardium at least during the transient response period when the bSSFP sequence is executed, and the T1 value. and memorize it. Note that the “steady state” referred to here is defined as, for example, a state in which the differential value of the time change of the MR signal is less than a predetermined value. In this case, the predetermined value is arbitrarily set according to, for example, an instruction from the operator. For example, when the predetermined value is set to be large, the timing of reaching the steady state is advanced, and as a result, the period before reaching the steady state is shortened (that is, the transient response period is shortened). Conversely, if the predetermined value is set small, the timing of reaching the steady state is delayed, and as a result, the period before reaching the steady state becomes longer (that is, the transient response period becomes longer).

For example, the storage circuit 15 stores, as reference information, information of a curve indicating temporal changes in the signal value of the MR signal during the transient response period when the bSSFP sequence is executed. Here, the memory circuit 15 stores reference information on normal myocardium and reference information on abnormal myocardium as reference information. The storage circuit 15 also stores reference information on abnormal myocardium for each case.

3 and 4 are diagrams showing examples of reference information stored by the storage circuit 15 according to the first embodiment.

For example, as shown in FIG. 3, memory circuitry 15 stores information indicative of curve 21 for normal myocardium. The storage circuit 15 also stores a curve 22 for case A, a curve 23 for case B, a curve 24 for case C, and a curve 25 for case D as information indicating curves for abnormal myocardium.

Here, the storage circuit 15 stores this reference information for each of a plurality of predetermined myocardial segments. For example, as shown in FIG. 4, the storage circuit 15 stores reference information for each of six segments S1 to S6 obtained by dividing an annular region representing the myocardium. The number of myocardial compartments is not limited to six, and may be five or less, or may be seven or more.

The storage circuit 15 also stores the reference information in association with the attribute information of the subject, the sequence information, and the T1 and T2 values used to create the reference information. Here, the attribute information of the subject is, for example, sex, age, weight, case of disease, and the like. Also, the sequence information is, for example, information about the free precession and the excitation angle by the RF pulse.

Note that although the example shown in FIG. 3 shows the signal values of the MR signals in the transient response period, the reference information includes the signal values of the MR signals in the steady state in addition to the signal values of the MR signals in the transient response period. may be included.

In this embodiment, the acquisition function 17a of the processing circuit 17 shown in FIG. 1 acquires data based on the MR signals in the period before reaching the steady state by executing the bSSFP sequence. For example, the acquisition function 17a acquires time-series data based on MR signals at least during the transient response period by executing the bSSFP sequence. Note that the collection function 17a is an example of a means for realizing the collection unit.

Specifically, the acquisition function 17a acquires time-series data by executing a non-orthogonal measurement pulse sequence. The non-orthogonal system measurement here refers to scanning the k-space radially or spirally around the origin, in contrast to the orthogonal system measurement that scans the k-space along an axis. This is a method of performing non-orthogonal scanning.

In this embodiment, as an example, the acquisition function 17a performs radial acquisition in which data is sequentially acquired while shifting the angle by one line for each of a plurality of lines radially set around the origin in the k-space. Time series data are collected by executing the pulse sequence.

Generally, in cardiac examination methods using an MRI apparatus, multiple types of examinations such as cine examination, flow examination, perfusion examination, delayed contrast enhancement examination, and coronary artery examination are performed. protocol is executed sequentially.

Here, for example, a cine examination is an examination for observing the shape and movement of the heart muscle and valves, and a protocol for acquiring short-axis cine images in time series is executed. A flow test is a test for determining the presence or absence of backflow of blood, and a protocol for imaging the speed of blood flow is executed. A perfusion examination is an examination for determining the presence or absence of ischemia, and a protocol for acquiring a perfusion image using a contrast medium is executed. A delayed contrast-enhanced examination is an examination for determining the presence or absence of myocardial infarction, and a protocol for acquiring delayed contrast-enhanced images is executed. A coronary artery examination is an examination for determining the presence or absence of coronary artery stenosis, and a protocol for imaging the running state of the coronary arteries throughout the heart is executed.

In such cardiac examination methods, the bSSFP sequence is usually used in cine examination protocols. Therefore, in the present embodiment, the acquisition function 17a acquires time-series data at least during the transient response period when executing the bSSFP sequence with the protocol for cine inspection.

Further, in the present embodiment, the generating function 18a of the processing circuit 18 shown in FIG. to generate an image of Note that the generation function 18a is an example of means for realizing a generation unit.

Specifically, the generation function 18a selects data of the number of lines required for image generation from the time-series data collected by the collection function 17a in chronological order by the number of lines, and generates a plurality of time-series images. Generate.

5 and 6 are diagrams showing a first example of image generation by the generation function 18a according to the first embodiment.

For example, as shown in FIG. 5, the generation function 18a generates four consecutive reconstruction time phases (#1 to #4) for each heartbeat based on the synchronization information associated with each data. , one image is generated using the data collected at each phase.

Here, for example, as shown on the left side of FIG. 6, when a plurality of lines of data are collected by radial acquisition, it is assumed that the number of lines of data required to generate one image is 128 lines. . In this case, for example, as shown on the right side of FIG. 6, the generation function 18a selects data one block at a time in chronological order to generate an image, with 128 consecutive lines of data as one block.

For example, the generation function 18a first selects 128 lines of data (Prj#: 1 to 128) starting with the first line (B1) from multiple lines of data collected in time series by radial collection. to generate an image of the first reconstructed time phase (#1). Next, the generation function 18a selects data (Prj#: 129 to 256) for 128 lines starting from the 129th line (B1), and generates the second reconstruction time phase (#2). Generate an image. After that, similarly, the generation function 18a repeatedly selects data for each 128 lines in chronological order, and sequentially generates the images of the reconstructed phases after the third one.

Alternatively, the generation function 18a selects data from the time-series data collected by the collection function 17a while shifting the data of the number of lines necessary for image generation in chronological order by a number smaller than the number of lines, and selects data from the time-series data collected by the collection function 17a. Multiple images may be generated (slide reconstruction).

7 and 8 are diagrams showing a second example of image generation by the generation function 18a according to the first embodiment.

For example, as shown in FIG. 7, the generation function 18a generates a plurality of consecutive reconstruction time phases (#1, #2, # 3, #4, #5 . . . ), one image is generated using the data collected at each phase.

Here, for example, as shown on the left side of FIG. 8, when a plurality of lines of data are acquired by radial acquisition, it is assumed that the number of lines of data required to generate one image is 128 lines. . In this case, for example, as shown on the right side of FIG. 8, the generation function 18a selects the data while shifting the position by one block in chronological order, with data for 32 consecutive lines as one block, and creates an image. to generate

For example, the generation function 18a first selects 128 lines of data (Prj#: 1 to 128) starting from the first line (B1) from multiple lines of data collected by radial collection, An image of the first reconstruction time phase (#1) is generated. Next, the generation function 18a selects data for 128 lines starting from the 33rd line (B2) which is shifted by 32 lines in chronological order from the first line B1, and selects the data for the second line. to generate an image of the reconstruction time phase (#2) of . After that, similarly, the generation function 18a repeatedly selects data while shifting the position of the first line by 32 lines in chronological order (B3-), and reconstructs the third and subsequent reconstruction time phases (#3-). Generate images sequentially.

The generation function 18a may generate an image by a predetermined method among the methods of the first example and the method of the second example described above, or may generate an image by a method selected by the operator. may Alternatively, the generation function 18a displays the generated image on the display 14 after generating the image by the method of the first example, and when requested by the operator to generate the image with higher time resolution , the image may be regenerated by the method of the second example.

Further, for example, the generation function 18a may switch the method of image generation from the method of the second example to the method of the first example when the transient response period ends. In this case, for example, the generation function 18a, based on the signal values of the MR signals sequentially obtained from the data acquired by the bSSFP sequence, at the timing when the magnitude of change in the signal value becomes equal to or less than a predetermined threshold, It is determined that the transient response period has ended. This allows short-axis cine images for cine inspection to be generated by the method of the first example from data collected after steady state.

In this embodiment, as shown in FIG. 1, the processing circuit 19 has a creation function 19b, a correction function 19c, a specification function 19d, an estimation function 19e, and a determination function 19f.

The creation function 19b uses existing case data (for example, Non-Patent Document 1: Scott A. Hamlin et al., “Mapping the Future of Cardiac MR Imaging: Case-based Review of T1 and T2 Mapping Techniques”, Radiographics. 2014 Oct ;34(6):1594-611., Non-Patent Document 2: Philippe Germain et al., “Native T1 Mapping of the Heart - A Pictorial Review”, Clin Med Insights Cardiol., 2014; 8(Suppl 4): 1 -11., non-patent document 3: Dina Radenkovic et al., ``T1 mapping in cardiac MRI'', Heart Fail Rev. 2017 Jul;22(4):415-430. to create Note that the creating function 19b is an example of means for realizing the creating unit.

Specifically, the creation function 19b uses, as case data, clinical data based on measurement results recorded in hospitals and epidemiological data recognized in literature such as medical papers, etc., which are installed in hospitals. retrieved from the database. Then, the creating function 19b uses the acquired data to create reference information by simulating the signal value of the myocardial MR signal during the transient response period when the bSSFP sequence is executed.

For example, the creation function 19b obtains the signal value of the myocardial MR signal during the transient response period when the bSSFP sequence is executed by performing a simulation using Bloch's equation. Bloch's equation is known as an equation capable of expressing macroscopic magnetization (spin) behavior, and is used in MRI simulators and the like.

The change in spin (magnetization) behavior from one instant to the next in the bSSFP sequence can be represented by a discrete system and matrix based on the Bloch equation. For example, the change in magnetization behavior during the transient response of the bSSFP sequence can be calculated by equation (1) below.

M(n+1)=AM(n)+B (1)

where M(n), M(n+1) are three-dimensional vectors representing the magnetization (Mx, My, Mz) at the n-th and n+1-th steps of the bSSFP sequence, and A is a 3×3 matrix. , B are three-dimensional vectors. These vectors and matrices are determined from the free precession, the excitation angle by the RF pulse, and the T1 and T2 values, respectively.

Also, the MR signal of the bSSFP sequence can be expressed by the following equation (2) using a complex signal.

S _ssfp (Mxy∝) = Mx + iMy (2)

Here, Mx and My are magnetization vectors in the aforementioned X-axis direction and Y-axis direction, and i is an imaginary unit (i ² =-1).

After that, the creation function 19b derives an approximated curve by approximating the time-series data based on the signal value of the MR signal represented by Equation (2) with a curve represented by Equation (3) below. Then, the creation function 19b stores the derived approximate curve in the storage circuit 15 as reference information.

S _ssfp =α+β*exp(−1*χ/τ) (3)

Here, the creating function 19b creates such reference information for each myocardial segment described above and stores it in the storage circuit 15. FIG. Further, the creating function 19b associates such reference information with the attribute information of the subject, the sequence information, and the T1 and T2 values used when creating the reference information, and stores the reference information in the memory circuit. Store in 15.

For example, the creation function 19b creates reference information when the MRI apparatus 100 is installed in a hospital or the like. Also, for example, the creation function 19b creates reference information at the timing requested by the operator.

A correction function 19c corrects the reference information using the data collected using the phantom. Note that the correction function 19c is an example of means for implementing the correction unit.

Specifically, the correction function 19c compares the data collected using a phantom with known T1 and T2 values with reference information corresponding to the same T1 and T2 values, so that the storage circuit 15 A correction factor for correcting the reference information stored in is obtained. Then, the correction function 19c corrects other reference information stored in the storage circuit 15 using the obtained correction coefficient.

For example, the correction function 19c corrects the reference information using data collected using a calibration phantom when the MRI apparatus 100 is installed in a hospital or the like. Also, for example, the correction function 19c corrects the reference information at the timing requested by the operator.

By correcting the reference information in this way, even if the time-series behavior of the longitudinal magnetization and transverse magnetization of the myocardium changes depending on the environment in which the MRI apparatus 100 is installed, the appropriate reference information can be used for each environment. be able to

The identifying function 19d compares the data collected by the collecting function 17a with reference information stored in the storage circuit 15, and identifies reference information having a predetermined relationship. For example, the specifying function 19d selects time-series data based on similar MR signals from the reference information stored in the storage circuit 15 based on signal values of time-series data based on MR signals collected by the collecting function 17a. Identify reference information for data. Note that the specific function 19d is an example of means for realizing the specific unit.

Specifically, the specifying function 19d is represented by an approximated curve of the signal value of the MR signal obtained from the time-series data based on the MR signal collected by the collecting function 17a and each reference information stored in the storage circuit 15. The reference information of the time-series data based on similar MR signals is specified by pattern matching with the curve obtained.

More specifically, the identification function 19d identifies reference information of time-series data based on similar MR signals based on the plurality of time-series images generated by the generation function 18a. Here, the specifying function 19d first sets a region of interest in one of the plurality of time-series images generated by the generating function 18a.

FIG. 9 is a diagram showing an example of setting a region of interest by the specifying function 19d according to the first embodiment.

For example, as shown in FIG. 9, the specifying function 19d extracts a myocardial region 32 from one image 31 of a plurality of time-series images, and sets the extracted region 32 as a region of interest. Here, as a method for extracting the myocardial region from the image, various known region extraction methods can be used. After that, the identifying function 19d identifies, for each voxel, a voxel corresponding to the same portion in each of the other images, for a plurality of voxels included in the extracted region of interest. Here, as a method of specifying voxels corresponding to the same portion, various known tracking methods can be used.

Then, the specifying function 19d derives an approximated curve of the signal value of the MR signal at least during the transient response period for each voxel corresponding to the same portion within the region of interest.

FIG. 10 is a diagram showing an example of derivation of approximated curves by the specific function 19d according to the first embodiment.

Here, the plurality of white circles shown in FIG. 10 indicate the signal values of the MR signals specified from each of the plurality of time-series images for one voxel included in the region of interest. For example, as shown in FIG. 10, the specific function 19d derives an approximation curve 41 by approximating the signal values of the time-series MR signals with the curve represented by the above equation (3).

Then, the identifying function 19d performs pattern matching between the derived approximated curve and the curve represented by each piece of reference information stored in the storage circuit 15 to identify the most similar curve. At this time, the specifying function 19d specifies the segment to which the voxel to be processed belongs by dividing the region of interest into a plurality of segments so as to correspond to the plurality of segments of the myocardium, and the reference information corresponding to the specified segment. and the approximation curve are pattern-matched.

Here, the specifying function 19d performs pattern matching between the reference information curve corresponding to the attribute information of the subject to be diagnosed and the sequence information regarding the bSSFP sequence executed by the collecting function 17a, and the approximate curve. . At this time, the reference information stored in the storage circuit 15 corresponds to the attribute information of the subject to be diagnosed and also to the sequence information about the bSSFP sequence executed by the collection function 17a. If it does not exist, the specifying function 19d instructs the creating function 19b to create reference information that meets the conditions and use it for pattern matching.

Further, for example, as described above, if the reference information stored in the storage circuit 15 also includes the signal value of the MR signal in the steady state, the specific function 19d may Pattern matching may be performed using the signal values of the MR signals in both. Thereby, the accuracy of specifying the reference information can be improved.

FIG. 11 is a diagram showing an example of identification of reference information by the identification function 19d according to the first embodiment.

For example, as shown in FIG. 11, the specifying function 19d specifies the curve 23 regarding case B as the curve most similar to the approximated curve 41 shown in FIG. 10 among the plurality of curves shown in FIG.

Note that, for example, the specifying function 19d may specify reference information of time-series data based on similar MR signals based on a machine learning algorithm. For example, the specifying function 19d uses a machine learning algorithm such as a neural network or deep learning to identify MR signals similar to the time-series data of the collected MR signals from among the reference information stored in the storage circuit 15. Identify the reference information of the time series data based on.

The estimation function 19e determines properties of the myocardium based on the reference information identified by the identification function 19d. For example, the estimation function 19e estimates the T1 value based on the reference information specified by the specification function 19d. Note that the estimation function 19e is an example of means for implementing the determination unit and the estimation unit.

Specifically, the estimating function 19e acquires the T1 value associated with the reference information specified by the specifying function 19d for each voxel included in the region of interest, and sets the T1 value as the estimated T1 value. Then, the estimation function 19 e generates a T1 map image by mapping the estimated T1 value obtained for each voxel by color, and displays the generated T1 map image on the display 14 .

The determination function 19f determines whether the myocardium is normal or abnormal based on the reference information specified by the specification function 19d. Note that the discrimination function 19f is an example of means for implementing the discrimination unit.

Specifically, the determining function 19f determines that the myocardium is normal when the reference information specified by the specifying function 19d is reference information relating to normal myocardium. Further, the determining function 19f determines that the myocardium is abnormal when the reference information specified by the specifying function 19d is reference information relating to an abnormal myocardium.

Note that, for example, the determination function 19f may determine whether the myocardium is normal or abnormal based on a machine learning algorithm. For example, the determination function 19f uses a machine learning algorithm such as a neural network or deep learning to determine whether the reference information identified by the identification function 19d relates to normal myocardium or abnormal myocardium.

Then, the determination function 19f displays normal/abnormal determination on the display 14. FIG.

For example, the discriminating function 19f determines changes over time in MR signals obtained from time-series data based on MR signals acquired by the acquiring function 17a, and MR signals in reference information on normal myocardium stored in the storage circuit 15. The display 14 displays information indicating the degree of correlation with changes over time. Further, when determining that the myocardium is abnormal, the determining function 19f displays information indicating the case of the specified reference information on the display 14 based on the specified reference information. Also, the display 14 displays the reference information specified by the specifying function 19d.

FIG. 12 is a diagram showing an example of information display by the discrimination function 19f according to the first embodiment.

For example, as shown in FIG. 12, the determination function 19f uses the approximate curve 41 derived by the specific function 19d and the reference information on the normal myocardium stored in the storage circuit 15 for each voxel included in the region of interest. Display curve 21 for normal myocardium shown and curve 23 for case B identified by identification function 19d.

The discrimination function 19f also provides information indicating the degree of correlation between the approximated curve 41 derived by the specifying function 19d and the curve 21 relating to normal myocardium, and the case of the curve 23 relating to abnormal myocardium specified by the specifying function 19d (FIG. 12 In the example shown in FIG. 2, "case B is suspected"), and information 53 indicating the degree of correlation between the approximated curve 41 derived by the specific function 19d and the curve 23 related to the abnormal myocardium is displayed together with the above-described curves. 14 further display.

Here, an example in which the determination function 19f displays information for each voxel has been described, but information may be displayed for each myocardial segment, for example. In this case, for example, the discrimination function 19f displays on the display 14 a curve 51 indicating the average signal value of the MR signal in each voxel included in the region of interest.

FIG. 13 is a flow chart showing a processing procedure of processing performed by the MRI apparatus 100 according to the first embodiment.

For example, as shown in FIG. 13, in this embodiment, first, the main control function 19a sets imaging conditions for the bSSFP sequence (step S101). This step S101 is realized, for example, by the processing circuit 19 reading out a predetermined program corresponding to the main control function 19a from the storage circuit 15 and executing it.

Subsequently, the acquisition function 17a acquires time series data at least during the transient response period by executing the bSSFP sequence based on the imaging conditions set by the main control function 19a (step S102). Also, the acquisition function 17a measures synchronization information indicating the cardiac time phase based on the trigger signal output from the ECG circuit 12 (step S103). These steps S102 and S103 are realized, for example, by the processing circuit 17 reading out a predetermined program corresponding to the collection function 17a from the storage circuit 15 and executing it.

Subsequently, the generation function 18a generates time-series images based on the time-series data collected by the collection function 17a (step S104). This step S104 is realized, for example, by the processing circuit 18 reading out a predetermined program corresponding to the generation function 18a from the storage circuit 15 and executing it.

Subsequently, the identifying function 19d sets a region of interest in one of the time-series images generated by the generating function 18a (step S105), and for each voxel corresponding to the same portion in the region of interest, at least transient An approximate curve of the signal value of the MR signal in the response period is derived (step S106). Then, the specifying function 19d specifies the most similar curve by pattern matching the derived approximated curve and the curve represented by each piece of reference information stored in the storage circuit 15 (step S107). These steps S105 to S107 are realized, for example, by the processing circuit 19 reading out a predetermined program corresponding to the specific function 19d from the storage circuit 15 and executing it.

Subsequently, the estimation function 19e estimates the T1 value based on the curve specified by the specification function 19d (step S108). Then, the estimation function 19e generates a T1 map image based on the estimated T1 value (step S109), and displays the generated T1 map image on the display 14 (step S110). These steps S108 to S110 are realized, for example, by the processing circuit 19 reading out a predetermined program corresponding to the estimation function 19e from the storage circuit 15 and executing it.

Subsequently, the discrimination function 19f discriminates normality/abnormality of the myocardium based on the curve specified by the specification function 19d (step S111), and displays the normality/abnormality discrimination result on the display 14 (step S112). These steps S111 and S112 are realized, for example, by the processing circuit 19 reading out a predetermined program corresponding to the discrimination function 19f from the storage circuit 15 and executing it.

As described above, in the first embodiment, data collected during at least the transient response period of the bSSFP sequence can be utilized to estimate myocardial tissue quantification. Thus, according to the first embodiment, it is possible to provide information for assisting diagnosis of myocardial properties as a simple biomarker.

For example, there is known a T1 mapping method for displaying a T1 map image in which a color representing a T1 value is assigned to each pixel. However, clinically, it is often difficult to diagnose myocardial properties with such conventional map images.

For this reason, as an example of a method for diagnosing myocardial properties, a T1 mapping method using ECV (Extracellular Volume) in combination is also being considered. . In addition, at present, there is no established data collection method for the T1 mapping method (although there is the MOLLI method as an example), and there are many error factors depending on the data collection method, subject, and the like. In addition, the T1 mapping method is difficult to use clinically because the setting of imaging conditions, data acquisition, and post-processing are complicated.

On the other hand, since the method described in the first embodiment does not use a contrast medium, it is possible to reduce the burden on the patient who is the subject. In addition, the method described in the first embodiment can be used, for example, as an alternative method when follow-up is required (for example, two of the three examinations method). In addition, the method described in the first embodiment can be measured in a short period of time (about 2 to 3 heartbeats). It is possible to obtain the measured results. Moreover, the method described in the first embodiment is used at approximately the same timing as short-axis cine imaging generally performed in a normal heart examination method (e.g., left ventricular function evaluation) using an MRI apparatus. As such, it can be easily incorporated into protocols that are performed in routine cardiac examinations.

As described above, in general, a heart examination method using an MRI apparatus sequentially executes a plurality of protocols. Examples include those involving the injection of a contrast medium, such as protocols for imaging, and those that do not involve the injection of a contrast medium, such as protocols for cine examinations and protocols for flow examinations.

Therefore, for example, when a plurality of protocols including a protocol involving injection of a contrast medium into the subject S are successively executed, the acquisition function 17a, before the injection of the contrast medium, waits for a period of time during the transient response period. When series data is collected and the myocardium is determined to be normal by the determination function 19f, the injection of the contrast medium and the execution of the protocol involving the injection of the contrast medium may be omitted. That is, the acquisition function 17a executes the injection of the contrast agent and the protocol involving the injection of the contrast agent only when the determination function 19f determines that the myocardium is abnormal.

With such a configuration, it is possible to perform injections of the contrast agent and protocols involving the injection of the contrast agent only when necessary, thereby reducing the frequency of using the contrast agent. This makes it possible to reduce the burden on a patient who is a subject.

In the first embodiment described above, the collection unit in this specification is realized by the collection function 17a of the processing circuit 17, the generation unit is realized by the generation function 18a of the processing circuit 18, and the generation unit, the correction unit, the identification Although an example in which the unit, the estimating unit, and the discriminating unit are realized by the creating function 19b, the correcting function 19c, the specifying function 19d, the estimating function 19e, and the discriminating function 19f of the processing circuit 19, respectively, has been described, the embodiment is directed to this. Not limited. For example, the collecting unit, generating unit, creating unit, correcting unit, specifying unit, estimating unit, and determining unit in this specification are respectively the collecting function 17a, the generating function 18a, the creating function 19b, and the correcting function 19c described in the embodiment. , the specifying function 19d, the estimating function 19e, and the determining function 19f, the same functions may be realized by hardware alone or by a mixture of hardware and software.

(Second embodiment)
As described above, in the first embodiment, an example in which the technology disclosed by the present application is applied to an MRI apparatus has been described, but the embodiment is not limited to this. For example, the technology disclosed by the present application can also be applied to an image processing apparatus connected to the MRI apparatus 100 via a network. Therefore, an embodiment of an image processing apparatus will be described below as a second embodiment.

FIG. 14 is a diagram illustrating an example configuration of an image processing apparatus according to the second embodiment. For example, as shown in FIG. 14, an image processing apparatus 300 according to this embodiment is communicably connected to an MRI apparatus 100 and an image storage apparatus 200 via a network 400 .

The MRI apparatus 100 acquires image data of a subject using magnetic resonance phenomena. Specifically, the MRI apparatus 100 acquires magnetic resonance data from a subject by executing various imaging sequences based on imaging conditions set by an operator. The MRI apparatus 100 generates two-dimensional or three-dimensional image data by performing image processing such as Fourier transform processing on the collected magnetic resonance data.

The image storage device 200 stores image data acquired by the MRI apparatus 100 . Specifically, the image storage apparatus 200 acquires image data from the MRI apparatus 100 via the network 400 and stores the acquired image data in a storage circuit provided inside or outside the apparatus. For example, the image storage device 200 is realized by computer equipment such as a server device.

The image processing apparatus 300 acquires image data from the MRI apparatus 100 or the image storage apparatus 200 via the network 400, and performs various image processing on the acquired image data. For example, the image processing apparatus 300 is implemented by computer equipment such as a workstation.

Specifically, the image processing device 300 has a NW interface 310 , a memory circuit 320 , an input interface 330 , a display 340 and a processing circuit 350 .

The NW interface 310 controls transmission and communication of various data exchanged between the image processing apparatus 300 and another apparatus connected via the network 400 . Specifically, the NW interface 310 is connected to the processing circuit 350 and transmits image data output from the processing circuit 350 to the MRI apparatus 100 or the image storage apparatus 200 . The NW interface 310 also outputs image data received from the MRI apparatus 100 or the image storage apparatus 200 to the processing circuit 350 . For example, the NW interface 310 is implemented by a network card, network adapter, NIC (Network Interface Controller), or the like.

The storage circuit 320 stores various data. Specifically, the storage circuit 320 is connected to the processing circuit 350 and stores input data or transfers stored data to the processing circuit 350 according to commands sent from the processing circuit 350 . Output. For example, the storage circuit 320 is implemented by a semiconductor memory device such as a RAM (Random Access Memory) or flash memory, a hard disk, an optical disk, or the like. Note that the storage circuit 320 is an example of means for realizing a storage unit.

The input interface 330 receives input operations of various instructions and various information from the operator. Specifically, the input interface 330 is connected to the processing circuit 350, converts an input operation received from the operator into an electrical signal, and outputs the electrical signal to the control circuit. For example, the input interface 330 includes a trackball for setting a region of interest (ROI), a switch button, a mouse, a keyboard, a touch pad for performing an input operation by touching an operation surface, a display screen and a touch panel. It is realized by a touch screen integrated with a pad, a non-contact input interface using an optical sensor, a voice input interface, or the like. In this specification, the input interface 330 is not limited to having physical operation parts such as a mouse and a keyboard. For example, the input interface 330 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the control circuit. It should be noted that the input interface 330 is an example of means for realizing an input unit.

The display 340 displays various information and various images. Specifically, the display 340 is connected to the processing circuit 350, converts various information and image data sent from the processing circuit 350 into electrical signals for display, and outputs the electrical signals. For example, the display 340 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like. Note that the display 340 is an example of means for realizing the display unit.

The processing circuit 350 controls the components of the image processing apparatus 300 according to input operations received from the operator via the input interface 330 . For example, the processing circuit 350 causes the storage circuit 320 to store image data output from the NW interface 310 . Also, for example, the processing circuit 350 reads image data from the storage circuit 320 and displays it on the display 340 .

With such a configuration, in the present embodiment, the storage circuit 320 stores a reference signal based on the MR signal that reflects the behavior of the longitudinal magnetization and transverse magnetization of the tissue in the period before reaching the steady state when the bSSFP sequence is executed. Accumulate and store information. For example, the memory circuit 320, like the memory circuit 15 described in the first embodiment, is based on MR signals reflecting the behavior of longitudinal magnetization and transverse magnetization of the myocardium at least during the transient response period when the bSSFP sequence is executed. Accumulate and store reference information based on the relationship between the time-series data and the T1 value.

Further, in this embodiment, the processing circuit 350 has an acquisition function 351 , a creation function 352 , a correction function 353 , a specification function 354 , an estimation function 355 and a determination function 356 .

Acquisition function 351 acquires data based on the MR signal during the period prior to steady state by executing the bSSFP sequence. For example, the acquisition function 17 a acquires from the MRI apparatus 100 or the image archive apparatus 200 time-series data based on MR signals during at least the transient response period acquired by executing the bSSFP sequence by the MRI apparatus 100 . It should be noted that the acquisition function 351 is an example of means for realizing an acquisition unit.

Specifically, the acquisition function 351 acquires from the MRI apparatus 100 or the image storage apparatus 200 a plurality of time-series images representing the signal values of the MR signals generated based on the data acquired by the bSSFP sequence.

The creation function 352 creates reference information by simulation using existing case data, like the creation function 19b described in the first embodiment. Note that the creating function 352 is an example of means for realizing the creating unit.

The correction function 353 corrects reference information using data collected by the MRI apparatus 100 using a phantom, like the correction function 19c described in the first embodiment. Note that the correction function 353 is an example of means for implementing the correction unit.

The identification function 354 compares the data collected by the acquisition function 351 and the reference information stored in the storage circuit 320 to identify reference information having a predetermined relationship. For example, the specific function 354 is stored in the storage circuit 320 based on the signal value of the time series data based on the MR signal acquired by the acquisition function 351, similar to the specific function 19d described in the first embodiment. Reference information of time-series data based on similar MR signals is identified from the existing reference information. It should be noted that the specific function 354 is an example of means for realizing the specific unit.

The estimating function 355 estimates the T1 value based on the reference information specified by the specifying function 354, like the estimating function 19e described in the first embodiment. Note that the estimation function 355 is an example of means for realizing the estimation unit.

The discrimination function 356 discriminates whether the myocardium is normal or abnormal based on the reference information specified by the specification function 354, like the discrimination function 19f described in the first embodiment. Note that the determination function 356 is an example of means for implementing the determination unit.

For example, the processing circuitry 350 described above is implemented by a processor. In this case, each processing function of processing circuit 350 is stored in storage circuit 320 in the form of a computer-executable program. Then, the processing circuit 350 reads out each program from the storage circuit 320 and executes it, thereby realizing the function corresponding to each program. In other words, the processing circuit 350 with each program read has each function shown in each processing circuit in FIG. In the example shown in FIG. 14, it is assumed that each processing function is realized by a single processor. It does not matter if the function is realized. Also, the processing functions of the processing circuit 350 may be appropriately distributed or integrated in a single or a plurality of processing circuits. Further, in the example shown in FIG. 14, the single memory circuit 320 has been described as storing a program corresponding to each processing function. A configuration in which the corresponding program is read out from the circuit may be used.

According to the above-described configuration, in the second embodiment, similarly to the first embodiment, the data collected during the transient response period of the bSSFP sequence can be used to estimate the myocardial tissue quantification value. . Thus, the second embodiment can also provide information for assisting diagnosis of myocardial properties.

In the above-described second embodiment, the acquisition unit, creation unit, correction unit, identification unit, estimation unit, and determination unit in this specification are respectively replaced by the acquisition function 351, the creation function 352, and the correction function of the processing circuit 350. 353, a specific function 354, an estimating function 355, and a determining function 356 have been described, but the embodiment is not limited to this. For example, the acquiring unit, creating unit, correcting unit, specifying unit, estimating unit, and determining unit in this specification are the acquiring function 351, creating function 352, correcting function 353, specifying function 354, and estimating function described in the embodiment, respectively. 355 and determination function 356, the same function may be realized by hardware alone or by a mixture of hardware and software.

The first and second embodiments have been described above. Here, in each of the embodiments described above, an example of using the bSSFP sequence has been described, but the embodiment is not limited to this. For example, each of the above-described embodiments can be similarly applied even when using an SSFP sequence in which the gradient magnetic field in one TR is positive and negative asymmetric.

Also, in each of the above-described embodiments, an example in which the tissue to be diagnosed is the myocardium has been described, but the embodiments are not limited to this. For example, each of the above-described embodiments can be similarly applied even when a tissue other than the myocardium is a tissue to be diagnosed.

Moreover, although each embodiment mentioned above demonstrated the example in the case of estimating a T1 value, embodiment is not restricted to this. For example, each of the above-described embodiments can be similarly applied when estimating other tissue quantification values such as T2 value, T1* value, T2* value, T1ρ value, and T2ρ value.

(Third Embodiment)
Further, in each of the above-described embodiments, an example in which the estimation function 19e estimates the tissue quantification value based on the reference information specified by the specification function 19d has been described. By doing so, the estimation accuracy of the tissue quantitative value may be improved. An example of such a case will be described below as a third embodiment. In addition, in the third embodiment, the points different from the first embodiment will be mainly described, and detailed descriptions of the contents common to the first embodiment will be omitted.

FIG. 15 is a diagram showing a configuration example of an MRI apparatus according to the third embodiment.

For example, as shown in FIG. 15, the MRI apparatus 500 according to this embodiment includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power supply 3, a WB coil 4, a transmission circuit 5, a local coil 6, a reception circuit 7, It comprises an RF shield 8, a cradle 9, a bed 10, an ECG sensor 11, an ECG circuit 12, an interface 13, a display 14, a memory circuit 15, processing circuits 16, 517, 18 and 519.

The processing circuit 517 has an acquisition function 517a. As in the first embodiment, the acquisition function 517a executes a pulse sequence that brings the longitudinal magnetization and transverse magnetization of the tissue to a steady state by continuously irradiating RF pulses, thereby Data is acquired based on the MR signal over a period of time, and in the present embodiment, the pulse sequence is performed multiple times. It should be noted that the collection function 517a is an example of means for realizing the collection unit.

The processing circuit 519 comprises a main control function 19a, a creation function 19b, a correction function 19c, a specification function 519d, an estimation function 19e, a calculation function 519g, an evaluation function 519h and an adjustment function 519i. Here, the main control function 19a, creation function 19b, correction function 19c, and estimation function 19e each perform the processing described in the first embodiment. Note that the processing circuit 519 may further include the determination function 19f described in the first embodiment.

The specifying function 519d performs the same processing as the specifying function 19d described in the first embodiment. The degree of similarity with the reference information that is provided is calculated, and the reference information with the highest degree of similarity is specified. Note that the specific function 519d is an example of means for realizing the specific unit.

Each time the acquisition function 517a executes a pulse sequence, the calculation function 519g reaches a steady state through a simulation using the measurement conditions when the pulse sequence was executed and the tissue quantification value estimated by the estimation function 19e. A simulated MR signal in the previous period is calculated, and simulated time series data based on the simulated MR signal is generated. Note that the calculation function 519g is an example of a means for realizing the calculation unit.

The evaluation function 519h compares the MR signal-based time series data collected by the collection function 517a with the simulated time series data generated by the calculation function 519g each time the simulated time series data is generated by the calculation function 519g. By doing so, the tissue quantitative value estimated by the estimation function 19e is evaluated. Note that the evaluation function 519h is an example of means for realizing the evaluation unit.

The adjustment function 519i adjusts the parameters of the next pulse sequence to be executed by the acquisition function 517a based on the evaluation result of the tissue quantification value by the evaluation function 519h each time the tissue quantification value is evaluated by the evaluation function 519h. Note that the adjustment function 519i is an example of means for realizing the adjustment unit.

In this embodiment, the collection function 517a repeatedly executes the pulse sequence using the parameters adjusted by the adjustment function 519i until the evaluation result of the tissue quantification value by the evaluation function 519h satisfies a predetermined condition.

Processing performed by each function described above will be described in detail below.

FIG. 16 is a flow chart showing the procedure of processing performed by the MRI apparatus according to the third embodiment.

For example, as shown in FIG. 16, in this embodiment, first, the main control function 19a sets a measurement target according to an instruction from the operator (step S201). For example, the main control function 19a sets the myocardium as the measurement target, as in the first embodiment. After that, the main control function 19a sets measurement conditions (imaging conditions) for the bSSFP sequence (step S202).

Subsequently, the acquisition function 517a measures the MR signal at least during the transient response period by executing the bSSFP sequence based on the measurement conditions set by the main control function 19a (step S203). Here, the acquisition function 517a measures MR signals in the same manner as the acquisition function 17a described in the first embodiment.

Subsequently, the specifying function 519d performs curve fitting on the MR signals measured by the acquisition function 517a using the nonlinear least-squares method to derive an approximate function (step S204). After that, the specific function 519d uses the derived approximation function to generate time-series data (approximation data) (step S205).

Further, the specific function 519d vectorizes the generated time-series data to calculate a time-series vector, and uses the time-series vector and the reference information stored in the storage circuit 15 to obtain the time-series vector of the MR signal. , and the reference information with the highest similarity is specified (step S206).

Subsequently, the estimating function 19e estimates tissue quantified values based on the reference information specified by the specifying function 19d, as in the first embodiment (step S207). Subsequently, the evaluation function 519h evaluates the similarity by comparing the similarity calculated by the specifying function 519d with the first threshold (step S208). Here, as the first threshold, a similarity value (for example, 95%) is set when it can be determined that the tissue quantification value specified by the specification function 519 has a certain degree of reliability. As a similarity evaluation method performed by the evaluation function 519h, for example, a determination method using cosine similarity is used.

Subsequently, the calculation function 519g performs a simulation using the measurement conditions when the pulse sequence is executed by the acquisition function 517a and the tissue quantification value estimated by the estimation function 19e. is calculated (step S209). Here, for example, the calculation function 519g calculates a simulated MR signal by performing a simulation using Bloch's equation, like the creation function 19b described in the first embodiment.

After that, the calculation function 519g generates simulated time-series data (approximate data) based on the calculated simulated MR signal using a predetermined approximation function (step S210). Here, for example, the calculation function 519g generates simulated time-series data using an approximation function derived by means similar to Equation (3) in the first embodiment. The calculation function 519g also vectorizes the generated simulated time-series data to calculate a time-series vector (step S211).

Subsequently, the evaluation function 519h compares the time-series vector calculated by the specific function 519d and the time-series vector calculated by the calculation function 519g to calculate their similarity (step S212). After that, the evaluation function 519h evaluates the similarity by comparing the calculated similarity with the first threshold (step S213). After that, the evaluation function 519h evaluates the tissue quantification value estimated by the estimation function 519e by comparing the similarity with the similarity calculated by the specification function 519d (step S214).

Here, the evaluation function 519h compares the similarity between the time-series data collected by the collection function 517a and the simulated time-series data generated by the calculation function 519g and the similarity calculated by the specification function 519d. , when the similarity difference is less than the second threshold and the similarity calculated by the specific function 519d is equal to or greater than the first threshold, it is determined that the target estimation accuracy has been achieved (steps S215, Yes). Note that the target estimation accuracy referred to here is the tissue quantitative value specified by the specifying function 519 as a result of evaluation by comparing the MR signal obtained by actual measurement with the simulated MR signal obtained by calculation (simulation). It is an estimation accuracy that can be judged to have a certain degree of reliability as an estimated quantitative value. Also, here, an example of using two thresholds will be described, but the evaluation function 519h does not use the first threshold, and the As a result of comparing the similarity with the simulated time-series data and the similarity calculated by the specific function 519d, if the difference in similarity is less than the second threshold, it is determined that the target estimation accuracy has been achieved. may

When the evaluation function 519h determines that the target estimation accuracy has been achieved, the evaluation function 519h outputs the tissue quantitative value estimated by the estimation function 19e to the display 14 (step S216). When the evaluation function 519h thus determines that the target estimation accuracy has been achieved, the collection function 517a terminates execution of the pulse sequence.

On the other hand, if the evaluation function 519h does not determine that the target estimation accuracy has been achieved (step S215, No), the adjustment function 519i adjusts the parameters of the pulse sequence executed by the collection function 517a (step S217). . At this time, for example, the adjustment function 519i adjusts the excitation angle (flip angle) by the RF pulse. Here, an example in which the adjustment function 519i adjusts the excitation angle will be described, but parameters to be adjusted are not limited to the excitation angle, and may be TE, TR, or the like.

Acquisition function 517a then repeatedly executes the pulse sequence using the parameters adjusted by adjustment function 519i until evaluation function 519h determines that the target estimation accuracy has been achieved.

Here, in the above-described processing procedure, steps S201 and S202 are realized, for example, by the processing circuit 519 reading out a predetermined program corresponding to the main control function 19a from the storage circuit 15 and executing it. Further, step S203 is realized, for example, by the processing circuit 517 reading out a predetermined program corresponding to the collection function 517a from the storage circuit 15 and executing it. Further, steps S204 to S206 are implemented by, for example, the processing circuit 519 reading out a predetermined program corresponding to the specific function 519d from the storage circuit 15 and executing it. Further, step S207 is realized, for example, by the processing circuit 519 reading out a predetermined program corresponding to the estimation function 19e from the storage circuit 15 and executing it. Further, steps S209 to S211 are implemented by, for example, the processing circuit 519 reading a predetermined program corresponding to the calculation function 519g from the storage circuit 15 and executing it. Further, steps S208 and S212 to S216 are implemented by, for example, the processing circuit 519 reading out a predetermined program corresponding to the evaluation function 519h from the storage circuit 15 and executing it. Further, step S217 is realized, for example, by the processing circuit 519 reading out a predetermined program corresponding to the adjustment function 519i from the storage circuit 15 and executing it.

Also, although not shown in FIG. 16, when the evaluation function 519h determines that the target estimation accuracy has been achieved, the time-series data collected by the collection function 517a and the tissue quantitative value estimated by the estimation function 19e The reference information based on the relationship of is newly created and stored in the storage circuit 15 . As a result, reference information with better estimation accuracy (fitting) and analysis efficiency is stored in the storage circuit 15 .

16, the processing performed by the calculation function 519g, the evaluation function 519h, and the adjustment function 519i is executed using, for example, a recovery time provided between successive pulse sequences. be done. Alternatively, for example, the processing performed by the calculation function 519g, the evaluation function 519h, and the adjustment function 519i may be performed sequentially (real time) for each repetition time TR during measurement.

Further, in the processing procedure shown in FIG. 16, the collection function 517a repeatedly executes the pulse sequence until it is determined that the target estimation accuracy has been achieved, but an upper limit may be set for the number of times the pulse sequence is executed. . Specifically, the acquisition function 517a repeatedly executes the pulse sequence until the evaluation function 519h determines that the target estimation accuracy has been achieved or the number of pulse sequence executions exceeds a predetermined number. When the target estimation accuracy is not achieved and the pulse sequence execution count reaches a predetermined number, the evaluation function 519h outputs the tissue quantitative value estimated by the estimation function 19e at that time to the display 14. . At this time, for example, the evaluation function 519h may display information indicating the estimation accuracy of the tissue quantification value together with the estimated tissue quantification value. For example, the evaluation function 519h displays information divided into levels according to the magnitude of the difference between the similarity calculated by the specifying function 519d and the first threshold as information indicating the estimation accuracy of the tissue quantitative value. For example, the evaluation function 519h divides into three levels "estimation accuracy: high", "estimation accuracy: medium" and Either "estimation accuracy: low" is displayed as information indicating estimation accuracy.

According to the third embodiment described above, by updating the n-th measurement conditions based on at least the (n−1)-th (n is a natural number of 2 or more) measurement signal and the analysis result, the tissue quantification value estimation accuracy can be improved.

For example, in the third embodiment, the adjusting function 519i adjusts the angle of excitation by RF pulses. By changing the excitation angle of the RF pulse, it is possible to evaluate the dependence of the tissue quantification value (T1 value/T2 value) considering the off-resonance component θ.

FIG. 17 is a diagram showing the relationship between the excitation angle, tissue quantification value, and off-resonance by the RF pulse according to the third embodiment.

Here, FIG. 17 shows the relationship between the MR signal obtained by the SSFP at the tissue quantification value T2/T1=0.1 (T1=1000 [ms], T2=100 [ms]), the excitation angle α, and the off-resonance component θ. showing relationships.

For example, as shown in FIG. 17, the MR signal obtained by SSFP depends on the excitation angle α (and tissue quantification values (T1, T2) and off-resonance component θ), but does not have a simple proportional relationship. . Therefore, in the case of remeasurement, the noise (off-resonance component θ that depends on magnetic field inhomogeneity, etc.) unique to the MRI apparatus 100 and the measurement object is reflected in the MR signal, and the recalculation and remeasurement It is necessary to evaluate the degree of divergence between the calculated tissue quantitative value and the measured tissue quantitative value by changing the excitation angle at times.

A change in spin (magnetization) behavior from an arbitrary time point to the next time point in the bSSFP sequence including the off-resonance component θ can be expressed by a discrete system and matrix based on Bloch's equation. For example, the change in magnetization behavior during the transient response period of the bSSFP sequence containing the off-resonance component θ can be calculated by Equation (4) below.

M(n)= _Rx (±α)[ _Dz ( _ΔθTR ) _E2 (TR,T2,T1)M(n-1)+ _E1 (TR,T1)] (4)

Here, M(n), M(n-1) are three-dimensional vectors representing the magnetization (Mx, My, Mz) at the nth and n-1th steps of the bSSFP sequence, and R _x (±α ) is the excitation by the RF pulse with the excitation angle α, D _z (Δθ _TR ) is a 3×3 matrix representing the phase dispersion (Dephasing) between the TRs, and Δθ _TR is the phase dispersion between the TRs depending on the off-resonance component θ. are represented by a 3×3 matrix E ₂ (TR, T2, T1) and a vector E ₁ (TR, T1) as parameters dependent on the T1 and T2 relaxation times.

FIG. 18 is a diagram showing an example of measurement and calculation of MR signals according to the third embodiment.

Here, the left side of FIG. 18 shows the measurement result and the calculation result of the MR signal under the initial measurement conditions, and shows an example in which the excitation angle (FA) by the RF pulse is 30°. The right side of FIG. 18 shows the measurement results and calculation results of the MR signal under the n-th (≠first time) measurement conditions, and shows an example in which the excitation angle (FA) by the RF pulse is 60°. ing.

For example, as shown in FIG. 18, by adjusting the excitation angle (FA) by the RF pulse from 30° to 60°, the MR signal under the first measurement condition and the MR signal under the nth (≠ first time) measurement condition can reduce the divergence between the measurement result and the calculation result. As a result, it is possible to improve the estimation accuracy of the tissue quantitative value.

In each of the above-described embodiments, an example in which an SSFP sequence, which is a pulse sequence that brings the longitudinal magnetization and transverse magnetization of tissue to a steady state, is executed has been described. The method for improving the estimation accuracy of is not limited to this. That is, the method for improving the estimation accuracy of the tissue quantification value described in the third embodiment can be used even when estimating the tissue quantification value using time-series data of MR signals acquired by a pulse sequence other than the SSFP sequence. A similar application is possible.

Further, the term "processor" used in the description of each embodiment described above is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an application specific integrated circuit (ASIC). , programmable logic devices (e.g., Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs)), etc. means Instead of storing the program in the memory circuit, the program may be directly incorporated in the circuit of the processor. In this case, the processor implements its functions by reading and executing the program embedded in the circuit. Further, each processor of the present embodiment is not limited to being configured as a single circuit for each processor, but may be configured as one processor by combining a plurality of independent circuits to realize its function. good. Additionally, multiple components in FIGS. 1 and 14 may be integrated into a single processor to implement its functionality.

Here, the program to be executed by the processor is provided by being pre-installed in, for example, a ROM (Read Only Memory), a storage circuit, or the like. This program is a file in a format that can be installed in these devices or a file in a format that can be installed on a computer such as CD (Compact Disk)-ROM, FD (Flexible Disk), CD-R (Recordable), DVD (Digital Versatile Disk), etc. may be recorded on a readable storage medium and provided. Also, this program may be provided or distributed by being stored on a computer connected to a network such as the Internet and downloaded via the network. For example, this program is composed of modules including each functional unit described above. As actual hardware, the CPU reads out a program from a storage medium such as a ROM and executes it, so that each module is loaded onto the main storage device and generated on the main storage device.

According to at least one embodiment described above, it is possible to provide information for assisting diagnosis of tissue characterization.

While several embodiments of the invention have been described, these embodiments have been 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, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, as well as the scope of the invention described in the claims and equivalents thereof.

100 magnetic resonance imaging (MRI) apparatus 17 processing circuit 17a acquisition function 18 processing circuit 18a generation function 19 processing circuit 19b creation function 19c correction function 19d specific function 19e estimation function 19f discrimination function

Claims (28)

A pulse sequence in which the longitudinal magnetization and transverse magnetization of tissue are brought to a steady state by continuously irradiating RF pulses, and the steady state when the pulse sequence is executed to measure one magnetic resonance signal for each RF pulse. a storage unit that accumulates and stores reference information based on magnetic resonance signals that reflect the behavior of longitudinal magnetization and transverse magnetization of the tissue in a period before reaching the
an acquisition unit that acquires data based on the magnetic resonance signals in a period before reaching the steady state by executing the pulse sequence;
A magnetic resonance imaging apparatus, comprising: an identifying unit that compares data collected by the collecting unit with reference information stored in the storage unit and identifies reference information having a predetermined relationship. The reference information is information based on the relationship between time-series data based on magnetic resonance signals obtained by executing the pulse sequence and characteristics of the tissue.
The magnetic resonance imaging apparatus according to claim 1. further comprising a determining unit that determines the characteristics of the tissue based on the reference information specified by the specifying unit;
3. The magnetic resonance imaging apparatus according to claim 1 or 2. The reference information is information based on the relationship between time-series data based on magnetic resonance signals obtained by executing the pulse sequence and tissue quantification values,
A magnetic resonance imaging apparatus according to any one of claims 1 to 3. further comprising an estimating unit that estimates the tissue quantification value based on the reference information specified by the specifying unit;
5. The magnetic resonance imaging apparatus according to claim 4. further comprising a display unit that displays the reference information specified by the specifying unit;
The magnetic resonance imaging apparatus according to any one of claims 1-5. The storage unit stores, as the reference information, curve information indicating a change in the signal value of the magnetic resonance signal over time at least during a transient response period,
The identifying unit patterns an approximate curve of signal values of the magnetic resonance signals obtained from time series data based on the collected magnetic resonance signals and a curve represented by each piece of reference information stored in the storage unit. Identify reference information for time-series data based on similar magnetic resonance signals by matching;
The magnetic resonance imaging apparatus according to claim 1. The storage unit stores, as the reference information, reference information regarding normal tissue and reference information regarding abnormal tissue,
further comprising a determining unit that determines whether the tissue is normal or abnormal based on the reference information specified by the specifying unit;
The magnetic resonance imaging apparatus according to claim 7. Reflects the behavior of the longitudinal magnetization and transverse magnetization of the tissue in the period before reaching the steady state when a pulse sequence is executed to bring the longitudinal magnetization and transverse magnetization of the tissue to a steady state by continuously irradiating RF pulses. a storage unit that accumulates and stores reference information based on magnetic resonance signals;
an acquisition unit that acquires data based on the magnetic resonance signals in a period before reaching the steady state by executing the pulse sequence;
an identification unit that compares the data collected by the collection unit with reference information stored in the storage unit and identifies reference information having a predetermined relationship;
a determination unit that determines whether the tissue is normal or abnormal;
with
The storage unit stores, as the reference information, at least curve information indicating temporal changes in signal values of the magnetic resonance signals in a transient response period, and reference information on normal tissues and abnormal tissues. store reference information and
The identifying unit patterns an approximate curve of signal values of the magnetic resonance signals obtained from time series data based on the collected magnetic resonance signals and a curve represented by each piece of reference information stored in the storage unit. Identify reference information for time-series data based on similar magnetic resonance signals by matching,
The determining unit determines whether the tissue is normal or abnormal based on the reference information specified by the specifying unit,
The collecting unit collects the time-series data before the injection of the contrast medium when a plurality of protocols including a protocol involving injection of a contrast medium into the subject is continuously executed, and the determining unit omitting the injection of the contrast agent and the execution of the protocol involving the injection of the contrast agent when the tissue is determined to be normal by
Magnetic resonance imaging equipment. The discriminating unit determines changes over time in the magnetic resonance signals obtained from time-series data based on the collected magnetic resonance signals, and changes over time in the magnetic resonance signals in the reference information on normal tissues stored in the storage unit. display on the display unit information indicating the degree of correlation with changes in
The magnetic resonance imaging apparatus according to claim 8 or 9 . The storage unit stores reference information about the abnormal tissue for each case,
When the determination unit determines that the tissue is abnormal, based on the specified reference information, the determination unit displays information indicating a case of the reference information on the display unit.
The magnetic resonance imaging apparatus according to any one of claims 8-10 . The determination unit determines whether the tissue is normal or abnormal based on a machine learning algorithm.
The magnetic resonance imaging apparatus according to any one of claims 8-11. The identifying unit identifies reference information of time-series data based on the similar magnetic resonance signals based on a machine learning algorithm.
The magnetic resonance imaging apparatus according to any one of claims 7-12. Further comprising a generation unit that generates a plurality of time-series images showing signal values of the magnetic resonance signals based on time-series data based on the collected magnetic resonance signals,
The specifying unit specifies reference information of time-series data based on the similar magnetic resonance signals, based on the plurality of time-series images.
The magnetic resonance imaging apparatus according to any one of claims 7-13. The acquisition unit acquires the time-series data by executing a non-orthogonal measurement pulse sequence,
The generation unit selects data of the number of lines necessary for image generation from the collected time-series data based on the magnetic resonance signals in chronological order by the number of lines, and generates a plurality of time-series images. 15. A magnetic resonance imaging apparatus according to claim 14. The acquisition unit acquires the time-series data by executing a non-orthogonal measurement pulse sequence,
The generation unit selects data of the number of lines required for image generation from the collected time-series data based on the magnetic resonance signals while shifting the number of lines in chronological order by a number less than the number of lines, and selects a plurality of the time-series data. produces an image of
15. A magnetic resonance imaging apparatus according to claim 14. Reflects the behavior of the longitudinal magnetization and transverse magnetization of the tissue in the period before reaching the steady state when a pulse sequence is executed to bring the longitudinal magnetization and transverse magnetization of the tissue to a steady state by continuously irradiating RF pulses. a storage unit that accumulates and stores reference information based on magnetic resonance signals;
an acquisition unit that acquires data based on the magnetic resonance signals in a period before reaching the steady state by executing the pulse sequence;
an identification unit that compares the data collected by the collection unit with reference information stored in the storage unit and identifies reference information having a predetermined relationship;
a determination unit that determines whether the tissue is normal or abnormal;
with
The reference information is information based on the relationship between time-series data based on magnetic resonance signals obtained by executing the pulse sequence and tissue quantitative values,
an estimating unit that estimates the tissue quantitative value based on the reference information specified by the specifying unit;
A simulated magnetic resonance signal in a period before reaching the steady state is calculated by a simulation using the measurement conditions when the pulse sequence is executed by the acquisition unit and the tissue quantification value estimated by the estimation unit, and a calculation unit that generates simulated time-series data based on simulated magnetic resonance signals;
An evaluation unit that evaluates the tissue quantitative value estimated by the estimation unit by comparing time-series data based on magnetic resonance signals collected by the collection unit and simulated time-series data generated by the calculation unit. and,
A magnetic resonance imaging apparatus, further comprising: an adjustment unit that adjusts parameters of the next pulse sequence based on the evaluation result of the tissue quantification value by the evaluation unit. The calculation unit generates the simulated time-series data each time the pulse sequence is executed by the acquisition unit;
The evaluation unit evaluates the tissue quantitative value each time the simulated time-series data is generated by the calculation unit,
The adjustment unit adjusts the parameter each time the tissue quantification value is evaluated by the evaluation unit,
The acquisition unit executes the pulse sequence using the parameter adjusted by the adjustment unit until the evaluation result of the tissue quantification value by the evaluation unit satisfies a predetermined condition.
18. A magnetic resonance imaging apparatus according to claim 17 . The identifying unit calculates similarity between the time-series data based on the magnetic resonance signals collected by the collecting unit and the reference information stored by the storage unit, and identifies the reference information with the highest similarity. death,
The evaluation unit calculates a degree of similarity between the time-series data based on the magnetic resonance signals collected by the collection unit and the simulated time-series data generated by the calculation unit. evaluating the tissue quantification value estimated by the estimation unit by comparing the calculated similarity;
19. A magnetic resonance imaging apparatus according to claim 17 or 18 . The evaluation unit compares the similarity between the time-series data collected by the collection unit and the simulated time-series data generated by the calculation unit and the similarity calculated by the identification unit. If the difference is less than a predetermined threshold, determine that the target estimation accuracy has been achieved,
The collection unit repeatedly executes the pulse sequence using the parameters adjusted by the adjustment unit until the evaluation unit determines that the target estimation accuracy has been achieved.
The magnetic resonance imaging apparatus according to any one of claims 17-19 . When the evaluation unit determines that the target estimation accuracy has been achieved, the evaluation unit outputs the tissue quantitative value estimated by the estimation unit to a display unit.
21. A magnetic resonance imaging apparatus according to claim 20 . When the evaluation unit determines that the target estimation accuracy has been achieved, the evaluation unit newly creates reference information based on the relationship between the time-series data collected by the collection unit and the tissue quantitative value estimated by the estimation unit. and store it in the storage unit;
22. A magnetic resonance imaging apparatus according to claim 20 or 21 . The adjustment unit adjusts at least an excitation angle by the RF pulse,
The magnetic resonance imaging apparatus according to any one of claims 17-22 . The tissue quantification value is T1 value, T2 value, T1* value, T2* value, T1ρ value, or T2ρ value,
24. The magnetic resonance imaging apparatus according to any one of claims 4 , 5 , 17-23 . Further comprising a creation unit that creates the reference information by simulation using existing case data,
A magnetic resonance imaging apparatus according to any one of claims 1-24 . Further comprising a correction unit that corrects the reference information using data collected using the phantom,
A magnetic resonance imaging apparatus according to any one of claims 1-25 . The pulse sequence is an SSFP (Steady-State Free Precession) sequence in which the gradient magnetic field is positive and negative symmetrical within one TR (Repetition Time).
A magnetic resonance imaging apparatus according to any one of claims 1-26 . A pulse sequence in which the longitudinal magnetization and transverse magnetization of tissue are brought to a steady state by continuously irradiating RF pulses, and the steady state when the pulse sequence is executed to measure one magnetic resonance signal for each RF pulse. a storage unit that accumulates and stores reference information based on magnetic resonance signals that reflect the behavior of longitudinal magnetization and transverse magnetization of the tissue in a period before reaching the
an acquisition unit that acquires data based on the magnetic resonance signals in a period before reaching the steady state acquired by executing the pulse sequence with a magnetic resonance imaging apparatus;
An image processing apparatus, comprising: a specifying unit that compares data acquired by the acquiring unit with reference information stored in the storage unit, and specifies reference information having a predetermined relationship.

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