Medical information processing device and medical information processing method

Abstract

To accurately estimate denoise intensity.SOLUTION: A medical information processing device according to an embodiment comprises a first calculation unit and a second calculation unit. The first calculation unit calculates noise intensity from first data collected by a first scan. The second calculation unit calculates denoise intensity to be used for denoise processing to be applied to second data obtained by a second scan on the basis of the noise intensity and a difference between an imaging condition of the first scan and an imaging condition of the second scan.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] An embodiment of the present invention relates to a medical information processing apparatus and a medical information processing method. [Background technology]

[0002] In recent years, a method called shrinkage method has become known as a denoising method applicable to magnetic resonance imaging (MRI). In this method, the denoising intensity (amount of noise) contained in an image is estimated, and a threshold value for the denoising process is determined based on the estimated denoising intensity. In order to ensure denoising performance, it is necessary to accurately estimate the denoising intensity contained in an image. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2015-181840 A Summary of the Invention [Problem to be solved by the invention]

[0004] An object of the present invention is to provide a medical image processing apparatus and a medical image processing method capable of estimating denoising intensity with high accuracy. [Means for solving the problem]

[0005] The medical image processing apparatus according to the embodiment includes a first calculator and a second calculator. The first calculator calculates a noise intensity from first data acquired by a first scan. The second calculator calculates a denoising intensity used in denoising processing performed on second data acquired by the second scan based on the noise intensity and a difference between an imaging condition of the first scan and an imaging condition of a second scan. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram showing an example of the overall configuration of an MRI apparatus according to the first embodiment. [Diagram 2] FIG. 2 is a flowchart showing a processing procedure performed by the MRI apparatus according to the first embodiment. [Diagram 3] FIG. 3 is a diagram for explaining the processing function of the MRI apparatus according to the first embodiment. [Figure 4] FIG. 4 is a diagram for explaining the processing function of the MRI apparatus according to the first embodiment. [Diagram 5] FIG. 5 is a diagram for explaining the processing function of the MRI apparatus according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, a medical image processing apparatus and a medical image processing method according to an embodiment will be described with reference to the drawings. Note that the embodiment is not limited to the following embodiment. Furthermore, the contents described in one embodiment can be similarly applied to other embodiments in principle.

[0008] (First embodiment) Fig. 1 is a diagram showing an example of the overall configuration of an MRI apparatus according to the first embodiment. For example, as shown in Fig. 1, an MRI apparatus 100 according to this embodiment includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power supply 3, a WB (Whole Body) coil 4, a receiving coil device 5, a bed 6, a transmitting circuit 7, a receiving circuit 8, a gantry 9, an interface 10, a display 11, a memory circuit 12, and processing circuits 13, 14, 15, and 16. Note that the MRI apparatus 100 does not include a subject S (e.g., a human body). Also, the MRI apparatus 100 is an example of a medical information processing apparatus.

[0009] The static magnetic field magnet 1 generates a static magnetic field in an imaging space in which a subject S is placed. Specifically, the static magnetic field magnet 1 is formed in a hollow, approximately cylindrical shape (including a shape in which the cross section perpendicular to the central axis is elliptical), and generates a static magnetic field in an imaging space arranged on the inner circumference side. For example, the static magnetic field magnet 1 has a cooling container formed in an approximately cylindrical shape, and a magnet such as a superconducting magnet immersed in a coolant (e.g., liquid helium, etc.) filled in the cooling container. Note that the static magnetic field magnet 1 may be one that generates a static magnetic field using, for example, a permanent magnet.

[0010] The gradient magnetic field coil 2 generates a gradient magnetic field in an imaging space in which the subject S is placed. Specifically, the gradient magnetic field coil 2 is formed in a hollow, approximately cylindrical shape (including a shape in which the cross section perpendicular to the central axis is elliptical) and has a plurality of approximately cylindrical gradient magnetic field coils stacked in the radial direction. Here, the plurality of gradient magnetic field coils generate gradient magnetic fields along the mutually orthogonal X-axis, Y-axis, and Z-axis directions in the imaging space placed on the inner periphery side, based on a current supplied from a gradient magnetic field power supply 3.

[0011] More specifically, the gradient magnetic field coil 2 has an X coil that generates a gradient magnetic field along the X-axis direction, a Y coil that generates a gradient magnetic field along the Y-axis direction, and a Z coil that generates a gradient magnetic field along the Z-axis direction. Here, the X-axis, Y-axis, and Z-axis form an apparatus coordinate system specific to the MRI apparatus 100. For example, the X-axis is set in a horizontal direction perpendicular to the central axis of the gradient magnetic field coil 2, and the Y-axis is set in a vertical direction perpendicular to the central axis of the gradient magnetic field coil 2. The Z-axis is set along the central axis of the gradient magnetic field coil 2.

[0012] The gradient magnetic field power supply 3 generates gradient magnetic fields along the X-axis, Y-axis, and Z-axis directions in the imaging space by supplying currents individually to the X-coil, Y-coil, and Z-coil of the gradient magnetic field coil 2. Specifically, the gradient magnetic field power supply 3 generates gradient magnetic fields along the mutually orthogonal readout direction, phase encoding direction, and slice direction by supplying currents appropriately to the X-coil, Y-coil, and Z-coil. Here, the axis along the readout direction, the axis along the phase encoding direction, and the axis along the slice direction form a logical coordinate system for defining a slice region or a volume region to be imaged.

[0013] In the following, as an example, a case will be described in which the axes along the readout direction, the phase encoding direction, and the slice direction constituting the logical coordinate system correspond to the X-axis, the Y-axis, and the Z-axis constituting the device coordinate system, respectively. However, the correspondence between the logical coordinate system and the device coordinate system is not limited to this, and can be arbitrarily changed.

[0014] The gradient magnetic fields along the readout direction, the phase encoding direction, and the slice direction are superimposed on the static magnetic field generated by the static magnetic field magnet 1 to provide spatial position information to the magnetic resonance (MR) signal generated from the subject S. Specifically, the gradient magnetic field Gro in the readout direction changes the frequency of the MR signal according to the position in the readout direction, thereby providing the MR signal with position information along the readout direction. The gradient magnetic field Gpe in the phase encoding direction changes the phase of the MR signal along the phase encoding direction, thereby providing the MR signal with position information along the phase encoding direction. The gradient magnetic field Gss in the slice direction provides the MR signal with position information along the slice direction. For example, when the imaging region is a slice region, the gradient magnetic field Gss in the slice direction is used to determine the direction, thickness, and number of slice regions, and when the imaging region is a volume region, it is used to change the phase of the MR signal according to the position in the slice direction.

[0015] The WB coil 4 is disposed inside the gradient magnetic field coil 2, and is an RF coil having a function of a transmitting coil that applies an RF (Radio Frequency) magnetic field to an imaging space in which the subject S is disposed, and a function of a receiving coil that receives an MR signal generated from the subject S under the influence of the RF magnetic field. Specifically, the WB coil 4 is formed in a hollow, substantially cylindrical shape (including a shape in which the cross section perpendicular to the central axis of the cylinder is elliptical), and applies an RF magnetic field to the imaging space disposed inside the cylinder based on a high-frequency pulse signal supplied from the transmitting circuit 7. In addition, the WB coil 4 receives the MR signal generated from the subject S under the influence of the RF magnetic field, and outputs the received MR signal to the receiving circuit 8.

[0016] The receiving coil device 5 is an RF coil that receives an MR signal generated from the subject S. For example, the receiving coil device 5 is prepared for each part of the subject S, and is placed near the part to be imaged when imaging the subject S. The receiving coil device 5 has one or more independent coil elements (hereinafter, simply referred to as "elements") that receive an MR signal generated from the subject S due to the influence of the RF magnetic field applied by the WB coil 4. The receiving coil device 5 amplifies the MR signal received by the element, and outputs the MR signal to the receiving circuit 8. The receiving coil device 5 may further have a function of a transmitting coil that applies an RF magnetic field to the subject S. In that case, the receiving coil device 5 is connected to a transmitting circuit 7, and applies an RF magnetic field to the subject S based on an RF pulse signal supplied from the transmitting circuit 7.

[0017] The bed 6 includes a top plate 6a on which the subject S is placed, and the top plate 6a on which the subject S is placed is moved to an imaging space when imaging the subject S. For example, the bed 6 is installed so that the longitudinal direction of the top plate 6a is parallel to the central axis of the static magnetic field magnet 1.

[0018] The transmission circuit 7 outputs an RF pulse signal corresponding to a resonance frequency (Larmor frequency) specific to a target atomic nucleus placed in a static magnetic field to the WB coil 4. Specifically, the transmission circuit 7 has a pulse generator, an RF generator, a modulator, and an amplifier. The pulse generator generates a waveform of an RF pulse signal. The RF generator generates an RF signal at a resonance frequency. The modulator generates an RF pulse signal by modulating the amplitude of the RF signal generated by the RF generator with the waveform generated by the pulse generator. The amplifier amplifies the RF pulse signal generated by the modulator and outputs it to the WB coil 4.

[0019] The receiving circuitry 8 generates MR signal data based on the MR signals received by the WB coil 4 or the receiving coil device 5. Then, the receiving circuitry 8 outputs the generated MR signal data to the processing circuitry .

[0020] The gantry 9 has a hollow bore 9a formed in a substantially cylindrical shape (including one in which the cross section perpendicular to the central axis has an elliptical shape), and supports the static magnetic field magnet 1, the gradient magnetic field coil 2, and the WB coil 4. Specifically, the gantry 9 supports the static magnetic field magnet 1, the gradient magnetic field coil 2, and the WB coil 4 in a state in which the gradient magnetic field coil 2 is disposed on the inner circumference side of the static magnetic field magnet 1, the WB coil 4 is disposed on the inner circumference side of the gradient magnetic field coil 2, and the bore 9a is disposed on the inner circumference side of the WB coil 4. Here, the space within the bore 9a of the gantry 9 becomes an imaging space in which the subject S is disposed when imaging the subject S.

[0021] Here, an example will be described in which the MRI apparatus 100 has a so-called tunnel-type configuration in which the static magnetic field magnet 1, the gradient magnetic field coil 2, and the WB coil 4 are each formed in a substantially cylindrical shape, but the embodiment is not limited to this. For example, the MRI apparatus 100 may have a so-called open-type configuration in which a pair of static magnetic field magnets, a pair of gradient magnetic field coil units, and a pair of RF coils are arranged to face each other across an imaging space in which a subject S is placed. In this case, the space sandwiched between the pair of static magnetic field magnets, the pair of gradient magnetic field coil units, and the pair of RF coils corresponds to a bore in the tunnel-type configuration.

[0022] The interface 10 accepts input operations of various instructions and various information from an operator. Specifically, the interface 10 is connected to a processing circuit 16, converts the input operations received from the operator into electrical signals, and outputs the electrical signals to the processing circuit 16. For example, the interface 10 is realized by a trackball for setting imaging conditions and a region of interest (ROI), a switch button, a mouse, a keyboard, a touchpad for performing input operations by touching the operation surface, a touch screen in which a display screen and a touchpad are integrated, a non-contact input circuit using an optical sensor, and a voice input circuit. Note that in this specification, the interface 10 is not limited to only those having physical operation parts such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to a control circuit is also included as an example of the interface 10.

[0023] The display 11 displays various types of information and images. Specifically, the display 11 is connected to a processing circuit 16, and converts various types of information and image data sent from the processing circuit 16 into electrical signals for display and outputs them. For example, the display 11 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like.

[0024] The memory circuitry 12 stores various data. Specifically, the memory circuitry 12 stores MR signal data and image data. For example, the memory circuitry 12 is realized by a semiconductor memory element such as a random access memory (RAM) or a flash memory, a hard disk, an optical disk, or the like.

[0025] The processing circuitry 13 has a bed control function 13a. The bed control function 13a controls the operation of the bed 6 by outputting a control electrical signal to the bed 6. For example, the bed control function 13a receives an instruction to move the top board 6a in the longitudinal direction, the up-down direction, or the left-right direction from the operator via the interface 10, and operates a moving mechanism for the top board 6a of the bed 6 so as to move the top board 6a according to the received instruction.

[0026] The processing circuitry 14 has an acquisition function 14a. The acquisition function 14a executes various pulse sequences to acquire MR signal data of the subject S. Specifically, the acquisition function 14a executes a pulse sequence by driving the gradient magnetic field power supply 3, the transmission circuitry 7, and the reception circuitry 8 according to sequence execution data output from the processing circuitry 16. Here, the sequence execution data is data representing a pulse sequence, and is information that specifies the timing and strength of the current that the gradient magnetic field power supply 3 supplies to the gradient magnetic field coil 2, the strength and supply timing of the RF pulse signal that the transmission circuitry 7 supplies to the WB coil 4, and the detection timing of the reception circuitry 8 to detect an MR signal. Then, the acquisition function 14a receives MR signal data from the reception circuitry 8 as a result of executing the pulse sequence, and stores the received MR signal data in the storage circuitry 12. Here, a set of MR signal data received by the acquisition function 14a is arranged two-dimensionally or three-dimensionally according to the position information given by the readout gradient magnetic field, phase encoding gradient magnetic field, and slice gradient magnetic field described above, and is stored in the storage circuitry 12 as data constituting a k-space.

[0027] The processing circuitry 15 has a reconstruction function 15a. The reconstruction function 15a generates image data based on the MR signal data stored in the memory circuitry 12. Specifically, the reconstruction function 15a reads out the MR signal data stored in the memory circuitry 12 by the acquisition function 14a, and generates image data by performing post-processing, i.e., reconstruction processing such as Fourier Transform (FT), on the read out MR signal data. In addition, the reconstruction function 15a stores the generated image data in the memory circuitry 12.

[0028] The processing circuitry 16 has a main control function 16a, a first calculation function 16b, a second calculation function 16c, and a denoising processing function 16d. The main control function 16a controls the entire MRI apparatus 100 by controlling each component of the MRI apparatus 100. Specifically, the main control function 16a displays a GUI (Graphical User Interface) for receiving input operations of various instructions and various information from an operator on the display 11. Then, the main control function 16a controls each component of the MRI apparatus 100 in response to an input operation received via the interface 10. For example, the main control function 16a receives input of imaging conditions from an operator via the interface 10. Then, the main control function 16a generates sequence execution data based on the received imaging conditions, and transmits the sequence execution data to the processing circuitry 14 to execute various pulse sequences. Also, for example, the main control function 16a reads image data from the storage circuitry 12 and outputs it to the display 11 in response to a request from the operator. The first calculation function 16b, the second calculation function 16c, and the denoising processing function 16d will be described later.

[0029] Here, the above-mentioned processing circuits 13, 14, 15, and 16 are realized by, for example, a processor. In this case, the processing functions of each processing circuit are stored in the storage circuit 12 in the form of, for example, a program executable by a computer. Each processing circuit realizes the function corresponding to each program by reading and executing each program from the storage circuit 12. Here, each processing circuit may be configured by multiple processors, and each processor may realize each processing function by executing a program. In addition, the processing functions of each processing circuit may be realized by being appropriately distributed or integrated in a single or multiple processing circuits. In addition, although it has been described here that a single storage circuit 12 stores a program corresponding to each processing function, a configuration in which multiple storage circuits are distributed and arranged, and the processing circuits read out the corresponding programs from the individual storage circuits, may also be used.

[0030] The above describes the configuration of the MRI apparatus 100 according to the first embodiment. With this configuration, the MRI apparatus 100 executes the following processing functions in order to accurately estimate the denoising intensity.

[0031] The MRI apparatus 100 according to the first embodiment can perform high-speed imaging using parallel imaging (PI). In PI, for example, by utilizing the fact that sensitivity differs depending on the arrangement of coils, k-space data is simultaneously imaged using multiple coils with thinning, and an image (reconstructed image) is reconstructed from the obtained k-space data while suppressing artifacts. In the following description, a case where PI is performed will be described, but the embodiment is not limited to this.

[0032] A processing procedure by the MRI apparatus 100 according to the first embodiment will be described with reference to Fig. 2. Fig. 2 is a flowchart showing the processing procedure by the MRI apparatus 100 according to the first embodiment. The processing procedure shown in Fig. 2 is started, for example, in response to an imaging start request input by an operator.

[0033] Note that Fig. 2 will be described with reference to Fig. 3 and Fig. 4. Fig. 3 and Fig. 4 are diagrams for explaining the processing functions of the MRI apparatus 100 according to the first embodiment.

[0034] 2, the main control function 16a sets imaging conditions (step S101). For example, the main control function 16a accepts input of imaging conditions from an operator via the interface 10. Then, the main control function 16a generates sequence execution data based on the accepted imaging conditions, and transmits the generated sequence execution data to the collection function 14a.

[0035] Next, the acquisition function 14a executes a preparatory scan (step S102). For example, the acquisition function 14a executes the preparatory scan by driving the gradient magnetic field power supply 3, the transmission circuitry 7, and the reception circuitry 8 according to sequence execution data output from the main control function 16a. The preparatory scan includes, for example, a scan for acquiring an image for positioning, a shimming scan for correcting the inhomogeneity of the static magnetic field, and a scan for acquiring a sensitivity map (sensitivity map scan).

[0036] In the sensitivity map scan, the collecting function 14a collects "noise data" and "coil sensitivity information". For example, the collecting function 14a collects "noise data" by performing a noise scan for each of the WB coil 4 and each element, which scans the WB coil 4 and each element without applying an RF magnetic field. The noise data is usually used to calculate a noise gain for correcting the deviation between channels of the signal value obtained by the noise scan, and a noise matrix showing the correlation between elements. The collecting function 14a also performs individual scans for each of the WB coil 4 and each element. The collecting function 14a then generates "coil sensitivity information" for each element by comparing information (or image data) scanned by the WB coil 4 with information (or image data) scanned by each element.

[0037] In order to collect only noise data, it is preferable to perform a noise scan without transmitting an RF pulse. However, an RF pulse may be applied as long as the generated NMR (Nuclear Magnetic Resonance) signal (MR signal) does not affect the estimation of the noise amount. For example, an RF pulse with an extremely small flip angle may be applied in a noise scan.

[0038] In other words, the preparatory scan includes a scan for collecting noise data without transmitting an RF pulse of a certain intensity or more, where the RF pulse of a certain intensity or more is an RF pulse of a certain intensity or more that does not affect the estimation of the noise amount of the generated NMR signal.

[0039] Then, the first calculation function 16b calculates the noise intensity and a conversion coefficient at the time of the preparatory scan (step S103). For example, the first calculation function 16b calculates the noise intensity from the noise data collected by the preparatory scan.

[0040] The calculation process of the noise intensity will be described with reference to FIG. 3. As shown in FIG. 3, for example, the reconstruction function 15a performs a Fast Fourier Transform (FFT) on the collected data of each channel (each element) collected by the noise scan to generate noise data, which is data of the real space. Then, the first calculation function 16b calculates the complex average value and the standard deviation of each pixel value of the noise data of all slices and all channels by complex calculation. The first calculation function 16b outputs the calculated standard deviation as the noise intensity "NoiseSD_map". In other words, the first calculation function 16b calculates the noise intensity based on the data obtained by converting the k-space data collected by the preparatory scan into the real space.

[0041] The first calculation function 16b calculates a conversion coefficient for the preparatory scan based on the imaging conditions of the preparatory scan. The conversion coefficient is a coefficient for adjusting the difference between the imaging conditions of the preparatory scan and the imaging conditions of the main scan. The conversion coefficient for the preparatory scan (sensitivity map scan) is an example of the first conversion coefficient.

[0042] For example, the first calculation function 16b calculates the conversion coefficient during the preparatory scan using the following formula (1). In formula (1), Rg indicates the signal amplification rate in the receiving circuit 8. Nex indicates the number of additions in k-space. Nx indicates the matrix size in the frequency encoding direction. Ny indicates the matrix size in the phase encoding direction. Nz indicates the number of slice encodings during 3DFT. BW indicates the bandwidth per pixel (BW=BandWidth / Nx). g is a factor representing the degree of amplification of noise components due to unfolding processing in parallel imaging. A indicates the speed-up rate in high-speed imaging using compressed sensing (CS).

[0043]

number

[0044] Each parameter Rg, Nex, Nx, Ny, Nz, BW, g, and A included in formula (1) can be acquired from the imaging conditions. That is, the first calculation function 16b acquires each parameter Rg, Nex, Nx, Ny, Nz, BW, g, and A from the imaging conditions of the preparatory scan. Then, the first calculation function 16b applies each acquired parameter to formula (1) to calculate the conversion coefficient "C_map" during the preparatory scan.

[0045] In this way, the first calculation function 16b calculates the noise intensity "NoiseSD_map" and the conversion coefficient "C_map" during the preparatory scan. Note that FIG. 3 and formula (1) are merely examples, and the embodiment is not limited to these. For example, the conversion coefficient may be multiplied by any coefficient. Also, application of the above formula (1) is not limited to 3DFT and CS. For example, in the case of 2DFT, Nz is "1". Also, when CS is not applied, A is "1".

[0046] The acquisition function 14a executes the main scan (step S104). For example, the acquisition function 14a executes the main scan by driving the gradient magnetic field power supply 3, the transmission circuitry 7, and the reception circuitry 8 according to sequence execution data output from the main control function 16a. Note that the main scan is executed, for example, to generate a diagnostic image.

[0047] The reconstruction function 15a generates a reconstructed image (step S105). For example, the reconstruction function 15a uses the sensitivity map to reconstruct an image (reconstructed image) without aliasing from the MR signals collected by the main scan. Then, the reconstruction function 15a stores the reconstructed image data in the storage circuitry 12. Note that the reconstructed image generated here is image data before denoising processing.

[0048] The first calculation function 16b calculates a conversion coefficient for the main scan (step S106). For example, the first calculation function 16b calculates the conversion coefficient for the main scan based on the imaging conditions for the main scan. The conversion coefficient for the main scan is an example of the second conversion coefficient.

[0049] For example, the first calculation function 16b acquires the parameters Rg, Nex, Nx, Ny, Nz, BW, g, and A from the imaging conditions of the main scan. Then, the first calculation function 16b applies each acquired parameter to the formula (1) to calculate the conversion coefficient "C" for the main scan.

[0050] The second calculation function 16c calculates a denoising intensity based on the noise intensity, the conversion coefficient during the preparatory scan, and the conversion coefficient during the main scan (step S107). Then, the denoising processing function 16d executes denoising processing on the reconstructed image based on the denoising intensity (step S108).

[0051] The calculation process of the denoising intensity and the denoising process will be described with reference to Fig. 4. For example, the second calculation function 16c acquires the noise intensity "NoiseSD_map" calculated by the first calculation function 16b, the conversion coefficient "C_map" during the preparatory scan, and the conversion coefficient "C" during the main scan. The second calculation function 16c then applies the acquired parameters to the formula shown in Fig. 4 to calculate the denoising intensity "NoiseSD". In the formula shown in Fig. 4 (NoiseSD = NoiseSD_map * C / C_map * α), α is an adjustment coefficient.

[0052] In other words, the second calculation function 16c calculates the denoising intensity based on the noise intensity, the first conversion coefficient according to the imaging conditions of the preparatory scan, and the second conversion coefficient according to the imaging conditions of the main scan. Specifically, the second calculation function 16c calculates the denoising intensity based on a value obtained by multiplying the noise intensity by the ratio of the second conversion coefficient to the first conversion coefficient.

[0053] Then, the denoising function 16d performs denoising by the shrinkage method using the denoising intensity "NoiseSD" with the reconstructed image acquired by the main scan as a filter input image. For the denoising by the shrinkage method, any known technology can be selected and applied. For example, the denoising function 16d determines a threshold value for the denoising by the shrinkage method using the denoising intensity. Then, the denoising function 16d performs denoising using the determined threshold value. In this way, the denoising function 16d generates a denoising image in which noise has been removed from the reconstructed image.

[0054] In this way, the second calculation function 16c calculates the denoising intensity used in the denoising process performed on the data obtained in the main scan based on the noise intensity and the difference between the imaging conditions of the preparatory scan and the main scan. Then, the denoising process function 16d performs the denoising process on the reconstructed image acquired by the main scan based on the denoising intensity calculated by the second calculation function 16c.

[0055] The main control function 16a displays the denoised image (step S109). For example, the main control function 16a displays the denoised image stored in the memory circuit 12 on the display 11. When displaying the denoised image, the main control function 16a can appropriately apply a known image processing technique such as a rendering process.

[0056] The processing procedure described in FIG. 2 is merely an example, and is not limited to the contents shown in the figure. For example, the MRI apparatus 100 can appropriately change and execute the processing procedure within a range where there is no contradiction in the processing contents. For example, the calculation processing of step S103 does not necessarily have to be executed immediately after the processing of step S102, as long as it is completed before the processing of step S107. For example, the calculation processing of step S103 may be executed simultaneously with the calculation processing of step S106. Also, for example, the reconstruction processing of step S105 does not necessarily have to be executed immediately after the processing of step S104, as long as it is completed before the processing of step S108.

[0057] As described above, in the MRI apparatus 100 according to the first embodiment, the first calculation function 16b calculates the noise intensity from the noise data collected by the preparatory scan. Then, the second calculation function 16c calculates the denoising intensity used in the denoising process from the noise intensity based on the difference between the imaging conditions of the preparatory scan and the imaging conditions of the main scan. This allows the MRI apparatus 100 to accurately estimate the denoising intensity. As a result, the MRI apparatus 100 can generate a high-quality reconstructed image from which noise has been accurately removed.

[0058] Furthermore, the MRI apparatus 100 can execute the above-described calculation process of the denoising intensity without adding a special scan. Therefore, the MRI apparatus 100 can accurately estimate the denoising intensity while minimizing the extension of the examination time.

[0059] (Modification 1 of the first embodiment) In the first embodiment, the case where PI is executed has been described, but the embodiment is not limited thereto. For example, even when PI is not executed, the MRI device 100 can execute the above-mentioned calculation process of the denoising intensity by additionally executing a noise scan. For example, when PI is not executed, the MRI device 100 executes the above-mentioned process by setting "g" in formula (1) (a factor representing the amplification degree of the noise component due to the unfolding process in parallel imaging) to "1".

[0060] (Modification 2 of the first embodiment) In the first embodiment, the denoising intensity of the main scan is calculated based on the difference between the imaging conditions of the preparatory scan and the main scan, but the embodiment is not limited to this. For example, as long as the noise intensity can be calculated, the scan does not necessarily have to be a "preparatory scan". In this case, the denoising intensity is calculated based on the difference between the imaging conditions of the "main scan A" in which the noise intensity was calculated and the imaging conditions of the "main scan B" in which the denoising process is performed.

[0061] Note that main scan A may be referred to as the "first scan." Furthermore, the data collected by the first scan may be referred to as the "first data." Furthermore, main scan B may be referred to as the "second scan." Furthermore, the data collected by the second scan may be referred to as the "second data."

[0062] (Modification 3 of the first embodiment) In the first embodiment, the case where the preparatory scan is performed before the main scan has been described, but the embodiment is not limited to this. For example, the scan for calculating noise intensity (a scan equivalent to the preparatory scan) may be performed after the main scan. In other words, the scan for calculating noise intensity may be a scan that is performed separately from the main scan.

[0063] Second Embodiment In the first embodiment, the case where the converted value of the noise intensity of the noise data is used as the denoising intensity has been described, but the embodiment is not limited to this. The MRI apparatus 100 according to the second embodiment can estimate the denoising intensity based on the pixel values ​​of the background region of the reconstructed image acquired by the main scan.

[0064] The MRI apparatus 100 according to the second embodiment has a similar configuration to the MRI apparatus 100 shown in Fig. 1, but is different in part of the processing of the second calculation function 16c and part of the processing of the denoising processing function 16d. Hereinafter, the second embodiment will be described with reference to the differences from the first embodiment.

[0065] Fig. 5 is a diagram for explaining the processing function of the MRI apparatus 100 according to the second embodiment. As shown in Fig. 5, the second calculation function 16c acquires the noise intensity "NoiseSD_map" calculated by the first calculation function 16b, the conversion coefficient "C_map" during the preparatory scan, and the conversion coefficient "C" during the main scan. Then, the second calculation function 16c applies the acquired parameters to the formula (NoiseSDtmp=NoiseSD_map*C / C_map) shown in Fig. 5 to calculate a provisional denoising intensity "NoiseSDtmp" (S11).

[0066] Here, the provisional denoising intensity corresponds to a value calculated by setting the adjustment coefficient (α) to "1" for the "denoising intensity (NoiseSD)" described in the first embodiment. That is, the noise intensity "NoiseSD_map" used to calculate the provisional denoising intensity is a value calculated by the processing of the first calculation function 16b according to the first embodiment (see FIG. 3). Note that the "provisional denoising intensity" may be a value multiplied by an adjustment coefficient other than "1". That is, the provisional denoising intensity is substantially the same parameter as the denoising intensity described in the first embodiment, except that it is not directly used in the denoising process.

[0067] Then, the second calculation function 16c sets a threshold based on the tentative denoising intensity (S12). This threshold corresponds to a range of pixel values ​​for identifying a background region in the reconstructed image. For example, the second calculation function 16c sets "0.0" as the lower limit of the threshold. Also, the second calculation function 16c sets "P*NoiseSDtmp" as the upper limit of the threshold. The value of P is determined taking into consideration the noise distribution. For example, in the case of a normal distribution, the value of P is selected to be "3" so that 95% of the distribution is included.

[0068] Then, the second calculation function 16c extracts pixels whose absolute values ​​of signal intensity (pixel value) are within the threshold range in the filter input image (reconstructed image) (S13). The extracted pixels generally correspond to the background region of the reconstructed image.

[0069] Then, the second calculation function 16c calculates the standard deviation of the pixel values ​​of the extracted pixels by complex calculation, and calculates a value obtained by multiplying the calculated standard deviation by an adjustment coefficient (α) as a denoising intensity "NoiseSD" (S14).

[0070] Then, the denoising function 16d performs denoising processing on the filter input image by the shrinkage method using the denoising strength "NoiseSD" (S15). For the denoising processing by the shrinkage method, any known technology can be selected and applied. In this way, the denoising function 16d generates a denoising image in which noise has been removed from the reconstructed image.

[0071] In this way, in the MRI apparatus 100 according to the second embodiment, the second calculation function 16c determines the range of signal values ​​of the background region using the denoising intensity, and calculates the standard deviation of the pixel values ​​of the background region in the reconstructed image. Then, the denoising processing function 16d performs denoising processing using the calculated standard deviation. This allows the MRI apparatus 100 to estimate the denoising intensity based on the pixel values ​​of the background region of the reconstructed image acquired by the main scan.

[0072] That is, the second calculation function 16c identifies a background region in the reconstructed image using the denoising intensity (temporary denoising intensity) and calculates the standard deviation of pixel values ​​of a plurality of pixels included in the identified background region. Then, the denoising processing function 16d performs denoising processing using the calculated standard deviation.

[0073] In MRI, it is preferable to estimate the denoising intensity from a "background region" that does not include an NMR signal. Therefore, the MRI apparatus 100 according to the second embodiment does not directly use the "denoising intensity (NoiseSD)" described in the first embodiment, but estimates the background region of the reconstructed image using this as a "temporary denoising intensity (NoiseSDtmp)". Then, the MRI apparatus 100 according to the second embodiment calculates the true denoising intensity "NoiseSD" based on the pixel values ​​of a plurality of pixels included in the estimated background region of the reconstructed image. This allows the MRI apparatus 100 according to the second embodiment to more appropriately calculate the denoising intensity.

[0074] 5 is merely an example, and the embodiment is not limited thereto. For example, when CS is applied, the second calculation function 16c may set the upper limit of the threshold value by taking into account the regularization coefficient.

[0075] (Other embodiments) In addition to the above-described embodiment, the present invention may be embodied in various different forms.

[0076] (Another parameter indicating noise intensity) In the above embodiment, the case where the standard deviation is used as the noise intensity has been described, but the embodiment is not limited to this. The reason why the standard deviation is used in the above embodiment is that the noise distribution in the MR image (real space image) follows a Gaussian distribution. For example, when the denoising process is performed in a space other than the real space, the noise distribution may have a distribution that does not follow the Gaussian distribution. In such a case, other parameters (such as the average value or other statistical values) instead of the standard deviation may be used as the noise intensity depending on the distribution.

[0077] (Conversion factor parameters) In addition, the parameters used to calculate the conversion coefficient are not limited to Rg, Nex, Nx, Ny, Nz, BW, g, and A shown in formula (1). For example, when zero padding / zero filling is performed, the number of data to be zero-filled may be used as a parameter used to calculate the conversion coefficient. Zero padding places a value that is completely "zero" and does not contain noise in k-space. Therefore, the zero-filled k-space data appears to have a reduced amount of noise according to the number of zero-filled data. Therefore, when zero padding is performed, it is preferable to adjust the conversion coefficient to be smaller according to the number of zero-filled data.

[0078] In other words, the first calculation function 16b calculates the first conversion coefficient based on at least one of the signal amplification rate in the receiving circuit, the number of additions in k-space, the matrix size in the frequency encoding direction, the matrix size in the phase encoding direction, the number of slice encodings, the bandwidth per pixel, a factor representing the expansion performance in parallel imaging, the speed-up rate in high-speed imaging using compressed sensing, and the number of data to be zero-padded, which are included in the imaging conditions of the preparatory scan.

[0079] In addition, the first calculation function 16b calculates a second conversion coefficient based on at least one of the signal amplification rate in the receiving circuit, the number of additions in k-space, the matrix size in the frequency encoding direction, the matrix size in the phase encoding direction, the number of slice encodings, the bandwidth per pixel, a factor representing the expansion performance in parallel imaging, the speed-up rate in high-speed imaging using compressed sensing, and the number of data to be zero-padded, which are included in the imaging conditions of the main scan.

[0080] (Medical information processing device) In the above embodiment, the MRI apparatus 100 is used as an example of the medical information processing apparatus, but the embodiment is not limited thereto. For example, an information processing apparatus such as a personal computer or a workstation may be used as the medical information processing apparatus.

[0081] In this case, the processing circuit of the medical image processing device executes the same processing functions as the first calculation function 16b, the second calculation function 16c, and the denoising processing function 16d, respectively. The medical image processing device receives, for example, information on noise data, imaging conditions of the preparatory scan, and imaging conditions of the main scan from the MRI device, thereby executing each processing function and calculating the denoising intensity.

[0082] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or a part of it can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc. Furthermore, each processing function performed by each device can be realized in whole or in any part by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0083] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified.

[0084] The medical information processing method described in the above embodiment can be realized by executing a prepared medical information processing program on a computer such as a personal computer or a workstation. This medical information processing program can be distributed via a network such as the Internet. This medical information processing method can also be recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, an MO, or a DVD, and executed by being read from the recording medium by a computer.

[0085] According to at least one of the embodiments described above, it is possible to estimate the denoising intensity with high accuracy.

[0086] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention.

[0087] Regarding the above embodiment, the following supplementary notes are disclosed as one aspect and optional features of the invention. (Appendix 1) a first calculation unit that calculates a noise intensity from first data collected by a first scan; a second calculation unit that calculates, based on a difference between an imaging condition of the first scan and an imaging condition of the second scan, a denoising intensity used in a denoising process performed on second data obtained by the second scan from the noise intensity; A medical information processing device comprising: (Appendix 2) The second calculation unit may calculate the denoising intensity based on the noise intensity, a first conversion coefficient according to an imaging condition of the first scan, and a second conversion coefficient according to an imaging condition of the second scan. (Appendix 3) The second calculation unit may calculate the denoising intensity based on a value obtained by multiplying the noise intensity by a ratio of the second conversion coefficient to the first conversion coefficient. (Appendix 4) The first calculation unit may calculate the first conversion coefficient based on at least one of a signal amplification rate in a receiving circuit, a number of additions in k-space, a matrix size in a frequency encoding direction, a matrix size in a phase encoding direction, a number of slice encodings, a bandwidth per pixel, a factor representing expansion performance in parallel imaging, a speed-up rate in high-speed imaging using compressed sensing, and a number of data to be zero-padded, which are included in the imaging conditions of the first scan. (Appendix 5) The first calculation unit may calculate the second conversion coefficient based on a signal amplification rate in a receiving circuit, a number of additions in k-space, a matrix size in a frequency encoding direction, a matrix size in a phase encoding direction, a number of slice encodings, and a bandwidth per pixel, which are included in the imaging conditions of the second scan. (Appendix 6) The first calculation unit may calculate the second conversion coefficient based on a factor indicating an amplification degree of a noise component caused by unfolding processing in parallel imaging included in imaging conditions for the second scan. (Appendix 7) The first calculation unit may calculate the second conversion coefficient based on a speed-up rate in high-speed imaging using compressed sensing included in imaging conditions for the second scan. (Appendix 8) The imaging apparatus may further include a denoising processor that performs the denoising process on second data acquired by the second scan based on the denoising intensity. (Appendix 9) The denoising processing unit is determining a threshold value for denoising processing by a shrinkage method using the denoising intensity; The denoising process may be performed using the determined threshold value. (Appendix 10) The second calculation unit is using the denoising intensity to identify background regions in the image; Calculating a standard deviation of pixel values ​​of a plurality of pixels included in the identified background region; The denoising processor may perform the denoising process using the calculated standard deviation. (Appendix 11) The first scan may include a scan in which the first data is acquired without transmitting an RF pulse having a certain strength or more. (Appendix 12) The RF pulse having a certain intensity or more may be an RF pulse whose generated NMR (Nuclear Magnetic Resonance) signal does not affect the estimation of the noise amount. (Appendix 13) The first calculation unit may calculate the noise intensity based on data obtained by converting k-space data acquired by the first scan into real space. (Appendix 14) The medical information processing device may be a magnetic resonance imaging device. (Appendix 15) The second scan may be a main scan for producing a diagnostic image; The first scan may be a scan that is performed separately from the main scan. (Appendix 16) Calculating noise intensity from first data collected by the first scan; calculating, from the noise intensity, a denoising intensity used in a denoising process performed on second data obtained by the second scan, based on a difference between an imaging condition of the first scan and an imaging condition of the second scan; A medical information processing method comprising: [Explanation of symbols]

[0088] 100 MRI machine 16 Processing circuit 16b First calculation function 16c Second calculation function 16d denoising function

Claims

1. An acquisition unit that acquires information regarding noise contained in first data based on first data obtained by a first scan performed under first imaging conditions; a calculation unit that calculates a denoising intensity used in a denoising process performed on second data obtained by a second scan that is executed after the first scan is executed and that is executed based on second imaging conditions different from the first imaging conditions, based on the information, a first value corresponding to the first imaging conditions, and a second value that corresponds to the second imaging conditions and is different from the first value; A medical information processing device comprising:

2. The calculation unit calculates the denoising intensity based on a ratio between the first value and the second value. The medical information processing device according to claim 1 .

3. the calculation unit calculates the denoising intensity based on a value obtained by multiplying a ratio of the second value to the first value by the noise intensity indicated by the information. The medical information processing device according to claim 2 .

4. The acquisition unit calculates the first value based on at least one of a signal amplification rate in a receiving circuit, a number of additions in k-space, a matrix size in a frequency encoding direction, a matrix size in a phase encoding direction, a number of slice encodings, a bandwidth per pixel, a factor representing expansion performance in parallel imaging, a speed-up rate in high-speed imaging using compressed sensing, and a number of data to be zero-padded, which are included in the imaging conditions of the first scan. The medical information processing device according to claim 1 .

5. The acquisition unit calculates the second value based on a signal amplification rate in a receiving circuit, a number of additions in k-space, a matrix size in a frequency encoding direction, a matrix size in a phase encoding direction, a number of slice encodings, and a bandwidth per pixel, which are included in the imaging conditions of the second scan. The medical information processing device according to claim 1 .

6. the acquisition unit calculates the second value based on a factor representing an amplification degree of a noise component due to an unfolding process in parallel imaging included in the imaging conditions of the second scan; The medical information processing device according to claim 1 .

7. The acquisition unit calculates the second value based on a speed-up rate in high-speed imaging using compressed sensing included in the imaging conditions of the second scan. The medical information processing device according to claim 1 .

8. a denoising processing unit that performs the denoising process on second data acquired by the second scan based on the denoising intensity. The medical information processing device according to claim 1 .

9. The denoising processing unit is determining a threshold value for denoising processing by a shrinkage method using the denoising intensity; performing the denoising process using the determined threshold value; The medical information processing device according to claim 8 .

10. The calculation unit is identifying a background region in the second data using the denoising intensity; Calculating a standard deviation of pixel values ​​of a plurality of pixels included in the identified background region; The denoising processing unit performs the denoising process using the calculated standard deviation. The medical information processing device according to claim 8 or 9.

11. the first scan includes a scan for acquiring the first data without transmitting an RF pulse having a certain strength or more; The medical image processing device according to claim 1 .

12. The RF pulse having a certain strength or more is an RF pulse having a strength such that the generated NMR (Nuclear Magnetic Resonance) signal does not affect the estimation of the noise amount. The medical information processing device according to claim 11.

13. The acquisition unit acquires the information based on the first data obtained by converting k-space data acquired by the first scan into a real space. The medical image processing device according to claim 1 .

14. A magnetic resonance imaging device. The medical information processing device according to claim 1 .

15. The second scan is a main scan for producing a diagnostic image, The first scan is a scan that is performed separately from the main scan. The medical information processing device according to claim 1 .

16. Based on first data obtained by a first scan performed under first imaging conditions, information regarding noise contained in the first data is obtained; calculating a denoising intensity used in a denoising process performed on second data obtained by a second scan which is performed after the first scan is performed and which is performed based on second imaging conditions different from the first imaging conditions, based on the information, a first value corresponding to the first imaging conditions, and a second value which corresponds to the second imaging conditions and is different from the first value; A medical information processing method comprising:

Images