Magnetic resonance imaging device, image processing device, and image processing method

Abstract

The present invention relates to a magnetic resonance imaging apparatus, an image processing apparatus, and an image processing method. Appropriate opacity settings are automatically performed from 3D MRI images to generate a volume-rendered image. A three-dimensional image of a subject is received, the distribution of pixel values in the three-dimensional image is determined, and pixel values of a predetermined feature are calculated based on the pixel value distribution. The opacity of each pixel value included in the three-dimensional image is then set based on the pixel values of the feature value. This allows for automatic opacity setting. A volume-rendered image of the three-dimensional image is generated using the opacity.

Description

Technical Field

[0001] The present invention relates to a technology for creating a volume rendering image of a desired tissue from a three-dimensional (3D) image captured by a magnetic resonance imaging (hereinafter referred to as "MRI") apparatus or the like. Background Art

[0002] An MRI device measures nuclear magnetic resonance (NMR) signals generated by the spins of the nuclei of an object, particularly tissues that make up the human body, to produce two-dimensional or three-dimensional images of the morphology and function of the head, abdomen, limbs, and other parts of the body. During imaging, the NMR signals are phase-encoded using a gradient magnetic field and frequency-encoded, measuring them as time-series data. The measured NMR signals are then reconstructed into an image using a two-dimensional or three-dimensional Fourier transform.

[0003] In order to perform imaging diagnosis of cerebral vascular lesions such as cerebral aneurysms, the following method is used: by taking pictures using a 3D TOF (time of flight) sequence, an image of the cerebral blood vessels with high signals is obtained without the use of a contrast agent. By projecting the obtained 3D TOF image at a desired angle using the MIP (maximum projection) method, the vascular image can be observed from the desired angle. In addition, before projection using the MIP method, the signal of unnecessary tissue other than the blood vessels of the observation object, such as subcutaneous fat and superficial blood vessels, is removed (cropping), and the opacity of the pixels is set so that the brain substance is not transparently displayed. This allows for a clearer image to be obtained that allows the morphology of the cerebral blood vessels to be observed. However, the cropping process and opacity setting are performed manually, which is a tedious and time-consuming task.

[0004] Therefore, Patent Document 1 discloses a technique for automatically performing the aforementioned cropping process. This technique processes a 3D MRI image to generate a brain extraction mask image for extracting the brain region and a vascular mask image for extracting the vascular region. The mask resulting from combining these two mask images is then used to extract images of the brain region and the blood vessels within the brain from the 3D image. This method enables the extraction (cropping) of blood vessels within the brain region and at the base of the skull necessary for cerebrovascular observation from a single 3D MRI image.

[0005] Meanwhile, Patent Document 2 discloses a technique that determines the outline of the brain in an X-ray CT image, obtains the MR signal intensity of pixels located within the brain outline in the corresponding MRI image, and sets an opacity curve based on the signal intensity, so that pixels within the brain outline are displayed opaquely, while areas outside the outline are displayed transparently. This opacity curve represents the relationship between MR signal values and the opacity of displayed pixel values. Specifically, the range of MRI values indicated by pixels within the brain outline is set to an opacity of 1, while the range of MRI values outside this range is set to an opacity of 0. This allows only the brain outline to be displayed as a pseudo-3D image.

[0006] Prior art literature

[0007] Patent Literature

[0008] Patent Document 1: Japanese Patent Application Publication No. 2020-39507

[0009] Patent Document 2: Japanese Patent Application Laid-Open No. 2012-100955

[0010] The technology of Patent Document 1 is a technology that automatically performs a clipping process, and requires manual setting of opacity when creating a volume rendering image.

[0011] On the other hand, the method of Patent Document 2 requires both X-ray CT images and MRI images. Furthermore, while Patent Document 2 discloses a method for setting opacity to render only the MRI signal representing the brain's outline opaque, it does not disclose a method for setting opacity to render only the outline of brain blood vessels opaque. Since brain blood vessels are more complex than the brain's outline, it is assumed that setting opacity using the technique of Patent Document 2 is not easy.

[0012] Furthermore, since the method of Patent Document 2 does not perform cropping, if the method of Patent Document 2 is used to set the opacity of brain blood vessels, signals of unnecessary tissues other than the blood vessels of the observation object, such as subcutaneous fat and superficial blood vessels, will remain in the MRI image, requiring complicated cropping.

[0013] Furthermore, assuming the clipping process of Patent Document 1 and the opacity processing of Patent Document 2 are combined, the technique of Patent Document 2 sets opacity based on the MR signal intensity histogram of the contour pixels of the region to be extracted. In contrast, the technique of Patent Document 1 uses a merged mask to extract brain and cerebrovascular images from 3D images and then performs clipping to remove unnecessary tissue. This causes the MR signal intensity histogram to change compared to the pre-clipped histogram. Consequently, it may not be possible to set an appropriate opacity for vascular evaluation based on the changed MRI signal intensity histogram. Summary of the Invention

[0014] The present application aims to automatically perform appropriate opacity setting on a 3D MRI image to obtain a volume-rendered image.

[0015] To achieve the above-mentioned object, the magnetic resonance imaging apparatus of the present invention comprises: a static magnetic field generator for generating a static magnetic field in a space where an imaging portion of a subject is located; a measurement control unit for applying a gradient magnetic field and a high-frequency magnetic field to the subject and detecting nuclear magnetic resonance signals generated from the imaging portion; and a processing unit for reconstructing a three-dimensional image of the subject using the detected nuclear magnetic resonance signals. The processing unit obtains a distribution of pixel values in the three-dimensional image, calculates pixel values of predetermined feature quantities based on the distribution of pixel values, and sets the opacity of each pixel value included in the three-dimensional image based on the pixel values of the feature quantities.

[0016] Effects of the Invention

[0017] According to the present invention, since appropriate opacity can be automatically set from 3D MRI images, MRI volume rendering images can be automatically created, thereby reducing the operator's tedious work and improving efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a block diagram showing an example of the overall configuration of the MRI apparatus according to the present invention.

[0019] Figure 2 This is an example of a 3D TOF imaging pulse sequence.

[0020] Figure 3 This is a flowchart showing the processing of the arithmetic processing unit according to the first embodiment of the present invention.

[0021] Figure 4 This is a flowchart showing the opacity setting process of the arithmetic processing unit according to the first embodiment of the present invention.

[0022] Figure 5 (a) to (c) are diagrams showing images before, during, and after processing according to the first embodiment of the present invention.

[0023] Figure 6 This is a graph showing a histogram and an opacity curve generated for opacity setting by the arithmetic processing unit according to the first embodiment of the present invention.

[0024] Figure 7 This is a flowchart showing the processing of the arithmetic processing unit according to the second embodiment of the present invention.

[0025] Figure 8 This is a flowchart showing the processing of the arithmetic processing unit according to the third embodiment of the present invention.

[0026] Figure 9 This is a flowchart showing the processing of the arithmetic processing unit according to the fourth embodiment of the present invention.

[0027] Description of Reference Numerals

[0028] 1: Subject, 2: Static magnetic field generator, 3: Gradient magnetic field generator, 4: Sequencer, 5: Transmitter, 6: Receiver, 7: Signal processor, 8: Arithmetic processing unit (image processing device, CPU), 9: Gradient magnetic field coil, 10: Gradient magnetic field power supply, 11: High-frequency oscillator, 12: Modulator, 13: High-frequency amplifier, 14a: High-frequency coil (transmitter coil), 14b: High-frequency coil (receiver coil), 15: Signal amplifier, 16: Quadrature phase detector, 17: A / D converter, 18: Magnetic disk, 19: Optical disk, 20: Display, 21: ROM, 22: RAM, 23: Trackball or mouse, 24: Keyboard DETAILED DESCRIPTION

[0029] Hereinafter, preferred embodiments of the MRI apparatus of the present invention will be described in detail with reference to the accompanying drawings. In all figures used to illustrate the embodiments of the invention, elements having the same functions are denoted by the same reference numerals, and their repeated descriptions are omitted.

[0030] The MRI apparatus of this embodiment captures a 3D TOF image, automatically performs cropping processing and transparency setting, and creates an optimal volume-rendered image.

[0031] Initially, based on Figure 1 An overall overview of an example of the MRI apparatus according to the present invention will be described. Figure 1 This is a block diagram showing the overall structure of an embodiment of the MRI apparatus of the present invention. The MRI apparatus uses the NMR phenomenon to obtain a cross-sectional image of the subject, such as Figure 1As shown, the MRI apparatus includes a static magnetic field generator 2, a gradient magnetic field generator 3, a transmitter 5, a receiver 6, a signal processor 7, a sequencer 4, and a processing unit (CPU) 8. The gradient magnetic field generator 3, the transmitter 5, the receiver 6, and the sequencer 4 are collectively referred to as a measurement control unit 100.

[0032] The static magnetic field generating unit 2 generates a static magnetic field in the space surrounding the imaging portion of the subject 1. The static magnetic field generating unit 2 is a permanent magnet, conventional conductive, or superconductive static magnetic field generating source positioned around the subject 1. If the static magnetic field generating source uses a vertical magnetic field, it generates a uniform static magnetic field in a direction perpendicular to the body axis of the subject 1. If the static magnetic field generating source uses a horizontal magnetic field, it generates a uniform static magnetic field in the direction of the body axis of the subject 1.

[0033] The gradient magnetic field generator 3 comprises gradient magnetic field coils 9 for applying gradient magnetic fields in the three X, Y, and Z axes, which form the coordinate unit (stationary coordinate unit) of the MRI apparatus, and gradient magnetic field power supplies 10 for driving each of the gradient magnetic field coils. The gradient magnetic field power supplies 10 for each coil are driven in accordance with commands from a sequencer 4, described later, to apply gradient magnetic fields Gx, Gy, and Gz in the three X, Y, and Z axes. During imaging, a slice-selective gradient magnetic field pulse (Gs) is applied in a direction orthogonal to the slice plane (imaging section) to set the slice plane for the subject 1. Phase-encoding gradient magnetic field pulses (Gp) and frequency-encoding gradient magnetic field pulses (Gf) are applied in the remaining two directions, which are orthogonal to the slice plane and to each other, to encode positional information in each direction into the echo signal.

[0034] The transmitter 5 irradiates the subject 1 with high-frequency magnetic field pulses (hereinafter referred to as "RF pulses") to induce nuclear magnetic resonance in the nuclear spins of atoms constituting the biological tissue of the subject 1. The transmitter 5 comprises a high-frequency oscillator 11, a modulator 12, a high-frequency amplifier 13, and a high-frequency coil (transmitting coil) 14a on the transmitting side. The modulator 12 amplitude-modulates the RF pulses output from the high-frequency oscillator 11 at a timing based on instructions from the sequencer 4, described later. The high-frequency amplifier 13 amplifies these amplitude-modulated RF pulses and then supplies them to the high-frequency coil 14a, which is positioned close to the subject 1. This irradiates the subject 1 with RF pulses from the high-frequency coil 14a.

[0035] The receiving unit 6 detects echo signals (hereinafter referred to as NMR signals) emitted by nuclear magnetic resonance (NMR) of the nuclear spins of the biological tissue constituting the subject 1, and is composed of a high-frequency coil (receiving coil) 14b on the receiving side, a signal amplifier 1, an orthogonal phase detector 16 and an A / D converter 17.

[0036] The NMR signal of the response of the subject 1 caused by the electromagnetic waves irradiated from the transmitting coil 14a is detected by the receiving coil 14b arranged close to the subject 1, and after being amplified by the signal amplifier 15, it is divided into two orthogonal signals by the orthogonal phase detector 16 at the timing based on the instructions from the sequence generator 4, and they are converted into digital quantities by the A / D converter 17 and sent to the signal processing unit 7.

[0037] The sequencer 4 is a control unit that repeatedly applies RF pulses and gradient magnetic field pulses according to a given pulse sequence and receives the resulting NMR signals at predetermined timings. Operating under the control of the arithmetic processing unit 8, the sequencer 4 sends various commands to the transmitter 5, the gradient magnetic field generator 3, and the receiver 6 according to the pulse sequence, collecting the NMR signal data necessary to generate tomographic images of the subject 1.

[0038] The signal processing unit 7 performs various data processing and displays and stores the processing results, and includes an external storage device such as an optical disk 19 and a magnetic disk 18 and a display 20 such as a CRT.

[0039] When NMR signal data is input from the receiving unit 6 to the processing unit (CPU) 8, the processing unit 8 performs signal processing and image reconstruction, thereby reconstructing a tomographic image of the subject 1. The processing unit 8 displays the tomographic image of the subject 1 on the display 20 and stores it on an external storage device such as a disk 18.

[0040] The operation unit 25 is used to input various control information for the MRI apparatus and control information for processing performed by the signal processing unit 7, and includes a trackball or mouse 23 and a keyboard 24. The operation unit 25 is located near the display 20, and the operator interactively controls various processing of the MRI apparatus through the operation unit 25 while viewing the display 20.

[0041] In addition, Figure 1 In the vertical magnetic field method, the high-frequency coil 14a and the gradient magnetic field coil 9 on the transmitting side are arranged opposite to the subject 1 within the static magnetic field space of the static magnetic field generator 2 inserted into the subject 1. In the horizontal magnetic field method, the high-frequency coil 14a and the gradient magnetic field coil 9 on the transmitting side are arranged to surround the subject 1 within the static magnetic field space of the static magnetic field generator 2 inserted into the subject 1. On the other hand, the high-frequency coil 14b on the receiving side is arranged to face or surround the subject 1. Furthermore, as described above, the gradient magnetic field generator 3, the transmitter 5, the receiver 6, and the sequencer 4 are collectively referred to as the measurement control unit 100.

[0042] Current MRI systems use hydrogen nuclei (protons), the primary constituent of the subject, as the most commonly imaged nuclei in clinical practice. By visualizing information related to the spatial distribution of proton density and the spatial distribution of relaxation times in excited states, MRI systems can capture the morphology and function of the human head, abdomen, limbs, and other parts of the body in two or three dimensions.

[0043] As a technique for imaging cerebral blood vessels using MRI, there is a pulse sequence using a 3D time of flight (TOF) method. Figure 2 This is an example of a sequence diagram of the 3D TOF method. Figure 2 In the figure, RF represents the timing of applying the RF pulse 401 and obtaining the NMR signal 402, and Gs, Gp, and Gr represent the timing and magnitude of applying the gradient magnetic field pulses 403 to 408 in the slice direction, phase encoding direction, and readout direction. The number after the hyphen after the 3-digit number represents the number of repetitions. For example, the "1" in 402 "-1" represents the pulse applied and the signal obtained at the first repetition, and the "2" in "-2" represents the pulse applied and the signal obtained at the second repetition. That is, Figure 2 The pulse sequence for the 3D TOF method repeats the following sequence a predetermined number of times: Simultaneously applying a gradient magnetic field pulse 403 in the slice direction Gs, followed by irradiation with an RF pulse 401, then applying a gradient magnetic field pulse 404 in the slice direction Gs, a gradient magnetic field pulse 405 in the phase encoding direction Gp, and a gradient magnetic field pulse 408 in the readout direction Gr. Subsequently, applying a gradient magnetic field pulse 407 in the readout direction while receiving NMR signals 402. With each repetition, the magnitude of the gradient magnetic field pulse 404 in the slice direction Gs and the magnitude of the gradient magnetic field pulses 405 and 406 in the phase encoding direction Gp are varied, thereby changing the slice position and phase encoding, thereby acquiring the NMR signals necessary for generating a 3D TOF image.

[0044] The following describes the following processing: In the MRI apparatus of this embodiment, the arithmetic processing unit 8 automatically performs cropping and opacity setting on 3D TOF images to create volume-rendered images. The processing performed by the arithmetic processing unit 8 in each embodiment is image processing, and the arithmetic processing unit 8 functions as an image processing device.

[0045] <<Implementation Method 1>>

[0046] based on Figure 3 as well as Figure 4 The processing of clipping and opacity setting of the MRI apparatus of embodiment 1 is described in detail with reference to the flowchart of Figure 5 (a) to (c) show the images being processed. Figure 6A histogram of pixel values is shown in .

[0047] In the first embodiment, the processing unit 8 obtains the pixel value distribution of a three-dimensional image (3D TOF image), calculates the pixel value of a predetermined feature value based on the pixel value distribution, and sets the opacity of each pixel value included in the three-dimensional image based on the pixel value of the feature value. When the imaging site is the brain, the processing unit 8 sets the opacity of each pixel value so that the opacity is set to 0 in the range of pixel values corresponding to the image of the brain parenchyma in the three-dimensional image, and the opacity is set to 1 in the range of pixel values corresponding to the image of blood vessels.

[0048] Specifically, the average value of the distribution of pixel values in the three-dimensional image is used as the pixel value of the feature quantity. The processing unit 8 sets an opacity curve such that the opacity changes from 0 to 1 as the pixel value increases, with the opacity being determined by a predetermined value greater than the pixel value (average value) of the feature quantity as the boundary. For example, the predetermined value is set based on the variance (σ) calculated from the distribution of pixel values.

[0049] More specifically, the calculation processing unit 8 sets an opacity curve in which opacity changes linearly or nonlinearly from 0 to 1 with a pixel value greater than the pixel value of the feature amount by a predetermined value (3σ) as a boundary.

[0050] Hereinafter, the processing of the calculation processing unit 8 will be described in further detail.

[0051] Figure 3 as well as Figure 4 The processing flow of the processing unit 8 shown is pre-stored as a program on disk 18. The processing unit (CPU) 8 reads and executes this program from disk 1, thereby implementing the process using software. Alternatively, part or all of the processing unit 8 can be implemented using hardware. For example, a custom IC such as an ASIC (Application Specific Integrated Circuit) or a programmable IC such as an FPGA (Field-Programmable Gate Array) can be used to construct part or all of the processing unit 8, and the circuit design can be performed to achieve the desired functionality.

[0052] (Step S201)

[0053] The operator places the imaging part of the subject 1 (here, the brain) in the static magnetic field space generated by the static magnetic field generator 2. The arithmetic processing unit 8 receives setting input of imaging conditions for a sequence of 3D TOF images from the operator via the operating unit 25.

[0054] (Step S202)

[0055] The calculation processing unit 8 instructs the sequencer 4 to execute the image processing under the imaging conditions set in step S201. Figure 2 The 3D TOF pulse sequence is used. Thus, the subject 1 is imaged based on the 3D TOF pulse sequence. The acquired NMR signals are processed by the arithmetic processing unit 8 to reconstruct a 3D TOF image. The generated image data is stored in the disk 18. Here, as an example, an image of the brain is imaged.

[0056] (Step S203)

[0057] The calculation processing unit 8 receives the selection of a 3D TOF image to be processed from among the images stored in the disk 18 from the operator via the operation unit 25 ( Figure 5 (a)). The processing unit 8 starts the volume rendering image creation process for the selected 3D TOF image. In addition, if step S203 is not performed and the image is captured in step S202, the processing unit 8 may select the captured image as the processing target.

[0058] (Step S204)

[0059] The calculation processing unit 8 automatically performs a cropping process on the 3D TOF image obtained in step S202 using a known method ( Figure 5 In this embodiment, for example, the subcutaneous fat area and the skull area are removed using the method of Patent Document 1 to obtain an image including the brain parenchyma area containing blood vessels and the blood vessels of the skull base.

[0060] (Step S205)

[0061] The calculation processing unit 8 calculates the opacity of the 3D TOF image obtained in step S204 after the cropping process. Figure 4 The process of step S205 is described in detail.

[0062] (Step S41)

[0063] The arithmetic processing unit 8 normalizes the 3D TOF image after the clipping process, for example, so that the signal value (pixel value) falls within the range of 0 to 255.

[0064] (Step S42)

[0065] The calculation processing unit 8 calculates the threshold value 61 for removing the background area by using the discriminant analysis method (refer to Figure 6) to maximize the separation of the pixel values of the standardized 3D TOF image and replace the pixel values below the threshold 61 with 0, thereby removing the background area of the 3D TOF image.

[0066] (Step S43)

[0067] The calculation processing unit 8 generates a histogram (distribution of pixel values) of the pixel values and the number of pixels of the 3D TOF image after the background area is removed. Figure 6 An example of a histogram is shown. The 3D TOF image after the background area is removed is roughly composed of images of the brain parenchyma area and the blood vessel area. The pixels in the blood vessel area have the characteristics of large signal values but small number of pixels, and the pixels in the brain parenchyma area have the characteristics of smaller signal values than the blood vessel area but much more pixels than the blood vessel area. Therefore, the histogram of the pixel values of the 3D TOF image after the background area is removed is as follows: Figure 6 Thus, the pixels of the brain parenchyma have a distribution with a peak, and the pixels of the blood vessel region are distributed in a region with a larger pixel value than the distribution of the pixels of the brain parenchyma.

[0068] (Step S44)

[0069] In order to set the opacity to 0 (transparent) for the pixel value range of the brain parenchyma and to 1 (opaque) for the pixels of the blood vessels, the arithmetic processing unit 8 calculates the mean value (Mean) and variance (σ) of the pixel values (signal values) as feature quantities of the histogram, sets the opacity to 0 (transparent) by defining the range from the minimum pixel value 0 to the pixel value of Mean (mean value) + 3σ (= pixel value C1) as the pixel value range of the brain parenchyma, and sets the opacity to a value greater than 0 by defining the range of pixel values greater than Mean + 3σ as the pixel value range of the blood vessels. Here, as Figure 6 The opacity curve A is set in such a way that the range of pixel values below Mean (average value) + 3σ (= pixel value C1) is opacity 0, the range from Mean+3 (= pixel value C1) to Mean+5 (= pixel value C2) changes the opacity linearly or nonlinearly from 0 to 1, and the range above Mean+5 (= pixel value C2) is opacity 1 (opaque). Figure 6 , an opacity curve A is shown as an example. In this opacity curve A, the opacity value increases linearly (in proportion to the pixel value) in the range from Mean+3 (=pixel value C1) to Mean+5 (=pixel value C2).

[0070] (Step S206)

[0071] The processing unit 8 uses the cropped 3D TOF image obtained in step S204 to create a volume rendering image. Here, the value of the opacity curve set in step S205 is used when creating the volume rendering image. That is, for each pixel of the cropped 3D TOF image obtained in step S204, if its pixel value is between 0 and Mean+3σ, the opacity is set to 0, and if it is greater than Mean (average value)+5σ, the opacity is set to 1, and the transparency of the linear or nonlinear opacity curve A is set for the pixels in between to perform volume rendering. In this way, it is possible to Figure 5 As shown in (c) of FIG. 1 , the pixels of the brain parenchyma are made transparent to obtain a volume rendering image of a blood vessel.

[0072] (Step S207)

[0073] The calculation processing unit 8 displays the volume rendering image calculated in step S206 , with the clipping and opacity set, on the display 20 , and stores the image.

[0074] As described above, in the first embodiment, the processing unit 8 automatically performs cropping and opacity setting on 3D TOF images, thereby automating the creation of MRI volume-rendered images. This reduces the operator's workload and improves efficiency.

[0075] In addition, in the above embodiment, in step 205, if Figure 6 As for the setting of opacity, an opacity curve A is set that linearly changes from opacity 0 to 1 between Mean (average value) + 3σ and Mean (average value) + 5σ, but it can also be set as follows Figure 6 It is set to an S-shaped curve like curve B.

[0076] In addition, regarding the opacity curve, an opacity curve can be set as follows, in which the opacity value changes from 0 to 1 in stages, with the pixel value (for example, Mean (average) + 3σ) being larger than the pixel value of the feature value by a predetermined value as the boundary.

[0077] In this embodiment, the average value (Mean) of the pixel values of the cropped 3D image is used as the feature value, and the pixel values C1 and C2 of the boundary where the opacity changes are set based on the feature value, but the feature value does not necessarily need to be the average value. Figure 6 The peak of the histogram is used as the feature value, and the pixel values that are away from the peak by a given value are used as pixel values C1 and C2.

[0078] <<Implementation Method 2>>

[0079] based on Figure 7The trimming process and opacity setting process of the MRI apparatus according to the second embodiment will be described in detail with reference to the flowchart of FIG.

[0080] The second embodiment is similar to the first embodiment in that it automatically performs cropping and automatic opacity setting on the 3D TOF image, automating the creation of the MRI volume rendering image. Figure 3 Step S301 is performed between steps S205 and S206 of the process, thereby searching so that the pixel values C1 and C2 that are the boundaries for changing the opacity from 0 to 1 become more optimal pixel values.

[0081] Specifically, the calculation processing unit 8 sets a plurality of opacity curves while shifting the pixel values C1 and C2 serving as boundaries, calculates the distribution of pixel values of the three-dimensional image using the plurality of opacity curves, and selects one opacity curve based on the calculation result.

[0082] The following is the specific use Figure 7 In addition, Figure 7 In the embodiment, the same steps as those in the first embodiment are denoted by the same step reference numerals and descriptions thereof are omitted.

[0083] (Steps S201 to S205)

[0084] Since this is the same as in Embodiment 1, its description is omitted. In step S205, based on the histogram of pixel values of the 3D TOF image, an opacity curve A is set such that the opacity changes linearly from 0 to 1 between the average value of the pixel values Mean+3σ (=pixel value C1) and the average value Mean+5σ (=pixel value C2).

[0085] (Step S301)

[0086] The processing unit 8 further determines the pixel values C1 and C2 that change the opacity from 0 to 1 by searching. For example, the pixel values C1 and C2 determined in step S205 are used as initial values, and while changing (shifting) the values of the pixel values C1 and C2 that change the opacity from 0 to 1 (C1 = Mean + 3σ, C2 = Mean + 5σ), an opacity curve A is set that changes the opacity from 0 to 1, for example, linearly. For example, in the search for C1, step S206 is performed while changing the value of C1, so that Figure 5For a volume-rendered image such as that shown in (c), the volume of the region remaining in the volume-rendered image is calculated, and the change in the volume is used as a feature value for search. Specifically, using pixel value C1 as the initial value, the pixel value is varied while the change in the volume remaining in the volume-rendered image obtained each time step S206 is performed is calculated. If the change is less than a predetermined threshold, the pixel value at that time is determined as the value of C1 in the opacity curve A. This allows the determination of a more appropriate pixel value C1, which allows only blood vessels to remain in the volume-rendered image without leaving any image of the brain parenchyma.

[0087] Similarly, while causing the pixel value C2 to change in the same manner, step S206 is performed to obtain a volume-rendered image. The volume of the remaining region in the volume-rendered image is calculated, and if the amount of change is smaller than a predetermined threshold, the pixel value at that time is determined as the value of C2 of the opacity curve A.

[0088] This makes it possible to set appropriate pixel values C1 and C2 so that only blood vessels remain in the volume-rendered image.

[0089] Alternatively, in step S301 , instead of using the volume of the remaining region in the volume-rendered image, the area of a given cross section of the volume-rendered image or the area of a two-dimensional image obtained by projecting the volume-rendered image onto a given plane may be used, and the amount of change therein may be used as a feature value to search for pixel values C1 and C2.

[0090] The search method is not limited to these methods, and a method such as integrating the set opacity curve A and the histogram and searching for pixel values C1 and C2 whose area of the integrated histogram curve fits within a predetermined area range of only major blood vessels may be used.

[0091] (Steps S206-S207)

[0092] Volume rendering is performed using the pixel values calculated in step S301 . The method is the same as that of the first embodiment.

[0093] As described above, in the second embodiment of the present invention, the processing unit 8 automatically performs cropping processing and searches for pixel values for opacity setting on 3D TOF images, sets optimal opacity, and automates the creation of MRI volume-rendered images. This enables the production of volume-rendered images with optimal opacity settings.

[0094] In the second embodiment, instead of the linear opacity curve A, a curve B such as the S-shaped curve described in the first embodiment may be used.

[0095] <<Implementation Method 3>>

[0096] based on Figure 8The processing of 3D TOF images by the MRI apparatus of embodiment 3 is described in detail with reference to the flowchart of FIG.

[0097] In the third embodiment, similar to the first embodiment, the 3D TOF image is automatically clipped and the opacity is automatically set to automate the creation of the MRI volume rendering image. Figure 3 Step S401 is performed between steps S206 and S207 of the flow, thereby deleting the region that remains after volume rendering and is unnecessary for evaluation.

[0098] Specifically, the arithmetic processing unit 8 performs a predetermined image processing on the volume rendering image to extract an unnecessary region and deletes the extracted unnecessary region. For example, the predetermined image processing may include extracting a discontinuous region.

[0099] Specific use Figure 8 In addition, Figure 8 In the embodiment, the same steps as those in the first embodiment are denoted by the same step reference numerals and descriptions thereof are omitted.

[0100] (Steps S201 to S206)

[0101] Since the process is the same as that of the first embodiment, the description thereof will be omitted. After automatically performing clipping and opacity setting on the 3D TOF image, a volume rendering image is generated.

[0102] (Step S401)

[0103] The processing unit 8 deletes unnecessary areas such as subcutaneous fat remaining in the volume rendering image. For example, all remaining areas are labeled, discontinuous areas are extracted, and the extracted areas are considered as non-vascular areas and deleted.

[0104] Alternatively, a method may be used in which a feature value is calculated for each region of a volume-rendered image, and regions other than blood vessels are identified based on the feature value and deleted.

[0105] Alternatively, a method may be employed in which regions other than blood vessels are deleted based on image recognition using a learning model that is trained in advance using a large number of volume-rendered images through machine learning or the like to distinguish between blood vessels and unnecessary regions.

[0106] (Step S207)

[0107] The volume-rendered image after the unnecessary area is deleted is displayed and stored in the same manner as in the first embodiment.

[0108] As described above, in the third embodiment of the present invention, the processing unit 8 automatically performs cropping and opacity setting on the 3D TOF image, thereby automatically deleting remaining areas unnecessary for diagnosis. This allows the operator to create a volume-rendered image more suitable for diagnosis without performing tedious operations.

[0109] <<Implementation Method 4>>

[0110] based on Figure 9 The processing of the 3D TOF image by the MRI apparatus of the fourth embodiment is described in detail with reference to the flowchart of FIG.

[0111] In the fourth embodiment, similar to the first embodiment, the 3D TOF image is automatically clipped and the opacity is automatically set to automate the creation of the MRI volume rendering image. Figure 3 Step S501 is performed between steps S206 and S207 in the flow, whereby a predetermined specific blood vessel image is extracted from the blood vessel image from the volume rendering image. The specific blood vessel image is a predetermined main blood vessel or a blood vessel on the left or right side of the subject.

[0112] Specific use Figure 9 In addition, Figure 9 In the embodiment, the same steps as those in the first embodiment are denoted by the same step reference numerals and descriptions thereof are omitted.

[0113] (Steps S201 to S206)

[0114] Since the process is the same as that of the first embodiment, the description thereof will be omitted. After automatically performing clipping and opacity setting on the 3D TOF image, a volume rendering image is generated.

[0115] (Step S501)

[0116] The processing unit 8 extracts an image of a predetermined major blood vessel and the left and / or right blood vessels of the imaging site (here, the brain) of the subject 1 from the volume-rendered image generated in step 206, thereby creating a volume-rendered image. Specifically, the processing unit 8 extracts the initial point of the major blood vessel or the left and right blood vessels from the volume-rendered image, and utilizes the continuity of the blood vessels to trace the blood vessel image from the initial point, thereby extracting the blood vessel.

[0117] For example, when distinguishing between left and right, the left and right center positions of the general body (brain) are determined based on the image position and patient position in the DICOM information included in the MRI image. A left region and a right region are set in the volume-rendered image. The pixels with the highest signal values on the blood vessels in the left and right regions are respectively determined and used as the starting points. From these starting points, continuous pixels are continuously traced on the image using a region dilation method or the like. In this way, continuous blood vessels in the left region and continuous blood vessels in the right region are extracted respectively.

[0118] When extracting major blood vessels, the initial point is first set to the pixel with the largest pixel value. From this initial point, vessels are extracted using a region dilation method or other method, leveraging their continuity. Subsequently, the initial point is set to the pixel with the largest pixel value in the remaining image to extract a second vessel. This process is repeated to sequentially extract multiple vessels. After labeling the extracted vessels, their positional information and continuity are used to identify the largest continuous region as a major vessel, such as the middle cerebral artery, and the second largest continuous region as a vessel, such as the vertebral artery, for extraction. These vessels can be extracted using deep learning-based labeling techniques, using a pre-trained learning model.

[0119] (Step S207)

[0120] One or more volume-rendered images in which desired blood vessels are extracted are displayed and stored in the same manner as in the first embodiment.

[0121] As described above, in the fourth embodiment of the present invention, the processing unit 8 automatically performs cropping and opacity setting on 3D TOF images, and further automates the creation of volume-rendered images divided by left and right, and by major blood vessels. This allows the operator to create volume-rendered images more suitable for diagnosis without having to perform tedious operations.

[0122] Although the embodiments of the present invention have been described above, it goes without saying that the present invention is not limited thereto.

Claims

1. A magnetic resonance imaging device, characterized in that have: a static magnetic field generating unit for generating a static magnetic field in a space where an imaging portion of the subject is arranged; a measurement control unit that applies a gradient magnetic field and a high-frequency magnetic field to the subject and detects a nuclear magnetic resonance signal generated from the imaging portion; and a processing unit that generates a three-dimensional image of the subject using the detected nuclear magnetic resonance signal; The arithmetic processing unit performs the following processing: generating a 3D TOF image of a brain parenchymal region containing blood vessels as the three-dimensional image, Obtain a histogram of pixel values of the 3D TOF image, By removing pixel values of the 3D TOF image below a given threshold, i.e., pixels of the background area, from the histogram, a histogram is obtained, wherein the histogram shows that the pixels of the brain parenchyma have a distribution with a peak value, and the pixels of the blood vessels are distributed in an area with larger pixel values than the distribution of the pixels of the brain parenchyma with the peak value. The average value and variance of the pixel values of the histogram are calculated, and the range of pixel values below the pixel value C1, which is the sum of three times the average value and the variance, is set as the pixel value range of the brain parenchyma to set the opacity value to 0. The range of pixel values greater than pixel value C1 is set as the range of pixel values of the blood vessel. In order to set the opacity to a value greater than 0, the following opacity curve is set. This opacity curve changes the opacity value from 0 to 1 between the pixel value C1 and the pixel value C2 which is the sum of 5 times the average value and the variance. Pixel values greater than pixel value C2 have an opacity value of 1.

2. The magnetic resonance imaging apparatus according to claim 1, wherein The arithmetic processing unit generates a volume-rendered image of the blood vessel by performing volume rendering on the three-dimensional image using the opacity value.

3. The magnetic resonance imaging apparatus according to claim 2, wherein: The arithmetic processing unit extracts an unnecessary region by performing predetermined image processing on the volume-rendered image, and performs processing for deleting the extracted unnecessary region.

4. The magnetic resonance imaging apparatus according to claim 2, wherein The arithmetic processing unit further performs a process of extracting an image of a predetermined specific blood vessel from the volume-rendered image of the blood vessel.

5. A magnetic resonance imaging device, characterized in that have: a static magnetic field generating unit for generating a static magnetic field in a space where an imaging portion of the subject is arranged; a measurement control unit that applies a gradient magnetic field and a high-frequency magnetic field to the subject and detects a nuclear magnetic resonance signal generated from the imaging portion; and a processing unit that generates a three-dimensional image of the subject using the detected nuclear magnetic resonance signal; The arithmetic processing unit performs the following processing: generating a 3D TOF image of a brain parenchymal region containing blood vessels as the three-dimensional image, Obtain a histogram of the pixel values of the 3D TOF image, calculate the average value and variance of the pixel values of the histogram, and set an opacity value of 0 in a range of pixel values below the sum of three times the average value and the variance, i.e., the pixel value C1. An opacity curve is set, which varies the opacity value from 0 to 1 between the pixel value C1 and the pixel value C2 which is the sum of the average value and 5 times the variance, and the pixel values above the pixel value C2 become opacity values 1. The processing unit generates a volume-rendered image of the three-dimensional image using the opacity of the opacity curve while shifting at least one of the pixel values C1 and C2 of the opacity curve, calculates the volume of an image remaining in the volume-rendered image and / or the area of a two-dimensional image generated from the volume-rendered image, and determines the pixel values C1 and / or C2 used in the opacity curve based on the volume and / or area.

6. A magnetic resonance imaging apparatus, characterized in that: have: a static magnetic field generating unit for generating a static magnetic field in a space where an imaging portion of the subject is arranged; a measurement control unit that applies a gradient magnetic field and a high-frequency magnetic field to the subject and detects a nuclear magnetic resonance signal generated from the imaging portion; and a processing unit that generates a three-dimensional image of the subject using the detected nuclear magnetic resonance signal; The arithmetic processing unit performs the following processing: Obtaining a distribution of pixel values of the three-dimensional image, calculating a pixel value of a predetermined characteristic quantity based on the distribution of the pixel values, and setting an opacity curve having a pixel value greater than the pixel value of the characteristic quantity by a predetermined value as a boundary, wherein opacity changes from 0 to 1 as the pixel value increases. while moving the pixel value serving as the boundary, setting a plurality of opacity curves, calculating the distribution of the pixel values of the three-dimensional image using the plurality of opacity curves, and selecting one opacity curve based on the calculation result. performing volume rendering on the three-dimensional image using the selected opacity curve, extracting unnecessary areas by performing predetermined image processing on the obtained volume rendering image, and deleting the extracted unnecessary areas. The predetermined image processing is a process of extracting a discontinuous area.

7. An image processing device, characterized in that: have: The processing unit receives the 3D TOF image of the subject's head and performs processing. The arithmetic processing unit performs the following processing: generating a 3D TOF image of a brain parenchymal region including blood vessels from the 3D TOF image of the head, Obtaining a histogram of pixel values of a 3D TOF image of a brain parenchymal region containing the blood vessel, By removing pixel values of the 3D TOF image below a given threshold, i.e., pixels of the background area, from the histogram, a histogram is obtained, wherein the histogram shows that the pixels of the brain parenchyma have a distribution with a peak value, and the pixels of the blood vessels are distributed in an area with larger pixel values than the distribution of the pixels of the brain parenchyma with the peak value. The average value and variance of the pixel values of the histogram are calculated, and the range of pixel values below the pixel value C1, which is the sum of three times the average value and the variance, is set as the pixel value range of the brain parenchyma to set the opacity value to 0. The range of pixel values greater than pixel value C1 is set as the range of pixel values of the blood vessel. In order to set the opacity to a value greater than 0, the following opacity curve is set. This opacity curve changes the opacity value from 0 to 1 between the pixel value C1 and the pixel value C2 which is the sum of 5 times the average value and the variance. Pixel values greater than pixel value C2 have an opacity value of 1.

8. An image processing method, characterized in that: Receive 3D TOF images of the subject's head, generating a 3D TOF image of a brain parenchymal region including blood vessels from the 3D TOF image of the head, Obtaining a histogram of pixel values of a 3D TOF image of a brain parenchymal region containing the blood vessel, By removing pixel values of the 3D TOF image below a given threshold, i.e., pixels of the background area, from the histogram, a histogram is obtained, wherein the histogram shows that the pixels of the brain parenchyma have a distribution with a peak value, and the pixels of the blood vessels are distributed in an area with larger pixel values than the distribution of the pixels of the brain parenchyma with the peak value. The average value and variance of the pixel values of the histogram are calculated, and the range of pixel values below the pixel value C1, which is the sum of three times the average value and the variance, is set as the pixel value range of the brain parenchyma to set the opacity value to 0. The range of pixel values greater than pixel value C1 is set as the range of pixel values of the blood vessel. In order to set the opacity to a value greater than 0, an opacity curve is set such that the opacity value changes from 0 to 1 between pixel value C1 and pixel value C2, which is the sum of 5 times the mean value and the variance. Pixel values greater than pixel value C2 have an opacity value of 1. A volume-rendered image of a 3D TOF image of a brain parenchymal region containing the blood vessel is generated using the opacity value.

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