JP7183055B2 - Image reconstruction method and reconstruction device - Google Patents

Description

TECHNICAL FIELD Embodiments of the present invention relate to an image reconstruction method and a reconstruction apparatus.

In Magnetic Resonance Imaging (MRI), synchronous imaging synchronized with biological signals of a subject is known as an imaging method for imaging a site that performs periodic motion. As an example of synchronized imaging, there is known electrocardiographic gated imaging in which imaging is performed in synchronization with an electrocardiographic signal of a subject.

Here, electrocardiographic gated imaging includes a prospective gating method and a retrospective gating method. Prospective gating is a method of collecting data at specific predetermined cardiac phases. For example, in the prospective gating method, the timing of the R wave is detected, and the R wave is used as a trigger to repeatedly collect data for each cardiac phase. In the prospective gating method, a waiting time occurs before a trigger occurs, so repeated imaging of two or more heartbeats is required to image an entire heartbeat. As a result, for example, even if parallel imaging is used together and a speed-up rate of 4 times is set, the substantial speed-up rate will be 2 times or less as long as repeated imaging of two or more heartbeats is required.

The retrospective gate method is a method of reconstructing an image by extracting data of the same cardiac time phase from a series of continuously acquired data. For example, in the retrospective gating method, data are continuously acquired without being synchronized with the electrocardiographic signal, and the electrocardiographic signal at the time of data acquisition is acquired. Then, using the acquired electrocardiographic signals, rearrangement is performed ex post facto so that the cardiac time phases of the series of acquired data are aligned, and then reconstruction is performed. Since the retrospective gating method does not require waiting for a trigger, the imaging time can be shortened compared to the prospective gating method.

JP 2017-035296 A JP 2013-240571 A

A problem to be solved by the present invention is to provide an image reconstruction method and a reconstruction apparatus that can suitably perform imaging of a moving imaging target.

An image reconstruction method according to an embodiment provides a plurality of first k-space data acquired from a subject by non-simple thinning sampling in which the sampling pattern changes in the time direction, and a first 1 Acquisition time and time-series biological signal information of the subject are acquired. The image reconstruction method inversely transforms intermediate data obtained by transforming the plurality of first k-space data, thereby filling at least a portion of the thinning region for full sampling with respect to the plurality of first k-space data. A plurality of second k-space data are generated. The image reconstruction method generates a pseudo second acquisition time for each of the plurality of second k-space data based on the first acquisition time. The image reconstruction method performs rearrangement processing of the plurality of second k-space data based on the second acquisition time and the biosignal information. The image reconstruction method generates a plurality of reconstructed images by performing reconstruction processing on the plurality of second k-space data after the rearrangement processing.

FIG. 1 is a block diagram showing an MRI apparatus according to the first embodiment. FIG. 2 is a diagram showing an example of sampling positions obtained by thinning the kt space in the time phase direction. FIG. 3 is a flow chart showing processing procedures by the MRI apparatus according to the first embodiment. FIG. 4 is a diagram showing an example of kt space data according to the first embodiment. FIG. 5 is a diagram for explaining an example of a sequence according to the first embodiment; FIG. 6 is a diagram for explaining processing of a calculation function according to the first embodiment. 7A and 7B are diagrams for explaining processing of the combining function according to the first embodiment. FIG. 8 is a diagram for explaining the processing of the combining function according to the first embodiment. FIG. 9 is a diagram for explaining the processing of the combining function according to the first embodiment; 10A and 10B are diagrams for explaining processing of the combining function according to the first embodiment. 11A and 11B are diagrams for explaining processing of the combining function according to the first embodiment. FIG. FIG. 12 is a diagram for explaining processing of a reconstruction function and a selection function according to the first embodiment; 13A and 13B are diagrams for explaining processing of the reconstruction function and the selection function according to the first embodiment. FIG. 14 is a diagram showing an example of kt space data according to Modification 1 of the first embodiment. FIG. 15 is a diagram showing an example of kt space data according to modification 2 of the first embodiment. FIG. 16 is a flow chart showing processing procedures by the MRI apparatus according to the second embodiment. FIG. 17 is a diagram for explaining processing of a selection function according to the second embodiment. FIG. 18 is a flow chart showing processing procedures by the MRI apparatus according to the third embodiment. FIG. 19 is a flow chart showing processing procedures by the MRI apparatus according to the fourth embodiment. FIG. 20 is a diagram for explaining processing of the MRI apparatus according to the fourth embodiment. FIG. 21 is a diagram for explaining processing of the MRI apparatus according to the fourth embodiment. FIG. 22 is a diagram for explaining processing of the MRI apparatus according to the fourth embodiment. FIG. 23 is a diagram for explaining processing of the MRI apparatus according to the fourth embodiment. FIG. 24 is a diagram for explaining processing of the MRI apparatus according to the fourth embodiment. FIG. 25 is a diagram for explaining processing of the MRI apparatus according to the fourth embodiment. FIG. 26 is a diagram for explaining processing of the MRI apparatus according to the fourth embodiment. FIG. 27 is a flow chart showing processing procedures by the MRI apparatus according to the fifth embodiment. FIG. 28 is a diagram for explaining processing of the MRI apparatus according to the fifth embodiment. FIG. 29 is a diagram for explaining processing of the MRI apparatus according to the sixth embodiment. FIG. 30 is a diagram for explaining a sampling order by an MRI apparatus according to another embodiment; FIG. 31 is a diagram for explaining processing of a combination function according to another embodiment; FIG. 32 is a diagram for explaining processing of an MRI apparatus according to another embodiment. FIG. 33 is a diagram for explaining processing of an MRI apparatus according to another embodiment. FIG. 34 is a block diagram showing a configuration example of a reconstruction device according to another embodiment.

An image reconstruction method and reconstruction apparatus according to embodiments will be described below with reference to the drawings. In addition, embodiment is not restricted to the following embodiments. Also, the content described in one embodiment can be similarly applied to other embodiments in principle.

(First embodiment)
FIG. 1 is a block diagram showing an MRI apparatus 100 according to the first embodiment. As shown in FIG. 1, the MRI apparatus 100 includes a static magnetic field magnet 101, a gradient magnetic field coil 102, a gradient magnetic field power supply 103, a bed 104, a bed control circuit 105, a transmission coil 106, a transmission circuit 107, It comprises 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 the subject P (eg, human body). Also, the MRI apparatus 100 is an example of a reconstruction apparatus.

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

The gradient magnetic field coil 102 is a coil formed in a hollow cylindrical shape (including one having 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 mutually orthogonal X, Y, and Z axes, and these three coils are individually supplied with current from the gradient magnetic field power supply 103 to generate gradient magnetic fields with magnetic field strengths varying along the X, Y, and Z axes. Here, the X-, Y-, and Z-axis gradient magnetic fields generated by the gradient magnetic field coil 102 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, respectively. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging section. The phase-encoding gradient magnetic field Ge is used to change the phase of the MR signal according to the spatial position. The readout gradient magnetic field Gr is used to change the frequency of the MR signal according to the spatial position.

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

The bed 104 has a tabletop 104a on which the subject P is placed. Under the control of the bed control circuit 105, the tabletop 104a is moved into the cavity ( (imaging opening). The bed 104 is usually installed so that its longitudinal direction is parallel to the central axis of the static magnetic field magnet 101 .

The bed control circuit 105 is a processor that drives the bed 104 and moves the top board 104a in the longitudinal direction and the vertical direction under the control of the computer system 120 .

The transmission coil 106 is arranged inside the gradient magnetic field coil 102, receives RF pulses supplied from the transmission circuit 107, and generates a high-frequency magnetic field.

The transmission circuit 107 supplies the transmission coil 106 with an RF pulse corresponding to the Larmor frequency determined by the type of target atoms and the strength of the magnetic field.

The receiving coil array 108 is arranged inside the gradient magnetic field coil 102 and receives magnetic resonance signals (hereinafter referred to as MR signals) emitted from the subject P under the influence of the high-frequency magnetic field. Upon receiving the MR signal, the receiving coil array 108 outputs the received MR signal to the receiving circuit 109 . Note that, in the first embodiment, the receiving coil array 108 is a coil array having one or more, typically a plurality of receiving coils.

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

Note that the receiving circuit 109 may be provided on the side of the gantry that includes the static magnetic field magnet 101, the gradient magnetic field coil 102, and the like. Here, in the first embodiment, the MR signals output from each coil element (receiving coil) of the receiving coil array 108 are appropriately distributed and synthesized, and are sent to the receiving circuit 109 in units called channels. output. For this reason, the MR data are handled for each channel in subsequent processing after receiving circuit 109 . The relationship between the total number of coil elements and the total number of channels may be the same, or the total number of channels may be less than the total number of coil elements, or conversely, the total number of channels may be greater than the total number of coil elements. In some cases. In the following description, the term "per channel" indicates that the process may be performed for each coil element, or may be performed for each channel in which the coil elements are distributed and synthesized. . Note that the timing of distribution/synthesis is not limited to the timing described above. The MR signal or MR data may be distributed and synthesized in units of channels before reconstruction processing, which will be described later.

The sequence control circuit 110 images the subject P by driving the gradient magnetic field power supply 103 , the transmission circuit 107 and the reception circuit 109 based on the 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 imaging. The sequence information includes the strength of the power supplied by the gradient magnetic field power supply 103 to the gradient magnetic field coil 102 and the timing of supplying the power, the strength of the RF pulse transmitted by the transmission circuit 107 to the transmission coil 106, the timing of applying the RF pulse, and the reception The timing at which the circuit 109 detects the MR signal and the like are defined.

When the sequence control circuit 110 receives MR data from the receiving circuit 109 as a result of imaging the subject P by driving the gradient magnetic field power supply 103, the transmitting circuit 107, and the receiving circuit 109, the received MR data is transferred to the computer system 120. transfer to

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

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

The computer system 120 performs overall control of the MRI apparatus 100, data acquisition, image reconstruction, and the like. The computer system 120 has an interface circuit 121 , a memory circuit 122 , a processing circuit 123 , an input interface 124 and a display 125 .

The interface circuit 121 transmits sequence information to the sequence control circuit 110 and receives MR data from the sequence control circuit 110 . Further, upon receiving the MR data, the interface circuit 121 stores the received MR data in the storage circuit 122 . The MR data stored in the storage circuit 122 are arranged in k-space by the processing circuit 123 . As a result, the memory circuit 122 stores k-space data for a plurality of channels. Thus, k-space data is acquired. The interface circuit 121 is implemented by, for example, a network interface card.

The storage circuit 122 stores MR data received by the interface circuit 121, time series data (kt space data) arranged in k space by an acquisition function 123a described later, and an image generated by a reconstruction function 123d described later. store data, etc. The storage circuit 122 also stores various programs. The storage circuit 122 is implemented by, for example, a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk, an optical disk, or the like.

The input interface 124 receives various instructions and information inputs from operators such as doctors and radiological technologists. The input interface 124 is implemented by, for example, a trackball, switch buttons, mouse, keyboard, and the like. The input interface 124 is connected to the processing circuit 123 , converts an input operation received from the operator into an electric signal, and outputs the electric signal to the processing circuit 123 .

Under the control of the processing circuit 123, the display 125 displays various GUIs (Graphical User Interfaces), MR (Magnetic Resonance) images generated by the reconstruction function 123d, and the like.

The processing circuit 123 performs overall control of the MRI apparatus 100 . Specifically, the processing circuit 123 controls imaging by generating sequence information based on the imaging conditions input by the operator via the input interface 124 and transmitting the generated sequence information to the sequence control circuit 110. do. The processing circuit 123 also controls image reconstruction based on MR data sent from the sequence control circuit 110 as a result of imaging, and controls display on the display 125 . The processing circuit 123 is realized by a processor. The processing circuitry 123 has an acquisition function 123a, a calculation function 123b, a combination function 123c, a reconstruction function 123d, and a selection function 123e. Note that the acquisition function 123a is an example of an acquisition unit. Also, the calculation function 123b is an example of a calculation unit. Also, the coupling function 123c is an example of a coupling section. Also, the reconstruction function 123d is an example of a reconstruction unit. Also, the selection function 123e is an example of a selection unit.

Here, for example, each of the processing functions of the acquisition function 123a, the calculation function 123b, the combination function 123c, the reconstruction function 123d, and the selection function 123e, which are components of the processing circuit 123, are stored in the storage circuit in the form of programs executable by a computer. 122. The processing circuit 123 reads each program from the storage circuit 122 and executes each read program, thereby realizing a function corresponding to each program. In other words, the processing circuit 123 with each program read has each function shown in the processing circuit 123 of FIG. In FIG. 1, it is assumed that the single processing circuit 123 implements the acquisition function 123a, the calculation function 123b, the combination function 123c, the reconstruction function 123d, and the selection function 123e. A plurality of independent processors may be combined to configure the processing circuit 123, and each processor may implement each processing function by executing each program.

The term "processor" used in the above description is, for example, a CPU (central preprocessing unit), a GPU (Graphics Processing Unit), or an application specific integrated circuit (ASIC), a programmable logic device (e.g., Circuits such as Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), and Field Programmable Gate Array (FPGA)). Note that instead of storing the program in the memory circuit 122, the program may be configured to be directly installed in the circuit of the processor. In this case, the processor implements its functions by reading and executing the program embedded in the circuit.

Here, the MRI apparatus uses coils to measure electromagnetic waves emitted from a subject. A signal obtained by digitizing the measured electromagnetic wave is called k-space data.

The k-space data is, for example, two-dimensional or three-dimensional data obtained by repeating one-dimensional imaging. An atomic distribution image inside the subject is obtained by subjecting the k-space data to Fourier transform (hereinafter, Fourier transform may include inverse Fourier transform). The obtained atomic distribution image is called an MR image, and the process of calculating an MR image from k-space data is called reconstruction, image reconstruction, or image generation. The central portion of the k-space data corresponds to the low-frequency component when the MR image is Fourier transformed, and the peripheral portion of the k-space data corresponds to the high-frequency component when the MR image is Fourier transformed.

An MRI apparatus obtains k-space data necessary for reconstruction by repeatedly performing one-dimensional imaging, but it is known that this imaging generally takes time. Furthermore, it is also known that the image quality of reconstructed MR images deteriorates when the subject's condition changes over time. Therefore, when acquiring time-series data in which the subject's condition changes and the amount of data is large, for example, when imaging the heart, there is a strong demand for shortening the imaging time. Therefore, in order to perform higher-speed imaging, for example, using the fact that the sensitivity varies depending on the arrangement of the coils, the k-space data is simultaneously thinned and imaged with a plurality of coils, and the artifacts are detected from the obtained plurality of k-space data. Parallel Imaging (PI), which reconstructs MR images while suppressing the distortion, has been researched and developed.

Generally, in PI, the imaging time is shortened by thinning out and acquiring k-space data in the phase encoding direction. Since aliased images are generated from the k-space data that has been collected after being thinned out, in PI, the difference in sensitivity between channels is used for k-space data that have been collected in a plurality of channels with different sensitivities, and aliased images are generated. Reconstruct missing images. That is, in PI, it is possible to increase the speed according to the thinning rate. Note that the thinning rate is sometimes called a multiplication factor. For example, if the thinning rate is 4, the imaging time is shortened to about 1/4.

Further speedup is expected by combining PI with the retrospective gate method. That is, the electrocardiogram signal is acquired while acquiring the k-space data group thinned out in the phase encoding direction over a plurality of time phases. Then, using the acquired electrocardiographic signals, rearrangement is performed ex post facto so that the cardiac time phases of the series of k-space data are aligned. For example, using SENSE (Sensitivity Encoding), which is one of the PIs, k-space data thinned out in the phase encoding direction are collected in a plurality of time phases, and rearranged after the fact so that the cardiac time phases are aligned. Then, the rearranged k-space data group is reconstructed using the difference in sensitivity between channels.

By the way, in order to further shorten the imaging time, it is effective to thin out the k-space data in the temporal direction (temporal direction) in addition to the phase encoding direction. As a technique for acquiring k-space data by thinning it in the temporal direction, for example, techniques called k-t BLAST (k-space time Broad-use Linear Acquisition Speed-up Technique) and kt SENSE are known. However, these techniques may not work well in combination with the retrospective gating method. This is because these methods are based on the premise that the k-space thinning pattern along the time series changes regularly. In other words, during rearrangement using the cardiac time phase, which is necessary for reconstruction using the retrospective gate method, the k-space thinning pattern along the time series becomes irregular, and kt BLAST and Technologies such as kt SENSE will not be available. It should be noted that kt BLAST is called kt BLAST when the number of coils is small relative to the ratio of the thinned sample, and kt SENSE otherwise. Inclusively, it is called kt SENSE. In the following, the case of multiple coils will be mainly described, but as a special case of kt BLAST, the case of one coil is also allowed. Even if there is only one coil, it will be called kt SENSE for convenience.

In kt SENSE, the acquired k-space data group is transformed into xf-space data consisting of image space and temporal spectrum by Fourier transform. Then, in this xf space data, the xf space data from which aliasing signals are removed is generated using the sensitivity map on the xf space. Then, by transforming the generated xf space data into xt space data by inverse Fourier transform, a plurality of MR images arranged in time series are generated.

An example of sampling the kt space by thinning it in the temporal direction will be described with reference to FIG. FIG. 2 is a diagram showing an example of sampling positions in kt space. In FIG. 2, "k" shown on the vertical axis corresponds to the phase encoding direction, and "t" shown on the horizontal axis corresponds to the time phase direction. For convenience of explanation, FIG. 2 illustrates kt-space data in which k-space data are arranged at 8 positions (frames) in the phase encoding direction and 16 positions (frames) in the time phase direction. A black dot indicates a position where one line of k-space data is acquired. In other words, the boxes without bullets are locations where k-space data is not collected. The k-space data of each time phase corresponds to a two-dimensional k-space consisting of a one-dimensional frequency encoding direction and a one-dimensional phase encoding direction. It is also assumed that a period of one heartbeat or more is included between phases T'1 to T'16. In kt BLAST and kt SENSE, calibration imaging that acquires information about the xf space without thinning in the temporal direction performed before or during the main imaging, and There is a main imaging in which the kt space is sampled by thinning, and the example of the sampling positions shown in FIG. 2 can be considered as an example of the sampling positions in the main imaging. For simplification, FIG. 2 does not show the sampling positions in the actual imaging. In addition, in the technique disclosed in Japanese Patent No. 6073627, which does not necessarily require calibration imaging, FIG. 2 can be considered as an example of sampling positions in main imaging.

In the example shown in FIG. 2, sampling is performed by shifting the sampling position by one sample in the phase encoding direction for each unit time phase. For example, a plurality of k-space data at time phase T'2 are sampled at positions shifted by one sample in the phase encoding direction (upward direction in the figure) compared to a plurality of k-space data at time phase T'1. be done. Also, the plurality of k-space data at time phase T'3 are sampled at positions shifted by one sample in the phase encoding direction compared to the plurality of k-space data at time phase T'2. Also, the plurality of k-space data at time phase T'4 are sampled at positions shifted by one sample in the phase encoding direction compared to the plurality of k-space data at time phase T'3. That is, in the example of FIG. 2, the k-space data thinned to 1/4 is periodically sampled every 4 unit time phases.

In this way, when the sampling pattern in the phase encoding direction is changed along the kt space along the time phase direction, the k space data having the same phase encoding amount is generated at a rate of 1 time phase for every 4 time phases. only exists. Therefore, if the k-space data are rearranged by focusing only on the cardiac time phase, it may not be possible to acquire the data of each phase encoding amount necessary for reconstruction. For example, even if the k-space data are arranged in the order of time phases T'1, T'16, T'3, and T'4, they cannot be reconstructed. This is because the sampling pattern of the k-space data necessary for reconstruction in combination with the phases T'1, T'3, and T'4 is the same as that of the phase T'2. This is because the sampling pattern differs from the time phase T'16.

Therefore, the MRI apparatus 100 according to the first embodiment enables imaging with high spatial resolution and high temporal resolution for a region that undergoes periodic motion by means of the processing functions described below. Although a case where the processing function according to the present embodiment is applied to kt SENSE will be described below, the present invention is not limited to this. Other application examples will be described separately. In addition, although the case where the heart is applied as a site that performs periodic exercise will be described below, the present invention is not limited to this. For example, this embodiment is also applicable to chest imaging that is affected by respiratory motion.

A processing procedure by the MRI apparatus 100 according to the first embodiment will be described with reference to FIG. FIG. 3 is a flow chart showing a processing procedure by the MRI apparatus 100 according to the first embodiment. The processing procedure shown in FIG. 3 is started, for example, triggered by an imaging start request input by the operator.

In step S101, the acquisition function 123a collects a plurality of k-space data. That is, the acquisition function 123a acquires a plurality of k-space data collected in a predetermined sampling pattern in a plurality of time phases.

For example, the acquisition function 123 a generates sequence information based on imaging conditions input by the operator via the input interface 124 . For example, the acquisition function 123a generates sequence information based on the period corresponding to one cardiac cycle input by the operator and the number of acquired MR images per one cardiac cycle.

For example, the operator defines a time period (RR interval) corresponding to one cardiac cycle. For example, if the heartbeat of the subject is approximately 1000 msec, the operator defines one cardiac cycle as "1000 msec". The reason why the period corresponding to one cardiac cycle is defined here is that there is fluctuation in the heartbeat. For example, it is known that the RR interval of each heartbeat fluctuates in the range of about 900 msec to 1100 msec even in a healthy person. Therefore, the MRI apparatus 100 expands and contracts the RR interval of each heartbeat according to the period defined by the operator, thereby capturing time-series MR images having a desired cardiac cycle. Note that although one cardiac cycle is defined as "1000 msec" here, it can be set to any time.

The operator also sets the number of MR images to be acquired per cardiac cycle. For example, the operator sets the number of sheets to be acquired to "24". As a result, the MRI apparatus 100 captures 24 MR images arranged at regular intervals during one cardiac cycle. Although a case where "24" MR images are acquired per cardiac cycle will be described here, the number of MR images can be set arbitrarily.

An example of kt space data according to the first embodiment will be described with reference to FIG. FIG. 4 is a diagram showing an example of kt space data according to the first embodiment. In FIG. 4, "k" indicated on the vertical axis corresponds to the phase encoding direction, and "t" indicated on the horizontal axis corresponds to the time phase direction. For convenience of explanation, FIG. 4 illustrates kt-space data in which k-space data are arranged at 48 positions (frames) in the phase encoding direction and 28 positions (frames) in the time phase direction. A black dot indicates a position where one line of k-space data is acquired. In other words, the boxes without bullets are locations where k-space data is not collected. For simplification, the frequency encoding direction is not shown in FIG. In FIG. 4, the k-space data in the frequency encoding direction is filled in the direction perpendicular to the plane of the paper.

As shown in FIG. 4, the acquisition function 123a sets the kt space data so that the sampling position is shifted by one sample in the phase encoding direction for each unit time phase, for example. That is, the set sampling pattern is regularly thinned out in the phase-encoding direction of the k-space, and phase-encoding lines (positions) to be acquired differ between consecutive frames in the time-phase direction. In other words, the k-space data is a sampling pattern that is regularly thinned out in the phase-encoding direction of the k-space, and phase-encoding lines are acquired in different sampling patterns between successive frames in the time-phase direction. be. In the example of FIG. 4, the k-space data thinned to 1/4 is periodically sampled every 4 unit time phases, but the thinning rate is not limited to this. do not have. Further, the sampling pattern is not limited to a sampling pattern in which one sample is shifted for each unit time phase, and any pattern that is periodically sampled for each predetermined unit time phase may be used. That is, sampling in kt SENSE is an example of non-simple decimation sampling in which the sampling pattern is time-varying.

As shown in FIG. 4, kt spatial data is collected in three segments AC. Here, segment B corresponds to the center segment, and segment A and segment C correspond to edge segments, which are segments other than the center segment. That is, segment B includes a plurality of k-space data corresponding to the central portion in the phase encoding direction. In addition, segment A and segment C include a plurality of k-space data corresponding to edge portions in the phase encoding direction. Segment B is an example of the first data group. Also, segment A and segment C are examples of a second data group.

Here, the kt space data shown in FIG. 4 are set so as to satisfy about 120% of the period of one cardiac cycle and the number of obtained images set by the operator. For example, when the number of acquired images is set to "24", the number of phases corresponding to about 28 images is set. Therefore, 28 sampling positions (28 time phases) are set in the time phase direction of the kt space data shown in FIG. If one cardiac cycle is defined as "1000 msec", kt space data for 1200 msec is acquired.

In this way, the acquisition function 123a generates sequence information based on imaging conditions input by the operator. For example, the acquisition function 123a generates sampled kt spatial data based on imaging conditions. Note that the description of FIG. 4 is merely an example, and is not limited to the illustrated contents. For example, the segment division number and division width illustrated in FIG. 4 can be arbitrarily changed by setting the phase encoding amount.

The acquisition function 123 a controls imaging by transmitting the generated sequence information to the sequence control circuit 110 . For example, the sequence control circuit 110 performs sampling based on the sequence information received from the acquisition function 123a.

An example of the order of acquiring k-space data according to the first embodiment will be described with reference to FIG. FIG. 5 is a diagram for explaining an example of the acquisition order of k-space data according to the first embodiment.

As shown in FIG. 5, the sequence control circuit 110 collects the kt space data shown in FIG. 4 divided into three segments AC. For example, the sequence control circuit 110 acquires k-space data for 28 time phases in the order of segment A, segment B, and segment C, respectively. Although not shown, the sequence control circuit 110 can insert dummy shots and wait times as appropriate to execute the sequence.

Thus, the sequence control circuit 110 performs sampling based on the sequence information received from the acquisition function 123a. Note that the description of FIG. 5 is merely an example, and is not limited to the illustrated contents. For example, in FIG. 5, the case of collecting in the order of segment A, segment B, and segment C has been described, but the order is not limited to this and can be collected in any order.

Thus, the sequence control circuit 110 acquires a plurality of segmented k-space data and sends the acquired plurality of k-space data to the acquisition function 123a as imaging results. Thereby, the acquisition function 123a acquires a plurality of k-space data acquired in a predetermined sampling pattern in a plurality of time phases and divided into a plurality of segments in the phase encoding direction.

Also, the acquisition function 123a acquires from the storage circuit 122 a trigger signal at the time of k-space data acquisition. For example, the acquisition function 123a acquires the detection time (timing) of the trigger signal. The interval between consecutive trigger signals corresponds to the RR interval. Note that the detection time of the trigger signal is an example of biological signal information.

In other words, the acquisition function 123a acquires a plurality of k-space data acquired from the subject by non-simple thinning-out sampling in which the sampling pattern changes in the time direction, the acquisition time that is the time at which each k-space data was acquired, and the to acquire time-series biosignal information.

In step S102, the calculation function 123b calculates cardiac phase information of the central segment. For example, the calculation function 123b calculates the cardiac time phase information of the line located substantially at the center of the phase encoding direction for k-space data of each time phase included in segment B, and calculates the cardiac time phase information of each time phase of segment B as Calculated as information.

Processing of the calculation function 123b according to the first embodiment will be described with reference to FIG. FIG. 6 is a diagram for explaining the processing of the calculation function 123b according to the first embodiment. "PE Line number" shown in FIG. 6 is a number indicating a line of acquired k-space data. "Time" is information indicating the acquisition time of the acquired k-space data. "RR Interval" is information indicating the RR interval of heartbeats in which the collected k-space data is included. "trigger" is information indicating the time at which the heartbeat trigger signal containing the acquired k-space data was detected. "Phase center" is information indicating whether or not the collected data of each line is substantially at the center in the phase encoding direction within the segment. This Phase center can be set at the stage when the sampling pattern, the number of segment divisions, and the division width are determined. "Cardiac phase information" is information indicating the position in the phase direction in one cardiac cycle. For example, when the RR interval is 100%, the cardiac time phase information indicates what percentage of the acquired k-space data was acquired from the starting point of the RR interval.

In the example shown in FIG. 6, a case will be described in which 8 lines with PE Line numbers from "2561" to "2568" are included in one segment. In this case, among the 8 lines, the data positioned substantially at the center of the segment in the phase encoding direction is the line with the PE Line number "2564". Therefore, the line with the PE Line number “2564” is set as the Phase center, and “1” is registered. Note that "0" is registered for lines that are not set as the phase center.

Here, the calculation function 123b calculates the cardiac phase information of the line set as the Phase center. For example, the line with the PE Line number “2564” is set as the Phase center. Therefore, the calculation function 123b calculates the cardiac phase information of the line with the PE Line number “2564”. Specifically, the calculation function 123b divides the difference between Time and Trigger by RR Interval and converts it into a percentage to calculate the cardiac phase information “54.97” of this segment.

Also, the calculation function 123b describes a case where one segment includes 8 lines with PE Line numbers from "2569" to "2576". In this case, among the 8 lines, the data positioned substantially at the center in the phase encoding direction within the segment is the line with the PE Line number "2572". Therefore, the line of PE Line number "2572" is set as the Phase center, and "1" is registered. Therefore, the calculation function 123b calculates the cardiac phase information of the line with the PE Line number “2572”. Specifically, the calculation function 123b divides the difference between Time and Trigger by the RR Interval and converts it into a percentage to calculate the cardiac phase information “58.37” of this segment.

In this way, the calculation function 123b calculates the cardiac time phase information of the central segment including the data of the line acquired with the reference phase encoding amount among the plurality of lines. It should be noted that FIG. 6 is merely an example, and the illustrated example is not limited. For example, the calculating function 123b may calculate the cardiac phase information of the line of PE Line number "2565" instead of the line of PE Line number "2564". In other words, "substantially the center" means that it is not limited to the line closest to the center in the phase encoding direction within the segment.

In step S103, the calculation function 123b calculates the cardiac phase information of the marginal segment. For example, the calculation function 123b calculates the cardiac time phase information of the line located substantially at the center in the phase encoding direction for the k-space data of each time phase included in segment A. Calculated as information. In addition, the calculation function 123b calculates the cardiac time phase information of the line located substantially at the center of the phase encoding direction for the k-space data of each time phase included in segment C, and calculates the cardiac time phase information of each time phase of segment C. Calculated as information.

That is, the calculation function 123b calculates the cardiac temporal information of the limbal segment that includes a plurality of k-space data acquired with a different amount of phase encoding than the central segment. The processing for calculating the cardiac phase information of the marginal segment is the same as the processing for calculating the cardiac phase information of the central segment, so the explanation is omitted.

In step S104, the merging function 123c merges, with respect to the central segment, marginal segments having cardiac temporal information close to that of the central segment. For example, the combining function 123c associates close cardiac phase information among the cardiac phase information of the marginal segments with each of the cardiac phase information of the central segment, and based on the associated cardiac phase information, The central segment and the marginal segment are combined to create combined data for multiple phases.

Processing of the combining function 123c according to the first embodiment will be described with reference to FIGS. 7 to 11. FIG. 7 to 11 are diagrams for explaining the processing of the combining function 123c according to the first embodiment. 7 to 11 exemplify the sequences executed by the sequence control circuit 110 at the top. The lower part illustrates the process of arranging the k-space data acquired by the upper sequence in the kt space according to a preset sampling pattern. Note that the upper sequence is the same as the sequence shown in FIG. In the lower kt space, k-space data are arranged in the sampling pattern shown in FIG.

Here, for convenience of explanation, the 28 time phases included in the segment A are denoted as TA1, TA2, TA3, . . . TA28 in order of the time phase direction. Also, the 28 time phases included in segment B are denoted by TB1, TB2, TB3, . Also, the 28 time phases included in the segment C are denoted by TC1, TC2, TC3, .

As shown in FIG. 7, the combining function 123c arranges k-space data of 28 time phases included in segment B, which is the central segment, in kt space. At this time, the combining function 123c arranges the k-space data of 28 time phases collected in a predetermined sampling pattern in the region R1 of the kt space without rearranging in the order of collection. That is, the temporal direction of the kt space corresponds to TB1, TB2, TB3, . . . TB28. As a result, in the center of the kt space in the phase encoding direction, k-space data with a sampling pattern shifted by one sample per unit time phase is arranged, as in FIG.

Next, the combining function 123c combines segment A having cardiac phase information close to the cardiac phase information of segment B with segment B of phase TB1 arranged as a reference. For example, as shown in FIG. 8, the combining function 123c reads the cardiac phase information of segment B (region R2) of phase TB1. Then, the combining function 123c selects the segment A having the cardiac phase information closest to the cardiac phase information of the segment B of the phase TB1 from among the segments A of the phases TA1 to TA28.

Here, the combining function 123c selects segment A based on a preset sampling pattern. For example, the combining function 123c identifies the sampling pattern arranged in the region R3 by referring to the preset sampling pattern (FIG. 4). Specifically, the connecting function 123c identifies a pattern in which k-space data are arranged at the 4th, 8th, 12th, and 16th positions from the top of the 16 frames of the region R3. Then, the combining function 123c selects a segment A having the specified sampling pattern and having cardiac phase information close to the cardiac phase information of the segment B of the phase TB1. Then, the join function 123c joins the selected segment A (for example, the segment of the phase TA3) to the segment B of the phase TB1.

Subsequently, as shown in FIG. 9, the combining function 123c reads the cardiac phase information of segment B (region R4) of phase TB2. Then, the combining function 123c selects the segment A having cardiac phase information close to the cardiac phase information of the segment B of the phase TB2 from among the segments A of the phases TA1 to TA28.

Then, the combining function 123c identifies the sampling pattern arranged in the region R5 by referring to the preset sampling pattern (FIG. 4). Specifically, the connecting function 123c identifies a pattern in which k-space data are arranged at the 3rd, 7th, 11th, and 15th positions from the top of the 16 frames of the region R5. The combining function 123c then selects a segment A that has the specified sampling pattern and has cardiac phase information that is close to the cardiac phase information of the segment B of the phase TB2. Then, the join function 123c joins the selected segment A to the segment B of the time phase TB2.

In this way, the combining function 123c selects a segment A that has a preset sampling pattern and that has cardiac phase information that is close to the cardiac phase information of segment B. FIG. Then, the linking function 123c links the selected segment A to the segment B in units of segments. As a result, as shown in FIG. 10, segment A having the closest cardiac phase can be combined with each segment B of phases TB1 to TB28 without disturbing the preset sampling pattern. . That is, the combining function 123c creates combined data such that predetermined sampling patterns are regularly arranged along the time phase direction.

Further, as shown in FIG. 11, the linking function 123c links segment C to segment B by performing the same process as for segment A. FIG. As a result, the combining function 123c selects a segment C having a preset sampling pattern and cardiac phase information close to the cardiac phase information of the segment B, and combines the selected segments.

Thus, the combining function 123c selects edge segments that have a preset sampling pattern and that have cardiac phase information close to that of the central segment. Then, the combining function 123c creates combined data by combining the central segment and the selected peripheral segment in segment units. Then, the combining function 123c creates combined data of each time phase for each time phase included in the kt space data. In other words, the combining function 123c combines k-space data having a cardiac time phase close to the cardiac time phase of the central segment among the plurality of k-space data included in the peripheral segment into the plurality of k-space data included in the central segment. bind to

In step S105, the reconstruction function 123d executes reconstruction processing. For example, the reconstruction function 123d performs reconstruction processing including Fourier transform (for example, discrete Fourier transform) on the combined data of multiple time phases created by the combining function 123c to generate MR images of multiple time phases. do. Note that the MR image is an example of a reconstructed image.

In step S106, the selection function 123e selects an MR image having cardiac phase information close to each preset phase information. For example, the selection function 123e selects a reconstructed image having cardiac phase information close to each of a plurality of preset time phase information from among the MR images of multiple time phases generated by the reconstruction function 123d.

Processing of the reconstruction function 123d and the selection function 123e according to the first embodiment will be described with reference to FIGS. 12 and 13. FIG. 12 and 13 are diagrams for explaining the processing of the reconstruction function 123d and the selection function 123e according to the first embodiment. The upper part of FIG. 12 illustrates the combined data of multiple time phases created by the combining function 123c, that is, the kt space data shown in the lower part of FIG. For convenience of illustration, FIG. 12 illustrates a portion of the kt space data. Further, FIG. 13 illustrates a phase corresponding to the preset number of MR images acquired per cardiac cycle, and temporal information indicating the position of each MR image in the temporal direction.

For example, the reconstruction function 123d transforms the combined data of multiple phases into xf space data consisting of image space and time spectrum by Fourier transform. Also, when kt SENSE is used, the reconstruction function 123d uses the sensitivity map on the xf space to generate xf space data from which aliasing signals in the xf space data are removed. . Then, the reconstruction function 123d converts the generated xf space data into xt space data by inverse Fourier transform, thereby generating a plurality of time-series MR images.

That is, as shown in FIG. 12, the reconstruction function 123d executes reconstruction processing in accordance with a preset sampling pattern to convert kt space data of 28 time phases to 28 time phases (28 images). ) to generate an MR image. Note that these 28 MR images correspond to the 28 time phases of segment B. FIG.

Then, the selection function 123e selects the number of MR images corresponding to the preset acquisition number from the 28 MR images. As shown in FIG. 13, when the number of acquired images is set to "24", the phases of each MR image in one cardiac cycle correspond to 24 phases P1 to P24. Here, the temporal information of each MR image of P1 to P24 corresponds to the position of each MR image of P1 to P23 by what percentage from the starting point of one cardiac cycle when P24 (24th image) is 100%. indicate whether to Specifically, the temporal information of the MR image of P1 is 4.1667(%), and the temporal information of the MR image of P2 is 8.3333(%). Thus, the temporal information of each MR image of P1 to P24 is calculated.

Then, as shown in FIG. 12, the selection function 123e selects the MR image having the cardiac phase information closest to the phase information of P1 to P24 from the 28-phase MR images. Here, since the MR image of 28 time phases corresponds to the time phases TB1 to TB28 of segment B, the cardiac time phase information of segment B of each time phase can be used as the cardiac time phase information of the MR image. In the example shown in FIG. 12, the selection function 123e selects the MR image of phase TB2 as the MR image of phase P1. The selection function 123e also selects the MR image of phase TB3 as the MR image of phase P2. Further, the selection function 123e selects the MR image of phase TB28 as the MR image of phase P3.

In this way, the selection function 123e selects an MR image having cardiac phase information close to each of a plurality of preset phase information from among the 28-phase MR images generated by the reconstruction function 123d. .

As a result, the processing circuit 123 generates MR images of multiple time phases that satisfy the imaging conditions set by the operator. The generated MR images of multiple time phases are displayed on the display 125 or stored in the storage circuit 122 according to instructions from the processing circuit 123 .

Note that the processing procedure shown in FIG. 3 is merely an example, and the embodiment is not limited to this. For example, the processes shown in steps S102 and S103 do not necessarily have to be executed in the illustrated order. For example, the processes of steps S102 and S103 may be performed simultaneously, or the process of step S103 may be performed and then the process of step S102 may be performed.

As described above, in the MRI apparatus 100 according to the first embodiment, the processing circuit 123 collects k-space data in a predetermined sampling pattern over a plurality of time phases in each of a plurality of segments divided in the phase encoding direction. do. The processing circuit 123 calculates the cardiac time phase information of the reference center segment and the cardiac time phase information of the marginal segments different from the center segment among the plurality of segments. Processing circuitry 123 generates combined data for multiple phases by combining the central segment with marginal segments having cardiac temporal information close to that of the central segment. The processing circuit 123 generates a reconstructed image of multiple time phases by reconstructing the combined data of multiple time phases based on a predetermined sampling pattern. The processing circuit 123 selects, from the reconstructed images of the plurality of time phases, reconstructed images having cardiac phase information close to a plurality of predetermined cardiac phase information. According to this, the MRI apparatus 100 according to the first embodiment enables imaging with high spatial resolution and high temporal resolution for a part that periodically moves.

For example, the MRI apparatus 100 according to the first embodiment samples the heart, which is a part that undergoes periodic motion, by regularly changing the k-space thinning pattern along the time series. Then, the MRI apparatus 100 aligns the cardiac phases in units of segments, not in units of lines. Therefore, the MRI apparatus 100 can combine segments having preset sampling patterns. Therefore, the MRI apparatus 100 can post-arrange the kt space data so that the cardiac time phases are aligned. According to this, the operator can define imaging conditions for high-speed imaging with a higher degree of freedom.

Also, the main component of the morphological information rendered in the MR image corresponds to the central part in the k-space. Therefore, the MRI apparatus 100 creates kt space data to be reconstructed based on the central segment corresponding to the central portion. This preserves the accuracy of the cardiac phase of the central segment.

Strictly speaking, a plurality of pieces of k-space data included in a certain temporal segment have different acquisition times even if they are acquired continuously. Differences in collection time may also occur between segments to be combined. However, the MRI apparatus 100 allows for these acquisition time differences and forms kt spatial data arranged in a predetermined sampling pattern. For this reason, the MRI apparatus 100 realizes high-speed imaging while permitting a certain degree of deviation of the cardiac time phase.

(Modification 1 of the first embodiment)
In the above embodiment, as an example, the case of sampling with the sampling pattern shown in FIG. 4 has been described, but the embodiment is not limited to this.

An example of kt space data according to Modification 1 of the first embodiment will be described with reference to FIG. FIG. 14 is a diagram showing an example of kt space data according to Modification 1 of the first embodiment. In the example shown in FIG. 14, segment A and segment C are the same as the kt space data in FIG. That is, in segment A and segment C, the k-space data thinned to 1/4 in the phase-encoding direction is sampled with a one-sample shift in the phase-encoding direction for each unit time phase.

Segment B, on the other hand, is sampled twice as densely as the kt spatial data in FIG. That is, in segment B, the k-space data thinned to 1/2 in the phase-encoding direction is sampled by shifting one sample in the phase-encoding direction for each unit time phase.

That is, the preset sampling pattern may be different between consecutive frames along the time phase direction. When the kt space data of such a sampling pattern is set, the MRI apparatus 100 can post-arrange the data so that the cardiac time phases are aligned in units of segments by the above-described processing. In other words, the k-space data is collected with different sampling patterns between successive frames along the temporal direction.

(Modification 2 of the first embodiment)
Further, in the above embodiment, a case has been described in which the k-space thinning pattern along the time series changes regularly, but the embodiment is not limited to this. For example, the processing functions according to the above-described embodiments are also applicable to Compressed Sensing (CS). Sampling in compressed sensing is an example of non-simple thinning sampling. Radial collection and Cartesian collection are suitable as collection methods in compression sensing.

Compressed sensing is an imaging technique that exploits the sparsity of the signal to reconstruct an image from a small number of k-space data. For example, in compressed sensing, when filling k-space data into k-space, sampling is performed by irregularly thinning out in the phase encoding direction. As a result, compressed sensing can reduce data collection time while introducing sparsity.

An example of kt space data according to Modification 2 of the first embodiment will be described with reference to FIG. FIG. 15 is a diagram showing an example of kt space data according to modification 2 of the first embodiment. It should be noted that the kt space data shown in FIG. 15 is acquired after being divided into a plurality of segments in the phase encoding direction.

In the example shown in FIG. 15, sampling is performed by irregularly thinning out in the phase encoding direction and also irregularly in the cine phase direction (time phase direction). Thereby, shortening of collection time by compressed sensing is realized.

That is, in compressed sensing, an irregular thinning pattern in the phase encoding direction and the cine phase direction is set in advance as a predetermined sampling pattern. Therefore, the MRI apparatus 100 performs ex post facto analysis such that the cardiac time phases in kt space data are aligned even when compressed sensing is used, as in the case of regularly changing the k-space thinning pattern along the time series. Sorting becomes applicable.

That is, in the MRI apparatus 100, the acquisition function 123a acquires a plurality of k-space data arranged in a predetermined sampling pattern and divided into a plurality of segments in the phase encoding direction over a plurality of time phases. The calculation function 123b calculates cardiac time phase information of a reference center segment and cardiac time phase information of a marginal segment different from the center segment among the plurality of segments. The combining function 123c generates combined data of multiple phases by combining the center segment and edge segments having cardiac phase information close to the cardiac phase information of the center segment. The reconstruction function 123d generates reconstructed images of multiple time phases by reconstructing combined data of multiple time phases based on a predetermined sampling pattern. The selection function 123e selects a reconstructed image having cardiac phase information close to a plurality of preset cardiac phase information from among the reconstructed images of a plurality of time phases. According to this, the MRI apparatus 100 enables faster imaging of a region that periodically moves even when compression sensing is applied.

It should be noted that FIG. 15 is only an example, and the content of the illustration is not limited. For example, as for the sampling pattern in compression sensing, it is sufficient if the phase encoding lines to be collected are irregular at least between consecutive frames in the time phase direction. Also, the combining function 123c creates combined data such that predetermined sampling patterns are arranged irregularly along the time phase direction. In other words, the k-space data is acquired with an irregular sampling pattern of phase-encoding lines acquired at least between consecutive frames in the time phase direction.

Also, for example, the processing of the MRI apparatus 100 may not be based on a predetermined sampling pattern. Then, when the image quality of the reconstructed image is low, the MRI apparatus 100 does not focus on the cardiac phase information closest to that of the center segment, but on the peripheral segment having the second closest cardiac phase information. Can be attached to a segment.

(Second embodiment)
In the first embodiment, a case has been described in which a plurality of MR images corresponding to one heartbeat are selected from MR images of a plurality of time phases corresponding to a period of one heartbeat or more. However, when reproducing a plurality of MR images, it is conceivable that a discontinuous impression may be given at the time of image switching.

For example, in the process shown in FIG. 12, the MR image of phase TB3 is selected as the MR image of phase P2, whereas the MR image of phase TB28 is selected as the MR image of phase P3. That is, the two MR images of time phase TB3 and time phase TB28 obtained at different heartbeats are reproduced as continuous frames. Since the two MR images of phases TB3 and TB28 are selected as MR images of desired cardiac phases, they are expected to be sufficient as images of the heart. It is conceivable that there is a difference in the images. In such a case, if these two MR images are played back, there is a possibility that a sense of incongruity will be given when switching between the two.

Therefore, the MRI apparatus 100 according to the second embodiment can suppress discomfort even when MR images obtained with mutually different heartbeats are consecutive by executing the following processing.

A processing procedure by the MRI apparatus 100 according to the second embodiment will be described with reference to FIG. FIG. 16 is a flow chart showing a processing procedure by the MRI apparatus 100 according to the second embodiment. The processing procedure shown in FIG. 16 is started, for example, triggered by an imaging start request input by the operator. Note that the processing of steps S201 to S205 shown in FIG. 16 is the same as the processing of steps S101 to S105 shown in FIG. 3, so description thereof will be omitted.

In step S206, the selection function 123e generates a plurality of k-space data by inverse Fourier transform. In other words, the selection function 123e generates a plurality of fully-sampled k-space data by executing inverse Fourier transform on the reconstructed images of a plurality of time phases.

Processing of the selection function 123e according to the second embodiment will be described with reference to FIG. FIG. 17 is a diagram for explaining the processing of the selection function 123e according to the second embodiment. The upper part of FIG. 17 illustrates 28 time-phase combined data (kt space data) created by the combining function 123c. In addition, in FIG. 17, 28 reconstructed images are generated by reconstructing the combined data of 28 time phases. FIG. 17 illustrates part of the kt space data for convenience of illustration.

As shown in FIG. 17, the selection function 123e performs an inverse Fourier transform (IFFT) on the 28 reconstructed images to generate a plurality of k-spaces in which the 28-phase kt-space is fully sampled. Data is generated (the middle part of FIG. 17).

In step S207, the selection function 123e gives pseudo time stamps to a plurality of pieces of k-space data. That is, the selection function 123e assigns a pseudo acquisition time of each k-space data to each of a plurality of full-sampling k-space data.

For example, the selection function 123e allocates the acquisition time for the k-space data acquired in the kt space shown in the upper part of FIG. 17 among the plurality of full-sampled k-space data shown in the middle part of FIG. . In the upper part of FIG. 17, k-space data has already been collected in the first and fifth stages from the bottom of time phase TB1. Therefore, the selection function 123e allocates the acquisition times of the first and fifth k-space data to the k-space data at the corresponding sampling positions in the middle of FIG. Then, the selection function 123e calculates the second, third, and fourth k-space data from the bottom based on the acquisition times of the first and fifth k-space data. For example, the selection function 123e calculates the pseudo acquisition times (timestamps) of the second, third, and fourth k-space data so as to equally divide the first and fifth stages. do. In this way, the selection function 123e calculates the pseudo acquisition time and assigns the calculated pseudo acquisition time to each k-space data. In other words, the selection function 123e generates pseudo collection times for each of the plurality of k-space data corresponding to full sampling based on the collection time for each k-space data collected by non-simple thinning sampling. Here, a plurality of k-space data corresponding to full sampling are data supplemented with k-space data corresponding to k-space data thinned out by non-simple thinning-out sampling.

In step S208, the selection function 123e rearranges the plurality of k-space data by the retrospective gate method. That is, the selection function 123e selects k-space data corresponding to each of a plurality of preset cardiac time phases based on the pseudo acquisition time of each k-space data. In other words, the selection function 123e rearranges a plurality of k-space data corresponding to full sampling based on the pseudo acquisition time and biological signal information.

For example, k-space data of 28 time phases are arranged in the kt space in the middle of FIG. Therefore, the selection function 123e selects k-space data corresponding to preset 24 phases (P1 to p24). As a result, the selection function 123e generates kt space data in which the time phase direction has been converted to 24 phases (lower part of FIG. 17).

In step S209, the reconstruction function 123d executes reconstruction processing. For example, the reconstruction function 123d reconstructs k-space data corresponding to each of the plurality of cardiac phases, thereby generating reconstructed images corresponding to each of the plurality of cardiac phases. In the example shown in FIG. 17, the reconstruction function 123d reconstructs 24 MR images from kt space data whose temporal direction has been converted to 24 phases. In other words, the reconstruction function 123d generates a plurality of reconstructed images by performing reconstruction processing on a plurality of pieces of k-space data after rearrangement processing.

In this way, the MRI apparatus 100 according to the second embodiment generates a plurality of full-sampled k-space data by executing the inverse Fourier transform on the reconstructed image of 28 time phases. Then, the MRI apparatus 100 assigns pseudo time stamps to the plurality of fully-sampled k-space data, and performs post-arrangement processing (retrospective gate method) on a line-by-line basis. The MRI apparatus 100 then generates a plurality of k-space data corresponding to desired cardiac phases. The MRI apparatus 100 then reconstructs this to generate a plurality of reconstructed images corresponding to desired cardiac phases. As a result, the MRI apparatus 100 can generate time-series reconstructed images with higher image quality.

(Modification of Second Embodiment)
Note that in the second embodiment, when generating k-space data corresponding to full sampling, a process of temporarily converting (reconstructing) an MR image (reconstructed image) as intermediate data has been described. The form is not limited to this. In other words, the MRI apparatus 100 can generate k-space data corresponding to full sampling without necessarily converting to an MR image.

For example, the reconstruction function 123d performs reconstruction processing corresponding to kt SENSE on the combined data of multiple time phases created by the combination function 123c. In this process, reconstruction function 123d generates xf spatial data with aliasing removed, as described above. This xf space data is intermediate data before being converted into image data (real space data).

Here, the reconstruction function 123d according to the modification of the second embodiment performs processing including Fourier transform (inverse Fourier transform) on this xf space data. Thereby, the reconstruction function 123d can generate k-space data corresponding to full sampling from the combined data of multiple time phases created by the combining function 123c. That is, the reconstruction function 123d generates a plurality of k-space data corresponding to full sampling from a plurality of k-space data thinned in a predetermined sampling pattern by processing including Fourier transform corresponding to non-simple thinning sampling. do.

Note that the reconstruction function 123d performs a process of generating a plurality of k-space data corresponding to the above-described full sampling instead of the processes of steps S205 and S206. Since the processing after step S207 is the same as the content explained in FIG. 16, the explanation is omitted.

Further, k-space data generated by processing including Fourier transform does not necessarily have to be filled with a plurality of k-space data corresponding to full sampling. For example, it is sufficient to reproduce a sampling pattern that allows kt reconstruction. As an example, the k-space data generated by a process involving a Fourier transform may be a plurality of k-space data corresponding to simple decimated sampling. In other words, the reconstruction function 123d inversely transforms the intermediate data obtained by transforming the plurality of k-space data, so that, for the plurality of k-space data, at least a portion of the thinning region for full sampling is filled in. to generate k-space data for

(Third Embodiment)
In the above embodiment, the case of performing segmentation has been described, but the embodiment is not limited to this. For example, when segmentation is not performed, processing can be performed as follows.

A processing procedure by the MRI apparatus 100 according to the third embodiment will be described with reference to FIG. FIG. 18 is a flow chart showing a processing procedure by the MRI apparatus 100 according to the third embodiment. The processing procedure shown in FIG. 18 is started, for example, triggered by an imaging start request input by the operator.

In step S301, the acquisition function 123a collects a plurality of k-space data. This process is basically the same as the process of step S101 in FIG. 3, but is different in that segmentation is not performed. That is, the acquisition function 123a according to the third embodiment acquires a plurality of k-space data corresponding to the sampling pattern defined by kt SENSE without segmentation. At this point, kt space data similar to the kt space data (combined data) shown in FIG. 11 are obtained.

In step S302, the reconstruction function 123d executes reconstruction processing. This process is basically the same as the process of step S105 in FIG. That is, the reconstruction function 123d performs reconstruction processing corresponding to kt SENSE on the plurality of pieces of k-space data acquired by the acquisition function 123a in step S301 to generate MR images of multiple time phases.

In step S303, the reconstruction function 123d generates a plurality of k-space data by inverse Fourier transform. This process is basically the same as the process of step S206 in FIG. That is, the selection function 123e generates a plurality of k-space data corresponding to full sampling by executing inverse Fourier transform on the reconstructed images of the plurality of time phases.

In step S304, the selection function 123e gives pseudo time stamps to a plurality of pieces of k-space data. This process is basically the same as the process of step S207 in FIG. That is, the selection function 123e assigns a pseudo acquisition time of each k-space data to each of a plurality of full-sampling k-space data.

In step S305, the selection function 123e rearranges the plurality of k-space data by a retrospective gate method. This process is basically the same as the process of step S208 in FIG. That is, the selection function 123e selects k-space data corresponding to each of a plurality of preset cardiac time phases based on the pseudo acquisition time of each k-space data.

In step S306, the reconstruction function 123d executes reconstruction processing. This process is basically the same as the process of step S209 in FIG. That is, the reconstruction function 123d generates reconstructed images corresponding to each of the plurality of cardiac phases by performing reconstruction processing on k-space data corresponding to each of the plurality of cardiac phases.

Note that the processing procedure shown in FIG. 18 is merely an example, and the embodiment is not limited to this. For example, in the processing procedure shown in FIG. 18, the processing order can be changed as appropriate within a range in which there is no contradiction in the processing content.

That is, in the MRI apparatus 100 according to the third embodiment, the acquisition function 123a acquires a plurality of k-space data acquired from the subject by non-simple thinning sampling, the acquisition time of each k-space data, and the time of the subject. obtain sequential biological signal information; The reconstruction function 123d generates a plurality of k-space data corresponding to full sampling from a plurality of first k-space data by processing including Fourier transform corresponding to non-simple thinning sampling. The selection function 123e generates pseudo acquisition times for each of the plurality of k-space data corresponding to full sampling based on the acquisition times. The selection function 123e rearranges a plurality of k-space data corresponding to full sampling based on the pseudo acquisition time and biological signal information. The reconstruction function 123d generates a plurality of reconstructed images by performing reconstruction processing on a plurality of pieces of k-space data after rearrangement processing. As a result, the MRI apparatus 100 can preferably perform imaging of a moving imaging target. For example, the MRI apparatus 100 can perform imaging with high spatial resolution and high temporal resolution for a site (heart) that undergoes periodic motion.

(Fourth embodiment)
In the first embodiment, when segmentation is performed, the k-space data of the marginal segments whose cardiac time phase is close to the k-space data of the central segment are combined on a group-by-group basis. Although the case of generating a constituent image has been described, embodiments are not limited to this. For example, the MRI apparatus 100 can generate the final reconstructed image by generating calibration data for full sampling of the k-space data of the marginal segments from the k-space data of the central segment. is.

A processing procedure by the MRI apparatus 100 according to the fourth embodiment will be described with reference to FIG. FIG. 19 is a flow chart showing a processing procedure by the MRI apparatus 100 according to the fourth embodiment. The processing procedure shown in FIG. 19 is started, for example, triggered by an imaging start request input by the operator.

19 will be described with reference to FIGS. 20 to 26. FIG. 20 to 26 are diagrams for explaining the processing of the MRI apparatus 100 according to the fourth embodiment. 20 to 26, the trigger table corresponds to the time direction of each kt spatial data. Note that the contents described with reference to FIGS. 19 to 26 are merely examples, and the embodiment is not limited to these.

In step S401, the acquisition function 123a collects a plurality of k-space data. In other words, the acquisition function 123a acquires a plurality of k-space data collected in a plurality of segments including a center segment corresponding to the center of k-space and edge segments different from the center segment.

For example, the acquisition function 123a collects multiple k-space data in two segments, a central segment and a peripheral segment, as shown in FIG. Here, for example, if the number of acquisitions set by the operator is "13", k-space data for 16 time phases is collected so as to satisfy about 120% of the number of acquisitions. Also, since the central segment and the edge segment are collected at different timings, the trigger signals are detected at different timings.

Note that FIG. 20 exemplifies the case of dividing into two segments and collecting, but it is also possible to divide into three or more segments and collect.

In step S402, the reconstruction function 123d performs full sampling of the central segment. That is, the reconstruction function 123d generates a plurality of k-space data corresponding to full sampling of the center segment from a plurality of k-space data contained in the center segment by processing including a Fourier transform corresponding to non-simple decimation sampling. .

For example, the reconstruction function 123d generates a plurality of k-space data corresponding to full sampling from a plurality of k-space data contained in the central segment, as shown in FIG. This process is the same as the process of steps S205 and S206 in FIG. 16, except that the processing target is a plurality of k-space data included in the central segment.

In step S403, the selection function 123e gives a pseudo time stamp. This process is the same as the process of step S207 in FIG. 16, except that the processing target is a plurality of k-space data included in the central segment.

In step S404, the selection function 123e sorts the fully-sampled central segment k-space data according to the cardiac phase of the peripheral segment. That is, the selection function 123e sorts the plurality of k-space data corresponding to full sampling according to the cardiac phase of the limbic segment.

For example, the selection function 123e calculates the cardiac phase information of the k-space data located substantially at the temporal center among the four lines of k-space data included in each time phase of the marginal segment shown in FIG. . Then, the selection function 123e selects a k-space data having the same phase encoding amount and closest to the calculated cardiac time-phase information from among a plurality of k-space data included in the fully sampled central segment. Select data. Accordingly, the selection function 123e generates calibration data by rearranging a plurality of k-space data corresponding to full sampling as shown in FIG. The trigger detection timing in the calibration data (right diagram of FIG. 22) approximately matches the trigger detection timing of the kt spatial data of the edge segment (right diagram of FIG. 20). That is, this calibration data is kt-space data obtained by rearranging a plurality of k-space data corresponding to full sampling of the center segment so that the trigger detection timing is substantially the same as the trigger detection timing in the peripheral segment. is.

In step S405, the selection function 123e extracts k-space data corresponding to the sampling pattern of the edge segments from the reordered central segment k-space data.

For example, the selection function 123e generates extracted data by extracting k-space data corresponding to the sampling pattern of the edge segments from the calibration data, as shown in FIG. That is, the extracted data is kt-space data obtained by thinning the k-space data of the calibration data so that the sampling pattern is the same as the sampling pattern of the edge segment. Thus, the selection function 123e extracts, from the rearranged k-space data, a plurality of k-space data corresponding to the sampling pattern of the plurality of k-space data included in the edge segment.

In step S406, the join function 123c joins the extracted k-space data with the k-space data of the edge segment.

For example, as shown in FIG. 24, the combining function 123c generates combined data by combining the extracted data with the kt space data of the edge segment shown in the right diagram of FIG.
Here, since the trigger detection timing in the extracted data is substantially the same as the trigger detection timing in the marginal segment, the k-space data of each time phase can be combined.

In step S407, the reconstruction function 123d performs full sampling of all segments. In other words, the reconstruction function 123d performs processing including Fourier transform corresponding to non-simple decimation sampling on the extracted plurality of k-space data and the plurality of k-space data included in the edge segment. , to generate a plurality of k-space data corresponding to full sampling.

For example, the reconstruction function 123d generates a plurality of k-space data corresponding to full sampling from the combined data, as shown in FIG. This process is the same as the process of steps S205 and S206 in FIG. Thus, the reconstruction function 123d can use the calibration data to fully sample the k-space data of the edge segment.

In step S408, the selection function 123e gives a pseudo time stamp. This process is the same as the process of step S207 in FIG.

In step S409, the selection function 123e selects k-space data corresponding to the center segment k-space data from the fully sampled k-space data of all segments.

For example, as shown in FIG. 26, the selection function 123e selects a plurality of k-space data corresponding to regions R11 and R12 from among a plurality of k-space data included in all fully sampled segments in the right diagram of FIG. Select spatial data. Specifically, the selection function 123e selects the cardiac time phase information of the k-space data positioned substantially at the temporal center among the four lines of k-space data included in each time phase of the central segment shown in FIG. calculate. Then, the selection function 123e selects k-space data having the same phase encoding amount and closest to the calculated cardiac time-phase information from among a plurality of k-space data included in the fully-sampled marginal segment. Select spatial data. As a result, the selection function 123e selects a plurality of k-space data corresponding to the regions R11 and R12, as shown in the right diagram of FIG. Then, the selection function 123e combines the plurality of k-space data corresponding to the selected regions R11 and R12 with the plurality of k-space data included in the central segment in the left diagram of FIG. generates the combined data (second combined data) shown in the right figure of . The second combined data is arranged so that the trigger detection timing is approximately the same as the trigger detection timing in the central segment.

In step S410, the reconstruction function 123d executes reconstruction processing. For example, the reconstruction function 123d performs reconstruction processing corresponding to non-simple thinning sampling on the second combined data shown in the right diagram of FIG. 26 to generate a 16-phase MR image. Then, the selection function 123e selects an MR image corresponding to the preset acquisition number "13" from among the 16-phase MR images generated by the reconstruction function 123d. For example, the reconstruction function 123d calculates cardiac phases that divide one heartbeat period into 13 equal parts, and selects MR images having cardiac phase information close to the calculated cardiac phases. This process is the same as the process of steps S105 and S106 in FIG.

In this way, the MRI apparatus 100 according to the fourth embodiment generates calibration data for full sampling of the k-space data of the peripheral segment from the k-space data of the center segment. A reconstructed image can be generated.

Note that the processing procedure shown in FIG. 19 is merely an example, and the embodiment is not limited to this. For example, in the processing procedure shown in FIG. 19, the processing order can be changed as appropriate within a range in which there is no contradiction in the processing content. Further, the full sampling processing described in steps S402 and S407 can be applied to full sampling processing without conversion to an MR image (processing of the modification of the second embodiment).

Also, the processing of steps S409 and S410 is merely an example, and the embodiment is not limited to this. For example, when the process of step S408 is completed, a plurality of k-space data corresponding to full sampling have been obtained for each of the central segment and the marginal segment. Therefore, the reconstruction function 123d can perform reconstruction processing by appropriately selecting arbitrary k-space data, not limited to the processing of steps S409 and S410 described above.

(Fifth embodiment)
In the above-described embodiment, the process in which multiple segments of k-space data are acquired without electrocardiographic gating has been described, but the embodiment is not limited to this. For example, the MRI apparatus 100 can perform electrocardiographic gating to acquire multiple segments of k-space data.

A processing procedure by the MRI apparatus 100 according to the fifth embodiment will be described with reference to FIG. FIG. 27 is a flow chart showing a processing procedure by the MRI apparatus 100 according to the fifth embodiment. The processing procedure shown in FIG. 27 is started, for example, triggered by an imaging start request input by the operator.

Note that FIG. 27 will be described with reference to FIG. 28 . FIG. 28 is a diagram for explaining the processing of the MRI apparatus 100 according to the fifth embodiment. Note that the contents described with reference to FIGS. 27 and 28 are merely examples, and the embodiment is not limited to these.

In step S501, the acquisition function 123a acquires k-space data for the central segment and k-space data for the peripheral segment by electrocardiographic gating.

For example, as shown in FIG. 28, the acquisition function 123a performs sampling of the center segment and sampling of the peripheral segment at the timing when the trigger signal (R wave) is detected. Here, since the time required for the dummy shot inserted before each sampling is constant, the trigger detection timing in the central segment and the trigger detection timing in the peripheral segment are substantially the same.

In this way, the acquisition function 123a obtains a plurality of k-spaces each including a central segment corresponding to the center of the k-space and edge segments different from the central segment, which are synchronously acquired based on biosignal information. Get spatial data.

In step S502, the join function 123c joins the k-space data of the center segment and the k-space data of the edge segments. For example, the combine function 123c combines k-space data contained in the center segment with k-space data contained in the edge segments. Here, since the trigger detection timing in the central segment and the trigger detection timing in the peripheral segment are substantially the same, the k-space data of each time phase can be combined.

Since the processing of steps S503 to S507 is the same as the processing of steps S205 to S209 shown in FIG. 16, description thereof is omitted.

In this manner, the MRI apparatus 100 according to the fifth embodiment performs electrocardiographic gating and acquires k-space data of multiple segments. Then, the MRI apparatus 100 combines the collected k-space data of multiple segments to generate a plurality of k-space data corresponding to full sampling. Then, the MRI apparatus 100 can perform rearrangement processing of a plurality of k-space data corresponding to full sampling, and reconstruct MR images of the number of phases corresponding to the acquired number of images.

(Sixth embodiment)
Although the above embodiment describes the case where the collection period of the center segment and the collection period of the edge segment are the same, the embodiment is not limited to this. For example, the MRI apparatus 100 makes the acquisition period of the peripheral segment longer than the acquisition period of the central segment, thereby reducing the deviation of the cardiac time phase that occurs between the plurality of k-space data constituting each reconstructed image. can be done.

FIG. 29 is a diagram for explaining processing of the MRI apparatus 100 according to the sixth embodiment. Note that the content described with reference to FIG. 29 is merely an example, and the embodiment is not limited to this.

For example, as shown in FIG. 29, the acquisition function 123a acquires a plurality of k-space data contained in the edge segment during an acquisition period longer than the acquisition period of the central segment. Here, for example, if the number of acquired images set by the operator is "13", k-space data for 16 time phases are collected for the center segment so as to satisfy about 120% of the acquired number of images. In addition, for edge segments, k-space data for 32 time phases are collected so as to satisfy about 240% of the acquired number.

Thus, if the collection period of the edge segment is longer than the collection period of the center segment, there are more options for the edge segment to couple to the center segment. This allows the merge function 123c to merge edge segments that have cardiac temporal information closer to that of the central segment. As a result, the MRI apparatus 100 can reduce the deviation of the cardiac time phase that occurs between a plurality of pieces of k-space data forming each reconstructed image.

(Other embodiments)
Various different forms may be implemented in addition to the embodiments described above.

(Sampling order of k-space data)
For example, in the above embodiments, a case has been described in which a plurality of k-space data included in each time phase are sampled in one direction in the phase encoding direction, but the embodiments are not limited to this. For example, the MRI apparatus 100 can alternately sample a plurality of pieces of k-space data included in each time phase in both directions in the phase encoding direction.

FIG. 30 is a diagram for explaining the sampling order by the MRI apparatus 100 according to another embodiment. The upper part of FIG. 30 shows the kt spatial data for the central and marginal segments. The lower part of FIG. 30 shows a plurality of arrows indicating the sampling order of a plurality of k-space data included in each time phase.

For example, as shown in FIG. 30, the acquisition function 123a samples the central segment in one direction in the phase encoding direction. Specifically, the acquisition function 123a acquires each k-space data in the sampling order of S11, S12, S13, S14, S15, S16, S17, and S18.

On the other hand, the acquisition function 123a alternately samples the edge segments in both directions in the phase encoding direction. Specifically, the acquisition function 123a acquires k-space data in the sampling order of S21, S22, S23, and S24 for the first time phase. Next, the acquisition function 123a acquires k-space data in the sampling order of S25, S26, S27, and S28 for the second phase.

The reason why the k-space data is acquired while alternately changing the sampling direction of the edge segment for each time phase is to reduce the influence of eddy currents. For example, the acquisition function 123a acquires the k-space data of S25 after the k-space data of S24, thereby reducing the difference in the amount of phase encoding between two consecutive k-space data. As a result, the acquisition function 123a can suppress the generation of eddy currents and reduce the influence of eddy currents.

In FIG. 30, the central segment is sampled in one direction, but k-space data may be collected while the sampling direction is alternately changed for each time phase. However, the acquisition function 123a preferably unidirectionally samples the k-space data of the central segment in order to capture changes in the motion of the heart in each cardiac phase.

(Combining in units of blocks)
Further, for example, in the above embodiment, a case has been described in which a plurality of pieces of k-space data included in each time phase are combined in units of segments (in units of groups), but the embodiments are not limited to this. For example, the MRI apparatus 100 can be combined in units of blocks in kt SENSE.

Here, a “block” means a group of k-space data of the number of temporal phases defined in reconstruction processing in kt SENSE. For example, if the thinning rate in kt SENSE is "4", each block contains k-space data for four time phases.

FIG. 31 is a diagram for explaining processing of the combining function 123c according to another embodiment. The upper part of FIG. 31 illustrates the sequence executed by the sequence control circuit 110 . The lower part of FIG. 31 illustrates the process of arranging the k-space data acquired by the upper sequence in the kt space according to a preset sampling pattern. Note that FIG. 31 illustrates a case where the thinning rate in kt SENSE is "4". That is, in FIG. 31, a plurality of pieces of k-space data included in region R21 correspond to one block in segment B. FIG. A plurality of pieces of k-space data included in region R22 correspond to one block in segment A. FIG.

First, the calculation function 123b calculates cardiac phase information for each block included in the segment B. FIG. For example, the calculation function 123b calculates the cardiac time phase information of the k-space data positioned substantially at the temporal center of the 16 lines of k-space data included in each block as the cardiac time phase information of each block. Similarly, the calculation function 123b calculates cardiac phase information for each block included in segment A. FIG.

Then, the combining function 123c combines each block of segment B with a block of segment A having cardiac phase information close to the cardiac phase information of each block of segment B, as shown in FIG. Specifically, the combining function 123c selects a block having cardiac phase information closest to the cardiac phase information of the block in the region R21 from among the blocks in the segment A. Then, the combining function 123c arranges the selected block of the segment A in the region R22. In this way, the combining function 123c can combine the k-space data of each segment on a block-by-block basis.

(Search for limbic segments in compressed sensing)
Also, if compressed sensing is used, the sampling patterns of the central and edge segments may not necessarily match. Therefore, the MRI apparatus 100 can also search for k-space data to be combined from the marginal segments and combine them with the k-space data of the central segment.

For example, the combining function 123c searches and combines a plurality of k-space data to be combined from among a plurality of k-space data included in the edge segment so that the image quality of the reconstructed image satisfies a certain standard. Let

FIG. 32 is a diagram for explaining processing of the MRI apparatus 100 according to another embodiment. In FIG. 32, the trigger table corresponds to the time direction of each kt space data. Note that in the example shown in FIG. 32, the k-space data of the central segment (left diagram in FIG. 32) and edge segment (right diagram in FIG. 32) are collected randomly in the temporal direction.

For example, as shown in FIG. 32, the plurality of k-space data included in region R31 of the central segment include the plurality of k-space data included in region R32 of the marginal segment having cardiac phase information close to the cardiac phase information of region R31. k-space data are combined. The image is then reconstructed when the k-space data of the edge segments are combined with the k-space data of all the central segments.

Here, the combining function 123c determines whether the image quality of the reconstructed image is disturbed based on whether the image quality of the reconstructed image satisfies a certain standard. As an example, the combining function 123c calculates the difference between two reconstructed images of consecutive time phases among the plurality of reconstructed images. Then, the combining function 123c determines that the image quality is disturbed when the calculated difference exceeds a certain value (threshold value) in the heart region. Note that the process of determining whether or not the image quality is disturbed is not limited to this process, and a wide range of known image analysis techniques can be applied.

Then, when the image quality of the reconstructed image is disturbed, the combining function 123c searches for and combines a plurality of k-space data to be combined from among the plurality of k-space data included in the edge segment. . For example, the combining function 123c moves the region R32 back and forth to search for k-space data of the optimum time phase.

Specifically, after region R32, join function 123c searches for a region of limbic segments having cardiac phase information close to that of region R31. Then, the combining function 123c combines the plurality of k-space data included in the searched area with the plurality of k-space data included in the area R31. Then, the reconstruction function 123d generates a reconstructed image using the combined k-space data.

Then, the combining function 123c determines again whether or not the image quality of the reconstructed image is disturbed. In this manner, the combining function 123c repeatedly executes the search process described above until the image quality of the reconstructed image satisfies a certain standard.

Then, when the image quality of the reconstructed image satisfies a certain standard, the MRI apparatus 100 performs the same processing as in the above embodiment. For example, the MRI apparatus 100 uses the generated reconstructed image to execute the processes from step S206 onward in FIG.

In this way, the combining function 123c searches for a plurality of k-space data to be combined from among the plurality of k-space data included in the edge segment until the image quality of the reconstructed image satisfies a certain standard. combine. Thereby, the MRI apparatus 100 can improve the image quality of the reconstructed image.

(Applications other than cardiac cine imaging)
Also, in the above embodiment, the case of performing cardiac cine imaging has been described, but the embodiment is not limited to this. For example, the above-described processing functions of the MRI apparatus 100 can be applied even when contrast imaging is performed.

For example, when performing contrast imaging, the MRI apparatus 100 acquires the injection start time of the contrast agent. Then, the MRI apparatus 100 rearranges the plurality of pieces of k-space data based on the injection start time of the contrast agent to generate a plurality of reconstructed images.

That is, the MRI apparatus 100 collects a plurality of k-space data collected from the subject by non-simple thinning-out sampling, the collection time of each k-space data, and time information (contrast agent injection start time) and Then, the MRI apparatus 100 generates a plurality of k-space data corresponding to full sampling from a plurality of k-space data by processing including Fourier transform corresponding to non-simple thinning sampling. Then, the MRI apparatus 100 generates pseudo acquisition times for each of the plurality of k-space data corresponding to full sampling based on the acquisition times. Then, the MRI apparatus 100 rearranges a plurality of k-space data corresponding to full sampling based on the pseudo acquisition time and time information. Then, the MRI apparatus 100 generates a plurality of reconstructed images by performing reconstruction processing on the plurality of pieces of k-space data after the rearrangement processing. As a result, the MRI apparatus 100 can preferably perform imaging of a moving imaging target.

Also, for example, the MRI apparatus 100 can be applied to flow collection by a PC (Phase Contrast) method. For example, as shown in FIG. 33, the MRI apparatus 100 performs flow acquisition by the PC method to image blood flow. FIG. 33 is a diagram for explaining processing of the MRI apparatus 100 according to another embodiment.

In the example shown in FIG. 33, black circles and white circles indicate k-space data. The MRI apparatus 100 individually reconstructs the k-space data indicated by black circles and the k-space data indicated by white circles, thereby generating two systems of reconstructed image groups. Then, the MRI apparatus 100 generates a high-quality two-system reconstructed image group by performing the processing from step S206 onward in FIG. 16, for example, on the two-system reconstructed image group. Then, the MRI apparatus 100 can generate a high-quality blood flow image by taking the difference between the two systems of high-quality reconstructed image groups. Note that the example shown in FIG. 33 is merely an example, and known flow collection can be arbitrarily applied.

It should be noted that the present invention can be widely applied to two-dimensional scanning, three-dimensional scanning, and the like, in addition to contrast-enhanced imaging and flow collection.

(reconstruction device)
Also, for example, the processing according to the above-described embodiments can be provided as a reconfiguration device on a network. This reconfiguration device can provide, for example, an information processing service (cloud service) via a network.

FIG. 34 is a block diagram showing a configuration example of a reconstruction device according to another embodiment. As shown in FIG. 34, for example, a reconfiguring device 200 is installed in a service center that provides information processing services. A reconstruction device 200 is connected to an operation terminal 201 . Also, the reconstruction device 200 is connected to a plurality of client terminals 203A, 203B, . . . Note that the reconstruction device 200 and the operation terminal 201 may be connected via the network 202 . Also, when collectively referring to the plurality of client terminals 203A, 203B, .

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

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

The reconstruction device 200 comprises a communication interface 210 , a storage circuit 220 and a processing circuit 230 . Communication interface 210, storage circuitry 220, and processing circuitry 230 are communicatively connected to each other.

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

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

The processing circuit 230 is an electronic device (processor) that controls the overall processing in the reconstruction device 200 . Processing circuitry 230 performs an acquisition function 231 , a calculation function 232 , a combination function 233 , a reconstruction function 234 and a selection function 235 . Each processing function executed by the processing circuit 230 is recorded in the storage circuit 220 in the form of a computer-executable program, for example. The processing circuit 230 reads out and executes each program to realize the function corresponding to each read program. The acquisition function 231, the calculation function 232, the combination function 233, the reconstruction function 234, and the selection function 235 are similar to the acquisition function 123a, the calculation function 123b, the combination function 123c, the reconstruction function 123d, and the selection function 123e shown in FIG. Basically similar processing can be executed.

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

The reconstruction device 200 then receives a plurality of k-space data transmitted from the client terminal 203 . Then, in the reconstruction device 200, the acquisition function 231 acquires a plurality of k-space data collected in a predetermined sampling pattern in a plurality of time phases. The calculation function 232 calculates the cardiac time phase information of a first data group including a plurality of k-space data acquired with a reference phase encoding amount among the plurality of k-space data, and the cardiac time phase information different from the first data group. and calculating cardiac phase information of a second data group including a plurality of k-space data acquired with a phase encoding amount. The linking function 233 associates each piece of cardiac time phase information of the first data group with the closest cardiac time phase information among the cardiac time phase information of the second data group, and combines the associated cardiac time phase information. The first data group and the second data group are combined based on to create combined data of a plurality of time phases. The reconstruction function 234 generates a reconstructed image of multiple time phases by reconstructing the combined data of multiple time phases based on a predetermined sampling pattern. The selection function 235 selects a reconstructed image having cardiac phase information close to a plurality of preset cardiac phase information from among the reconstructed images of the plurality of time phases. Then, the reconstruction device 200 transmits (downloads) the selected reconstructed image to the client terminal 203 . As a result, the reconstruction device 200 enables imaging with high spatial resolution and high temporal resolution for a site that periodically moves.

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the illustrated one, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured. Furthermore, each processing function performed by each device may be implemented in whole or in part by a CPU and a program analyzed and executed by the CPU, or implemented as hardware based on wired logic.

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

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

According to at least one of the embodiments described above, it is possible to perform faster imaging of a region that undergoes periodic motion.

While several embodiments of the invention have been described, these embodiments have been presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, as well as the scope of the invention described in the claims and equivalents thereof.

100 MRI apparatus 123 processing circuit 123a acquisition function 123b calculation function 123c combination function 123d reconstruction function 123e selection function

Claims (17)

A plurality of first k-space data collected from a subject by non-simple thinning sampling in which the sampling pattern changes in the time direction; Acquiring sequential biological signal information,
By inversely transforming the intermediate data obtained by transforming the plurality of first k-space data, a plurality of second k-space data in which at least part of the thinning region for full sampling is filled with respect to the plurality of first k-space data to generate
generating a pseudo second acquisition time for each of the plurality of second k-space data based on the first acquisition time;
performing rearrangement processing of the plurality of second k-space data based on the second acquisition time and the biosignal information;
generating a plurality of reconstructed images by performing reconstruction processing on the plurality of second k-space data after the rearrangement processing;
An image reconstruction method, comprising: The obtaining process obtains the plurality of first k-space data acquired by dividing into a plurality of segments including a first segment corresponding to the center of the k-space and a second segment different from the first segment. ,
The process of generating the second k-space data includes:
By inversely transforming intermediate data obtained by transforming the k-space data included in the first segment, a plurality of third k-space data in which at least a portion of the thinning region for full sampling is filled with respect to the first segment is generated. generate and
rearranging the plurality of 3rd k-space data according to the cardiac phase of the second segment;
extracting a plurality of 3rd k-space data corresponding to the sampling pattern of the plurality of first k-space data included in the second segment from the plurality of 3rd k-space data after rearrangement;
generating the plurality of second k-space data using the plurality of extracted third k-space data and the plurality of first k-space data included in the second segment;
2. The image reconstruction method according to claim 1. The obtaining process obtains the plurality of first k-space data acquired by dividing into a plurality of segments including a first segment corresponding to the center of the k-space and a second segment different from the first segment. ,
The process of generating the second k-space data includes:
Among the plurality of first k-space data included in the second segment, the first k-space data having a cardiac time phase close to the cardiac time phase of the first segment is selected from the plurality of first k-space data included in the first segment. Combine for 1k spatial data,
generating the plurality of second k-space data by inversely transforming intermediate data obtained by transforming the plurality of combined first k-space data;
2. The image reconstruction method according to claim 1. the collection period of the second segment is longer than the collection period of the first segment;
4. The image reconstruction method according to claim 3. In the acquiring process, each of a plurality of segments including a first segment corresponding to the center of k-space and a second segment different from the first segment is synchronously acquired based on the biosignal information. acquire the first k-space data of
The process of generating the second k-space data includes:
combining a plurality of first k-space data included in the first segment and a plurality of first k-space data included in the second segment;
generating the plurality of second k-space data by inversely transforming intermediate data obtained by transforming the plurality of combined first k-space data;
2. The image reconstruction method according to claim 1. the first segment includes a plurality of k-space data corresponding to a central portion of k-space;
wherein the second segment includes a plurality of k-space data corresponding to an edge of k-space;
4. The image reconstruction method according to claim 3. The cardiac phase is information indicating a position in the temporal direction in one cardiac cycle,
4. The image reconstruction method according to claim 3. The first k-space data is collected with different sampling patterns between successive frames along the temporal direction.
2. The image reconstruction method according to claim 1. The first k-space data has a sampling pattern that is regularly thinned out in the phase encoding direction of the k space, and phase encoding lines to be acquired are acquired in different sampling patterns between successive frames in the time phase direction. ,
9. The image reconstruction method according to claim 8. In the process of generating the second k-space data, the sampling patterns are combined so as to be regularly arranged along the time phase direction.
10. The image reconstruction method according to claim 9. The first k-space data is collected with an irregular sampling pattern of phase-encoded lines collected at least between consecutive frames in the time phase direction.
9. The image reconstruction method according to claim 8. In the process of generating the second k-space data, the sampling patterns are combined so as to be arranged irregularly along the time phase direction.
12. The image reconstruction method according to claim 11. The combining process selects a plurality of first k-space data to be combined from among the plurality of first k-space data included in the second segment so that the image quality of the reconstructed image satisfies a certain standard. explore and combine
6. The image reconstruction method according to claim 5. A plurality of first k-space data collected from a subject by non-simple thinning sampling in which the sampling pattern changes in the time direction; an acquisition unit that acquires sequential biological signal information;
By inversely transforming the intermediate data obtained by transforming the plurality of first k-space data, a plurality of second k-space data in which at least part of the thinning region for full sampling is filled with respect to the plurality of first k-space data and generates a pseudo second acquisition time for each of the plurality of second k-space data based on the first acquisition time;
a rearrangement unit that rearranges the plurality of second k-space data based on the second acquisition time and the biosignal information;
A reconstruction device, comprising: a reconstruction unit that generates a plurality of reconstructed images by performing reconstruction processing on the plurality of second k-space data after the rearrangement processing. A magnetic resonance imaging device,
15. A reconstruction device according to claim 14. A plurality of first k-space data collected from a subject by non-simple thinning-out sampling in which the sampling pattern changes in the time direction, a first collection time that is the time at which each of the first k-space data is collected, and the movement of the object to be imaged. Acquiring reference time information and
By inversely transforming the intermediate data obtained by transforming the plurality of first k-space data, a plurality of second k-space data in which at least part of the thinning region for full sampling is filled with respect to the plurality of first k-space data to generate
generating a pseudo second acquisition time for each of the plurality of second k-space data based on the first acquisition time;
rearranging the plurality of second k-space data based on the second collection time and the time information;
generating a plurality of reconstructed images by performing reconstruction processing on the plurality of second k-space data after the rearrangement processing;
An image reconstruction method, comprising: A plurality of first k-space data collected from a subject by non-simple thinning-out sampling in which the sampling pattern changes in the time direction, a first collection time that is the time at which each of the first k-space data is collected, and the movement of the object to be imaged. an acquisition unit that acquires reference time information;
By inversely transforming the intermediate data obtained by transforming the plurality of first k-space data, a plurality of second k-space data in which at least part of the thinning region for full sampling is filled with respect to the plurality of first k-space data and generates a pseudo second acquisition time for each of the plurality of second k-space data based on the first acquisition time;
a rearrangement unit that rearranges the plurality of second k-space data based on the second collection time and the time information;
A reconstruction device, comprising: a reconstruction unit that generates a plurality of reconstructed images by performing reconstruction processing on the plurality of second k-space data after the rearrangement processing.

Images