JP2013180009A - Image processing device and control program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing device allowing an operator to easily check an abnormal image.SOLUTION: An image processing device includes: an image obtaining part for obtaining a cross-sectional image of a subject; an image generating part for generating a difference image by using a plurality of cross-sectional images; a computing part for computing a degree of abnormality of the cross-sectional image by using a threshold from a pixel value of each pixel in the difference image; and a display part for the display for an operator to confirm the presence/absence of an abnormal image by using the degree of abnormality.

Description

  Embodiments described herein relate generally to an image processing apparatus and a control program.

  After imaging a subject with an image diagnostic apparatus such as an MRI (Magnetic Resonance Imaging) apparatus, it is necessary to check for the presence of abnormal images such as artifacts. When imaging a subject using an MRI apparatus, it is necessary to acquire a T1-weighted image, a T2-weighted image, and a PD (Proton Density: proton density) -weighted image. And they are combined to make a diagnosis. At this time, the presence or absence of an abnormal image is checked every time imaging in each series is completed. Currently, as a checking method, a captured image is automatically displayed, and an operator visually checks the abnormality of the captured image. The imaging of the subject in the MRI apparatus is the same as the T1-weighted image, T2-weighted image, and PD-weighted image, and the subject once placed on the bed must finish all scans without moving the posture. Don't be. Here, dozens or hundreds of images are taken, and if even one of them is abnormal, it must be taken again from the beginning. Then, when each scan is completed, it is converted into a DICOM (Digital Imaging and Communications in Medicine) format. Therefore, the presence / absence of abnormal images should be checked one by one at the stage of conversion to the DICOM format.

  Here, if an abnormal image is found in the cross-sectional image during the T1-weighted image acquisition scan at the stage where all of the T1-weighted image acquisition scan → T2-weighted image acquisition scan → PD-weighted image acquisition scan is performed, all the scans are performed again. must not. Therefore, it is necessary to move to the next scan after confirming that there is no abnormal image at the end of each scan.

  In recent years, with the increase in the number of captured images, it is not uncommon to capture several hundred images at a time. Further, the image display is abnormally fast due to the speeding up of the system, and it is difficult for the operator to find the abnormal image in the automatic display, and the burden on the operator is increased.

JP 2007-61545 A

  The problem to be solved by the present invention is to provide an image processing apparatus that allows an operator to easily check an abnormal image.

  In order to solve the above problem, an image processing apparatus according to an embodiment includes an image acquisition unit that acquires a cross-sectional image of a subject, an image generation unit that generates a differential image using a plurality of the cross-sectional images, A calculation unit that calculates a degree of abnormality of the cross-sectional image using a threshold value from a pixel value of each pixel in the difference image, and a display unit that performs display for allowing an operator to check whether there is an abnormal image using the degree of abnormality. With.

It is the schematic of the MRI apparatus in this embodiment. FIG. 4 is a schematic view showing a state in which a subject P is continuously imaged with a slice thickness X and a slice gap Y. It is the schematic which showed the example which calculates using a difference image, derives | leads-out the numerical value showing an abnormal degree, and the example which graphs a numerical value. It is the schematic which showed the example calculated using a difference image. It is the schematic which showed an example of the table which showed the abnormal threshold value depending on the slice thickness for every imaging region, a slice gap, a function, and a function, and a function. 3 is a flowchart according to the first embodiment. It is the schematic which showed an example which shows the abnormality degree of an original image in 1st Embodiment. It is a flowchart concerning a 2nd embodiment. It is the schematic which showed the example which sets the 2 area | region as ROI in a condition setting part, and graphs the abnormality degree of an original image from the calculation result using the difference image in ROI.

  Hereinafter, embodiments for carrying out the invention will be described.

(First embodiment)
The outline of the image processing apparatus according to the first embodiment will be described below. Here, an example using an MRI apparatus is shown.

  FIG. 1 is a schematic view of an MRI apparatus in the present embodiment.

  The MRI apparatus in the present embodiment includes a gantry 10, a gradient magnetic field power supply 20, an RF transmission unit 30, an RF reception unit 40, a sequence control unit 50, a bed 60, a bed control unit 70, and a computer system 100. Is provided.

  The first image generation unit 111 provided in the data processing unit 110 in the computer system 100 captures the subject P placed on the bed 60 for each successive slice and generates a cross-sectional image (hereinafter referred to as an original image). . The storage unit 105 stores a large number of original images generated by the first image generation unit 111. The second image generation unit 112 generates a difference image using the adjacent original images. Then, the calculation unit 114 calculates using the difference image and obtains an abnormal value that is the degree of abnormality of the original image. Then, the graph generation unit 113 graphs the abnormal values to make it easy for the operator to intuitively understand whether there is an abnormal image.

  Hereinafter, the MRI apparatus will be described.

  The gantry 10 is a device that irradiates a subject P placed in a static magnetic field with a high-frequency magnetic field and thereby detects a magnetic resonance signal generated from the subject P. The gantry 10 includes a static magnetic field magnet 11, a gradient magnetic field coil 12, and the like. And an RF (Radio Frequency) coil 13 for transmission and reception.

  The static magnetic field magnet 11 is a superconducting magnet that is formed in, for example, a hollow cylindrical shape and generates a uniform static magnetic field in an internal space. The static magnetic field magnet 11 may be a superconducting magnet that generates a uniform static magnetic field in an open MRI apparatus.

  The gradient magnetic field coil 12 is a coil formed in a hollow cylindrical shape, and is disposed inside the static magnetic field magnet 11. The gradient coil 12 is formed by combining three coils corresponding to the X, Y, and Z axes orthogonal to each other. In the present embodiment, these three coils are individually supplied with a current from a gradient magnetic field power source 20 to be described later, and the magnetic field in the Z-axis direction with respect to the distance from the center of the gradient magnetic field in each of the X, Y, and Z axes. Thus, a gradient magnetic field that changes linearly is generated.

  The gradient magnetic fields of the X, Y, and Z axes generated by the gradient coil 12 correspond to, for example, the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr, respectively. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging section. The phase encoding gradient magnetic field Ge is used to change the phase of the magnetic resonance signal in accordance with the spatial position. The readout gradient magnetic field Gr is used to change the frequency of the magnetic resonance signal in accordance with the spatial position.

  As shown in FIG. 1, the transmission / reception RF coil 13 is disposed inside the gradient magnetic field coil 12 and generates a high-frequency magnetic field by being supplied with a high-frequency pulse from an RF transmission unit 30 described later. While transmitting to the sample P, the magnetic resonance signal which generate | occur | produces from the subject P under the influence of a high frequency magnetic field is received. Note that a magnetic resonance signal generated from the subject P may be received by a reception-only RF coil (not shown).

  The gradient magnetic field power supply 20 supplies a current to the gradient magnetic field coil 12. The RF transmitter 30 transmits a high-frequency pulse corresponding to the Larmor frequency to the transmission / reception RF coil 13. The RF receiver 40 generates raw data by digitizing the magnetic resonance signal output from the transmission / reception RF coil 13.

  The sequence control unit 50 controls the scanning of the subject P by driving the gradient magnetic field power source 20, the RF transmission unit 30, and the RF reception unit 40 based on the sequence information transmitted from the computer system 100. When the raw data is transmitted from the RF receiver 40 as a result of scanning the subject P, the sequence control unit 50 transfers the received raw data to the computer system 100.

  Here, the sequence information means the strength of the power supplied from the gradient magnetic field power supply 20 to the gradient magnetic field coil 12 and the timing of supplying the power, the strength of the high frequency pulse transmitted from the RF transmitter 30 to the transmission / reception RF coil 13, and the high frequency pulse. Information defining the procedure for performing the scan, such as the timing at which the RF receiver 40 detects the NMR (Nuclear Magnetic Resonance) signal.

  The bed 60 includes a top plate 61 on which the subject P is placed, and the cavity of the gradient magnetic field coil 12 with the subject P placed on the top plate 61 under the control of a later-described bed control unit 70. Insert into. Usually, the bed 60 is installed such that the longitudinal direction is parallel to the central axis of the static magnetic field magnet 11.

  The couch controller 70 drives the couch 60 to move the top board 61 in the longitudinal direction and the vertical direction.

  The computer system 100 performs overall control of the MRI apparatus, data collection, image reconstruction, and the like, and includes an interface unit 101, a data processing unit 110, a condition setting unit 106, a storage unit 105, an input unit 102, a display unit 103, and a control. Part 104.

  The interface unit 101 controls input / output of various signals exchanged with the sequence control unit 50. For example, the interface unit 101 transmits sequence information to the sequence control unit 50 and receives raw data from the sequence control unit 50.

  Note that the raw data received by the interface unit 101 is based on the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr generated by the gradient magnetic field coil 12, in the SE (Slice Encode) direction, PE ( The information is stored in the storage unit 105 as Fourier space data (k-space data) in which the spatial frequency information in the Phase Encode (RO) direction and the RO (Read Out) direction is associated.

  Further, the interface unit 101 transmits a bed movement request input via the input unit 102 described later to the bed control unit 70, and the bed control unit 70 drives the bed 60 based on the received bed movement request. .

  The data processing unit 110 includes a first image generation unit 111, a second image generation unit 112, and a calculation unit 114. The first image generation unit 111 performs post-processing, i.e., reconstruction such as Fourier transform, on the Fourier space data stored in the storage unit 105 as k-space data, thereby generating an original image for each successive slice. Generate. The second image generation unit 112 generates a difference image using a plurality of original images obtained by the first image generation unit 111. At this time, it is preferable to generate a difference image obtained by subtracting corresponding pixels using two adjacent original images. However, adjacent original images may be used instead of the adjacent original images.

  The input unit 102 includes a pointing device such as a mouse and a trackball for receiving various instructions and information input from the operator, a selection device such as a mode change switch, or an input device such as a keyboard.

  The display unit 103 is a monitor that is referred to by the operator, and displays various information such as a magnetic resonance image to the operator and receives commands from the operator via the input unit 102 under the control of the control unit 104. The GUI (Graphical User Interface) is displayed.

  The storage unit 105 stores Fourier space data transferred from the interface unit 101 and a magnetic resonance image generated by the first image generation unit 111 performing Fourier transform on the Fourier space data. Further, a table for setting a different slice thickness and slice gap is stored for each imaging region set by the condition setting unit 106. Details will be described later with reference to FIG.

  The calculation unit 114 performs calculation using the difference image obtained by the second image generation unit 112. The calculation is to determine an abnormal threshold value that differs depending on the imaging region or the like using a function, and finally obtain an abnormal value that is the degree of abnormality of the original image. The abnormal value at this time is obtained from the number of pixels having a pixel value equal to or higher than the abnormal threshold or the ratio of the number of pixels having a pixel value equal to or higher than the abnormal threshold to all pixels.

  Here, the abnormal threshold value refers to a threshold value for determining whether or not the pixel value of the difference image is abnormal. The abnormal threshold value is derived from the ratio of the pixel value of the original image, and hereinafter, both the pixel value obtained using the ratio and the ratio are expressed as the abnormal threshold value. For example, when the pixel value of a certain pixel of the original image is 50, the abnormal threshold is 10 when the abnormal threshold determined depending on the slice condition and the subject is 20%. Therefore, an abnormal value is obtained based on the number of pixels having a pixel value of 10 or more in the difference image.

  The condition setting unit 106 sets an abnormal threshold value for determining whether or not the pixel value of the difference image is abnormal when the calculation unit 114 performs an arithmetic process using the original image and the difference image to obtain an abnormal value. The abnormal threshold at this time depends on a function and depends on the imaging region, slice thickness, and slice gap. Details will be described later with reference to FIG. The condition setting unit 106 sets a graph display threshold and an original image display threshold in the graph (see FIG. 3) generated by the graph generation unit 113. Details will be described later with reference to FIG.

  The graph generation unit 113 graphs an abnormal value indicating the degree of abnormality of the original image obtained by the calculation unit 114. All abnormal values may be graphed, or abnormal values that are greater than or equal to a preset graph display threshold may be graphed.

  The control unit 104 includes a CPU (Central Processing Unit), a memory, and the like, and performs overall control of the MRI apparatus. For example, the control unit 104 generates sequence information based on imaging conditions input from the operator via the input unit 102 and parameters set when designing the block setting, and the generated sequence information is sequenced. The scan of the subject P is executed by transmitting it to the control unit 50. In addition, the control unit 104 performs control to display the graph obtained by the graph generation unit 113 on the display unit 103.

  FIG. 2 is a schematic view showing a state in which the subject P is continuously imaged with the slice thickness X and the slice gap Y.

  In using the MRI apparatus, continuous original images are often captured as shown in FIG. And since the parts of the human body are basically connected, it is unlikely that a large change will occur between the front and rear slices. However, an abnormal image such as an artifact may occur at a certain place as in the original image 3. Artifacts are states that appear in an image different from the original shape of the subject due to various causes. There are motion artifacts due to body movement, chemical artifacts that occur due to the difference in frequency between the water component and the fat component, which are the main signals measured by MRI. An artifact such as the original image 3 in FIG. 2 is an example called an N / 2 artifact. The N / 2 artifact is an artifact in which the image is doubled by shifting by half of the FOV (Field Of View: imaging range) in the phase encoding direction. Also, an abnormal image such as the original image 3 may be detected even with motion artifacts.

  The second image generation unit 112 generates a difference image using these continuous original images. Next, the calculation unit 114 uses the difference image to perform calculation processing for checking the presence or absence of an abnormal image. The graph generation unit 113 generates a graph as a result obtained by the calculation unit 114 as a numerical value as an abnormal value. Details will be described below with reference to FIG. In addition, when the cross section of the subject is imaged for each slice as shown in FIG. 2, there is not much difference between the different parts of the consecutive images. Therefore, unless an abnormal image such as an artifact appears, an image whose shape is gradually changed is captured as in the original image 1, the original image 2 and the original image 4 shown in FIG.

  FIG. 3A is a schematic diagram illustrating an example in which a calculation is performed using a difference image to derive an abnormal value representing the degree of abnormality. Here, description will be made assuming that the original image 3 is an abnormal image.

  In order to create a graph that intuitively indicates the degree of abnormality of the original image, the second image generation unit 112 generates a difference image 1 between the original image 1 and the original image 2. And the calculating part 114 calculates using the pixel value of the difference image 1 obtained by the 2nd image generation part 112, and obtains an abnormal value. At this time, the calculation unit 114 performs calculation using an arbitrary function, and an abnormal threshold is determined.

  Next, the second image generation unit 112 generates the difference image 2 using the original image 2 and the original image 3, and the calculation unit 114 calculates the abnormality degree of the original image 2 as an abnormal value 2 in order to quantify the difference image 2. 2 is used for calculation. The computing unit 114 obtains an abnormal value 2 indicating the degree of abnormality of the original image 2 by performing the same calculation as described above. The above procedure is performed for each original image, and the graph generation unit 113 generates a graph as shown in FIG.

  FIG. 3B is a graph showing the degree of abnormality for each original image generated by the graph generation unit 113 based on the numerical values obtained by the processing shown in FIG. The horizontal axis represents position information indicating the slice position of the basic original image, and the vertical axis represents the degree of abnormality digitized by the calculation unit 114. Here, as shown in FIG. 2, it is assumed that the original image 3 is an abnormal image. Since the original image 3 is an abnormal image, the abnormal value of the original image 2 obtained from the difference image 2 (original image 2 -original image 3) also increases as shown in FIG. In such a case, in consideration of the order of the slice positions of the original image, processing such as showing a mark near the graph near the original image 3 where the abnormal image originally appears may be performed.

  Note that it is only necessary to be able to check “whether or not there is at least one abnormal image among a plurality of original images”, and therefore it is not always necessary to specify the exact occurrence position of the abnormal image. As shown in FIG. 3C, when an abnormal image is detected in each of the series 1 to 4, a symbol or a mark may be indicated instead of the exact position. FIG. 3C indicates that an abnormal image is detected in the T2 SG sequence. T2 SG here means a sequence for scanning the sagittal plane with a T2-weighted image.

  Further, when obtaining an abnormal value of the original image, not only the difference image but also the original image may be used. For example, the function and the abnormality threshold are changed according to the ratio of the pixel value between the difference image and the original image.

  Specifically, the following two cases will be considered and described.

In a pixel,
(1) The pixel value of the original image 1 is 100, the pixel value of the original image 2 is 95, and the pixel value of the difference image is 5
(2) The pixel value of the original image 1 is 10, the pixel value of the original image 2 is 5, and the pixel value of the difference image is 5.
In the case of (1), the pixel value of the difference image is 5% of the pixel value of the original image 1, whereas in the case of (2), the pixel value is 50%. By using this ratio, the calculation unit 114 may obtain the abnormal value in consideration of not only the pixel value of the function and the difference image 1 but also the pixel value of the original image 1. A dotted arrow in FIG. 3A suggests that an abnormal value is obtained in consideration of the pixel value of the original image.

  The graph may display all the numerical value data obtained by the calculation unit 114, or an operator may set a graph display threshold value in advance in the condition setting unit 106, and the graph generation unit 113 may exceed the graph display threshold value. Only numerical data of may be displayed in a graph. At this time, an original image having an abnormal value equal to or higher than the original image display threshold preset by the operator in the condition setting unit 106 is displayed by clicking the input unit 102 on the graph. Further, when the degree of abnormality suddenly increases, the graph generation unit 113 may automatically display an image near the graph. The display method and the like of these images may be set by the operator using the condition setting unit 106.

  Further, instead of showing only the graph as shown in FIG. 3B, the original image may be displayed side by side with the graph. In that case, by clicking the vicinity of the graph, the original image may be moved and displayed so that the operator can easily confirm it, or various controls such as enlarged display may be performed. Then, a graph display or an image display may be performed by clicking a series list as shown in FIG.

  Hereinafter, with reference to FIGS. 4 and 5, an example in which the calculation unit 114 performs calculation based on the values of the table stored in the storage unit 105 in advance will be described. The operator may set the slice thickness and the slice gap by the input unit 102 instead of the preset conditions. The function in that case may be uniquely determined depending on the slice thickness and the slice gap, or may be set by the operator using the input unit 102.

  FIG. 4 is a schematic diagram illustrating an example in which a difference image is generated from the original image 1 and the original image 2 and is calculated using the difference image.

  FIG. 5A is a table showing slice thicknesses, slice gaps, and abnormal threshold values depending on them, which are stored in the storage unit 105 for each imaging region.

  The second image generation unit 112 generates a difference image using the original image 1 and the original image 2. Then, the calculation unit 114 uses the abnormal threshold value to determine whether or not the pixel value is less than the abnormal threshold value previously determined by the condition setting unit 106 in all the pixels of the difference image 1. As a result, the pixel values of all the pixels in the difference image 1 are divided into two types depending on whether or not they are less than the abnormal threshold. At this time, the calculation unit 114 quantifies the degree of abnormality of the original image 1 as an abnormal value using the number of pixels or the ratio divided depending on whether or not the pixel value is less than the abnormal threshold. The ratio refers to the number of pixels having a pixel value equal to or greater than the abnormal threshold with respect to all pixels.

  Here, the change in shape between adjacent original images differs depending on the imaging region, slice thickness, slice gap, and subject. Therefore, when the calculation unit 114 digitizes the degree of abnormality, the abnormality threshold is determined by taking them into account.

  Further, even if the imaging site is the same, the degree of change in shape varies depending on the physique of the subject P. For example, as shown in FIG. 5B, even when the imaging site is the same and the slice thickness and the gap are the same, the degree of change in shape depends on the physique of the subject P (difference in organ size). It may be different.

  FIG. 5C shows an example of a function corresponding to the physique of the subject P.

  The organ or the like of the subject P having a large physique is larger than the subject P having a small physique. Therefore, the degree of change in shape is small even in the same imaging region and the same slice condition. Therefore, as described above, a function corresponding to the physique of the subject P is used from the viewpoint of obtaining an abnormal value of the original image clearly according to the physique of the subject P.

  In FIG. 5C, when comparing the subject A (small physique) and the subject B (large physique), the abnormal threshold in the subject B whose shape change is smaller than that of the subject A becomes smaller.

  Further, FOV may be used for setting the abnormal threshold. This is because when the size of the subject P is large, the FOV needs to be increased correspondingly, and thus the size of the subject P depends on the size of the FOV. The FOV may be set by the operator, or several types of FOVs may be stored in the storage unit 105 in advance and used. Hereinafter, description will be made on the assumption that, for example, the abnormal threshold is 20% of the pixel value in the original image.

  4, (X1, Y1) has a pixel value of 50 in the original image 1, a pixel value of 46 in the original image 2, and a pixel value of 4 in the difference image. On the other hand, (X2, Y1) has a pixel value of 50 in the original image, a pixel value of 10 in the original image 2, and a pixel value of 40 in the difference image. Since the abnormality threshold is 20% of the original image 1, if the pixel value of the difference image is less than 10, the pixel value of the pixel is less than the abnormality threshold. That is, (X1, Y1) is less than the abnormal threshold, and (X2, Y1) is greater than or equal to the abnormal threshold. The calculation unit 114 performs the above procedure for all the pixels, and counts the number of pixels having a pixel value equal to or greater than the abnormal threshold in the difference image. Then, the calculation unit 114 derives an abnormal value indicating the degree of abnormality of the original image 1 based on the number of pixels having a pixel value equal to or greater than the abnormality threshold. At this time, the calculation unit 114 may derive an abnormal value from the number of pixels having a pixel value equal to or greater than the abnormal threshold, or from the ratio of the number of pixels having a pixel value equal to or greater than the abnormal threshold in all pixels. An outlier may be derived. Incidentally, in FIG. 4, the pixels having the pixel value of the difference image equal to or higher than the abnormal threshold are surrounded by dotted lines (X2, Y1), (X2, Y2), (X3, Y2), (X4, Y2), There are six pixels (X1, Y3) and (X3, Y3), and the ratio of the number of pixels having a pixel value equal to or greater than the abnormal threshold is 37.5%.

  Here, assuming that the abnormal threshold is 10% of the original image 1, the pixel values of (X2, Y4) and (X3, Y4) are also higher than the abnormal threshold.

  Next, the operation of the image processing apparatus in this embodiment will be described.

  FIG. 6 is a flowchart according to the present embodiment.

  In step S1, the condition setting unit 106 specifies a part to be imaged and a range to be imaged based on information input by the operator. Since the slice thickness and the slice gap are determined, the abnormality threshold value used in step S5 is also uniquely determined. At this time, as described above, it may be considered that the physique differs depending on the subject. Here, for example, SAR (Specific Absorption Rate) may be used. When a magnetic field is applied to the subject, electricity flows in the subject and Joule heat is generated accordingly. The amount of current at this time depends on the tissue and is called local SAR. The SAR is finely classified according to the imaging site. At the time of diagnosis, the SAR value is determined by inputting the height and weight of the subject, and thereby the number of slices is also determined.

  Various conditions can be set in detail by the operator without using the data stored in the storage unit 105 shown in FIG.

  In step S <b> 2, the first image generation unit 111 generates an original image indicating the form information of the subject P by performing Fourier transform on the Fourier space data stored in the storage unit 105. An original image is continuously generated in the range, slice thickness, and slice gap determined in step S1. By this step S2, a continuous original image whose shape gradually changes as shown in FIG. 2 is obtained. At this time, an abnormal image due to various artifacts may be captured as in the original image 3.

  In step S3, the second image generation unit 112 generates a difference image using two adjacent original images obtained in step S2. The second image generation unit 112 calculates a difference between pixel values of pixels at the same position in the two original images. The second image generation unit 112 generates a difference image from the two original images by performing the same operation on all pixels.

  In step S4, the control unit 104 checks whether or not a difference image has been generated using all the original images obtained in step S2. If there is an original image that has not yet been used to generate a difference image, the process proceeds to step S3. If all original images are used, the process proceeds to step S5.

  In step S5, the calculation unit 114 obtains an abnormal value of the original image using the difference image acquired in step S3. At this time, the calculation unit 114 performs calculation using an abnormal threshold value that depends on a function based on a table (see FIG. 5) stored in the storage unit 105. The calculation unit 114 calculates the number of pixels having a pixel value that is less than (or more than) the abnormal threshold in the difference image. The number of pixels at this time may be an abnormal value that is the degree of abnormality of the original image, or the abnormal value of the original image is calculated from the ratio of the number of pixels that have a pixel value that is less than the abnormal threshold (or more than the abnormal threshold) for all pixels. An estimate may be made.

  In step S <b> 6, the control unit 104 confirms whether or not the calculation unit 114 has obtained abnormal values for all original images. If there is an original image for which an abnormal value has not yet been obtained (N), the process proceeds to step S5. If abnormal values are obtained for all original images (Y), the process proceeds to step S7.

  In step S7, the graph generation unit 113 graphs the abnormal value obtained in step S5 as shown in FIG. The horizontal axis of the graph represents information such as slice positions of continuous original images, and the vertical axis represents abnormal values of the original images. Incidentally, the abnormal value of the original image 1 is derived from the difference image 1 (original image 1-original image 2), and the abnormal value of the original image 2 is derived from the difference image 2 (original image 2-original image 3).

  In step S8, the control unit 104 causes the display unit 103 to display the graph and the original image obtained in step S7. The control unit 104 controls the graph display based on the graph display threshold set by the operator in the condition setting unit 106 in advance. When the graph display threshold is set, only an abnormal value (obtained in step S5) equal to or higher than the graph display threshold is displayed in a graph. On the other hand, when the graph display threshold is not set, the control unit 104 performs control for displaying all numerical values in a graph.

Further, the control unit 104 controls the original image display based on the original image display threshold that is set in advance by the condition setting unit 106 by the operator. For example, the control unit 104 may perform control for displaying an original image having a numerical value higher than the original image display threshold when the operator clicks the vicinity of the graph that is equal to or higher than the original image display threshold by the input unit 102. . (See Fig. 3 (b))
Although an example in which the operator sets the graph display threshold and the original image display threshold using the condition setting unit 106 is shown here, the present invention is not limited to this. Similar to the abnormal threshold used by the calculation unit 114 in step S5, the graph display threshold and the original image display threshold may be automatically set based on the imaging region or the like.

  In step S9, the operator checks the graph and original image displayed on the display unit 103, and determines the type of scan to be performed next. At this time, the operator may reset the abnormality threshold value again using the condition setting unit 106. In that case, the calculation unit 114 obtains an abnormal value of the original image based on the abnormal threshold value set again. Then, the graph generation unit 113 generates a graph of the abnormal value obtained based on the reset abnormal threshold value, and the display unit 103 displays the graph. If it is determined that the same kind of scan needs to be performed again (Y), the process proceeds to step S10A. If it is determined that a different type of scan is to be performed (N), the process proceeds to step S10B. If it is determined that there is no need to perform the next scan, the step ends.

  In step S <b> 10 </ b> A, the operator may reset the imaging conditions using the condition setting unit 106. Step S1 and subsequent steps are as described above.

  In step S <b> 10 </ b> B, the operator sets an imaging condition in the condition setting unit 106 so as to perform different types of scanning. Step S1 and subsequent steps are as described above.

  Even when an artifact occurs and there is an abnormal image, there is a case where it is not necessary to repeat the same scan again, such as when one image comes out toward the end in the imaging range. This is determined by the operator.

  Here, an example of graphing to make it easy for the operator to intuitively recognize the degree of abnormality of the original image has been shown, but the present embodiment is not limited to this. For example, as shown in FIG. 7, a plurality of original images may be displayed, and only original images with a high degree of abnormality may be marked or colored so as to stand out. In addition, an original image for which an abnormal value equal to or greater than the set original image display threshold value is obtained may be automatically displayed. Further, a graph as shown in FIG. 3B may be displayed at the same time.

  Further, when diagnosing the subject P, an abnormal image may be detected using past data without displaying a graph each time. In this case, the abnormal value increase pattern when the abnormal image is generated is referred to. Past data may be classified according to the type of artifact.

  Other various means such as displaying only an original image with a high degree of abnormality in a pop-up, increasing the size of the image, and the like can be applied.

  Next, effects of the image processing apparatus according to the present embodiment will be described.

  According to the present embodiment, the degree of abnormality of the original image can be appropriately quantified as an abnormal value by the arithmetic unit performing arithmetic processing according to conditions using the original image and the difference image. Then, the graph generation unit graphs based on the calculation result of the calculation unit, so that the operator can easily intuitively recognize the presence or absence of an abnormal image. Further, the operator can grasp the graph shape pattern by confirming the graph displayed for each diagnosis. That is, by creating a graph and notifying the operator of the degree of abnormality of the original image, the operator can get used to checking for the presence of an abnormal image. Further, by using a pattern recognition technique based on past graphed data, the presence or absence of an abnormal image can also be confirmed without graphing.

  Therefore, it is not necessary to visually check the presence or absence of abnormal images one by one from hundreds of original images, saving the operator's trouble. Furthermore, since the degree of abnormality is automatically digitized from the pixel values in the original image and the difference image, the degree of abnormality can be detected with higher accuracy than visual inspection by the operator.

  In addition, the present embodiment makes it possible to accurately and easily check for the presence or absence of abnormal images, so that the MRI apparatus can be used efficiently.

  And when imaging a subject with an MRI apparatus, it is necessary to perform each scan in order as mentioned above. In this embodiment, the calculation unit calculates the degree of abnormality of the original image captured in each scan, and the result is displayed on the display unit in an easy-to-understand manner for the operator. Therefore, the operator can check the abnormal image quickly and accurately, which not only allows the MRI apparatus to be used efficiently, but also reduces the burden on the subject who is forced to place on the bed for a long time. You can also And since it becomes possible to use an MRI apparatus efficiently without waste, waste of electric power can be prevented.

(Second Embodiment)
The second embodiment is different from the first embodiment in that a ROI (Region Of Interest) is set in step S25 in FIG. An image processing apparatus according to the second embodiment will be outlined with reference to FIG.

  FIG. 8 is a flowchart according to the present embodiment. A description of the same steps as those in the first embodiment will be omitted, and the description will start from step S25. Steps S1 to S4 in FIG. 6 correspond to steps S21 to S24 in FIG.

  In step S25, the condition setting unit 106 sets the ROI based on the information input by the operator. When an area necessary for diagnosis in the original image is determined in advance, the ROI is set without setting all the pixels as the calculation target as in the first embodiment. The ROI is not limited to one area, and a plurality of areas may be set. For example, when detecting an abnormal image due to an artifact as shown in FIG. 8, it is preferable to set two regions as the ROI.

  Note that the ROI may be set before step S23 and a difference image may be generated only within the set ROI range. By performing the difference process only within the set ROI, the processing time can be shortened.

Details will be described below with reference to FIG.

  FIG. 9 is a schematic diagram illustrating an example in which two regions are set as ROIs by the condition setting unit 106 and the abnormality degree of the original image is graphed from the calculation result in the ROI of the difference image.

  In FIG. 9, in the original image 3, an abnormal image due to N / 2 artifact is captured. In ROI1, since there is not much difference in pixel values from the original images 1 to 4, there is no significant change on the graph. However, in ROI2, the influence of the artifact in the original image 3 is reflected, and the degree of abnormality is greatly increased on the graph.

  In step S26, the calculation unit 114 obtains an abnormal value based on the pixel value in the ROI specified in step S25 in the difference image acquired in step S23. At this time, the calculation unit 114 performs calculation using an abnormal threshold value that depends on a function based on the table stored in the storage unit 105. Then, the calculation unit 114 calculates the number of pixels having pixel values that are less than the abnormal threshold (or higher than the abnormal threshold) in the difference image. The number of pixels at this time may be an abnormal value that is the degree of abnormality of the original image, or the ratio of the number of pixels having a pixel value that is less than the abnormal threshold (or more than the abnormal threshold) with respect to all pixels in the ROI. An abnormal value may be estimated. When a plurality of ROIs are set, the calculation unit 114 performs calculation for each ROI and obtains an abnormal value for each ROI.

  In step S <b> 27, the control unit 104 confirms whether or not an abnormal value of the abnormality degree of all original images has been obtained. If there is an original image for which an abnormal value has not yet been obtained (N), the process proceeds to step S26. When abnormal values are obtained for all original images (Y), the process proceeds to step S28.

  In step S28, the graph generation unit 113 graphs the abnormal value obtained in step S26. The horizontal axis represents information such as the positions of successive original images, and the vertical axis represents the digitized abnormal values of the original images. Here, when a plurality of ROIs are set, the graph generation unit 113 generates a graph for each ROI. That is, when three ROIs are set, there are three types of graphs.

  Hereinafter, effects of the image processing apparatus according to the present embodiment will be described.

  According to the present embodiment, the time required for the arithmetic processing can be shortened by setting the ROI in the condition setting unit. Also, as shown in FIG. 9, by setting a plurality of ROIs and displaying a plurality of graphs according to the number of ROIs, it is possible to shorten the time required for arithmetic processing and improve the accuracy of abnormal image detection.

  Further, by setting the ROI in advance, it is possible to easily determine which artifact has occurred depending on the set position and range. This is because, for example, in the case of the N / 2 artifact shown in FIG. 9, the image is doubled, which is greatly different from the abnormal state in the case of the susceptibility artifact that appears due to the susceptibility effect. In addition, the ROI can be changed or newly set after the graph is created. Thereby, the abnormal image can be checked more accurately.

  That is, according to the present embodiment, it is possible to further reduce the burden on the patient through shortening the arithmetic processing and improving the accuracy of abnormal image detection.

  The example using the difference image generated using a plurality of adjacent original images has been described above, but the present embodiment is not limited to this. For example, even if the difference processing is not performed, the pixel values of the pixels at the same position in a plurality of original images may be compared, and the abnormal value of the original image may be estimated from the ratio. Other various means can be applied.

  Further, although the case where the MRI apparatus is used has been described, the present embodiment is not limited to this. The present invention can also be used for image data shot with other modalities. Further, the processing may be performed by a display device that is generally used for interpretation, generally called a medical image viewer. In that case, the medical image viewer has a function of acquiring image data acquired by each modality in the image acquisition unit, performing various calculations using the image data, and deriving abnormal values. The image acquisition unit at this time is some means for acquiring image data generated by a modality such as an MRI apparatus. Image data may be transmitted / received using a cable, or wireless.

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

DESCRIPTION OF SYMBOLS 10 Gantry 11 Static magnetic field magnet 12 Gradient magnetic field coil 13 Transmission / reception RF coil 20 Gradient magnetic field power supply 30 RF transmission part 40 RF reception part 50 Sequence control part 60 Sleeper 61 Top plate 70 Sleeper control part 100 Computer system 101 Interface part 102 Input part 103 Display unit 104 Control unit 105 Storage unit 106 Condition setting unit 110 Data processing unit 111 First image generation unit 112 Second image generation unit 113 Graph generation unit 114 Calculation unit

Claims (14)

An image acquisition unit for acquiring a cross-sectional image of the subject;
An image generation unit that generates a difference image using a plurality of the cross-sectional images;
From the pixel value for each pixel in the difference image, a calculation unit that calculates the degree of abnormality of the cross-sectional image using a threshold value;
Using the degree of abnormality, a display unit that performs a display for allowing an operator to confirm the presence or absence of an abnormal image;
An image processing apparatus.   The image processing apparatus according to claim 1, wherein the cross-sectional image is obtained by an MRI apparatus. A graph generator for generating a graph indicating the degree of abnormality and position information of the cross-sectional image;
An input unit for receiving information from an operator;
The image processing apparatus according to claim 1, wherein the display unit displays the cross-sectional image corresponding to a position on the graph designated by information from the operator.   The image processing apparatus according to claim 3, wherein the threshold is determined based on at least one of a slice thickness, a slice gap, and an imaging region designated by information from the operator.   The image processing apparatus according to claim 4, wherein the calculation unit obtains an abnormal value that is an abnormality degree of the cross-sectional image based on a comparison between the threshold value and a pixel value of the difference image.   The image processing apparatus according to claim 5, wherein the abnormal value is based on a number of pixels having a pixel value higher than the threshold value in the difference image.   The image processing apparatus according to claim 5, wherein the abnormal value is a ratio of the number of pixels having a pixel value higher than the threshold to the total number of pixels in the difference image.   The image processing device according to claim 5, wherein the calculation unit obtains the abnormal value based on a ratio of a pixel value of the difference image to a pixel value of the slice image in each pixel in the difference image and the slice image. . A condition setting unit for setting the threshold;
The condition setting unit sets at least one region in the difference image;
The arithmetic unit quantifies the degree of abnormality in the at least one region for each region,
The image processing apparatus according to claim 1, wherein the control unit performs control for causing the display unit to display the calculated degree of abnormality for each region. The condition setting unit sets the at least one region at the same pixel position in the cross-sectional image and the difference image,
The image processing apparatus according to claim 9, wherein the second image generation unit performs difference processing only within the at least one region. An image acquisition unit for acquiring a cross-sectional image of the subject;
A calculation unit that performs calculation using a threshold value and a plurality of the cross-sectional images;
When there is an abnormal image among a plurality of the cross-sectional images in one sequence, using the calculated abnormality degree, a display unit that displays whether or not an abnormal image has occurred in the sequence;
An image processing apparatus. An image acquisition unit for acquiring a cross-sectional image of the subject;
An arithmetic unit that calculates the degree of abnormality of the cross-sectional image from a threshold value and a pixel value for each pixel in the plurality of cross-sectional images,
A display unit for displaying the presence or absence of an abnormal image using the degree of abnormality;
An image processing apparatus.   The image processing apparatus according to claim 12, wherein the cross-sectional image is an MRI apparatus. In the image processing device,
Function to generate a cross-sectional image of a subject and a differential image obtained from the cross-sectional image,
Function to calculate the degree of abnormality of the cross-sectional image using the difference image,
A control program for an image processing apparatus that causes a function of displaying the degree of abnormality on a display unit.

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