Image processing device and image processing method

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

[Technical Field]

[0001] The embodiments disclosed in this specification and the drawings relate to an image processing device and an image processing method. [Background technology]

[0002] Magnetic resonance imaging (MRI) may be applied to determine fat quantification in the examined area of ​​a subject. MRI-based fat quantification methods can non-invasively provide quantitative and spatial information about fat accumulation in the human body for diagnostic purposes.

[0003] MRI methods of fat quantification require long scan times, so bipolar acquisition and parallel imaging techniques are typically used to reduce scan times.

[0004] In bipolar acquisition technology, data is acquired by applying positive and negative gradients, but the acquired data has phase errors due to the influence of eddy currents present in the system. To correct this phase error, a one-dimensional or two-dimensional phase error model is typically estimated from unencoded or encoded template scans, respectively, and the phase error is corrected according to these phase error models.

[0005] Parallel imaging techniques involve undersampling data with multi-channel coils and reconstructing images using methods including Generalized Auto-calibrating Partially Parallel Acquisition (GRAPPA) and Sensitivity Encoding (SENSE).

[0006] A more complex parallel imaging reconstruction method is the bipolar GRAPPA technique, which is used to synthesize missing k-space data in echo-planar imaging (EPI) and correct the intrinsic phase errors due to opposite readout gradient polarity. Bipolar GRAPPA provides high image quality in regions where higher-order EPI phase errors commonly occur.

[0007] However, in bipolar acquisition and parallel imaging techniques, one-dimensional phase error correction cannot completely remove phase errors, especially those along the phase encoding (PE) direction. Two-dimensional phase error correction is performed in image space and requires unwrapping from the phase error map, which is usually difficult and may fail in some highly phase-wrapped regions. Furthermore, undersampled images must first be unwrapped using SENSE or GRAPPA, and any motion artifacts will degrade the quality of the phase error correction.

[0008] Additionally, parallel imaging reconstruction methods such as SENSE require acquisition of separate mapping scans, and when imaging the abdomen, respiratory motion can cause errors in image reconstruction. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Special Publication No. 2020-522344 Summary of the Invention [Problem to be solved by the invention]

[0010] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to improve image quality. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0011] 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 composite k-space data generation unit, and an image generation unit. The imaging k-space data acquisition unit acquires multiple pieces of imaging k-space data by performing a first bipolar multi-echo acquisition on an examination region of a subject. The template k-space data acquisition unit acquires multiple pieces of template k-space data by performing a second bipolar multi-echo acquisition on the examination region with a readout gradient polarity opposite to that of the first bipolar multi-echo acquisition. The target k-space data generation unit generates target k-space data without phase error based on the template k-space data and imaging k-space data acquired at the same echo time. The kernel calculation unit calculates multiple kernels based on the target k-space data and the imaging k-space data to correct a phase error in the imaging k-space data or to generate unsampled data in the imaging k-space data. The composite k-space data generation unit generates composite k-space data by combining the multiple kernels with each of the imaging k-space data. The image generator generates an image of the examination region using the combined k-space data. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing the basic structure of a magnetic resonance imaging apparatus according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing the structure of the image processing device according to the embodiment. [Figure 3]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. [Figure 4] FIG. 4 is a diagram illustrating generation of target k-space data according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating the generation of three kernels according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating generation of composite k-space data using three kernels according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an embodiment in which an average kernel is generated for multiple isotropic imaging k-space data. [Figure 8] FIG. 8 is a flowchart of an image processing method according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments of an image processing device and an image processing method will be described in detail with reference to the drawings.

[0014] A magnetic resonance imaging apparatus 1 according to the present invention will be described below with reference to FIGS.

[0015] Fig. 1 is a block diagram showing the basic structure of a magnetic resonance imaging apparatus 1 according to the present invention. Each component 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 formed of a superconducting magnet, a permanent magnet, or the like.

[0016] The gradient magnetic field coil 20 generates a gradient magnetic field. For example, the gradient magnetic field coil has an X coil, a Y coil, and a Z coil corresponding to the X axis, Y axis, and Z axis, which are orthogonal to each other. The X coil, Y coil, and 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. The X axis is set along the horizontal direction orthogonal to the Z axis. The Y axis is set along a direction orthogonal to both the Z axis and the X axis.

[0017] The gradient magnetic field power supply 30 supplies a current to the gradient magnetic field coil 20. By supplying a current to the gradient magnetic field coil 20, the gradient magnetic field power supply 30 can cause the gradient magnetic field coil 20 to generate a gradient magnetic field.

[0018] The RF (Radio Frequency) coil 40 applies a high-frequency magnetic field to a subject placed in the imaging space and receives NMR (Nuclear Magnetic Resonance) signals generated from the subject. The high-frequency magnetic field is sometimes called an RF pulse. The RF coil 40 includes a whole-body RF coil 41 placed to surround the imaging space and a local RF coil 42 placed close to the subject. The functions of the RF coil 40 are broadly divided into transmitting a high-frequency magnetic field and receiving an NMR signal. Either the whole-body RF coil 41 or the local RF coil 42 may have both transmitting and receiving functions, or both the whole-body RF coil 41 and the local RF coil 42 may be used for transmitting and receiving. Furthermore, multiple local RF coils 42 may be used simultaneously. Different local RF coils 42 may be provided for different parts of the subject.

[0019] The transmission circuit 50 outputs to the RF coil 40 a radio frequency pulse signal corresponding to the Larmor frequency specific to the target atomic nucleus provided in the static magnetic field.

[0020] The receiving circuitry 60 generates magnetic resonance (MR) data based on the NMR signals received by the RF coil 40, and outputs the generated MR data to the processing circuitry 100.

[0021] The bed 70 includes a top plate 71 on which a subject is placed, and the top plate 71 can be moved vertically and horizontally.

[0022] The input interface 80 accepts input operations of various instructions and information from an operator. Specifically, the input interface 80 is connected to the processing circuit 100, converts the input operations received from the operator into electrical signals, and outputs the electrical signals to the processing circuit 100. For example, the input interface 80 may be realized by a trackball, a switch button, a mouse, a keyboard, a touchpad that performs input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, and a voice input circuit. Note that in this specification, the input interface 80 is not limited to those that include physical operation components such as a mouse and a keyboard. For example, 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 a control circuit is also included as an example of the input interface 80.

[0023] The display 81 displays various types of information and images. Specifically, the display 81 is connected to the processing circuit 100, and converts the data of various types of information and images sent from the processing circuit 100 into electrical signals for display and outputs the signals. For example, the display 81 is realized by a liquid crystal monitor, an LED monitor, a touch panel, or the like.

[0024] The memory circuitry 90 stores various data and programs. Specifically, the memory circuitry 90 is connected to the processing circuitry 100 and stores various data and programs input and output by each processing circuit. For example, the memory circuitry 90 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like.

[0025] The processing circuit 100 includes a bed control unit 101. The bed control unit 101 controls the operation of the bed 70 by outputting control electrical signals to the bed 70. For example, the bed control unit 101 receives an instruction to move the top 71 from an operator via the input interface 80, and operates a moving mechanism for the top 71 of the bed 70 so as to move the top 71 in accordance with the received instruction. For example, when imaging the subject, the bed control unit 101 moves the top 71, on which the subject is placed, into an imaging space.

[0026] The processing circuitry 100 includes an acquisition unit 102. The acquisition unit 102 executes various pulse sequences to acquire MR data of the subject. Specifically, the acquisition unit 102 executes various pulse sequences by driving the gradient magnetic field power supply 30, the transmission circuitry 50, and the reception circuitry 60 in accordance with sequence execution data output from the processing circuitry 100. Here, the sequence execution data is data representing a pulse sequence, and is information that specifies the timing and strength of the supplied current when the gradient magnetic field power supply 30 supplies a current to the gradient magnetic field coil 20, the timing and strength of the supplied radio frequency pulse when the transmission circuitry 50 supplies a radio frequency pulse signal to the RF coil 40, and the timing when the reception circuitry 60 samples a magnetic resonance signal. The sequence execution data is pre-stored in the storage circuitry 90 or is generated by receiving an input from the operator via the input interface 80. Alternatively, the operator may edit the sequence execution data pre-stored in the storage circuitry 90. The acquisition unit 102 then receives the MR data output from the reception circuitry 60 as a result of executing the pulse sequence and stores the data in the storage circuitry 90. At this time, the MR data stored in the memory circuitry 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 the slice selection gradient magnetic field. The k-space data of the phase encoding amount according to the applied gradient magnetic field is read out using a readout gradient magnetic field. The readout gradient magnetic field is also called a frequency encoding gradient magnetic field. For example, when performing three-dimensional imaging, 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 a 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 each formed by gradient magnetic fields generated by one or more of the X coil, Y coil, and Z coil described above.

[0027] The processing circuitry 100 includes a correction unit 103. The correction unit 103 uses at least a 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.

[0028] The processing circuitry 100 includes an MR image generation unit 104. The MR image generation unit 104 generates various MR images using the k-space data acquired by the acquisition unit 102. For example, the MR images are generated by performing reconstruction processing such as Fourier transform on the k-space data. The MR image generation unit 104 can also perform image processing on the reconstructed images as post-processing.

[0029] The processing circuitry 100 includes a display control unit 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 images stored in the memory circuitry 90 or an external storage and display them on the display 81.

[0030] The processing circuits 100 described above are each realized by a processor. In this case, the processing functions of each processing circuit are stored in the storage circuitry 90, for example, in the form of a program executable by a computer. Each processing circuit then reads out each program from the storage circuitry 90 and executes it to realize the processing function corresponding to each program. In other words, after reading out each program, each processing circuit has the functions of the parts shown in each processing circuit in FIG. 1.

[0031] Although each processing circuit has been described as being implemented by a single processor, embodiments are not limited to this. Each processing circuit may be configured by combining multiple independent processors, and each processor may execute a program to implement each processing function. The processing functions of each processing circuit may be appropriately distributed or integrated among a single or multiple processing circuits. While the example shown in FIG. 1 has been described as a single storage circuit 90 storing programs corresponding to each processing function, multiple storage circuits may be distributed and arranged, and each processing circuit may read corresponding programs from individual storage circuits.

[0032] The magnetic resonance imaging apparatus 1 further includes an image processing device 200. Fig. 2 is a structural block diagram of the image processing device 200 according to the present invention.

[0033] The image processing device 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 calculation unit 204, a composite k-space data generation unit 205, and an image generation unit 206. In the magnetic resonance imaging apparatus 1, the image processing device 200 may be configured independently or may be configured integrated into the processing circuitry 100. The bipolar multi-echo acquisition method will be described in detail below.

[0034] 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 a first bipolar multi-echo acquisition D1 on an examination region of a subject.

[0035] The template k-space data acquisition unit 202 performs a second bipolar multi-echo collection D2 with a readout gradient polarity opposite to that of the first bipolar multi-echo collection D1 on the examination area of ​​the subject, and acquires template k-space data S1' to S3' with a readout gradient polarity opposite to that of the first bipolar multi-echo collection D1 at echo times TE1' to TE3' corresponding to echo times TE1 to TE3 at which the imaging k-space data acquisition unit 201 acquires each of the multiple imaging k-space data S1 to S3.

[0036] In the first bipolar multi-echo acquisition D1, imaging k-space data S1-S3 are formed using the acquired line data. In the imaging k-space data S1-S3, the regions on the periphery (upper and lower peripheries) of the k-space data are called peripheral region P, and the region in the center of the k-space data is called central region C (center part). The imaging k-space data S1-S3 are data obtained by fully sampling the central region C (center part) of k-space and undersampling the peripheral region P (periphery part) of k-space.

[0037] Specifically, in the first shot, rightward line data L1 is output at echo time TE1 and collected by an acquisition unit (not shown), leftward line data L2 is output at echo time TE2 and collected by the acquisition unit, and rightward line data L3 is output at echo time TE3 and collected by the acquisition 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 acquisition unit, leftward line data L8 is output at echo time TE2 and collected by the acquisition unit, and rightward line data L9 is output at echo time TE3 and collected by the acquisition unit. The line data L7 to L9 are positioned at the top of the central region C of each k-space.

[0038] As shown in FIG. 3, the undersampled portions L4 to L6 are indicated by dashed lines between the line data L1 to L3 and the line data L7 to L9 in each k-space. The undersampled portions L4 to L6 are where data that should be collected when fully sampling the k-space is located. However, to shorten the imaging time, data that should be located in the undersampled portions L4 to L6 is not output. In other words, no data exists in the undersampled portions L4 to L6 of the k-space. In FIG. 3, the line data L1 and the undersampled portion L4 constitute the undersampled data S12 in the imaging k-space data S1, the line data L2 and the undersampled portion L5 constitute the undersampled data S22 in the imaging k-space data S2, and the line data L3 and the undersampled portion L6 constitute the undersampled data S32 in the imaging k-space data S3.

[0039] 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 full-sampled data, and the peripheral region P at the bottom of Figure 3 is undersampled to form under-sampled data S13, under-sampled data S23, and under-sampled data S33.

[0040] Thus, multiple line data are acquired using the above acquisition method over multiple shots. Line data L1, L7, etc. acquired at echo time TE1 constitute imaging k-space data S1, line data L2, L8, etc. acquired at echo time TE2 constitute imaging k-space data S2, and line data L3, L9, etc. acquired at echo time TE3 constitute imaging k-space data S3. Each of the imaging k-space data S1-S3 comprises undersampling data including an undersampled portion (non-sampled data) formed in peripheral region P of FIG. 3, and full sampling data formed in central region C. In other words, the imaging k-space data S1-S3 consist of the undersampling data in peripheral region P and the full sampling data in central region C.

[0041] 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 of the first bipolar multi-echo acquisition D1, and only k-space data in the central region (center part) of k-space is acquired by the acquisition unit. The template k-space data S1' to S3' are data obtained by full sampling of the central region (center part) of k-space.

[0042] Specifically, in the second bipolar multi-echo acquisition D2, the first shot to start data acquisition involves acquiring leftward line data L1' at echo time TE1', rightward line data L2' at echo time TE2', and leftward line data L3' at echo time TE3'. In the second shot, leftward line data L4' at echo time TE1', rightward line data L5' at echo time TE2', and leftward line data L6' at echo time TE3'. Thus, multiple line data are acquired using the above acquisition method over multiple shots. Line data L1', L4', etc. acquired at echo time TE1' constitute template k-space data S1', line data L2', L5', etc. acquired at echo time TE2' constitute template k-space data S2', and line data L3', L6', etc. acquired at echo time TE3' constitute template k-space data S3'.

[0043] The readout directions of the template k-space data S1' to S3' acquired at echo times TE1' to TE3' are opposite to those of the imaging k-space data S1 to S3 acquired at the corresponding echo times TE1 to TE3, respectively.

[0044] The order of the first bipolar multi-echo acquisition D1 and the second bipolar multi-echo acquisition D2 may be such that the first bipolar multi-echo acquisition D1 is completed before the second bipolar multi-echo acquisition D2, or such that the first shot of the first bipolar multi-echo acquisition D1 is completed before the first shot of the second bipolar multi-echo acquisition D2 is completed, and then the second shot of the first bipolar multi-echo acquisition D1 is completed, and then the second shot of the second bipolar multi-echo acquisition D2 is completed, etc. The first bipolar multi-echo acquisition D1 and the second bipolar multi-echo acquisition D2 may be performed in various orders and are not limited thereto. Furthermore, it is preferable to perform the first bipolar multi-echo acquisition D1 and the corresponding second bipolar multi-echo acquisition D2 during a single breath-holding process of the subject.

[0045] Also, in FIG. 3, an example is shown in which line data is acquired at three echo times to form three k-space data in the first bipolar multi-echo collection D1 and the second bipolar multi-echo collection D2, but the echo time is not limited to three and may be any number other than three.

[0046] The target k-space data generator 203 generates target k-space data without phase error of the imaging k-space data based on the template k-space data and imaging k-space data acquired at the same echo time. The same echo time here refers to the echo time at which the imaging k-space data is acquired and the echo time at which the template k-space data corresponding to the imaging k-space data is acquired, and in FIG. 3, this refers to echo times TE1, TE1', echo times TE2, TE2', or echo times TE3, TE3'.

[0047] As shown in Fig. 4, the k-space data in the lower 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. 3, and the k-space data S21 in the upper left of Fig. 4 is k-space data in the center of the imaging k-space data S2 acquired at the same echo time TE2 (i.e., the echo time corresponding to the echo time TE2' at which the template k-space data S2' is acquired). The target k-space data generator 203 combines the template k-space data S2' and the imaging k-space data S21, and adds them together to generate target k-space data T2 without phase error.

[0048] Here, an example has been described in which target k-space data T2 is generated for imaging k-space data S2. However, in the embodiment, not only is target k-space data T2 generated for imaging k-space data S2, but target k-space data is also generated for each of multiple imaging k-space data, such as imaging k-space data S1 and S3. The description will be given with reference to FIGS. 3 and 4 as an example. The target k-space data generator 203 combines template k-space data S1′ with imaging k-space data S11, adding them together to generate target k-space data T1 without phase error, combines template k-space data S3′ with imaging k-space data S31, adding them together to generate target k-space data T3 without phase error, and also generates target k-space data T2 as described above. The target k-space data T1 to T3 are collectively referred to as target k-space data T.

[0049] The kernel calculation unit 204 calculates, based on target k-space data of the imaging k-space data and the imaging k-space data, a plurality of kernels for correcting a phase error of at least one imaging k-space data including the imaging k-space data or for generating unsampled data in the at least one imaging k-space data.

[0050] As shown in FIG. 5, three kernels are generated for each imaging k-space data S, which are a first kernel k1 for correcting the phase of the k-space data in the peripheral region of the imaging k-space data S, a second kernel k2 for correcting the phase of the k-space data in the central region of the imaging k-space data S, and a third kernel k3 for generating unsampled data in the peripheral region of the imaging k-space data S.

[0051] 5(a), the kernel calculation unit 204 calculates a first kernel k1 based on target k-space data T of the imaging k-space data S and this imaging k-space data S. Specifically, the kernel calculation unit 204 calculates the first kernel k1 based on the target k-space data T, particularly a point t located in the central portion thereof (i.e., the central portion of L10, L20, and L30), and three points in line data L10, three points in line data L20, and three points in line data L30 of the imaging k-space data S. The line data L10 to L30 are each separated by one row, i.e., the line data L10 and the line data L20 are separated by one row, and the line data L20 and the line data L30 are separated by one row. The first kernel k1 is calculated by the formula shown in FIG. 5(d), i.e., matrix multiplication (S*k=T). Specifically, when the imaging k-space data S and the target k-space data T are known, the coefficient matrix k can be obtained as the first kernel k1.

[0052] In Fig. 5(a), the line data L10 to L30 are each separated by one line, but the line data may be separated by one or more lines, for example, two or three lines, and the number of lines may be the same as the number of lines of non-sampled data between the sampled data. In other words, the number of lines is not particularly limited. Also, in Fig. 5(a), three points are used as an example in each line data, but other numbers of points may be used, and there is no particular limit. In other words, it is sufficient to calculate a first kernel k1 that corrects the phase of k-space data in the peripheral region of the imaging k-space data S.

[0053] 5(b), the kernel calculation unit 204 calculates the second kernel k2 based on the target k-space data T of the imaging k-space data S and this imaging k-space data S. Specifically, the kernel calculation unit 204 calculates the second kernel k2 based on the target k-space data T, particularly point t located in the center thereof, and three points on each of five adjacent line data L of the imaging k-space data S. The second kernel k2 is calculated by the equation shown in FIG. 5(d), i.e., matrix multiplication (S*k=T). Specifically, when the imaging k-space data S and the target k-space data T are known, the coefficient matrix k can be obtained as the second kernel k2.

[0054] 5(b) has been described as an example in which three points are located on each of five adjacent lines of line data L, but the number of adjacent lines of line data L may be three or more odd-numbered lines, or any number of points other than three may be used, and is not particularly limited. In other words, it is only necessary to calculate the second kernel k2 that corrects the phase of the k-space data in the central region of the imaging k-space data S.

[0055] 5(c), the kernel calculation unit 204 calculates a third kernel k3 based on target k-space data T of the imaging k-space data S and this imaging k-space data S. Specifically, the kernel calculation unit 204 calculates the third kernel k3 based on the target k-space data T, particularly point t in its central portion (i.e., the central portion between L40 and L50), and three points in line data L40 and three points in line data L50 of the imaging k-space data S. Note that in FIG. 5(c), L40 and L50 are data relating to a portion where undersampling was not performed in undersampling acquisition, i.e., a portion where acquisition was performed, and point t in the central portion between L40 and L50 is data relating to a portion where undersampling was performed.

[0056] The line data L40 and the line data L50 are separated by one row. The third kernel k3 is calculated by the equation shown in Fig. 5(d), i.e., matrix multiplication (S*k=T). Specifically, when the imaging k-space data S and the target k-space data T are known, the coefficient matrix k can be obtained as the third kernel k3.

[0057] In Fig. 5(c), the line data L40 and the line data L50 are separated by one line, but the line data L40 and the line data L50 may be separated by an odd number of lines, such as three or five lines, and the number of lines may be the same as the number of lines of non-sampled data between the sampled data. In other words, the number of lines is not particularly limited. Also, in Fig. 5(c), three points are used as an example in each line data, but other numbers of multiple points may also be used and are not particularly limited. In other words, it is sufficient to calculate the third kernel k3 that generates non-sampled data in the peripheral region of the imaging k-space data S.

[0058] The composite k-space data generator 205 generates composite k-space data by combining the plurality of kernels with at least one piece of imaging k-space data.

[0059] As shown in Figures 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, are combined with the imaging k-space data S to generate combined k-space data HS.

[0060] Specifically, as shown in Fig. 6, the first kernel k1 is used to correct the phase of a point t100 in line data L100 in the peripheral region P of the imaging k-space data S. Fig. 6 illustrates an example in which the first kernel k1 is used to correct a point in line data L100 in the peripheral region P of the imaging k-space data S, but in reality, all collected line data in the peripheral region P of the imaging k-space data S can be corrected using the first kernel k1.

[0061] The second kernel k2 is used to correct the phase of a point t200 located at L200 in the central region C of the imaging k-space data S. While Fig. 6 illustrates an example in which the second kernel k2 is used to correct a point of the line data L200 in the central region C of the imaging k-space data S, in practice, all line data in the central region C of the imaging k-space data S can be corrected using the second kernel k2.

[0062] The third kernel k3 is used to generate a point t300 in an undersampled portion L300 in the peripheral region P of the imaging k-space data S. While Fig. 6 illustrates an example in which the third kernel k3 is used to generate a point in the undersampled portion L300 in the peripheral region P of the imaging k-space data S, in practice, it is possible to generate line data for all undersampled portions in the peripheral region P of the imaging k-space data S using the third kernel k3.

[0063] In this way, each of the acquired line data of the imaging k-space data S is corrected using the first kernel k1, the second kernel k2, and the third kernel k3 to generate line data of the undersampled portion of the imaging k-space data S, and then synthetic k-space data HS is generated for the imaging k-space data S.

[0064] The image generator 206 generates an image of the examination region using the combined k-space data HS. Specifically, the image generator 206 performs processing such as Fourier transform on the combined k-space data HS to generate an MR image of the examination region.

[0065] In the above embodiment, the kernel calculation unit 204 calculates, for each piece of imaging k-space data, three kernels for the imaging k-space data, namely, a first kernel, a second kernel, and a third kernel.

[0066] The kernel calculation unit 204 may be configured to calculate, based on the target k-space data of the imaging k-space data and the imaging k-space data, three kernels for correcting phase errors of isotropic imaging k-space data having the same readout direction as the imaging k-space data or for generating unsampled data in the isotropic imaging k-space data, i.e., the target k-space data and the imaging k-space data have the same readout direction.

[0067] The apparatus may be configured to generate an average kernel of a plurality of imaging k-space data having the same readout direction by averaging or weighted averaging the corresponding kernels of the plurality of imaging k-space data, and to generate synthesized k-space data of the respective imaging k-space data by combining the average kernel with each of the imaging k-space data.

[0068] Specifically, an example will be given using Fig. 7. Fig. 7 shows three sets of imaging k-space data S1, S3, and S5 having the same readout direction, and for simplicity of the drawing, only one line of data is shown for each of the imaging k-space data S1, S3, and S5.

[0069] The kernel calculation unit 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. In Fig. 7, the first kernel is used as an example, and the kernel calculation unit 204 generates a first average kernel k10 by averaging or weighting the first kernel k11 calculated for the imaging k-space data S1, the first kernel k13 calculated for the imaging k-space data S3, and the first kernel k15 calculated for the imaging k-space data S5. Although not shown, the second average kernel is generated by averaging or weighting the three calculated second kernels, and the third average kernel is generated by averaging or weighting the three calculated third kernels. That is, each of the multiple imaging k-space data consists of multiple data, and the kernel calculation unit 204 uses the multiple imaging k-space data to calculate multiple first kernels, multiple second kernels, and multiple third kernels, averages or weighted averages the calculated multiple first kernels to generate a first average kernel, averages or weighted averages the calculated multiple second kernels to generate a second average kernel, and averages or weighted averages the calculated multiple third kernels to generate a third average kernel.

[0070] The composite k-space data generator 205 generates composite k-space data by combining the first average kernel k10, the second average kernel, and the third average kernel with the imaging k-space data S1, S3, and S5, respectively.

[0071] When the kernel calculation unit 204 calculates a kernel for each imaging k-space data, the kernel for each individual imaging k-space data will be inaccurate due to the influence of signal noise. As described above, by averaging or weighted averaging corresponding kernels of multiple isotropic imaging k-space data, the accuracy of the kernel can be improved and more accurate composite k-space data can be generated for each imaging k-space data.

[0072] Next, a method for imaging an examination region of a subject will be described with reference to FIG.

[0073] In step S11, the imaging k-space data acquisition unit 201 acquires a plurality of imaging k-space data by performing a first bipolar multi-echo acquisition on the examination area of ​​the subject, and the plurality of imaging k-space data are arranged so that the readout directions of adjacent imaging k-space data are opposite to each other.

[0074] In step S12, the template k-space data acquisition unit 202 performs a second bipolar multi-echo acquisition on the examination area of ​​the subject, and acquires template k-space data with an opposite readout gradient polarity at an echo time corresponding to the echo time at which each of the multiple imaging k-space data is acquired, thereby acquiring template k-space data in the center of the k-space in the direction opposite to the readout direction for each imaging k-space data.

[0075] In step S13, the target k-space data generator 203 generates target k-space data without phase error of the imaging k-space data based on the template k-space data and imaging k-space data acquired at the same echo time. Specifically, the target k-space data generator 203 combines the template k-space data with k-space data in the center of the imaging k-space data acquired at the same echo time to generate target k-space data without phase error.

[0076] In step S14, the kernel calculation unit 204 calculates, based on the target k-space data of the imaging k-space data and the imaging k-space data, a plurality of kernels for correcting a phase error of at least one imaging k-space data including the imaging k-space data or for generating unsampled data in the at least one imaging k-space data. The plurality of kernels are coefficient matrices and include a first kernel for correcting the phase of the 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, and a third kernel for generating unsampled data in the peripheral region of the imaging k-space data.

[0077] In step S15, the composite k-space data generator 205 generates composite k-space data by combining the multiple kernels with at least one piece of imaging k-space data. Specifically, for each piece of imaging k-space data, the kernel is combined with the imaging k-space data to generate composite k-space data in which phase-corrected, unsampled data is generated.

[0078] In step S16, the image generator 206 generates an image of the examination region using the combined k-space data for each imaging k-space data.

[0079] In step S14, the kernel calculation unit 204 may be configured to average or weighted average first kernels of isotropic imaging k-space data that are a plurality of k-space data and have the same readout direction to generate a first average kernel, average or weighted average second kernels of isotropic imaging k-space data that are a plurality of k-space data to generate a second average kernel, and average or weighted average third kernels of isotropic imaging k-space data that are a plurality of k-space data to generate a third average kernel. Furthermore, in step S15, the composite k-space data generation unit 205 may be configured to combine the first average kernel, the second average kernel, and the third average kernel with each of the isotropic imaging k-space data to generate composite k-space data.

[0080] In the above-described embodiment, the three generated kernels can perform phase correction on the imaging k-space data while simultaneously reconstructing (generating) unsampled imaging k-space data, thereby enabling more reliable and efficient imaging than prior art imaging methods in which the process of solving the phase error map and the phase correction process are performed separately.

[0081] Furthermore, in the above-described embodiment, first bipolar multi-echo acquisition and corresponding second bipolar multi-echo acquisition can be performed during a single breath-holding process of the subject to acquire imaging k-space data and corresponding template k-space data. This prevents errors in image reconstruction due to the influence of respiratory motion, and allows for more reliable imaging than SENSE mapping scanning.

[0082] In the above-described embodiment, more accurate and reliable phase correction and imaging can be performed, and therefore the accuracy of fat quantification determination can be improved in fat quantification processing such as water-fat segmentation.

[0083] According to at least one of the embodiments described above, image quality can be improved.

[0084] Although several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0085] 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

1. an imaging k-space data acquisition unit that acquires a plurality of imaging k-space data by performing a first bipolar multi-echo acquisition on an examination region of a subject; a template k-space data acquisition unit that performs second bipolar multi-echo acquisition on the examination region with a readout gradient polarity opposite to that of the first bipolar multi-echo acquisition, and acquires a plurality of template k-space data; a target k-space data generating unit that generates target k-space data by adding template k-space data and imaging k-space data acquired at the same echo time; a kernel calculation unit that calculates a plurality of kernels for correcting a phase error in the imaging k-space data or for generating unsampled data in the imaging k-space data based on the target k-space data and the imaging k-space data; a synthetic k-space data generator that generates synthetic k-space data based on the plurality of kernels and each of the imaging k-space data; an image generator that generates an image of the examination region using the synthesized k-space data; An image processing device comprising:

2. the imaging k-space data is data obtained by fully sampling a central portion of the k-space and undersampling a peripheral portion of the k-space; The template k-space data is data obtained by fully sampling the central part of the k-space. The image processing device according to claim 1 .

3. 3. The image processing device according to claim 2, wherein the target k-space data generation unit generates the target k-space data by adding the template k-space data and k-space data in a central part of the imaging k-space data acquired at the same echo time.

4. The image processing apparatus according to claim 1 , wherein the target k-space data and the imaging k-space data have the same readout direction.

5. the plurality of kernels are coefficient matrices; The plurality of kernels include: a first kernel for correcting the phase of the 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 a peripheral region of the imaging k-space data; The image processing device according to claim 4 , comprising:

6. each of the plurality of imaging k-space data includes a plurality of data; the kernel calculation unit calculates a plurality of the first kernels, the second kernels, and the third kernels using the plurality of imaging k-space data, averages or weighted averages the calculated plurality of first kernels to generate a first average kernel, averages or weighted averages the calculated plurality of second kernels to generate a second average kernel, and averages or weighted averages the calculated plurality of third kernels to generate a third average kernel, The image processing apparatus according to claim 5 , wherein the composite k-space data generating unit generates the composite k-space data based on the first average kernel, the second average kernel, and the third average kernel and imaging k-space data.

7. performing a first bipolar multi-echo acquisition on an examination region of a subject to acquire a plurality of imaging k-space data; performing a second bipolar multi-echo acquisition on the examination region 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 by adding the template k-space data and the imaging k-space data acquired at the same echo time; calculating a plurality of kernels for correcting a phase error in the imaging k-space data or for generating unsampled data in the imaging k-space data based on the target k-space data and the imaging k-space data; generating composite k-space data based on the plurality of kernels and each of the imaging k-space data; an image processing method using the composite k-space data to generate an image of the examination region;

8. the imaging k-space data is data obtained by fully sampling a central portion of the k-space and undersampling a peripheral portion of the k-space; 8. The image processing method according to claim 7, wherein the template k-space data is data obtained by full sampling of a central portion of the k-space.

9. 9. The image processing method according to claim 8, wherein the target k-space data is generated by adding the template k-space data and k-space data in a central portion of the imaging k-space data acquired at the same echo time.

10. The image processing method according to claim 8 , wherein the target k-space data and the imaging k-space data have the same readout direction.

11. the plurality of kernels are coefficient matrices; The plurality of kernels include: 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 k-space data in a central region of the imaging k-space data; and a third kernel for generating unsampled data in the peripheral region of the imaging k-space data. The image processing method of claim 10 , comprising:

12. each of the plurality of imaging k-space data includes a plurality of data; using the plurality of imaging k-space data, calculating a plurality of the first kernels, the second kernels, and the third kernels, averaging or weighting the calculated plurality of first kernels to generate a first average kernel, averaging or weighting the calculated plurality of second kernels to generate a second average kernel, and averaging or weighting the calculated plurality of third kernels to generate a third average kernel; The image processing method of claim 11 , further comprising generating composite k-space data based on the first averaging kernel, the second averaging kernel, and the third averaging kernel and imaging k-space data.

13. The image processing device described in Claim 1, wherein the synthetic k-space data generation unit generates the synthetic k-space data by using the multiple kernels to correct the phase of line data points in regions of the imaging k-space data corresponding to each of the multiple kernels.

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