Image reconstruction method and reconstruction apparatus

Abstract

To avoid reimaging when an arrhythmia occurs.SOLUTION: In a method according to the embodiment, k-space data is divided into a k-space center segment and a k-space edge segment. The method collects the k-space center segment in a first-time interval, and collects the k-space edge segment in a second-time interval different from the first-time interval. The method reconstructs an MR (Magnetic Resonance) image from k-space data obtained by combining the data of the collected k-space center segment with the data of the collected k-space edge segment. The first-time interval includes a plurality of cardiac cycles. The k-space center segment is repeatedly collected over the plurality of cardiac cycles. Of the plurality of cardiac cycles, data of cardiac cycles that are less affected by arrhythmia is selected as the center segment of the k-space data for use in the reconstruction of the MR image.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the present invention relate to an image reconstruction method and a reconstruction apparatus.

Background Art

[0002] In magnetic resonance imaging (MRI), as an imaging method for imaging a site that performs periodic motion, synchronous imaging synchronized with the biological signal of a subject is known. As an example of synchronous imaging, electrocardiogram synchronous imaging that performs imaging synchronized with the electrocardiogram signal of a subject is known.

[0003] Here, electrocardiogram synchronous imaging includes a prospective gating method and a retrospective gating method. The prospective gating method is a method of collecting data at a specific cardiac phase determined in advance. For example, in the prospective gating method, the timing of the R wave is detected, and data of each cardiac phase is repeatedly collected using this R wave as a trigger.

[0004] The retrospective gating method is a method of extracting data of the same cardiac phase from a series of continuously collected data and reconstructing an image. For example, in the retrospective gating method, data is continuously collected without synchronizing with the electrocardiogram signal, and the electrocardiogram signal at the time of data collection is acquired. Then, using the acquired electrocardiogram signal, after performing post hoc sorting so that the cardiac phases of a series of collected data are aligned, reconstruction is performed.

[0005] During the above electrocardiogram synchronous imaging, arrhythmia of a patient (subject) may occur. Usually, when arrhythmia occurs during imaging, artifacts appear in the image. Therefore, in order to capture a moving image that does not include the influence of arrhythmia, sampling for a sufficiently long period or re-imaging may be performed.

Prior Art Documents

Patent Documents

[0006] Patent Document 1 Japanese Unexamined Patent Application Publication No. 2007-082753 Patent Document 2 Japanese Translation of PCT International Publication No. 2010-510856 SUMMARY OF THE INVENTION PROBLEMS TO BE SOLVED BY THE INVENTION

[0007] The problem to be solved by the present invention is to provide an image reconstruction method and a reconstruction apparatus capable of avoiding re-imaging during the occurrence of arrhythmia. MEANS FOR SOLVING THE PROBLEMS

[0008] The method according to the embodiment segments k-space data into a k-space central portion and a k-space peripheral portion. The method collects the k-space central portion in a first time interval and collects the k-space peripheral portion in a second time interval different from the first time interval. The method reconstructs an MR (Magnetic Resonance) image from the k-space data obtained by combining the data of the collected k-space central portion and the k-space peripheral portion. Further, the first time interval includes a plurality of cardiac cycles. The k-space central portion is repeatedly collected over the plurality of cardiac cycles. As the central portion of the k-space data used for the reconstruction of the MR image, data of a cardiac cycle with a low influence of arrhythmia is selected from among the plurality of cardiac cycles. BRIEF DESCRIPTION OF THE DRAWINGS

[0009]

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MODE FOR CARRYING OUT THE INVENTION

[0010] Hereinafter, with reference to the drawings, an image reconstruction method and a reconstruction apparatus according to an embodiment will be described. Note that the embodiment is not limited to the following embodiments. In addition, the content described in one embodiment is generally applicable to other embodiments as well.

[0011] (First Embodiment) FIG. 1 is a block diagram showing an MRI apparatus 100 according to the first embodiment. As shown in FIG. 1, the MRI apparatus 100 includes a static magnetic field magnet 101, a gradient magnetic field coil 102, a gradient magnetic field power supply 103, a bed 104, a bed control circuit 105, a transmission coil 106, a transmission circuit 107, a reception coil array 108, a reception circuit 109, a sequence control circuit 110, an ECG (Electrocardiogram) circuit 111, and a computer system 120. Note that the MRI apparatus 100 does not include a subject P (for example, a human body). Also, the MRI apparatus 100 is an example of a reconstruction apparatus.

[0012] The static magnetic field magnet 101 is a magnet formed in a hollow cylindrical shape (including those having an elliptical cross section orthogonal to the axis of the cylinder), and generates a uniform static magnetic field in the internal space. The static magnetic field magnet 101 is, for example, a permanent magnet, a superconducting magnet, or the like.

[0013] The gradient magnetic field coil 102 is a coil formed in a hollow cylindrical shape (including those with an elliptical cross-section perpendicular to the axis of the cylinder) and is disposed inside the static magnetic field magnet 101. The gradient magnetic field coil 102 is formed by combining three coils corresponding to the respective X, Y, and Z axes that are perpendicular to each other. These three coils are individually supplied with current from the gradient magnetic field power supply 103 to generate a gradient magnetic field in which the magnetic field strength changes along the X, Y, and Z axes. Here, the gradient magnetic fields of the X, Y, and Z axes generated by the gradient magnetic field coil 102 respectively correspond to, for example, the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging section. The phase encoding gradient magnetic field Ge is used to change the phase of the MR signal according to the spatial position. The readout gradient magnetic field Gr is used to change the frequency of the MR signal according to the spatial position.

[0014] The gradient magnetic field power supply 103 supplies current to the gradient magnetic field coil 102. For example, the gradient magnetic field power supply 103 individually supplies current to each of the three coils forming the gradient magnetic field coil 102.

[0015] The examination table 104 includes a top plate 104a on which the subject P is placed, and under the control of the examination table control circuit 105, inserts the top plate 104a into the cavity (imaging opening) of the gradient magnetic field coil 102 with the subject P placed thereon. Usually, the examination table 104 is installed such that its longitudinal direction is parallel to the central axis of the static magnetic field magnet 101.

[0016] The examination table control circuit 105 is a processor that drives the examination table 104 to move the top plate 104a in the longitudinal direction and the vertical direction under the control of the computer system 120.

[0017] The transmission coil 106 is disposed inside the gradient magnetic field coil 102, receives the supply of RF pulses from the transmission circuit 107, and generates a high-frequency magnetic field.

[0018] The transmission circuit 107 supplies an RF pulse corresponding to the Larmor frequency determined by the type of target atom and the intensity of the magnetic field to the transmission coil 106.

[0019] The receive coil array 108 is disposed inside the gradient magnetic field coil 102 and receives a magnetic resonance signal (hereinafter referred to as an MR signal) emitted from the subject P due to the influence of the high-frequency magnetic field. When the receive coil array 108 receives the MR signal, the received MR signal is output to the receive circuit 109. In the first embodiment, the receive coil array 108 is a coil array having one or more, typically a plurality of, receive coils.

[0020] The receive circuit 109 generates MR data based on the MR signal output from the receive coil array 108. For example, the receive circuit 109 generates MR data by digitally converting the MR signal output from the receive coil array 108. Further, the receive circuit 109 transmits the generated MR data to the sequence control circuit 110.

[0021] Note that the receive circuit 109 may be provided on the gantry device side including the static magnetic field magnet 101, the gradient magnetic field coil 102, and the like. Here, in the first embodiment, the MR signals output from the respective coil elements (each receive coil) of the receive coil array 108 are appropriately distributed and combined and then output to the receive circuit 109 in units called channels or the like. For this reason, the MR data is handled for each channel in the subsequent processing after the receive circuit 109. The relationship between the total number of coil elements and the total number of channels may be the same, or the total number of channels may be less than the total number of coil elements, or conversely, the total number of channels may be more than the total number of coil elements. In the following, when expressed as "for each channel", it indicates that the processing may be performed for each coil element or may be performed for each channel in which the coil elements are distributed and combined. Note that the timing of distribution and combination is not limited to the timing described above. The MR signal or MR data may be distributed and combined in units of channels before the reconstruction processing described later.

[0022] Based on the sequence information transmitted from the computer system 120, the sequence control circuit 110 performs imaging of the subject P by driving the gradient magnetic field power supply 103, the transmission circuit 107, and the reception circuit 109. For example, the sequence control circuit 110 is realized by a processor. Here, the sequence information is information that defines the procedure for performing imaging. The sequence information defines, for example, the strength of the power supply that the gradient magnetic field power supply 103 supplies to the gradient magnetic field coil 102 and the timing of supplying the power supply, the strength of the RF pulse that the transmission circuit 107 transmits to the transmission coil 106 and the timing of applying the RF pulse, and the timing at which the reception circuit 109 detects the MR signal.

[0023] In addition, when the sequence control circuit 110 drives the gradient magnetic field power supply 103, the transmission circuit 107, and the reception circuit 109 to image the subject P and then receives MR data from the reception circuit 109, the received MR data is transferred to the computer system 120.

[0024] Based on the electrocardiogram signal output from the ECG sensor 111a, the ECG circuit 111 detects a predetermined electrocardiogram waveform. The ECG sensor 111a is a sensor that is attached to the body surface of the subject P and detects the electrocardiogram signal of the subject P. The ECG sensor 111a outputs the detected electrocardiogram signal to the ECG circuit 111.

[0025] For example, the ECG circuit 111 detects an R wave as a predetermined electrocardiogram waveform. Then, the ECG circuit 111 generates a trigger signal at the timing when the R wave is detected, and outputs the generated trigger signal to the interface circuit 121. The trigger signal is stored in the memory circuit 122 by the interface circuit 121. Here, the trigger signal may be transmitted from the ECG circuit 111 to the interface circuit 121 by wireless communication. In the present embodiment, the case where the electrocardiogram signal is detected by the ECG sensor 111a is described, but the present invention is not limited thereto. For example, it may be detected by a pulse wave meter. Further, in FIG. 1, an example in which the ECG sensor 111a and the ECG circuit 111 are part of the MRI apparatus 100 has been described, but the present invention is not limited thereto. That is, the MRI apparatus 100 may acquire an electrocardiogram signal obtained from the ECG sensor 111a and the ECG circuit 111 provided separately from the MRI apparatus 100.

[0026] The computer system 120 performs overall control of the MRI apparatus 100, data collection, image reconstruction, and the like. The computer system 120 includes an interface circuit 121, a memory circuit 122, an input interface 123, a display 124, and a processing circuit 130.

[0027] The interface circuit 121 transmits sequence information to the sequence control circuit 110 and receives MR data from the sequence control circuit 110. Further, when the interface circuit 121 receives MR data, the received MR data is stored in the memory circuit 122. The MR data stored in the memory circuit 122 is arranged in the k-space by the processing circuit 130. As a result, the memory circuit 122 stores k-space data for a plurality of channels. In this way, the k-space data is collected. The interface circuit 121 is realized by, for example, a network interface card.

[0028] The memory circuit 122 stores MR data received by the interface circuit 121, time-series data (k-t space data) arranged in the k-space by the acquisition function 131 described later, MR image data generated by the second reconstruction function 137 described later, and the like. The memory circuit 122 also stores various programs. The memory circuit 122 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, or the like.

[0029] The input interface 123 receives various instructions and information inputs from operators such as doctors and radiological technologists. The input interface 123 is realized by, for example, a trackball, a switch button, a mouse, a keyboard, or the like. The input interface 123 is connected to the processing circuit 130, and converts the input operation received from the operator into an electrical signal and outputs it to the processing circuit 130.

[0030] The display 124 displays various GUIs (Graphical User Interface) and MR image data generated by the second reconstruction function 137 under the control of the processing circuit 130.

[0031] The processing circuit 130 performs overall control of the MRI apparatus 100. Specifically, the processing circuit 130 generates sequence information based on imaging conditions input from the operator via the input interface 123, and controls imaging by transmitting the generated sequence information to the sequence control circuit 110. Further, the processing circuit 130 controls the reconstruction of an image performed based on the MR data sent from the sequence control circuit 110 as a result of imaging, and controls the display by the display 124. The processing circuit 130 is realized by a processor.

[0032] The processing circuit 130 includes an acquisition function 131, a calculation function 132, a combination function 133, a first reconstruction function 134, a generation function 135, a selection function 136, a second reconstruction function 137, and an output control function 138. Note that the acquisition function 131 is an example of an acquisition unit. Also, the calculation function 132 is an example of a calculation unit. Also, the combination function 133 is an example of a combination unit. Also, the first reconstruction function 134 is an example of a first generation unit. Also, the generation function 135 is an example of a second generation unit. Also, the selection function 136 is an example of a selection unit. Also, the second reconstruction function 137 is an example of a reconstruction unit. Also, the output control function 138 is an example of an output control unit.

[0033] Here, for example, each processing function of the acquisition function 131, the calculation function 132, the combination function 133, the first reconstruction function 134, the generation function 135, the selection function 136, the second reconstruction function 137, and the output control function 138, which are components of the processing circuit 130, is stored in the storage circuit 122 in the form of a program executable by a computer. The processing circuit 130 reads each program from the storage circuit 122 and executes each read program, thereby realizing the function corresponding to each program. In other words, the processing circuit 130 in the state of having read each program has each function shown in the processing circuit 130 of FIG. 1. In FIG. 1, although it has been described that each processing function of the acquisition function 131, the calculation function 132, the combination function 133, the first reconstruction function 134, the generation function 135, the selection function 136, the second reconstruction function 137, and the output control function 138 is realized by a single processing circuit 130, it is also possible to configure the processing circuit 130 by combining a plurality of independent processors, and each processor executes each program to realize each processing function.

[0034] As used in the above description, the term "processor" means, for example, a CPU (central preprocess unit), a GPU (Graphics Processing Unit), or a circuit such as an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (for example, a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). Note that instead of storing a program in the storage circuit 122, the program may be directly incorporated into the circuit of the processor. In this case, the processor realizes its functions by reading and executing the program incorporated in the circuit.

[0035] Here, generally, the MRI apparatus 100 measures electromagnetic waves emitted from a subject using a coil. A signal obtained by digitizing the measured electromagnetic waves is called k-space data.

[0036] The k-space data is, for example, two-dimensional or three-dimensional data obtained by repeating one-dimensional imaging. Then, an atomic distribution image inside the subject is obtained by performing a Fourier transform (hereinafter, the Fourier transform may include the inverse Fourier transform) on the k-space data. The obtained atomic distribution image is called an MR image, and the process of calculating an MR image from the k-space data is called reconstruction, image reconstruction, image generation, etc. The central part of the k-space data corresponds to the low-frequency component when a Fourier transform is performed on the MR image, and the edge part of the k-space data corresponds to the high-frequency component when a Fourier transform is performed on the MR image.

[0037] In order to shorten the imaging time, in addition to the phase encoding direction, it is effective to subsample the k-space data also in the temporal direction (time direction). As techniques for subsampling the k-space data in the temporal direction, for example, techniques called k-t BLAST (k-space time Broad-use Linear Acquisition Speed-up Technique) and k-t SENSE are known. However, these techniques may not work well even when combined with the retrospective gating method. This is because these techniques assume that the subsampling pattern of the k-space along the time series changes regularly. That is, when rearranging using the cardiac phase, which is necessary when performing reconstruction using the retrospective gating method, the subsampling pattern of the k-space along the time series becomes irregular, and techniques such as k-t BLAST and k-t SENSE cannot be used. Note that when the number of coils is small relative to the ratio of the subsampled samples, it is called k-t BLAST, and when it is not, it is called k-t SENSE. However, in the following description, unless explicitly distinguished, it will be called k-t SENSE including k-t BLAST. Hereinafter, mainly the case of multiple coils will be described, but as a special case of k-t BLAST, the case where the number of coils is one is also allowed. Even when there is one coil, for convenience, it will be called k-t SENSE.

[0038] In k-t SENSE, the collected group of k-space data is converted into x-f space data consisting of an image space and a time spectrum by Fourier transform. Then, in this x-f space data, x-f space data with the aliasing signal removed is generated using the sensitivity map on the x-f space. Then, by converting the generated x-f space data into x-t space data by inverse Fourier transform, a plurality of MR images arranged in time series are generated.

[0039] An example of sampling by thinning the k-t space in the temporal direction will be described with reference to FIG. 2. FIG. 2 is a diagram showing an example of sampling positions in the k-t space. In FIG. 2, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the temporal direction. In FIG. 2, for the sake of explanation, k-t space data in which collected data is arranged at 8 positions in the phase encoding direction and 20 positions (frames) in the temporal direction is exemplified. Also, the black dots indicate the positions where one line of data is collected. In other words, the frames where the black dots are not arranged are the positions where data is not collected. Also, it is assumed that a period of more than one heartbeat is included between temporal phases P'1 to P'20. In k-t BLAST and k-t SENSE, there are a calibration imaging for acquiring information regarding the x-f space without thinning in the temporal direction and this imaging for sampling the k-t space by thinning in the temporal direction before or during this imaging. However, the example of the sampling position shown in FIG. 2 can be considered as an example of the sampling position in this imaging. For simplicity, in FIG. 2, the sampling position in this imaging is not shown. Also, in a technique that does not necessarily require calibration imaging as disclosed in Japanese Patent No. 6073627, FIG. 2 can be considered as an example of the sampling position in this imaging.

[0040] In the example shown in FIG. 2, the sampling positions are shifted one by one in the phase encoding direction for each unit temporal phase. For example, the k-space data of temporal phase P'2 is sampled at a position shifted by one sample in the phase encoding direction (the upward direction in the figure) compared to the k-space data of temporal phase P'1. Also, the k-space data of temporal phase P'3 is sampled at a position shifted by one sample in the phase encoding direction compared to the k-space data of temporal phase P'2. Also, the k-space data of temporal phase P'4 is sampled at a position shifted by one sample in the phase encoding direction compared to the k-space data of temporal phase P'3. That is, in the example of FIG. 2, the k-space data thinned to one-fourth is periodically sampled every four unit temporal phases.

[0041] Thus, when changing the sampling pattern in the phase-encoding direction along the temporal direction in k-space, k-space data having the same phase-encoding amount exists only at a ratio of 1 out of 4 time phases. For this reason, if the k-space data is rearranged focusing only on the cardiac phases, data for each phase-encoding amount necessary for reconstruction may not be collected. For example, even if the k-space data is arranged in the order of time phases P’1, P’20, P’3, P’4, it cannot be reconstructed. This is because the sampling pattern of the k-space data necessary for reconstruction in combination with time phases P’1, P’3, P’4 is the same as that of time phase P’2, and the sampling patterns are different between time phase P’2 and time phase P’20.

[0042] In addition, in electrocardiogram-synchronized imaging for a patient (subject) with arrhythmia, in order to avoid the occurrence of artifacts due to the influence of arrhythmia, sampling may be performed for a sufficiently long period (for example, 3 heartbeats, etc.) or re-imaging may be performed. However, extending the sampling period hinders the speeding up of imaging. In addition, since re-imaging means performing the same imaging twice, it extends the examination time spent on one patient.

[0043] Therefore, the MRI apparatus 100 according to the first embodiment enables high-speed electrocardiogram-synchronized imaging while avoiding re-imaging during the occurrence of arrhythmia by the processing functions described below.

[0044] Note that, in the following, the case where the present embodiment is applied to k-t SENSE will be described, but it is not limited thereto. For example, the present embodiment is also applicable to k-t BLAST and Compressed Sensing (CS). Compressed Sensing is a high-speed imaging method that samples by randomly thinning in the phase encoding direction and reconstructs an image from a small number of k-space data by utilizing the sparsity of the signal. Note that the sampling in k-t SENSE, k-t BLAST, and Compressed Sensing collects a plurality of k-space data with different sampling patterns between consecutive time phases, and in the following description, it will be referred to as "non-uniform thinning sampling" in contrast to a normal PI that performs simple thinning sampling.

[0045] The processing procedure by the MRI apparatus 100 according to the first embodiment will be described with reference to FIG. 3. FIG. 3 is a flowchart showing the processing procedure by the MRI apparatus 100 according to the first embodiment. The processing procedure shown in FIG. 3 is started, for example, triggered by an imaging start request input by an operator. Note that the numerical values exemplified in the following embodiments are merely examples and can be arbitrarily changed by the operator.

[0046] In step S101, the acquisition function 131 collects a plurality of k-space data. For example, the acquisition function 131 generates sequence information based on the imaging conditions input from the operator via the input interface 123. For example, the acquisition function 131 generates sequence information based on the period of one cardiac cycle input from the operator and the number of MR images acquired per cardiac cycle.

[0047] For example, the operator defines a period of one cardiac cycle suitable for the subject. This period of one cardiac cycle is, for example, the RR interval (trigger interval). The operator refers to biological information such as an ECG or PPG (Photoplethysmography) and defines the period of one cardiac cycle of the subject, for example, in milliseconds. Also, since the RR interval may fluctuate even in a healthy person, the allowable amount of variation in the RR interval may be further specified. For example, if the amount of variation in the RR interval is defined as 10%, the MRI apparatus 100 determines that a range from 720 msec to 880 msec is a normal heartbeat. On the other hand, if the RR interval is extremely short or long, it is considered an arrhythmia. Therefore, the MRI apparatus 100 determines an RR interval that is outside the range of the period of one cardiac cycle defined by the operator or the period range considering the allowable amount of variation in the period of one cardiac cycle as an arrhythmia. In the present embodiment, a case where "800 msec" is defined as the period of one cardiac cycle used as a reference will be described, but it can be set to any time. Also, the unit for receiving the setting can be arbitrarily changed.

[0048] The operator sets the "number of acquired images" of image data per cardiac cycle. For example, the operator sets the number of acquired images to "24 images". Thereby, the MRI apparatus 100 captures 24 MR images during one cardiac cycle. Note that since this number of acquired images also represents how many time phases of one cardiac cycle are imaged, it corresponds to the "number of time phases (number of phases)" to be imaged. In the present embodiment, a case where "24 images (24 phases)" of MR images are acquired per cardiac cycle will be described, but it can be set to any number (number of phases).

[0049] That is, the acquisition function 131 generates sequence information based on the input of imaging conditions including the period corresponding to one cardiac cycle and the number of acquired MR images per cardiac cycle. Then, the acquisition function 131 controls imaging by transmitting the generated sequence information to the sequence control circuit 110. The sequence control circuit 110 samples k-space data based on the sequence information received from the acquisition function 131.

[0050] For example, the sequence control circuit 110 collects k-space data by dividing it into a plurality of segments with different phase encoding amounts. The sequence control circuit 110 sends the collected plurality of k-space data to the acquisition function 131 as an imaging result. As a result, the acquisition function 131 acquires a plurality of k-space data collected by dividing them into a plurality of segments with different phase encoding amounts.

[0051] The processing of the acquisition function 13 according to the first embodiment will be described with reference to FIGS. 4, 5, and 6. FIGS. 4, 5, and 6 are diagrams for explaining the processing of the acquisition function 13 according to the first embodiment. FIG. 4 illustrates an imaging sequence in which k-t space data is divided into segment A and segment B and collected. FIG. 5 illustrates the k-t space data of segment A. FIG. 6 illustrates the k-t space data of segment B. In FIGS. 4, 5, and 6, "t" shown on the horizontal axis corresponds to the time direction. For convenience of explanation, the time direction is partially omitted in the illustration here. Also, in FIGS. 5 and 6, "k" shown on the vertical axis corresponds to the phase encoding direction. Also, in FIGS. 5 and 6, the black circles indicate the positions where one line of k-space data is collected. In other words, the frames where the black circles are not arranged are the positions where k-space data is not collected. Each k-space data corresponds to a two-dimensional k-space composed of a one-dimensional frequency encoding direction and a one-dimensional phase encoding direction. Here, for simplicity, the frequency encoding direction is not shown, but the k-space data in the frequency encoding direction is filled in a direction perpendicular to the paper surface. Also, although not shown, the sequence control circuit 110 can execute an imaging sequence by appropriately inserting dummy shots and wait times.

[0052] As shown in FIG. 4, the sequence control circuit 110 executes an imaging sequence in the order of segment A and segment B. Here, segment A includes 72 phases from phase PA1, phase PA2, phase PA3... to phase PA72. Further, segment B includes 58 phases from phase PB1, phase PB2, phase PB3... to phase PB60. That is, the sequence control circuit 110 collects k-space data for 72 phases for segment A and k-space data for 58 phases for segment B.

[0053] As shown in FIG. 5, segment A collects k-space data corresponding to 16 phase encoding amounts close to the center of k-space for 72 time phases in the time direction. That is, segment A corresponds to the central segment. Note that the central segment is also referred to as the first segment. The central segment corresponds to the central portion of k-space.

[0054] As shown in FIG. 6, segment B collects k-space data corresponding to 8 phase encoding amounts alternately at both edge portions of k-space for 58 time phases in the time direction. That is, segment B corresponds to the edge segment. Note that the edge segment is also referred to as the second segment. The edge segment corresponds to the edge portion of k-space.

[0055] Here, the acquisition of segment A and segment B by k-t SENSE will be described. k-t SENSE is a sampling pattern that is regularly decimated in the phase encoding direction of k-space, and collects a plurality of k-space data with a sampling pattern in which the phase encoding lines collected between consecutive time phases are different. For example, in segment A, a plurality of k-space data decimated by a factor of 1 / 4 are sampled at positions shifted by one sample in the phase encoding direction for each unit time phase. For example, a plurality of k-space data at time phase PA2 are sampled at positions shifted by one sample in the phase encoding direction (the upward direction in the figure) compared to a plurality of k-space data at time phase PA1. Also, a plurality of k-space data at time phase PA3 are sampled at positions shifted by one sample in the phase encoding direction compared to a plurality of k-space data at time phase PA2. Thus, the sampling pattern of segment A is a pattern in which a plurality of k-space data decimated by a factor of 1 / 4 are periodically repeated every 4 unit time phases. Also, since the sampling pattern of segment B is the same as that of segment A except that the amount of phase encoding of the k-space data to be collected is different, the description thereof will be omitted.

[0056] Here, the acquisition period of the central segment (segment A) is set to a value corresponding to 200 to 300% of the "RR interval" defined by the operator so that k-space data for one cardiac cycle can be obtained even if arrhythmia occurs. In the present embodiment, since the RR interval set by the operator is 800 msec, when it is equivalent to 300%, the acquisition period of segment A is "800 msec × 3 = 2400 msec". Also, in the present embodiment, since the number of acquisitions per 800 msec (one cardiac cycle) set by the operator is 24 (24 phases), the number of phases of segment A is "2400 msec × 24 phases / 800 msec = 72 phases". The acquisition period of the central segment (segment A) corresponds to the first time interval.

[0057] In addition, the acquisition period of the edge segment (segment B) is set to a smaller number of phases compared to the central segment. For example, the acquisition period of the edge segment is set to about 120% of the set one cardiac cycle period regardless of the occurrence of arrhythmia. In this embodiment, the RR interval set by the operator is 800 msec, and the number of acquired images per 800 msec set as the period of one cardiac cycle by the operator is 24 images (24 phases). Therefore, the number of phases of segment B is 28 phases. The acquisition period of the above-described edge segment is shorter than the acquisition period of the central segment and does not consider the presence or absence of arrhythmia. This is because the information included in the central segment contributes more to the diagnosis using cine images than the information included in the edge segment. The acquisition period of the edge segment (segment B) corresponds to the second time interval.

[0058] That is, in this embodiment, the k-space data included in the edge segment is collected over an acquisition period shorter than the acquisition period of the central segment. For example, when the acquisition period of the central segment is set as a value corresponding to 200 to 300% of the reference RR interval set by the operator, it is preferable that the acquisition period of the edge segment is set as a value less than 200%, and more preferably set as a value of about 120%.

[0059] In addition, by combining (concatenating) the k-space data included in segment A, segment B1, and segment B2 respectively, a set of k-space data having the phase encoding amount necessary for reconstruction can be aligned. The process of combining the k-space data will be described later.

[0060] In this way, the sequence control circuit 110 collects a plurality of k-space data divided into segments and sends the collected plurality of k-space data to the acquisition function 131 as an imaging result. Thereby, the acquisition function 131 acquires a plurality of k-space data collected separately into a plurality of segments having different phase encoding amounts.

[0061] Note that in FIGS. 5 and 6, for the sake of illustration, although the plurality of k-space data included in each time phase are illustrated at the same position in the time direction, strictly speaking, they are collected at different times. For example, the four k-space data included in time phase PA1 are collected in order from the one with the smallest phase encoding amount. The collection time of each k-space data is associated with each k-space data. That is, the acquisition function 131 acquires a plurality of k-space data and the collection time of each k-space data.

[0062] Also, the acquisition function 131 acquires electrocardiogram information of the subject P together with non-uniform subsampling. For example, when non-uniform subsampling is started, the ECG circuit 111 starts recording an electrocardiogram signal. The ECG circuit 111 detects an R wave from the electrocardiogram signal detected by the ECG sensor 111a. Then, the ECG circuit 111 generates a trigger signal at the timing when the R wave is detected. Then, the ECG circuit 111 stores the generated trigger signal in the memory circuit 122 via the interface circuit 121. The detection time of the trigger signal can be associated with the collection time of the k-space data. The acquisition function 131 acquires the detection time of the trigger signal stored in the memory circuit 122 as the electrocardiogram information of the subject P. Note that the electrocardiogram information is an example of heartbeat information.

[0063] In this way, the acquisition function 131 acquires a plurality of k-space data collected from the subject P by non-uniform subsampling, the collection time of each k-space data, and the electrocardiogram information of the subject P. Note that the k-space data acquired by the acquisition function 131 is also referred to as first k-space data. Also, the collection time of the first k-space data is also referred to as the first collection time.

[0064] Note that the description of the above acquisition function 131 is merely an example and is not limited by the above description. For example, in FIG. 4, the case of collecting in the order of segment A and segment B was described, but it is also possible to collect in the reverse order. Further, when collecting by dividing into three or more segments, the three segments can be collected in any order. Further, the number of positions (frames) in the phase encoding direction and the time direction illustrated in FIGS. 5 and 6 can be arbitrarily changed.

[0065] In step S102, the calculation function 132 calculates the cardiac phase information of the central segment. Here, the "cardiac phase information" is information indicating the position in the phase direction in one cardiac cycle. For example, in each phase included in segment A, the calculation function 132 calculates the cardiac phase information of the data serving as the representative point from among the phase encoding amounts of the k-space data included in each phase as the cardiac phase information of each phase included in segment A. Note that as the data serving as the representative point, for example, data having a phase encoding amount approximately at the center among the phase encoding amounts of the k-space data included in each phase is selected.

[0066] The processing of the calculation function 132 according to the first embodiment will be described with reference to FIG. 7. FIG. 7 is a diagram for explaining the processing of the calculation function 132 according to the first embodiment. In FIG. 7, "PE Line number" is a number indicating the line regarding the phase encoding direction of each collected data. Further, "Time" is information indicating the collection time of each collected data. Further, "RR Interval" is information indicating the RR interval of the heartbeat included in each collected data, and is "820 msec" in the example of FIG. 7. "trigger" is information indicating the detection time of the trigger signal of the heartbeat included in each collected data. "Phase center" is information indicating whether the data of each collected line is approximately at the center in the phase encoding direction within the segment. This Phase center can be set at the stage when the sampling pattern, the number of segments, and the segment width are determined. The "cardiac phase information" indicates, for example, at what percentage position from the start of the RR interval the collected k-space data is collected when the RR interval is 100%.

[0067] In the example shown in FIG. 7, a case will be described where the k-space data of 4 lines with PE Line numbers from "1" to "4" corresponds to the k-space data of 4 lines included in the time phase PA1 of FIG. 5. Specifically, the k-space data with the smallest phase encoding amount among the time phases of PA corresponds to the PE Line number "1", and the k-space data with the largest phase encoding amount corresponds to the PE Line number "4". Among the k-space data of these 4 lines, the k-space data located approximately at the center in the phase encoding direction is the line of the PE Line number "3". Among the phase encoding amounts of these 4 lines, the k-space data having the phase encoding amount approximately at the center is the line of the PE Line number "3". Therefore, the line of the PE Line number "3" is set as the Phase center, and "1" is registered. Note that "0" is registered for the lines not set as the Phase center.

[0068] Here, the calculation function 132 calculates the cardiac phase information of the line set as the Phase center. For example, the calculation function 132 calculates the cardiac phase information of the line of the PE Line number "3" set as the Phase center. Specifically, the calculation function 132 divides the difference between Time and trigger by the RR Interval and converts it into a percentage to calculate the cardiac phase information "67.26" of this segment. Also, although the description is omitted here, the calculation function 132 similarly calculates the cardiac phase information of the line located approximately at the center in the phase encoding direction for each of the time phases PA2, PA3... PA72 as the cardiac phase information of each time phase included in segment A.

[0069] In this way, the calculation function 132 calculates the cardiac phase information of the central segment including a plurality of k-space data with a reference phase encoding amount among the plurality of k-space data. Note that the content illustrated in FIG. 7 is merely an example and is not limited to the illustrated example. For example, the calculation function 132 may calculate the cardiac phase information of the line with the PE Line number "2" and the cardiac phase information of the line with the PE Line number "3", and use the average value thereof as the cardiac phase information of the phase PA1 of the segment A. That is, the "substantially central" is not limited to only the line closest to the center in the phase encoding direction within the segment.

[0070] In step S103, the calculation function 132 calculates the cardiac phase information of the edge segment. For example, in each phase included in the segment B, the calculation function 132 calculates the cardiac phase information of the k-space data having the substantially central phase encoding amount among the phase encoding amounts of the plurality of k-space data included in each phase as the cardiac phase information of each phase included in the segment B. In the example of FIG. 6, the calculation function 132 calculates the cardiac phase information of the data with the larger phase encoding amount among the two data included in the k-space of the phase PB1 as the cardiac phase information of the phase PB1. This is because among the phase encoding amounts covered by the segment B1, the data is located substantially centrally with respect to the phase encoding direction.

[0071] That is, the calculation function 132 calculates the cardiac phase information of the edge segment including a plurality of k-space data with a phase encoding amount different from that of the central segment. Note that the process of calculating the cardiac phase information of the edge segment is the same as the process of calculating the cardiac phase information of the central segment, so the description thereof is omitted.

[0072] In step S104, the combining function 133 combines an edge segment having cardiac phase information close to the cardiac phase information of the central segment with the central segment. For example, the combining function 133 combines the k-space data of each phase included in segment B with the k-space data of each phase included in segment A, taking segment A, which is the central segment, as a reference. Here, the combining function 133 identifies the k-space data to be combined based on a preset sampling pattern. Hereinafter, the plurality of k-space data included in each phase combined by the combining function 133 is referred to as combined data.

[0073] The processing of the combining function 133 according to the first embodiment will be described with reference to FIGS. 8, 9, 10, and 11. FIGS. 8, 9, 10, and 11 are diagrams for explaining the processing of the combining function 133 according to the first embodiment. In FIGS. 8, 9, 10, and 11, the k-t space data of segment A is illustrated on the left side, and the k-t space data of segment B is illustrated on the right side. Note that the k-t space data shown in FIGS. 8, 9, 10, and 11 corresponds to the k-t space data shown in FIGS. 5 and 6. In FIGS. 8, 9, 10, and 11, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the time direction. For convenience of explanation, the time direction is partially omitted in the drawings here. Also, the black dots indicate one line on the k-space data filled with data.

[0074] In FIG. 8, a process of identifying a target for combination to be combined with the k-space data included in region R1 of segment A from the k-space data of segment B1 will be described. Region R1 includes 4-line k-space data collected at time phase PA1. The sampling pattern of this region R1 is the sampling pattern in which the first of every 4 frames is collected, in order from the one with the smallest k-space data phase encoding amount, such as the 1st, 5th, 9th, and 13th. Therefore, the combining function 133 identifies a phase in the k-space data of segment B1 that has the same sampling pattern as the sampling pattern of region R1 and has a cardiac phase information close to the cardiac phase information of time phase PA1. Here, since segment B1 and segment B2 are collected alternately and each sampling pattern is periodically repeated every 4 unit phases, the sampling patterns that are the same as the sampling pattern of region R1 are time phases PB1, PB9, PB17... PB57. Then, the combining function 133 identifies time phase PB1 (region R2) as the phase having the cardiac phase information closest to the cardiac phase information of time phase PA1 among time phases PB1, PB9, PB17... PB57. Then, the combining function 133 combines the k-space data included in region R1 by arranging the k-space data included in region R2 in region R3.

[0075] Also, in FIG. 9, a process of identifying a target for combination to be combined with the k-space data included in region R4 of segment A from the k-space data of segment B1 will be described. Region R4 includes 4-line k-space data collected at time phase PA2. The sampling pattern of this region R4 is the sampling pattern in which the second one out of every 4 frames is collected, in order from the one with a small phase-encoding amount, such as the second, sixth, tenth, and fourteenth. Therefore, the combining function 133 identifies a time phase from the k-space data of segment B1 that has the same sampling pattern as the sampling pattern of region R4 and has a cardiac phase information close to the cardiac phase information of time phase PA2. Here, the sampling patterns that are the same as the sampling pattern of region R4 are time phases PB2, PB10, PB18 ··· PB58. Then, the combining function 133 identifies the time phase of region R5 as the time phase having the cardiac phase information closest to the cardiac phase information of time phase PA2 among time phases PB2, PB10, PB18 ··· PB58. Then, the combining function 133 combines the k-space data included in region R5 with the k-space data included in region R2 by arranging the k-space data included in region R5 in region R6.

[0076] In this way, the combining function 133 identifies the k-space data of segment B1 that has a preset sampling pattern and has cardiac phase information close to the cardiac phase information of segment A. Then, the combining function 133 combines the identified k-space data of segment B1 with the k-space data of segment A in units of segments. As a result, as shown in FIG. 10, the k-space data of segment B1 collected at the closest cardiac phase can be combined with the k-space data of each time phase of time phases PA1, PA2, PA3 ··· PA72 of segment A without disturbing the preset sampling pattern.

[0077] Also, as shown in FIG. 11, the combining function 133 combines the k-space data of segment B2 with the k-space data of segment A on a segment-by-segment basis. Note that the process of combining the k-space data of segment B2 is the same as the process of combining the k-space data of segment B1, so the description thereof is omitted. As a result, as shown in FIG. 11, for the k-space data of segment A at each time phase of time phases PA1, PA2, PA3... PA72, the k-space data of segment B2 collected at the closest cardiac time phase can be combined without disturbing the preset sampling pattern.

[0078] In this way, the combining function 133 combines the k-space data at each time phase of the central segment with the k-space data at the time phase having cardiac time phase information close to the cardiac time phase information of each time phase of the central segment among the peripheral segments. Thereby, the combining function 133 generates combined data for each time phase for each of the time phases PA1, PA2, PA3... PA72.

[0079] Note that the above description of the combining function 133 is merely an example, and the embodiments are not limited thereto. For example, in the above description, the case where the combining function 133 performs a combining process without performing a process of specifying a plurality of k-space data that are not affected (less affected) by arrhythmia among the k-space data included in the peripheral segments is described, but the embodiments are not limited thereto. That is, the combining function 133 can also perform a process of specifying a plurality of k-space data that are not affected by arrhythmia among the k-space data included in the peripheral segments. The process of the combining function 133 in this case will be described later with reference to FIG. 18.

[0080] Returning to the description of FIG. 3. In step S105, the first reconstruction function 134 executes the first reconstruction process. For example, the first reconstruction function 134 reconstructs a plurality of image data from a plurality of k-space data by a reconstruction process corresponding to non-uniform subsampling (e.g., k-t SENSE). Note that the image data reconstructed by the first reconstruction function 134 is an MR image as an intermediate image.

[0081] In step S106, the generation function 135 generates a plurality of k-space data corresponding to full sampling by performing an inverse Fourier transform process. For example, the generation function 135 performs an inverse Fourier transform process on the plurality of image data reconstructed by the first reconstruction function 134 to generate a plurality of k-space data corresponding to full sampling. Note that the k-space data generated by the generation function 135 is also referred to as second k-space data. Here, the plurality of k-space data corresponding to full sampling is data in which at least a part of the k-space data corresponding to the k-space data decimated by non-uniform decimation sampling is filled (filled). That is, the generation function 135 generates a plurality of second k-space data in which at least a part of the region decimated by non-uniform decimation sampling is filled from the plurality of first k-space data by a process including a Fourier transform corresponding to non-uniform decimation sampling.

[0082] In step S107, the generation function 135 assigns a pseudo acquisition time to each of the plurality of k-space data. For example, the generation function 135 generates a pseudo acquisition time for each k-space data corresponding to full sampling. Note that the pseudo acquisition time is also referred to as a second acquisition time.

[0083] The processing of the first reconstruction function 134 and the generation function 135 according to the first embodiment will be described with reference to FIG. 12. FIG. 12 is a diagram for explaining the processing of the first reconstruction function 134 and the generation function 135 according to the first embodiment. In the upper part of FIG. 12, combined data of a plurality of time phases generated in FIG. 11 is illustrated. In the middle part of FIG. 12, a plurality of time-phase images (MR images) reconstructed from the combined data of the plurality of time phases in the upper part of FIG. 12 are illustrated. In the lower part of FIG. 12, a plurality of k-space data corresponding to full sampling generated from the images in the upper part of FIG. 12 are illustrated. In FIG. 12, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the time direction. Also, for convenience of explanation, in FIG. 12, the phase encoding direction and the time direction are partially omitted. Also, the black circles indicate the positions where one line of k-space data is arranged.

[0084] As shown in FIG. 12, the first reconstruction function 134 converts the combined data (plural k-space data) of each phase into x-f space data composed of an image space and a time spectrum by Fourier transform. Further, the first reconstruction function 134 uses a sensitivity map in the x-f space to generate x-f space data in which the aliasing signal in the x-f space data is removed. Then, the first reconstruction function 134 generates a plurality of time-series image data by converting the generated x-f space data into x-t space data by inverse Fourier transform.

[0085] That is, the reconstruction function 134 reconstructs a plurality of image data by performing a reconstruction process on the plurality of k-space data combined by the combining function 133. Specifically, the reconstruction function 134 generates image data of each phase from the combined data of each phase of the phase PA1, phase PA2, phase PA3 ··· phase PA72.

[0086] Then, the generation function 135 executes an inverse Fourier transform process (IFFT) on the image data of each phase of the phase PA1, phase PA2, phase PA3 ··· phase PA72. Thereby, the generation function 135 generates a plurality of k-space data corresponding to a state in which the k-spaces of each phase of the phase PA1, phase PA2, phase PA3 ··· phase PA72 are fully sampled.

[0087] For example, the generation function 135 generates 32 k-space data S'1, S'2, S'3, ··· S'32 by performing an inverse Fourier transform process on the image data of the phase PA1. Here, these 32 k-space data S'1, S'2, S'3, ··· S'32 correspond to the full sampling of 32 sampling positions of the phase PA1. The generation function 135 similarly generates a plurality of k-space data corresponding to full sampling for other phases.

[0088] Then, the generation function 135 assigns pseudo acquisition times to a plurality of k-space data corresponding to full sampling. For example, the k-space data S’1 has the same temporal phase and the same phase encoding amount as the k-space data S1 in the upper part of FIG. 12. Therefore, the generation function 135 assigns the acquisition time of the k-space data S1 as the acquisition time of the k-space data S’1. Also, the k-space data S’5 has the same temporal phase and the same phase encoding amount as the k-space data S2 in the upper part of FIG. 12. Therefore, the generation function 135 assigns the acquisition time of the k-space data S2 as the acquisition time of the k-space data S’5.

[0089] Also, the k-space data S’2, S’3, S’4 are arranged at equal intervals between the k-space data S’1 and S’5. Therefore, the generation function 135 assigns the times obtained by equally dividing the time between the acquisition time of the k-space data S’1 and the acquisition time of the k-space data S’5 as the acquisition times of the respective k-space data S’2, S’3, S’4. Specifically, the acquisition time of the k-space data S’3 is the median of the acquisition time of the k-space data S’1 and the acquisition time of the k-space data S’5. Also, the acquisition time of the k-space data S’2 is the median of the acquisition time of the k-space data S’1 and the acquisition time of the k-space data S’3. Also, the acquisition time of the k-space data S’4 is the median of the acquisition time of the k-space data S’3 and the acquisition time of the k-space data S’5. In this way, the generation function 135 calculates and assigns the second acquisition time of the second k-space data corresponding to full sampling based on the first acquisition time of the first k-space data.

[0090] Returning to the description of FIG. 3. In step S108, the selection function 136 executes arrhythmia removal rearrangement processing. This arrhythmia removal rearrangement processing removes (excludes) k-space data affected by arrhythmia from among a plurality of k-space data corresponding to full sampling and then performs rearrangement processing (sorting processing by the retrospective gating method).

[0091] That is, the selection function 136 identifies, based on the electrocardiogram information, a plurality of k-space data corresponding to full sampling that are not affected by arrhythmia among the plurality of k-space data. Then, the selection function 136 selects, based on the pseudo-acquisition time, a plurality of k-space data corresponding to each of a plurality of preset cardiac time phases from among the plurality of k-space data not affected by arrhythmia that have been identified. Note that the process of removing k-space data affected by arrhythmia from the processing target is substantially equivalent to the process of identifying a plurality of k-space data not affected by arrhythmia.

[0092] Using FIG. 13, the processing procedure of the arrhythmia removal rearrangement process according to the first embodiment will be described. FIG. 13 is a flowchart showing the processing procedure of the arrhythmia removal rearrangement process according to the first embodiment. The processing procedure shown in FIG. 13 corresponds to the processing procedure of the arrhythmia removal rearrangement process in step S108 of FIG. 3.

[0093] Hereinafter, the processing procedure of the arrhythmia removal rearrangement process will be described with reference to FIGS. 14, 15, and 16. FIGS. 14, 15, and 16 are diagrams for explaining the processing of the selection function 136 according to the first embodiment. In FIGS. 14, 15, and 16, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the time direction. Also, for convenience of explanation, the time direction is partially omitted and illustrated here. Also, the circles indicate the positions where one line of k-space data is arranged. Among the circles, the black circles indicate k-space data not affected by arrhythmia. Among the circles, the white circles indicate k-space data affected by arrhythmia.

[0094] As shown in FIG. 13, in step S201, the selection function 136 identifies k-space data affected by arrhythmia. For example, the selection function 136 identifies, based on the electrocardiogram information, k-space data affected by arrhythmia from among the plurality of k-space data corresponding to full sampling. Specifically, when the RR interval calculated based on the electrocardiogram signal is less than the threshold value, the selection function 136 identifies the k-space data included in that RR interval as a plurality of k-space data affected by arrhythmia.

[0095] As shown in FIG. 14, the selection function 136 distinguishes, based on the electrocardiogram information of the subject P, among the k-space data of a plurality of time phases corresponding to the full sampling generated by the generation function 135, those affected by arrhythmia and those not affected. For example, the selection function 136 calculates the RR interval based on the electrocardiogram information acquired by the acquisition function 131. The RR interval is calculated as the time difference between the detection times of two consecutive trigger signals among a series of trigger signals included in the electrocardiogram information. Then, when the calculated RR interval is less than the threshold value, the selection function 136 determines that arrhythmia has occurred at that RR interval. As an example, this threshold value is set to a value of the RR interval (for example, an arbitrary value of about 100 to 500 msec) that cannot occur with the normal fluctuation of the heartbeat. Note that the trigger signal in FIG. 14 is obtained from the electrocardiogram information collected during a period corresponding to the collection period of the central segment.

[0096] In the example of FIG. 14, the RR interval between the trigger signal Tg1 and the trigger signal Tg2 is less than the threshold value. In this case, the selection function 136 determines that arrhythmia has occurred between the trigger signal Tg1 and the trigger signal Tg2. Then, the selection function 136 identifies the k-space data included between the detection time of the trigger signal Tg1 and the detection time of the trigger signal Tg2 as the k-space data affected by arrhythmia. Thereby, the k-space data indicated by the white circles in FIG. 14 is identified.

[0097] In step S202, the selection function 136 determines whether there is k-space data not affected by arrhythmia for one heartbeat. For example, the selection function 136 determines whether the number of phases including the k-space data corresponding to the black circles in FIG. 14 is equal to or more than one cardiac cycle. Here, when it is determined that there is k-space data not affected by arrhythmia for one heartbeat (step S202, Yes), the selection function 136 proceeds to the process of step S203. On the other hand, when it is determined that there is no k-space data not affected by arrhythmia for one heartbeat (step S202, No), the selection function 136 proceeds to the process of step S207.

[0098] In step S203, the selection function 136 removes the k-space data affected by arrhythmia. For example, the selection function 136 removes the k-space data indicated by the white circles in FIG. 14 from the processing target.

[0099] In step S204, the selection function 136 determines whether there is k-space data for a plurality of consecutive time phases within one heartbeat. For example, by removing the k-space data affected by arrhythmia from the processing target, the k-space data for a plurality of time phases from time phase PA1 to time phase PA72 is segmented and becomes discontinuous. Therefore, the selection function 136 determines whether there is k-space data for a plurality of consecutive time phases within one heartbeat among the k-space data for a plurality of time phases after the removal. If it is determined that there is k-space data for a plurality of consecutive time phases within one heartbeat (step S204, Yes), the selection function 136 proceeds to the process of step S205. On the other hand, if it is determined that there is no k-space data for a plurality of consecutive time phases within one heartbeat (step S204, No), the selection function 136 proceeds to the process of step S206.

[0100] In step S205, the selection function 136 executes a sorting process using the k-space data for a plurality of consecutive time phases. In the example shown in FIG. 15, it shows a case where there is k-space data for a plurality of consecutive time phases within one heartbeat (one cardiac cycle or more) during period T1.

[0101] In this case, the selection function 136 sorts the k-space data for a plurality of time phases included in period T1 by the retrospective gating method. That is, the selection function 136 selects the k-space data corresponding to each of the plurality of preset cardiac time phases based on the pseudo-acquisition time of each k-space data.

[0102] Here, the acquisition number preset by the operator is "24". Therefore, the selection function 136 rearranges the k-space data of a plurality of time phases included in the period T1 into the k-space data for 24 phases of time phase P1, time phase P2, time phase P3 ··· time phase P24. Specifically, the selection function 136 calculates the cardiac time phase information for each time phase on the assumption that each time phase of time phase P1, time phase P2, time phase P3 ··· time phase P24 is arranged at equal intervals within one cardiac cycle. Then, the selection function 136 selects the k-space data having cardiac time phase information close to the cardiac time phase information of each time phase from the k-space data included in the period T1. In this way, the selection function 136 executes the rearrangement process using the k-space data of a plurality of consecutive time phases. When the rearrangement process is completed, the selection function 136 ends the process of step S205 and sends the k-space data of time phase P1 to time phase P24 after the rearrangement process to the second reconstruction function.

[0103] In step S206, the selection function 136 executes a rearrangement process using the k-space data of a plurality of non-consecutive time phases. In the example shown in FIG. 16, the case where the k-space data of a plurality of time phases in the period T2 and the k-space data of a plurality of time phases in the period T3 are both less than one heartbeat (more than one cardiac cycle) is shown. Note that the k-space data in the period T2 and the k-space data in the period T3 are discontinuous.

[0104] In this case, the selection function 136 rearranges the k-space data of a plurality of time phases included in the period T2 and the k-space data of a plurality of time phases included in the period T3 by the retrospective gating method. As a result, the selection function 136 rearranges the k-space data of a plurality of time phases included in the period T2 and the k-space data of a plurality of time phases included in the period T3 into the k-space data for 24 phases of time phase P1, time phase P2, time phase P3 ··· time phase P24. Note that the description of the rearrangement process is omitted because it is the same as the process of step S205. When the rearrangement process is completed, the selection function 136 ends the process of step S206 and sends the k-space data of time phase P1 to time phase P24 after the rearrangement process to the second reconstruction function.

[0105] In step S207, the selection function 136 executes the sorting process without removing the k-space data affected by arrhythmia. In this case, the selection function 136 sorts the k-space data corresponding to the black circles in FIG. 14 and the k-space data corresponding to the white circles by the retrospective gating method. Thereby, the selection function 136 sorts the k-space data corresponding to the black circles in FIG. 14 and the k-space data corresponding to the white circles into the k-space data for 24 phases of time phases P1, P2, P3... time phase P24. Note that the sorting process is the same as the process in step S205, so the description is omitted. When the sorting process is completed, the selection function 136 ends the process of step S207 and sends the k-space data of time phases P1 to P24 after the sorting process to the second reconstruction function.

[0106] Return to the description of FIG. 3. In step S109, the second reconstruction function 137 executes the second reconstruction process. The second reconstruction function 137 reconstructs a plurality of image data corresponding to a plurality of preset cardiac time phases using a plurality of k-space data corresponding to each of the selected plurality of cardiac time phases. For example, the second reconstruction function 137 generates the image data of time phase P1 by executing the reconstruction process using 32 k-space data included in time phase P1. Similarly, the second reconstruction function 137 generates the image data of each time phase of time phases P2, P3... time phase P24 by executing the reconstruction process using 32 k-space data included in each of time phases P2, P3... time phase P24. Note that as the reconstruction process executed by the second reconstruction function 137, a known reconstruction process can be appropriately applied.

[0107] In step S110, the output control function 138 outputs a plurality of image data. For example, the output control function 138 cine-plays the 24-phase image data generated by the second reconstruction function 137. Note that the output control function 138 is not limited to cine-play, and for example, a plurality of image data arranged in time series can also be arranged and displayed. Further, the output control function 138 can also store a plurality of image data in the storage circuit 122 or send the network to a device outside the MRI apparatus 100 via a storage medium.

[0108] As described above, the MRI apparatus 100 acquires a plurality of k-space data collected from a subject by non-uniform subsampling, the collection time of each k-space data, and the electrocardiogram information of the subject. Further, the MRI apparatus 100 reconstructs a plurality of image data from the plurality of k-space data by a reconstruction process corresponding to non-uniform subsampling. Further, the MRI apparatus 100 generates a plurality of k-space data corresponding to full sampling by performing an inverse Fourier transform process on the reconstructed plurality of image data, and generates a pseudo collection time for each generated k-space data. Further, the MRI apparatus 100 identifies a plurality of second k-space data that are not affected by arrhythmia among the plurality of k-space data based on the electrocardiogram information. Further, the MRI apparatus 100 selects a plurality of k-space data corresponding to each of a plurality of preset cardiac phases from among the plurality of k-space data that are not affected by the identified arrhythmia based on the pseudo collection time. Further, the MRI apparatus 100 reconstructs a plurality of image data corresponding to the plurality of cardiac phases using the plurality of k-space data corresponding to each of the selected plurality of cardiac phases. Thereby, the MRI apparatus 100 can avoid re-imaging at the time of arrhythmia occurrence while performing high-speed electrocardiogram synchronous imaging.

[0109] For example, when there is k-space data unaffected by arrhythmia for one cardiac cycle, the MRI apparatus 100 executes rearrangement processing by the retrospective gating method using the k-space data. Here, the presence or absence of the influence of arrhythmia is determined based on electrocardiogram information (trigger signal) collected during a period corresponding to the acquisition period of the central segment. Therefore, the MRI apparatus 100 can perform high-speed electrocardiogram synchronous imaging by k-t SENSE after excluding the influence of arrhythmia at least for the central segment. Further, when there is no k-space data unaffected by arrhythmia for one cardiac cycle, the MRI apparatus 100 executes rearrangement processing by the retrospective gating method without removing the k-space data affected by arrhythmia. Therefore, even if arrhythmia has occurred during imaging, the MRI apparatus 100 can perform high-speed electrocardiogram synchronous imaging by k-t SENSE. Thus, the MRI apparatus 100 can execute rearrangement processing by the retrospective gating method according to each situation whether there is k-space data unaffected by arrhythmia for one cardiac cycle or not, so that re-imaging can be avoided.

[0110] Also, for example, the MRI apparatus 100 collects the k-space data included in the peripheral segment over an acquisition period shorter than the acquisition period of the central segment. Then, the MRI apparatus 100 generates combined data without performing a process of specifying a plurality of k-space data unaffected by arrhythmia among the k-space data included in the peripheral segment. Therefore, the MRI apparatus 100 can speed up the imaging required for the peripheral segment while tolerating arrhythmia occurring in the peripheral segment.

[0111] (Modification Example 1 of the First Embodiment) In the above embodiment, the case where the k-space data included in the RR interval determined to be arrhythmia is specified as the k-space data affected by arrhythmia has been described. However, it is conceivable that arrhythmia may also affect the movement of the heart before and after it. Therefore, the MRI apparatus 100 can also specify the k-space data included within a predetermined period before and after arrhythmia as the k-space data affected by arrhythmia.

[0112] For example, when the RR interval calculated based on the electrocardiogram signal is less than a threshold value, the selection function 136 identifies the k-space data included in a predetermined period including the RR interval as a plurality of k-space data affected by arrhythmia.

[0113] Using FIG. 17, the processing of the selection function 136 according to Modification 1 of the first embodiment will be described. FIG. 17 is a diagram for explaining the processing of the selection function 136 according to Modification 2 of the first embodiment. In FIG. 17, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the time direction. Also, for the convenience of explanation, the time direction is partially omitted and illustrated here. Also, the circles indicate the positions where one line of k-space data is arranged. Among the circles, the black circles indicate k-space data not affected by arrhythmia. Among the circles, the white circles indicate k-space data affected by arrhythmia.

[0114] In the example of FIG. 17, the RR interval between the trigger signal Tg1 and the trigger signal Tg2 is less than the threshold value. In this case, the selection function 136 determines that arrhythmia has occurred between the trigger signal Tg1 and the trigger signal Tg2. Then, the selection function 136 identifies the k-space data included in the period T4 including the trigger signal Tg1 and the trigger signal Tg2 as k-space data affected by arrhythmia. Here, the period T4 corresponds to, for example, the period from 50 msec before the detection time of the trigger signal Tg1 to 50 msec after the detection time of the trigger signal Tg2. Thereby, the k-space data indicated by the white circles in FIG. 17 is identified.

[0115] In this way, the MRI apparatus 100 identifies the k-space data included within a predetermined period before and after the arrhythmia as a plurality of k-space data affected by arrhythmia. Thereby, the MRI apparatus 100 can reconstruct an MR image with less influence of arrhythmia.

[0116] (Modification 2 of the First Embodiment) In addition, in the above-described embodiment, the case of removing the influence of the arrhythmia generated in the central segment has been described. However, it is also possible to remove the influence of the arrhythmia generated in the peripheral segment.

[0117] Based on the electrocardiogram information, the combining function 133 identifies a plurality of k-space data that are not affected by arrhythmia among the k-space data included in the peripheral segment. Then, the combining function 133 combines the k-space data of the peripheral segment having cardiac phase information close to the cardiac phase information of the central segment among the identified plurality of k-space data that are not affected by arrhythmia with the k-space data of the central segment. Note that this process is executed before the process of step S104 in FIG. 3.

[0118] The process of the combining function 133 according to Modification 2 of the first embodiment will be described with reference to FIG. 18. FIG. 18 is a diagram for explaining the process of the combining function 133 according to Modification 2 of the first embodiment. In FIG. 18, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the time direction. For convenience of explanation, the time direction is partially omitted and illustrated here. Also, the circles indicate the positions where one line of k-space data is arranged. Among the circles, the black circles indicate k-space data not affected by arrhythmia. Among the circles, the white circles indicate k-space data affected by arrhythmia.

[0119] As shown in FIG. 18, the combining function 133 distinguishes, based on the electrocardiogram information of the subject P, among the k-space data of a plurality of time phases included in segment B, those affected by arrhythmia and those not affected. For example, the combining function 133 calculates the RR interval based on the electrocardiogram information acquired by the acquisition function 131. The RR interval is calculated as the time difference between the detection times of two consecutive trigger signals among a series of trigger signals included in the electrocardiogram information. Then, when the calculated RR interval is less than the threshold value, the combining function 133 determines that arrhythmia has occurred at that RR interval. As an example, this threshold value is set to a value of the RR interval (for example, an arbitrary value of about 100 to 500 msec) that cannot occur due to the general fluctuation of the heartbeat. Note that the trigger signal in FIG. 18 is obtained from the electrocardiogram information collected during a period corresponding to the collection period of the edge segment.

[0120] In the example of FIG. 18, the RR interval between the trigger signal Tg3 and the trigger signal Tg4 is less than the threshold value. In this case, the combining function 133 determines that arrhythmia has occurred between the trigger signal Tg3 and the trigger signal Tg4. Then, the combining function 133 specifies the k-space data included between the detection time of the trigger signal Tg3 and the detection time of the trigger signal Tg4 as the k-space data affected by arrhythmia. Thereby, the k-space data indicated by the white circles in FIG. 18 is specified.

[0121] Then, for the k-space data of each time phase of segment A, the combining function 133 specifies and combines, from among the k-space data of a plurality of time phases not affected by arrhythmia (that is, the black circles in FIG. 18), the k-space data of the time phase having heart time phase information close to the heart time phase information of each time phase of segment A. Since this process is the same as the process of step S104 shown in FIG. 3, the description thereof is omitted.

[0122] In this way, based on the electrocardiogram information, the MRI apparatus 100 identifies a plurality of k-space data in the edge segment that are not affected by arrhythmia among the k-space data included in the edge segment, and among the identified plurality of k-space data, the k-space data of the edge segment having a cardiac phase information close to the cardiac phase information of the central segment is combined with the k-space data of the central segment. Thereby, the MRI apparatus 100 can reconstruct an MR image with less influence of arrhythmia.

[0123] (Modification Example 3 of the First Embodiment) Also, in the first embodiment, when generating k-space data corresponding to full sampling, the process of once converting to an MR image (reconstructed image) as an intermediate image (first reconstruction process) was described, but the embodiment is not limited to this. That is, the MRI apparatus 100 can generate k-space data corresponding to full sampling without necessarily converting to an intermediate image.

[0124] For example, the first reconstruction function 134 performs a reconstruction process corresponding to k-t SENSE on the combined data of a plurality of phases created by the combining function 133. In this process, as described above, the reconstruction function 123d generates x-f space data from which the aliasing signal has been removed. This x-f space data is data before being converted into image data (real space data).

[0125] Here, the first reconstruction function 134 performs a process including Fourier transform (inverse Fourier transform) on this x-f space data. Thereby, the first reconstruction function 134 can generate k-space data corresponding to full sampling from the combined data of a plurality of phases created by the combining function 133. That is, the first reconstruction function 134 generates a plurality of k-space data corresponding to full sampling from a plurality of k-space data thinned out in a predetermined sampling pattern by a process including a Fourier transform corresponding to non-uniform subsampling.

[0126] Note that the first reconstruction function 134 performs a process of generating a plurality of k-space data corresponding to the above full sampling instead of the processes of steps S105 and S106. Since the processes after step S107 are the same as those described in FIG. 3, the description thereof is omitted.

[0127] (Second Embodiment) In the first embodiment, the case where the acquisition period is preset has been described, but the embodiment is not limited thereto. For example, the MRI apparatus 100 can dynamically monitor the occurrence of arrhythmia during sampling and stop sampling when sufficient k-space data unaffected by arrhythmia is obtained.

[0128] That is, the acquisition function 131 monitors the occurrence of arrhythmia based on electrocardiogram information while non-uniform subsampling is being performed. Then, when the period without the occurrence of arrhythmia satisfies a predetermined condition, the acquisition function 131 ends the non-uniform subsampling.

[0129] The process of the acquisition function 131 according to the second embodiment will be described with reference to FIG. 19. FIG. 19 is a diagram for explaining the process of the acquisition function 131 according to the second embodiment. The upper part of FIG. 19 illustrates an imaging sequence for collecting k-space data of segment A. Also, the lower part of FIG. 19 illustrates the detection times of trigger signals monitored in the imaging sequence in the upper part of FIG. 19. In FIG. 19, "t" shown on the horizontal axis corresponds to the time direction. Although not shown, the sequence control circuit 110 can execute the imaging sequence by appropriately inserting dummy shots and wait times.

[0130] As shown in FIG. 19, the sequence control circuit 110 executes the imaging sequence of segment A. At this time, the acquisition function 131 calculates the RR interval based on the electrocardiogram information while the imaging sequence of segment A is being performed. The RR interval is calculated as the time difference between the detection times of two consecutive trigger signals among a series of trigger signals included in the electrocardiogram information. And when the calculated RR interval is less than the threshold value, the acquisition function 131 determines that an arrhythmia has occurred at that RR interval. As an example, this threshold value is set to a value of the RR interval (for example, an arbitrary value of about 100 to 500 msec) that cannot occur due to the general fluctuation of the heartbeat.

[0131] In the example of FIG. 19, the RR interval between the trigger signal Tg5 and the trigger signal Tg6 is less than the threshold value. In this case, the selection function 136 determines that an arrhythmia has occurred between the trigger signal Tg5 and the trigger signal Tg6. Then, the acquisition function 131 starts counting the collection period (period T5) starting from the detection time of the trigger signal Tg6 (the time when the arrhythmia is considered to have disappeared).

[0132] And when the period T5 reaches the predetermined period, the acquisition function 131 stops the sampling of segment A. This predetermined period is set, for example, to satisfy about 120% of one cardiac cycle. When the RR interval set by the operator is 800 msec, the sampling of segment A is stopped 960 msec after the detection time of the trigger signal Tg6.

[0133] In this way, the acquisition function 131 dynamically monitors the occurrence of arrhythmia while the imaging sequence of segment A is being performed, and stops sampling when sufficient k-space data unaffected by arrhythmia is obtained. Also, although not described in FIG. 19, the acquisition function 131 can similarly monitor the occurrence of arrhythmia while sampling is being performed for the imaging sequence of segment B, and stop sampling when sufficient k-space data unaffected by arrhythmia is obtained.

[0134] In addition, if the acquisition function 131 detects the recurrence of arrhythmia before the period T5 reaches the predetermined period, it restarts the count of the collection period starting from the time when the recurrence of arrhythmia is recognized to have disappeared. Then, when the collection period counted again reaches the predetermined period, the acquisition function 131 stops the sampling.

[0135] In this way, the MRI apparatus 100 according to the second embodiment dynamically monitors the occurrence of arrhythmia while sampling is being performed, and stops the sampling when sufficient k-space data unaffected by arrhythmia has been obtained. Thereby, the MRI apparatus 100 can obtain a plurality of k-space data unaffected by arrhythmia according to the required amount.

[0136] Note that the above description is merely an example and is not limited by the above description. For example, in FIG. 19, the case where the acquisition function 131 uses a collection period counted starting from the detection time of the trigger signal Tg6 as a predetermined condition for determination has been described, but it is not limited to this. For example, the acquisition function 131 may use a collection period counted starting from a predetermined time (for example, 50 msec) after the detection time of the trigger signal Tg6 as a predetermined condition for determination. This is to eliminate the influence in consideration of the possibility that arrhythmia may also affect the movement of the heart before and after it. In addition, the acquisition function 131 may perform collection for a certain period without performing the above-described dynamic monitoring for the segment B (edge segment).

[0137] (Third Embodiment) In the above embodiment, the case where the process of removing the influence of arrhythmia is performed after full sampling has been described, but the embodiment is not limited to this. For example, the MRI apparatus 100 can also perform the process of removing the influence of arrhythmia before performing full sampling.

[0138] That is, in the MRI apparatus 100 according to the third embodiment, the acquisition function 131 acquires a plurality of k-space data collected from a subject by non-uniform subsampling, the collection time of each k-space data, and the electrocardiogram information of the subject. The selection function 136 identifies a plurality of k-space data among the plurality of k-space data that are not affected by arrhythmia based on the electrocardiogram information. The first reconstruction function 134 generates a plurality of k-space data corresponding to full sampling from the identified plurality of k-space data that are not affected by arrhythmia by a process including a Fourier transform corresponding to non-uniform subsampling. The generation function 135 generates a pseudo collection time for each second k-space data. The selection function 136 selects a plurality of k-space data corresponding to each of a plurality of preset cardiac phases from among the generated k-space data corresponding to full sampling based on the pseudo collection time. The second reconstruction function 137 reconstructs a plurality of image data corresponding to the plurality of cardiac phases using the plurality of k-space data corresponding to each of the selected plurality of cardiac phases. Note that the selection function 136 is an example of a specifying unit.

[0139] Using FIG. 20, the processing procedure by the MRI apparatus 100 according to the third embodiment will be described. FIG. 20 is a flowchart showing the processing procedure by the MRI apparatus 100 according to the third embodiment. The processing procedure shown in FIG. 20 starts, for example, upon receipt of an imaging start request input by an operator.

[0140] In addition, in FIG. 20, the description will be given with reference to FIG. 21. FIG. 21 is a diagram for explaining the processing of the selection function 136 according to the third embodiment. In the k-t space data in the upper part of FIG. 21, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the time direction. Also, for convenience of explanation, the time direction is partially omitted and illustrated here. The circles indicate the positions where one line of k-space data is arranged. The black circles among the circles indicate k-space data unaffected by arrhythmia. The white circles among the circles indicate k-space data affected by arrhythmia. Also, the trigger signal shown in the lower part of FIG. 21 corresponds to the time direction of the k-t space data. Note that the content described in FIG. 21 is merely an example, and the embodiment is not limited thereto.

[0141] In step S301, the acquisition function 131 collects a plurality of k-space data. For example, the acquisition function 131 collects a plurality of k-space data included in segment A and segment B, respectively. This process is the same as the process of step S101 in FIG. 3.

[0142] In step S302, the selection function 136 identifies k-space data affected by arrhythmia. For example, the selection function 136 identifies k-space data affected by arrhythmia from among a plurality of k-space data included in segment A (central segment) based on electrocardiogram information. Specifically, when the RR interval calculated based on the electrocardiogram signal is less than the threshold value, the selection function 136 identifies the k-space data included in that RR interval as a plurality of k-space data affected by arrhythmia.

[0143] As shown in FIG. 21, the selection function 136 distinguishes, based on the electrocardiogram information of the subject P, among the k-space data of a plurality of time phases included in segment A, those affected by arrhythmia and those not affected. For example, the selection function 136 calculates the RR interval based on the electrocardiogram information acquired by the acquisition function 131. The RR interval is calculated as the time difference between the detection times of two consecutive trigger signals among a series of trigger signals included in the electrocardiogram information. Then, when the calculated RR interval is less than the threshold value, the selection function 136 determines that arrhythmia has occurred at that RR interval. As an example of this threshold value, a value of the RR interval (for example, an arbitrary value of about 100 to 500 msec) that cannot occur due to the normal fluctuation of the heartbeat is set. Note that the trigger signal in FIG. 21 is obtained from the electrocardiogram information collected during a period corresponding to the collection period of segment A.

[0144] In the example of FIG. 21, the RR interval between the trigger signal Tg5 and the trigger signal Tg6 is less than the threshold value. In this case, the selection function 136 determines that arrhythmia has occurred between the trigger signal Tg5 and the trigger signal Tg6. Then, the selection function 136 identifies the k-space data included between the detection time of the trigger signal Tg5 and the detection time of the trigger signal Tg6 as the k-space data affected by arrhythmia. Thereby, the k-space data indicated by the white circle marks in FIG. 21 is identified.

[0145] In step S303, the selection function 136 removes the k-space data affected by arrhythmia. For example, the selection function 136 removes the k-space data affected by arrhythmia from the processing target in block units.

[0146] Here, "block" means a group in which the k-space data of the number of time phases defined in the reconstruction process in k-t SENSE is grouped together. For example, if the decimation ratio in k-t SENSE is "4", each block includes the k-space data for four time phases.

[0147] In the example shown in FIG. 21, the regions R11, R12, R13, and R14 each contain 16 lines of k-space data corresponding to one block. Among these, three regions R12, R13, and R14 contain k-space data affected by arrhythmia. Therefore, the selection function 136 removes the 48 lines of k-space data contained in the three regions R12, R13, and R14 from the processing target.

[0148] Also, the selection function 136 removes blocks with a number of consecutively sequential blocks less than a predetermined number from the processing target. Here, the predetermined number is, for example, "3". In the example shown in FIG. 21, since the block contained in the region R11 is not consecutive with other blocks, it is a removal target. Therefore, the selection function 136 removes the 16 lines of k-space data contained in the region R11 from the processing target.

[0149] In this way, the selection function 136 removes the 64 lines of k-space data contained in the four regions R11, R12, R13, and R14 from the processing target. As a result, the selection function 136 selects a plurality of k-space data contained in the region R15 as the processing target for subsequent processing. It is assumed that the region R15 contains three or more blocks consecutively in time series.

[0150] Note that in FIG. 21, the case where k-space data affected by arrhythmia is removed from the central segment and not removed from the edge segment has been described, but the embodiment is not limited to this. That is, similarly for the edge segment, it is possible to remove k-space data affected by arrhythmia. The process of removing k-space data affected by arrhythmia from the edge segment may be the same as the process described in FIG. 18, or may be removed in block units.

[0151] Also, in FIG. 21, the case where continuous k-space data of three or more blocks is the processing target was described, but the embodiment is not limited to this. The number of blocks to be processed can be arbitrarily set. Also, it is possible to combine k-space data of less than one block with k-space data of other blocks and make it the processing target. However, in order to maintain the image quality of the reconstructed image, it is preferable to process a predetermined number or more consecutive blocks in time series.

[0152] Returning to the description of FIG. 20. The processing of steps S304 to S309 is the same as the processing of steps S102 to S107 shown in FIG. 3, except that the k-space data affected by arrhythmia is removed from the processing target. As a result, k-space data equivalent to full sampling included in region R15 is generated.

[0153] In step S310, the selection function 136 executes a sorting process. For example, the selection function 136 sorts the k-space data equivalent to full sampling included in region R15 by the retrospective gating method. That is, the selection function 136 selects k-space data corresponding to each of a plurality of preset cardiac phases based on the pseudo-acquisition time of each k-space data.

[0154] The processing of steps S311 to S312 is the same as the processing of steps S109 to S110 shown in FIG. 3.

[0155] Through the above processing, the MRI apparatus 100 according to the third embodiment can execute processing for removing the influence of arrhythmia before performing full sampling. As a result, the MRI apparatus 100 can perform high-speed electrocardiogram-synchronized imaging while avoiding re-imaging during arrhythmia occurrence.

[0156] (Other Embodiments) In addition to the above-described embodiments, it may be implemented in various different forms.

[0157] (Use of Calibration Data) In the above embodiment, a case has been described in which, when segmentation is performed, the k-space data of the peripheral segments having a cardiac time phase close to that of the central segment are combined in groups to generate a final reconstructed image, but the embodiment is not limited to this. For example, the MRI device 100 can generate a final reconstructed image by generating calibration data for full sampling of the k-space data of the peripheral segments from the k-space data of the central segment.

[0158] A processing procedure by the MRI apparatus 100 according to another embodiment will be described with reference to Fig. 22. Fig. 22 is a flowchart showing the processing procedure by the MRI apparatus 100 according to another embodiment. The processing procedure shown in Fig. 22 is started, for example, in response to an imaging start request input by an operator.

[0159] In addition, Fig. 22 will be described with reference to Figs. 23 to 29. Figs. 23 to 29 are diagrams for explaining the processing of the MRI apparatus 100 according to other embodiments. In Figs. 23 to 29, the trigger table corresponds to the time direction of each kt space data. In addition, the contents described in Figs. 22 to 29 are merely examples, and the embodiment is not limited thereto.

[0160] In step S401, the acquisition function 131 acquires a plurality of k-space data. That is, the acquisition function 131 acquires a plurality of k-space data acquired by dividing the plurality of k-space data into a plurality of segments including a central segment corresponding to the center of the k-space and a peripheral segment different from the central segment.

[0161] For example, the acquisition function 131 acquires a plurality of k-space data by dividing the data into two segments, a central segment and a peripheral segment, as shown in Fig. 23. Here, the central segment and the peripheral segment are acquired at different timings, and therefore trigger signals are detected at different timings.

[0162] In FIG. 23, the case of collecting data in two segments is illustrated, but it is also possible to collect data in three or more segments.

[0163] In step S402, the generation function 135 performs full sampling of the central segment. That is, the generation function 135 generates a plurality of k-space data corresponding to the full sampling of the central segment from a plurality of k-space data included in the central segment by a process including a Fourier transform corresponding to non-uniform decimation sampling.

[0164] For example, as shown in FIG. 24, the generation function 135 generates a plurality of k-space data corresponding to full sampling from a plurality of k-space data included in the central segment. This process is the same as the processes of steps S105 and S106 in FIG. 3, except that the processing target is a plurality of k-space data included in the central segment.

[0165] In step S403, the generation function 135 assigns a pseudo collection time. This process is the same as the process of step S107 in FIG. 3, except that the processing target is a plurality of k-space data included in the central segment.

[0166] In step S404, the selection function 136 rearranges the k-space data of the fully sampled central segment according to the cardiac phase of the peripheral segment. That is, the selection function 136 rearranges a plurality of k-space data corresponding to full sampling according to the cardiac phase of the peripheral segment.

[0167] For example, the selection function 136 calculates the cardiac phase information of the k-space data located approximately at the center in terms of time among the four lines of k-space data included in each cardiac phase of the edge segments shown in FIG. 23. Then, the selection function 136 selects, from among the plurality of k-space data included in the fully sampled center segment, the k-space data having the same phase encoding amount and the cardiac phase information closest to the calculated cardiac phase information. Thereby, as shown in FIG. 25, the selection function 136 generates calibration data by rearranging a plurality of k-space data corresponding to full sampling. The trigger detection timing in the calibration data (the right figure in FIG. 25) substantially coincides with the trigger detection timing of the k-t space data of the edge segment (the right figure in FIG. 23). That is, this calibration data is k-t space data in which a plurality of k-space data corresponding to the full sampling of the center segment are rearranged so as to have substantially the same trigger detection timing as the trigger detection timing in the edge segment.

[0168] In step S405, the selection function 136 extracts, from the k-space data of the center segment after rearrangement, the k-space data corresponding to the sampling pattern of the edge segment.

[0169] For example, as shown in FIG. 26, the selection function 136 generates extraction data by extracting, from the calibration data, the k-space data corresponding to the sampling pattern of the edge segment. That is, this extraction data is k-t space data in which the k-space data of the calibration data is decimated so as to have the same sampling pattern as the sampling pattern of the edge segment. In this way, the selection function 136 extracts, from the rearranged plurality of k-space data, a plurality of k-space data corresponding to the sampling pattern of the plurality of k-space data included in the edge segment.

[0170] In step S406, the combining function 133 combines the extracted k-space data with the k-space data of the edge segment.

[0171] For example, as shown in FIG. 27, the combining function 133 generates combined data by combining the extracted data with the k-t space data of the edge segments shown in the right diagram of FIG. 23. Here, since the trigger detection timing in the extracted data is substantially the same as the trigger detection timing in the edge segments, the k-space data of each time phase can be combined respectively.

[0172] In step S407, the generation function 135 performs full sampling of all segments. That is, the generation function 135 performs a process including a Fourier transform corresponding to non-uniform decimation sampling on the plurality of extracted k-space data and the plurality of k-space data included in the edge segments, thereby generating a plurality of k-space data corresponding to full sampling.

[0173] For example, as shown in FIG. 28, the generation function 135 generates a plurality of k-space data corresponding to full sampling from the combined data. This process is the same as the processes of steps S105 and S106 in FIG. 3. In this way, the generation function 135 can full sample the k-space data of the edge segments by using the calibration data.

[0174] In step S408, the selection function 136 assigns a pseudo time stamp. This process is the same as the process of step S107 in FIG. 3.

[0175] In step S409, the selection function 136 selects the k-space data corresponding to the k-space data of the central segment from the full-sampled k-space data of all segments.

[0176] For example, as shown in FIG. 29, the selection function 136 selects a plurality of k-space data corresponding to regions R21 and R22 from among a plurality of k-space data included in the fully sampled all segments in the right diagram of FIG. 28. Specifically, the selection function 136 calculates the cardiac phase information of the k-space data located approximately at the center in time among the 4 lines of k-space data included in each cardiac phase of the central segment shown in FIG. 23. Then, the selection function 136 selects, from among a plurality of k-space data included in the fully sampled edge segments, the k-space data having the same phase encoding amount and the cardiac phase information closest to the calculated cardiac phase information. As a result, the selection function 136 selects a plurality of k-space data corresponding to regions R21 and R22 as shown in the right diagram of FIG. 29. Then, the selection function 136 generates combined data (second combined data) in the right diagram of FIG. 29 by combining the selected plurality of k-space data corresponding to regions R21 and R22 with the plurality of k-space data included in the central segment in the left diagram of FIG. 23. The second combined data is arranged to have a trigger detection timing substantially the same as the trigger detection timing in the central segment.

[0177] In step S410, the selection function 136 executes arrhythmia removal rearrangement processing. This arrhythmia removal rearrangement processing is the same as the processing in step S108 of FIG. 3.

[0178] In step S411, the second reconstruction function 137 executes reconstruction processing. This reconstruction processing is the same as the second reconstruction processing in step S109 of FIG. 3.

[0179] In step S412, the output control function 138 outputs a plurality of image data. This processing is the same as the processing in step S110 of FIG. 3.

[0180] As described above, the MRI apparatus 100 according to other embodiments can generate a final reconstructed image by generating calibration data for fully sampling the k-space data of the edge segments from the k-space data of the central segment.

[0181] Note that the processing procedure shown in FIG. 22 is merely an example, and the embodiments are not limited thereto. For example, the processing procedure shown in FIG. 22 can be appropriately changed in the processing order as long as there is no contradiction in the processing content. Further, for the full-sampling processing described in steps S402 and S407, a processing method of performing full-sampling without converting to an MR image is applicable.

[0182] Also, the processing in step S409 is merely an example, and the embodiments are not limited thereto. For example, when the processing in step S408 is completed, for each of the central segment and the edge segment, a plurality of k-space data corresponding to full-sampling are obtained. Therefore, the reconstruction function 137 can perform reconstruction processing by appropriately selecting arbitrary k-space data, not limited to the processing in step S409 above.

[0183] (Change in Collection Density in Phase Encoding Direction) In the above embodiment, sampling with uniform thinning to 1 / 4 in the phase encoding direction is exemplified, but the embodiments are not limited thereto. For example, in the non-uniform thinning sampling according to the embodiments, it is also possible to increase the collection density near the center in the phase encoding direction for collection.

[0184] An example of k-t space data according to other embodiments will be described with reference to FIG. 30. FIG. 30 is a diagram showing an example of sampling positions in k-t space according to other embodiments. It is a diagram showing an example of k-t space data according to other embodiments. In FIG. 30, "t" shown on the horizontal axis corresponds to the time direction, and "k" shown on the vertical axis corresponds to the phase encoding direction. FIG. 30 exemplifies k-t space data having 27 positions (frames) in the phase encoding direction of the k-space and 30 positions in the time direction. Also, in FIG. 30, black circles indicate positions where one line of k-space data is collected.

[0185] As shown in FIG. 30, the sequence control circuit 110 collects k-space data without decimation at all time phases for positions near the center (the 14th to 16th) in the phase encoding direction.

[0186] Thereby, the MRI apparatus 100 can collect k-space data corresponding to the main frequency components constituting the MR image at high density, and can improve the image quality of the finally reconstructed MR image.

[0187] (Application to Compressed Sensing) Also, for example, the processing functions according to the above-described embodiments are also applicable to Compressed Sensing (CS).

[0188] Compressed Sensing is an imaging method that reconstructs an image from a small number of k-space data by utilizing the sparsity of a signal. For example, in Compressed Sensing, when filling k-space with k-space data, sampling is performed with irregular decimation in the phase encoding direction. As an example, in Compressed Sensing, sampling is performed with irregular decimation by Cartesian acquisition or radial acquisition (Golden Angle Radial acquisition). As a result, in Compressed Sensing, it is possible to shorten the data acquisition time while introducing sparsity.

[0189] That is, the non-uniform decimation sampling according to the embodiment collects a plurality of k-space data with an irregular sampling pattern for the phase encoding lines collected between consecutive time phases. The acquisition function 131 acquires a plurality of k-space data collected with an irregular sampling pattern for the phase encoding lines collected between consecutive time phases.

[0190] (Case of not using non-uniform decimation sampling) In the above-described embodiment, the case of using non-uniform subsampling has been described, but the embodiment is not limited thereto. For example, while collecting the k-space edge portion for about one cardiac cycle, the k-space central portion is repeatedly collected over a plurality of cardiac cycles, and for the k-space central portion, a normal section without arrhythmia is selected and used for reconstruction. Thereby, even in normal sampling (sampling that fills the k-space without subsampling), the MRI apparatus 100 can avoid re-imaging during the occurrence of arrhythmia.

[0191] That is, the MRI apparatus 100 segments the k-space data into a k-space central portion and a k-space edge portion. The MRI apparatus 100 collects the k-space central portion in a first time interval, and collects the k-space edge portion in a second time interval different from the first time interval. The MRI apparatus 100 reconstructs an MR (Magnetic Resonance) image from the k-space data obtained by combining the data of the collected k-space central portion and k-space edge portion respectively. Also, in the MRI apparatus 100, the first time interval includes a plurality of cardiac cycles. The k-space central portion is repeatedly collected over a plurality of cardiac cycles. As the central portion of the k-space data used for the reconstruction of the MR image, the data of the cardiac cycle with a low influence of arrhythmia among the plurality of cardiac cycles is selected. According to this, the MRI apparatus 100 can avoid re-imaging during the occurrence of arrhythmia.

[0192] (Selection of data of a cardiac cycle with a low influence of arrhythmia) In the above-described embodiment, the case where the case where the RR interval is less than the threshold is specified as data affected by arrhythmia and other data other than the specified data is selected as data of a cardiac cycle with a low influence of arrhythmia has been described, but the embodiment is not limited thereto. For example, by comparing the RR intervals of a plurality of cardiac cycles obtained from the subject P with each other, it is also possible to select data of a cardiac cycle with a low influence of arrhythmia.

[0193] That is, the MRI apparatus 100 further acquires the heartbeat information of the subject whose k-space data is to be collected. When selecting data of a cardiac cycle with low influence of arrhythmia, the MRI apparatus 100 selects data with a normal cardiac cycle range from the central part of the k-space data based on the acquired heartbeat information. For example, the MRI apparatus 100 selects data with a normal cardiac cycle range by selecting data having an RR interval corresponding to the center of the distribution of RR intervals of a plurality of cardiac cycles.

[0194] Here, a case where the RR intervals of five cardiac cycles obtained from the subject P are 1000 msec, 810 msec, 820 msec, 850 msec, and 700 msec will be described. For example, when classifying the data by dividing the range every 50 msec into 700 - 750 msec, 750 - 800 msec, 800 - 850 msec, 850 - 900 msec, 900 - 950 msec, 950 - 1000 msec, three data (810 msec, 820 msec, 850 msec) are concentrated in the range of 800 - 850 msec. In this case, the MRI apparatus 100 can select any data (for example, data of 820 msec corresponding to the median) from the three concentrated data.

[0195] Note that the above selection method is merely an example, and the embodiment is not limited thereto. For example, the MRI apparatus 100 may calculate the average value of the RR intervals of a plurality of cardiac cycles and select data having an RR interval close to the calculated average value. Also, the MRI apparatus 100 may identify the median from the RR intervals of a plurality of cardiac cycles and select data having an RR interval corresponding to the identified median.

[0196] (Generation of a pseudo trigger signal) In the inspection, the ECG sensor 111a may cause a detection omission of the trigger signal. In this case, the data selected from among the data of a plurality of cardiac cycles will include data for two cardiac cycles (two heartbeats). Therefore, when referring to the MR image reconstructed from the data for these two cardiac cycles, the depicted heart appears to be beating twice, so that the operator can recognize that the ECG sensor 111a has caused a detection omission of the trigger signal.

[0197] Therefore, when the operator recognizes a detection omission of the trigger signal, the MRI apparatus 100 provides a GUI for inputting a pseudo trigger signal. That is, the MRI apparatus 100 accepts an input of the time of the trigger signal in the heartbeat information. The MRI apparatus 100 generates a pseudo trigger signal at the input time based on the input. The MRI apparatus 100 selects data within the normal range of the cardiac cycle from among the central portions of the k-space data based on the heartbeat information including the pseudo trigger signal.

[0198] FIG. 31 is a diagram showing an example of input of a pseudo trigger signal according to another embodiment. In the upper part “Sampling” of FIG. 31, an imaging sequence for collecting k-space data of a central segment and an edge segment is illustrated. Also, in the middle and lower parts “Trigger Signal” of FIG. 31, the detection times of the trigger signals monitored in the imaging sequence in the upper part of FIG. 31 are illustrated. In FIG. 31, “t” shown on the horizontal axis corresponds to the time direction. Although not shown, the sequence control circuit 110 can execute the imaging sequence by appropriately inserting dummy shots and wait times.

[0199] As shown in FIG. 31, the sequence control circuit 110 executes an imaging sequence for a central segment and an edge segment. At this time, the acquisition function 131 detects trigger signals Tg11 to Tg17 in parallel with the execution of the imaging sequence. In FIG. 31, an MR image is reconstructed using the k-space data included in the period T10.

[0200] Here, when the operator notices a detection omission of the trigger signal, the operator previews and plays back the MR image to identify the phase (end-diastolic phase) when the trigger signal should originally be present. Then, the operator inputs the identified phase. As a result, the selection function 136 generates a pseudo trigger signal Tg20 at the input phase (time). And the selection function 136 selects data with an appropriate RR interval from the central part of the k-space using the cardiac cycle information including the trigger signal Tg20. In this case, the selection function 136 may select either the divided period T11 or period T12 of the originally adopted period T10, or may select from another period such as the period T20, for example.

[0201] (Combination between segments based on RR interval) In addition, the combination function 133 can combine the first k-space data in the central part of the k-space and the first k-space data in the edge part of the k-space based not only on the cardiac phase information but also on the trigger interval (RR interval) of the data of the cardiac cycle with a low influence of the selected arrhythmia.

[0202] As an example, a case where the cardiac phase information of the k-space data in the central part of the k-space is "50%" and the RR interval of the cardiac cycle including the k-space data is "800 msec" will be described. Here, as candidates for combination targets, there are a first candidate with cardiac phase information "55%" and an RR interval "1000 msec", and a second candidate with cardiac phase information "40%" and an RR interval "800 msec". In this case, when selecting the combination target based on the closer cardiac phase information, the first candidate is selected, but when selecting considering not only the cardiac phase information but also the proximity of the RR interval, the second candidate is selected.

[0203] (Reconfiguration device on the network) Also, for example, the processing function according to the above-described embodiment can be provided as a reconfiguration device on the network. This reconfiguration device can provide, for example, an information processing service (cloud service) via the network.

[0204] FIG. 32 is a block diagram showing a configuration example of a reconstruction device according to another embodiment. As shown in FIG. 32, for example, a reconstruction device 200 is installed in a service center that provides information processing services. The reconstruction device 200 is connected to an operation terminal 201. Further, the reconstruction device 200 is connected to a plurality of client terminals 203A, 203B, ···, 203N via a network 202. Note that the reconstruction device 200 and the operation terminal 201 may be connected via the network 202. Also, when collectively referring to the plurality of client terminals 203A, 203B, ···, 203N without distinction, it is described as "client terminal 203".

[0205] The operation terminal 201 is an information processing terminal used by a person (operator) who operates the reconstruction device 200. For example, the operation terminal 201 includes an input device for receiving various instructions and setting requests from the operator, such as a mouse, a keyboard, and a touch panel. Further, the operation terminal 201 includes a display device for displaying images or a GUI for the operator to input various setting requests using the input device. The operator can send various instructions and setting requests to the reconstruction device 200 or view the information inside the reconstruction device 200 by operating the operation terminal 201. Also, the network 202 is an arbitrary communication network such as the Internet, a WAN (Wide Area Network), or a LAN (Local Area Network).

[0206] The client terminal 203 is an information processing terminal operated by a user who uses the information processing service. Here, the user is, for example, a medical worker such as a doctor or a technician working in a medical institution. For example, the client terminal 203 corresponds to an information processing device such as a personal computer or a workstation, or an operation terminal of a medical image diagnostic device such as a console device included in an MRI device. The client terminal 203 has a client function that can use the information processing service provided by the reconstruction device 200. Note that this client function is pre-recorded in the client terminal 203 in the form of a program executable by a computer.

[0207] The reconfiguration device 200 includes a communication interface 210, a memory circuit 220, and a processing circuit 230. The communication interface 210, the memory circuit 220, and the processing circuit 230 are connected to be communicable with each other.

[0208] The communication interface 210 is, for example, a network card or a network adapter. By connecting to the network 202, the communication interface 210 performs information communication between the reconfiguration device 200 and an external device.

[0209] The memory circuit 220 is, for example, a NAND (Not AND) type flash memory or an HDD (Hard Disk Drive), and stores medical image data, various programs for displaying a GUI, and information used by the programs.

[0210] The processing circuit 230 is an electronic device (processor) that controls the overall processing in the reconfiguration device 200. The processing circuit 230 has an acquisition function 231, a calculation function 232, a combination function 233, a first reconfiguration function 234, a generation function 235, a selection function 236, a second reconfiguration function 237, and an output control function 238. Each processing function executed by the processing circuit 230 is recorded in the memory circuit 220 in the form of, for example, a program executable by a computer. The processing circuit 230 reads out each program and realizes the function corresponding to each read program. The acquisition function 231, the calculation function 232, the combination function 233, the first reconfiguration function 234, the generation function 235, the selection function 236, the second reconfiguration function 237, and the output control function 238 can execute basically the same processing as the acquisition function 131, the calculation function 132, the combination function 133, the first reconfiguration function 134, the generation function 135, the selection function 136, the second reconfiguration function 137, and the output control function 138 shown in FIG. 1.

[0211] For example, the user operates the client terminal 203 to input an instruction to transmit (upload) a plurality of k-space data to the reconstruction device 200 in the service center. When an instruction to transmit a plurality of k-space data is input, the client terminal 203 transmits a plurality of k-space data to the reconstruction device 200. Here, the plurality of k-space data are a plurality of k-space data divided into segment units collected by the sequence control circuit 110.

[0212] Then, the reconstruction device 200 receives a plurality of k-space data transmitted from the client terminal 203. As a result, in the reconstruction device 200, the acquisition function 231 acquires a plurality of k-space data collected from the subject by non-uniform sampling, the collection time of each k-space data, and the electrocardiogram information of the subject. Then, the first reconstruction function 234 reconstructs a plurality of image data from the plurality of k-space data by a reconstruction process corresponding to non-uniform sampling. Then, the generation function 235 generates a plurality of k-space data corresponding to full sampling by performing an inverse Fourier transform process on the reconstructed plurality of image data, and generates a pseudo collection time for each generated k-space data. Then, the selection function 236 identifies a plurality of second k-space data that are not affected by arrhythmia among the plurality of k-space data based on the electrocardiogram information. Then, the selection function 236 selects a plurality of k-space data corresponding to each of a plurality of preset cardiac time phases from among the plurality of k-space data that are not affected by the identified arrhythmia based on the pseudo collection time. Then, the second reconstruction function 237 reconstructs a plurality of image data corresponding to a plurality of cardiac time phases using the plurality of k-space data corresponding to each of the selected plurality of cardiac time phases. Then, the output control function 138 transmits (downloads) the reconstructed image data to the client terminal 203. As a result, the reconstruction device 200 can perform high-speed electrocardiogram-synchronized imaging while avoiding re-imaging during arrhythmia occurrence.

[0213] Moreover, each component of each illustrated device is functionally conceptual and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of the distribution and integration of each device is not limited to that shown in the figure, and all or part of it can be functionally or physically distributed and integrated in any unit according to various loads, usage situations, etc. Furthermore, each processing function performed by each device can be realized in whole or in any part by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware by wired logic.

[0214] Also, among the processes described in the above-described embodiments, all or part of the processes described as being automatically performed can be manually performed, or all or part of the processes described as being manually performed can be automatically performed by a known method. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified.

[0215] Also, the image reconstruction method described in the above-described embodiments can be realized by executing a pre-prepared image reconstruction program on a computer such as a personal computer or a workstation. This image reconstruction program can be distributed via a network such as the Internet. In addition, this ultrasonic imaging method can also be executed by being recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, an MO, or a DVD and read from the recording medium by a computer.

[0216] According to at least one of the embodiments described above, it is possible to avoid re-imaging during arrhythmia while performing high-speed electrocardiogram synchronous imaging.

[0217] Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, as well as in the invention described in the claims and its equivalent scope.

Explanation of Reference Numerals

[0218] 100 MRI apparatus 130 Processing circuit 131 Acquisition function 132 Calculation function 133 Combining function 134 First reconstruction function 135 Generation function 136 Selection function 137 Second reconstruction function 138 Output control function

Claims

1. Segmenting the k-space data into a central k-space portion and a peripheral k-space portion; acquiring the central portion of k-space over a first time interval; acquiring the k-space edge portion at a second time interval different from the first time interval; An image reconstruction method for reconstructing an MR (Magnetic Resonance) image from k-space data combining collected data of the k-space central portion and the k-space peripheral portion, comprising: the first time interval includes a plurality of cardiac cycles; the central k-space portion is repeatedly acquired over the multiple cardiac cycles; data of a cardiac cycle that is less affected by arrhythmia is selected from the plurality of cardiac cycles as a central portion of k-space data used for reconstructing the MR image; Further acquiring heart rate information of a subject from which the k-space data is collected; In the selection of cardiac cycle data with low influence of arrhythmia, data in a normal cardiac cycle range is selected from a central portion of the k-space data based on the acquired heart rate information; Accepting an input of a time of a trigger signal in the heart rate information; generating a pseudo trigger signal at the input time based on the input; selecting data in which the cardiac cycle is in a normal range from a central portion of the k-space data based on heartbeat information including the pseudo trigger signal; Image reconstruction method.

2. the second time interval includes at least one cardiac cycle and is shorter than the first time interval; The image reconstruction method according to claim 1 .

3. Further acquiring heart rate information of a subject from which the k-space data is collected; acquiring a plurality of first k-space data corresponding to the k-space central portion and the k-space peripheral portion by non-simple thinning sampling; generating a plurality of second k-space data from the plurality of first k-space data, in which at least a part of a region thinned out by the non-simple thinning sampling is filled, by processing including a Fourier transform corresponding to the non-simple thinning sampling; generating a pseudo second acquisition time of each second k-space data based on the first acquisition time of each first k-space data; Identifying a plurality of second k-space data that are less affected by arrhythmia among the plurality of second k-space data based on the heartbeat information; selecting a plurality of second k-space data corresponding to a plurality of predetermined cardiac phases from among the plurality of second k-space data having a low influence of the identified arrhythmia based on the second acquisition time; reconstructing a plurality of the MR images corresponding to the plurality of cardiac phases using a plurality of second k-space data corresponding to each of the selected plurality of cardiac phases; 3. The image reconstruction method according to claim 1.

4. The process of generating the second k-space data includes: Calculating cardiac phase information of a plurality of first k-space data included in the k-space central portion and cardiac phase information of a plurality of first k-space data included in the k-space peripheral portion; combining each of the plurality of first k-space data included in the central portion of the k-space with the first k-space data of the peripheral portion of the k-space having cardiac phase information close to cardiac phase information of the central portion of the k-space; performing a process including the Fourier transform on the combined plurality of first k-space data to generate the plurality of second k-space data; The image reconstruction method according to claim 3 .

5. The combining process further combines the first k-space data of the k-space central portion and the first k-space data of the k-space peripheral portion based on a trigger interval of data of the selected cardiac cycle having a low influence of arrhythmia. The image reconstruction method according to claim 4.

6. The bonding process includes: Identifying a plurality of first k-space data that are less affected by arrhythmia among the first k-space data included in the k-space edge portion based on the heartbeat information; Among the plurality of first k-space data having a low influence of the identified arrhythmia, first k-space data of the k-space peripheral portion having cardiac phase information close to cardiac phase information of the k-space central portion is combined with the first k-space data of the k-space central portion. The image reconstruction method according to claim 4.

7. The process of generating the second k-space data includes performing the combining process without performing a process of identifying a plurality of first k-space data that are less affected by arrhythmia among the first k-space data included in the k-space peripheral portion. The image reconstruction method according to claim 4.

8. The cardiac phase information is information indicating a position in a cardiac cycle in a phase direction. The image reconstruction method according to any one of claims 4 to 7.

9. The acquiring process includes: monitoring the occurrence of arrhythmia based on the heart rate information while the non-simple thinning sampling is being performed; when the period during which the arrhythmia does not occur satisfies a predetermined condition, the non-simple thinning-out sampling is terminated. The image reconstruction method according to any one of claims 4 to 8.

10. The process of identifying includes, when an RR interval calculated based on the heart rate information is less than a threshold, identifying second k-space data included in the RR interval as a plurality of second k-space data affected by the arrhythmia. The image reconstruction method according to any one of claims 4 to 9.

11. The process of identifying includes, when an R-R interval calculated based on the heart rate information is less than a threshold value, identifying second k-space data included in a predetermined period including the R-R interval as a plurality of second k-space data affected by the arrhythmia. The image reconstruction method according to any one of claims 4 to 9.

12. The non-simple thinning sampling acquires the plurality of first k-space data with different sampling patterns between consecutive time phases. The image reconstruction method according to any one of claims 4 to 11.

13. The non-simple thinned sampling acquires the plurality of first k-space data using a sampling pattern in which the sampling pattern is regularly thinned in a phase encoding direction of the k-space, and the phase encoding lines acquired between successive time phases are different. The image reconstruction method according to claim 12.

14. The non-simple thinning sampling acquires the plurality of first k-space data in a sampling pattern in which lines of phase encoding are acquired between successive time phases in an irregular manner. The image reconstruction method according to claim 12.

15. Further acquiring heart rate information of a subject from which the k-space data is collected; acquiring a plurality of first k-space data corresponding to the k-space central portion and the k-space peripheral portion by non-simple thinning sampling; Identifying a plurality of first k-space data that are less affected by arrhythmia among the plurality of first k-space data based on the heartbeat information; generating a plurality of second k-space data in which at least a part of a region thinned out by the non-simple thinning-out sampling is filled from a plurality of first k-space data having a low influence of the identified arrhythmia by a process including a Fourier transform corresponding to the non-simple thinning-out sampling; generating a pseudo second acquisition time of each second k-space data based on the first acquisition time of each first k-space data; selecting a plurality of second k-space data corresponding to a plurality of preset cardiac phases from the generated second k-space data based on the second acquisition time; reconstructing a plurality of the MR images corresponding to the plurality of cardiac phases using a plurality of second k-space data corresponding to each of the selected plurality of cardiac phases; 3. The image reconstruction method according to claim 1.

16. an acquisition unit that segments k-space data into a k-space central portion and a k-space peripheral portion, acquires the k-space central portion in a first time interval, and acquires the k-space peripheral portion in a second time interval different from the first time interval; a reconstruction unit that reconstructs an MR (Magnetic Resonance) image from k-space data that combines the collected data of the k-space central portion and the k-space peripheral portion; A reconstruction device comprising: the first time interval includes a plurality of cardiac cycles; the central k-space portion is repeatedly acquired over the multiple cardiac cycles; data of a cardiac cycle that is less affected by arrhythmia is selected from the plurality of cardiac cycles as a central portion of k-space data used for reconstructing the MR image; Heart rate information of a subject from whom the k-space data is collected is further acquired; In the selection of data of cardiac cycles with low influence of arrhythmia, data of a cardiac cycle in a normal range is selected from a central portion of the k-space data based on the acquired heartbeat information; An input of a time of a trigger signal in the heartbeat information is accepted; a pseudo trigger signal is generated at the input time based on the input; data in which the cardiac cycle is in a normal range is selected from a central portion of the k-space data based on heartbeat information including the pseudo trigger signal; Reconfiguration device.

17. A magnetic resonance imaging device. The reconstruction device according to claim 16.

Images