Image reconstruction method and reconstruction apparatus

Abstract

The goal is to shorten imaging time while minimizing the effects of respiration. [Solution] The image reconstruction method according to the embodiment detects a time-series respiratory signal, collects data from the central part of k-space by non-simple decimation sampling so as to include two consecutive electrocardiogram trigger signals based on the respiratory signal, and reconstructs image data from the k-space data including the data from the central part of k-space.

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to an image reconstruction method and a reconstruction apparatus.

Background Art

[0002] As a method of observing the heart by magnetic resonance imaging (MRI), cardiac cine imaging is performed. In cardiac cine imaging, various imaging methods have been proposed in order to reduce the influence (artifact) due to the variation in the position of the heart accompanying the respiration of the subject. Such imaging methods include, for example, "apnea imaging" in which cine imaging is performed under electrocardiogram synchronization while the subject holds their breath, and "respiratory synchronization imaging" in which imaging is performed under free breathing in synchronization with a respiratory signal detected by a respiratory sensor or the like.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to shorten the imaging time while suppressing the influence of respiration. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the respective effects of the respective configurations shown in the embodiments described later can also be regarded as other problems.

Means for Solving the Problems

[0005] The image reconstruction method according to the embodiment detects a time-series respiratory signal, collects data from the central part of k-space by non-simple decimation sampling so as to include two consecutive electrocardiogram trigger signals based on the respiratory signal, and reconstructs image data from the k-space data including the data from the central part of k-space. [Brief explanation of the drawing]

[0006] [Figure 1] Figure 1 is a block diagram showing an MRI apparatus according to an embodiment. [Figure 2] Figure 2 shows an example of a sampling location in kt SENSE. [Figure 3] Figure 3 shows an example of the sampling location for the central segment. [Figure 4] Figure 4 shows an example of the sampling position of the central segment according to the embodiment. [Figure 5] Figure 5 is a flowchart showing the processing procedure using the MRI apparatus according to this embodiment. [Figure 6] Figure 6 is a flowchart showing the processing procedure for k-space data acquisition according to the embodiment. [Figure 7] Figure 7 is a diagram illustrating the k-space data acquisition process according to the embodiment. [Figure 8] Figure 8 is a diagram illustrating the reconstruction process according to the embodiment. [Figure 9] Figure 9 is a diagram illustrating the reconstruction process according to the embodiment. [Figure 10] Figure 10 is a flowchart showing the processing procedure for k-space data acquisition according to Modification 1. [Figure 11] Figure 11 is a diagram illustrating the k-space data acquisition process related to Modification Example 1. [Figure 12] Figure 12 is a flowchart showing the processing procedure for k-space data acquisition according to Modification 2. [Figure 13] Figure 13 is a diagram illustrating the k-space data acquisition process related to Modification Example 2. [Figure 14] Figure 14 is a flowchart showing the processing procedure for k-space data acquisition according to Modification 3. [Figure 15] Figure 15 is a diagram illustrating the k-space data acquisition process related to Modification Example 3. [Figure 16] Figure 16 is a diagram illustrating the processing of the collection function and reconstruction function related to Modification Example 5. [Figure 17] Figure 17 is a block diagram showing an example of the configuration of a reconstruction apparatus according to another embodiment. [Modes for carrying out the invention]

[0007] The image reconstruction method and reconstruction apparatus according to the embodiment will be described below with reference to the drawings. Note that the embodiments are not limited to those described below. Furthermore, the contents described in one embodiment are, in principle, applicable to other embodiments as well.

[0008] (Embodiment) Figure 1 is a block diagram showing an MRI apparatus 100 according to an embodiment. As shown in Figure 1, the MRI apparatus 100 comprises a static magnetic field magnet 101, a gradient magnetic field coil 102, a gradient magnetic field power supply 103, a patient table 104, a patient table control circuit 105, a transmitting coil 106, a transmitting circuit 107, a receiving coil array 108, a receiving 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 (e.g., a human body). Also, the MRI apparatus 100 is an example of a reconstruction device.

[0009] The static magnetic field magnet 101 is a magnet formed in the shape of a hollow cylinder (including those with an elliptical cross-section perpendicular to the axis of the cylinder), and generates a uniform static magnetic field in the internal space. Examples of the static magnetic field magnet 101 include permanent magnets and superconducting magnets.

[0010] 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 arranged inside the static magnetic field magnet 101. The gradient magnetic field coil 102 is formed by combining three coils corresponding to the 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 the 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.

[0011] 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.

[0012] The bed 104 includes a top plate 104a on which the subject P is placed, and under the control of the bed control circuit 105, inserts the top plate 104a into the cavity (imaging aperture) of the gradient magnetic field coil 102 with the subject P placed thereon. Usually, the bed 104 is installed such that its longitudinal direction is parallel to the central axis of the static magnetic field magnet 101.

[0013] The bed control circuit 105 is a processor that drives the bed 104 to move the top plate 104a in the longitudinal direction and the vertical direction under the control of the computer system 120.

[0014] The transmission coil 106 is arranged inside the gradient magnetic field coil 10, and receives the supply of RF pulses from the transmission circuit 107 to generate a high-frequency magnetic field.

[0015] The transmitting circuit 107 supplies the transmitting coil 106 with RF pulses corresponding to the Larmor frequency, which is determined by the type of atom being targeted and the strength of the magnetic field.

[0016] The receiving coil array 108 is positioned inside the gradient magnetic field coil 102 and receives magnetic resonance signals (hereinafter referred to as MR signals) emitted from the subject P due to the influence of the high-frequency magnetic field. When the receiving coil array 108 receives an MR signal, it outputs the received MR signal to the receiving circuit 109. In this embodiment, the receiving coil array 108 is a coil array having one or more, typically multiple, receiving coils.

[0017] The receiving circuit 109 generates MR data based on the MR signal output from the receiving coil array 108. For example, the receiving circuit 109 generates MR data by digitally converting the MR signal output from the receiving coil array 108. The receiving circuit 109 also transmits the generated MR data to the sequence control circuit 110.

[0018] The receiving circuit 109 may be provided on the mounting device side, which includes a static magnetic field magnet 101 and gradient magnetic field coils 102. In this embodiment, the MR signals output from each coil element (each receiving coil) of the receiving coil array 108 are distributed and combined as appropriate and output to the receiving circuit 109 in units called channels. Therefore, the MR data is handled channel by channel in the subsequent processing after the receiving circuit 109. The relationship between the total number of coil elements and the total number of channels may be the same, the total number of channels may be less than the total number of coil elements, or conversely, the total number of channels may be greater than the total number of coil elements. In the following, when we refer to it as "per channel," it means that the processing may be performed for each coil element, or for each channel in which the coil elements have been distributed and combined. Note that the timing of distribution and combination is not limited to the timing described above. The MR signals or MR data only need to be distributed and combined channel by channel before the reconstruction processing described later.

[0019] The sequence control circuit 110 performs imaging of the subject P by driving the gradient power supply 103, the transmitting circuit 107, and the receiving circuit 109 based on sequence information transmitted from the computer system 120. For example, the sequence control circuit 110 is implemented by a processor. Here, the sequence information is information that defines the procedure for performing imaging. The sequence information defines the strength of the power supply that the gradient power supply 103 supplies to the gradient coil 102 and the timing of the power supply supply, the strength of the RF pulse that the transmitting circuit 107 transmits to the transmitting coil 106 and the timing of the RF pulse application, and the timing at which the receiving circuit 109 detects the MR signal.

[0020] Furthermore, the sequence control circuit 110 drives the gradient magnetic field power supply 103, the transmitting circuit 107, and the receiving circuit 109 to image the subject P. When it receives MR data from the receiving circuit 109, it transfers the received MR data to the computer system 120.

[0021] The ECG circuit 111 collects an electrocardiogram waveform based on the electrocardiogram signal output from the ECG sensor 111a. The electrocardiogram waveform is, for example, a time-series electrocardiogram signal. The ECG sensor 111a is attached to the body surface of the subject P and is a sensor that sequentially detects the electrocardiogram signal of the subject P. The ECG sensor 111a outputs the detected electrocardiogram signal to the ECG circuit 111. Note that the ECG circuit 111 is an example of a detection unit.

[0022] Furthermore, for example, the ECG circuit 111 detects the R wave from the electrocardiogram waveform. The ECG circuit 111 then generates an electrocardiogram trigger signal at the time of R wave detection and outputs the generated electrocardiogram trigger signal to the interface circuit 121. The electrocardiogram trigger signal is stored in the memory circuit 122 by the interface circuit 121. Here, the electrocardiogram trigger signal may also be transmitted from the ECG circuit 111 to the interface circuit 121 by wireless communication. In this embodiment, the case in which the electrocardiogram signal is detected by the ECG sensor 111a is described, but it is not limited to this, and for example, it may be detected by a pulse wave meter. Also, in Figure 1, an example in which the ECG sensor 111a and the ECG circuit 111 are part of the MRI device 100 is described, but it is not limited to this. In other words, the MRI device 100 may acquire the electrocardiogram signal obtained from the ECG sensor 111a and the ECG circuit 111 which are provided separately from the MRI device 100.

[0023] The computer system 120 performs overall control of the MRI device 100, as well as data acquisition and image reconstruction. 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.

[0024] The interface circuit 121 transmits sequence information to the sequence control circuit 110 and receives MR data from the sequence control circuit 110. Upon receiving MR data, the interface circuit 121 stores the received MR data in the memory circuit 122. The MR data stored in the memory circuit 122 is then placed in k-space by the processing circuit 130. As a result, the memory circuit 122 stores k-space data for multiple channels. In this way, k-space data is collected. The interface circuit 121 can be implemented, for example, by a network interface card.

[0025] The memory circuit 122 stores MR data received by the interface circuit 121, time-series data (kt-space data) arranged in k-space by the acquisition function 131 described later, and MR image data generated by the second reconstruction function 137 described later. The memory circuit 122 also stores various programs. The memory circuit 122 can be implemented using semiconductor memory elements such as RAM (Random Access Memory) or flash memory, a hard disk, or an optical disk.

[0026] The input interface 123 receives various instructions and information inputs from operators such as doctors and radiologic technologists. The input interface 123 can be implemented using, for example, a trackball, switch buttons, a mouse, or a keyboard. The input interface 123 is connected to the processing circuit 130 and converts the input operations received from the operator into electrical signals, which are then output to the processing circuit 130.

[0027] The display 124, under the control of the processing circuit 130, displays various GUIs (Graphical User Interfaces) and MR image data generated by the second reconstruction function 137.

[0028] The processing circuit 130 performs overall control of the MRI device 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. The processing circuit 130 also controls image reconstruction performed based on 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 implemented by a processor.

[0029] The processing circuit 130 includes a detection function 131, an acquisition function 132, a reconstruction function 133, and an output control function 134. The detection function 131 is an example of a detection unit. The acquisition function 132 is an example of an acquisition unit. The reconstruction function 133 is an example of a reconstruction unit. The output control function 134 is an example of an output control unit.

[0030] Here, for example, the detection function 131, the collection function 132, the reconstruction function 133, and the output control function 134, which are components of the processing circuit 130, are stored in the memory circuit 122 in the form of programs that can be executed by a computer. The processing circuit 130 reads each program from the memory circuit 122 and executes each program that has been read, thereby realizing the function corresponding to each program. In other words, the processing circuit 130 in the state in which each program has been read will have the functions shown in the processing circuit 130 of Figure 1. Note that in Figure 1, the processing functions 131, 132, 133, and 134 are realized by a single processing circuit 130, but it is also possible to configure the processing circuit 130 by combining multiple independent processors, and each processor will realize each processing function by executing each program.

[0031] In the above description, the term "processor" refers to circuits such as a CPU (central preprocessor unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), or a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). Alternatively, instead of storing the program in the memory circuit 122, the program may be directly embedded within the processor's circuitry. In this case, the processor functions by reading and executing the program embedded within the circuitry.

[0032] Generally, an MRI device 100 measures electromagnetic waves emitted from a subject using a coil. The signal obtained by digitizing these measured electromagnetic waves is called k-space data.

[0033] k-space data is, for example, two-dimensional or three-dimensional data obtained by repeatedly taking one-dimensional images. The atomic distribution image inside the subject is obtained by applying a Fourier transform (hereafter, Fourier transform may also include the inverse Fourier transform) to the k-space data. The obtained atomic distribution image is called an MR image, and the process of calculating the MR image from k-space data is called reconstruction, image reconstruction, or image generation. The central part (central segment) of the k-space data corresponds to the low-frequency components when the MR image is subjected to a Fourier transform. The peripheral part (peripheral segment) of the k-space data corresponds to the high-frequency components when the MR image is subjected to a Fourier transform.

[0034] In cardiac cine imaging, parallel imaging (PI), a known high-speed imaging technique, is often combined with breath-hold imaging or respiratory-gated imaging to reduce imaging time while suppressing the effects (artifacts) caused by respiration.

[0035] PI (Primary Image Processing) is a technique that utilizes the fact that sensitivity differs depending on the coil arrangement to simultaneously acquire k-space data using multiple coils with selective sampling, and then reconstructs an MR image from the obtained k-space data while suppressing artifacts. Generally, PI aims to speed up acquisition by collecting k-space data by selectively sampling it in the phase encoding direction. Since aliased images are generated from the selectively collected k-space data, PI reconstructs an image without aliasing by utilizing the difference in sensitivity between channels for k-space data collected with multiple channels with different sensitivities. In other words, PI enables speedup in proportion to the selective sampling rate. The selective sampling rate is sometimes called the speed-up rate. For example, if the selective sampling rate is 4, the acquisition time is reduced to approximately one-quarter.

[0036] However, even when combined with PI, breath-hold imaging and respiratory-gated imaging may not be applicable to actual examinations. For example, the suitability of breath-hold imaging depends on whether the imaging time can be completed within the time the subject (patient) can hold their breath. Since the time a patient can hold their breath may be shorter depending on the patient's disease and physical condition, breath-hold imaging cannot be applied if the imaging time cannot be completed within the time the patient can hold their breath, even when combined with PI. Also, for example, respiratory-gated imaging theoretically selectively images during periods with minimal respiratory influence, but if data is acquired redundantly under free breathing in combination with PI, it takes about 30 to 40 heartbeats to acquire one slice. Therefore, cardiac cine imaging would take about 15 minutes, which is insufficient for actual clinical practice, and there are not many cases in which it can be applied in reality.

[0037] Incidentally, in order to further increase speed (reduce imaging time), techniques have been proposed to acquire k-space data by downsampling not only in the phase encoding direction but also in the temporal direction (time direction). Known techniques for this purpose include kt BLAST (k-space time Broad-use Linear Acquisition Speed-up Technique) and kt SENSE. Note that when the number of coils is small relative to the ratio of downsampled samples, it is called kt BLAST, and when it is not, it is called kt SENSE. In the following explanation, unless explicitly distinguished, kt BLAST will also be referred to as kt SENSE. The following explanation will mainly focus on the case with multiple coils, but as a special case of kt BLAST, the case with one coil is also acceptable. For convenience, even in the case with one coil, it will be referred to as kt SENSE.

[0038] In kt SENSE, the collected k-space data is transformed into xf-space data consisting of image space and time spectrum using a Fourier transform. Then, in this xf-space data, aliasing signals are removed using a sensitivity map in xf space to generate xf-space data. Finally, by transforming the generated xf-space data into xt-space data using an inverse Fourier transform, multiple MR images arranged in time series are generated.

[0039] Figure 2 shows an example of sampling positions in kt SENSE. In Figure 2, "k" on the vertical axis corresponds to the phase encoding direction, and "t" on the horizontal axis corresponds to the temporal direction. For the sake of explanation, Figure 2 illustrates kt spatial data with collected data placed in 8 positions (frames) in the phase encoding direction and 20 positions in the temporal direction. The black circles indicate the positions where one line of data is collected. In other words, frames without black circles are positions where data is not collected. It is also assumed that a period of one heartbeat or more is included between temporal phases P'1 to P'20. Note that in kt BLAST and kt SENSE, there are calibration imaging, which acquires information about the xf space without downsampling in the temporal direction, and main imaging, which samples the kt space by downsampling in the temporal direction, either before or during the main imaging. The example of sampling positions shown in Figure 2 can be considered an example of sampling positions in main imaging. For simplification, the sampling positions in main imaging are not shown in Figure 2. Furthermore, in technologies that do not necessarily require calibration imaging, such as those disclosed in Japanese Patent No. 6073627, Figure 2 can be considered an example of the sampling position in this imaging.

[0040] In the example shown in Figure 2, the sampling position is shifted by one sample in the phase encoding direction for each time phase unit. For example, the k-space data of time phase P'2 is sampled at a position shifted by one sample in the phase encoding direction (upward in the figure) compared to the k-space data of time phase P'1. Similarly, the k-space data of time phase P'3 is sampled at a position shifted by one sample in the phase encoding direction compared to the k-space data of time phase P'2. Furthermore, the k-space data of time phase P'4 is sampled at a position shifted by one sample in the phase encoding direction compared to the k-space data of time phase P'3. In other words, in the example in Figure 2, the k-space data, which has been downsampled to one-quarter of its original size, is sampled periodically every four time phase units. Note that a group of time phases (four time phases in Figure 2) that constitute this sampling period is referred to as a "block".

[0041] Thus, kt SENSE reduces k-space data not only in the phase encoding direction but also in the temporal direction (time direction) by regularly changing the k-space reduction pattern along the time series. As a result, kt SENSE achieves faster imaging than PI.

[0042] Here, the inventors focused on the fact that even when kt SENSE is combined with respiratory-gated imaging, respiratory effects may still occur.

[0043] Figure 3 shows an example of the sampling position of the central segment. In Figure 3, the top panel shows the electrocardiogram signal, the middle panel shows the respiratory signal, and the bottom panel shows the sampling position in the scan. The respiratory signal is, for example, the craniocaudal displacement of the diaphragm detected using the navigator signal, and if this value is below the threshold (Th), it is considered that artifacts due to changes in cardiac position are suppressed. Also, the rightward direction in the figure corresponds to elapsed time, and the numbers in the rectangles indicate the order of the reconstructed images in the cardiac time phase. Note that for simplicity, Figure 3 shows an example where three images are reconstructed, but this is not the only number of images that can be used.

[0044] For example, if sampling is performed by combining kt SENSE with retrospective gating-gated electrocardiogram imaging, k-space data of the central segment is collected at the timing shown in Figure 3, and multiple images are reconstructed. The order of the reconstructed images in the cardiac time phase is such that the first image is the one after the R wave (ECG trigger signal), the second image is the one after the R wave, and the third image is the one before the R wave. Here, the second and third images show discontinuities in the changes in cardiac position, which can be unsettling for the viewer.

[0045] Therefore, the MRI apparatus 100 according to this embodiment can shorten the imaging time while suppressing the effects of respiration through the processing functions described below.

[0046] Specifically, the MRI device 100 includes a detection function 131, an acquisition function 132, a reconstruction function 133, and an output control function 134. The detection function 131 detects the respiratory signal. The acquisition function 132 acquires data from the central part of k-space by non-simple decimation sampling, based on the respiratory signal, so as to include two consecutive electrocardiogram trigger signals. The reconstruction function 133 reconstructs image data from the k-space data, including the data from the central part of k-space, based on the electrocardiogram signal.

[0047] For example, as shown in Figure 4, the MRI device 100 collects k-space data of the central segment so as to include two consecutive R waves. As a result, the order of the multiple images to be reconstructed in the cardiac time phase is sequential, starting with the image after the first R wave, then the second image, the third image, and so on. This allows the MRI device 100 to shorten the imaging time without causing discomfort due to discontinuities in changes in the cardiac position. Figure 4 is a diagram showing an example of the sampling position of the central segment according to the embodiment.

[0048] In the following description, this embodiment will be explained in the case where it is applied to kt SENSE, but it is not limited to this. For example, this embodiment can also be applied to kt BLAST and Compressed Sensing (CS). Compressed sensing is a high-speed imaging method that reconstructs an image from a small amount of k-space data by performing sampling with irregular decimation in the phase encoding direction and utilizing the sparsity of the signal. In kt SENSE, kt BLAST, and compressed sensing, sampling involves collecting multiple k-space data with different sampling patterns over consecutive time phases, and in the following description, this will be referred to as "non-simple decimation sampling" in contrast to normal PI which performs simple decimation sampling.

[0049] Furthermore, this embodiment will describe a case where segment collection is applied. Segment collection is a collection method in which kt spatial data is divided into multiple segments with different phase encoding amounts and collected. In this embodiment, we will show a case where the data is divided into three segments and collected. Of the three divided segments, the segment with the central phase encoding amount will be referred to as the "central segment," one side of the segments flanking the central segment will be referred to as the "first edge segment," and the other side as the "second edge segment."

[0050] The processing procedure of the MRI apparatus 100 according to this embodiment will be explained using Figure 5. Figure 5 is a flowchart of the processing procedure of the MRI apparatus 100 according to this embodiment. The processing procedure shown in Figure 5 is started, for example, when an imaging start request is input by the operator. Note that the numerical values ​​exemplified in the following embodiment are merely examples and can be changed at the operator's discretion.

[0051] Furthermore, Figure 5 will be explained with reference to Figures 6 to 9. Figure 6 is a flowchart showing the processing procedure of the k-space data acquisition process according to the embodiment. Figure 7 is a diagram for explaining the k-space data acquisition process according to the embodiment. Figures 8 and 9 are diagrams for explaining the reconstruction process according to the embodiment.

[0052] Prior to the start of the processing procedure shown in Figure 5, imaging conditions are set. For example, the processing circuit 130 sets imaging conditions based on various information input from the operator via the input interface 123. One imaging condition is the "number of time phases (number of images)" of image data generated per cardiac cycle. As an example, the operator inputs "24" as the desired number of time phases. Upon receiving this input, the MRI device 100 generates 24 MR images arranged at equal intervals within one cardiac cycle. Note that the number of time phases is not limited to "24" and can be set to any number (number of phases).

[0053] In step S101, the detection function 131 starts detecting electrocardiogram signals and respiratory signals. For example, the detection function 131 controls the ECG circuit 111 in response to the imaging start request to sequentially detect electrocardiogram signals and collect them as a time-series electrocardiogram signal. The collected time-series electrocardiogram signal is stored, for example, in the memory circuit 122.

[0054] Furthermore, for example, the detection function 131 collects an MR signal called the navigator signal from a region set near the diaphragm of the subject by executing a pulse sequence that includes a pulse train for collecting the navigator signal. The detection function 131 then uses the collected navigator signal to measure the craniocaudal displacement of the diaphragm. The detection function 131 sequentially detects the measured displacement as a respiratory signal and collects it as a time-series respiratory signal. The collected time-series respiratory signal is stored, for example, in the memory circuit 122.

[0055] Furthermore, the method for detecting respiratory signals is not limited to methods using navigator signals, and known technologies can be applied. For example, a method using a respiratory sensor attached to the subject's chest may be used to detect respiratory signals. Also, the detection function 131 may include the functions of the ECG circuit 111.

[0056] In step S102, the acquisition function 132 performs k-space data acquisition processing. For example, the acquisition function 132 separately acquires data from the central part of k-space and data from the peripheral part of k-space by non-simple decimation sampling. The acquisition function 132 also acquires data from the central part of k-space based on the respiratory signal, so as to include two consecutive electrocardiogram trigger signals.

[0057] While the R wave is typically used as the electrocardiogram trigger signal, it is not limited to this. For example, it is preferable to use features on the electrocardiogram that are used as the start and end points of one cardiac cycle (one heartbeat period) as the electrocardiogram trigger signal. In this embodiment, when performing cardiac cine imaging with the period from the R wave (start point) to the next R wave (end point) as one cardiac cycle, it is preferable to use the R wave as the electrocardiogram trigger signal.

[0058] The processing procedure of the k-space data acquisition process (processing in step S102) according to the embodiment will be explained using Figure 6. Figure 6 will also be explained with reference to Figure 7. The upper part of Figure 7 shows the electrocardiogram signal. The middle part of Figure 7 shows the respiratory signal. The respiratory signal is, for example, the craniocaudal displacement of the diaphragm, and if this falls below the threshold (Th), it is considered that artifacts due to fluctuations in the heart position are suppressed. The lower part of Figure 7 shows the scan status. The white areas indicate the sampling positions, and the black areas indicate dummy pulses that stabilize the signal. The rightward direction in the figure corresponds to elapsed time.

[0059] In step S201, the data collection function 132 collects k-spatial data of the central segment over a predetermined period including multiple heartbeats. In step S202, the data collection function 132 collects k-spatial data of the first peripheral segment over a predetermined period including multiple heartbeats. In step S203, the data collection function 132 collects k-spatial data of the second peripheral segment over a predetermined period including multiple heartbeats.

[0060] Here, the "prescribed period" can be set arbitrarily, but if it is too long, the imaging time will be prolonged, so a period including about 2 to 3 heartbeats is preferable. Since one heartbeat is approximately 800 to 1200 msec, it is preferable to set the prescribed period (acquisition period) to about 3000 msec. Note that the prescribed period can be appropriately changed depending on the age and disease of the subject, and for example, in the case of a patient with arrhythmia, it may be set to a longer period (for example, about 4000 to 5000 msec) depending on the symptoms.

[0061] As shown in Figure 7, the acquisition function 132 acquires k-spatial data of the central segment Sa10 over 3000 msec. The acquisition function 132 also acquires k-spatial data of the peripheral segment Sb10 over 3000 msec. Furthermore, the acquisition function 132 acquires k-spatial data of the peripheral segment Sc10 over 3000 msec. This allows the acquisition function 132 to acquire spatial data for each segment, including two consecutive electrocardiogram trigger signals. While the timing for starting data acquisition can be set arbitrarily, it is preferable to start data acquisition immediately after signal stabilization using a dummy pulse.

[0062] In step S204, the acquisition function 132 selects k-space data for an appropriate period from the k-space data of the central segment of a predetermined period, based on the respiratory signal and the electrocardiogram signal. For example, the acquisition function 132 selects a period that satisfies the condition of "containing two consecutive R waves" (electrocardiogram signal condition) and the condition of "short distance from the resting position" (respiratory signal condition). The period selected here is preferably a period corresponding to one cardiac cycle (heartbeat), for example, 1100 msec. The conditions used here can be arbitrarily set (changed).

[0063] The process of selecting central segment Sa11 from central segment Sa10 will be explained using Figure 7. As shown in Figure 7, central segment Sa10 contains three R waves. Here, there are two periods in central segment Sa10 that satisfy the electrocardiogram signal condition "contains two consecutive R waves": the period containing the first and second R waves, and the period containing the second and third R waves. Of these, the respiratory signal condition "short distance from resting position" is satisfied by the period containing the second and third R waves, so the collection function 132 selects this period as central segment Sa11.

[0064] Furthermore, the calculation and comparison of the "distance from the stationary position" can be implemented using any arbitrary method. A typical method is to calculate the "distance" by determining the area between the respiratory signal waveform and the stationary position during the selected period, and then compare the distances for each selected period. Another method is to calculate the distance between the respiratory signal and the stationary position at any given time phase within the selected period (typically the start phase, end phase, etc.), and then compare the distances for each selected period.

[0065] In step S205, the acquisition function 132 selects k-space data for an appropriate period from the k-space data of the first peripheral segment for a predetermined period, based on the respiratory signal. For example, the acquisition function 132 selects a period that satisfies the condition that "the distance from the resting position is short" (the respiratory signal condition). The period selected here is preferably a period equivalent to one heartbeat (e.g., 1100 msec), similar to the case of the central segment.

[0066] The process of selecting peripheral segment Sb11 from peripheral segment Sb10 (first peripheral segment) will be explained using Figure 7. As shown in Figure 7, among peripheral segment Sb10, the respiratory signal condition "short distance from resting position" is met in the first half, where the period below the threshold Th is long. Therefore, the collection function 132 selects this period as peripheral segment Sb11. The processing method for calculating and comparing "distance from resting position" can be implemented using any processing method, similar to the case of the central segment.

[0067] In step S206, the acquisition function 132 selects k-space data for an appropriate period from the k-space data of the second marginal segment for a predetermined period, based on the respiratory signal. The processing in step S206 can be implemented by the same process as in step S205. As a result, the acquisition function 132 selects marginal segment Sc11 from marginal segment Sc10 (second marginal segment).

[0068] In this way, the acquisition function 132 acquires k-space data for each of the central segment Sa11, peripheral segment Sb11, and peripheral segment Sc11 for one heart cycle (e.g., 1100 msec).

[0069] The contents described in Figures 6 and 7 are merely examples and are not limited to those shown. For example, the processing procedure shown in Figure 6 is an example and can be modified as appropriate. For instance, the processing in step S204 can be performed in any order as long as it follows the processing in step S201. Also, although Figure 6 describes the case where the central segment Sa10, edge segment Sb10, and edge segment Sc10 are collected in that order, each segment can be collected in any order.

[0070] Furthermore, while Figure 6 illustrates the case where k-space data is divided into three segments for collection, the embodiment is not limited to this. For example, the collection function 132 can also collect k-space data by dividing it into four or more segments.

[0071] Furthermore, the various conditions described in Figure 7 are merely examples and can be set arbitrarily. For example, in steps S205 and S206, the acquisition function 132 may select k-space data for an appropriate period based on the condition that "the waveform of the respiratory signal is similar to the waveform of the respiratory signal of central segment Sa11," rather than the condition that "the distance from the stationary position is short." Also, in steps S205 and S206, the acquisition function 132 may select k-space data for an appropriate period using the electrocardiogram signal condition in addition to the respiratory signal condition. Moreover, the conditions used in each process of steps S205 and S206 may be set individually.

[0072] Returning to the explanation of Figure 5, in step S103, the reconstruction function 133 combines the central segment, the first peripheral segment, and the second peripheral segment based on the electrocardiogram signal. For example, the reconstruction function 133 combines the k-space data of the central segment Sa11, the peripheral segment Sb11, and the peripheral segment Sc11 in block units.

[0073] The processing performed by the reconstruction function 133 will be explained using Figure 8. Figure 8 is a diagram illustrating the processing performed by the reconstruction function 133. In Figure 8, "k" on the vertical axis corresponds to the phase encoding direction, and "t" on the horizontal axis corresponds to the temporal direction. Also, in Figure 8, the black circles indicate the positions of the k-space data.

[0074] First, the reconstruction function 133 calculates cardiac phase information for each of the central segment Sa11, peripheral segment Sb11, and peripheral segment Sc11. Here, "cardiac phase information" refers to information indicating the position in the temporal direction within one cardiac cycle. For example, cardiac phase information indicates what percentage of the RR interval (starting point) the collected k-spatial data was collected at, assuming the RR interval is 100%. Cardiac phase information can be calculated based on the collection time of each k-spatial data and the detection times of the electrocardiogram trigger signals before and after that k-spatial data.

[0075] The example shown in Figure 8 illustrates the process by which the reconstruction function 133 calculates the cardiac phase information for a block in region R1. The block in region R1 is a block containing the k-space data of phases PA1 to PA4 from the k-space data of the central segment Sa11. In this case, the reconstruction function 133 calculates the average value of the cardiac phase information of the 16 lines of k-space data included in phases PA1 to PA4 as the cardiac phase information for the block in region R1. Through a similar process, the reconstruction function 133 calculates the cardiac phase information for each block included in the central segment Sa11.

[0076] Furthermore, the reconstruction function 133 calculates cardiac phase information for each block included in the peripheral segment Sb11 and peripheral segment Sc11. This process is the same as the process for calculating cardiac phase information for each block included in the central segment Sa11, so a detailed explanation is omitted.

[0077] Next, the reconstruction function 133 combines the k-space data of the central segment Sa11, the peripheral segment Sb11, and the peripheral segment Sc11 in block units.

[0078] The example shown in Figure 8 illustrates the process by which the reconstruction function 133 combines the k-spatial data of the central segment Sa11 with the k-spatial data of the peripheral segment Sb11. For example, the reconstruction function 133 compares the cardiac phase information (approximately 97%) of the block in region R1 of the central segment Sa11 with the cardiac phase information of each block in the peripheral segment Sb11. Then, the reconstruction function 133 identifies the block in region R2 as the block with the cardiac phase information closest to the cardiac phase information of the block in region R1 among the multiple blocks contained in the peripheral segment Sb11. The reconstruction function 133 then combines the eight k-spatial data contained in the identified block in region R2 by placing (duplicating) them in region R3 on a block-by-block basis. Through a similar process, the reconstruction function 133 combines the k-spatial data of the peripheral segment Sb11 with the k-spatial data of each block contained in the central segment Sa11.

[0079] Furthermore, the reconstruction function 133 combines the k-space data of the central segment Sa11 with the k-space data of the peripheral segment Sc11. This process is the same as the process of combining the k-space data of the central segment Sa11 with the k-space data of the peripheral segment Sb11, so the explanation is omitted.

[0080] In this way, the reconstruction function 133 combines the k-space data of the central segment Sa11, the peripheral segment Sb11, and the peripheral segment Sc11 in block units based on the electrocardiogram signal. In other words, the k-space data after the combining process includes the k-space data of the central segment Sa11, the peripheral segment Sb11, and the peripheral segment Sc11. This allows the reconstruction function 133 to combine the k-space data of each segment without disrupting the sampling pattern by kt SENSE (non-simple decimation sampling).

[0081] It should be noted that the explanation in Figure 8 is merely an example and is not limited to what is shown. For example, Figure 8 illustrates the case where 72 time phases are collected from both the central segment Sa11 and the peripheral segment Sb11, but the number of time phases collected during one cardiac cycle can be arbitrarily set according to the performance of the MRI device 100, etc.

[0082] Furthermore, while Figure 8 illustrates the case where the average value of the cardiac phase information of each k-space data included in each block is applied as the cardiac phase information for each block, this is not the only option. For example, the cardiac phase information of the k-space data collected at approximately the center time among the k-space data included in each block may be applied as a representative value.

[0083] In step S104, the reconstruction function 133 performs a first reconstruction process. In step S105, the reconstruction function 133 generates k-space data corresponding to full sampling by performing an inverse Fourier transform. In step S106, the reconstruction function 133 assigns pseudo-acquisition times to multiple k-space data. In step S107, the reconstruction function 133 performs a second reconstruction process.

[0084] Using Figure 9, the processing from step S104 to step S107 by the reconstruction function 133 will be explained. Figure 9 is a diagram illustrating the processing by the reconstruction function 133. In Figure 9, "k" on the vertical axis corresponds to the phase encoding direction, and "t" on the horizontal axis corresponds to the temporal direction. Also, in Figure 9, the black circles indicate the positions where one line of data was collected. In other words, frames where no black circles are placed (empty frames) are positions where no data was collected.

[0085] As shown in Figure 9, the reconstruction function 133 reconstructs the image group IG0 from the k-space data after the merging process by a reconstruction process (first reconstruction process) corresponding to kt SENSE (non-simple decimation sampling).

[0086] For example, the reconstruction function 133 converts the k-space data after the concatenation of each time phase into xf-space data consisting of image space and time spectrum using a Fourier transform. The reconstruction function 133 also generates xf-space data from which aliasing signals in the xf-space data have been removed using a sensitivity map in xf-space. Then, the reconstruction function 133 generates the image group IG0 by converting the generated xf-space data into xt-space data using an inverse Fourier transform. The image data and the number of time phases (number of images) included in the image group IG0 depend on the number of time phases of the k-space data after concatenation, and for example, it will be image data of 72 time phases from PA1 to PA72. The multiple image data included in the image group IG0 are MR images as intermediate images.

[0087] Next, the reconstruction function 133 generates multiple k-space data equivalent to full sampling by performing an inverse Fourier transform. For example, the reconstruction function 133 generates multiple k-space data equivalent to full sampling by performing an inverse Fourier transform on multiple image data contained in the image group IG0. Here, the multiple k-space data equivalent to full sampling are data in which at least a portion of the k-space data equivalent to the k-space data that was thinned out by non-simple decimation sampling has been filled in.

[0088] The reconstruction function 133 then calculates a pseudo-acquisition time for each of the multiple k-space data points corresponding to full sampling. For example, among the multiple k-space data points corresponding to full sampling, the k-space data point at the same position as the k-space data point after the merging process can be considered to have the same acquisition time as that k-space data point. Therefore, the reconstruction function 133 calculates the acquisition time for the position of the frame where no black circle is placed (empty frame) based on the acquisition time of the position of the black circle in the k-space data point after the merging process. This calculation process can be performed using any process, such as linear interpolation. Then, the reconstruction function 133 assigns the acquisition time of the corresponding empty frame position as a pseudo-acquisition time for each k-space data point corresponding to full sampling.

[0089] The reconstruction function 133 then generates image data (image group IG1) with a desired number of time phases by performing a reconstruction process (second reconstruction process) on k-space data equivalent to full sampling.

[0090] For example, if "12" is set as the desired number of time phases, the reconstruction function 133 calculates the acquisition time for each time phase so that one heart cycle is divided into 12 equal parts. Then, the reconstruction function 133 appropriately selects k-space data with values ​​close to the calculated acquisition time from the k-space data equivalent to full sampling, and reconstructs the image data for each time phase. In this way, the reconstruction function 133 reconstructs the image group IG1, which includes image data for the number of time phases "12", from the k-space data equivalent to full sampling. Note that known reconstruction processes can be appropriately applied as the reconstruction process performed here.

[0091] Thus, the reconstruction function 133 reconstructs image data for each time phase from the combined k-space data, which includes k-space data of the central segment Sa11, peripheral segment Sb11, and peripheral segment Sc11, based on the electrocardiogram signal. Specifically, the reconstruction function 133 generates multiple k-space data points from the k-space data including the data of the central part of k-space, by processing including a Fourier transform corresponding to non-simple decimation sampling, in which at least a portion of the region decimated by non-simple decimation sampling is filled. Then, the reconstruction function 133 reconstructs image data for the desired number of time phases from the generated multiple k-space data points in which at least a portion is filled.

[0092] Returning to the explanation of Figure 5, in step S108, the output control function 134 outputs image data of a desired number of time phases. For example, the output control function 134 performs a cine playback of 12 time phase image data generated by the reconstruction function 133. The output control function 134 is not limited to cine playback; for example, it can also display multiple image data arranged in a time series. Furthermore, the output control function 134 can store multiple image data in the memory circuit 122, or send the data to an external device of the MRI device 100 via a network or storage medium.

[0093] As described above, in the MRI device 100, the detection function 131 detects the respiratory signal. Based on the respiratory signal, the acquisition function 132 collects data from the central part of k-space by non-simple decimation sampling so as to include two consecutive electrocardiogram trigger signals. Based on the electrocardiogram signal, the reconstruction function 133 reconstructs image data from the k-space data including the data from the central part of k-space. With this, the MRI device 100 can shorten the imaging time while suppressing the effects of respiration.

[0094] For example, the MRI device 100 collects k-space data of the central segment such that two consecutive R waves are included within one cardiac cycle. As a result, the central segment of one cardiac cycle that is collected contains k-space data for each time phase from the first R wave (start point) to the next R wave (end point) continuously. Therefore, in the multi-phase image data reconstructed from the k-space data of this central segment, the changes in cardiac position due to respiration become continuous. Consequently, the MRI device 100 can shorten the imaging time without causing discomfort due to discontinuities in cardiac position changes.

[0095] Furthermore, for example, the MRI device 100 promptly starts data acquisition after signal stabilization using a dummy pulse, and performs segment acquisition for a predetermined period (specified period). This allows the MRI device 100 to keep the imaging time within a fixed period regardless of fluctuations in respiratory signals or electrocardiogram signals.

[0096] (Variation 1) In the above embodiment, a case was described in which k-spatial data for an appropriate period is selected from k-spatial data for a predetermined period that includes multiple heartbeats, but the embodiment is not limited thereto. For example, the collection function 132 may be configured to collect k-spatial data that satisfies predetermined conditions.

[0097] Using Figure 10, the processing procedure for k-space data acquisition (processing in step S102) according to Modification Example 1 will be explained. Figure 10 is a flowchart of the processing procedure for k-space data acquisition according to Modification Example 1. Figure 10 will be explained with reference to Figure 11. Figure 11 is a diagram for explaining the k-space data acquisition process according to Modification Example 1. The upper part of Figure 11 shows the electrocardiogram signal. The middle part of Figure 11 shows the respiratory signal. The white circles in the middle part indicate the acquisition start timing t1 to t5. The lower part of Figure 11 shows the scan status. The white areas indicate the sampling position, and the black areas indicate dummy pulses to stabilize the signal. Also, the rightward direction in the figure corresponds to elapsed time. Note that, except for the k-space data acquisition process (processing in step S102), the overall processing by the MRI device 100 is the same as the processing shown in Figure 5, so the explanation will be omitted.

[0098] In step S301, the collection function 132 collects k-spatial data of the central segment. In step S302, the collection function 132 determines whether the central segment meets the acceptance criteria based on the electrocardiogram signal and respiratory signal. If it is determined that the acceptance criteria are not met (negative in step S302), the collection function 132 returns to the process in step S301 and collects k-spatial data of the central segment again. On the other hand, if it is determined that the acceptance criteria are met (positive in step S302), the collection function 132 terminates the collection of k-spatial data of the central segment and proceeds to the process in step S303.

[0099] The processes of steps S301 and S302 will be explained using Figure 11. The example shown in Figure 11 explains the case where data acquisition is started at regular intervals. This interval is preferably a period equivalent to one cardiac cycle (heartbeat), for example, 1100 msec. In other words, the acquisition start timings t1 to t5 are set at 1100 msec intervals.

[0100] As shown in Figure 11, the data collection function 132 starts data collection from the start timing t1 and collects k-space data of the central segment Sa21. The data collection function 132 then determines whether the collected central segment Sa21 satisfies the acceptance criteria. The acceptance criteria include, for example, the condition "contains two consecutive R waves" (ECG signal condition) and the condition "respiratory signal is below the threshold (distance from resting position is below the threshold Th)" (respiratory signal condition). The acceptance criteria used here can be arbitrarily set (changed).

[0101] Here, the central segment Sa21 contains only one R wave, so it does not meet the conditions for an electrocardiogram signal. Also, the central segment Sa21 is above the threshold Th for most of the period, so it does not meet the conditions for a respiratory signal. Thus, if at least one of the conditions for an electrocardiogram signal and a respiratory signal is not met, the collection function 132 determines that the selection criteria are not met. Then, the collection function 132 starts collecting data from the central segment Sa22 from the next collection start timing t2.

[0102] Next, the data collection function 132 determines whether the collected central segment Sa22 meets the acceptance criteria. The central segment Sa22 contains two consecutive R waves, so it meets the electrocardiogram signal criteria. Also, the central segment Sa22 is below the threshold Th for most of the period, so it meets the respiratory signal criteria. Thus, if both the electrocardiogram signal criteria and the respiratory signal criteria are met, the data collection function 132 determines that the acceptance criteria are met (accepts), terminates data collection of the central segment, and moves on to data collection of the peripheral segment.

[0103] In step S303, the collection function 132 collects k-space data of the first peripheral segment. In step S304, the collection function 132 determines, based on the breathing signal, whether the first peripheral segment meets the acceptance criteria. If it is determined that the acceptance criteria are not met (negation in step S304), the collection function 132 returns to the process in step S303 and collects k-space data of the first peripheral segment again. On the other hand, if it is determined that the acceptance criteria are met (affirmation in step S304), the collection function 132 terminates the collection of k-space data of the first peripheral segment and proceeds to the process in step S305.

[0104] As shown in Figure 11, the acquisition function 132 starts data acquisition from the acquisition start timing t3 and acquires k-space data of the peripheral segment Sb21. The acquisition function 132 then determines whether the acquired peripheral segment Sb21 satisfies the acceptance criteria. Here, the acceptance criteria include, for example, the condition that "it is similar to the waveform of the respiratory signal of the accepted central segment Sa22" (respiratory signal condition). Note that the acceptance criteria used here can be arbitrarily set (changed).

[0105] Here, the peripheral segment Sb21 has periods where the respiratory signal is above the threshold Th, but it is generally below the threshold Th and satisfies the condition that "it is similar to the respiratory signal waveform of the adopted central segment Sa22." In this case, the acquisition function 132 determines that the adoption condition is met (adopted). Then, the acquisition function 132 starts acquiring data from the peripheral segment Sc21 from the next acquisition start timing t4.

[0106] In step S305, the collection function 132 collects k-space data of the second peripheral segment. In step S306, the collection function 132 determines, based on the breathing signal, whether the second peripheral segment meets the acceptance criteria. If it is determined that the acceptance criteria are not met (negative in step S306), the collection function 132 returns to the process in step S305 and collects k-space data of the second peripheral segment again. On the other hand, if it is determined that the acceptance criteria are met (positive in step S306), the collection function 132 terminates the collection of k-space data of the second peripheral segment and ends the processing procedure in Figure 10.

[0107] As shown in Figure 11, the acquisition function 132 starts data acquisition from the acquisition start timing t4 and acquires k-space data of the peripheral segment Sc21. The acquisition function 132 then determines whether the acquired peripheral segment Sc21 satisfies the acceptance criteria. Here, the acceptance criteria include, for example, the condition that "the waveform is similar to the respiratory signal waveform of the accepted central segment Sa22" (respiratory signal condition), similar to the conditions in step S304. Note that the acceptance criteria used here can be arbitrarily set (changed).

[0108] In this case, the peripheral segment Sc21 has a long period during which the respiratory signal is above the threshold Th, and therefore does not satisfy the condition that it is "similar to the respiratory signal waveform of the adopted central segment Sa22." In this case, the acquisition function 132 determines that the peripheral segment Sc21 does not satisfy the adoption conditions. Then, the acquisition function 132 starts acquiring data from the peripheral segment Sc22 from the next acquisition start timing t5.

[0109] Next, the data collection function 132 determines whether the collected peripheral segment Sc22 meets the acceptance criteria. Here, the peripheral segment Sc22 satisfies the respiratory signal condition that the respiratory signal is below the threshold Th for a long period and is "similar to the respiratory signal waveform of the accepted central segment Sa22". In this case, the data collection function 132 determines that the acceptance criteria are met (accepts), and terminates data collection for the second peripheral segment.

[0110] Thus, in the MRI apparatus 100 according to Modification 1, the acquisition function 132 sets predetermined conditions and acquires k-space data that satisfies those conditions. As a result, the MRI apparatus 100 acquires data from the central part of k-space so as to include two consecutive electrocardiogram trigger signals, thereby reducing the imaging time while suppressing the effects of respiration.

[0111] The information presented in Figures 10 and 11 is merely an example and is not limited to this. For example, the processing procedure shown in Figure 10 is an example and can be modified as needed. For instance, Figures 10 and 11 describe the case where the central segment, the first edge segment, and the second edge segment are collected in that order, but each segment can be collected in any order.

[0112] Furthermore, while Figures 10 and 11 illustrate the case where k-space data is divided into three segments for collection, the embodiment is not limited to this. For example, the collection function 132 can also collect k-space data by dividing it into four or more segments.

[0113] Furthermore, the various conditions described in Figure 11 are merely examples and can be set arbitrarily. For example, in steps S304 and S306, the acquisition function 132 may be determined using the condition "short distance from the stationary position" instead of the condition "similar to the waveform of the respiratory signal of the adopted central segment Sa22".

[0114] Furthermore, while Figure 11 illustrates the case where data collection is started at regular intervals, the timing of data collection start may also be determined based on respiratory signals or electrocardiogram signals. For example, the data collection function 132 may determine the time to start data collection as the time when at least one of the following conditions is met: "respiratory signal is below threshold Th" (respiratory signal condition) and "R wave detected" (electrocardiogram signal condition), and start collecting data for each segment.

[0115] (Modification 2) Furthermore, for example, the acquisition function 132 can also acquire k-space data of the peripheral segment using a simplified configuration without using either electrocardiogram signals or respiratory signals.

[0116] For example, high-frequency components in k-space data are said to contribute to the depiction of fine structures in the final reconstructed image data. In cardiac cine imaging, the "regions with large motion" in the reconstructed image are mainly the contours of the heart wall, which contain very few fine structures and are therefore easily depicted clearly. For this reason, even if k-space data from a time phase with a long distance from the stationary position is used as the k-space data corresponding to the high-frequency components in regions with large motion, the image quality does not deteriorate much. Therefore, the acquisition function 132 in Modification 2 shortens the imaging time by using k-space data acquired in a short time, regardless of the distance from the stationary position, for the high-frequency components.

[0117] Using Figure 12, the processing procedure for k-space data acquisition (processing in step S102) according to Modification Example 2 will be explained. Figure 12 is a flowchart illustrating the processing procedure for k-space data acquisition according to Modification Example 2. Figure 12 will be explained with reference to Figure 13. Figure 13 is a diagram illustrating the k-space data acquisition process according to Modification Example 2. The upper part of Figure 13 shows the electrocardiogram signal. The middle part of Figure 13 shows the respiratory signal. The lower part of Figure 13 shows the scan status. The white areas indicate the sampling position, and the black areas indicate dummy pulses that stabilize the signal. Also, the rightward direction in the figure corresponds to elapsed time. Note that, except for the k-space data acquisition process (processing in step S102), the overall processing by the MRI device 100 is the same as the processing shown in Figure 5, so the explanation will be omitted.

[0118] In step S401, the acquisition function 132 collects k-spatial data of the central segment over a predetermined period including multiple heartbeats. For example, as shown in Figure 13, the acquisition function 132 collects k-spatial data of the central segment Sa30 over a period of 3000 msec. This process is the same as the process in step S201 shown in Figure 6, so its explanation is omitted.

[0119] In step S402, the acquisition function 132 selects appropriate k-space data from the k-space data of the central segment for a predetermined period, based on the respiratory signal and the electrocardiogram signal. For example, as shown in Figure 13, the acquisition function 132 selects central segment Sa31 as a period that satisfies the conditions of "containing two consecutive R waves" (electrocardiogram signal condition) and "short distance from the resting position" (respiratory signal condition). This process is the same as the process in step S204 shown in Figure 6, so the explanation is omitted.

[0120] In step S403, the acquisition function 132 acquires a minimum amount of k-space data from the first peripheral segment. Here, "minimum amount" refers to the minimum amount used in the reconstruction process corresponding to non-simple decimation sampling, and is preferably, for example, one cardiac cycle (e.g., 1100 msec). With k-space data for one cardiac cycle, in the subsequent merging process, the k-space data of the peripheral segment having the cardiac phase information closest to the cardiac phase information of the k-space data of the central segment can be merged.

[0121] As shown in Figure 13, the acquisition function 132 collects k-spatial data of the peripheral segment Sb30 for a period of 1100 msec. In other words, the acquisition function 132 collects k-spatial data of the peripheral segment Sb30 without using either electrocardiogram signals or respiratory signals.

[0122] In step S404, the collection function 132 collects the minimum amount of k-space data for the second edge segment. For example, as shown in Figure 13, the collection function 132 collects k-space data for the edge segment Sc30 for a period of 1100 msec. This process is the same as the process in step S403, so its explanation is omitted.

[0123] In this way, the acquisition function 132 can shorten the imaging time while simplifying the configuration by acquiring k-space data of the peripheral segment without using either electrocardiogram signals or respiratory signals.

[0124] It should be noted that the explanations in Figures 12 and 13 are merely examples and are not limiting. For example, the above explanation described the case where the "minimum amount" is "one cardiac cycle," but it could also be "one block." This is because, for the same reasons as above, in regions with large motion, the image quality hardly deteriorates even if k-space data from other time phases is used as k-space data corresponding to high-frequency components. In other words, the acquisition function 132 in Modification 2 can shorten the imaging time by combining one block (four time phases in Figure 8) of k-space data into all time phases, regardless of cardiac time phase information, for high-frequency components.

[0125] (Variation 3) In the above embodiment, a case was described in which data acquisition is started at regular intervals, but the timing of data acquisition may be determined based on respiratory signals or electrocardiogram signals.

[0126] Using Figure 14, the processing procedure for k-space data acquisition (processing in step S102) according to Modification Example 3 will be explained. Figure 14 is a flowchart illustrating the processing procedure for k-space data acquisition according to Modification Example 3. Figure 14 will be explained with reference to Figure 15. Figure 15 is a diagram illustrating the k-space data acquisition process according to Modification Example 3. The upper part of Figure 15 shows the electrocardiogram signal. The middle part of Figure 15 shows the respiratory signal. The lower part of Figure 15 shows the scan status. The white areas indicate the sampling position, and the black areas indicate dummy pulses that stabilize the signal. Also, the rightward direction in the figure corresponds to elapsed time. Note that, except for the k-space data acquisition process (processing in step S102), the overall processing by the MRI device 100 is the same as the processing shown in Figure 5, so the explanation will be omitted.

[0127] In step S501, the data collection function 132 determines whether it is time to start data collection. For example, the data collection function 132 determines whether it is time to start data collection based on the respiratory signal and the electrocardiogram signal. If the data collection function 132 determines that it is time to start data collection (affirmative in step S501), it proceeds to the process in step S502 and collects k-space data of the central segment. On the other hand, if the data collection function 132 does not determine that it is time to start data collection (negative in step S501), it does not start the process from step S502 onwards and remains in a waiting state.

[0128] As shown in Figure 15, the collection function 132 determines that it is time to start collection when the conditions "respiratory signal is below threshold Th" (respiratory signal condition) and "R wave detected" (electrocardiogram signal condition) are met, and starts collecting k-space data of the central segment Sa40.

[0129] Thus, the acquisition function 132 starts acquiring k-space data of the central segment based on the respiratory signal and electrocardiogram signal.

[0130] In step S503, the data collection function 132 determines whether or not it is time to end data collection. For example, the data collection function 132 determines whether or not it is time to end data collection based on the respiratory signal and the electrocardiogram signal. If the data collection function 132 determines that it is time to end data collection (step S503 affirmative), it terminates the collection of k-space data for the central segment. On the other hand, if the data collection function 132 does not determine that it is time to end data collection (step S503 negative), it proceeds to the process in step S502 and continues the collection of k-space data for the central segment.

[0131] For example, the collection function 132 determines that it is time to end collection when the condition "detection of the second R wave" (an electrocardiogram signal condition) is met, and terminates the collection of k-space data for the central segment Sa40.

[0132] In step S504, the acquisition function 132 acquires k-space data of the first peripheral segment. For example, the acquisition function 132 acquires k-space data of the peripheral segment Sb40 over a predetermined period. The predetermined period is preferably a period corresponding to one cardiac cycle (heartbeat), for example, 1100 msec.

[0133] In step S505, the acquisition function 132 acquires k-space data of the second peripheral segment. For example, the acquisition function 132 acquires k-space data of the peripheral segment Sc40 over a predetermined period. The predetermined period is preferably a period equivalent to one cardiac cycle (heartbeat), for example, 1100 msec.

[0134] As described above, the data acquisition function 132 in the modified example 3 acquires k-space data for the central segment Sa40, the peripheral segment Sb40, and the peripheral segment Sc40. The MRI device 100 then uses the k-space data for the central segment Sa40, the peripheral segment Sb40, and the peripheral segment Sc40 to perform the processing from step S103 onward.

[0135] According to this, the MRI device 100 according to modified example 3 can collect k-space data at an appropriate timing.

[0136] Furthermore, a delay of several milliseconds (Δt) occurs between the determination of the timing to start data collection and the actual start of data collection. As a result, the central segment collected by the data collection function 132 according to Modification 3 actually does not include the first R wave (start point). In other words, the data collection function 132 collects data from the central part of k-space using non-simple decimation sampling, based on the respiratory signal, so as to include the second (end point) electrocardiogram trigger signal.

[0137] The explanations in Figures 14 and 15 are merely examples and are not limiting. For example, the various conditions used to determine the start and end timings of data acquisition can be arbitrarily set (changed). For example, in determining the end timing of data acquisition, the acquisition function 132 may use the condition "respiratory signal is less than the threshold Th" (respiratory signal condition). In other words, the acquisition function 132 may terminate the acquisition of data from the central part of k-space based on at least one of the respiratory signal and the electrocardiogram signal.

[0138] Furthermore, the data collection function 132 may perform k-space data collection of the central segment for a predetermined period (for example, 1100 msec). In this case, since data collection ends when the predetermined period has elapsed, the processing in step S503 does not need to be performed.

[0139] Furthermore, in the collection of edge segments (processing in steps S504 and S505), the same determination of the start timing of collection and the end timing of collection may be used as in the case of the central segment.

[0140] (Modification 4) In addition to the above embodiments, a process may be performed to select k-space data for heart rate periods that are unaffected (or have little effect) by arrhythmias, in preparation for the occurrence of arrhythmias.

[0141] For example, the data collection function 132 collects data from the central part of k-space over a predetermined period, including multiple heartbeats. Then, from the collected data from the central part of k-space over the predetermined period, the data collection function 132 selects data from heartbeat periods with less influence from arrhythmias. The predetermined period (collection period) is preferably set to approximately 3000 msec.

[0142] Here, arrhythmia is a condition in which the pulse becomes irregular, resulting in extremely long or short heartbeats. For this reason, a "heartbeat duration with minimal impact from arrhythmia" is, for example, 800 to 1200 msec. Therefore, the data collection function 132 removes heartbeats shorter than 800 msec and longer than 1200 msec as arrhythmias. This period can be set (changed) arbitrarily.

[0143] For example, prior to processing in step S204 in Figure 6, the data collection function 132 excludes heart rate periods affected by arrhythmias. Specifically, the data collection function 132 identifies the location of the electrocardiogram trigger signal from the k-space data of the central segment Sa10 of 3000 msec and detects the heart rate period. Then, the data collection function 132 removes heart rate periods of less than 800 msec and 1200 msec or more from the detected heart rate periods. Finally, the data collection function 132 executes the processing from step S204 onward. Note that the process of excluding heart rate periods affected by arrhythmias is essentially synonymous with the process of selecting heart rate periods with less influence from arrhythmias.

[0144] As a result, the data collection function 132 in the modified example 4 can select k-spatial data from heart rate periods with less influence from arrhythmias.

[0145] Furthermore, the process of selecting k-space data for heart rate periods with minimal impact from arrhythmias is not limited to the above explanation; for example, the process described in Japanese Patent Publication No. 2020-157063 can be applied as appropriate.

[0146] (Variation 5) In Modification 4, we described a process for excluding heart rate periods affected by arrhythmias, but it is also possible to reconstruct the image from k-space data of heart rate periods affected by arrhythmias.

[0147] In other words, the acquisition function 132 acquires data from the central part of k-space over a predetermined period that includes multiple heartbeats. The reconstruction function 133 then reconstructs the image data with a desired number of time phases for each heartbeat period of the multiple heartbeats included in the predetermined period, based on the multiple k-space data that are at least partially filled.

[0148] The processing of the acquisition function 132 and the reconstruction function 133 in Modification 5 will be explained using Figure 16. Figure 16 is a diagram for explaining the processing of the acquisition function 132 and the reconstruction function 133 in Modification 5. The upper part of Figure 16 shows the electrocardiogram trigger signal, and the lower part of Figure 16 shows the scan status. The white areas indicate the sampling position, and the black areas indicate dummy pulses that stabilize the signal. Also, the rightward direction in the figure corresponds to elapsed time.

[0149] The processes of the data collection function 132 and the reconstruction function 133 shown in Figure 16 are basically the same as those shown in Figures 5 and 12, except that they perform the processes described below. The following explanation will refer to Figures 5 and 12.

[0150] As shown in Figure 16, the data collection function 132 collects k-spatial data of the central segment Sa50 for a predetermined period of 3000 msec, which includes multiple heartbeats. This process is the same as the process in step S401 in Figure 12.

[0151] Next, as part of step S402, the data acquisition function 132 selects k-space data corresponding to each heartbeat duration from the k-space data of the central segment Sa50 for a predetermined period, based on the electrocardiogram signal. In the example shown in Figure 16, the central segment Sa50 includes three heartbeat durations of 500 msec, 1000 msec, and 800 msec. In this case, the data acquisition function 132 selects k-space data for the three central segments Sa51, Sa52, and Sa53, respectively, corresponding to these three heartbeat durations.

[0152] The collection function 132 then collects k-space data of the marginal segments Sb50 and Sc50. This process is the same as steps S403 and S404 in Figure 12.

[0153] As a result, the data collection function 132 in the modified example 5 outputs the k-space data for each of the central segments Sa51, Sa52, Sa53, Sb50, and Sc50 to the reconstruction function 133.

[0154] The reconstruction function 133 then performs the processing shown in Figure 5 from step S103 onwards onwards for the k-space data of each of the three central segments Sa51, Sa52, and Sa53.

[0155] For example, in step S103, the reconstruction function 133 combines the k-space data of the central segment Sa51, the peripheral segment Sb50, and the peripheral segment Sc50 in block units to generate k-space data for the first heartbeat period (500 msec). The reconstruction function 133 also combines the k-space data of the central segment Sa52, the peripheral segment Sb50, and the peripheral segment Sc50 in block units to generate k-space data for the second heartbeat period (1000 msec). The reconstruction function 133 also combines the k-space data of the central segment Sa53, the peripheral segment Sb50, and the peripheral segment Sc50 in block units to generate k-space data for the third heartbeat period (800 msec). Note that the k-space data generated (combined) here all correspond to k-space data that has been thinned by non-simple decimation sampling.

[0156] In step S104, the reconstruction function 133 performs a first reconstruction process on the k-space data for the first heartbeat period, the second heartbeat period, and the third heartbeat period. As a result, the reconstruction function 133 generates image sets for the first heartbeat period, the second heartbeat period, and the third heartbeat period. Note that the number of time phases in the image sets generated here depends on the number of time phases in the k-space data after the merging process and does not necessarily match the desired number of time phases.

[0157] In step S105, the reconstruction function 133 generates k-space data corresponding to the full sampling of the first, second, and third heartbeat periods by performing an inverse Fourier transform on the image sets for each of the first, second, and third heartbeat periods.

[0158] In step S106, the reconstruction function 133 assigns a pseudo-acquisition time to the k-space data corresponding to the full sampling of the first heartbeat period, the second heartbeat period, and the third heartbeat period.

[0159] In step S107, the reconstruction function 133 performs a second reconstruction process on the k-space data corresponding to the full sampling of the first, second, and third heartbeat periods, thereby generating image data with a desired number of time phases for each of the first, second, and third heartbeat periods. As a result, the reconstruction function 133 reconstructs, for example, 24 images (the desired number of time phases) for each of the first, second, and third heartbeat periods.

[0160] Thus, the MRI device 100 according to Modification 5 is capable of reconstructing a desired number of cardiac cine images that include k-space data of the heartbeat period affected by arrhythmia. As a result, for example, the MRI device 100 can provide the user with image data of a heart affected by arrhythmia.

[0161] The information presented in Figure 16 is merely an example and is not limited thereto. For example, the data collection function 132 and reconstruction function 133 in Modification 5 can be performed regardless of whether or not a period of heartbeat affected by arrhythmia is included. Furthermore, the data collection function 132 and reconstruction function 133 in Modification 5 may be performed when a period of heartbeat affected by arrhythmia is detected in the central segment Sa50. Note that a period of heartbeat affected by arrhythmia can be detected by the process described in Modification 4.

[0162] (Other embodiments) In addition to the embodiments described above, the device may be implemented in various other forms.

[0163] (Network reconfiguration device) Furthermore, for example, the processing function according to the above embodiment can be provided as a reconfiguration device on a network. This reconfiguration device can, for example, provide information processing services (cloud services) via the network.

[0164] Figure 17 is a block diagram showing an example configuration of a reconfiguration device according to another embodiment. As shown in Figure 17, for example, a reconfiguration device 200 is installed in a service center that provides information processing services. The reconfiguration device 200 is connected to an operation terminal 201. The reconfiguration device 200 is also connected to a plurality of client terminals 203A, 203B, ..., 203N via a network 202. Note that the reconfiguration device 200 and the operation terminal 201 may be connected via the network 202. Furthermore, when referring to the plurality of client terminals 203A, 203B, ..., 203N collectively without distinction, they are referred to as "client terminal 203".

[0165] The operation terminal 201 is an information processing terminal used by the operator who operates the reconfiguration device 200. For example, the operation terminal 201 is equipped with input devices such as a mouse, keyboard, and touch panel to receive various instructions and setting requests from the operator. The operation terminal 201 is also equipped with a display device that can display images and a GUI for the operator to input various setting requests using the input devices. By operating the operation terminal 201, the operator can send various instructions and setting requests to the reconfiguration device 200 and view information inside the reconfiguration device 200. The network 202 is any communication network, such as the Internet, WAN (Wide Area Network), or LAN (Local Area Network).

[0166] The client terminal 203 is an information processing terminal operated by a user utilizing the information processing service. Here, the user is, for example, a medical professional such as a doctor or technician working in a medical institution. For instance, the client terminal 203 corresponds to an information processing device such as a personal computer or workstation, or an operating terminal for a medical imaging diagnostic device such as a console included in an MRI machine. The client terminal 203 has client functionality that allows it to utilize the information processing service provided by the reconstruction device 200. This client functionality is pre-recorded on the client terminal 203 in the form of a program executable by a computer.

[0167] 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 each other so as to be able to communicate with one another.

[0168] The communication interface 210 is, for example, a network card or a network adapter. By connecting to the network 202, the communication interface 210 enables information communication between the reconfiguration device 200 and external devices.

[0169] 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 those programs.

[0170] The processing circuit 230 is an electronic device (processor) that controls the entire process in the reconstruction device 200. The processing circuit 230 has a detection function 231, an acquisition function 232, a reconstruction function 233, and an output control function 234. Each processing function performed by the processing circuit 230 is recorded in the memory circuit 220 in the form of a program that can be executed by a computer, for example. The processing circuit 230 reads each program and executes it to realize the function corresponding to each program that has been read. The detection function 231, acquisition function 232, reconstruction function 233, and output control function 234 can perform basically the same processing as the detection function 131, acquisition function 132, reconstruction function 133, and output control function 134 shown in Figure 1.

[0171] For example, a user operates the client terminal 203 to input an instruction to send (upload) multiple k-space data to the reconstruction device 200 located in the service center. Upon receiving the instruction to send multiple k-space data, the client terminal 203 sends the multiple k-space data to the reconstruction device 200. Here, the multiple k-space data are multiple k-space data that have been divided into segment units collected by the sequence control circuit 110.

[0172] The reconstruction device 200 then receives multiple k-space data transmitted from the client terminal 203. Based on this, the detection function 231 in the reconstruction device 200 detects the respiratory signal. The acquisition function 232 then collects data from the central part of k-space using non-simple decimation sampling, based on the respiratory signal, so that it includes two consecutive electrocardiogram trigger signals. The reconstruction function 233 then reconstructs image data from the k-space data, including the data from the central part of k-space, based on the electrocardiogram signal. Finally, the output control function 134 transmits (allows download) the reconstructed image data to the client terminal 203. This allows the MRI device 100 to shorten the imaging time while suppressing the effects of respiration.

[0173] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions. Moreover, each processing function performed by each device can be implemented, in whole or in any part, by a CPU and the program that is analyzed and executed by that CPU, or by hardware using wired logic.

[0174] Furthermore, among the processes described in the embodiments described above, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified.

[0175] Furthermore, the image reconstruction method described in the above-mentioned embodiments can be implemented by executing a pre-prepared image reconstruction program on a computer such as a personal computer or workstation. This image reconstruction program can be distributed via a network such as the Internet. In addition, this ultrasound imaging method can also be executed by recording it on a computer-readable recording medium such as a hard disk, flexible disk (FD), CD-ROM, MO, or DVD, and then reading it from the recording medium by a computer.

[0176] According to at least one embodiment described above, imaging time can be shortened while suppressing the effects of respiration.

[0177] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0178] 100 MRI machine 130 Processing Circuits 131 Detection function 132 Collection Function 133 Reconfiguration function 134 Output control function

Claims

1. Detects time-series respiratory signals, Based on the respiratory signal, data from the central part of k-space is collected by non-simple decimation sampling to include two consecutive electrocardiogram trigger signals. Image data is reconstructed from k-space data including the data of the central part of the k-space. Image reconstruction method.

2. The aforementioned detection process detects time-series electrocardiogram signals, The aforementioned data collection process involves separately collecting data from the central part of k-space and data from the peripheral part of k-space. The aforementioned reconstruction process is: Based on the electrocardiogram signal, the data from the central part of the k-space and the data from the peripheral part of the k-space are combined. The image data is reconstructed from the k-space data, which includes the combined data of the central part of the k-space and the data of the peripheral part of the k-space. The image reconstruction method according to claim 1.

3. The aforementioned detection process detects time-series electrocardiogram signals, The aforementioned data collection process is: By collecting data from the central part of k-space over a predetermined period, including multiple heartbeats, Based on the electrocardiogram signal and the respiratory signal, an appropriate data point for the central part of k-space is selected from the data point for the central part of k-space during the predetermined period. The image reconstruction method according to claim 1.

4. The aforementioned data collection process is: By collecting data from the k-space periphery over a predetermined period that includes multiple heartbeats, Based on the respiratory signal, data from the k-space periphery for the predetermined period is selected to represent the appropriate central portion of the k-space. The image reconstruction method according to claim 1.

5. The aforementioned detection process detects time-series electrocardiogram signals, The aforementioned data collection process involves collecting data from the central part of the k-space that satisfies predetermined conditions, based on the electrocardiogram signal and the respiratory signal. The image reconstruction method according to claim 1.

6. The aforementioned data collection process involves collecting data from the k-space edge portion that satisfies predetermined conditions based on the respiratory signal. The image reconstruction method according to claim 2.

7. The aforementioned data collection process involves collecting data from the k-space periphery over a predetermined period without using either the electrocardiogram signal or the respiratory signal. The image reconstruction method according to claim 2.

8. The aforementioned data collection process collects the minimum amount of k-space peripheral data used in the reconstruction process corresponding to the non-simple decimation sampling. The image reconstruction method according to claim 1.

9. The aforementioned detection process detects time-series electrocardiogram signals, The aforementioned data collection process starts collecting data from the central part of k-space based on the respiratory signal and the electrocardiogram signal. The image reconstruction method according to claim 1.

10. The aforementioned data collection process terminates the collection of data from the central part of k-space based on at least one of the respiratory signal and the electrocardiogram signal. The image reconstruction method according to claim 9.

11. The aforementioned data collection process is: By collecting data from the central part of k-space over a predetermined period, including multiple heartbeats, From the data of the central part of the k-space over the predetermined period, data from heart rate periods with minimal influence from arrhythmias are selected. The image reconstruction method according to claim 1.

12. The aforementioned reconstruction process is: By a process including a Fourier transform corresponding to the non-simple decimation sampling, a plurality of k-space data are generated from the k-space data including the data of the central part of the k-space, in which at least a portion of the region decimated by the non-simple decimation sampling is filled. From the generated k-space data, at least a portion of which is filled, the image data of a desired time phase is reconstructed. The image reconstruction method according to claim 1.

13. The aforementioned data collection process collects data from the central part of k-space over a predetermined period, including multiple heartbeats. The reconstruction process reconstructs the image data of a desired number of time phases for each of the multiple heartbeats included in the predetermined period, based on the multiple k-space data that are at least partially filled. The image reconstruction method according to claim 12.

14. A detection unit that detects respiratory signals, Based on the aforementioned respiratory signal, an acquisition unit collects data from the central part of k-space by non-simple decimation sampling so as to include two consecutive electrocardiogram trigger signals. A reconstruction unit that reconstructs image data from k-space data including the data of the central part of k-space. A reconfiguration device equipped with the following features.

Images