JP6723059B2 - Image processing apparatus and magnetic resonance imaging apparatus - Google Patents

Description

Embodiments of the present invention relate to an image processing apparatus and a magnetic resonance imaging apparatus.

As a method of setting a region of interest in magnetic resonance imaging, there is a method of manually setting parameters relating to the region of interest. Further, as a method of setting a region of interest in magnetic resonance imaging, a predetermined landmark is detected from volume data (for example, three-dimensional data or two-dimensional multi-slice data) obtained in the previous imaging, and the detected landmark is detected. There is a method of automatically setting a region of interest by obtaining the axial direction of one part (for example, a predetermined bone).

However, the method of manually setting the region of interest has poor reproducibility. In addition, the method of automatically setting the region of interest by obtaining the axial direction of one region may not be able to set the region of interest appropriately.

Japanese Unexamined Patent Application Publication No. 2015-06599

Embodiments of the present invention provide an image processing apparatus capable of appropriately setting a region of interest.

The image processing apparatus according to the embodiment includes an acquisition unit and a setting unit. The acquisition unit acquires first data regarding the first bone and second data regarding the second bone based on the volume data. The setting unit determines the interest based on the first point on the first straight line obtained from the first data and the second point on the second straight line obtained from the second data. Set the area.

FIG. 1 is a diagram showing an image processing apparatus according to the first embodiment. FIG. 2 is a diagram illustrating the background of the image processing apparatus according to the first embodiment. FIG. 3 is a diagram illustrating the background of the image processing apparatus according to the first embodiment. FIG. 4 is a diagram illustrating the background of the image processing apparatus according to the first embodiment. FIG. 5 is a flowchart showing a procedure of processing performed by the image processing apparatus according to the first embodiment. FIG. 6 is a diagram illustrating a process performed by the image processing apparatus according to the first embodiment. FIG. 7 is a diagram illustrating a process performed by the image processing apparatus according to the first embodiment. FIG. 8 is a diagram illustrating processing performed by the image processing apparatus according to the first embodiment. FIG. 9 is a diagram illustrating a process performed by the image processing apparatus according to the first modified example of the first embodiment. FIG. 10 is a diagram illustrating a process performed by the image processing device according to the first modification of the first embodiment. FIG. 11 is a diagram illustrating a process performed by the image processing apparatus according to the second embodiment. FIG. 12 is a diagram showing an image processing apparatus according to the second embodiment. FIG. 13 is a flowchart showing a procedure of processing performed by the image processing apparatus according to the second embodiment. FIG. 14 is a diagram showing a magnetic resonance imaging apparatus according to the third embodiment. FIG. 15 is a flowchart illustrating the processing performed by the magnetic resonance imaging apparatus according to the third embodiment. FIG. 16 is a diagram illustrating the hardware configuration of the image processing apparatus according to the embodiment.

Hereinafter, an image processing apparatus and a magnetic resonance imaging apparatus according to embodiments will be described in detail with reference to the accompanying drawings.

(First embodiment)
Hereinafter, the image processing device according to the first embodiment will be described in detail.

FIG. 1 is a diagram showing an image processing apparatus 100 according to the first embodiment. The image processing apparatus 100 is, for example, an image processing apparatus that processes data captured by a magnetic resonance imaging apparatus to generate an image or sets a predetermined setting. The image processing apparatus 100 is, for example, a dedicated or general-purpose computer. The image processing apparatus 100 is, for example, an image processing apparatus connected to a magnetic resonance imaging apparatus via a network, an image processing apparatus forming a part of the magnetic resonance imaging apparatus, a PC (workstation for performing image processing on medical images. ), or an image processing apparatus included in a medical image management apparatus (server) that stores and manages medical images.

The image processing apparatus 100 according to the first embodiment includes a processing circuit 150, a storage circuit 132, an input device 134, and a display device 135. The processing circuit 150 includes a detection function 150a, an acquisition function 150b, a first estimation function 150c, a second estimation function 150d, a setting function 150f, and a reception function 150g. Details of the detection function 150a, the acquisition function 150b, the first estimation function 150c, the second estimation function 150d, the setting function 150f, and the reception function 150g will be described later.

In the first embodiment, each processing function performed by the detection function 150a, the acquisition function 150b, the first estimation function 150c, the second estimation function 150d, the setting function 150f, and the reception function 150g is a computer-executable program. It is stored in the storage circuit 132 in the form. The processing circuit 150 is a processor that realizes a function corresponding to each program by reading the program from the storage circuit 132 and executing the program. In other words, the processing circuit 150 in the state where each program has been read out has the functions shown in the processing circuit 150 of FIG. In FIG. 1, the processing functions performed by the detection function 150a, the acquisition function 150b, the first estimation function 150c, the second estimation function 150d, the setting function 150f, and the reception function 150g are performed by the single processing circuit 150. Although described as being realized, a plurality of independent processors may be combined to configure the processing circuit 150, and each processor may realize a function by executing a program.

In other words, each function described above may be configured as a program, and one processing circuit may execute each program, or a specific function may be implemented in a dedicated independent program execution circuit. May be.

The detection function 150a, the acquisition function 150b, and the reception function 150g included in the processing circuit 150 are examples of a detection unit, an acquisition unit, and a reception unit, respectively. The first estimating function 150c, the second estimating function 150d, and the setting function 150f are examples of the setting unit. Further, the first estimation function 150c, the second estimation function 150d, and the setting function 150f are examples of a calculation unit.

The word "processor" used in the above description is, for example, a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), or an application-specific integrated circuit (ASIC), a programmable logic device (e.g., simple logic device). A programmable programmable logic device (SPLD), a complex programmable logic device (Complex Programmable Logic Device: CPLD), a field programmable gate array (meaning a Field Programmable Gate Array: FPGA), and the like. The processor realizes the function by reading and executing the program stored in the storage circuit 132. Instead of storing the program in the memory circuit 132, the program may be directly incorporated in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program incorporated in the circuit.

The storage circuit 132 stores data and the like associated with various processes performed by the image processing apparatus 100 as needed. For example, the storage circuit 132 is a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk, an optical disk, or the like. Further, the processing performed by the storage circuit 132 in the processing circuit 150 may be replaced by a storage device outside the image processing apparatus 100.

The input device 134 receives various instructions and information input from the operator. The input device 134 is, for example, a pointing device such as a mouse or a trackball, or an input device such as a keyboard.

The display device 135 displays various information such as image data. The display device 135 is, for example, a display device such as a liquid crystal display. The processing performed by the display device 135 in the processing circuit 150 may be replaced by a display device outside the image processing device 100.

Subsequently, the background of the image processing apparatus 100 according to the first embodiment will be briefly described with reference to FIGS. 2 to 4. 2 to 4 are diagrams illustrating the background of the image processing apparatus 100 according to the first embodiment.

FIG. 2 shows various imaging cross sections regarding the subject P. The sagittal surface 1a is a sagittal surface and is a plane stretched by two vectors, a vector representing the front-back direction of the subject P and a vector representing the up-down direction of the subject P. The coronal surface 1b is a coronal surface and is a plane stretched by two vectors, a vector representing the horizontal direction of the subject P and a vector representing the vertical direction of the subject P. The axial surface 1c is an axial surface, and is a plane stretched by two vectors, a vector representing the left-right direction of the subject P and a vector representing the front-back direction of the subject P.

Next, setting of the imaging coronal surface using the sagittal surface will be described with reference to FIGS. 3 and 4. 3 and 4, the case where the knee is the imaging target will be described. As will be described later, the imaging coronal surface is a plane inclined as compared with the coronal surface.

In FIG. 3, the knee 2a represents the knee of the subject P. The sagittal surface 2b represents a sagittal surface. The coronal surface 2c represents a coronal surface obtained by cutting the place corresponding to the upper part of the knee with the coronal surface. Further, the coronal surface 2d represents a coronal surface obtained by cutting the place corresponding to the lower part of the knee with the coronal surface. As can be seen from FIG. 3, in FIG. 3, since the direction of the knee of the subject P is not necessarily in the vertical direction, the upper knee is photographed while the lower knee is photographed on the coronal surface 2c, for example. Not not. On the other hand, on the coronal surface 2d, the lower knee is photographed while the upper knee is not photographed. Therefore, it is not possible to photograph the entire knee with one coronal surface.

The setting of the imaging coronal surface using the sagittal surface will be described with reference to FIG. In FIG. 4, the knee 2a represents a knee, and the sagittal surface 2b represents a sagittal surface. The imaging coronal surface 3 is a surface on which imaging for diagnosis is performed, and represents an imaging coronal surface which is a plane on which imaging is performed in a direction substantially equal to the coronal surface. Imaging for diagnosis is sometimes called, for example, main imaging to distinguish it from positioning imaging. The imaging coronal surface 3 is a plane inclined as compared with the coronal surface 2c and the coronal surface 2d, and the imaging coronal surface 3 is a plane in which the entire knee is included in the imaging coronal surface 3. The processing circuit 150 sets the orientation of the imaging coronal surface 3 based on the image displayed on the sagittal surface 2b, for example.

As an example of such a method, there is a method of setting a region of interest by detecting a predetermined landmark from volume data obtained by positioning and imaging, and obtaining the axial direction of one region from the detected landmark. In this case, for example, the processing circuit 150 obtains, in the sagittal plane 2b, an axial direction in which both long bones of the femur and the tibia forming the knee 2a are included in the region of interest as much as possible, whereby the imaging coronal plane 3 is obtained. And set the region of interest.

However, if the region of interest is set by obtaining the axial direction of one region, it may be difficult to set the region of interest appropriately. For example, in knee magnetic resonance imaging, since both the axial direction of the femur and the axial direction of the tibia are important, it is necessary to appropriately adjust them according to the relative positional relationship between these two parts (for example, the angle between the femur and the tibia). It is desirable that the region of interest be set.

With this background, the image processing apparatus 100 according to the first embodiment causes the first point on the first straight line obtained from the first data regarding the first bone and the second point regarding the second bone. The region of interest is set based on the second point on the second straight line obtained from the data.

Subsequently, processing performed by the image processing apparatus 100 according to the first embodiment will be described with reference to FIGS. 5 to 8. FIG. 5 is a flowchart showing a procedure of processing performed by the image processing apparatus 100 according to the first embodiment. 6 to 8 are diagrams illustrating the processing performed by the image processing apparatus 100 according to the first embodiment. Hereinafter, a case will be described in which the image processing apparatus 100 processes an image related to knee magnetic resonance imaging and sets a region of interest including a knee in coronal imaging using a positioning image of a sagittal plane. 6 to 8 describe the processing performed by the image processing apparatus 100 according to the first embodiment based on the positioning image of the sagittal surface.

In FIG. 6, the femur 10 represents the femur. The tibia 11 represents a tibia. The femoral center 13 represents the femoral center that is a landmark. The straight line 8 indicates an example of a straight line passing through the femoral center 13. The angle 14 represents an angle indicating the direction of the straight line 8. The neighboring region 12 is
The predetermined area|region defined with respect to the straight line 8 which is one of the several directions which pass along the femoral center 13 is shown. The neighborhood area 12 has, for example, a predetermined point on the straight line 8 whose center of gravity is located, and a predetermined long side and short side whose long sides and short sides are perpendicular or parallel to the straight line 8 (long side and short side (Including cases where the two are equal). For example, when the angle 14 is θ, the neighboring region 12 can be expressed as R _f (θ) as a function of θ. When the angle 14 changes due to the change in the direction of the straight line 8, the neighboring region 12 represents a different region accordingly.

In FIG. 7, the femur 10 represents the femur. The tibia 11 represents a tibia. The tibia center 15 represents a tibia center that is a landmark. A straight line 9 is an example of a straight line passing through the tibia center 15. The angle 17 represents the angle indicating the direction of the straight line 9. The neighboring area 16 is a predetermined area defined with respect to the straight line 9 which is one of the straight lines passing through the tibial center 15 in a plurality of directions. The vicinity region 16 is, for example, a center of gravity at a predetermined point on the straight line 9, and long sides and short sides thereof are perpendicular or parallel to the straight line 9 and have predetermined long and short sides (long and short side lengths). (Including cases where the two are equal).

In FIG. 8, the first straight line 20 is a straight line calculated by the processing circuit 150 by the first estimating function 150c in step S120 described later. The second straight line 21 is a straight line calculated by the processing circuit 150 by the second estimating function 150d in step S140 described later. The angle 23 is the angle of the first straight line 20. The angle 25 is the angle of the second straight line 21. The third straight line 28 is a third straight line described later. The fourth straight line 29 is a fourth straight line parallel to the third straight line 28. The first point 22 is the intersection of the first straight line 20 and the third straight line 28. The second point 24 is the intersection of the second straight line 21 and the fourth straight line 29. The straight line 27 is a straight line that passes through the first point 22 and the second point 24. The angle 26 is the angle of the straight line 27.

Returning to FIG. 5, the processing circuit 150 uses the acquisition function 150b to acquire predetermined volume data. Here, the volume data is volume data obtained by imaging with a medical image diagnostic apparatus such as a magnetic resonance imaging apparatus. Examples of volume data are, for example, three-dimensional data and two-dimensional multi-slice data.

The processing circuit 150 detects a landmark from the acquired volume data by the detection function 150a (step S100). Specifically, the processing circuit 150 uses the acquisition function 150b to acquire first data regarding the first bone and second data regarding the second bone based on the acquired volume data. Subsequently, the processing circuit 150 uses the detection function 150a to detect the first landmark in the first bone from the acquired first data, and to detect the second landmark in the second bone from the second data. To detect.

Here, a specific example of the first bone is, for example, the femur 10. At this time, the first data is data regarding the femur 10. A specific example of the second bone is, for example, the tibia 11. At this time, the second data is data regarding the tibia 11. An example of the first landmark is, for example, the femoral center 13 which is the central position of the femur 10. An example of the second landmark is the tibia center 15 which is the center position of the tibia 11.

In such a case, in step S100, the processing circuit 150 acquires the data regarding the femur 10 and the data regarding the tibia 11 by the acquisition function 150b based on the acquired volume data. Then, the processing circuit 150 detects the femoral center 13 which is the center position of the femur 10 from the acquired data on the femur 10 by the detection function 150a, and detects the center position of the tibia 11 from the acquired data on the tibia 11. A certain tibial center 15 is detected.

Here, the central position of the femur 10 or the tibia 11 typically refers to a point located at the axial center of the femur 10 or the tibia 11 at the boundary between the diaphysis and the epiphysis, but is not limited to this. It may be on the bone 10 or the tibia 11. For example, it is an example of the center position of the femur where the femoral center 13 in FIG. 6 is applied. Further, for example, the center 15 of the tibia in FIG. 7 is an example of the center position of the tibia.

The processing circuit 150 executes the processing of step S100 by the detection function 150a by using, for example, a classifier that learns and constructs a three-dimensional image pattern around the central position by Extremely Randomized Trees. In addition, the processing circuit 150, for example,
The detection function 150a executes the process of step S100 using an identification machine constructed by performing learning using another machine learning method such as Support Vector Machine or deep learning. Further, as another example, the processing circuit 150 applies, for example, a statistical shape model in which a landmark position is known based on a method other than the machine learning method, for example, template matching, Active Shape Model, and the position of the landmark is determined. The processing of step S100 is executed by the detection function 150a using a technique such as registration with a known atlas image. Further, as another example, the processing circuit 150 detects the position of the landmark by machine learning, and then increases the accuracy of the detected position by a combination of methods such as using template matching in the vicinity of the detected position. The detection function 150a executes the process of step S100. Further, at this time, the processing circuit 150 may perform the isotropicization of the image and the normalization of the luminance value by the histogram flattening or the histogram expansion before the detection.

Subsequently, the processing circuit 150 determines the first plane that is the plane related to the first bone by the first estimation function 150c (step S110). For example, the processing circuit 150 uses the first estimation function 150c to determine the femoral plane that is the plane relating to the femur 10. The first plane is, for example, a sagittal plane that passes through the femoral center 13. As another example, the first plane is a plane that passes through the femoral center 13 and the tibial center 15 and is perpendicular to the coronal plane of the volume data. The first plane is used by the processing circuit 150 to estimate the first straight line 20 by the first estimation function 150c in step S120.

Subsequently, the processing circuit 150 causes the first estimation function 150c to determine the first straight line 20 that is a straight line passing through the first landmark and indicating the axial direction of the first bone as the first data. It is calculated based on (step S120). For example, the processing circuit 150 uses the first estimation function 150c to form a first straight line 20 that is a straight line that passes through the femoral center 13 that is the first landmark and that indicates the axial direction of the femur 10, It is estimated based on the first data which is the data of the femur.

In step S120, for example, the processing circuit 150 uses the first estimation function 150c to set predetermined regions (proximity regions) R(θ) with respect to the directions θ of a plurality of straight lines passing through the femoral center 13. Then, the first straight line 20 characterized by the angle θ _f is estimated by comparing the integrated value of a predetermined function E(θ) described later for each direction θ.

The angle θ _f (the angle between the first straight line 20 and the horizontal direction in FIG. 6) that characterizes the first straight line 20 is represented by, for example, the value φ on the left side of Expression (1) below.

Here, argmax means a process of changing the angle θ and calculating the angle θ such that the predetermined function E(θ) takes the maximum value. The predetermined function E(θ) is also called an energy function, and its expression is given by, for example, the following expression (2).

Here, x and y indicate the x coordinate and the y coordinate of the pixel. For example, in FIG. 6, x is a horizontal position and y is a vertical position. Since FIG. 6 is a sagittal plane, the horizontal direction means the front-back direction of the subject P, and the vertical direction means the vertical direction of the subject P. I _grad (x, y) indicates the absolute value of the gradient vector of pixel values at the pixel position (x, y). Further, I _dir (x, y) represents an angle corresponding to the direction in which the magnitude of the gradient vector of the pixel value at the pixel position (x, y) is maximum, in the range of 0° to 360°. Is a function. Further, R(θ) represents the above-mentioned neighboring region 12 as a function of the angle 14(θ).

As can be seen from Expression (2), the predetermined function E(θ) is a function that increases as the gradient direction of a pixel having a large gradient strength is perpendicular to θ. Here, in the bone image, the direction in which the gradient of the pixel value is the largest is considered to be the lateral direction of the bone. On the contrary, in the image of the bone, the direction perpendicular to the direction in which the gradient of the pixel value is the largest is considered to be the longitudinal direction of the bone (axial direction of the bone). Therefore, the direction θ in which the function E(θ), which increases as the direction in which the gradient of the pixel value becomes the largest is perpendicular to θ, becomes the largest, is considered to be the axial direction of the bone.

The predetermined function E(θ) is a function having a value determined for each pixel, and is the maximum gradient direction I _dir (x, y) of the pixel value and the first value of the pixel (x, y). It is a function determined based on the positional relationship with respect to the landmark (for example, the femoral center 13) or the second landmark (for example, the tibial center 15). The processing circuit 150 may calculate the angle 23 (θ _f ) representing the first straight line 20 by using another functional system having such a property.

As described above, in step S120, the processing circuit 150 causes the first estimating function 150c to perform a predetermined function E(θ in a predetermined area set for each of straight lines in a plurality of directions passing through the first landmark. The first straight line 20 is estimated by comparing the integrated value of) for each direction θ.

Subsequently, the processing circuit 150 uses the second estimation function 150d to determine the second plane that is the plane related to the second bone (step S130). For example, the processing circuit 150 determines the tibial plane that is the plane related to the tibia 11 by the second estimation function 150d. The second plane is, for example, a sagittal plane of volume data passing through the tibia center 15. Further, as another example, the second plane is a plane that passes through the femoral center 13 and the tibial center 15 and is perpendicular to the coronal plane. The second plane is used by the processing circuit 150 to estimate the second straight line 21 by the second estimation function 150d in step S120. The second plane may be the same plane as the first plane or a different plane.

Subsequently, the processing circuit 150 causes the second estimation function 150d to determine the second straight line 21, which is a straight line passing through the second landmark and indicating the axial direction of the second bone, as the second data. It is calculated based on (step S140). For example, the processing circuit 150 uses the second estimation function 150d to set a second straight line 21 that is a straight line that passes through the tibia center 15 that is the second landmark and that indicates the axial direction of the tibia 11, to the tibia 11. It is estimated based on the second data which is the data of.

In step S140, for example, the processing circuit 150 uses the second estimation function 150d to determine a predetermined region (proximity region 16) for each direction θ (angle 17) of a plurality of straight lines passing through the tibial center 15. The second straight line 21 characterized by the angle φ is calculated by comparing the integrated value of the energy function E(θ) in R(θ) for each direction θ.

The angle φ (angle 25) (the angle between the second straight line 21 and the horizontal direction in FIG. 7) that characterizes the second straight line 21 is represented by, for example, the above-described formula (1). Further, the expression of the energy function E(θ) is given by the above-mentioned expression (2).

As described above, in step S140, the processing circuit 150 causes the second estimation function 150d to integrate the integrated value of the predetermined function in the predetermined regions defined for the straight lines in the plurality of directions passing through the second landmark. Is determined for each direction to determine the second straight line 21.

When step S140 is completed, subsequently, the processing circuit 150 causes the setting function 150f to obtain the first point 22 on the first straight line 20 obtained from the first data and the first point 22 obtained from the second data. The region of interest is set based on the second point 24 on the second straight line 21 (step S150).

More specifically, the processing circuit 150 causes the setting function 150f to set the first point 22 as the intersection of the first straight line 20 and the third straight line 28, as indicated by _pf in FIG. To calculate. Further, the processing circuit 150 uses the setting function 150f to calculate the second point 24 as the intersection of the second straight line 21 and the fourth straight line 29, as indicated by p _t in FIG.

Here, the third straight line 28 is, for example, a predetermined straight line that represents the upper end of the femur surface. Further, the fourth straight line 29 is, for example, a predetermined straight line that represents the lower end of the tibial surface. The third straight line 28 and the fourth straight line 29 may be set at fixed positions in advance, or may be set by user input. In this case, the processing circuit 150 receives the input of information specifying the third straight line 28 and the fourth straight line 29 by the receiving function 150g. The processing circuit 150 uses the setting function 150f to calculate the first point 22 and the second point 24 based on the received input.

Subsequently, the processing circuit 150 uses the setting function 150f to calculate a straight line 27 passing through the first point 22 and the second point 24. The processing circuit 150 uses the setting function 150f to set the imaging coronal surface based on the direction indicated by the straight line 27 passing through the first point 22 and the second point 24. In this way, the processing circuit 150 sets the imaging coronal surface based on the information on the predetermined sagittal surface. As a method of setting the imaging coronal surface, for example, as already described in FIG. 4, the processing circuit 150 uses the setting function 150f to include the direction indicated by the straight line 27 passing through the first point 22 and the second point 24. Set the imaging coronal plane with. The imaging coronal plane set in this manner includes a large number of femurs and tibias in the region of interest.

Further, the embodiment is not limited to this. For example, the method of finding the optimum direction of the region of interest is not limited to this. For example, the processing circuitry 150 may calculate the optimal angle using the lines on each long bone in the axial direction of the bone. For example, the first point 22 may be the end point of a line segment that passes through the femoral center 13 and has a predetermined length at an angle 23, and the second point 24 passes through the tibial center 15 and has a predetermined length at an angle 25. It may be the end point of a line segment having a length.

In addition, in step S100 and the like, when the landmark does not exist on the femur surface, the intersection with the perpendicular drawn from the landmark to the femur surface or two coordinates of the three-dimensional coordinates of the landmark are used. The processing circuit 150 may use the points projected on the surface of the femur as landmarks by the detection function 150a.

In the first embodiment, the case where the image processing apparatus 100 is concerned with magnetic resonance imaging of the knee has been described, but the embodiment is not limited to this. For example, the image processing apparatus 100 may be related to magnetic resonance imaging of another part, for example, a finger.

Further, in the first embodiment, the case where the image processing apparatus 100 performs the process on the area formed of two parts has been described, but the embodiment is not limited to this. For example, the image processing apparatus 100 may perform processing on an area composed of a plurality of parts.

Further, in the flowchart of FIG. 5, for example, steps S110 to S140 do not necessarily have to be performed in this order. For example, the processing circuit 150 may perform processing in the order of step S110, step S130, step S120, and step S140. Further, the processing circuit 150 may perform processing in the order of step S130, step S140, step S110, and step S120, for example.

Moreover, the above-mentioned first plane and the second plane may be the same plane or different planes. If the first plane and the second plane are different planes, the straight line 27 in FIG. 8 does not belong to either the first plane or the second plane and is a straight line that is three-dimensionally inclined in FIG. Become.

Further, the embodiment is not limited to the case of setting the imaging coronal section. For example, an arbitrary doctor uses for diagnosis from three-dimensional data that is obtained by a scan capable of three-dimensional imaging, such as a magnetic resonance imaging device or a CT (Computed Tomography) device, and typically has higher definition than a positioning image It may be a case where a cross section is displayed. Further, the volume data is not limited to the volume data obtained by the magnetic resonance imaging apparatus, and may be an image obtained by using, for example, a CT apparatus.

Further, in step S150 of FIG. 5, the processing circuit 150 may use a landmark to set an axis other than the axis in the direction of the straight line 27. For example, in step S100, the processing circuit 150 may detect the lateral condyle and the medial condyle of the femur as landmarks and make the setting using the angle of the line connecting these two points.

Further, the processing circuit 150 detects two or more landmarks in step S100, and sets the region of interest in step S150 based on the angle of the regression line generated using the detected two or more landmarks. May be. In addition, the processing circuit 150 may set the region of interest in step S150 based on the average position of the detected two or more landmarks. For example, the processing circuit 150 detects the lateral condyle and medial condyle of the patella and femur as landmarks in step S100, and sets the region of interest in step S150 based on the average position of the detected landmarks. Good.

As described above, according to the image processing apparatus 100 according to the first embodiment, when two regions are included in the region of interest, it is possible to set the region of interest in which the two regions are included as much as possible.

(First Modification of First Embodiment)
In the first embodiment, the case where the processing circuit 150 sets the imaging coronal surface 3 using the sagittal surface 2b has been described. This allows the processing circuit 150 to set an appropriate imaging coronal surface 3 even when the knee is tilted forward and backward. However, the embodiment is not limited to this. For example, the processing circuit 150 may set the imaging sagittal surface using the coronal surface. This allows the processing circuit 150 to set an appropriate imaging sagittal plane even when the knee is tilted to the left or right.

Processing in such a case will be described with reference to FIGS. 9 and 10. 9 and 10 are diagrams illustrating the processing performed by the image processing apparatus according to the first modification of the first embodiment.

In FIG. 9, the knee and the like of the subject P are shown. The coronal surface 4a indicates the coronal surface of the subject P. The sagittal surface 4b represents a sagittal surface obtained by cutting the place corresponding to the lower part of the knee of the subject P with the sagittal surface. Further, the sagittal surface 4c represents a sagittal surface obtained by cutting the place corresponding to the upper part of the knee of the subject P with the sagittal surface. As can be seen from FIG. 9, since the knee of the subject P has a width in the left and right directions in FIG. 9, for example, in the sagittal surface 4b, the lower knee is photographed while the upper knee is not photographed. On the other hand, for example, on the sagittal surface 4c, the upper part of the knee is photographed while the lower part of the knee is not photographed. Therefore, in FIG. 9, it is not possible to image the entire knee with one sagittal surface.

The setting of the imaging sagittal plane 5 using the coronal plane 4a will be described with reference to FIG. In FIG. 10, the coronal surface 4a represents a coronal surface. Moreover, the imaging sagittal surface 5 represents an imaging sagittal surface. The imaging sagittal surface 5 is a plane inclined as compared with the sagittal surface 4b and the sagittal surface 4c, and the imaging sagittal surface 5 is a plane in which the entire knee is included in the imaging sagittal surface 5.

Returning to FIG. 9, the straight line 4d is a straight line on which the processing circuit 150 sets the imaging sagittal surface. The processing circuit 150 performs the same processing as that of FIG. 5 in the first embodiment to set the imaging sagittal plane. Specifically, the processing circuit 150 uses the detection function 150a to detect, for example, a landmark on the coronal surface 4a from the acquired volume data (step S100). Specifically, the processing circuit 150 uses the acquisition function 150b to acquire the first data regarding the first bone and the second data regarding the second bone on the coronal surface 4a. Subsequently, the processing circuit 150 uses the detection function 150a to detect the first landmark in the first bone from the acquired first data, and to detect the second landmark in the second bone from the second data. To detect.

Subsequently, the processing circuit 150 determines the first plane that is the plane related to the first bone by the first estimation function 150c (step S110). Subsequently, the processing circuit 150 uses the first estimation function 150c to convert the first straight line, which is a straight line passing through the first landmark and indicating the axial direction of the first bone, into the first data. It estimates based on (step S120).

Subsequently, the processing circuit 150 determines the second plane that is the plane related to the second bone by the second estimation function 150d (step S130). Subsequently, the processing circuit 150 causes the second estimation function 150d to determine the second straight line 21, which is a straight line passing through the second landmark and indicating the axial direction of the second bone, as the second data. (Step S140).

When step S140 is completed, subsequently, the processing circuit 150 causes the setting function 150f to set the first point on the first straight line obtained from the first data and the second point obtained from the second data. A region of interest is set based on the second point on the straight line (step S150).

Specifically, the processing circuit 150 uses the setting function 150f to set the imaging sagittal plane based on the direction indicated by the straight line 4d passing through the first point and the second point. The imaging sagittal plane set in this way includes a large number of femurs and tibias in the region of interest.

(Second embodiment)
In the first embodiment, the case where the processing circuit 150 executes the setting of the imaging coronal surface by the setting function 150f using the data of the sagittal surface obtained from the volume data has been described. In the second embodiment, a case where the processing circuit 150 executes the setting of the imaging sagittal surface, for example, the setting of the center position of the imaging sagittal surface, the number of slices, etc. by the setting function 150f using the data of the axial surface will be described. ..

Processing performed by the image processing apparatus 100 according to the second embodiment will be briefly described with reference to FIG. 11. FIG. 11 is a diagram illustrating a process performed by the image processing apparatus according to the second embodiment.

In FIG. 11, sagittal surface 47a, sagittal surface 47b, and sagittal surface 47c represent sagittal surfaces. Each of the sagittal surface 47a, the sagittal surface 47b, and the sagittal surface 47c corresponds to one slice, for example, the sagittal surface 47a, the sagittal surface 47b, and the sagittal surface 47c, which correspond to a total of three slices. The width 46 represents the width of the sagittal plane in the slice direction. The larger the width 46, the larger the number of slices required for imaging, and conversely, the smaller the width 46, the smaller the number of slices required for imaging.

Here, the sagittal surface 47a, the sagittal surface 47b, and the sagittal surface 47c may be normal sagittal surfaces, for example, the sagittal surface 4b or the sagittal surface 4c shown in FIG. 9, when the knee is not tilted in the left-right direction. Further, the sagittal surface 47a, the sagittal surface 47b, and the sagittal surface 47c may have an inclined cross section like the imaging sagittal surface 5 in FIG. 10, for example, when the knee is inclined in the left-right direction. In this case, the sagittal surface 47a, the sagittal surface 47b, and the sagittal surface 47c are flat surfaces that include the entire knee as much as possible.

The axial surface 47e represents an axial surface. In the second embodiment, the processing circuit 150 calculates the width 46 and thus the number of slices based on the axial surface 47e. On the axial surface 47e, the contour 40 represents the contour of the bone on the axial surface 47e. The straight line 41 represents one tangent line tangent to the contour 40. Further, the straight line 42 represents one tangent line that is in contact with the contour 40. The straight line 41 and the straight line 42 form one rectangle. The center position 44 is the center of gravity of the constructed rectangle, that is, the center point. The width 45 is the width of the rectangle in the direction of the straight line 42. The direction of the straight line 41 corresponds to, for example, the front-back direction of the subject P, and the direction of the straight line 42 corresponds to the left-right direction of the subject P.

For example, the direction of the straight line 42 may be set to be the same as the direction of the width 46. Conversely, the direction of the straight line 42 may be determined from the characteristic points of the axial image 47e, and the direction of the imaging sagittal surface and the direction of the width 46 when performing sagittal imaging may be determined based on the direction of the straight line 42.

FIG. 12 is a diagram showing an image processing device 100 according to the second embodiment. Similar to the first embodiment, the image processing apparatus 100 includes a processing circuit 150, a storage circuit 132, an input device 134, and a display device 135. In addition, the processing circuit 150 includes a detection function 150a, an acquisition function 150b, a first estimation function 150c, a second estimation function 150d, and a setting function 150f, as in the first embodiment. In addition to this, the processing circuit 150 includes a third estimation function 150e. The function of the third estimation function 150e will be described later. Further, the processes other than the third estimation function 150e are the same as those in the first embodiment, and thus detailed description thereof will be omitted.

Next, the background of the second embodiment will be briefly described. In setting the sagittal section for imaging, there is a method of setting the region of interest position so that the femur is included in the axial surface 47e. The processing circuit 150 performs imaging with a plurality of slices (with a width 46) such as the sagittal surface 47a, the sagittal surface 47b, and the sagittal surface 47c so as to include, for example, the femur. In this case, the width 46 is preferably the minimum necessary width so as not to increase the imaging time. Therefore, the processing circuit 150 calculates the width 46 in which the femur is completely included from the data of the axial surface 47e. For example, in FIG. 11, the width 45 on the axial surface 47e corresponds to the width 46, and the center position 44 corresponds to the center position in the slice direction.

FIG. 13 is a flowchart showing the procedure of processing performed by the image processing apparatus 100 according to the second embodiment.

In steps S100 to S150, the image processing apparatus 100 performs the same processing as that in the first embodiment. Hereinafter, as in the case of the first embodiment, a case where the first bone is the femur and the second bone is the tibia will be described.

First, the processing circuit 150 uses the acquisition function 150b to acquire predetermined volume data. The processing circuit 150 detects a landmark from the acquired volume data by the detection function 150a (step S100).

In the second embodiment, in addition to the first embodiment, the processing circuit 150 detects a point on the contour of the first bone (femur) on the axial surface 47e as a landmark.

Subsequently, the processing circuit 150 uses the first estimation function 150c to determine the first plane (femur surface) that is the plane related to the first bone (step S110). The processing circuit 150 uses the first estimation function 150c to determine the first straight line 20 (the femoral axis direction), which is a straight line passing through the first landmark and showing the axial direction of the first bone, as the first straight line. It is estimated based on the data of 1 (step S120).

Subsequently, the processing circuit 150 determines the second plane (tibial plane) that is the plane related to the second bone (tibia) by the second estimation function 150d (step S130). Subsequently, the processing circuit 150 causes the second estimating function 150d to generate a second straight line 21 (tibial axis direction) that is a straight line passing through the second landmark and indicating the axial direction of the second bone. , And estimates based on the second data (step S140).

When step S140 is completed, subsequently, the processing circuit 150 causes the setting function 150f to obtain the first point 22 on the first straight line 20 obtained from the first data and the first point 22 obtained from the second data. The region of interest is set based on the second point 24 on the second straight line 21 (step S150). Specifically, the processing circuit 150 uses the setting function 150f to set in one direction (the direction of the straight line 27) of the region of interest based on the first point 22 and the second point 24 described above. That is, the processing circuit 150 performs the same processing as that of the first embodiment to calculate the angle 26 in which the femur 10 and the tibia 11 are included in the region of interest in a large amount, and the calculated angle 26. Using, the setting is performed for one direction of the region of interest.

Subsequently, the processing circuit 150 uses the acquisition function 150b to acquire at least one of the contour 40 of the first bone and the contour of the second bone based on the data of the axial surface 47e based on the volume data. To do. (Step 160). For example, the processing circuit 150 acquires the contour 40 of the femur using the landmark on the contour of the femur by the acquisition function 150b. As a method of acquiring the contour 40, for example, the shortest path problem between the landmarks on the axial surface 47e where the landmark on the contour 40 exists or on the cross section calculated from the landmarks on the contour 40 by the least square method. There is a method of connecting the landmarks with a line by solving and acquiring the contour 40 of the femur. Here, when there is no landmark on the axial surface 47e, the processing circuit 150 determines an intersection point with a perpendicular line drawn from the landmark to the axial surface 47e, or two coordinates of the three-dimensional coordinates of the landmark. Alternatively, a point projected on the axial surface 47e may be used. As a method of solving the shortest path problem, for example, there is a method of using Fast Marching Method to set a growth rate that becomes faster as the brightness gradient becomes larger and the brightness value becomes lower. Another method for obtaining the contour 40 is to use a landmark on the contour 40 and project it onto a model in which the landmark and the contour 40 of the femur are known.

Further, the processing circuit 150 uses the setting function 150f to set a direction different from the direction set based on the first point 22 and the second point 24 based on at least one of the contours (for example, the width 46). Direction). For example, the processing circuit 150 uses the setting function 150f to set the sagittal surface based on at least one of the contours described above. For example, the processing circuit 150 calculates a rectangle circumscribing the contour 40 (a rectangle formed by the straight line 41 and the straight line 42) from the contour 40 on the axial surface 47e, and calculates the width 45 from the calculated rectangle. Based on the width 45, a setting (width 46) is made as to which slice the imaging sagittal plane is to be performed. Here, the directions of the straight line 41 and the straight line 42, which are rectangular components, may be determined in advance, for example, based on the direction of the width 46. Further, as another example, the directions of the straight line 41 and the straight line 42, which are rectangular components, may be determined based on the feature points extracted from the contour 40.

Further, as another example, the processing circuit 150 calculates a rectangle (a rectangle formed by the straight line 41 and the straight line 42) circumscribing the contour 40 from the contour 40 on the axial surface 47e, and calculates the center position 44 from the calculated rectangle. May be calculated, and the center position in the slice direction of the imaging sagittal plane may be set based on the calculated center position 44. In this case, the component of the center position 44 in the direction of the straight line 41 is less important, and the position of the center position 44 in the width 45 direction is used for setting, for example. That is, the processing circuit 150 uses the setting function 150f to use at least one of the center position 44 of one side and the length (width 46) of one side of the circumscribed rectangle with respect to the contour 40 of the first bone, The setting is performed in a direction different from the direction set based on the first point 22 and the second point 24. That is, the processing circuit 150 uses the setting function 150f to set the imaging sagittal plane based on the calculated rectangle and the center position 44.

Further, the processing circuit 150 may calculate the size (the number of slices) of the region of interest based on not only the position of the region of interest in the slice direction but also the width 46 calculated from the width 45.

The embodiment is not limited to this. In step S170 of FIG. 13, the processing circuit 150 may set the position of the region of interest in the up-down direction or the front-back direction based on the average value of the coordinates of the landmark group on the femoral contour acquired in step S100. ..

As described above, in the second embodiment, the image processing apparatus 100 sets the imaging sagittal plane using, for example, the axial plane 47e. Thereby, for example, the width 46 and the position in the left-right direction can be appropriately set.

(Third Embodiment)
In the third embodiment, a case where the image processing apparatus 100 described in the first embodiment and the second embodiment is incorporated in a magnetic resonance imaging apparatus will be described.

FIG. 14 is a diagram showing a magnetic resonance imaging apparatus 50 according to the third embodiment. The magnetic resonance imaging apparatus 50 is roughly composed of an imaging system, a control system, and an image processing apparatus 100.

The imaging system includes a static magnetic field magnet 61, a gradient magnetic field coil 62, a gradient magnetic field power supply device 63, a bed 64, a bed control circuit 65, a transmission coil (RF coil for transmission) 66, a transmission circuit 67, and a reception coil (for reception). RF coils) 68a to 68e, a receiving circuit 69, and a sequence control circuit 120.

The static magnetic field magnet 61 generates a static magnetic field in the bore (internal space of the static magnetic field magnet 61) which is the imaging region of the subject (patient). The static magnetic field magnet 61 contains, for example, a superconducting coil, and the superconducting coil is cooled to a cryogenic temperature by liquid helium. The static magnetic field magnet 61 generates a static magnetic field by applying a current supplied from a static magnetic field power supply (not shown) to the superconducting coil in the excitation mode, and then shifts to the permanent current mode. To be separated. Once the static magnetic field magnet 61 is switched to the permanent current mode, the static magnetic field magnet 61 continues to generate a large static magnetic field for a long time, for example, for one year or longer. The static magnetic field magnet 61 may be composed of a permanent magnet.

The gradient magnetic field coil 62 is a gradient magnetic field generation unit that is disposed inside the static magnetic field magnet 61 and that generates a gradient magnetic field in the internal space. The gradient magnetic field coil 62 is formed by combining three coils corresponding to X, Y, and Z axes that are orthogonal to each other. These three coils are individually supplied with current from the gradient magnetic field power supply device 63, and generate a gradient magnetic field whose magnetic field strength changes along each of the X, Y, and Z axes.

Here, the gradient magnetic fields of the X, Y, and Z axes generated by the gradient magnetic field coil 62 correspond to, for example, the gradient magnetic field Gr for readout, the gradient magnetic field Ge for phase encoding, and the gradient magnetic field Gs for slice selection, respectively. doing. The read-out gradient magnetic field Gr is used to change the frequency of an MR (Magnetic Resonance) signal according to a spatial position. The phase encoding gradient magnetic field Ge is used to change the phase of the MR signal according to the spatial position. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging region of interest.

The gradient magnetic field power supply device 63 supplies a current to the gradient magnetic field coil 62 based on the pulse sequence execution data sent from the sequence control circuit 120.

The bed 64 includes a top plate 64a on which the subject P is placed. Under the control of the bed control circuit 65, which will be described later, the bed 64 inserts the top plate 64a into the cavity (imaging port) of the gradient magnetic field coil 62 with the subject P placed thereon. Usually, the bed 64 is installed so that its longitudinal direction is parallel to the central axis of the static magnetic field magnet 61.

Under the control of the sequence control circuit 120, the bed control circuit 65 drives the bed 64 and moves the table 64a in the longitudinal direction and the vertical direction.

The transmission coil 66 is arranged inside the gradient magnetic field coil 62, receives an RF pulse signal from the transmission circuit 67, and generates an RF pulse.

The transmission circuit 67 transmits an RF pulse signal corresponding to the Larmor frequency to the transmission coil 66 based on the pulse sequence execution data transmitted from the sequence control circuit 120.

The receiving coils 68a to 68e are arranged inside the gradient magnetic field coil 62 and receive the magnetic resonance signals emitted from the imaging region of the subject P under the influence of the high frequency magnetic field. Here, each of the receiving coils 68a to 68e is an array coil having a plurality of element coils that respectively receive the magnetic resonance signals emitted from the imaging region of the subject P, and each element coil receives the magnetic resonance signals. Then, the received magnetic resonance signal is output to the receiving circuit 69.

The receiving coil 68a is a head coil attached to the head of the subject P. The receiving coils 68b and 68c are coils for the spine arranged between the back of the subject P and the top plate 64a, respectively. The receiving coils 68d and 68e are coils for the abdomen attached to the abdominal side of the subject P, respectively.

The receiving circuit 69 generates a magnetic resonance signal based on the magnetic resonance signals output from the receiving coils 68a to 68e based on the pulse sequence execution data sent from the sequence control circuit 120. Further, when the reception circuit 69 generates the magnetic resonance signal, the reception circuit 69 transmits the magnetic resonance signal to the control system via the sequence control circuit 120.

The receiving circuit 69 has a plurality of receiving channels for receiving magnetic resonance signals output from a plurality of element coils included in the receiving coils 68a to 68e. Then, when the control system notifies of the element coil used for imaging, the receiving circuit 69 receives the element coil so that the magnetic resonance signal output from the notified element coil is received. Assign receive channels.

Here, the “channel” means that the magnetic resonance signals output from the plurality of element coils are
This refers to a unit output to the receiving circuit 69 in the subsequent stage, and the magnetic resonance data is handled for each channel in the processing after the receiving circuit 69. The relationship between the total number of coil elements and the total number of channels may be the same, the total number of channels may be smaller than the total number of coil elements, or conversely, the total number of channels may be larger than the total number of coil elements. In some cases. The expression "each channel" indicates that the process may be performed for each coil element, or for each channel in which the coil elements are distributed and combined.

The sequence control circuit 120 is connected to the gradient magnetic field power supply device 63, the bed control circuit 65, the transmission circuit 67, the reception circuit 69, and the like. The sequence control circuit 120 controls information necessary for driving the gradient magnetic field power supply device 63, the bed control circuit 65, the transmission circuit 67, and the receiving circuit 69, for example, the intensity of the pulse current to be applied to the gradient magnetic field power supply device 63. Sequence information describing operation control information such as application time and application timing is stored.

Further, the sequence control circuit 120 drives the bed control circuit 65 in accordance with the stored predetermined sequence to move the table 64a forward and backward with respect to the gantry in the Z direction. Further, the sequence control circuit 120 drives the gradient magnetic field power supply device 63, the transmission circuit 67, and the reception circuit 69 in accordance with the stored predetermined sequence, so that the X-axis gradient magnetic field Gx and the Y-axis gradient magnetic fields Gy, Z are provided in the gantry. An axial gradient magnetic field Gz and an RF pulse signal are generated.

The sequence control circuit 120 executes a pulse sequence related to diagnostic imaging. The sequence control circuit 120 also stores the data obtained by the executed pulse sequence in the storage circuit 132 included in the image processing apparatus 100. In addition, the sequence control circuit 120 executes a pulse sequence relating to imaging for positioning. The sequence control circuit 120 also stores the data obtained by the executed pulse sequence in the storage circuit 132 included in the image processing apparatus 100.

Here, the pulse sequence related to the imaging for positioning may be the same as or different from the pulse sequence related to the imaging for diagnosis. Examples of the pulse sequence for imaging for positioning include an FFE (Fast Field Echo) sequence and an SSFP (Steady State Free Precession) sequence.

The sequence control circuit 120 is an example of a first sequence control unit and a second sequence control unit.

A control circuit (not shown) controls the entire magnetic resonance imaging apparatus 50.

Further, the image processing apparatus 100 described in the first embodiment or the second embodiment is connected to the sequence control circuit 120 or the like. As described above, the image processing apparatus 100 has the processing circuit 150, the storage circuit 132, the input device 134, the display device 135, and the like. The processing circuit 150 also has a detection function 150a, an acquisition function 150b, a first estimation function 150c, a second estimation function 150d, a third estimation function 150e, a setting function 150f, and a reception function 150g. Hereinafter, with respect to the same functions as those described in the first embodiment and the second embodiment, repeated description will be appropriately omitted.

In addition to the processing described in the above embodiment, the storage circuit 132 stores the magnetic resonance signal acquired from the reception circuit 69 through the sequence control circuit 120 as needed.

In addition to the processing described in the above-described embodiment, the processing circuit 150 uses a not-shown image generation function to perform Fourier transform or the like on the magnetic resonance signal acquired from the reception circuit 69 or the magnetic resonance signal acquired from the storage circuit 132. Reconstruction processing is performed. The processing circuit 150 also generates an image for diagnosis by an image generation function (not shown). Examples of generated images include, for example, T2-weighted images, T1-weighted images, FLAIR, Diffusion, and T2*-weighted images.

FIG. 15 is a flowchart illustrating the processing performed by the magnetic resonance imaging apparatus 50 according to the third embodiment. In FIG. 15, the processing of steps S100 to S170 is the same processing as described in FIG. 13, for example, and thus the repeated description will be omitted.

The sequence control circuit 120 performs the first imaging on the subject P. The processing circuit 150 acquires the data based on the first imaging performed on the subject P by the acquisition function 150b, and generates the first image which is the volume data by the image generation function (not shown) ( Step S90). Here, the first imaging performed on the subject P is, for example, positioning imaging.

Subsequently, the magnetic resonance imaging apparatus 50 performs the same processing as steps S100 to S170 in FIG. 13 with respect to steps S100 to S170. (Steps S100 to S170).

After the end of step S170, the display device 135 displays the region of interest set in step S170 to the user for confirmation, for example. At this time, the display device 135 may display the volume data generated in step S90 to the user. Further, the input device 134 may appropriately receive an input from the user for changing the region of interest.

Subsequently, the sequence control circuit 120 performs the second imaging on the subject P based on the region of interest set in this way (step S180). The second imaging is, for example, diagnostic imaging.

Subsequently, the processing circuit 150 generates a second image based on the second image pickup performed in step S180 by an image generation function (not shown) (step S190). The second image is, for example, a diagnostic image.

The second image capturing performed in step S180 is not limited to one image capturing, and may be multiple image capturing. For example, the second imaging performed in step S180 is performed a plurality of times including the imaging based on the first setting performed in step S150 and the imaging based on the second setting performed in step S170. May be imaged.

Further, the region of interest set in step S150 or step S170 is not limited to the case of the imaging range. The region of interest may be an auxiliary region set separately from the imaging range. For example, when the pre-saturation pulse is used in the second imaging, the auxiliary region includes a region that is brought into a saturated state by the pre-saturation pulse (pre-saturation region). Further, for example, the auxiliary region may be a labeling region (or tag region) used in the Time-SLIP method or the like. In that case, the fluid moving from the labeling area to the outside of the area can be observed.

Further, the first image generated in step S90 does not need to be taken by the magnetic resonance imaging apparatus 50, and may be an image generated by another medical image diagnostic apparatus such as an X-ray CT apparatus. Good.

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

Further, the OS (operating system) running on the computer based on the instructions of the program installed from the storage medium to the computer or the embedded system, the database management software, the MW (middleware) such as the network, and the like are the above-described embodiments. You may perform a part of each process for implement|achieving.

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

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

The computer or the embedded system in the embodiment is for executing each processing in the above-described embodiment based on the program stored in the storage medium, and includes one device such as a personal computer and a microcomputer, and a plurality of devices. The device may have any configuration such as a system in which the device is connected to a network.

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

(Hardware configuration)
FIG. 16 is a diagram showing a hardware configuration of the image processing apparatus 100 according to the embodiment. The image processing apparatus 100 according to the above-described embodiment is connected to a control device such as a CPU (Central Processing Unit) 310, a storage device such as a ROM (Read Only Memory) 320 or a RAM (Random Access Memory) 330, and a network. A communication interface 340 for performing communication by means of communication and a bus 301 for connecting the respective units are provided. The program executed by the image processing apparatus 100 according to the above-described embodiment is provided by being incorporated in the ROM 320 or the like in advance. Further, the program executed by the image processing apparatus 100 according to the above-described embodiment can cause a computer to function as each unit of the image processing apparatus 100 described above. In this computer, the CPU 310 can read the program from the computer-readable storage medium onto the main storage device and execute the program.

According to the image processing apparatus 100 and the magnetic resonance imaging apparatus 50 of at least one embodiment described above, the region of interest can be set appropriately.

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

150 Processing circuit 150a Detection function 150b Acquisition function 150c First estimation function 150d Second estimation function 150f Setting function 150g Reception function

Claims (14)

An acquisition unit for acquiring first data regarding the first bone and second data regarding the second bone based on the volume data;
A first point on a first straight line, which is a straight line indicating the axial direction of the first bone, obtained from the first data, and the second bone obtained from the second data And a second point on a second straight line that is a straight line indicating the axial direction of the image processing apparatus. The acquisition unit further acquires at least one contour of the contours of the first bone and the second bone based on the volume data,
The setting unit performs setting for one direction of the region of interest based on the first point and the second point, and a direction different from the one direction based on the at least one contour. The image processing apparatus according to claim 1, wherein the setting is performed with respect to. The image processing apparatus according to claim 1, wherein the setting unit sets the coronal surface based on a direction indicated by a straight line passing through the first point and the second point. The image processing apparatus according to claim 2, wherein the setting unit sets a sagittal surface based on the at least one contour. Further comprising a detector that detects a first landmark in the first bone from the first data and a second landmark in the second bone from the second data,
The image processing apparatus according to claim 2, wherein the setting unit sets the region of interest based on a result detected by the detection unit. A straight line passing through the first landmark, which is a straight line indicating the axial direction of the first bone, is calculated based on the first data, and the second landmark is calculated. A second straight line that is a straight line that passes through and that indicates the axial direction of the second bone, and further includes a calculation unit that calculates the second straight line based on the second data.
The image processing apparatus according to claim 5, wherein the setting unit sets the region of interest based on a result calculated by the calculation unit. The calculation unit calculates the first straight line by comparing, for each direction, integral values of a predetermined function in predetermined regions defined respectively with respect to straight lines in a plurality of directions passing through the first landmark. The second straight line is calculated by comparing the integrated value of the predetermined function in a predetermined region defined for each of a plurality of straight lines passing through the second landmark, for each direction. The image processing apparatus according to claim 6, which calculates. The predetermined function is a function having a value determined for each pixel, and is based on the maximum gradient direction of the pixel value and the positional relationship of the pixel with respect to the first landmark or the second landmark. The image processing device according to claim 7, wherein the image processing device is a defined function. The first point is calculated as an intersection of the first straight line and a third straight line, and the second point is calculated as an intersection of the second straight line and a fourth straight line parallel to the third straight line. Further comprising a calculation unit for calculating the point
The image processing apparatus according to claim 1, wherein the setting unit sets the region of interest based on the first point and the second point. Further comprising a receiving unit that receives an input of information specifying the third straight line and the fourth straight line,
The image processing apparatus according to claim 9, wherein the calculation unit calculates the first point and the second point based on the input received by the reception unit. The setting unit performs setting in the different directions using at least one of a center position of one side and a length of the one side of a circumscribed rectangle with respect to the contour of the first bone. The image processing device described. The image processing apparatus according to claim 1, wherein the first bone is a femur and the second bone is a tibia. The image processing apparatus according to claim 1, wherein the region of interest is a region including a knee. A first sequence controller for performing a first imaging on the subject;
An acquisition unit for acquiring first data regarding the first bone and second data regarding the second bone based on the volume data generated based on the first imaging.
A first point on a first straight line, which is a straight line indicating the axial direction of the first bone, obtained from the first data , and the second bone obtained from the second data A setting unit that sets a region of interest based on a second point on a second straight line that is a straight line indicating the axial direction of
A magnetic resonance imaging apparatus comprising: a second sequence controller that performs a second imaging on the subject based on the set region of interest.

Images