JP2022145575A - Image processing device and image processing method - Google Patents

Abstract

To improve image quality.SOLUTION: An image processing device of the present invention includes an imaging k space data acquisition unit, a template k space data acquisition unit, a target k space data generation unit, composite k space data, and an image generation unit. The k space data acquisition unit acquires a plurality of pieces of imaging k space data by executing first bipolar multi-echo collection. The template k space data acquisition unit executes second bipolar multi-echo collection, and acquires a plurality of pieces of template k space data. The target k space data generation unit generates target k space data with no phase error. A kernel calculation unit calculates a plurality of kernels. The composite k space data generation unit generates composite k space data. The image generation unit generates an image of an examination site using the composite k space data.SELECTED DRAWING: Figure 1

Description

The embodiments disclosed in the specification and drawings relate to an image processing apparatus and an image processing method.

Magnetic Resonance Imaging (MRI) may be applied to determine fat quantification of an examination area of a subject. Methods of fat quantification by MRI can non-invasively provide quantitative and spatial information about fat depots in the human body for diagnostic purposes.

Methods of fat quantification by MRI require long scan times, so bipolar acquisition and parallel imaging techniques are usually used to reduce scan times.

Bipolar acquisition techniques collect data by applying positive and negative gradients, but there is a phase error in the collected data due to the effects of eddy currents present in the system. As a method of correcting this phase error, a one-dimensional phase error model or a two-dimensional phase error model is normally estimated from template scanning without or with encoding, respectively, and the phase error is corrected according to these phase error models.

Parallel imaging techniques undersample the data with a multichannel coil and reproduce the images in a manner that includes Generalized Auto-calibrating Partially Parallel Acquisition (GRAPPA) and Sensitivity Encoding (SENSE). Configure.

A more complex parallel imaging reconstruction method is the bipolar GRAPPA method, which synthesizes the missing k-space data in Echo Planar Imaging (EPI) as well as the reverse readout gradient. It is used to correct the inherent phase error due to polarity. The bipolar GRAPPA method exhibits high image quality in regions where high-order EPI phase errors commonly occur.

However, in bipolar acquisition techniques and parallel imaging techniques, etc., one-dimensional phase error correction cannot completely eliminate phase errors, especially along the phase encode (PE) direction. can't Two-dimensional phase error correction is performed in image space and needs to be unwrapped from the phase error map, which is usually difficult and can fail in certain highly phase-wrapped regions. Furthermore, the undersampled image must first be developed with SENSE or GRAPPA and any motion artifacts will degrade the quality of the phase error correction.

Also, parallel imaging reconstruction methods such as SENSE require that separate mapping scans be acquired, and when imaging the abdomen, are affected by respiratory motion and introduce errors in image reconstruction.

Japanese Patent Publication No. 2020-522344

One of the problems to be solved by the embodiments disclosed in the specification and drawings is to improve image quality. However, the problems to be solved by the embodiments disclosed in this specification and drawings are not limited to the above problems. A problem corresponding to each effect of each configuration shown in the embodiments described later can be positioned as another problem.

An image processing apparatus according to an embodiment includes an imaging k-space data acquisition unit, a template k-space data acquisition unit, a target k-space data generation unit, a kernel calculation unit, a synthetic k-space data generation unit, and an image generation unit. Prepare. An imaging k-space data acquisition unit acquires a plurality of imaging k-space data by performing first bipolar multi-echo acquisition on an examination site of a subject. The template k-space data acquisition unit acquires a plurality of template k-space data by performing second bipolar multi-echo acquisition on the inspection site with a readout gradient polarity opposite to that of the first bipolar multi-echo acquisition. . A target k-space data generator generates target k-space data without a phase error based on template k-space data and imaging k-space data acquired at the same echo time. a kernel calculator for correcting a phase error in the imaging k-space data or generating unsampled data in the imaging k-space data based on the target k-space data and the imaging k-space data; Compute the kernel. The synthesized k-space data generation unit generates synthesized k-space data by synthesizing the plurality of kernels and each of the imaging k-space data. The image generation unit generates an image of the inspection site using the synthesized k-space data.

FIG. 1 is a block diagram showing the basic structure of a magnetic resonance imaging apparatus according to an embodiment. FIG. 2 is a block diagram showing the structure of the image processing device according to the embodiment. FIG. 3 is a diagram illustrating generation of a plurality of imaging k-space data and corresponding template k-space data according to the embodiment. FIG. 4 is a diagram explaining generation of target k-space data according to the embodiment. FIG. 5 is a diagram explaining generation of three kernels according to the embodiment. FIG. 6 is a diagram illustrating generation of synthetic k-space data using three kernels according to the embodiment. FIG. 7 is a diagram describing an embodiment for generating an average kernel for multiple co-imaging k-space data. FIG. 8 is a flow chart of an image processing method according to the embodiment.

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

A magnetic resonance imaging apparatus 1 according to the present invention will be described below with reference to FIGS. 1 to 4. FIG.

FIG. 1 is a block diagram showing the basic structure of a magnetic resonance imaging apparatus 1 according to the invention. Each structure of the magnetic resonance imaging apparatus 1 will be described below. A static magnetic field magnet 10 generates a static magnetic field in an imaging space in which a subject is placed. For example, the static magnetic field magnet 10 is made of a superconducting magnet, a permanent magnet, or the like.

The gradient magnetic field coil 20 generates a gradient magnetic field. For example, the gradient magnetic field coils have X coils, Y coils, and Z coils corresponding to X, Y, and Z axes that are orthogonal to each other. The X coil, the Y coil, and the Z coil generate gradient magnetic fields along each axial direction by current supplied from the gradient magnetic field power supply 30 . Here, the Z axis is set along the magnetic flux of the static magnetic field generated by the static magnetic field magnet 10 . Also, the X-axis is set along the horizontal direction perpendicular to the Z-axis. The Y-axis is set along a direction orthogonal to both the Z-axis and the X-axis.

The gradient magnetic field power supply 30 supplies current to the gradient magnetic field coil 20 . The gradient magnetic field power supply 30 supplies a current to the gradient magnetic field coil 20 so that the gradient magnetic field coil 20 can generate a gradient magnetic field.

An RF (Radio Frequency) coil 40 applies a high-frequency magnetic field to a subject placed in an imaging space and receives NMR (Nuclear Magnetic Resonance) signals generated from the subject. A high frequency magnetic field is sometimes called an RF pulse. The RF coils 40 include a whole-body RF coil 41 that surrounds the imaging space and a local RF coil 42 that is placed close to the subject. The functions of the RF coil 40 are roughly divided into transmission of high-frequency magnetic fields and reception of NMR signals. Either the whole-body RF coil 41 or the local RF coil 42 may have both transmission and reception functions, or both the whole-body RF coil 41 and the local RF coil 42 may be used for transmission and reception. good too. Furthermore, multiple local RF coils 42 may be used simultaneously. The local RF coil 42 may be provided differently for each site of the subject.

The transmission circuit 50 outputs to the RF coil 40 a high-frequency pulse signal corresponding to the Larmor frequency specific to the target nucleus provided in the static magnetic field.

The receiving circuit 60 generates magnetic resonance (MR) data based on the NMR signal received by the RF coil 40 and outputs the generated MR data to the processing circuit 100 .

The bed 70 includes a top plate 71 on which the subject is placed, and the top plate 71 can be moved vertically and horizontally.

The input interface 80 receives input operations of various instructions and various information from the operator. Specifically, the input interface 80 is connected to the processing circuit 100 , converts an input operation received from an operator into an electrical signal, and outputs the electrical signal to the processing circuit 100 . For example, the input interface 80 includes a trackball, switch buttons, a mouse, a keyboard, a touch pad that performs input operations by touching an operation surface, a touch screen that integrates a display screen and a touch pad, and a non-optical sensor using an optical sensor. It is implemented by a contact input circuit, an audio input circuit, and the like. In this specification, the input interface 80 is not limited to those including physical operation parts such as a mouse and a keyboard. For example, the input interface 80 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the control circuit. .

The display 81 displays various information and various images. Specifically, the display 81 is connected to the processing circuit 100, converts various information and image data sent from the processing circuit 100 into electrical signals for display, and outputs the electrical signals. For example, the display 81 is implemented by a liquid crystal monitor, an LED monitor, a touch panel, or the like.

The storage circuit 90 stores various data and various programs. Specifically, the storage circuit 90 is connected to the processing circuit 100 and stores various data and various programs input/output by each processing circuit. For example, the storage circuit 90 is implemented by a semiconductor memory device such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, or the like.

The processing circuit 100 has a bed control section 101 . The bed control unit 101 controls the operation of the bed 70 by outputting electrical signals for control to the bed 70 . For example, the bed control unit 101 receives an instruction to move the tabletop 71 from the operator via the input interface 80, and moves the tabletop 71 of the bed 70 so that the tabletop 71 is moved according to the received instruction. Operate the mechanism. For example, the bed control unit 101 moves the tabletop 71 on which the subject is placed to the imaging space when imaging the subject.

The processing circuit 100 has a collection unit 102 . The acquisition unit 102 acquires MR data of the subject by executing various pulse sequences. Specifically, the acquisition unit 102 executes various pulse sequences by driving the gradient magnetic field power supply 30, the transmission circuit 50, and the reception circuit 60 according to the sequence execution data output from the processing circuit 100. FIG. Here, the sequence execution data is data representing a pulse sequence. This information defines the timing of supplying a signal, the strength of the supplied high-frequency pulse, the timing of sampling the magnetic resonance signal by the receiving circuit 60, and the like. The sequence execution data is stored in the memory circuit 90 in advance, or is generated by receiving input from the operator at the input interface 80 . Alternatively, the operator may edit the sequence execution data stored in advance in the storage circuit 90 . Then, the acquisition unit 102 receives the MR data output from the receiving circuit 60 as a result of executing the pulse sequence, and causes the storage circuit 90 to store the MR data. At this time, the MR data stored in the memory circuit 90 is stored as k-space data. For example, when performing two-dimensional imaging, a phase encoding gradient magnetic field is applied to a slice plane selected by a slice selection gradient magnetic field. The k-space data of phase-encoded quantities according to the applied magnetic field gradients are read out using readout magnetic field gradients. Readout magnetic field gradients are also called frequency-encoding magnetic field gradients. For example, when three-dimensional imaging is performed, a slice-encoding gradient magnetic field and a phase-encoding gradient magnetic field are applied, and the k-space data of the encoding amount according to the applied gradient magnetic field is read out using the readout gradient magnetic field. The slice selection gradient magnetic field, phase encoding gradient magnetic field, readout gradient magnetic field, and slice encoding gradient magnetic field described above are formed by gradient magnetic fields generated by one or more of the aforementioned X coil, Y coil, and Z coil, respectively. be done.

The processing circuit 100 has a correction section 103 . The correction unit 103 uses at least part of the k-space data acquired by the acquisition unit 102 to perform correction for suppressing motion artifacts caused by movement of the subject.

The processing circuitry 100 has an MR image generator 104 . The MR image generation unit 104 uses the k-space data acquired by the acquisition unit 102 to generate various MR images. For example, an MR image is generated by performing reconstruction processing such as Fourier transform on the k-space data. The MR image generator 104 can also apply image processing as post-processing to the reconstructed image.

The processing circuit 100 has a display control section 105 . The display control unit 105 outputs the image generated by the MR image generation unit 104 to the display 81 . It is also possible to acquire an image stored in the storage circuit 90 or an external storage and display it on the display 81 .

Each of the processing circuits 100 described above is implemented by a processor. In this case, the processing functions of each processing circuit are stored in the storage circuit 90 in the form of a computer-executable program, for example. Each processing circuit reads out each program from the storage circuit 90 and executes it, thereby realizing a processing function corresponding to each program. In other words, each processing circuit in a state where each program is read has the function of each unit shown in each processing circuit in FIG.

Also, here, each processing circuit has been described as being realized by a single processor, but the embodiments are not limited to this, and each processing circuit is configured by combining a plurality of independent processors, and each processor is a program. Each processing function may be realized by executing Also, the processing functions of each processing circuit may be appropriately distributed or integrated in a single or a plurality of processing circuits. In the example shown in FIG. 1, the single memory circuit 90 stores programs corresponding to each processing function. A configuration in which the corresponding program is read out from the circuit may be used.

The magnetic resonance imaging apparatus 1 further includes an image processing device 200 . FIG. 2 is a structural block diagram of an image processing device 200 according to the present invention.

The image processing apparatus 200 is for imaging an examination region of a subject in bipolar multi-echo acquisition, and includes an imaging k-space data acquisition unit 201, a template k-space data acquisition unit 202, a target k-space data generation unit 203, a kernel A calculation unit 204 , a synthetic k-space data generation unit 205 and an image generation unit 206 are provided. In the magnetic resonance imaging apparatus 1 , the image processing device 200 may be configured independently, or may be configured by combining with the processing circuit 100 . The bipolar multi-echo acquisition method will be described in detail below.

As shown in FIG. 3, the imaging k-space data acquisition unit 201 acquires a plurality of imaging k-space data S1 to S3 by performing the first bipolar multi-echo acquisition D1 on the examination site of the subject.

The template k-space data acquisition unit 202 performs the second bipolar multi-echo acquisition D2 on the inspection site of the subject with the readout gradient polarity opposite to that of the first bipolar multi-echo acquisition D1, and acquires the imaging k-space data. Opposite readout gradient polarity of the first bipolar multi-echo acquisition D1 at echo times TE1′-TE3′ corresponding to the echo times TE1-TE3 at which each of the plurality of imaging k-space data S1-S3 is acquired in section 201. to acquire the template k-space data S1′ to S3′.

In the first bipolar multi-echo acquisition D1, the acquired line data form imaging k-space data S1-S3, respectively. In the imaging k-space data S1 to S3, the region at the periphery (upper periphery and lower periphery) of the k-space data is called a peripheral region P, and the region at the center of the k-space data is called a central region C (central part). . The imaging k-space data S1 to S3 are data obtained by fully sampling the central region C (central portion) of the k-space and undersampling the peripheral region P (peripheral portion) of the k-space.

Specifically, in the first shot, rightward line data L1 is output at echo time TE1 and collected by a collection unit (not shown), and leftward line data L2 is output at echo time TE2 and collected by the collection unit. , and the rightward line data L3 is output at the echo time TE3 and collected by the collecting unit. The line data L1 to L3 are positioned at the top of the peripheral region P of each k-space. In the second shot, rightward line data L7 is output at echo time TE1 and collected by the collecting unit, leftward line data L8 is output and collected at echo time TE2, and rightward line data L8 is collected at echo time TE3. Line data L9 is output and collected by the collection unit. The line data L7 to L9 are positioned at the uppermost portion of the central region C of each k-space.

As shown in FIG. 3, portions L4 to L6 to be undersampled between line data L1 to L3 and line data L7 to L9 in each k-space are indicated by dashed lines. The undersampled portions L4-L6 are where the data to be collected when fully sampling k-space is located. However, in order to reduce the imaging time, data that should be located in the undersampled portions L4-L6 are not output. That is, there is no data at the positions of the undersampled portions L4 to L6 of the k-space. In FIG. 3, the line data L1 and the undersampled portion L4 are configured as undersampling data S12 in the imaging k-space data S1, and the line data L2 and the undersampled portion L5 are configured as undersampling data S12 in the imaging k-space data S2. The line data L3 and the under-sampled portion L6 configured as the data S22 are configured as the under-sampled data S32 in the imaging k-space data S3.

In subsequent shots, the central region C of each k-space is fully sampled to form imaging k-space data S11, imaging k-space data S21, and imaging k-space data S31, which are fully sampled data, and shown in the lower part of FIG. is undersampled to form undersampling data S13, undersampling data S23, and undersampling data S33, which are undersampling data.

Thus, in a plurality of shots, a plurality of line data are acquired by the above collection method. The line data L1, L7, etc. acquired at echo time TE1 are configured as imaging k-space data S1, the line data L2, L8, etc. acquired at echo time TE2 are configured as imaging k-space data S2, and at echo time TE3 The obtained line data L3, L9, etc. are configured as imaging k-space data S3. Each of the imaging k-space data S1 to S3 includes under-sampling data including an under-sampled portion (non-sampled data) formed in the peripheral region P in FIG. 3, full-sampling data formed in the central region C, Prepare. That is, the imaging k-space data S1 to S3 consist of under-sampled data in the peripheral region P and full-sampled data in the central region C. FIG.

In the second bipolar multi-echo acquisition D2, as shown in FIG. 3, template k-space data S1′ to S3′ are acquired with a readout gradient polarity opposite to that in the first bipolar multi-echo acquisition D1, and the acquisition unit Only the k-space data in the central region (central part) of k-space are acquired. The template k-space data S1' to S3' are data obtained by fully sampling the central region (central part) of the k-space.

Specifically, in the second bipolar multi-echo acquisition D2, in the first shot for starting data acquisition, leftward line data L1′ is acquired at echo time TE1′, and rightward line data L1′ is acquired at echo time TE2′. Data L2' is collected, and leftward line data L3' is collected at echo time TE3'. In the second shot, leftward line data L4' is collected at echo time TE1', rightward line data L5' is collected at echo time TE2', and leftward line data L6' is collected at echo time TE3'. . Thus, in a plurality of shots, a plurality of line data are acquired by the above acquisition method. The line data L1′, L4′, etc. acquired during the echo time TE1′ are configured as the template k-space data S1′, and the line data L2′, L5′, etc. acquired during the echo time TE2′ are configured as the template k-space data S2′. , and the line data L3', L6', etc. acquired at the echo time TE3' are configured as the template k-space data S3'.

Each of the readout directions of the template k-space data S1'-S3' acquired at the echo times TE1'-TE3' is in the opposite direction to the imaging k-space data S1-S3 acquired at the corresponding echo times TE1-TE3. becomes.

As for the order of the first bipolar multi-echo acquisition D1 and the second bipolar multi-echo acquisition D2, the first bipolar multi-echo acquisition D1 may be completed and then the second bipolar multi-echo acquisition D2 may be performed. Acquisition of the first shot of the first bipolar multi-echo acquisition D1 is completed, followed by acquisition of the first shot of the second bipolar multi-echo acquisition D2, and then the acquisition of the first shot of the first bipolar multi-echo acquisition D1. The acquisition of the first shot may be performed, and then the acquisition of the second shot of the second bipolar multi-echo acquisition D2 may be performed. The first bipolar multi-echo acquisition D1 and the second bipolar multi-echo acquisition D2 can be performed in various orders and are not limiting. Moreover, it is preferable to perform the first bipolar multi-echo acquisition D1 and the corresponding second bipolar multi-echo acquisition D2 during one breath-holding process of the subject.

In addition, FIG. 3 shows an example of acquiring line data in three echo times to form three k-space data respectively in the first bipolar multi-echo acquisition D1 and the second bipolar multi-echo acquisition D2. However, the number of echo times is not limited to three, and may be any number other than three.

A target k-space data generation unit 203 generates target k-space data without a phase error of the imaging k-space data based on template k-space data and imaging k-space data acquired at the same echo time. The same echo time here means the echo time for acquiring the imaging k-space data and the echo time for acquiring the template k-space data corresponding to this imaging k-space data. , TE1′, echo times TE2, TE2′, or echo times TE3, TE3′.

As shown in FIG. 4, the k-space data at the bottom left of FIG. 4 is template k-space data S2′ acquired at echo time TE2′ in the second bipolar multi-echo acquisition D2 of FIG. The k-space data S21 in the center of the imaging k-space data S2 acquired at the same echo time TE2 (that is, the echo time corresponding to the echo time TE2' for acquiring the template k-space data S2') is the k-space Data. The target k-space data generation unit 203 combines the template k-space data S2' and the imaging k-space data S21, and adds them here to generate target k-space data T2 without a phase error.

Here, the generation of the target k-space data T2 for the imaging k-space data S2 has been described as an example. However, in embodiments, rather than just generating target k-space data T2 for imaging k-space data S2, target k-space data to generate 3 and 4 will be described as examples. The target k-space data generation unit 203 combines the template k-space data S1′ and the imaging k-space data S11, and adds them here to generate target k-space data T1 with no phase error, and generates template k-space data S3. ' and imaging k-space data S31 are combined and added here to generate target k-space data T3 with no phase error, and target k-space data T2 is generated as described above. Target k-space data T1 to T3 are collectively referred to as target k-space data T. FIG.

A kernel calculator 204 corrects a phase error of at least one piece of imaging k-space data including the imaging k-space data based on the target k-space data of the imaging k-space data and the imaging k-space data, or the at least Compute multiple kernels for generating unsampled data in a piece of imaging k-space data.

As shown in FIG. 5, three kernels are generated for each imaging k-space data S, and the three kernels are respectively for correcting the phase of the k-space data in the peripheral regions of the imaging k-space data S. A first kernel k1, a second kernel k2 for correcting the phase of k-space data in a central region of the imaging k-space data S, and a second kernel k2 for generating unsampled data in a peripheral region of the imaging k-space data S. 3 kernel k3.

As shown in FIG. 5(a), the kernel calculation unit 204 calculates a first kernel k1 based on the target k-space data T of the imaging k-space data S and the imaging k-space data S. Specifically, , the kernel calculator 204 computes the target k-space data T, in particular the point t in its central portion (that is, the central portion of L10, L20, and L30), and the line data L10 in the imaging k-space data S. A first kernel k1 is calculated based on the three points, the three points in the line data L20, and the three points in the line data L30. Line data L10 to line data L30 are separated by one line, that is, line data L10 and line data L20 are separated by one line, and line data L20 and line data L30 are separated by one line. . The first kernel k1 is calculated by the formula shown in FIG. 5(d), that is, matrix multiplication (S*k=T). , the coefficient matrix k can be determined as the first kernel k1.

In FIG. 5(a), line data L10 to line data L30 are separated by one line from each other, but they may be separated by one or more same lines, such as two lines or three lines. may be the same as the number of rows of unsampled data between sampled data, in short, the number of rows separated is not particularly limited. Also, in FIG. 5A, three points in each line data have been described as an example, but other number of points may be used, and the number of points is not particularly limited. In other words, it suffices if the first kernel k1 for correcting the phase of the k-space data in the peripheral region of the imaging k-space data S can be calculated.

As shown in FIG. 5(b), the kernel calculator 204 calculates a second kernel k2 based on the target k-space data T of the imaging k-space data S and the imaging k-space data S. Specifically, , the kernel calculator 204 is based on the target k-space data T, particularly the point t in the center thereof, and each of the three points on the adjacent five line data L of the imaging k-space data S. Compute a second kernel k2. The second kernel k2 is calculated by the formula shown in FIG. 5(d), that is, matrix multiplication (S*k=T). , the coefficient matrix k can be determined as a second kernel k2.

In FIG. 5B, three points in each of five adjacent line data L are described as an example, but the line data L in three or more adjacent odd-numbered lines may be used, or three points may be used. A plurality of points other than the number of points may be used, and there is no particular limitation. In other words, it is sufficient if the second kernel k2 for correcting the phase of the k-space data in the central region of the imaging k-space data S can be calculated.

As shown in FIG. 5(c), the kernel calculator 204 calculates a third kernel k3 based on the target k-space data T of the imaging k-space data S and this imaging k-space data S, and specifically , the kernel calculator 204 computes the target k-space data T, particularly the point t in its central portion (i.e., the central portion between L40 and L50), and the imaging k-space data S, 3 points in the line data L40. A third kernel k3 is calculated based on the three points in the line data L50. In FIG. 5(c), L40 and L50 are data related to the part where undersampling is not performed in the collection with undersampling, that is, the part where the collection is performed. Point t is data relating to the portion where undersampling is performed.

One line is separated between the line data L40 and the line data L50. The third kernel k3 is calculated by the formula shown in FIG. 5(d), that is, matrix multiplication (S*k=T). , the coefficient matrix k can be determined as a third kernel k3.

In FIG. 5C, the line data L40 and the line data L50 are separated by one line, but they may be separated by one or more odd lines, such as three lines or five lines. It may be the same as the number of rows of data not sampled between sampled data, in short, the number of rows separated is not particularly limited. Also, in FIG. 5C, three points in each line data have been described as an example, but other number of points may be used, and the number of points is not particularly limited. In other words, it suffices if the third kernel k3 that generates non-sampled data in the peripheral region of the imaging k-space data S can be calculated.

A synthesized k-space data generation unit 205 generates synthesized k-space data by synthesizing a plurality of kernels and at least one piece of imaging k-space data.

As shown in FIGS. 5 and 6, three kernels generated for the imaging k-space data S, namely a first kernel k1, a second kernel k2, and a third kernel k3, and the imaging k-space data and S to generate synthesized k-space data HS.

Specifically, as shown in FIG. 6, the phase of point t100 in line data L100 in peripheral region P of imaging k-space data S is corrected using first kernel k1. FIG. 6 illustrates the case where the points of the line data L100 in the peripheral region P of the imaging k-space data S are corrected using the first kernel k1. All acquired line data in the peripheral region P of the imaging k-space data S can be corrected.

A second kernel k2 is used to phase correct the point t200 at L200 in the central region C of the imaging k-space data S. FIG. 6 illustrates the case of correcting the points of the line data L200 in the central region C of the imaging k-space data S using the second kernel k2. All line data in the central region C of the imaging k-space data S can be corrected.

A third kernel k3 is used to generate a point t300 in the undersampled portion L300 in the peripheral region P of the imaging k-space data S. FIG. 6 illustrates the case of generating the points of the undersampled portion L300 in the peripheral region P of the imaging k-space data S using the third kernel k3. can be used to generate line data for all undersampled portions in the peripheral region P of the imaging k-space data S.

Thus, undersampled imaging k-space data S is corrected for each of the acquired line data of imaging k-space data S using first kernel k1, second kernel k2, and third kernel k3. After generating the line data of the portion where the line data is located, synthetic k-space data HS is generated for the imaging k-space data S. As shown in FIG.

The image generation unit 206 generates an image of the inspection site using the synthesized k-space data HS. Specifically, the image generation unit 206 performs processing such as Fourier transform on the synthesized k-space data HS to generate an MR image of the examination site.

In the above-described embodiment, the kernel calculator 204 calculates, for each piece of imaging k-space data, three kernels for this imaging k-space data: a first kernel, a second kernel and a third kernel.

Based on the target k-space data of the imaging k-space data and this imaging k-space data, the kernel calculation unit 204 corrects the phase error of the same-direction imaging k-space data whose readout direction is the same as that of this imaging k-space data. , or to compute three kernels for generating unsampled data in this co-imaging k-space data. That is, the target k-space data and the imaging k-space data have the same readout direction.

For a plurality of imaging k-space data with the same readout direction, the corresponding kernels are averaged or weighted to generate an average kernel of the plurality of imaging k-space data, and the average kernel and each imaging k-space data are combined. to generate composite k-space data for each imaging k-space data.

Specifically, FIG. 7 will be described as an example. In FIG. 7, three imaging k-space data S1, S3, and S5 with the same readout direction are shown, and for simplification of the drawing, one line of each of the imaging k-space data S1, S3, and S5 is shown. Only line data are shown.

The kernel calculator 204 calculates a first kernel, a second kernel, and a third kernel for each of the three imaging k-space data S1, S3, and S5. FIG. 7 illustrates the first kernel as an example, and the kernel calculation unit 204 calculates the first kernel k11 calculated for the imaging k-space data S1 and the first kernel k11 calculated for the imaging k-space data S3. 1 kernel k13 and the first kernel k15 calculated for the imaging k-space data S5 are averaged or weighted to generate a first average kernel k10. Although not shown, the three calculated second kernels are averaged or weighted to generate a second average kernel, and the three calculated third kernels are averaged or weighted to generate a second average kernel. Generate 3 average kernels. That is, each of the plurality of imaging k-space data is composed of a plurality of data, and the kernel calculation unit 204 calculates the first kernel, the second kernel, and the third kernel using the plurality of imaging k-space data. a plurality of calculations, averaging or weighted averaging the plurality of calculated first kernels to generate a first average kernel, and averaging or weighting the plurality of calculated second kernels to generate a second average kernel and averaging or weighted averaging the plurality of calculated third kernels to generate a third average kernel.

A synthetic k-space data generation unit 205 synthesizes the first average kernel k10, the second average kernel, the third average kernel, and the imaging k-space data S1, S3, and S5 to generate synthetic k-space data. do.

When the kernel calculator 204 calculates kernels for each imaging k-space data, the effect of signal noise makes the kernels for individual imaging k-space data inaccurate. As described above, by averaging or weighting the corresponding kernels of multiple co-imaging k-space data, the accuracy of the kernels can be improved to produce a more accurate composite k-space data for each imaging k-space data. can be generated.

Next, based on FIG. 8, a method for imaging the inspection site of the subject will be described.

In step S11, the imaging k-space data acquisition unit 201 acquires a plurality of imaging k-space data by performing first bipolar multi-echo acquisition on the inspection site of the subject, and the plurality of imaging k-space data are arranged such that the readout directions of adjacent imaging k-space data are opposite.

In step S12, the template k-space data acquisition unit 202 performs second bipolar multi-echo acquisition on the examination site of the subject, and acquires each of the plurality of imaging k-space data at echo times corresponding to echo times for acquiring each of the plurality of imaging k-space data. , acquire template k-space data with opposite readout gradient polarities, such that for each imaging k-space data acquire template k-space data in the middle of k-space in the opposite direction to its readout direction. do.

In step S13, the target k-space data generator 203 generates target k-space data without a phase error of the imaging k-space data based on the template k-space data and the imaging k-space data acquired at the same echo time. Generate. Specifically, the target k-space data generation unit 203 combines the template k-space data and the k-space data in the central part of the imaging k-space data acquired at the same echo time to generate a target k-space data with no phase error. Generate spatial data.

In step S14, the kernel calculator 204 corrects the phase error of at least one piece of imaging k-space data including this imaging k-space data based on the target k-space data of the imaging k-space data and this imaging k-space data. or calculating a plurality of kernels for generating unsampled data in the at least one imaging k-space data. The plurality of kernels is a matrix of coefficients, a first kernel for correcting the phase of k-space data in a peripheral region of the imaging k-space data and a first kernel for correcting the phase of the k-space data in a central region of the imaging k-space data. and a third kernel for generating unsampled data in peripheral regions of the imaging k-space data.

In step S15, the synthesized k-space data generating unit 205 generates synthesized k-space data by synthesizing a plurality of kernels and at least one piece of imaging k-space data. Specifically, for each imaging k-space data, its kernel is combined with this imaging k-space data to combine the combined k-space data in which phase corrected, unsampled data is generated.

In step S16, the image generation unit 206 generates an image of the inspection site using the combined k-space data for each imaging k-space data.

In step S14, the kernel calculation unit 204 averages or weights the first kernels of the same-direction imaging k-space data, which are a plurality of k-space data and have the same readout direction, to obtain the first average kernel. averaging or weighting a second kernel of the plurality of k-space data co-imaging k-space data to generate a second average kernel; and A third kernel of the k-space data may be averaged or weighted to produce a third average kernel. Further, in step S15, the synthetic k-space data generation unit 205 synthesizes the first average kernel, the second average kernel, the third average kernel, and each co-directional imaging k-space data to generate a synthetic k-space. It may be configured to generate data.

In the embodiment described above, the three kernels generated are capable of performing phase correction on the imaging k-space data while simultaneously reconstructing (generating) the unsampled imaging k-space data, so that the phase error map is More reliable and efficient imaging can be achieved as compared with conventional imaging methods in which solving processing and phase correction processing are performed separately.

Further, in the above-described embodiment, the first bipolar multi-echo acquisition and the corresponding second bipolar multi-echo acquisition are performed in the course of one breath-holding of the subject, and the imaging k-space data and the corresponding Since template k-space data can be acquired, it is possible to obtain more reliable imaging than SENSE mapping scanning without causing an error in image reconstruction due to the influence of respiratory motion.

In the above-described embodiments, more accurate and reliable phase correction and imaging can be performed, so that the accuracy of fat quantification determination can be improved in fat quantification processing such as water-fat segmentation.

According to at least one embodiment described above, image quality can be improved.

While several embodiments have been described, these embodiments are provided by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, changes, and combinations of embodiments can be made without departing from the 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.

REFERENCE SIGNS LIST 100 processing circuit 200 image processing device 201 imaging k-space data acquisition unit 202 template k-space data acquisition unit 203 target k-space data generation unit 204 kernel calculation unit 205 synthetic k-space data generation unit 206 image generation unit

Claims (12)

an imaging k-space data acquisition unit that acquires a plurality of imaging k-space data by performing first bipolar multi-echo acquisition on an examination site of a subject;
a template k-space data acquisition unit that acquires a plurality of template k-space data by performing second bipolar multi-echo acquisition on the inspection site with a readout gradient polarity opposite to that of the first bipolar multi-echo acquisition;
a target k-space data generator that generates target k-space data without a phase error based on template k-space data and imaging k-space data acquired at the same echo time;
A kernel that calculates a plurality of kernels for correcting phase errors in the imaging k-space data or generating unsampled data in the imaging k-space data based on the target k-space data and the imaging k-space data. a calculation unit;
a synthetic k-space data generation unit that synthesizes the plurality of kernels and each of the imaging k-space data to generate synthetic k-space data;
an image generation unit that generates an image of the examination site using the synthesized k-space data;
An image processing device comprising: The imaging k-space data is data obtained by fully sampling the central portion of the k-space and undersampling the peripheral portion of the k-space,
The template k-space data is data obtained by fully sampling the central part of k-space,
The image processing apparatus according to claim 1. The target k-space data generation unit generates the target k-space data by combining the template k-space data and k-space data in the center of the imaging k-space data acquired at the same echo time. 3. The image processing apparatus according to claim 2. 2. The image processing apparatus according to claim 1, wherein said target k-space data and said imaging k-space data have the same readout direction. the plurality of kernels is a coefficient matrix;
The plurality of kernels are
a first kernel for correcting the phase of k-space data in peripheral regions of the imaging k-space data;
a second kernel for correcting the phase of k-space data in a central region of the imaging k-space data;
a third kernel for generating unsampled data in regions surrounding the imaging k-space data;
5. The image processing device according to claim 4, comprising: each of the plurality of imaging k-space data comprises a plurality of data;
The kernel calculation unit calculates a plurality of the first kernel, the second kernel, and the third kernel using the plurality of imaging k-space data, and calculates the plurality of calculated first kernels. averaging or weighting to produce a first average kernel, averaging or weighting the plurality of calculated second kernels to produce a second average kernel, and averaging or weighting the plurality of calculated third kernels to produce a first average kernel; averaging or weighted averaging to produce a third average kernel;
The synthetic k-space data generation unit generates synthetic k-space data by synthesizing the first average kernel, the second average kernel, the third average kernel, and the imaging k-space data. Item 6. The image processing apparatus according to item 5. Acquiring a plurality of imaging k-space data by performing a first bipolar multi-echo acquisition on the inspection site of the subject,
performing a second bipolar multi-echo acquisition on the examination site with a readout gradient polarity opposite to that of the first bipolar multi-echo acquisition to acquire a plurality of template k-space data;
generating target k-space data with no phase error based on template k-space data and imaging k-space data acquired at the same echo time;
calculating, based on the target k-space data and the imaging k-space data, a plurality of kernels for correcting phase errors in the imaging k-space data or generating unsampled data in the imaging k-space data;
combining the plurality of kernels with each of the imaging k-space data to generate combined k-space data;
An image processing method comprising generating an image of the inspection site using the synthesized k-space data. The imaging k-space data is data obtained by fully sampling the central portion of the k-space and undersampling the peripheral portion of the k-space,
8. The image processing method according to claim 7, wherein said template k-space data is data obtained by fully sampling a central portion of k-space. 9. The image processing of claim 8, wherein the template k-space data and k-space data in the center of the imaging k-space data acquired at the same echo time are combined to generate the target k-space data. Method. 9. The image processing method of claim 8, wherein the target k-space data and the imaging k-space data have the same readout direction. the plurality of kernels is a coefficient matrix;
The plurality of kernels are
a first kernel for correcting the phase of k-space data in a peripheral region of the imaging k-space data; a second kernel for correcting the phase of the k-space data in a central region of the imaging k-space data; a third kernel for generating unsampled data in peripheral regions of the spatial data;
11. The image processing method according to claim 10, comprising: each of the plurality of imaging k-space data comprises a plurality of data;
Using the plurality of imaging k-space data, calculating a plurality of the first kernel, the second kernel, and the third kernel, and averaging or weighted averaging the plurality of calculated first kernels generating a first average kernel, averaging or weighting the calculated plurality of second kernels to generate a second average kernel, and averaging or weighting the plurality of calculated third kernels; generate a third average kernel,
12. The image processing method of claim 11, wherein the first average kernel, the second average kernel and the third average kernel and the imaging k-space data are combined to generate combined k-space data.

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