JP2017136170A - Magnetic resonance imaging apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a magnetic resonance imaging apparatus and a magnetic resonance imaging method capable of improving the quality of an image while keeping an imaging time short.SOLUTION: A magnetic resonance imaging apparatus includes a sequence control part. The sequence control part performs undersampling about a second axis for a space k including a first axis in a lead-out direction in performing Cartesian sampling and the second axis different from the first axis in the lead-out direction in performing the Cartesian sampling, and performs a predetermined pulse sequence while performing oblique sampling in the space k.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to a magnetic resonance imaging apparatus.

  In magnetic resonance imaging using parallel imaging (Parallel Imaging), the greater the overlap between a signal and an aliasing signal (folding signal), the lower the reconstruction accuracy of the output target image. Therefore, as much as possible, an imaging method in which the position where the aliasing signal is arranged is an area where nothing is imaged, such as an air area, is desirable. Based on such a concept, for example, in a method such as 2D CAIPIRINHA, a method of undersampling in a two-dimensional space composed of, for example, a phase encoding (PE) direction and a slice encoding (SE) direction. By devising, the aliasing position is controlled. Therefore, the image quality can be improved.

  However, in the lead-out (RO) direction, even if sampling is performed with the k-space thinned out, TR (Repetition Time) is unlikely to be shortened.

Felix A. Breuer et al., "Controlled Aliasing in Volumetric Parallel Imaging (2D CAIPIRINHA)", Magnetic Resonance in Medicine, Vol.55, pp.549-556,2006 J. Tsao et al., "K-t BLAST and k-t SENSE: Dynamic MRI with High Frame Rate Exploting Spatiotemporal Correlations", Magnetic Resonance in Medicine, Vol.50, pp.1031-1042,2003

  The problem to be solved by the present invention is to provide a magnetic resonance imaging apparatus and a magnetic resonance imaging method capable of improving the image quality while keeping the imaging time short.

  The magnetic resonance imaging apparatus according to the embodiment includes a sequence control unit. The sequence control unit, for k-space including a first axis in the readout direction when performing Cartesian sampling and an axis different from the first axis in the readout direction when performing Cartesian sampling, Undersampling is performed on the second axis, and a predetermined pulse sequence is executed while performing oblique sampling on the k space.

FIG. 1 is a diagram illustrating a magnetic resonance imaging apparatus according to an embodiment. FIG. 2A is a diagram illustrating the background art of a magnetic resonance imaging apparatus. FIG. 2B is a diagram for explaining the background art of the magnetic resonance imaging apparatus. FIG. 3A is a diagram illustrating an overview of the magnetic resonance imaging apparatus according to the first embodiment. FIG. 3B is a diagram illustrating an overview of the magnetic resonance imaging apparatus according to the first embodiment. FIG. 4A is a diagram illustrating an overview of the magnetic resonance imaging apparatus according to the first embodiment. FIG. 4B is a diagram illustrating an overview of the magnetic resonance imaging apparatus according to the first embodiment. FIG. 5 is a diagram for explaining the outline of the magnetic resonance imaging apparatus according to the first embodiment. FIG. 6 is a diagram showing an example of a pulse sequence executed by the sequence control circuit in the first embodiment. FIG. 7 is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the first embodiment. FIG. 8 is a diagram illustrating processing performed by the magnetic resonance imaging apparatus according to the first embodiment. FIG. 9 is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the first embodiment. FIG. 10 is a flowchart illustrating an example of a procedure of processing performed by the magnetic resonance imaging apparatus according to the first modification of the first embodiment. FIG. 11 is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the second modification of the first embodiment. FIG. 12A is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the third modification of the first embodiment. FIG. 12B is a diagram illustrating processing performed by the magnetic resonance imaging apparatus according to the third modification of the first embodiment. FIG. 13 is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the fourth modification of the first embodiment. FIG. 14A is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the second embodiment. FIG. 14B is a diagram illustrating processing performed by the magnetic resonance imaging apparatus according to the second embodiment. FIG. 14C is a diagram illustrating processing performed by the magnetic resonance imaging apparatus according to the second embodiment. FIG. 15A is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the third embodiment. FIG. 15B is a diagram illustrating processing performed by the magnetic resonance imaging apparatus according to the third embodiment. FIG. 15C is a diagram for explaining processing performed by the magnetic resonance imaging apparatus according to the third embodiment.

  Hereinafter, embodiments will be described with reference to the drawings.

(First embodiment)
FIG. 1 is a block diagram showing a magnetic resonance imaging apparatus 100 according to the embodiment. As shown in FIG. 1, a magnetic resonance imaging apparatus 100 includes a static magnetic field magnet 101, a gradient magnetic field coil 102, a gradient magnetic field power source 103, a bed 104, a bed control circuit 105, a transmission coil 106, and a transmission circuit 107. A receiving coil array 108, a receiving circuit 109, a sequence control circuit 110, and a computer system 120. The magnetic resonance imaging apparatus 100 does not include the subject P (for example, a human body).

  The static magnetic field magnet 101 is a magnet formed in a hollow, substantially cylindrical shape, and generates a uniform static magnetic field in an internal space. The static magnetic field magnet 101 is, for example, a permanent magnet or a superconducting magnet. The gradient magnetic field coil 102 is a coil formed in a hollow, substantially cylindrical shape, and is disposed inside the static magnetic field magnet 101. The gradient magnetic field coil 102 is formed by combining three coils corresponding to the X, Y, and Z axes orthogonal to each other, and these three coils individually receive current from the gradient magnetic field power supply 103, A gradient magnetic field is generated in which the magnetic field strength varies along the X, Y, and Z axes. The Z-axis direction is the same direction as the static magnetic field.

  The gradient magnetic field power supply 103 supplies a current to the gradient magnetic field coil 102. Here, the gradient magnetic fields of the X, Y, and Z axes generated by the gradient magnetic field coil 102 correspond to, for example, the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr, respectively. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging section. The phase encoding gradient magnetic field Ge is used to change the phase of the magnetic resonance signal in accordance with the spatial position. The readout gradient magnetic field Gr is used for changing the frequency of the magnetic resonance signal in accordance with the spatial position.

  The couch 104 includes a couch 104a on which the subject P is placed. Under the control of the couch control circuit 105, the couch 104a is placed in the cavity of the gradient coil 102 (with the subject P placed). Insert it into the imaging port. Normally, the bed 104 is installed such that the longitudinal direction is parallel to the central axis of the static magnetic field magnet 101. Under the control of the computer system 120, the couch control circuit 105 drives the couch 104 to move the top board 104a in the longitudinal direction and the vertical direction.

  The transmission coil 106 is disposed inside the gradient magnetic field coil 102 and receives a supply of RF pulses from the transmission circuit 107 to generate a high-frequency magnetic field. The transmission circuit 107 supplies an RF pulse corresponding to a Larmor frequency determined by the type of target atom and the strength of the magnetic field to the transmission coil 106.

  The reception coil array 108 is disposed inside the gradient magnetic field coil 102 and receives a magnetic resonance signal emitted from the subject P due to the influence of the high-frequency magnetic field. When receiving the magnetic resonance signal, the reception coil array 108 outputs the received magnetic resonance signal to the reception circuit 109. In the first embodiment, the reception coil array 108 is a coil array having one or more, typically a plurality of reception coils.

  The reception circuit 109 generates magnetic resonance data based on the magnetic resonance signal output from the reception coil array 108. Specifically, the receiving circuit 109 generates magnetic resonance data by digitally converting the magnetic resonance signal output from the receiving coil array 108. In addition, the reception circuit 109 transmits the generated magnetic resonance data to the sequence control circuit 110. The receiving circuit 109 may be provided on the gantry device side including the static magnetic field magnet 101 and the gradient magnetic field coil 102. Here, in the first embodiment, the magnetic resonance signals output from the coil elements (reception coils) of the reception coil array 108 are appropriately distributed and synthesized to the reception circuit 109 in units called channels. Is output. For this reason, magnetic resonance data is handled for each channel in subsequent processing after the receiving circuit 109. The relationship between the total number of coil elements and the total number of channels may be the same, when the total number of channels is small with respect to the total number of coil elements, or conversely, the total number of channels is large with respect to the total number of coil elements In some cases. In the following description, when it is expressed as “for each channel”, it indicates that the processing may be performed for each coil element or may be performed for each channel in which the coil elements are distributed and combined. Note that the timing of distribution / combination is not limited to the timing described above. The magnetic resonance signal or magnetic resonance data may be distributed and synthesized for each channel before reconstruction processing by the processing circuit 150 having the image generation function 126 described later.

  The sequence control circuit 110 performs imaging of the subject P by driving the gradient magnetic field power supply 103, the transmission circuit 107, and the reception circuit 109 based on the sequence information transmitted from the computer system 120. Here, the sequence information is information defining a procedure for performing imaging. The sequence information includes the strength of the power supplied from the gradient magnetic field power supply 103 to the gradient magnetic field coil 102, the timing of supplying power, the strength of the RF pulse transmitted from the transmission circuit 107 to the transmission coil 106, the timing of applying the RF pulse, and reception. The timing at which the circuit 109 detects a magnetic resonance signal is defined.

  The sequence control circuit 110 drives the gradient magnetic field power supply 103, the transmission circuit 107, and the reception circuit 109 to image the subject P. As a result, when the magnetic resonance data is received from the reception circuit 109, the sequence control circuit 110 calculates the received magnetic resonance data. Transfer to system 120.

  The computer system 120 performs overall control of the magnetic resonance imaging apparatus 100, data collection, image reconstruction, and the like, and includes a storage circuit 122, an input device 124, a display 125, and a processing circuit 150. Further, the processing circuit 150 includes an interface function 121, a control function 123, and an image generation function 126. Specific processing of the image generation function 126 function will be described later.

  In the first embodiment, each processing function performed by the interface function 121, the control function 123, and the image generation function 126 is stored in the storage circuit 122 in the form of a program that can be executed by a computer. The processing circuit 150 is a processor that realizes a function corresponding to each program by reading the program from the storage circuit 122 and executing the program. In other words, the processing circuit 150 in a state where each program is read has each function shown in the processing circuit 150 of FIG. In FIG. 1, it has been described that the processing functions performed by the interface function 121, the control function 123, and the image generation function 126 are realized by a single processing circuit 150, but a plurality of independent processors are combined. The processing circuit 150 may be configured so that the functions are realized by each processor executing a program.

  In other words, each function described above may be configured as a program, and one processing circuit may execute each program, or a specific function may be implemented in a dedicated independent program execution circuit. May be. The image generation function 126 is an example of a generation unit and a determination unit, respectively. The sequence control circuit 110 and the input device 124 are examples of a sequence control unit and an input unit, respectively. In addition, a determination function (not shown), a first calculation function (not shown), and a second calculation function (not shown) in embodiments described later are examples of a determination unit, a first calculation unit, and a second calculation unit, respectively. is there.

  The term “processor” used in the above description refers to, for example, a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an application specific integrated circuit (ASIC), a programmable logic device (for example), or the like. It means a circuit such as a simple programmable logic device (SPLD), a complex programmable logic device (Complex Programmable Logic Device), and a field programmable gate array (FPGA). That. The processor implements a function by reading and executing a program stored in the storage circuit 122.

  Instead of storing the program in the storage circuit 122, the program may be directly incorporated in the processor circuit. In this case, the processor realizes the function by reading and executing the program incorporated in the circuit. Similarly, the bed control circuit 105, the transmission circuit 107, the reception circuit 109, the sequence control circuit 110, and the like are also configured by electronic circuits such as the above-described processor.

  The processing circuit 150 transmits the sequence information to the sequence control circuit 110 by the interface function 121 and receives the magnetic resonance data from the sequence control circuit 110. In addition, when the magnetic resonance data is received by the interface function 121, the processing circuit 150 stores the received magnetic resonance data in the storage circuit 122. The magnetic resonance data stored in the storage circuit 122 is arranged in the k space by the processing circuit 150. As a result, the storage circuit 122 stores k-space data for a plurality of channels.

  The storage circuit 122 stores magnetic resonance data received by the processing circuit 150 having the interface function 121, time-series data arranged in the k space, image data generated by an image generation function 126 described later, and the like. For example, the storage circuit 122 is a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk, an optical disk, or the like. The input device 124 receives various instructions and information input from the operator. The input device 124 is, for example, a pointing device such as a mouse or a trackball, a selection device such as a mode change switch, or an input device such as a keyboard. The display 125 displays various information such as spectrum data and image data under the control of the processing circuit 150 having the control function 123. The display 125 is a display device such as a liquid crystal display, for example.

  A processing circuit 150 having a control function 123 performs overall control of the magnetic resonance imaging apparatus 100. Specifically, the processing circuit 150 having the control function 123 generates sequence information based on the imaging conditions input from the operator via the input device 124 and transmits the generated sequence information to the sequence control circuit 110. The imaging is controlled accordingly. The processing circuit 150 having the control function 123 controls image generation performed based on magnetic resonance imaging data sent from the sequence control circuit 110 as a result of imaging, and controls display on the display 125. For example, the processing circuit 150 having the control function 123 is an electronic circuit such as an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA), a central processing unit (CPU), or a micro processing unit (MPU). is there.

  First, the background relating to the magnetic resonance imaging apparatus 100 according to the embodiment will be briefly described with reference to FIGS. 2A to 3B as appropriate. 2A and 2B are diagrams illustrating the background relating to the magnetic resonance imaging apparatus according to the first embodiment. FIG. 3A and FIG. 3B are diagrams for explaining the outline of the magnetic resonance imaging apparatus according to the first embodiment.

  It is known that two-dimensional or three-dimensional imaging using a magnetic resonance imaging apparatus takes time to collect, and in order to shorten the acquisition time, a simultaneous acquisition technique using an array coil and data of a plurality of coils are used. A parallel imaging technique combining a reconstruction technique is known. Examples of parallel imaging techniques include SMASH (Simultaneous Acquisition of Spatial Harmonics), SENSE (Sensitivity encoding), and GRAPPA (Generalized Autoparticulate Parity). Parallel imaging technology reduces the aliasing signal by collecting the thinned out resolution necessary for reconstruction and removing the aliasing signal generated by the thinning using the difference in the collected information for each coil. This is a technique for obtaining a reconstructed image.

  In the data acquisition of the magnetic resonance imaging apparatus, data is collected densely in the one-dimensional readout direction, and the position is set on the remaining one-dimensional or two-dimensional space orthogonal to the readout direction. Cartesian collection is often performed in which collection is repeated as many times as necessary while changing. For convenience, directions other than the readout direction are referred to as a phase encoding (PE) direction in the case of two dimensions, and a phase encoding direction and a slice encoding (SE) direction in the case of three dimensions. In the parallel imaging technique in Cartesian acquisition, acquisition is performed while thinning out at equal intervals for PE in two dimensions and PE and SE in three dimensions. In this case, the aliasing signal appears in the PE direction and the SE direction on the image.

  As described above, the parallel imaging technique is a technique for separating the aliasing signal. However, as the overlap between the signal and the aliasing signal increases, the reconstruction accuracy of the parallel imaging technique decreases.

  For example, FIGS. 2A to 3B show the relationship between the thinning pattern (undersampling sampling pattern) and aliasing.

FIG. 2A shows sampling when the sequence control circuit 110 performs thinning at a rate of one in two in the phase encoding direction k _y (PE direction) and does not thin out in the lead-out direction k _x (RO direction). The pattern is shown. In this case, the reduction factor R representing the thinning rate is 2.

FIG. 2B shows a folding signal (aliasing) appearing in the image space when the k-space data obtained when the sequence control circuit 110 performs such undersampling is Fourier transformed. Results sequence control circuit 110 has performed undersampling in the phase encoding direction k _y direction, the y direction of the image space, aliasing appears.

FIG. 3A shows a sampling pattern when the sequence control circuit 110 performs undersampling with the reduction factor R = 2 as a whole in the phase encoding direction k _y (PE direction) and the readout direction k _x (RO direction). Represents.

FIG. 3B shows the state of aliasing signals appearing in the image space when the k-space data obtained when the sequence control circuit 110 performs such undersampling is Fourier transformed. The sequence control circuit 110 phase encoding direction k _y direction and the readout direction k _x, Doing undersampling while shifting the sampling position, aliasing appears like Figure 2B. However, in FIG. 3B, the position of aliasing is different from that in FIG. 2B. Specifically, when the sequence control circuit 110 performs undersampling with the sampling pattern of FIG. 2B, aliasing appears around a straight line y = x. In other words, it performs the decimation sampling the phase encoding direction k _y, further when the sequence control circuit 110 in a pattern shifted sampling pattern in k _x direction for sampling the position of the aliasing is moved in the x direction. That is, the sequence control circuit 110 can control the aliasing position by changing the sampling pattern.

  In magnetic resonance imaging, the subject is rarely imaged over the entire area of the FOV (Field Of View) to be imaged, and the periphery of the FOV is often an area where nothing is imaged, such as an air area. . Therefore, if the control can be performed so that the aliasing signal is arranged in the air region as much as possible, the reconstruction accuracy of the parallel imaging technique is improved. Based on this concept, for the 3D imaging that can use the phase encoding direction and the slice encoding direction as the dimensions for which the acquisition position can be selected, the position to be thinned out in 2D space instead of being thinned out independently in each dimension 2D CAIPIRINHA is known which controls the aliasing position by defining

  In 2D CAIPIRINHA, only in the case of three-dimensional imaging, the aliasing position can be controlled so that many aliasing signals are arranged in the air region, and the reconstruction accuracy can be improved. Note that 2D of 2D CAIPIRINHA indicates a phase encoding direction and a slice encoding direction, and three-dimensional imaging is essential for imaging itself. A modification of CAIPIRINHA targeting multi-slice imaging with respect to a plurality of two-dimensional cross sections is also known, but this also allows acquisition positions to be selected in the slice direction in addition to the phase encoding direction. This technique utilizes the fact that the collection position can be selected.

  As another example, for dynamic imaging in which two phase encoding directions and a time direction can be used, a position to be thinned out in a two-dimensional space composed of a k-space and a time axis is determined to control the aliasing position. t BLAST and kt SENSE are known. In kt BLAST and kt SENSE, only in the case of dynamic imaging that can use the time axis, the aliasing position can be controlled by the same principle as 2D CAIPIRINHA, and the reconstruction accuracy can be improved. It is essential that the imaging is time-series imaging.

  However, these techniques assume that acquisition position selection is possible for at least two dimensions. There is no known method for controlling the position of the folding signal appearing in the reconstructed image when the acquisition position is selected only in one dimension, and the known technique has a three-dimensional, two-dimensional multi-slice, time-series It will be limited to either imaging. On the other hand, in the first embodiment, the sequence control circuit 110 controls the position of the folding signal that appears in the reconstructed image by undersampling even when the acquisition position is selected only in one dimension.

  In the first embodiment, the sequence control circuit 110 has a first axis in the readout direction when performing Cartesian sampling and a second axis different from the axis in the readout direction when performing Cartesian sampling (for example, A predetermined pulse sequence while performing undersampling on the axis (second axis) different from the axis in the readout direction with respect to the k space including the phase encoding axis) and performing oblique sampling on the k space. Execute.

First, Cartesian sampling and oblique sampling will be described. Cartesian sampling is collection in which the phase encoding direction and the readout direction are orthogonal to each other, for example. Here, typically, the phase-encoding direction, k _y direction, the lead-out direction is k _x direction. On the other hand, in oblique sampling, the sequence control circuit 110 performs sampling at an angle (oblique angle) where the readout direction is not orthogonal to the phase encoding direction, for example. In other words, the lead-out direction in oblique sampling is inclined (oblique) compared to the lead-out direction in Cartesian sampling. At this time, the readout direction in the oblique sampling, for example does not coincide with the k _x direction.

An example of Cartesian sampling is shown in FIG. 4A. Here, the vertical direction is _{k x} direction, the lateral direction represents the _{k y} direction. The k _y direction coincides with the phase encoding direction, and in Cartesian sampling, the k _x direction coincides with the reading direction (lead-out direction).

  The straight line 301 represents the k space collected by, for example, one collection (1TR (Repetition Time)). In many pulse sequences, k-space data is collected at a single TR time, for example, at multiple points in the readout direction. On the other hand, in many pulse sequences, k-space data is collected at one TR time, for example, only at one point in the phase encoding direction. Therefore, in the phase encoding direction, the imaging time can be shortened by undersampling and thinning out the data, but in the readout direction, the number of times that echo must be generated even if data is thinned out by undersampling. Does not change the imaging time.

On the other hand, an example of oblique sampling is shown in FIG. 4B. Here, the vertical direction is _{k x} direction, the lateral direction represents the _{k y} direction. Arrow 404a represents the phase encoding direction, and arrow 404b represents the readout direction. k _y direction corresponds to the phase encoding direction. In oblique sampling, k _x direction does not match the readout direction.

  Similarly, the straight line 401 represents a k-space collected by, for example, one (1TR) collection. In many pulse sequences, k-space data is collected at a single TR time, for example, at multiple points in the readout direction.

  When the sequence control circuit 110 according to the first embodiment performs collection in the same phase encoding step as Cartesian collection, k-space data is collected outside the domain of the k-space definition (outside the integrand area of Fourier integration). Conversely, there is a region in the domain of the k-space (in the integrand region of Fourier integration) where no k-space data is collected. For example, the area 402 is an example of an area in which k-space data is collected outside the k-space definition area. For example, the region 405 is an example of a region in which k-space data is not collected in the definition region of k-space.

  Note that the magnetic resonance imaging apparatus 100 according to the embodiment is a SENSE system (Sensitivity Encoding) that separates aliasing signals in an image space, or a SMASH system (Simultaneous Acquisition of Spatial Harmonics) that estimates missing signals in a k space. When performing reconstruction by imaging, it is particularly useful for reducing the size of the area where the aliasing signal exists. The reconstruction method is not limited to parallel imaging, which will be described later. Note that the SMASH parallel imaging includes GRAPPA variations such as GRAPPA and ARC (Autocalibrating Reconstruction for Cartesian imaging), which is a technique for executing GRAPPA in a hybrid space.

FIG. 5 illustrates an example of an image obtained when the sequence control circuit 110 performs oblique sampling. The image 501 is a reference image, that is, an image obtained when the sequence control circuit 110 performs full sampling without performing undersampling on the subject P. Here, the vertical direction, _{k x} direction, i.e. readout direction in Cartesian sampling, the horizontal direction represents the _{k y} direction, that is, the phase encoding direction _{k x.} Image 502, the sequence control circuit 110 performs undersampling in the phase encode direction _{(k y} direction), the _{k x} direction is an image obtained when performing Cartesian sampling without undersampling. An image 503 is an image obtained when the sequence control circuit 110 performs oblique sampling. Similarly to the image 503, the image 504 is an image obtained when the sequence control circuit 110 performs oblique sampling, but has an oblique angle larger than that of the image 503.

Code 502a, reference numeral 502b, the code 502c, reference numeral 502d, etc., a sequence control circuit 110 performs undersampling in the phase encoding direction, in the _{k x} direction when subjected to Cartesian sampling without undersampling aliasing signal ( Aliasing signal). Reference numerals 503a, 503b, 503c, 503d, and the like indicate aliasing signals when the sequence control circuit 120 performs undersampling with a smaller oblique angle than that of the image 504. Reference numerals 504a, 504b, 504c, 504d, and the like indicate aliasing signals when the sequence control circuit 110 performs undersampling with a larger oblique angle than that of the image 503. Thus, as the oblique angle increases, the area where aliasing occurs shifts to the outside, and the area where signals overlap decreases. In other words, the sequence control circuit 110 performs oblique sampling in such a direction that a region where signals overlap due to aliasing is smaller than a region where signals overlap due to aliasing when performing Cartesian sampling.

  Note that the sequence control circuit 110 may determine, for example, the above-described oblique angle according to at least one of an imaging region and an imaging section type (for example, an axial, sagittal, or coronal type). For example, the sequence control circuit 110 calculates the oblique angle based on a rough classification such as whether the imaging site is the brain, the heart, the abdomen, the spine, or the axial image. decide. Note that the sequence control circuit 110 may determine the oblique angle in consideration of the imaging angle, for example, in the case of oblique imaging in which the imaging section is oblique.

  As described above, when signal separation by parallel imaging is performed, signals in the overlapping region are separated, and an image close to the image 501 in the case of full sampling is obtained. However, reconstruction noise in the overlapping region is obtained. Is often stronger than reconstruction noise in the other areas. In contrast, in the magnetic resonance imaging apparatus 100 according to the first embodiment, collection is performed by giving an oblique angle in the readout direction. As a result, as shown in the image 503 and the image 504, the region where the signals overlap can be shifted according to the oblique angle. In the example of FIG. 5, the region where the signals overlap decreases as the oblique angle increases. Thus, in the magnetic resonance imaging apparatus 100 according to the first embodiment, reconstruction noise can be suppressed by giving an oblique angle in the readout direction.

  Next, the flow of processing performed by the magnetic resonance imaging apparatus 100 according to the first embodiment will be described with reference to FIGS. FIG. 6 is a diagram illustrating an example of a pulse sequence executed by the sequence control circuit 110 in the first embodiment. FIG. 7 and FIG. 9 are flowcharts illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus according to the first embodiment. FIG. 8 is a diagram illustrating processing performed by the magnetic resonance imaging apparatus 100 according to the first embodiment.

  6 represents an RF (Radio Frequency) pulse applied by the sequence control circuit 110 and a spin echo generated by the RF pulse. A pulse 10 represents a 90 ° RF pulse applied by the sequence control circuit 110. A pulse 14 represents a 180 ° RF pulse applied by the sequence control circuit 110. The echo 16 represents an echo generated by the pulse 10 and the pulse 14 applied by the sequence control circuit 110. The echo 16 is a so-called Hahn eco.

Second stage from the top in FIG. 6 illustrates a gradient magnetic field G _s slice encoding direction. As indicated by the gradient magnetic field 11, a gradient magnetic field in the slice encoding direction is applied by the sequence control circuit 110 simultaneously with the pulse 10 that is a 90 ° RF pulse. Further, as indicated by the gradient magnetic field 15, a gradient magnetic field in the slice encoding direction is applied by the sequence control circuit 110 simultaneously with the pulse 14 that is a 180 ° RF pulse.

Third stage from the top in FIG. 6 illustrates a gradient magnetic field G _p of the phase encoding direction. As indicated by the gradient magnetic field 12, a gradient magnetic field in the phase encoding direction is applied by the sequence control circuit 110. Here, the magnitude of the gradient magnetic field 12 applied by the sequence control circuit 110 is a magnitude corresponding to the phase encoding amount encoded during the TR interval, and is different for each TR interval. Further, oblique sampling is performed by applying the gradient magnetic field 17 by the sequence control circuit 110 simultaneously with the gradient magnetic field 18 in the readout direction, which will be described later, during the period in which the echo 16 is generated.

The bottom of FIG. 6 represents a gradient magnetic field G _r in a readout direction. As indicated by the gradient magnetic field 13, the gradient magnetic field in the readout direction is applied by the sequence control circuit 110 simultaneously with the phase encoding by the gradient magnetic field 12 in the phase encoding direction. Further, oblique sampling is performed by applying the gradient magnetic field 18 by the sequence control circuit 110 simultaneously with the gradient magnetic field 17 in the phase encoding direction during the period in which the echo 16 is generated.

  Here, the sequence control circuit 110 controls the ratio of the magnitude of the gradient magnetic field 17 in the phase encoding direction and the gradient magnetic field 18 in the readout direction during the period in which the echo 16 is generated, so that the oblique leakage in oblique sampling is performed. The corner can be controlled. For example, when the magnitude of the gradient magnetic field 17 applied by the sequence control circuit 110 in the phase encoding direction is 0, the oblique angle is 0 °, and normal Cartesian sampling is performed. Further, when the magnitude of the gradient magnetic field 18 in the readout direction applied by the sequence control circuit 110 is 0, the oblique angle is 90 °. Further, when the magnitude of the gradient magnetic field 17 in the phase encoding direction and the gradient magnetic field 18 in the readout direction applied by the sequence control circuit 110 is 1: 1, the oblique angle is 45 °.

  The pulse sequence applied by the sequence control circuit 110 is not limited to that shown in FIG. For example, the sequence control circuit 110 may apply a spin-echo pulse sequence other than that shown in FIG. 6, or other pulse sequences such as an Echo Planar method, an Inversion Recovery method, a Gradient Echo method, etc. A sequence may be used.

  FIG. 7 is a flowchart showing the flow of processing performed by the magnetic resonance imaging apparatus 100 according to the first embodiment. The input device 124 receives an oblique angle input from a user. When receiving the oblique angle input from the user, the processing circuit 150 having the interface function 121 transmits the oblique angle value to the sequence control circuit 110. In this way, the sequence control circuit 110 acquires the oblique angle from the processing circuit 150 having the interface function 121 (step S110). The embodiment is not limited to this, and the value of the oblique angle is determined in advance as, for example, a default value and stored in the storage circuit 122. The sequence control circuit 110 acquires the value of the oblique angle from the storage circuit 122 as necessary. May be.

  For example, the sequence control circuit 110 executes the pulse sequence shown in FIG. 6 and collects k-space data at the oblique angle. An example of the k-space line collected by the sequence control circuit 110 is shown in FIG. 8, for example. In FIG. 8, each arrow represents k-space data collected by the sequence control circuit 110 within one TR interval, for example. As shown in FIG. 8, the sequence control circuit 110 collects k-space data at an angle such that the readout direction is not orthogonal to the phase encoding direction (step S120). The sequence control circuit 110 repeats the process of step S120 while changing the phase encoding from the phase encoding start position to the phase encoding end position, and collects k-space data at the oblique angle. In other words, the sequence control circuit 110 performs the different axis (second axis) with respect to k-space including the first axis in the readout direction and the second axis different from the first axis when performing Cartesian sampling. In addition to undersampling, a predetermined pulse sequence is executed while performing oblique sampling in the k space. Here, the different axis (second axis) means, for example, an axis in the phase encoding direction. When the sequence control circuit 110 completes the collection up to the phase encoding end position, the process proceeds to step S130.

  As indicated by 402 in FIG. 4B, the sequence control circuit 110 collects k-space data in step S120, including data outside the domain of the k-space. Therefore, the processing circuit 150 having the image generation function 126 discards k-space data outside the k-space definition area (excludes it from image reconstruction targets) (step S130). Subsequently, the processing circuit 150 having the image generation function 126 generates an image based on k-space data obtained based on a predetermined pulse sequence executed by the sequence control circuit 110. The processing circuit 150 having the image generation function 126 generates an image by performing image reconstruction processing using k-space data other than the k-space data excluded from the image reconstruction target in step S130 (step S140). As a specific example of the image generation method, for example, the processing circuit 150 having the image generation function 126 performs parallel imaging to generate an image.

  In step S130, instead of discarding the k-space data, a weight value can be set for each sample value on the k-space data. The weight value may be gradually decreased from 1 as it approaches the outside of the area. When the sample value outside the domain of the k space has a non-zero value, for example, the value is considered to be circulating in the k space, for example, and is arranged on the opposite side in the direction out of the domain. By introducing the weight, an effect of suppressing the influence of the data truncation in the vicinity of the domain boundary in the k space on the reconstructed image can be expected. In other words, the processing circuit 150 having the image generation function 126 gives a lower weight to k-space data outside the k-space definition area than k-space data within the k-space definition area, and weighted k-space data. An image is generated using.

  In step S140, the processing circuit 150 having the image generation function 126 generates an image using, for example, a SENSE system reconstruction method that is a typical method for multi-coil reconstruction. When the processing circuit 150 performs image generation in step S140 by using the SENSE reconstruction method by the image generation function 126, the sequence control unit 110 converts the undersampled k-space data to the multi-coil in step S120. Collect with.

  FIG. 9 details the processing in step S140 performed by the processing circuit 150 having the image generation function 126. In step S140-1, the processing circuit 150 having the image generation function 126 acquires k-space data necessary for subsequent image generation. In step S140-2, the processing circuit 150 having the image generation function 126 generates an image from k-space data using gridding.

  Here, the gridding is a technique for obtaining equal interval signal values after inverse Fourier transform from vectors of non-equal intervals (Nonequispaced) Fourier coefficients (Non-patent document Jackson et al. IEEE-MI 1991).

  In typical gridding, the following procedure is performed for a given coefficient sequence that is not equally spaced. (1) A function obtained by convolving a predetermined window function with a given coefficient sequence is sampled at equal intervals to generate equal interval data. (2) Divide the generated equally spaced data by the sampling density function ρ (f). (3) Inverse Fourier transform is applied to the obtained equally spaced data. (4) Divide each signal value of the equally spaced signal sequence by the Fourier coefficient of the window function.

  By this operation, a signal can be restored from a data string sampled at a position that is not a lattice point. That is, the data obtained by gridding is a signal on which aliasing signals are superimposed. Subsequently, the processing circuit 150 having the image generation function 126 generates a reconstructed image using the SENSE parallel imaging for the generated image (step S140-3).

  As described above, the magnetic resonance imaging apparatus 100 according to the first embodiment can control the position where aliasing occurs by performing oblique sampling, and can improve the image quality of the magnetic resonance imaging apparatus 100. .

(First modification of the first embodiment)
In the first embodiment, the processing circuit 150 provided with the image generation function 126 excludes k-space data outside the k-space domain from the reconstruction target and is excluded from the image reconstruction target. The case where an image is generated using k-space data other than k-space data has been described. The embodiment is not limited to this. In the first modification of the first embodiment, when the processing circuit 150 having a determination function (not shown) determines whether it is outside the definition area of the k-space, Do not shoot outside parts. This point will be described with reference to FIG. FIG. 10 is a flowchart illustrating an example of a procedure of processing performed by the magnetic resonance imaging apparatus according to the first modification of the first embodiment.

  Steps S110 and S140 in FIG. 10 perform the same processing as in steps S110 and S140 in FIG. 7, that is, the first embodiment, and thus detailed description thereof is omitted. The first modification of the first embodiment is different from the first embodiment in that, in the first embodiment, after the sequence control circuit 110 collects all k-space data, it is out of definition in step S130. While the k-space data has been discarded, in the first modification of the first embodiment, the k-space data is collected from the beginning for the place where the processing circuit 150 determines not to collect the data. do not do.

  The sequence control circuit 110 repeats the process of step S119 (and step S120) while changing the phase encoding from the phase encoding start position to the phase encoding end position. The processing circuit 150 having a determination function (not shown) determines whether or not the k-space area to be collected is outside the k-space definition area. That is, the processing function 150 determines whether to collect data for each k-space point (step S119). The sequence control circuit 110 executes a predetermined pulse sequence based on the determination result of the processing circuit having the determination function. Specifically, when the processing circuit 150 having a determination function determines a point in k space that is determined not to collect data (Yes in step S119), the sequence control circuit 110 performs collection for the point in the k space. Instead, the process proceeds to the next phase encoding position after approximately TR time has elapsed. At this time, the sequence control circuit 110 controls the execution time of a predetermined pulse sequence by controlling the operation time of AD (Analog to Digital) conversion based on the result determined by the determination function. Conversely, when the processing circuit 150 having the determination function determines a point in the k space determined to collect data (No in step S119), for the k space, the sequence control circuit 110 performs collection as usual (step S120). ).

(Second modification of the first embodiment)
In the first embodiment and the first modification of the first embodiment, the sequence control circuit 110 performs an oblique sampling to perform a pulse sequence. While the oblique sampling position is advantageous for image reconstruction by oblique sampling, the sampling is performed at an angle, so that, for example, in the domain of the k-space, such as the area 405a and the area 405b in FIG. 4B. Thus, there is an area where data is not collected. Therefore, it is desirable that the value of k-space data at such a point can be estimated by some method.

  In view of such a background, in the second modification of the first embodiment, the sequence control circuit 110 performs asymmetric Fourier imaging (Asymmetric Fourier Imaging). It is known that sample values at symmetrical positions in the k space (point symmetry with respect to the origin in the k space) are often close to complex conjugates. A reconstruction technique that takes advantage of this property (also known as Hermitian symmetry) is known as asymmetric Fourier imaging (or partial Fourier imaging, half Fourier imaging, half scan imaging) and only collects half of the k-space periphery. This is a technique for reducing the amount of collection of k-space data by reconstructing the components that have been performed and not collected and have a value close to the complex conjugate. Although the reconstruction method is not particularly limited, for example, a POCS (Projection On Convex Sets) method can be used. In the case of using asymmetric Fourier imaging, one of the regions that are Hermitian symmetric is collected, and the information is used to estimate the region that was not collected. As described above, by performing asymmetric Fourier imaging, it is possible to calculate k-space data at a point where data is not collected based on k-space data at a point where data is collected.

  Next, processing performed by the magnetic resonance imaging apparatus 100 according to the second modification of the first embodiment will be described with reference to FIG. FIG. 11 is a flowchart illustrating an example of a procedure of processing performed by the magnetic resonance imaging apparatus 100 according to the second modification of the first embodiment. Since the process of step S110 is the same as that of the first embodiment, a detailed description thereof will be omitted.

In step S111, the processing circuit 150 having a determination function (not shown) calculates a range of k-space for collecting k-space data. Specifically, the processing circuit 150 having a determination function calculates a range in the k space such that one of all asymmetric regions on the k space is collected. Here, “all one of the asymmetric regions on the k space is collected” means that, for example, a point (k _x , k _y ) on a two-dimensional plane for any real number k _x and k _y Or any one of (−k _x , −k _y ) is collected. As a specific _example, on the k x -k _y space, area of _{k x} ≧ 0 is in the range of such k-space so as to satisfy the condition. As another _example, on k x -k _y space, area of _{k x} ≧ _{k y} is in the range of such k-space so as to satisfy the condition. Incidentally, "among the asymmetric region on the k-space, one all collection" in this description has been with, for example, a partial region of the k-space peripheral (both high region of k _x and k _y) is asymmetrically Although it is collected, it is possible to modify such that some areas are not collected but are treated as zero.

The sequence control circuit 110 repeats the process of step S119 (and step S120) while changing the phase encode from the phase encode start position to the phase encode end position, and collects k-space data at the oblique angle. The processing circuit 150 having a determination function (not shown) determines whether or not the region of k space in which collection is performed satisfies the above-described condition. That is, the processing function 150 determines whether to collect data for each k-space point. (Step S119). The sequence control circuit 110 executes a predetermined pulse sequence based on the determination result of the processing circuit having the determination function. Specifically, when the processing circuit 150 having the determination function is a k-space point that does not satisfy the above-described condition (Yes in step S119), the sequence control circuit 110 performs collection for the k-space point. Absent. On the other hand, when the processing circuit 150 having the determination function determines a point in the k space that is determined to satisfy the above-described condition (No in step S119), the sequence control circuit 110 performs the normal operation for the point in the k space. Collection is performed (step S120). For example, the above-mentioned conditions, "on _k x -k _y space, _{k x} ≧ 0" case, for points _{k x} is a positive value, the sequence control circuit 110 satisfies the above conditions (No in step S119), the sequence control circuit 110 collects the k-space points as usual (step S120). Further, for example, the aforementioned conditions, "on _k x -k _y space, _{k x} ≧ 0" case, for points _{k x} is a negative value, the sequence control circuit 110, the condition of the above It is determined that the condition is not satisfied (step S119 Yes), and the sequence control circuit 110 does not collect the k-space point.

Subsequently, the processing circuit 150 having the image generation function 126 generates an image while estimating k-space data of uncollected points by asymmetric Fourier imaging based on the k-space of points acquired by executing the pulse sequence. (Step S121). Specifically, the processing circuit 150 having the image generation function 126 is k-space data of a point in the domain of the k-space, and is not acquired by a predetermined pulse sequence. The data is calculated based on the k-space data of the second point that is point-symmetric with respect to the first point and the origin on the k-space and is collected by a predetermined pulse sequence. Thus, an image is generated. For example, a predetermined pulse sequence, "on _k x -k _y space, _{k x} ≧ 0" for the case of explaining. K-space data that has been collected by a given pulse sequence, "on k _x -k _y space, k _x ≧ 0" and k-space data points. On the other hand, the k-space data that are not collected by the predetermined pulse sequence is "on k _x -k _y space, k x _<0" and k-space data points. Now, assuming that the coordinates of the first point not collected by the predetermined pulse sequence are α <0 and (k _x , k _y ) = (α, β), the processing circuit 150 having the image generation function 126. Is asymmetrical information of k-space data including k-space data of the first point and second points (k _x , k _y ) = (− α, −β) collected by a predetermined pulse sequence. An image is generated while being estimated using Fourier imaging.

  Note that asymmetric Fourier imaging can be combined with other methods, for example, SENSE or SMASH parallel imaging.

  As described above, in the second modification example of the first embodiment, the processing circuit 150 having the image generation function 126 stores uncollected k-space data resulting from the sampling pattern becoming oblique sampling. Using symmetry, it can be restored based on already collected k-space data. As a result, there is no uncollected data in the k space, and the merit resulting from the shift of the aliasing position in the k space by oblique sampling can be enjoyed, leading to an improvement in the quality of the output image.

(Third Modification of First Embodiment)
Parallel imaging methods are roughly classified into so-called SENSE and SMASH systems. In the first embodiment, a case has been described in which the processing circuit 150 having the image reconstruction function 126 generates an output target image using the SENSE image reconstruction method in step S140. However, the embodiment is not limited thereto. The case where the processing circuit 150 having the image reconstruction function 126 generates an image to be output using an SMASH image reconstruction method will be described. In the third modification of the first embodiment, for example, the processing in the first embodiment shown in FIG. 7 and the same processing as step S140 are performed, and only step S140 is the first. Different from the embodiment. Therefore, detailed description of processes other than step S140 is omitted.

As a typical example of SMASH system reconfiguration, here is GRAPPA (Generalized
An example of reconstruction using Autocalibrating Partially Parallel Acquisition) will be described. In GRAPPA, a sample value to be estimated in k space for a certain coil is expressed as a weighted sum of sample values of all coils in the vicinity thereof. The weight is a weight estimated using a signal called an auto-calibration signal (ACS), typically near the center of k-space. The original GRAPPA is designed assuming Cartesian lattice points, but the present embodiment can be applied as it is.

  Processing performed by the magnetic resonance imaging apparatus 100 according to the third modification of the first embodiment will be described with reference to FIGS. 12A and 12B. FIG. 12A is a flowchart illustrating an example of a procedure of processing performed by the magnetic resonance imaging apparatus 100 according to the third modification of the first embodiment. FIG. 12B is a diagram for explaining processing performed by the magnetic resonance imaging apparatus 100 according to the third modification of the first embodiment.

  FIG. 12A details the processing in step S140 performed by the processing circuit 150 having the image generation function 126 in the third modification of the first embodiment. The processing circuit 150 having the image generation function 126 acquires k-space data necessary for subsequent image generation (step S140-1).

  Subsequently, the processing circuit 150 having the image generation function 126 estimates signal values of uncollected positions in the k space by SMASH parallel imaging (step S140-4). For example, in the case of GRAPPA, the processing circuit 150 having the image generation function 126 first performs calibration and then estimates the signal value. That is, the processing circuit 150 having the image generation function 126 estimates the signal value of k-space data at an uncollected position from the acquired k-space data.

  Such processing will be described using GRAPPA as an example with reference to FIG. 12B. The same idea can be applied to SMASH-based methods other than GRAPPA. In FIG. 12B, black dots such as data point 30a, data point 30b, and data point 30c represent sampled data points, and white circles such as data point 31a and data 31b are unsampled data points. The purpose is to obtain an estimate of the values of these data points. The processing circuit 150 having the image generation function 126 estimates the value of the data point 40 using the values of the data point 41a, the data point 41b, the data point 41c, the data point 41d, the data point 41e, and the data point 41f, for example. .

  Returning to FIG. 12B, the processing circuit 150 having the image generation function 126 applies gridding using the estimated signal value to generate a reconstructed image (step S140-5).

(Other modifications of the first embodiment)
In the first embodiment, the case where the processing circuit 150 having the image generation function 126 uses gridding is described as the reconstruction method in step S140. The embodiment is not limited to this. For example, the processing circuit 150 having the image generation function 126 converts the aliased signal into a distorted image by applying Fourier transform without using the gridding, and after separating the aliasing signal from the distorted image. May be corrected. In this case, the processing circuit 150 having the image generation function 126 generates an image by performing Fourier transform on k-space data that is non-equal interval data, for example. Further, the processing circuit 150 having the image generation function 126 may generate an image by performing unfolding on the image space.

  FIG. 13 details the processing in step S140 performed by the processing circuit 150 having the image generation function 126 in such a case. The processing circuit 150 having the image generation function 126 acquires k-space data necessary for subsequent image generation (step S140-1). Subsequently, the processing circuit 150 having the image generation function 126 generates an image with distortion from the acquired k-space data using Fourier transform (step S140-6). At this time, the processing circuit 150 having the image generation function 126 Since the step using gridding is replaced by Fourier transform, the obtained image becomes a distorted image. Next, the processing circuit 150 having the image generation function 126 separates the aliasing signal from the generated distorted image using SENSE parallel imaging (step S140-7). The processing circuit 150 having the image generation function 126 can separate the aliasing signal, but since the input is an distorted image, the image obtained by the image generation function 126 is also distorted. Finally, the processing circuit 150 having the image generation function 126 corrects the distortion of the image from which the aliasing signal is separated and generates a reconstructed image (step S140-8).

  Further, in the first embodiment, for example, the case where the sequence control circuit 110 in FIG. 7 collects k-space data by multi-coils has been described, but the embodiment is not limited thereto. For example, the sequence control circuit 110 may collect k-space data with a single coil. In this case, the processing circuit 150 having the image generation function 126 generates an image using, for example, a reconstruction method using prior knowledge (hereinafter referred to as aliasing signal separation using prior knowledge). As a method using prior knowledge, many methods are known as shown in, for example, BLAST (Broad-use Linear Acquisition Speed-up Technique). In this case, the processing circuit 150 having the image generation function 126 generates an image by aliasing signal separation using the prior knowledge for the k-space data of the single coil in step S140 of FIG. It should be noted that for multi-coil acquired data, a reconstruction that combines a technique using prior knowledge such as BLAST and parallel imaging may be used.

  In the above description, the processing circuit 150 having the image generation function 126 has been described with respect to the method of separating the aliasing signal by applying the Cartesian reconstruction method. However, the processing circuit 150 having the image generation function 126 may apply the non-cartesian parallel imaging reconstruction method in step S140 of FIG. Specifically, for example, the processing circuit 150 having the image generation function 126 may apply SENSE for Non Cartesian. Alternatively, the processing circuit 150 having the image generation function 126 may apply a technique in which prior knowledge is combined with the SENSE method for Non Cartesian.

  Further, when the aliasing signal is separated on the image space as in the SENSE system reconstruction, if the position after the separation is not on the integer pixel position (grid position) in the image space, an image interpolation process is required. . This interpolation process may slightly degrade the reconstructed image quality. Therefore, the processing circuit 150 having the image generation function 126 calculates the position of the aliasing signal from the thinning interval and the oblique angle, and adjusts the oblique angle or the thinning interval at the time of collection so that it becomes an integer. As a result, it is not necessary to perform image interpolation after the aliasing signal is separated.

  Further, although the case where the sequence control circuit 110 performs undersampling in the phase encoding direction and the readout direction has been described, the embodiment is not limited thereto. For example, when performing three-dimensional acquisition, the sequence control circuit 110 may perform undersampling in the slice encoding (SE) direction instead of the phase encoding direction.

(Second Embodiment)
In the first embodiment, it has been described that the oblique angle is given. However, the embodiment is not limited to this. For example, the oblique angle may be input by the user through the input device 124, or an automatic setting function or a user input assist function may be provided on the magnetic resonance imaging apparatus 100 side. Therefore, a method of giving an oblique angle (or assisting user input) on the magnetic resonance imaging apparatus 100 side will be described.

  In many cases, a device user who performs magnetic resonance imaging picks up an image after deciding an organ as a target of image diagnosis. In imaging, it is important that the image quality of a region of interest (ROI: Region of Interest) such as a target organ is not impaired. Unlike normal Cartesian collection, sampling at an oblique angle can be controlled such that the image quality of the region of interest is relatively high. Therefore, in the magnetic resonance imaging apparatus 100 according to the second embodiment, the user is made to input the region of interest, and the sequence control circuit 110 executes the pulse sequence with the optimized oblique angle using the region of interest. This point will be described with reference to FIGS. 14A to 14C.

  FIG. 14A is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus 100 according to the second embodiment. FIG. 14B and FIG. 14C are diagrams explaining the processing performed by the magnetic resonance imaging apparatus 100 according to the second embodiment.

  First, the input device 124 receives an input of a region of interest from a user and acquires a region of interest (ROI). (Step S110-1). FIG. 14B is an example of a user interface for receiving an input of a region of interest from a user. The image 80a and the image 80b represent scout images. A square frame 80c indicates a region of interest (ROI) displayed superimposed on the image 80b, which is a scout image. The input device 124 receives key input and mouse input from the user. For example, the input device 124 receives an input for enlarging the region of interest from the user. Further, the input device 124 receives an input for reducing the region of interest from the user. The user input result is displayed through the display 125.

  Next, the processing circuit 150 having the first calculation function (not shown) repeats the processing of Step S110-2 and Step S110-3 for a plurality of provisional values of oblique angles until the end condition is satisfied. Here, the processing circuit 150 having the first calculation function proceeds to the following steps when the processing of Step S110-2 and Step S110-3, which will be described later, is performed for a predetermined number, for example, 10 oblique angles. Alternatively, the processing circuit 150 having the first calculation function proceeds to subsequent processing when, for example, a noise index value to be described later falls below a predetermined value. The oblique angle referred to here is, for example, the deviation of the first angle indicating the direction of data collection when performing oblique sampling from the second angle indicating the direction of data collection when performing Cartesian sampling. It means the angle shown.

  Next, the processing circuit 150 having the first calculation function determines a provisional value of the oblique angle for the pulse sequence to be applied (step S110-2). Here, the provisional value of the oblique angle may be determined in advance. The provisional value of the oblique angle may be determined using an input from the user.

  Next, the processing circuit 150 calculates a noise index value, which is an estimated value of noise, based on the provisional value of the defined oblique angle and the region of interest (ROI) input by the user by the first calculation function ( Step S110-3). More specifically, the processing circuit 150 assumes that the sequence control circuit 110 has executed a predetermined pulse sequence at the oblique angle for the provisional value of the oblique angle determined by the first calculation function. An index value representing the magnitude of noise of the image generated in the next step is calculated.

  Here, the noise index value is a value representing the strength of noise when a certain oblique angle and a thinning rate for full sampling are given.

  In addition, as a first example of the index value calculated by the processing circuit 150 using the first calculation function, for example, a value calculated based on an estimated value of the size of an area where aliasing occurs. For example, if a scout image or a coil sensitivity map obtained by a map scan can be used, the decimation by the oblique angle and the thinning rate given to the image or the sensitivity map is simulated, and the signal region (object region) is simulated. The size of the overlapping region (overlapping area or overlapping volume) can be calculated, and the obtained value can be used as the noise index value. In the case of a scout image, it is possible to calculate the size of the overlapping region by applying a threshold value to each pixel value and determining a region where nothing exists (in the air). On the other hand, in the case of a coil sensitivity map, the area of nothing (air) is usually determined when the map is generated, and therefore the size of the overlapping area can be calculated as it is.

  A second example of the index value calculated by the processing circuit 150 using the first calculation function is, for example, a statistical value in the region of interest of g-factor. If a sensitivity map can be used, the oblique angle and the thinning rate given to the sensitivity map are simulated, the g-factor used in SENSE is calculated for each pixel, and the ROI of the g-factor is calculated. Statistical values (for example, average value, median, 80th percentile, 90th percentile) can be used as the noise index value. Compared to the calculation of the overlap region, the calculation of g-factor takes time, but there is a trade-off that the accuracy as a noise index value is increased, and either method can be used. Note that the noise index value is not limited to these two.

  The noise index value greatly depends on how to select the region of interest. This is illustrated, for example, in FIG. 14C. Here, the square 71 represents the region of interest. In the images 70a and 71b, the white elliptical area represents the aliasing signal. In the image 70a, since the aliasing signal is overlapped in the square 71 which is the region of interest, the reconstruction noise becomes large with respect to the region of interest. On the other hand, in the image 71b, since the aliasing signal does not overlap in the region of interest, the reconstruction noise is reduced with respect to the region of interest.

  When the processing circuit 150 having the first calculation function calculates a noise index value for a plurality of provisional values of the oblique angle and satisfies the above-described termination condition, the processing subsequently proceeds to the second calculation function of the processing circuit 150. (Not shown). Specifically, in the processing circuit 150, the sequence control circuit 110 executes a predetermined pulse sequence based on the plurality of noise index values calculated for each of the provisional values of the plurality of oblique angles by the second calculation function. The oblique angle value is calculated. For example, the processing circuit 150 determines the oblique angle so that the noise index value in the region of interest is reduced by the second calculation function. At this time, the processing circuit 150 uses the second calculation function to calculate the oblique angle at which the position where aliasing occurs in the pixel in the reconstructed image is an integer pixel position, and the value of the oblique angle at which a predetermined pulse sequence is executed. May be calculated as Once the noise index value is determined, it is possible to control the ROI image quality to be relatively high by adjusting the oblique angle so that the noise index value becomes small using a plurality of oblique angles as candidates.

  Here, when the processing circuit 150 calculates the optimum oblique angle value by the second calculation function, the processing circuit 150 notifies the sequence control circuit 110 of the optimum oblique angle value. As a result, the sequence control circuit 110 acquires the oblique angle of the pulse sequence to be executed (step S110 in FIG. 7). Here, for example, the processing returns to the flowchart of FIG. 7 described in the first embodiment, and the sequence control circuit 110 performs the same processing as described in the first embodiment.

As a method for searching for the optimum oblique angle, for example, candidates for the oblique angle may be listed to calculate all the noise index values, and the optimum oblique angle may be selected. Or you may use the search by the following step, for example.
Step 1: An appropriate initial section (for example, 0 to 45 degrees) is given as an oblique angle.
Step 2: Calculate a value that has not yet been obtained among the noise index values at both ends.
Step 3: Find the angle at the center of the section, and update the angle with the larger noise index value at both ends of the section with the angle at the center of the section.
Step 4: Repeat steps 2-3 several times.
Note that the oblique angle adjustment method is not limited to these two methods.

  As described above, in the second embodiment, for example, the processing circuit 150 calculates the noise index value with respect to the oblique angle. As a result, the oblique angle in the pulse sequence executed by the sequence control circuit 110 can be optimized.

(Third embodiment)
In the embodiments so far, the case where the sequence control circuit 110 executes a pulse sequence using a single oblique angle has been described. In the third embodiment, the sequence control circuit 110 executes a pulse sequence using a plurality of oblique angles. Examples of collecting multiple times for the same imaging region include, for example, time-series data imaging (for example, contrast examination and cardiac cine mode imaging), multiple times of acquisition and image quality improvement by averaging, and collection for parameter mapping such as T1 mapping. However, it is not limited to these. If the oblique angle is different, the place where aliasing occurs is different, so the place where the noise is large and the place where the noise is small are different. The sequence control circuit 110 included in the magnetic resonance imaging apparatus 100 according to the third embodiment executes a pulse sequence using a plurality of oblique angles, and synthesizes images of regions with low noise to generate, for example, one image. Thus, for example, a higher quality image can be generated than when a pulse sequence using a single oblique angle is executed. In other words, the sequence control circuit 110 can improve the reconstruction accuracy by executing the pulse sequence by changing the oblique angle for each acquisition. Here, “change the oblique angle for each collection” specifically refers to, for example, every time data in a domain defined in a predetermined k space is collected once or a predetermined TR (Repetition Time). ) Means that the oblique angle is changed every time the number is collected.

  Processing performed by the magnetic resonance imaging apparatus 100 according to the third embodiment will be described with reference to FIGS. 15A to 15C. FIG. 15A is a flowchart illustrating an example of a processing procedure performed by the magnetic resonance imaging apparatus 100 according to the third embodiment. FIG. 15B and FIG. 15C are diagrams explaining the processing performed by the magnetic resonance imaging apparatus 100 according to the third embodiment. When the case where there are two types of oblique angles will be described, the number of oblique angles that are changed for each collection may be three or more types.

  FIG. 15A is a flowchart illustrating a flow of processing performed by the magnetic resonance imaging apparatus 100 according to the third embodiment. The sequence control circuit 110 acquires the oblique angle from the processing circuit 150 having the interface function 121, as in the first embodiment (step S110X).

  For example, the sequence control circuit 110 executes the pulse sequence shown in FIG. 6 to collect k-space data at the first oblique angle. That is, the sequence control circuit 110 performs the first oblique sampling with the first oblique angle in the k space. The sequence control circuit 110 repeats the process of step S120X while changing the phase encoding from the phase encoding start position to the phase encoding end position, and collects the first k-space data at the first oblique angle.

  In addition, the sequence control circuit 110 acquires the second oblique angle from the processing circuit 150 having the interface function 121 as in the first embodiment (step S110Y).

  For example, the sequence control circuit 110 executes the pulse sequence shown in FIG. 6 and collects k-space data at the second oblique angle. That is, the sequence control circuit 110 performs the second oblique sampling with the second oblique angle in the k space. The sequence control circuit 110 repeats the process of step S120Y while changing the phase encoding from the phase encoding start position to the phase encoding end position, and collects the second k-space data at the second oblique angle.

  In step S150, the processing circuit 150 having the image generation function 126, for example, includes the first k-space data obtained by the first oblique sampling and the second k-space data obtained by the second oblique sampling. Based on the above, an image is generated.

  As an example, the processing circuit 150 having the image generation function 126 calculates a first reconstructed image from the first k-space data, and calculates a second reconstructed image from the second k-space data. Thereafter, the processing circuit 150 having the image generation function 126 reduces the reconstruction artifact of the nuclear reconstructed image using the first reconstructed image and the second reconstructed image.

  As shown in an image 90a and an image 90b in FIG. 15B, by changing the oblique angle, the appearance of the aliasing signal changes for each imaging. The structure of the object appearing on both is the structure of the object to be reconstructed, but the structure of the object appearing on only one of them is highly likely to be an aliasing signal. Therefore, it can be expected that the image quality of the reconstructed image will be improved if the reconstruction is performed in such a manner that only the structures of the objects appearing on both sides can be selectively reconstructed.

  As can be seen from the flowchart of FIG. 15A, step S120X for performing the first k-space data collection and step S120Y for performing the second k-space data collection may be processed in parallel processing. However, the first k-space data collection and the second k-space data collection are not necessarily performed separately. Specifically, for example, in the case of time-series imaging, two data can be reconstructed by simultaneously reconstructing the two data in such a manner that a penalty is imposed as the difference between the two images increases during reconstruction. The noise can be reduced compared to the case of reconfiguration independently.

  When averaging is performed on images obtained at a plurality of oblique angles, for example, the reconstructed image may be averaged. When parameter mapping such as T1 mapping is performed, parameter curve fitting using a reconstructed image is performed for each pixel as post-processing. Therefore, if the oblique angle is changed and the image is taken, the influence of the false signal appearing at a different position for every image pickup can be reduced by the parameter curve fitting even if no special action is taken.

  The integration of data collected at a plurality of oblique angles will be described with reference to FIG. 15C. FIG. 15C is a diagram for describing processing performed by the magnetic resonance imaging apparatus 100 according to the third embodiment. Now, for the pixel 1, the pixel value when measured at the first oblique angle is 5.0 ± 1.0, and the pixel value when measured at the second oblique angle is 4.0 ± 1.0. 2.0, and the pixel value when measured at the third oblique angle is 6.0 ± 0.5. Similarly, for the pixel 2, the pixel value when measured at the first oblique angle is 3.0 ± 0.5, and the pixel value when measured at the second oblique angle is 5 It is assumed that the pixel value when measured at the third oblique angle is 7.0 ± 1.0. Here, for example, a pixel value value of 5.0 ± 1.0 means that the signal value is 5.0 and the error (standard deviation) is 1.0. For example, measurement errors and reconstruction errors are collected together.

  When the “minimum error selection method” is used, for the pixel 1, the third oblique angle has the smallest error, so for the pixel 1, the third oblique angle is adopted. In addition, for the pixel 2, the first oblique angle has the smallest error, so the case of the first oblique angle is adopted for the pixel 2.

  When the “weighted average method” is used, a weighted average is taken with respect to pixels with a weight inversely proportional to the square of the error (standard deviation). Thus, for pixel 1, the oblique angle 1 has a weight of 1.0, the oblique angle 2 has a weight of 0.25, and the oblique angle 3 has a weight of 4.0. Therefore, taking a weighted average, the pixel value of the pixel 1 is (5.0 × 1.0 + 4.0 × 0.25 + 6 × 4.0) ÷ (1.0 + 0.25 + 4.0) = 5.71. In this way, the processing circuit 150 can integrate data obtained at a plurality of oblique angles.

  The magnetic resonance imaging apparatus 100 according to the third embodiment can be applied to, for example, moving image imaging. Further, the sequence control circuit 110 may apply a predetermined pulse sequence for simultaneously exciting a plurality of slices. Further, the sequence control circuit 110 may execute a sequence using CAIPIRINHA.

  Further, the sequence control circuit 110 performs a predetermined pulse sequence using the first oblique angle and the second oblique angle such that the sum of the first oblique angle and the second oblique angle becomes approximately 0 degrees. May be executed. Since this pulse sequence has high symmetry, the aliasing signal can be discriminated efficiently.

(program)
The instructions shown in the processing procedure shown in the above-described embodiment can be executed based on a program that is software. A general-purpose computer system stores this program in advance and reads this program, whereby it is possible to obtain the same effects as those obtained by the magnetic resonance imaging apparatus and the image processing apparatus of the above-described embodiment. The instructions described in the above-described embodiments are, as programs that can be executed by a computer, magnetic disks (flexible disks, hard disks, etc.), optical disks (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD). ± R, DVD ± RW, etc.), semiconductor memory, or a similar recording medium. As long as the computer or embedded system can read the storage medium, the storage format may be any form. If the computer reads the program from the recording medium and causes the CPU to execute instructions described in the program based on the program, the same operation as the magnetic resonance imaging apparatus and image processing apparatus of the above-described embodiment is realized. can do. Of course, when the computer acquires or reads the program, it may be acquired or read through a network.

  Further, an OS (operating system) operating on a computer based on instructions from a program installed in a computer or an embedded system from a storage medium, database management software, MW (middleware) such as a network, etc. A part of each process for realizing the above may be executed.

  Furthermore, the storage medium is not limited to a medium independent of a computer or an embedded system, but also includes a storage medium in which a program transmitted via a LAN (Local Area Network) or the Internet is downloaded and stored or temporarily stored.

  Further, the number of storage media is not limited to one, and the processing in the embodiment described above is executed from a plurality of media, and the configuration of the medium may be any configuration included in the storage medium in the embodiment. .

  The computer or the embedded system in the embodiment is for executing each process in the above-described embodiment based on a program stored in a storage medium, and includes a single device such as a personal computer or a microcomputer. The system may be any configuration such as a system connected to the network.

  In addition, the computer in the embodiment is not limited to a personal computer, and includes an arithmetic processing device, a microcomputer, and the like included in an information processing device, and is a generic term for devices and devices that can realize the functions in the embodiment by a program. .

According to the magnetic resonance imaging apparatus and method of at least one embodiment described above, the image quality can be improved while keeping the imaging time short.

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

110 Sequence control circuit 126 Image generation function 150 Processing circuit

Claims (21)

For a k-space including a first axis in the readout direction when performing Cartesian sampling and a second axis different from the first axis in the readout direction when performing Cartesian sampling, the first A magnetic resonance imaging apparatus comprising: a sequence control unit that performs undersampling on two axes and executes a predetermined pulse sequence while performing oblique sampling on the k space.   The sequence control unit according to claim 1, wherein the sequence control unit performs the oblique sampling in a direction in which a region where signals overlap due to aliasing is smaller than a region where signals overlap due to aliasing when performing Cartesian sampling. Magnetic resonance imaging device.   The magnetic resonance imaging apparatus according to claim 1, further comprising: a generation unit that generates an image based on k-space data obtained based on the predetermined pulse sequence executed by the sequence control unit.   The magnetic resonance imaging apparatus according to claim 3, wherein the generation unit generates the image by performing a Fourier transform on the k-space data that is non-uniformly spaced data.   The magnetic resonance imaging apparatus according to claim 1, wherein the second axis is an axis in a phase encoding direction.   The magnetic resonance imaging apparatus according to claim 3, wherein the generation unit generates the image by performing parallel imaging.   The magnetic resonance imaging apparatus according to claim 3, wherein the generation unit separates an aliasing signal using prior knowledge and generates the image.   The generating unit excludes k-space data outside the domain of k-space from image reconstruction targets, and uses k-space data other than k-space data excluded from image reconstruction targets, The magnetic resonance imaging apparatus according to claim 3, which generates an image.   The generating unit assigns a lower weight to k-space data outside the k-space domain than k-space data within the k-space domain, and generates the image using weighted k-space data. The magnetic resonance imaging apparatus according to claim 3. A determination unit for determining whether or not a region of k space to be collected is outside a definition region of k space;
The magnetic resonance imaging apparatus according to claim 1, wherein the sequence control unit executes the predetermined pulse sequence based on a result determined by the determination unit.   The said sequence control part controls execution of the said predetermined | prescribed pulse sequence by controlling the operation time of AD (Analog to Digital) conversion based on the determination result of the said determination part. Magnetic resonance imaging device.   The generation unit is k-space data of a point in a domain of k-space, and k-space data of a first point not collected by the predetermined pulse sequence, the first point, 4. The magnetic field according to claim 3, wherein the magnetic field is calculated based on k-space data of a second point that is point-symmetric with respect to the origin on the k-space and is acquired by the predetermined pulse sequence. Resonance imaging device. The provisional value of the oblique angle, which is an angle indicating the deviation of the first angle indicating the direction of data collection when performing oblique sampling from the second angle indicating the direction of data collecting when performing Cartesian sampling. A first calculation unit that calculates an index value representing a noise level of an image generated when the sequence control unit assumes that the predetermined pulse sequence is executed at an oblique angle of the provisional value;
A second calculation unit that calculates a value of an oblique angle at which the sequence control unit executes the predetermined pulse sequence based on the plurality of index values calculated for each of the plurality of provisional values;
The magnetic resonance imaging apparatus according to claim 3, further comprising: An input unit for receiving an input of a region of interest from the user;
The magnetic resonance imaging apparatus according to claim 13, wherein the first calculation unit calculates the estimated value of the noise based on the input region of interest.   The magnetic resonance imaging apparatus according to claim 14, wherein the index value is a statistical value of the g-factor within the region of interest.   The magnetic resonance imaging apparatus according to claim 13, wherein the index value is a value calculated based on an estimated value of a size of a region where aliasing occurs.   The second calculation unit calculates an oblique angle at which an aliasing position in a pixel in a reconstructed image is an integer pixel position, as an oblique angle value for executing the predetermined pulse sequence. Magnetic resonance imaging equipment.   The magnetic resonance imaging apparatus according to claim 3, wherein the generation unit generates the image by performing unfold processing on an image space. a sequence controller for performing a first pulse sampling with a first oblique angle on a k-space and performing a second pulse sampling with a second oblique angle to execute a predetermined pulse sequence;
A generator that generates an image based on the first k-space data obtained by the first oblique sampling and the second k-space data obtained by the second oblique sampling; Magnetic resonance imaging device.   The magnetic resonance imaging apparatus according to claim 19, wherein the predetermined pulse sequence is a pulse sequence for simultaneously exciting a plurality of slices.   The sequence control unit uses the first oblique angle and the second oblique angle so that a sum of the first oblique angle and the second oblique angle becomes approximately 0 degrees, and uses the predetermined oblique angle and the second oblique angle. 21. The magnetic resonance imaging apparatus according to claim 19 or 20, wherein the magnetic resonance imaging apparatus executes a pulse sequence.