WO2018037868A1 - Dispositif d'imagerie par résonance magnétique et procédé de reconstruction d'image - Google Patents

Dispositif d'imagerie par résonance magnétique et procédé de reconstruction d'image Download PDF

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WO2018037868A1
WO2018037868A1 PCT/JP2017/028159 JP2017028159W WO2018037868A1 WO 2018037868 A1 WO2018037868 A1 WO 2018037868A1 JP 2017028159 W JP2017028159 W JP 2017028159W WO 2018037868 A1 WO2018037868 A1 WO 2018037868A1
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processing
calculation unit
magnetic resonance
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calculation
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克成 長嶋
毅倫 村瀬
康弘 鎌田
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株式会社日立製作所
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging

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  • the present invention relates to a magnetic resonance imaging apparatus (hereinafter referred to as an MRI apparatus) to which a compression sensing technique is applied, and particularly to an image reconstruction method thereof.
  • an MRI apparatus magnetic resonance imaging apparatus
  • the MRI apparatus is a medical diagnostic imaging apparatus that mainly uses the nuclear magnetic resonance phenomenon of hydrogen nuclei, but has the problem that the examination time is long due to its characteristics. Generally, several tens of minutes are required for each specimen, and the burden on the patient is large. Therefore, it is desired to increase the imaging speed.
  • CS compressed sensing
  • Compressed sensing uses the sparseness of the signal and can restore the original signal with high accuracy from the sparse observation results.
  • Parallel imaging is a technology for speeding up imaging.
  • Parallel imaging uses multiple receiving coils with different sensitivity distributions to measure less data than is necessary for normal image reconstruction (undersampling), and thereby the aliasing that is included in the image is detected. This is a technique for removing by calculation using a sensitivity distribution. Combining this parallel imaging and compressed sensing has also been proposed (for example, Patent Document 3).
  • compressed sensing often has a huge amount of processing for signal restoration, and as a result, there is a problem that it takes a long time to display an image after the inspection is completed.
  • a reconstruction process for imaging after restoring an original signal with high accuracy by compressed sensing For example, 2DFFT, parallel imaging calculation, processing for correcting the sensitivity difference of the coil, and the like can be mentioned. Since these reconstruction processes are performed on the data after signal restoration, they are performed in order, and the time until image display is extended by a simple addition of the process related to compressed sensing and the reconstruction process.
  • This invention makes it a subject to shorten the time until an image is displayed after completion
  • the MRI apparatus of the present invention provides the above processing by providing means for processing in parallel with the compression sensing calculation for the calculation that can be started without waiting for the completion of the compression sensing calculation among the calculations related to the reconstruction other than the compression sensing calculation. Solve the problem.
  • the MRI apparatus of the present invention measures a nuclear magnetic resonance signal from a subject and acquires sparse measurement data, and image reconstruction using the sparse measurement data measured by the measurement unit
  • An image processing unit that performs processing necessary for the image processing unit, and a control unit that controls operations of the measurement unit and the image processing unit.
  • the image processing unit includes a first calculation unit and a second calculation that operate in parallel.
  • the control unit uses the sparse measurement data to perform restoration processing of data that has not been measured, and the first computation unit performs computations necessary for image reconstruction other than the restoration processing.
  • the operation of the image processing unit is controlled so that at least a part is performed by the second arithmetic unit.
  • FIG. 3 is a block diagram illustrating a configuration example of an image processing unit 220 according to the embodiment.
  • FIG. 3 is a diagram illustrating a processing flow of an image processing unit according to the first embodiment.
  • FIG. 5 is a diagram showing the effect of shortening the processing time of the first embodiment, where (A) shows the processing time of the conventional method, and (B) shows the processing time of the first embodiment.
  • FIG. 10 is a diagram illustrating a processing flow of an image processing unit according to the second embodiment.
  • FIG. 10 is a diagram illustrating a processing flow of an image processing unit according to a third embodiment.
  • FIG. 10 is a diagram illustrating a processing flow of an image processing unit according to a fourth embodiment.
  • FIG. 10 is a diagram illustrating a processing flow of an image processing unit according to a fifth embodiment.
  • FIG. 10 is a diagram illustrating a processing flow of an image processing unit according to a sixth embodiment.
  • FIG. 6A is a diagram showing the effect of shortening the processing time of the sixth embodiment.
  • FIG. 6A shows the processing time of the conventional method
  • FIG. FIG. 10 is a diagram illustrating a flow of processing of an image processing unit according to a seventh embodiment.
  • FIG. 10 is a diagram showing the effect of shortening the seventh processing time, where (A) shows the processing time of the conventional method and (B) shows the processing time of the seventh embodiment.
  • FIG. 1 (A) is a block diagram showing the overall configuration of the MRI apparatus 100 of the present embodiment.
  • the MRI apparatus 100 of the present embodiment is roughly divided into a measurement unit 110, a signal processing unit 120, and an operation unit 130.
  • the measurement unit 110 is a subject 150, where a static magnetic field generation unit 101 that generates a static magnetic field in a space where a person is placed, a gradient magnetic field coil 102 that generates a gradient magnetic field in the same space, and a high frequency magnetic field pulse applied to the subject 150 And a receiving coil 104 that receives a nuclear magnetic resonance signal generated by the subject 150.
  • the static magnetic field generation unit 101 is composed of a permanent magnet type, a normal conduction type or a superconductivity type static magnetic field generation source. If the vertical magnetic field type is used, the space around the subject 150 is perpendicular to the body axis in the space around the subject 150. If the horizontal magnetic field method is used, a uniform static magnetic field is generated in the body axis direction of the subject 150.
  • the gradient magnetic field coil 102 is composed of gradient magnetic field coils wound in the three-axis directions of X, Y, and Z, which are the coordinate system (stationary coordinate system) of the MRI apparatus 100, and each is connected to the gradient magnetic field power source 112.
  • the gradient magnetic field coil 102 is driven by a gradient magnetic field power source 112 to generate gradient magnetic fields Gx, Gy, and Gz in the three axis directions of X, Y, and Z, and a gradient magnetic field in an arbitrary direction can be generated by a combination thereof. .
  • a slice direction gradient magnetic field pulse (Gs) is applied in a direction orthogonal to the slice plane (imaging cross section) to set a slice plane for the subject 150, and the remaining planes orthogonal to the slice plane and orthogonal to each other are set.
  • a phase encoding direction gradient magnetic field pulse (Gp) and a frequency encoding direction gradient magnetic field pulse (Gf) are applied in two directions, and position information in each direction is encoded into an echo signal.
  • the transmission coil 103 is composed of a high-frequency coil arranged close to the subject 150, and is connected to a transmission unit 113 including a high-frequency oscillator, a modulator, and a high-frequency amplifier (not shown).
  • the RF pulse output from the high-frequency oscillator is amplitude-modulated by a modulator, then amplified by a high-frequency amplifier, and irradiated to the subject 150 from the transmission coil 103, so that the nuclear spin of the atoms constituting the living tissue of the subject 150 is converted to Causes nuclear magnetic resonance.
  • the reception coil 104 is a high-frequency coil that detects an echo signal (NMR signal) emitted from the subject 150 by nuclear magnetic resonance, and includes a signal amplifier (not shown), a quadrature detector, an A / D converter, and the like. Connected to the unit 114.
  • the NMR signal of the response induced by the RF pulse irradiated from the transmission coil 103 is detected by the reception coil 104 disposed in the vicinity of the subject 150, amplified by the signal amplifier, and orthogonally amplified by the quadrature detector.
  • the signals are divided into system signals, each of which is converted into a digital quantity by an A / D converter and sent to the signal processing unit 120 as measurement data.
  • the reception coil 104 uses a multiple array coil (MAC) in which a plurality of small RF coils 104-1 to 104-4 are combined as shown in FIG. 1 (B).
  • MAC multiple array coil
  • the receiving unit 114 described above is provided for each channel (for each small RF coil constituting the multiple array coil).
  • FIG. 1 an example of a four-channel multiple array coil is shown, but the number of small RF coils (channels) is not limited to that shown, but is arbitrary.
  • the signal processing unit 120 controls the entire apparatus, performs various data processing, displays and stores processing results, and includes a control unit 210, an image processing unit 220, and a storage unit 230.
  • the control unit 210 mainly has a function of controlling the measurement unit 110.
  • the control unit 210 controls to repeatedly apply an RF pulse and a gradient magnetic field pulse according to a predetermined pulse sequence, and performs various kinds of operations necessary for collecting measurement data.
  • the command is sent to the transmission unit 113, the gradient magnetic field power source 112, and the reception unit 114.
  • the measurement unit 110 is controlled to collect measurement data to which compressed sensing is applied. Measurement data to which compressed sensing is applied will be described later.
  • the image processing unit 220 performs various calculations necessary for image reconstruction on the measured data. In the present embodiment, compression sensing calculation, image correction, and calculation for the parallel imaging are performed.
  • the signal processing unit 120 specifically includes a storage device such as a CPU, a ROM, and a RAM, and an external storage device such as an optical disk and a magnetic disk.
  • the processing by the image processing unit 220 is realized by reading a program or a calculation algorithm stored in advance in a storage device. Some of the operations can also be realized by hardware such as ASIC and FPGA.
  • the operation unit 130 receives various control information of the MRI apparatus 100 itself and various control information of processing performed by the signal processing unit 120, and includes an input unit 320 including an input device such as a trackball, a mouse, and a keyboard, and an image And a display unit 310 for displaying a GUI and the like.
  • the operator interactively inputs information necessary for various processes of the MRI apparatus 100 via the input unit 320 while viewing the display unit 310.
  • the image processing unit 220 executes processing such as signal processing and image reconstruction, and displays a tomographic image of the subject 150 as a result of the processing.
  • the information is displayed on 310 and recorded in the storage unit 230.
  • FIG. 2 shows the basic processing in compressed sensing. As shown in the figure, in compressed sensing, sparse K-space data acquisition S101, signal or data restoration processing S102, and image reconstruction processing S103 other than restoration processing are executed. Hereinafter, each process is explained in full detail.
  • the measurement unit 110 adjusts the gradient magnetic field application pattern, and obtains K-space data with a predetermined number of data points (observation patterns) with fewer data points than the data points of the K-space data. collect.
  • observation patterns data points
  • K space if the low frequency components near the origin are not observed closely, the contrast and brightness of the reconstructed image will fluctuate greatly.
  • Observe elements As an observation pattern by such an observation method, a parallel line shape 31 as shown in FIG. 3, a radial shape 32 (see, for example, Patent Document 2), a random shape 33, and a spiral shape although not shown are conceivable. . Any of these may be employed in the present embodiment.
  • the signal processing unit 120 performs an operation using a compressed sensing algorithm.
  • the compressed sensing algorithm is a Fourier transform of the measured K-space data to an image space, and then a sparse conversion to solve optimization problems such as L1 norm minimization with an iterative algorithm. Conversion, combination of wavelet conversion and Curvelet conversion, TV (Total Variation), Ridgelet conversion, etc.
  • Such a compression sensing algorithm is known, and is available as a software package such as L1-SPIRiT, L1-ESPIRiT, SAKE-L1ESPIRiT, TVG (Total Generalized Variation: an improved version of TV).
  • the compression sensing algorithm is stored as a program in the storage device of the MRI apparatus or another storage device, and the image processing unit 220 uploads and executes the program.
  • Fig. 4 conceptually shows an example of restoration processing using L1 norm minimization of compressed sensing.
  • the observation data 41 here sparse K space data measured by the measurement unit 110, is Fourier transformed to be real space data 42 (S201).
  • the real space data 42 is compressed with a sparse transform operator to obtain a sparse image 43 (S202).
  • This sparse conversion process utilizes the fact that the signal intensity of the same tissue is ideally equal in MR images and the signal intensity changes only at the boundary of the tissue.
  • a sparse transform operator such as a wavelet transform or a discrete cosine transform.
  • the sparse image 43 is masked with a threshold value to obtain a sparse image 44 after the threshold value process. (In L1 norm minimization, this threshold is gradually reduced as the iteration is repeated.)
  • inverse sparse transform is performed on the sparse image 44 after threshold processing, and after compressed sensing processing.
  • Real space data 45 (S204) which is further subjected to inverse Fourier transform (S205) to obtain restored K space data 46.
  • L1 norm minimization is used for restoration processing, but various other restoration algorithms besides L1 norm minimization are known and can be adopted without being limited to L1 norm minimization. .
  • the K space data is collected for each small RF coil that constitutes the multiple array coil. To the data.
  • the image reconstruction processing S103 other than the restoration processing includes, for example, aliasing removal calculation (parallel imaging calculation) in the case of parallel imaging, shading (image shading) correction in the case of using a multiple array coil, and the like.
  • Parallel imaging operations can be broadly divided into methods that create data that is not collected in the measurement space (K space) (SMASH, GRAPPA, etc.), and methods that expand and remove aliases in the real space (SENSE, etc.) )
  • the former can also be processed by an algorithm combined with compressed sensing (for example, Patent Document 3) in the above-described restoration processing S102.
  • Patent Document 3 a method for expanding the fold in the real space will be described.
  • the measurement unit 110 performs measurement by thinning out the number of repetitions of phase encoding at a constant rate using a multiple array coil.
  • N-folding occurs in the image after the two-dimensional Fourier transform.
  • measurement is performed by thinning out 1 / N using M reception coils equal to or greater than N, and M images (images for each reception coil) in which N diffraction returns are obtained.
  • the signal processing unit 120 develops the folded image N times using the sensitivity distribution of the M receiving coils, and acquires one image from which the folding is removed.
  • the image S is represented by the product of the density distribution p of the subject and the sensitivity distribution C of the coil, as shown in Equation (1).
  • the image matrices in the x and y directions are X and Y, respectively, the coordinates (x, y) (x: 1 ⁇ x) in the image of the i (where 1 ⁇ i ⁇ M) th small receiving coil
  • the pixel value of ⁇ X, y: 1 ⁇ y ⁇ Y) is S i (x, y)
  • the sensitivity distribution is C i (x, y)
  • the density distribution of the subject is p (x, y).
  • equation (4) holds in the same manner for M RF receiving coils, it can be expressed as equation (5) when expressed in a matrix.
  • the density distribution P of the subject that is, an image without aliasing can be obtained by obtaining an inverse matrix of the sensitivity distribution as follows.
  • shading correction will be described as another example of image reconstruction processing S103 other than restoration processing.
  • the sensitivity distribution of each local coil is localized, so that shading occurs in the image.
  • the shading correction first, a correction coefficient for correcting the shading is obtained (processing 1), and then the correction data is multiplied by measurement data (image data) to obtain corrected image data (processing 2). As a result, measurement data in which shading is corrected is obtained.
  • the fitting curved surface F (x, y) is calculated independently for each slice, and the sensitivity distribution of the MAC (entire synthesized coil) is approximated.
  • F (x, y) to be calculated can be expressed by Expression (8) using six fitting coefficients a to f.
  • x and y indicate the position in the frequency encoding direction and the phase encoding direction, respectively.
  • Equation (9) the fitting curved surface calculated by Equation (8) for each slice is fitted with a quadratic function expressed by Equation (9) in the slice direction. Since the sensitivity of the multiple array coil is smoothly distributed in the three-dimensional space, it is possible to provide continuity also in the slice direction by fitting in the slice direction.
  • Equation (9) z indicates the position in the slice direction.
  • the fitting curved surface is obtained by Expression (10) by combining Expressions (8) and (9) described above.
  • Shading correction (Process 2) is obtained by multiplying the image data by the correction coefficient G obtained as described above, thereby correcting the shading including not only the receiving coil sensitivity but also the shading due to uneven illumination (corrected) S i (x, y, z)) is obtained.
  • the processing mainly performed by the image processing unit 220 of the MRI apparatus to which compressed sensing is applied has been described above.
  • the MRI apparatus according to the present embodiment is characterized in that, among these processes, in particular, in order to shorten the process performed by the image processing unit 220, parallelization is performed according to the content of the process. In other words, depending on the contents of the process, it is essential to be sequential (there is a constraint on the context), but by dividing one process appropriately, the divided processes can be parallelized, reducing the processing time. Can be achieved.
  • FIG. 5 shows a configuration example of the image processing unit 220 for shortening the processing time.
  • the image processing unit 220 includes a first calculation unit 221 and a second calculation unit 222 in order to process a plurality of calculations in parallel, and controls operations of the first calculation unit 221 and the second calculation unit 222.
  • the main control unit 223 is further provided.
  • the image processing unit 220 is a functional unit of the signal processing unit 120 including a CPU, and the first calculation unit 221 and the second calculation unit 222 can be constructed on the CPU. If parallel processing is performed on the same CPU, each processing time is extended, and there is a possibility that the effect cannot be obtained sufficiently.
  • the image processing unit 220 has a configuration capable of simultaneously performing a plurality of processes, such as assigning a plurality of CPUs or using a multi-core CPU or GPU.
  • the main control unit 223 may have the function of the control unit 210 that controls the measurement unit 110.
  • the restoration process is an iterative calculation including a sparse conversion and a threshold process as shown in FIG. 4, for example, and is executed for each reception coil when there are a plurality of reception coils.
  • the processing for each reception coil can be performed in parallel in the first calculation unit 221.
  • the second calculation unit 222 starts processing using the acquired sparse K space data (S201).
  • This process is a part of the image reconstruction process (S202) performed after restoration, and is performed using the data before restoration, for example, processing using data in a relatively dense low frequency region. Creation, correction coefficient calculation, and the like.
  • the image reconstruction process S202 includes 2DFFT, parallel imaging calculation, image data synthesis (MAC synthesis) of each receiving coil, shading correction using the sensitivity map of the receiving coil, etc.
  • the first computing unit 221 and the second computing unit Any of 222 may be performed, or a third arithmetic unit provided separately from the first arithmetic unit 221 and the second arithmetic unit 222 may execute.
  • the image reconstruction process S202 performed after the restoration process can be performed in a short time using the map, the coefficient, and the like necessary for the process already obtained in the process S201 by the second calculation unit 222. it can.
  • FIG. 7 shows the time saving effect of this embodiment.
  • FIG. 7A schematically shows the conventional processing time
  • FIG. 7B schematically shows the processing time of the present embodiment.
  • the image reconstruction process is conventionally performed after the restoration process, whereas in the present embodiment, by performing a part of the image reconstruction process during the time-consuming restoration process, The time for the image reconstruction process after the restoration process can be greatly reduced.
  • the main control unit 223 (or the control unit 210) performs the restoration processing and the calculation based on parallel imaging continuously in the first calculation unit 221, and performs the processing of the first calculation unit 221.
  • the second arithmetic unit 222 controls the image processing unit 220 to perform calculation based on parallel imaging and calculation of a correction value used for correction processing.
  • correction processing using the correction value calculated by the second calculation unit is performed.
  • the correction processing is, for example, shading correction
  • the second calculation unit calculates correction coefficients used for shading correction using sparse K space data before restoration.
  • FIG. 8 data at the time when the processing is performed (image diagram when the head is imaged) is also shown next to the block indicating the processing (the same applies to the following processing flow).
  • image refers to a real space image obtained by performing Fourier transform on K space data.
  • step S801 sparse K-space data measured by compressed sensing is acquired.
  • the image 81 obtained by reconstructing the K space data is an image with a blurred outline.
  • step S802 restoration processing is performed on the acquired sparse K space data to restore the K space data.
  • an image 82 with a clear outline is obtained.
  • step S803 a parallel imaging operation is performed on the image (82) data to expand the aliasing generated in the image. As a result, an image 83 without wrapping is obtained.
  • step S804 a value (correction coefficient) for correcting shading caused by a difference in coil sensitivity or uneven irradiation is calculated for the image after the parallel imaging calculation.
  • This calculation is a process of calculating the correction coefficient described in the above equations (8) to (11).
  • the correction coefficient 84 is a value that corrects the signal value low when the signal value is high and corrects the signal value high when the signal value is low.
  • step S805 correction processing is performed using the correction coefficient created in step S804 (formula (12)). As a result, an image 85 with no shading is completed.
  • the time until the image is completed after sparse K space data acquisition is as shown in FIG. This is the total processing time for each step.
  • the processing time of each step varies depending on the processing contents, and the restoration processing S802 and the correction coefficient calculation S804 for obtaining the fitting curved surface require a longer time than the parallel imaging calculation S803 and the correction processing S805. .
  • step S801 after acquiring sparse K-space data measured by compressed sensing, two processes that require a long time, a restoration process S802 and an image gradation correction coefficient calculation S804, are processed in parallel.
  • the first calculation unit 221 performs a restoration process S802 and subsequently a parallel imaging calculation S803, and in parallel thereto, the second calculation unit 222 performs a parallel imaging calculation S901 and subsequently a correction coefficient calculation S902. Since S802 and S803 have the same contents as conventional processing, description thereof will be omitted.
  • step S901 first, parallel imaging processing is performed on sparse K-space data to create an image 91 with a blurred outline without aliasing.
  • step S902 a correction coefficient for correcting shading is created using the image data created in S901.
  • the image data used in this process (image 91) is blurred in the outline of the original image data, but the shading appears characteristically, A correction coefficient 92 that is not different from the conventional one can be obtained.
  • step S805 When the processes performed in parallel are completed, the correction process in step S805 is performed using the correction coefficient 92 created in step S902. As a result, an image 85 with no shading that is different from the conventional method is completed.
  • the image processing unit 220 uses the central portion of the sparse K space data to calculate a value for correcting the shading of the image.
  • the processing of this embodiment will be described with reference to FIG. In FIG. 11, processes having the same contents as those shown in FIG.
  • the second calculation unit 222 first performs parallel imaging calculation, and then performs correction coefficient calculation using the data after aliasing removal.
  • the difference is that the correction coefficient is calculated using the center data.
  • step S911 the central part of sparse K-space data is extracted.
  • the central portion means a low-frequency region including the K-space origin, and is data of a region where data points are densely measured as shown in FIG. 3, and is determined by a compressed sensing measurement pattern (31 to 33).
  • the data acquired in step S801 is sparse K-space data as represented by the image 81, but there is no aliasing because the data is densely present in the central portion of the K-space. Therefore, an image 93 without aliasing can be obtained by reconstructing the K space data of the central portion without performing parallel imaging.
  • step S912 using the image data created in S911, a correction coefficient for correcting shading is calculated by fitting (Equations (8) to (11)).
  • step S805 the correction process using the correction value (correction coefficient) 94 created in step S912 is the same as in the first embodiment. Complete.
  • the processing time can be shortened as shown in FIG.
  • the present embodiment is characterized in that the image processing unit 220 performs a parallel imaging operation prior to the restoration process, and then performs the restoration process and correction coefficient calculation for shading correction in parallel.
  • the processing of this embodiment will be described with reference to FIG. 12, processes having the same contents as the processes shown in FIG. 9 are denoted by the same reference numerals, and redundant description is omitted.
  • step S921 parallel imaging calculation is performed. This calculation may be performed by either the first calculation unit 221 or the second calculation unit 222, or may be executed by a third calculation unit provided separately from the first calculation unit 221 and the second calculation unit 222. Also good.
  • the parallel imaging operation may be an operation on K space data (for example, SMASH) or an operation on image data (for example, SENSE).
  • step S922 the second calculation unit 222 creates a correction coefficient for correcting the shading using the image data obtained in step S921, as in the first embodiment.
  • the first calculation unit 221 performs the restoration process in step S923.
  • the correction processing S805 is performed using the restored image data and the correction coefficient calculated in step S922, as in the first embodiment. Also in this embodiment, the same effect of shortening the processing time as in the first embodiment can be obtained.
  • FIG. 13 shows a processing flow of this embodiment.
  • processes having the same contents as those shown in FIG. 9 are denoted by the same reference numerals.
  • step S802 and S803 performed by the first calculation unit 221 is the same as in the first embodiment except that the process is replaced with step S931.
  • a restoration process using a compression sensing algorithm that combines a Wavelet transform and a parallel imaging operation such as GRAPPA is performed.
  • a compressed sensing algorithm is disclosed in Patent Document 3, for example, and is also available as the above-described software package (L1-SPIRiT, L1-ESPIRiT).
  • the present embodiment is characterized in that, while the restoration processing is performed by the first computing unit 221, in parallel, the second computing unit 222 performs parallel imaging map creation and shading correction coefficient calculation. It is. That is, in the present embodiment, parallel imaging map creation is added as processing of the second computing unit 222 in the second embodiment, and step S803 of parallel imaging computation is performed after the completion of this processing.
  • step S802 After acquiring sparse K-space data (S801), in step S802, the first calculation unit 221 performs a restoration process using a predetermined compressed sensing algorithm. On the other hand, in step S911, the second calculation unit 222 extracts the central portion of the sparse K space data for each small coil. As described in the second embodiment, the central portion is data that does not cause aliasing because data is densely present.
  • step S941 a map for parallel imaging is created from the image 93 obtained by reconstructing the data of the central portion.
  • the map C i (x, y) can calculate the image data S i (x, y) of each small coil as follows.
  • the second arithmetic unit 222 creates a correction coefficient for correcting the shading using the image data obtained in step S941, as in the second embodiment.
  • step S803 parallel imaging calculation is performed using the map created in step S941, and then step S805 In step S912, shading correction is performed using the correction coefficient 94 calculated in step S912. As a result, an image 85 with no shading is completed in a short time as in other embodiments.
  • the processing of the first computing unit 221 and the processing of the second computing unit 222 are examples performed independently and in parallel. However, in the present embodiment, the processing of the first computing unit 221 is performed.
  • the feature is that the data being restored is passed to the second calculation unit 222, and the second calculation unit 222 performs correction coefficient calculation using the data being restored.
  • the sparse K-space data acquired in step S801 is taken in by the first calculation unit 221 and the restoration process S802 and then the parallel imaging calculation S803 are performed as in the first embodiment.
  • the first calculation unit 221 passes the data restored to some extent to the second calculation unit 222 during the restoration process. It is preferable that the degree of restoration of data to be passed to the second arithmetic unit 222 is such that it does not cause a problem in proceeding with the processing in the second arithmetic unit 222, and is not particularly limited.
  • the threshold value (soft threshold value) can be determined by the ratio to the initial threshold value and the number of repetitions.
  • the second calculation unit 222 captures intermediate data (step S951), performs parallel imaging calculation on the intermediate data in step S952, and removes the aliasing. Get 95. Thereafter, in step S953, correction coefficient calculation is performed to obtain the correction coefficient 96, as in the first embodiment.
  • the correction coefficient for shading correction can be calculated more accurately by using data that has been restored to some extent.
  • FIG. 16A shows the processing time of the conventional method.
  • “image density correction coefficient calculation” is performed in parallel from the stage where “restoration processing” is performed halfway. Therefore, compared to the first embodiment, the timing for starting the “image density correction value calculation” is delayed, but since the time required for the “restoration process” is long, the “image density correction value calculation” is faster. When completed, the same time reduction effect as in the first embodiment can be obtained. Even if the “restoration process” is completed first, the imaging time can be shortened by performing the correction process after waiting for the “image density correction value calculation” to be completed.
  • the sixth embodiment described above is an example in which data is transferred to the second calculation unit 222 during the processing of the first calculation unit 221, and the second calculation unit 222 performs processing using the intermediate data. Then, data is exchanged between the first calculation unit 221 and the second calculation unit 222, and the first calculation unit 221 controls the repetitive calculation of restoration using the processing result of the second calculation unit 222. This is a feature that reduces the time required for the restoration process.
  • the processing performed by the first calculation unit 221 after sparse K-space data acquisition (S801) is the processing subsequent to step S802 and step S803 as in the first embodiment, and the K-space data is restored in step S802.
  • the aliasing is developed by the parallel imaging calculation. It is the same processing flow as the conventional method.
  • step S961 parallel imaging processing is performed on the sparse K-space data in step S961, and an image 91 with a blurred outline without aliasing is created.
  • step S962 data is exchanged with the first calculation unit 221 in the middle of the restoration process of step S802 and used for the restoration process.
  • the image 91 created by the parallel imaging operation (S961) is compared with the image restored halfway by the restoration process S802, the degree of restoration is judged by the magnitude of the difference, and the coefficient value that determines the soft threshold value is Or change it. Thereby, the amount of calculation can be reduced according to the degree of restoration determined.
  • step S963 a correction coefficient for correcting the shading is obtained using the data before restoration (data after performing the parallel imaging operation in step S961). Further, instead of using the data before restoration, data in the middle of restoration exchanged in step S962 may be used as in the sixth embodiment.
  • step S805 shading correction (shading correction processing) is performed to obtain an image 85 from which aliasing is removed and shading is corrected.
  • the restoration process can be performed with more optimal accuracy, and the processing time can be shortened by omitting unnecessary processes.
  • FIG. 18A shows the processing time of the conventional method.
  • “restoration processing” and “image shading correction value calculation” are performed in parallel, so the imaging time is shorter than in the conventional method.
  • the processing time of the restoration process is shortened.
  • the embodiments of the MRI apparatus of the present embodiment have been described above, but the present invention is not limited to these embodiments, and various modifications can be made.
  • the calculation of the correction coefficient for shading correction is exemplified as the main process performed in parallel with the restoration process.
  • the calculation is related to image reconstruction and is a sparse K space. Any calculation using data or data being restored can be applied.
  • restoration processing and correction coefficient calculation are merely examples, and the use of other methods is also included in the present invention. Further, a configuration in which elements that are not essential are added or deleted is also included in the present invention.
  • 100 MRI device 110 measurement unit, 120 signal processing unit, 130 operation unit, 210 control unit, 220 image processing unit, 221 first calculation unit, 222 second calculation unit, 223 main control unit, 230 storage unit, 310 display unit 320 input section

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Abstract

Afin de réduire le temps nécessaire pour qu'un dispositif d'IRM, qui utilise le calcul d'acquisition comprimée, affiche une image après un examen, ce dispositif d'IRM possède une unité de traitement d'image munie d'une première unité de calcul et d'une seconde unité de calcul qui exécutent le traitement en parallèle. Lors de la reconstruction d'une image à l'aide de données de mesure incomplètes, l'unité de traitement d'image exécute un traitement pour la restauration des données de mesure incomplètes et un traitement subséquent tel qu'une reconstruction d'image et une correction d'ombrage. À ce moment, la première unité de calcul exécute le traitement de restauration et la seconde unité de calcul exécute, parallèlement à celle-ci, une partie du traitement qui est exécutée comme traitement pour la reconstruction d'image autre que le traitement de restauration. Le traitement restant concernant la reconstruction d'image est exécuté après le traitement de restauration par la première unité de calcul.
PCT/JP2017/028159 2016-08-22 2017-08-03 Dispositif d'imagerie par résonance magnétique et procédé de reconstruction d'image WO2018037868A1 (fr)

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