JP2020096752A - Medical image processing device, x-ray diagnostic device and medical image processing program - Google Patents

Abstract

To improve visibility of a lesion in a combined 2D image with tomosynthesis imaging.SOLUTION: A medical image processing device according to an embodiment includes a detection unit and an image processing unit. The detection unit detects a position of a lesion of a breast in a plurality of tomographic images contained in three-dimensional medical data obtained by tomosynthesis-imaging the breast of a subject. The image processing unit generates two-dimensional image data by combining the plurality of tomographic images on the basis of the detected position of the lesion.SELECTED DRAWING: Figure 1

Description

Embodiments of the present invention relate to a medical image processing apparatus, an X-ray diagnostic apparatus, and a medical image processing program.

Conventionally, in breast examination, tomosynthesis imaging by an X-ray diagnostic apparatus is executed. Specifically, the X-ray diagnostic apparatus first collects a plurality of projection data while changing the irradiation angle of X-rays on the breast. Next, the X-ray diagnostic apparatus generates three-dimensional medical data by reconstruction processing based on the plurality of collected projection data. Here, a user such as a doctor can sequentially read a plurality of tomographic images (slices) included in the three-dimensional medical data to observe a lesion.

Furthermore, a technique is known in which a plurality of slices included in three-dimensional medical data are combined to generate two-dimensional image data (combined 2D image). Such a synthetic 2D image is two-dimensional medical data having information on the entire breast. That is, the user can observe the entire breast by reading the composite 2D image. However, in the synthetic 2D image, the lesions appearing in the individual slices may be blurred, making it difficult to observe.

Japanese Patent Publication No. 2017-510323

The problem to be solved by the present invention is to improve the visibility of lesions in a synthetic 2D image obtained by tomosynthesis imaging.

The medical image processing apparatus according to the embodiment includes a detection unit and an image processing unit. The detection unit detects the position of the breast lesion in a plurality of tomographic images included in the three-dimensional medical data obtained by tomosynthesis imaging of the breast of the subject. The image processing unit generates two-dimensional image data by synthesizing the plurality of tomographic images based on the detected position of the lesion.

FIG. 1 is a block diagram showing an example of the configuration of a medical image processing system according to the first embodiment. FIG. 2 is a block diagram showing an example of the configuration of the X-ray diagnostic apparatus according to the first embodiment. FIG. 3A is a diagram for explaining tomosynthesis imaging according to the first embodiment. FIG. 3B is a diagram for explaining tomosynthesis imaging according to the first embodiment. FIG. 3C is a diagram for explaining tomosynthesis imaging according to the first embodiment. FIG. 4 is a diagram for explaining the reconstruction of the three-dimensional medical data according to the first embodiment. FIG. 5 is a diagram showing an example of three-dimensional medical data according to the first embodiment. FIG. 6A is a diagram illustrating an example of lesion detection according to the first embodiment. FIG. 6B is a diagram illustrating an example of lesion detection according to the first embodiment. FIG. 7 is a diagram illustrating an example of weighting processing according to the first embodiment. FIG. 8 is a diagram showing an example of a combined 2D image according to the first embodiment. FIG. 9A is a diagram illustrating an example of lesion detection according to the first embodiment. FIG. 9B is a diagram illustrating an example of lesion detection according to the first embodiment. FIG. 10 is a diagram illustrating an example of weighting processing according to the first embodiment. FIG. 11 is a flowchart for explaining a series of processing flows of the medical image processing apparatus according to the first embodiment. FIG. 12 is a diagram showing an example of the distribution of weights according to the second embodiment. FIG. 13 is a diagram illustrating an example of weighting processing according to the second embodiment. FIG. 14A is a diagram illustrating an example of lesion detection according to the second embodiment. FIG. 14B is a diagram illustrating an example of lesion detection according to the second embodiment. FIG. 15 is a diagram illustrating an example of calculation of a combined distribution according to the second embodiment. FIG. 16 is a diagram illustrating an example of the weighting process according to the second embodiment. FIG. 17 is a flowchart for explaining a series of processing flows of the medical image processing apparatus according to the second embodiment. FIG. 18 is a diagram for explaining generation of a synthetic 2D image according to the third embodiment. FIG. 19 is a flowchart for explaining a series of processing flows of the medical image processing apparatus according to the third embodiment. FIG. 20 is a block diagram showing an example of a processing circuit according to the fourth embodiment.

Hereinafter, a medical image processing apparatus, an X-ray diagnostic apparatus, and a medical image processing program according to an embodiment will be described with reference to the drawings.

(First embodiment)
First, the first embodiment will be described. In the first embodiment, a medical image processing system 1 including an X-ray diagnostic apparatus 10, an image storage apparatus 20, and a medical image processing apparatus 30 will be described.

FIG. 1 is a block diagram showing an example of the configuration of a medical image processing system 1 according to the first embodiment. As shown in FIG. 1, the medical image processing system 1 according to the first embodiment includes an X-ray diagnostic apparatus 10, an image storage apparatus 20, and a medical image processing apparatus 30. As shown in FIG. 1, the X-ray diagnostic apparatus 10, the image storage apparatus 20, and the medical image processing apparatus 30 are connected to each other via a network NW.

The X-ray diagnostic apparatus 10 is an apparatus that collects medical data from a subject. Specifically, the X-ray diagnostic apparatus 10 collects three-dimensional medical data by performing tomosynthesis imaging of the breast P of the subject. The configuration of the X-ray diagnostic apparatus 10 will be described later.

The image storage device 20 is a device that stores medical data collected by the X-ray diagnostic apparatus 10. For example, the image storage device 20 acquires the three-dimensional medical data from the X-ray diagnostic apparatus 10 via the network NW and stores the acquired three-dimensional medical data in a memory provided inside or outside the device. For example, the image storage device 20 is realized by a computer device such as a server device.

The medical image processing apparatus 30 acquires three-dimensional medical data from the image storage apparatus 20 via the network NW and executes various processes based on the acquired three-dimensional medical data. For example, the medical image processing apparatus 30 generates a synthetic 2D image based on the three-dimensional medical data. The processing performed by the medical image processing apparatus 30 will be described later. For example, the medical image processing apparatus 30 is realized by computer equipment such as a workstation.

If the X-ray diagnostic apparatus 10, the image storage apparatus 20, and the medical image processing apparatus 30 can be connected via the network NW, any place can be installed. For example, the medical image processing apparatus 30 may be installed in a hospital different from the X-ray diagnostic apparatus 10. That is, the network NW may be a local network closed in the hospital or a network via the Internet. Further, although one X-ray diagnostic apparatus 10 is shown in FIG. 1, the medical image processing system 1 may include a plurality of X-ray diagnostic apparatuses 10.

As shown in FIG. 1, the medical image processing apparatus 30 has an input interface 31, a display 32, a memory 33, and a processing circuit 34.

The input interface 31 receives various input operations from the operator, converts the received input operations into electric signals, and outputs the electric signals to the processing circuit 34. For example, the input interface 31 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad for performing an input operation by touching an operation surface, a touch screen in which a display screen and a touch pad are integrated, and an optical sensor. It is realized by the used non-contact input circuit, voice input circuit and the like. The input interface 31 may be composed of a tablet terminal or the like that can wirelessly communicate with the main body of the medical image processing apparatus 30. Further, the input interface 31 is not limited to the one including physical operation parts such as a mouse and a keyboard. For example, an electric signal processing circuit that receives an electric signal corresponding to an input operation from an external input device provided separately from the medical image processing apparatus 30 and outputs the electric signal to the processing circuit 34 is also included in the input interface 31. Included in the example.

The display 32 displays various information. For example, the display 32 displays a GUI (Graphical User Interface) for receiving various instructions and various settings from the operator via the input interface 31. The display 32 also displays various image data collected about the breast P of the subject. For example, the display 32 sequentially displays a plurality of slices included in the three-dimensional medical data. In addition, the display 32 displays a synthetic 2D image generated based on the three-dimensional medical data. For example, the display 32 is a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 32 may be a desktop type, or a tablet terminal or the like capable of wireless communication with the main body of the medical image processing apparatus 30.

The memory 33 is realized by, for example, a RAM (Random Access Memory), a semiconductor memory element such as a flash memory, a hard disk, an optical disk, or the like. For example, the memory 33 stores the three-dimensional medical data of the breast P acquired from the image storage device 20 and the synthetic 2D image generated by the processing circuit 34. Further, for example, the memory 33 stores a program for a circuit included in the medical image processing apparatus 30 to realize its function. The memory 33 may be realized by a server group (cloud) connected to the medical image processing apparatus 30 via the network NW. The memory 33 is an example of a storage unit.

The processing circuit 34 controls the overall operation of the medical image processing apparatus 30 by executing the control function 341, the detection function 342, the image processing function 343, and the display control function 344. Here, the detection function 342 is an example of a detection unit. The image processing function 343 is an example of an image processing unit. The display control function 344 is an example of the display control unit.

For example, the processing circuit 34 controls various functions of the processing circuit 34 based on an input operation received from an operator via the input interface 31 by reading a program corresponding to the control function 341 from the memory 33 and executing the program. To do. Further, the control function 341 controls transmission/reception of data via the network NW. For example, the control function 341 receives the three-dimensional medical data of the breast P from the image storage device 20 and stores it in the memory 33. Further, for example, the control function 341 transmits the combined 2D image generated by the image processing function 343 to the image storage device 20.

Further, for example, the processing circuit 34 detects the position of the lesion in the plurality of tomographic images (slices) included in the three-dimensional medical data of the breast P by reading the program corresponding to the detection function 342 from the memory 33 and executing the program. To do. Further, the processing circuit 34 reads out a program corresponding to the image processing function 343 from the memory 33 and executes the program to generate two-dimensional image data (composite 2D image) from a plurality of slices included in the three-dimensional medical data. The processing by the detection function 342 and the image processing function 343 will be described later. Further, the processing circuit 34 displays the combined 2D image generated by the image processing function 343 on the display 32 by reading the program corresponding to the display control function 344 from the memory 33 and executing the program.

In the medical image processing apparatus 30 shown in FIG. 1, each processing function is stored in the memory 33 in the form of a program executable by a computer. The processing circuit 34 is a processor that realizes a function corresponding to each program by reading the program from the memory 33 and executing the program. In other words, the processing circuit 34 in the state where each program is read has the function corresponding to the read program. Note that, in FIG. 1, the single processing circuit 34 has been described as realizing the control function 341, the detection function 342, the image processing function 343, and the display control function 344, but the processing is performed by combining a plurality of independent processors. The circuit 34 may be configured so that each processor implements a function by executing a program. Further, each processing function of the processing circuit 34 may be implemented by being appropriately dispersed or integrated into a single or a plurality of processing circuits.

Next, the X-ray diagnostic apparatus 10 will be described. The X-ray diagnostic apparatus 10 performs tomosynthesis imaging on the breast P of the subject and collects three-dimensional medical data. Hereinafter, the X-ray diagnostic apparatus 10 will be described as a mammography apparatus.

For example, the X-ray diagnostic apparatus 10 has a base 101 and a stand 102 as shown in FIG. The stand 102 is erected on the base 101 and supports an imaging table 103, a compression plate 104, an X-ray tube 105, an X-ray diaphragm 106, an X-ray detector 107, and a signal processing circuit 108. To do. Here, the stand 102 supports the imaging table 103, the compression plate 104, the X-ray detector 107, and the signal processing circuit 108 so as to be vertically movable. Note that FIG. 2 is a block diagram showing an example of the configuration of the X-ray diagnostic apparatus 10 according to the first embodiment.

The imaging table 103 is a table that supports the breast P of the subject, and has a support surface on which the breast P is placed. The compression plate 104 is disposed above the imaging table 103 and is provided so as to face the imaging table 103 in parallel. Here, the compression plate 104 is provided so as to be movable in a direction of approaching and separating from the imaging table 103. For example, the compression plate 104 compresses the breast P supported on the imaging table 103 by moving in a direction approaching the imaging table 103. The breast compressed by the compression plate 104 is spread thinly and the overlap of the mammary glands in the breast is reduced.

The X-ray tube 105 is a vacuum tube having a cathode (filament) that generates thermoelectrons and an anode (target) that generates X-rays when the thermoelectrons collide. The X-ray tube 105 generates X-rays by irradiating the thermoelectrons from the cathode to the anode using the high voltage supplied from the X-ray high voltage device 111. Here, the X-ray tube 105 is configured to be movable so as to change the irradiation angle of X-rays on the breast P. The movement of the X-ray tube 105 will be described later.

The X-ray narrowing device 106 is arranged between the X-ray tube 105 and the compression plate 104, and controls the X-rays generated by the X-ray tube 105. For example, the X-ray diaphragm 106 has a collimator that narrows down the X-ray irradiation range and a filter that adjusts the X-rays.

The collimator in the X-ray diaphragm device 106 has, for example, four slidable diaphragm blades, and by sliding these diaphragm blades, the X-rays generated by the X-ray tube 105 are narrowed down and irradiated onto the breast P. .. Here, the diaphragm blade is a plate-shaped member made of lead or the like, and is provided near the X-ray irradiation port of the X-ray tube 105 in order to adjust the X-ray irradiation range.

The filter in the X-ray diaphragm 106 changes the quality of the transmitted X-rays depending on the material and the thickness thereof for the purpose of reducing the exposure dose to the subject and improving the image quality of the X-ray image data, and is easily absorbed by the subject. The soft line component is reduced, and the high energy component that causes a reduction in image contrast is reduced. Further, the filter changes the dose and irradiation range of X-rays depending on the material, thickness, position, etc., and attenuates the X-rays so that the X-rays irradiated to the breast P have a predetermined distribution.

For example, the X-ray restrictor 106 has a drive mechanism such as a motor and an actuator, and controls the X-ray irradiation by operating the drive mechanism under the control of the processing circuit 114 described later. For example, the X-ray iris diaphragm 106 adjusts the opening of the diaphragm blade of the collimator by applying a driving voltage to the driving mechanism according to the control signal received from the processing circuit 114, and irradiates the breast P. The X-ray irradiation range is controlled. In addition, for example, the X-ray diaphragm 106 irradiates the breast P by adjusting the position of the filter by applying a drive voltage to the drive mechanism according to the control signal received from the processing circuit 114. Control the distribution of X-ray dose.

The X-ray detector 107 is, for example, an X-ray flat panel detector (FPD) having detection elements arranged in a matrix. The X-ray detector 107 detects X-rays emitted from the X-ray tube 105 and transmitted through the breast P, and outputs a detection signal corresponding to the detected X-ray dose to the signal processing circuit 108. The X-ray detector 107 may be an indirect conversion type detector having a grid, a scintillator array and a photosensor array, or a direct conversion type detector having a semiconductor element that converts incident X-rays into electric signals. It may be a detector.

For example, the X-ray detector 107 detects the X-ray pulse emitted from the X-ray tube 105 and generates a detection signal corresponding to the detected X-ray dose. Here, the X-ray detector 107 holds the generated detection signal. Further, the X-ray detector 107 outputs a detection signal to the signal processing circuit 108 after the irradiation of the X-ray pulse. Then, the signal processing circuit 108 generates projection data based on the detection signal output from the X-ray detector 107 and stores it in the memory 112.

Further, as shown in FIG. 2, the X-ray diagnostic apparatus 10 includes an input interface 109, a lifting drive device 110, an X-ray high voltage device 111, a memory 112, a display 113, and a processing circuit 114.

The input interface 109 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuit 114. For example, the input interface 109 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad for performing an input operation by touching an operation surface, a touch screen in which a display screen and a touch pad are integrated, and an optical sensor. It is realized by the used non-contact input circuit, voice input circuit and the like. The input interface 109 may be composed of a tablet terminal or the like that can wirelessly communicate with the processing circuit 114. Further, the input interface 109 is not limited to the one including physical operation parts such as a mouse and a keyboard. For example, an electric signal processing circuit that receives an electric signal corresponding to an input operation from an external input device provided separately from the main body of the X-ray diagnostic apparatus 10 and outputs the electric signal to the processing circuit 114 is also the input interface 109. Included in the example.

The lifting drive device 110 is connected to the imaging table 103 and the compression plate 104. For example, the lifting drive device 110 moves the imaging table 103 up and down. Further, for example, the elevating and lowering drive device 110 elevates and lowers the compression plate 104 in the vertical direction (the direction in which the compression plate 104 is brought into contact with and separated from the imaging table 103). For example, the elevation drive device 110 has a drive mechanism such as a motor and an actuator, and operates the drive mechanism under the control of the processing circuit 114 to control the elevation of the imaging table 103 and the compression plate 104.

The X-ray high voltage device 111 supplies a high voltage to the X-ray tube 105 under the control of the processing circuit 114. For example, the X-ray high-voltage device 111 has an electric circuit such as a transformer and a rectifier, and the X-ray tube 105 irradiates the high-voltage generator that generates a high voltage to be applied to the X-ray tube 105. And an X-ray control device for controlling the output voltage according to the X-rays. The high voltage generator may be of a transformer type or an inverter type.

The memory 112 is realized by, for example, a RAM, a semiconductor memory element such as a flash memory, a hard disk, an optical disk, or the like. For example, the memory 112 stores the projection data generated by the signal processing circuit 108 and the three-dimensional medical data generated by the processing circuit 114. In addition, for example, the memory 112 stores a program for a circuit included in the X-ray diagnostic apparatus 10 to realize its function. The memory 112 may be realized by a server group (cloud) connected to the X-ray diagnostic apparatus 10 via the network NW.

The display 113 displays various information. For example, the display 113 displays a GUI for accepting various instructions and various settings from the operator via the input interface 109. The display 113 also displays various image data collected for the breast P. For example, the display 113 sequentially displays a plurality of slices included in the three-dimensional medical data. Further, for example, the display 113 displays the synthetic 2D image generated by the medical image processing apparatus 30. For example, the display 113 is a liquid crystal display or a CRT display. The display 113 may be a desktop type or a tablet terminal or the like that can wirelessly communicate with the X-ray diagnostic apparatus 10 main body.

The processing circuit 114 controls the operation of the entire X-ray diagnostic apparatus 10 by executing the control function 114a and the display control function 114b.

For example, the processing circuit 114 controls various functions of the processing circuit 114 based on an input operation received from the operator via the input interface 109 by reading a program corresponding to the control function 114a from the memory 112 and executing the program. To do.

The control function 114a also controls the collection of three-dimensional medical data. Specifically, first, the control function 114a executes tomosynthesis imaging on the breast P to collect a plurality of projection data. Next, the control function 114a performs correction processing such as logarithmic conversion processing, offset correction, sensitivity correction, and beam hardening correction on the collected projection data to generate corrected projection data. Then, the processing circuit 114 reconstructs the three-dimensional medical data based on the corrected projection data.

The control function 114a can also collect mammography images such as MLO (Mediolateral-Oblique) images and CC (Cranio-Caudal) images. Specifically, the control function 114a fixes the positions of the imaging table 103 and the compression plate 104 in the MLO direction and the CC direction, and irradiates the breast P with X-rays while keeping the X-ray irradiation angle constant. , MLO images, CC images and other mammography images are acquired.

Further, the control function 114a controls transmission/reception of data via the network NW. For example, the control function 114a transmits to the image storage device 20 the three-dimensional medical data obtained by tomosynthesis imaging of the breast P of the subject. Further, the processing circuit 34 displays various image data on the display 32 by reading a program corresponding to the display control function 344 from the memory 33 and executing the program.

In the X-ray diagnostic apparatus 10 shown in FIG. 2, each processing function is stored in the memory 112 in the form of a program executable by a computer. The signal processing circuit 108 and the processing circuit 114 are processors that realize a function corresponding to each program by reading the program from the memory 112 and executing the program. In other words, the signal processing circuit 108 and the processing circuit 114 in the state where the program is read out have a function corresponding to the read program. Note that, in FIG. 2, it has been described that the control function 114a and the display control function 114b are realized by the single processing circuit 114, but the processing circuit 114 is configured by combining a plurality of independent processors, and each processor is configured as follows. The function may be realized by executing the program. Further, each processing function of the processing circuit 114 may be implemented by being appropriately dispersed or integrated into a single or a plurality of processing circuits.

The word "processor" used in the above description means, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), a programmable logic device (for example, It means a circuit such as a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA). The processor realizes the function by reading and executing the program stored in the memory 33 or the memory 112.

Note that, in FIGS. 1 and 2, the single memory 33 or the memory 112 has been described as storing a program corresponding to each processing function. However, the embodiment is not limited to this. For example, the plurality of memories 33 may be arranged in a distributed manner, and the processing circuit 34 may be configured to read the corresponding programs from the individual memories 33. Similarly, the plurality of memories 112 may be arranged in a distributed manner, and the processing circuit 114 may be configured to read the corresponding programs from the individual memories 112. Further, instead of storing the program in the memory 33 and the memory 112, 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.

Further, the processing circuit 34 and the processing circuit 114 may implement the function by using the processor of the external device connected via the network NW. For example, the processing circuit 34 reads a program corresponding to each function from the memory 33 and executes the program, and uses a server group (cloud) connected to the medical image processing apparatus 30 via the network NW as a computing resource. The functions shown in FIG. 1 are realized.

The medical image processing system 1 including the medical image processing apparatus 30 has been described above. With such a configuration, the medical image processing apparatus 30 in the medical image processing system 1 improves the visibility of the lesion in the synthetic 2D image by tomosynthesis imaging by the processing by the processing circuit 34.

First, the tomosynthesis imaging will be described. In the tomosynthesis imaging, the breast P of the subject is placed on the imaging table 103 and then compressed between the imaging table 103 and the compression plate 104. Next, the control function 114a collects a plurality of projection data by irradiating the breast P with X-rays while changing the X-ray irradiation angle. Hereinafter, collection of a plurality of projection data will be described with reference to FIGS. 3A, 3B and 3C. 3A, 3B, and 3C are diagrams for explaining tomosynthesis imaging according to the first embodiment.

3A shows the case where the X-ray tube 105 is located at the position X1, FIG. 3B shows the case where the X-ray tube 105 is located at the position X2, and FIG. 3C shows the case where the X-ray tube 105 is located at the position X3. Indicate the case. As shown in FIG. 3A, FIG. 3B, and FIG. 3C, the X-ray tube 105 moves in an orbit including a position X1, a position X2, and a position X3. The broken lines in FIGS. 3A, 3B, and 3C show a part of the X-rays emitted from the X-ray tube 105. As shown in FIGS. 3A, 3B, and 3C, the X-ray tube 105 moves so as to change the irradiation angle of X-rays.

Specifically, the control function 114a first irradiates the breast P with X-rays from the X-ray tube 105 at the position X1, as shown in the upper diagram of FIG. 3A. Here, the X-rays transmitted through the breast P are detected by the X-ray detector 107. The signal processing circuit 108 also generates projection data A101 shown in the lower diagram of FIG. 3A based on the detection signal output from the X-ray detector 107. In the projection data A101, the part a, the part b, and the part c of the breast P are projected at the positions shown in the lower diagram of FIG. 3A.

Next, as shown in the upper diagram of FIG. 3B, the control function 114a changes the irradiation angle of X-rays from the state shown in FIG. 3A, and causes the X-ray tube 105 at the position X2 to irradiate the breast P with X-rays. Here, the X-rays transmitted through the breast P are detected by the X-ray detector 107. The signal processing circuit 108 also generates projection data A102 shown in the lower diagram of FIG. 3B based on the detection signal output from the X-ray detector 107. In the projection data A102, the part a, the part b, and the part c of the breast P are projected so as to be superimposed on the positions shown in the lower diagram of FIG. 3B.

Further, as shown in the upper diagram of FIG. 3C, the control function 114a changes the irradiation angle of the X-ray from the state shown in FIG. 3B, and irradiates the breast P with the X-ray from the X-ray tube 105 at the position X3. Here, the X-rays transmitted through the breast P are detected by the X-ray detector 107. The signal processing circuit 108 also generates projection data A103 shown in the lower diagram of FIG. 3C based on the detection signal output from the X-ray detector 107. In the projection data A103, the part a, the part b, and the part c of the breast P are projected at the positions shown in the lower diagram of FIG. 3C, respectively.

For example, the control function 114a generates X-rays when the X-ray tube 105 reaches the position X1, the position X2, the position X3, etc. while continuously changing the position of the X-ray tube 105. Further, for example, the control function 114a generates X-rays from the X-ray tube 105 stopped at the position X1, the position X2, the position X3, etc. by alternately repeating the movement of the X-ray tube 105 and the irradiation of the X-rays. .. The orbit when the X-ray tube 105 moves may be arcuate as shown in FIGS. 3A, 3B, and 3C, may be linear, or may be another orbit.

Next, the control function 114a reconstructs three-dimensional medical data from the plurality of projection data collected by changing the irradiation angle. Reconstruction of three-dimensional medical data will be described below with reference to FIG. FIG. 4 is a diagram for explaining the reconstruction of the three-dimensional medical data according to the first embodiment. In FIG. 4, as an example, a case of reconstructing three-dimensional medical image data by the shift addition method will be described.

For example, the control function 114a adds the projection data A101, the projection data A102, and the projection data A103 shown in the upper diagram of FIG. 4 so that the positions where the part a is projected on the respective projection data are aligned, and the result is shown in the lower diagram of FIG. The slice Ba shown is generated. In the slice Ba, the parts of the projection data A101, the projection data A102, and the projection data A103 on which the part a is projected are aligned and added, so that the part a is clearly expressed.

Here, although each projection data also includes a signal indicating the part b or the part c, the signals of the part b or the part c on the slice Ba are overlapped because the positions are shifted in the slice Ba. It is diffuse and blurred. Therefore, as shown in the lower diagram of FIG. 4, only the part a is clearly expressed in the slice Ba. That is, the control function 114a generates the slice Ba by using the plane including the part a in the breast P as the reconstruction plane.

In the following, the direction parallel to the reconstruction plane is the X direction. In the following, the direction parallel to the reconstruction plane and orthogonal to the X direction will be referred to as the Y direction. That is, as shown in FIG. 4, the X direction and the Y direction are parallel to the slice. Moreover, below, let the direction orthogonal to an X direction and a Y direction be a Z direction. That is, the Z direction is a direction perpendicular to the slice. The Z direction is also referred to as the slice direction.

Similarly, the control function 114a adds the projection data A101, the projection data A102, and the projection data A103 so that the position where the part b is projected is aligned on each projection data to generate a slice Bb (not shown). That is, the control function 114a generates the slice Bb with the XY plane including the part b in the breast P as the reconstruction plane. In addition, the control function 114a adds the projection data A101, the projection data A102, and the projection data A103 so as to match the position where the part c is projected on each projection data, and generates a slice Bc (not shown). That is, the control function 114a generates the slice Bc with the XY plane including the part c in the breast P as the reconstruction plane.

As described above, the control function 114a uses a plurality of projection data to generate a plurality of slices while shifting the reconstruction plane. That is, the control function 114a reconstructs three-dimensional medical data including a plurality of slices using a plurality of projection data.

As an example, a case where the control function 114a reconstructs the three-dimensional medical data shown in FIG. 5 will be described below. That is, the case where the three-dimensional medical data includes “50” slices from slice B101 to slice B150 will be described below. FIG. 5 is a diagram showing an example of three-dimensional medical data according to the first embodiment. The control function 114a transmits the generated three-dimensional medical data to the image storage device 20.

The medical image processing apparatus 30 receives the three-dimensional medical data from the image storage apparatus 20 and stores it in the memory 33. The medical image processing apparatus 30 may receive the three-dimensional medical data from the X-ray diagnostic apparatus 10 without going through the image storage apparatus 20. The medical image processing apparatus 30 also generates two-dimensional image data (composite 2D image) by combining a plurality of slices included in the three-dimensional medical data.

Here, as a method of generating a synthetic 2D image from three-dimensional medical data, for example, maximum intensity projection (MIP: Maximum Intensity Projection), minimum intensity projection (MinIP: Minimum Intensity Projection) and the like are known. However, when a synthetic 2D image is generated by these methods, there are cases where lesions appearing in individual slices are blurred and difficult to observe. That is, a slice in which no lesion has appeared is combined with a slice in which a lesion has appeared, so that a lesion that was clear before combining may be blurred.

Therefore, the processing circuit 34 detects the position of the lesion of the breast P in a plurality of slices included in the three-dimensional medical data, and generates a composite 2D image from the plurality of slices based on the detected position of the lesion, thereby combining the slices. The visibility of lesions in 2D images is improved. Hereinafter, the generation of the synthetic 2D image by the processing circuit 34 will be described in detail.

First, the detection function 342 detects the position of the lesion of the breast P in a plurality of slices included in the three-dimensional medical data. Specifically, the detection function 342 performs computer-aided detection (CAD) on each of the slices B101 to B150 illustrated in FIG. The position of the lesion of P is detected.

Hereinafter, a case where a lesion is detected in the slice B120 illustrated in FIG. 6A and the slice B130 illustrated in FIG. 6B among the slices B101 to B150 will be described. Specifically, the detection function 342 detects the lesion L1 at the position shown in FIG. 6A in the slice B120, and detects the lesion L2 at the position shown in FIG. 6B in the slice B130. The pixel P1 shown in FIG. 6A is an example of a pixel at the position of the lesion L1. Hereinafter, the XY coordinates of the pixel P1 will be (x1, y1). Further, the pixel P2 shown in FIG. 6B is an example of the pixel at the position of the lesion L2. Hereinafter, the XY coordinates of the pixel P2 will be (x2, y2). 6A and 6B are diagrams illustrating an example of lesion detection according to the first embodiment.

Next, the image processing function 343 determines the weight for each pixel in the plurality of slices based on the position of the lesion detected by the detection function 342. That is, the image processing function 343 determines the weight for each slice and each pixel based on the position of the lesion.

For example, the image processing function 343 determines the weight of the pixel P1 (pixel at the position of the lesion L1) to be “1”, and determines the weight of a pixel having the same XY coordinates as the pixel P1 in another slice to be “0”. To do. That is, as shown in FIG. 7, the image processing function 343 determines that the weight of the pixel P1 whose XY coordinate is (x1, y1) in the slice B120 is “1”, and the other slices (slice B101 to slice B119, Also, in slices B121 to B150), the weight of the pixel whose XY coordinate is (x1, y1) is determined to be “0”. 7. FIG. 7 is a diagram illustrating an example of the weighting process according to the first embodiment.

As described above, the image processing function 343 determines the weight of the pixel in each slice for the XY coordinates (x1, y1). Further, the image processing function 343 determines the weight of the pixel in each slice for each of the XY coordinates included in the position of the lesion L1, similarly to the XY coordinates (x1, y1).

Similarly, the image processing function 343 determines the weight of the pixel P2 (pixel at the position of the lesion L2) to be “1” and sets the weight of the pixel having the same XY coordinates as the pixel P2 in other slices to “0”. ". That is, as shown in FIG. 7, the image processing function 343 determines that the weight of the pixel P2 whose XY coordinate is (x2, y2) in the slice B130 is “1”, and the other slices (slice B101 to slice B129, Also, in the slices B131 to B150), the weight of the pixel whose XY coordinate is (x2, y2) is determined to be "0". Further, the image processing function 343 determines the weight of the pixel in each slice for each of the XY coordinates included in the position of the lesion L2, similarly to the XY coordinates (x2, y2).

Depending on the size of the lesion, one lesion may be detected over a plurality of slices. That is, a lesion may be detected over a plurality of positions overlapping in the slice direction (Z direction). In this case, the image processing function 343 determines that the weight of the pixel at the position where the pixel value is the smallest among the plurality of positions where the lesion is detected is larger than the weight of the pixel at other positions. Determine the weight for each pixel.

For example, when the lesion L1 is detected over the slice B119, the slice B120, and the slice B121, the image processing function 343 compares pixel values between slices for each XY coordinate of the position where the lesion L1 is detected. It should be noted that the image processing function 343 compares the pixel values in a state where the gradation has not been inverted. That is, the image processing function 343 compares the pixel values, assuming that the pixel value becomes smaller as the region (bone, lesion, etc.) where X-rays are less likely to penetrate becomes smaller.

As an example, the image processing function 343 has a pixel having an XY coordinate of (x1, y1) in the slice B119, a pixel P1 having an XY coordinate of (x1, y1) in the slice B120, and an XY coordinate in the slice B121. The pixel value is compared with the pixel that is (x1, y1). Here, for example, when the pixel value of the pixel P1 is the minimum, the image processing function 343 determines that the weight of the pixel P1 is lower than the weight of each pixel whose XY coordinates are (x1, y1) in the slice B119 and the slice B121. The weight for each pixel in the plurality of slices is determined so as to be large. For example, the image processing function 343 determines the weight of the pixel P1 to be “1”, and determines the weight of each pixel whose XY coordinates are (x1, y1) in the slice B119 and the slice B121 to be “0”. In other words, the image processing function 343 determines the weight for each pixel in a plurality of slices so that when the lesion is detected over a plurality of positions overlapping in the slice direction, the weight becomes larger as the lesion becomes clearer. ..

Next, the image processing function 343 generates a combined 2D image by combining a plurality of slices included in the three-dimensional medical data based on the determined weight. For example, the image processing function 343 generates the two-dimensional image data C101 (composite 2D image) shown in FIG. 8 by executing the weighted average processing based on the determined weight for each XY coordinate. Note that FIG. 8 is a diagram showing an example of a composite 2D image according to the first embodiment.

For example, the image processing function 343 executes a weighted average process for the XY coordinates (x1, y1) using the pixel value of each pixel and the weight determined for each pixel. Specifically, the image processing function 343 calculates the product of the pixel value of the pixel P1 whose XY coordinate is (x1, y1) in the slice B120 and the weight “1”, and the XY coordinates in other slices ( The product of the pixel value of the pixel of x1, y1) and the weight “0” is calculated. Then, the image processing function 343 sets the calculated sum of products as the pixel value of the pixel having the XY coordinates of (x1, y1) in the two-dimensional image data C101.

Further, the image processing function 343 determines the pixel value of the two-dimensional image data C101 for each of the XY coordinates included in the position of the lesion L1, as with the XY coordinates (x1, y1). In this case, in the two-dimensional image data C101, the lesion L1 in the slice B120 is directly reflected in the region having the same XY coordinates as the lesion L1 in the slice B120 (region R1 in FIG. 8).

Further, the image processing function 343 executes a weighted average process for the XY coordinates (x2, y2) using the pixel value of each pixel and the weight determined for each pixel. Specifically, the image processing function 343 calculates the product of the pixel value of the pixel P2 whose XY coordinate is (x2, y2) in the slice B130 and the weight “1”, and the XY coordinates in other slices are ( The product of the pixel value of the pixel of x2, y2) and the weight “0” is calculated. Then, the image processing function 343 sets the calculated sum of products as the pixel value of the pixel of which the XY coordinate is (x2, y2) in the two-dimensional image data C101.

Further, the image processing function 343 determines the pixel value of the two-dimensional image data C101 for each of the XY coordinates included in the position of the lesion L2, similarly to the XY coordinates (x2, y2). In this case, in the two-dimensional image data C101, the lesion L2 in the slice B130 is directly reflected in the region having the same XY coordinates as the lesion L2 in the slice B130 (region R2 in FIG. 8).

For the XY coordinates (XY coordinates excluding the XY coordinates of the lesion L1 and the XY coordinates of the lesion L2) in which no lesion was detected in any of the slices, the image processing function 343 uses the arbitrary method to store the two-dimensional image data C101. The pixel value can be determined. For example, the image processing function 343 determines the pixel value of the two-dimensional image data C101 by the minimum intensity projection without determining the weight for the XY coordinates in which the lesion is not detected in any slice. That is, the image processing function 343 executes weighted averaging processing based on the determined weights when the weighted pixels are present in the slice direction, and when the weighted pixels are not present in the slice direction. Executes the minimum value projection process to generate the two-dimensional image data C101.

As described above, the image processing function 343 determines the weight for each pixel in a plurality of slices based on the position of the lesion, and synthesizes the plurality of slices based on the determined weights to generate the two-dimensional image data C101. To generate. As a result, the two-dimensional image data C101 represents the lesion in each slice. For example, in the two-dimensional image data C101, the region R1 represents the lesion L1 in the slice B120, and the region R2 represents the lesion L2 in the slice B130. Therefore, the medical image processing apparatus 30 can improve the visibility of the lesion in the two-dimensional image data C101 obtained by tomosynthesis imaging.

Here, the types of lesions include tumors, calcification, and disorder of construction. Of these, the tumor and calcification are represented by pixel values lower than those in other regions in the three-dimensional medical data. Therefore, tumors and calcifications usually also appear on the synthetic 2D images generated by eg minimum intensity projection. On the other hand, the disorder in the construction is a change in the shape of the mammary gland, so that the pixel values in the three-dimensional medical data do not differ greatly from those in other regions. Therefore, even if the construction disturbance appears in some slices, the construction disturbance is usually blurred in the synthetic 2D image generated by the minimum intensity projection or the like. On the other hand, the medical image processing apparatus 30 detects the position of the lesion in a plurality of slices included in the three-dimensional medical data, and generates a synthetic 2D image based on the position of the lesion, thereby visually confirming the disorder of the construction. It is possible to improve the sex.

The medical image processing apparatus 30 may generate the synthetic 2D image by further using not only the position of the lesion but also the type of the lesion. In this case, first, the detection function 342 detects the position and type of the lesion of the breast P in the plurality of slices included in the three-dimensional medical data. For example, the detection function 342 detects the position and type of the lesion of the breast P in the slices B101 to B150 by performing computer-aided detection (CAD) on each of the slices B101 to B150 shown in FIG. To do.

Hereinafter, as an example, a case will be described in which a lesion is detected in the slice B120 illustrated in FIG. 9A and the slice B130 illustrated in FIG. 9B among the slices B101 to B150. 9A and 9B are diagrams illustrating an example of lesion detection according to the first embodiment.

Specifically, the detection function 342 detects a lesion L3 that is a “construction disorder” at the position shown in FIG. 9A in the slice B120. The pixel P3 shown in FIG. 9A is an example of the pixel at the position of the lesion L3. Hereinafter, the XY coordinate of the pixel P3 is set to (x3, y3). Further, the detection function 342 detects a lesion L4 that is a “tumor” and a lesion L5 that is a “calcification” at the position shown in FIG. 9B in the slice B130. The pixel P4 shown in FIG. 9B is an example of the pixel at the position of the lesion L4. The XY coordinate of the pixel P4 is the same as that of the pixel P3 (x3, y3). Further, the pixel P5 shown in FIG. 9A is an example of the pixel at the position of the lesion L5. Hereinafter, the XY coordinates of the pixel P5 will be (x5, y5).

Next, the image processing function 343 determines the weight for each pixel in the plurality of slices based on the position and type of the lesion detected by the detection function 342. The weighting process based on the position and type of lesion will be described below with reference to FIG. FIG. 10 is a diagram illustrating an example of weighting processing according to the first embodiment.

For example, the image processing function 343 determines the weight of the pixel of the slice in which the lesion is detected to be “1” and the weight of the pixel of the other slice to be “0” for the XY coordinates in which one lesion is detected. To do. As an example, as shown in FIGS. 9A and 9B, only one lesion L5 is detected at the XY coordinates (x5, y5). In this case, the image processing function 343 determines the weight of the pixel P5 (pixel at the position of the lesion L5) to be “1”, and sets the weight of the pixel having the same XY coordinates as the pixel P5 in another slice to “0”. decide. That is, as shown in FIG. 10, the image processing function 343 determines the weight of the pixel P5 of which the XY coordinate is (x5, y5) in the slice B130 to be “1”, and the other slices (slice B101 to slice B129, Also, in the slices B131 to B150), the weight of the pixel whose XY coordinate is (x5, y5) is determined to be "0". Further, the image processing function 343 determines the weight of the pixel in each slice for each of the XY coordinates included in the position of the lesion L5, similarly to the XY coordinates (x5, y5).

Further, the image processing function 343 determines the pixel weight of each slice using the lesion type for the XY coordinates in which a plurality of lesions are detected. As an example, as shown in FIGS. 9A and 9B, two lesions (lesion L3 and lesion L4) are detected at the XY coordinates (x3, y3). In this case, the image processing function 343 weights the pixel P3 (the pixel at the position of the lesion L3) and the pixel P4 (the pixel at the position of the lesion L4) according to the type of the lesion, so that each slice Determine the pixel weight.

For example, the image processing function 343 acquires the ratio set for each combination of lesion types according to the types of the lesion L3 and the lesion L4. The ratio may be a preset value set in advance or set by the user. For example, the image processing function 343 acquires, from the memory 33, the ratio “4:1” of “disorder of construction” and “tumor” according to the types of the lesion L3 and the lesion L4.

Then, the image processing function 343 sets the pixel of each slice so that the ratio of the weight of the pixel P3 at the position of “construction disorder” and the weight of the pixel P4 at the position of “tumor” is “4:1”. Weight. For example, as shown in FIG. 10, the image processing function 343 assigns a weight “0.8” to the pixel P3 whose XY coordinate is (x3, y3) in the slice B120, and the XY coordinate is ( A weight “0.2” is given to the pixel P4 which is x3, y3). That is, the image processing function 343 weights the pixel P3 at the position of “disorder of construction” to the pixel at the position of lesion L4 of “tumor” when the lesion L3 of “disorder of construction” is detected. Weight more than.

Further, the image processing function 343 weights the pixels whose XY coordinates are (x3, y3) in other slices (slice B101 to slice B119, slice B121 to slice B129, and slice B131 to slice B150). "0" is added. That is, the image processing function 343 determines the weight for each pixel in a plurality of slices by weighting the pixels at the positions of the lesion L3 and the lesion L4 according to the type of lesion. Similarly, the image processing function 343 determines the pixel weight in each slice for each of the XY coordinates included in the positions of the lesion L3 and the lesion L4, similarly to the XY coordinates (x3, y3).

Then, the image processing function 343 generates a combined 2D image by combining a plurality of slices included in the three-dimensional medical data based on the determined weight. For example, the image processing function 343 generates the two-dimensional image data C102 (not shown) by performing the weighted average processing based on the determined weight for each XY coordinate.

For example, the image processing function 343 executes a weighted average process for the XY coordinates (x3, y3) using the pixel value of each pixel and the weight determined for each pixel. Specifically, the image processing function 343 calculates the product of the pixel value of the pixel P3 whose XY coordinate is (x3, y3) in the slice B120 and the weight “0.8”, and the XY coordinate in the slice B130 is ( The product of the pixel value of the pixel P4 that is x3, y3) and the weight “0.2” is calculated, and the pixel value of the pixel whose XY coordinate is (x3, y3) and the weight “0” are calculated in other slices. Calculate the product. Then, the image processing function 343 sets the calculated sum of the products as the pixel value of the pixel whose XY coordinate is (x3, y3) in the two-dimensional image data C102. Further, the image processing function 343 determines the pixel value of the two-dimensional image data C102 for each of the XY coordinates included in the positions of the lesion L3 and the lesion L4, similarly to the XY coordinates (x3, y3).

Further, the image processing function 343 executes weighted averaging processing for the XY coordinates (x5, y5) using the pixel value of each pixel and the weight determined for each pixel. Further, the image processing function 343 determines the pixel value of the two-dimensional image data C102 for each of the XY coordinates included in the position of the lesion L5, similarly to the XY coordinates (x5, y5). Further, the image processing function 343 determines the pixel value of the two-dimensional image data C102 by the minimum intensity projection without determining the weight for the XY coordinates in which the lesion is not detected in any slice. That is, the image processing function 343 executes weighted averaging processing based on the determined weights when the weighted pixels are present in the slice direction, and when the weighted pixels are not present in the slice direction. Performs minimum intensity projection processing to generate two-dimensional image data C102.

As described above, the image processing function 343 determines the weight for each pixel in a plurality of slices based on the position and type of the lesion, and synthesizes the plurality of slices based on the determined weights to generate the two-dimensional image data. Generate C102. Accordingly, the two-dimensional image data C102 appropriately represents each lesion even when a plurality of lesions overlap each other in the slice direction.

For example, when the “construction disorder” and the “tumor” overlap in the slice direction, the image processing function 343 weights the pixel at the “construction disorder” position to the pixel at the “tumor” position. The two-dimensional image data C102 is generated with a larger weight. Here, the “construction disorder” is not so different in the pixel value from other regions in the three-dimensional medical data, and therefore is easily blurred when synthesizing a plurality of slices. On the other hand, the image processing function 343 can reduce the blur of the “construction disorder” in the two-dimensional image data C102 by giving a large weight to the pixel at the “construction disorder” position. Further, other types of lesions such as “tumor” and “calcification” are represented by pixel values lower than those in other regions in the three-dimensional medical data, and even if they are given a small weight, they are two-dimensional. The image data C102 can be sufficiently viewed. That is, the image processing function 343 can enable both the “construction disorder” and the lesion of another type to be visually recognized by generating the two-dimensional image data C102 based on the position and type of the lesion. it can.

After the composite 2D image is generated by the image processing function 343, the display control function 344 causes the display 32 to display the composite 2D image. Accordingly, the display control function 344 can present the combined 2D image to the user of the medical image processing apparatus 30. Alternatively, the control function 341 may output the generated combined 2D image to an external device. For example, the control function 341 transmits the combined 2D image to the X-ray diagnostic apparatus 10 via the network NW. In this case, the display control function 114b of the X-ray diagnostic apparatus 10 causes the display 113 to display the composite 2D image. Accordingly, the display control function 114b can present the synthetic 2D image to the user of the X-ray diagnostic apparatus 10. Note that the gradation may be inverted when displaying the composite 2D image.

Next, an example of a procedure of processing by the medical image processing apparatus 30 will be described with reference to FIG. FIG. 11 is a flowchart for explaining a series of processing flows of the medical image processing apparatus 30 according to the first embodiment. Step S101 is a step corresponding to the control function 341. Step S102 is a step corresponding to the detection function 342. Step S103, step S104, step S105, step S106, step S107, step S108, step S109, step S110 and step S111 are steps corresponding to the image processing function 343. Note that, in FIG. 11, a case where a synthetic 2D image is generated based on the position and type of lesion will be described.

First, the processing circuit 34 acquires three-dimensional medical data from the X-ray diagnostic apparatus 10 or the image storage apparatus 20 (step S101). Next, the processing circuit 34 detects the position and type of the lesion of the breast P in the plurality of slices included in the three-dimensional medical data (step S102).

Next, the processing circuit 34 sets any of the XY coordinates included in the three-dimensional medical data (step S103), and determines whether or not a lesion exists in the slice direction (Z direction) at the set XY coordinates. The determination is made (step S104). That is, the processing circuit 34 determines whether or not there is a slice in which a lesion is detected at the set XY coordinates.

Here, when a lesion exists in the slice direction (Yes at Step S104), the processing circuit 34 determines whether or not there are a plurality of lesions (Step S105). Further, when there are a plurality of lesions (Yes in step S105), as in the XY coordinates (x3, y3) illustrated in FIGS. 9A and 9B, the processing circuit 34 sets the ratio set for each combination of lesion types. It is acquired (step S106).

As in the XY coordinates (x5, y5) shown in FIGS. 9A and 9B, when there are not a plurality of lesions (No at Step S105), or after Step S106, the processing circuit 34 makes a plurality of data included in the three-dimensional medical data. The weight for each pixel in the slice is determined (step S106). Here, when there are a plurality of lesions, the processing circuit 34 determines the weight for each pixel in the plurality of slices by using the ratio acquired in step S106.

Next, the processing circuit 34 executes the weighted average processing based on the determined weight (step S108). Alternatively, when there is no lesion in the slice direction (No at Step S104), the processing circuit 34 executes the minimum value projection process (Step S109). Through step S108 or step S109, the processing circuit 34 can determine the pixel value of the pixel of the combined 2D image at the set XY coordinates.

Here, the processing circuit 34 determines whether or not all the pixels in the three-dimensional medical data have been processed (step S110). If there are unprocessed pixels remaining (No at Step S110), the processing circuit 34 changes the setting of the XY coordinates (Step S111), and proceeds to Step S104 again. On the other hand, when all pixels have been processed (Yes at Step S110), the processing circuit 34 determines that the generation of the combined 2D image is completed, and ends the processing.

As described above, according to the first embodiment, the detection function 342 determines the position of the lesion of the breast P in the plurality of slices included in the three-dimensional medical data obtained by tomosynthesis imaging of the breast P of the subject. To detect. Further, the image processing function 343 generates two-dimensional image data (composite 2D image) by combining a plurality of slices based on the detected lesion position. Therefore, the medical image processing apparatus 30 according to the first embodiment can improve the visibility of the lesion in the synthetic 2D image obtained by tomosynthesis imaging. As a result, the medical image processing apparatus 30 can improve the throughput of image interpretation and reduce the possibility of overlooking a lesion.

Further, as described above, according to the first embodiment, the detection function 342 further detects the lesion type in the plurality of slices. Further, the image processing function 343 further uses the type of lesion to generate a synthetic 2D image. Therefore, the medical image processing apparatus 30 according to the first embodiment can generate a synthetic 2D image that appropriately represents each lesion even when a plurality of lesions overlap each other in the slice direction.

Further, as described above, according to the first embodiment, when the disorder of the construction is detected as the lesion, the image processing function 343 sets the pixel of the position of the disorder of the construction to the position of the lesion of another type. A larger weight is given than a pixel. Therefore, the medical image processing apparatus 30 according to the first embodiment can prevent the disorder of the construction from being blurred during the synthesis, and can improve the visibility of the disorder of the construction in the synthetic 2D image.

Moreover, as described above, according to the first embodiment, the image processing function 343 generates a composite 2D image from the three-dimensional medical data. That is, the medical image processing apparatus 30 according to the first embodiment can acquire both the three-dimensional image data and the two-dimensional image data by performing the photographing once. Therefore, the medical image processing apparatus 30 can reduce the number of times of imaging and reduce the exposure dose of the subject.

The display control function 344 may display three-dimensional medical data in addition to the composite 2D image. For example, the display control function 344 causes the display 32 to display any one of the plurality of slices in the three-dimensional medical data or to sequentially display the plurality of slices. Here, since the user has already grasped the position and type of the lesion by the synthetic 2D image, the user can quickly interpret the three-dimensional medical data. Then, the user can pay attention to the slice in which the lesion appears particularly clearly and perform more detailed observation.

(Second embodiment)
In the above-described first embodiment, the case has been described in which the weight for each pixel in a plurality of slices is determined based on the position and type of the lesion. On the other hand, in the second embodiment, a case will be described in which the weight for each pixel in a plurality of slices is determined based on the distribution of weights in the slice direction of the three-dimensional medical data.

The medical image processing system 1 according to the second embodiment has the same configuration as the medical image processing system 1 according to the first embodiment shown in FIGS. The parts are different. About the point which has the same composition as the composition explained in a 1st embodiment, the same numerals as Drawing 1-Drawing 2 are attached, and explanation is omitted.

First, the memory 33 stores the distribution of weights in the slice direction (Z direction) of the three-dimensional medical data for each lesion type. Here, the distribution of weights stored in the memory 33 will be described with reference to FIG. FIG. 12 is a diagram showing an example of the distribution of weights according to the second embodiment.

The weight distribution shown in FIG. 12 is set in advance and stored in the memory 33 prior to the generation of the synthetic 2D image. The setting of the distribution of weights may be automatically performed by the medical image processing apparatus 30 or may be performed by the user via the input interface 109. Alternatively, the medical image processing apparatus 30 may receive the weight distribution set by the external device and store it in the memory 33.

The horizontal axis of FIG. 12 corresponds to the slice direction (Z direction). More specifically, the horizontal axis of FIG. 12 corresponds to the number of slices. Note that the horizontal axis of FIG. 12 may be the length in the slice direction instead of the number of slices. Further, the horizontal axis of FIG. 12 corresponds to the magnitude of the weight given to each pixel of the plurality of slices.

FIG. 12 shows distributions of three weights set for three types of lesions of “calcification”, “disorder of construction”, and “tumor”. Here, the distribution of the three weights is set such that the spread in the slice direction becomes smaller in the order of “calcification”, “disorder of construction”, and “tumor”. That is, the memory 33 stores the distribution of weights set such that the kurtosis increases as the lesion size decreases. In addition, the distributions of the three weights are set so that their total areas are approximately the same. For example, the distribution of the weight of “tumor” is set such that the larger the spread in the slice direction, the smaller the peak value. Although FIG. 12 shows the distribution set continuously, the memory 33 may store the distribution (array) set discretely.

The processing circuit 34 determines a weight for each pixel in a plurality of slices by using the weight distribution shown in FIG. 12, thereby generating a combined 2D image. Specifically, first, the detection function 342 detects the position and type of lesion in a plurality of slices. Next, the image processing function 343 determines the weight for each pixel in the plurality of slices by applying the distribution of weights according to the type of lesion to the position of the lesion detected by the detection function 342. Further, the image processing function 343 generates a combined 2D image by combining a plurality of slices based on the determined weight.

Hereinafter, as an example, among the slices B101 to B150, a lesion L1 that is a “tumor” is detected in the slice B120 illustrated in FIG. 6A, and a lesion L2 that is “calcified” is detected in the slice B130 illustrated in FIG. 6B. The case where it is performed will be described. In this case, the image processing function 343 applies the distribution of the weight of “tumor” to the position of the lesion L1 of the slice B120 and the distribution of the weight of “calcification” to the position of the lesion L2 of the slice B130. By doing so, the weight for each pixel in the plurality of slices is determined.

Specifically, the image processing function 343 first reads from the memory 33 the weight distribution of “tumor” among the three weight distributions shown in FIG. Next, the image processing function 343 applies the distribution of the weight of “tumor” to the pixel P1 at the position of the lesion L1 of the slice B120 so that (0) in the distribution of weight of “tumor” is matched with the slice B120. To do. That is, the image processing function 343 matches (-2) with the slice B118, (-1) with the slice B119, (0) with the slice B120, (+1) with the slice B121, and (+2). The distribution of the weight of "tumor" is applied to the pixel P1 so as to match the slice B122.

By applying the distribution of the “tumor” weight to the pixel P1, the image processing function 343 determines the weight of the pixel P1 in the slice B120 and the pixel having the same XY coordinates as the pixel P1 in the other slices. .. For example, the image processing function 343 determines the weight of each pixel having the same XY coordinates (x1, y1) as the pixel P1, as shown in FIG. Note that FIG. 13 is a diagram illustrating an example of the weighting process according to the second embodiment.

That is, as shown in FIG. 13, the image processing function 343 determines the weight of the pixel whose XY coordinate is (x1, y1) in the slice B118 to be “0.1”, and the XY coordinate is (x1, y1) in the slice B119. The pixel weight of y1) is determined to be “0.2”, the weight of the pixel P1 of which the XY coordinate is (x1, y1) is determined to be “0.4” in the slice B120, and the XY coordinate is determined to be in the slice B121. The weight of the pixel having (x1, y1) is determined to be “0.2”, and the weight of the pixel having the XY coordinate of (x1, y1) is determined to be “0.1” in the slice B122. Further, the image processing function 343 determines the weight of the pixel having the XY coordinates (x1, y1) in the other slices (slice B101 to slice B117 and slice B123 to slice B150) to be “0”. Further, the image processing function 343 determines the weight of the pixel in each slice for each of the XY coordinates included in the position of the lesion L1, similarly to the XY coordinates (x1, y1).

Further, the image processing function 343 reads out the distribution of the weight of “calcification” from the memory 33 among the distributions of the three weights shown in FIG. Next, the image processing function 343 sets the distribution of the weight of “tumor” to the pixel P2 at the position of the lesion L2 of the slice B130 so that (0) in the distribution of the weight of “calcification” is matched with the slice B130. Apply. That is, the image processing function 343 aligns (-2) with the slice B128, (-1) with the slice B129, (0) with the slice B130, (+1) with the slice B131, and (+2). The distribution of the weight of "calcification" is applied to the pixel P2 so as to match the slice B132. Thereby, the image processing function 343 determines the weight of the pixel P2 in the slice B130 and the pixel having the same XY coordinate as the pixel P2 in the other slice.

That is, as shown in FIG. 13, the image processing function 343 determines the weight of the pixel whose XY coordinate is (x2, y2) in the slice B129 to be “0.1”, and in the slice B130, the XY coordinate is (x2, y2). The pixel weight of y2) is determined to be “0.8”, and the pixel weight of the slice B131 whose XY coordinate is (x2, y2) is determined to be “0.1”. Further, the image processing function 343 determines the weight of the pixel having the XY coordinates (x2, y2) in the other slices (slice B101 to slice B128 and slice B132 to slice B150) to be “0”. Further, the image processing function 343 determines the weight of the pixel in each slice for each of the XY coordinates included in the position of the lesion L2, similarly to the XY coordinates (x2, y2).

Then, the image processing function 343 generates a combined 2D image by combining a plurality of slices included in the three-dimensional medical data based on the determined weight. For example, the image processing function 343 generates the two-dimensional image data C201 (not shown) by performing the weighted average processing based on the determined weight for each XY coordinate. For example, the image processing function 343 executes weighted averaging processing based on the determined weights when the weighted pixels are present in the slice direction, and when the weighted pixels are not present in the slice direction. Performs minimum intensity projection processing to generate two-dimensional image data C201.

Next, a case where a plurality of lesions overlap in the slice direction will be described. As an example below, among the slices B101 to B150, a lesion L6 that is a “tumor” is detected in the slice B120 illustrated in FIG. 14A, and a lesion L7 that is a “calcification” is detected in the slice B130 illustrated in FIG. 14B. The case will be described. 14A and 14B are diagrams illustrating an example of lesion detection according to the second embodiment.

The pixel P6 and the pixel P7 illustrated in FIG. 14A are examples of pixels at the position of the lesion L6. Hereinafter, the XY coordinate of the pixel P6 is set to (x6, y6), and the XY coordinate of the pixel P7 is set to (x7, y7). The pixel P8 illustrated in FIG. 14B is an example of the pixel at the position of the lesion L7. The XY coordinate of the pixel P8 is the same as that of the pixel P6 (x6, y6). The pixel P9 illustrated in FIG. 14B is a pixel having the same XY coordinates (x7, y7) as the pixel P7. As shown in FIG. 14B, the pixel P9 is not included in the position of the lesion L7.

As shown in FIGS. 14A and 14B, the detection function 342 detects the lesion L6 and the lesion L7 at the XY coordinates (x6, y6). That is, FIGS. 14A and 14B show a case where the detection function 342 detects a plurality of lesions at positions overlapping in the slice direction. In this case, the image processing function 343 calculates the composite distribution by, for example, combining the distributions of the weights corresponding to the types of the plurality of lesions according to the positions of the plurality of lesions, and based on the calculated composite distributions. Determine the weight for each pixel in the plurality of slices.

Specifically, the image processing function 343 first reads from the memory 33 the weight distribution of “tumor” and the weight distribution of “calcification” among the three weight distributions shown in FIG. Next, the image processing function 343 adjusts the weight distribution of “tumor” to the slice B120 and the weight distribution of “calcification” to the slice B130, and then distributes the weight of “tumor” and the “calcification”. And the distribution of the weights of “” are combined to calculate a combined distribution.

When one lesion is detected over a plurality of slices, the image processing function 343 calculates the composite distribution in a state where the weight distribution is matched to the central slice of the plurality of slices, for example. For example, as illustrated in FIG. 15, when the lesion L6 that is a “tumor” is detected over the slice B119, the slice B120, and the slice B121, the image processing function 343 sets the weight of the “tumor” to the central slice B120. Match the distribution. Then, the image processing function 343 matches the distribution of the weight of “tumor” with the slice B120 and the distribution of the weight of “calcification” with the slice B130, and the distribution of the weight of “tumor” and the “calcification”. 15 and the distribution of the weights are combined to calculate a combined distribution shown in the lower part of FIG. Note that FIG. 15 is a diagram illustrating an example of calculation of the combined distribution according to the second embodiment.

Then, the image processing function 343 determines the weight for each pixel in the plurality of slices based on the calculated combined distribution. For example, the image processing function 343 aligns the peak m1 in the composite distribution shown in the lower diagram of FIG. 15 with the slice B120 and the peak m2 with the slice B130 so that the pixel P6 at the position of the lesion L6 and the pixel P8 at the position of the lesion L7. Apply the composite distribution to. Accordingly, the image processing function 343 determines the weights of the pixel P6 in the slice B120, the pixel P8 in the slice B130, and the pixels having the same XY coordinates as the pixels P6 and P8 in the other slices.

For example, as shown in FIG. 16, the image processing function 343 determines the weight of the pixel whose XY coordinate is (x6, y6) in the slice B118 to be “0.05”, and the XY coordinate is (x6, in the slice B119. The pixel weight of y6) is determined to be "0.1", the weight of the pixel whose XY coordinate is (x6, y6) is determined to be "0.2" in the slice B120, and the XY coordinate is ( The weight of the pixel having x6, y6) is determined to be “0.1”, and the weight of the pixel having the XY coordinate of (x6, y6) in the slice B122 is determined to be “0.05”. Further, the image processing function 343 determines the weight of the pixel having the XY coordinate of (x6, y6) in the slice B129 to be “0.05”, and the weight of the pixel having the XY coordinate of (x6, y6) in the slice B130. Is determined to be “0.4”, and the weight of the pixel whose XY coordinate is (x6, y6) in the slice B131 is determined to be “0.05”. In addition, the image processing function 343 weights the pixels whose XY coordinates are (x6, y6) in other slices (slice B101 to slice B117, slice B123 to slice B128, and slice B132 to slice B150) to be “0”. To decide. Further, the image processing function 343 determines the weight of the pixel in each slice for each of the XY coordinates included in the positions of both the lesion L6 and the lesion L7, similarly to the XY coordinates (x6, y6). Note that FIG. 16 is a diagram illustrating an example of weighting processing according to the second embodiment.

Further, as shown in FIGS. 14A and 14B, only one lesion (lesion L6 that is a “tumor”) is detected at the XY coordinates (x7, y7). In this case, the image processing function 343 applies the weight distribution of “tumor” to the position of the lesion L6 detected by the detection function 342 to determine the weight for each pixel in the plurality of slices.

Specifically, the image processing function 343 reads the weight distribution of “tumor” from the memory 33, and applies the weight distribution of “tumor” to the pixel P7 at the position of the lesion L6 of the slice B120. Accordingly, the image processing function 343 determines the weight of the pixel P7 in the slice B120 and the pixel having the same XY coordinates as the pixel P7 in the other slice. For example, the image processing function 343 determines the weight of each pixel having the same XY coordinates (x7, y7) as the pixel P7, as shown in FIG.

That is, the image processing function 343 determines the weight of the pixel of which the XY coordinate is (x7, y7) in the slice B118 to be "0.1", and the weight of the pixel of which the XY coordinate is (x7, y7) is in the slice B119. Is determined to be “0.2”, the weight of the pixel P7 whose XY coordinate is (x7, y7) in the slice B120 is determined to be “0.4”, and the XY coordinate is (x7, y7) in the slice B121. The weight of the pixel is determined to be “0.2”, and the weight of the pixel of which the XY coordinate is (x7, y7) in the slice B122 is determined to be “0.1”. Further, the image processing function 343 determines the weight of the pixel having the XY coordinates (x7, y7) in the other slices (slice B101 to slice B117 and slice B123 to slice B150) to be “0”. Further, the image processing function 343, for each of the XY coordinates included in the position of the lesion L6 and not included in the position of the lesion L7, sets the pixel weight in each slice in the same manner as the XY coordinates (x7, y7). decide.

Then, the image processing function 343 generates a combined 2D image by combining a plurality of slices included in the three-dimensional medical data based on the determined weight. For example, the image processing function 343 generates the two-dimensional image data C202 (not shown) by performing the weighted average processing based on the determined weight for each XY coordinate. For example, the image processing function 343 executes weighted averaging processing based on the determined weights when the weighted pixels are present in the slice direction, and when the weighted pixels are not present in the slice direction. Performs two-dimensional image data C202 by executing the minimum intensity projection process.

Next, an example of a processing procedure by the medical image processing apparatus 30 will be described with reference to FIG. FIG. 17 is a flowchart for explaining a series of processing flows of the medical image processing apparatus 30 according to the second embodiment. Step S201 is a step corresponding to the control function 341. Step S202 is a step corresponding to the detection function 342. Step S203, step S204, step S205, step S206, step S207, step S208, step S209, step S210, step S211 and step S212 are steps corresponding to the image processing function 343.

First, the processing circuit 34 acquires three-dimensional medical data from the X-ray diagnostic apparatus 10 or the image storage apparatus 20 (step S201). Next, the processing circuit 34 detects the position and type of the lesion of the breast P in the plurality of slices included in the three-dimensional medical data (step S202).

Next, the processing circuit 34 sets any of the XY coordinates included in the three-dimensional medical data (step S203) and determines whether or not a lesion exists in the slice direction (Z direction) at the set XY coordinates. The determination is made (step S204). That is, the processing circuit 34 determines whether or not there is a slice in which a lesion is detected at the set XY coordinates.

Here, when a lesion exists in the slice direction (Yes at Step S204), the processing circuit 34 determines whether or not there are a plurality of lesions (Step S205). When there are a plurality of lesions (Yes in step S205), as in the XY coordinates (x6, y6) shown in FIGS. 14A and 14B, the processing circuit 34 distributes the weights according to the types of the plurality of lesions. Is read from the memory 33, and the read distributions are combined according to the positions of the lesions to calculate a combined distribution (step S206).

When there are not a plurality of lesions like the XY coordinates (x7, y7) shown in FIGS. 9A and 9B (No at Step S205), or after Step S206, the processing circuit 34 applies the distribution to the plurality of slices. (Step S207). Here, when there are not a plurality of lesions, the processing circuit 34 applies the distribution according to the type of lesion to the plurality of slices. On the other hand, when there are a plurality of lesions, the processing circuit 34 applies the composite distribution calculated in step S206 to the plurality of slices. Accordingly, the processing circuit 34 determines the weight for each pixel in the plurality of slices (step S208).

Next, the processing circuit 34 executes the weighted average processing based on the determined weight (step S209). Alternatively, when there is no lesion in the slice direction (No at Step S204), the processing circuit 34 executes the minimum value projection process (Step S210). Through step S209 or step S210, the processing circuit 34 can determine the pixel value of the pixel of the combined 2D image at the set XY coordinates.

Here, the processing circuit 34 determines whether or not all pixels in the three-dimensional medical data have been processed (step S211). If there are unprocessed pixels remaining (No at Step S211), the processing circuit 34 changes the XY coordinate setting (Step S212), and the process proceeds to Step S204 again. On the other hand, when all the pixels have been processed (Yes in step S211), the processing circuit 34 determines that the generation of the combined 2D image is completed, and ends the processing.

As described above, according to the second embodiment, the memory 33 stores the distribution of weights in the slice direction of the three-dimensional medical data for each lesion type. Further, the detection function 342 detects the position and type of lesion in a plurality of slices. Further, the image processing function 343 determines the weight for each pixel in the plurality of slices by applying the distribution of the weights according to the type of the lesion to the position of the lesion detected by the detection function 342, and determines the determined weight. A composite 2D image is generated by combining a plurality of slices based on Therefore, the medical image processing apparatus 30 according to the second embodiment can improve the visibility of the lesion in the synthetic 2D image obtained by tomosynthesis imaging.

Further, as described above, the memory 33 stores the distribution of weights set so that the kurtosis increases as the lesion size decreases. That is, the medical image processing apparatus 30 reflects information from a large number of slices on a synthetic 2D image for a lesion having a large size such as a tumor or a disorder of construction, and a small number for a lesion having a small size such as calcification. The information from the slice is reflected in the composite 2D image. Therefore, the medical image processing apparatus 30 according to the second embodiment can improve the visibility of the lesion by reflecting the information of the lesion included in the plurality of slices on the composite 2D image without excess or deficiency.

Further, as described above, when the detection function 342 detects a plurality of lesions at positions overlapping in the slice direction, the image processing function 343 causes the distribution of weights according to the types of the plurality of lesions to be distributed to each of the plurality of lesions. A composite distribution is calculated by combining them according to the position, and a weight for each pixel in a plurality of slices is determined based on the composite distribution. Further, when the detection function 342 detects one lesion at a position overlapping in the slice direction, the image processing function 343 determines the weight for each pixel in a plurality of slices based on the distribution of weights according to the type of lesion. Therefore, the medical image processing apparatus 30 according to the second embodiment can generate a synthetic 2D image that appropriately represents each lesion even when a plurality of lesions overlap each other in the slice direction.

For example, in the XY coordinates (x6, y6) shown in FIGS. 14A and 14B, both the lesion L6 that is a “tumor” and the lesion L7 that is a “calcification” are weighted based on the composite distribution. It Therefore, in the XY coordinates (x6, y6) of the generated synthetic 2D image (two-dimensional image data C202), “tumor” and “calcification” are expressed in an overlapping manner. Further, for example, at the XY coordinates (x7, y7), the lesion L6 that is a “tumor” is weighted based on the distribution of the weight of the “tumor”. Therefore, in the XY coordinates (x7, y7) of the two-dimensional image data C202, "tumor" is preferentially expressed. That is, in the two-dimensional image data C202, both "tumor" and "calcification" can be visually recognized.

(Third Embodiment)
In the above-described first embodiment, a case has been described in which a composite 2D image is generated by combining a plurality of slices included in three-dimensional medical data. On the other hand, in the third embodiment, a case will be described in which image processing is performed on a plurality of slices and the plurality of slices after the image processing are combined to generate a combined 2D image.

The medical image processing system 1 according to the third embodiment has the same configuration as the medical image processing system 1 according to the first embodiment shown in FIGS. The parts are different. About the point which has the same composition as the composition explained in a 1st embodiment, the same numerals as Drawing 1-Drawing 2 are attached, and explanation is omitted.

First, the detection function 342 detects the position and type of lesion in a plurality of slices included in the three-dimensional medical data. Hereinafter, as an example, a case where a lesion is detected in the slice B120 illustrated in FIG. 18 and the slice B130 illustrated in FIG. 18 among the slices B101 to B150 will be described. Specifically, the detection function 342 detects a lesion L8 that is a “tumor” at the position shown in FIG. 18 in the slice B120. Further, the detection function 342 detects a lesion L9 that is “calcification” and a lesion L10 that is “disorder of construction” at the position shown in FIG. 18 in the slice B130. Note that FIG. 18 is a diagram for explaining the generation of a synthetic 2D image according to the third embodiment.

Next, the image processing function 343 performs image processing according to the type of lesion on the pixel corresponding to the position of the lesion detected by the detection function 342 among the pixels in the plurality of slices. The image processing function 343 may perform the image processing only on the pixels corresponding to the lesion position, or may perform the image processing on the pixels including the pixels corresponding to the lesion position. ..

Specifically, the image processing function 343 executes the image processing T1 according to the lesion type “tumor” on the pixel corresponding to the position of the lesion L8 in the slice B120. Here, the image processing T1 is, for example, image processing that emphasizes low-frequency components. As an example, the image processing function 343 executes filtering using a smoothing filter or processing for adding an emphasis component on the low frequency side as the image processing T1.

Further, the image processing function 343 executes the image processing T2 according to the lesion type “calcification” on the pixel corresponding to the position of the lesion L9 in the slice B130. Here, the image processing T2 is, for example, image processing that emphasizes high frequency components. As an example, the image processing function 343 executes filtering using an edge enhancement filter or processing for adding a high-frequency side enhancement component as the image processing T2.

Further, the image processing function 343 executes image processing T3 according to the lesion type “disorder of construction” on the pixel corresponding to the position of the lesion L10 in the slice B130. Here, the image processing T3 is, for example, image processing that emphasizes contrast. As an example, the image processing function 343 executes, as the image processing T3, filtering using an edge enhancement filter and processing for expanding the pixel value histogram of pixels corresponding to the position of the lesion L10.

Next, the image processing function 343 determines the weight for each pixel in the plurality of slices after the image processing based on the position and type of the lesion. For example, the image processing function 343 determines the pixel weight of the slice in which the lesion is detected to be “1” for the XY coordinates in which one lesion is detected (XY coordinates included in the position of the lesion L10). Then, the pixel weights of the other slices are set to “0”. Further, the image processing function 343 uses the ratio set for each combination of lesion types for the XY coordinates (XY coordinates included in the position of the lesion L9) in which a plurality of lesions are detected, and the slice The weights are determined, and the weights of the pixels of other slices are determined to be "0".

As another example, the image processing function 343 determines the weight for each pixel in a plurality of slices by applying a distribution of weights according to the type of lesion to the position of the lesion detected by the detection function 342. To do. Here, for the XY coordinates in which a plurality of lesions are detected, the image processing function 343 calculates a composite distribution by synthesizing distributions of weights according to the types of the plurality of lesions, and based on the composite distribution, Determine the weight for each pixel in multiple slices.

Then, the image processing function 343 generates the two-dimensional image data C301 (composite 2D image) shown in FIG. 18 by combining a plurality of slices based on the determined weight. That is, the image processing function 343, based on the determined weight, the slice B120 and the slice B130 after the image processing and other slices (slice B101 to slice B119, slice B121 to slice B129, and slice B131 to slice B150). By combining and, the two-dimensional image data C301 is generated. For example, the image processing function 343 executes weighted averaging processing based on the determined weights when the weighted pixels are present in the slice direction, and when the weighted pixels are not present in the slice direction. Performs minimum intensity projection processing to generate two-dimensional image data C301.

Here, in the two-dimensional image data C301, the visibility of each lesion is improved. For example, the region R3 of the two-dimensional image data C301 represents “tumor” in which the low frequency component is emphasized. Further, the region R4 of the two-dimensional image data C301 represents “calcification” in which high frequency components are emphasized. In addition, the region R5 of the two-dimensional image data C301 represents “construction disorder” in which the contrast is emphasized. That is, the medical image processing apparatus 30 can improve the visibility of the lesion in the two-dimensional image data C301 obtained by tomosynthesis imaging.

Next, an example of a processing procedure by the medical image processing apparatus 30 will be described with reference to FIG. FIG. 19 is a flowchart for explaining a series of processing flows of the medical image processing apparatus 30 according to the third embodiment. Step S301 is a step corresponding to the control function 341. Step S302 is a step corresponding to the detection function 342. Step S303, step S304, step S305, step S306, step S307, step S308, step S309, step S310, step S311, step S312 and step S313 are steps corresponding to the image processing function 343. Note that FIG. 19 illustrates, as an example, a case where the weight for each pixel is determined using the distribution of weights in the slice direction.

First, the processing circuit 34 acquires three-dimensional medical data from the X-ray diagnostic apparatus 10 or the image storage apparatus 20 (step S301). Next, the processing circuit 34 detects the position and type of the lesion of the breast P in the plurality of slices included in the three-dimensional medical data (step S302). Next, the processing circuit 34 performs image processing according to the type of lesion on the pixel corresponding to the position of the detected lesion among the pixels in the plurality of slices (step S303).

Next, the processing circuit 34 sets any of the XY coordinates included in the three-dimensional medical data (step S304), and determines whether or not a lesion exists in the slice direction (Z direction) at the set XY coordinates. The determination is made (step S305). That is, the processing circuit 34 determines whether or not there is a slice in which a lesion is detected at the set XY coordinates.

Here, when a lesion exists in the slice direction (Yes at Step S305), the processing circuit 34 determines whether or not there are a plurality of lesions (Step S306). Further, when there are a plurality of lesions (Yes at Step S306), the processing circuit 34 reads the distribution of weights corresponding to the types of the plurality of lesions from the memory 33, and the read plurality of distributions are stored in the plurality of lesions. The composite distribution is calculated by synthesizing according to the position (step S307).

When there are not a plurality of lesions (No at Step S306) or after Step S307, the processing circuit 34 applies the distribution to the plurality of slices (Step S308). Here, when there are not a plurality of lesions, the processing circuit 34 applies the distribution according to the type of lesion to the plurality of slices. On the other hand, when there are a plurality of lesions, the processing circuit 34 applies the composite distribution calculated in step S307 to the plurality of slices. Accordingly, the processing circuit 34 determines the weight for each pixel in the plurality of slices (step S309).

Next, the processing circuit 34 executes the weighted average processing based on the determined weight (step S310). Alternatively, when there is no lesion in the slice direction (No at step S305), the processing circuit 34 executes the minimum value projection process (step S311). Through step S310 or step S311, the processing circuit 34 can determine the pixel value of the pixel of the combined 2D image at the set XY coordinates.

Here, the processing circuit 34 determines whether or not all the pixels in the three-dimensional medical data have been processed (step S312). When there are unprocessed pixels remaining (No at Step S312), the processing circuit 34 changes the setting of the XY coordinates (Step S313), and the process proceeds to Step S305 again. On the other hand, when all the pixels have been processed (Yes in step S312), the processing circuit 34 determines that the generation of the combined 2D image is completed, and ends the processing.

As described above, according to the third embodiment, the detection function 342 detects the position and type of the lesion in the plurality of slices included in the three-dimensional medical data. Further, the image processing function 343 performs image processing according to the type of lesion on the pixel corresponding to the position of the lesion detected by the detection function 342 among the pixels in the plurality of slices. In addition, the image processing function 343 determines the weight for each pixel in the plurality of slices after the image processing based on the position and type of the lesion, and synthesizes the plurality of slices based on the determined weight to obtain a synthesized 2D image. To generate. Therefore, the medical image processing apparatus 30 according to the third embodiment can improve the visibility of the lesion in the synthetic 2D image obtained by tomosynthesis imaging.

Further, according to the third embodiment, when the structural disorder is detected as a lesion, the image processing function 343 gives the contrast to the pixel corresponding to the position of the structural disorder among the pixels in the plurality of slices. Performs image processing to emphasize. As a result, the image processing function 343 can represent a disorder of construction with high contrast even in a synthetic 2D image based on a plurality of slices. Therefore, the medical image processing apparatus 30 can improve the visibility of the disorder of the construction in the synthetic 2D image.

(Fourth Embodiment)
Now, the first to third embodiments have been described so far, but the embodiments may be implemented in various different forms other than the above-described embodiments.

For example, the medical image processing apparatus 30 may generate a plurality of synthetic 2D images from one piece of 3D medical data. For example, the detection function 342 detects the lesion L3 in the slice B120 and the lesion L4 and the lesion L5 in the slice B130, as illustrated in FIGS. 9A and 9B. That is, the detection function 342 detects a plurality of lesions (lesion L3 and lesion L4) at positions overlapping in the slice direction. Here, the image processing function 343 generates a composite 2D image for each of a plurality of lesions.

For example, the image processing function 343 generates a combined 2D image in which the visibility of the lesion L3 is improved and a combined 2D image in which the visibility of the lesion L4 is improved. For example, the image processing function 343 first determines the weight for each pixel in a plurality of slices so that the pixel at the position of the lesion L3 is weighted more than the pixel at the position of the lesion L4. To do. Then, the image processing function 343 generates the two-dimensional image data C401 by combining a plurality of slices based on the determined weight. Here, the two-dimensional image data C401 preferentially represents the lesion L3. That is, in the two-dimensional image data C401, the visibility of the lesion L3 is improved.

Further, the image processing function 343 determines the weight for each pixel in the plurality of slices so that the pixel at the position of the lesion L4 is weighted more than the pixel at the position of the lesion L3. Then, the image processing function 343 generates the two-dimensional image data C402 by combining a plurality of slices based on the determined weight. Here, the two-dimensional image data C402 preferentially represents the lesion L4. That is, in the two-dimensional image data C402, the visibility of the lesion L4 is improved.

Then, the display control function 344 displays the plurality of generated two-dimensional image data. For example, the display control function 344 displays the two-dimensional image data C401 and the two-dimensional image data C402 side by side on the display 32. Further, for example, the display control function 344 switches between the two-dimensional image data C401 and the two-dimensional image data C402 and displays the two-dimensional image data C401 according to the input operation by the user.

Moreover, in the above-described embodiment, the case where the processing circuit 34 in the medical image processing apparatus 30 generates a composite 2D image has been described. However, the embodiment is not limited to this. For example, the processing circuit 114 in the X-ray diagnostic apparatus 10 may generate a synthetic 2D image.

For example, as shown in FIG. 20, the processing circuit 114 further includes a detection function 114c and an image processing function 114d in addition to the control function 114a and the display control function 114b. Here, the detection function 114c is a function corresponding to the detection function 342. The image processing function 114d is a function corresponding to the image processing function 343. 20. FIG. 20 is a block diagram showing an example of the processing circuit 114 according to the fourth embodiment.

In the case shown in FIG. 20, first, the control function 114a collects three-dimensional medical data by executing tomosynthesis imaging on the breast P of the subject. Next, the detection function 114c detects the position of the lesion of the breast P in the plurality of slices included in the three-dimensional medical data. Then, the image processing function 114d generates a combined 2D image by combining a plurality of slices based on the detected lesion position. For example, the image processing function 114d determines a weight for each pixel in a plurality of slices based on the position of the lesion, and synthesizes the plurality of slices based on the determined weight to generate a synthesized 2D image.

In addition, the detection function 114c may further detect the type of lesion in a plurality of slices. In this case, the image processing function 114d determines the weight for each pixel in a plurality of slices, for example, by weighting the position of the lesion detected by the detection function 114c according to the type of lesion. Then, the image processing function 114d generates a combined 2D image by combining a plurality of slices based on the determined weight.

Alternatively, the image processing function 114d may use the distribution of weights in the slice direction of the three-dimensional medical data to determine the weight for each pixel in a plurality of slices. Specifically, the memory 112 stores the distribution of weights in the slice direction of the three-dimensional medical data. Further, the image processing function 114d determines a weight for each pixel in a plurality of slices by applying a distribution of weights according to the type of lesion to the position of the lesion detected by the detection function 114c. Then, the image processing function 114d generates a combined 2D image by combining a plurality of slices based on the determined weight.

Further, the image processing function 114d may perform image processing according to the type of lesion on the pixel corresponding to the position of the lesion detected by the detection function 114c among the pixels in the plurality of slices. In this case, the image processing function 114d determines the weight for each pixel in the plurality of slices after the image processing based on the position and the type of the lesion, and synthesizes the plurality of slices based on the determined weight, thereby performing the synthesis 2D. Generate an image.

Each component of each device according to the first to fourth embodiments is functionally conceptual and does not necessarily have to be physically configured as illustrated. That is, the specific form of distribution/integration of each device is not limited to that shown in the figure, and all or part of the device may be functionally or physically distributed/arranged in arbitrary units according to various loads and usage conditions. It can be integrated and configured. Further, each processing function performed by each device may be realized in whole or in part by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by a wired logic.

The medical image processing methods described in the first to fourth embodiments can be realized by executing a prepared medical image processing program on a computer such as a personal computer or a workstation. This medical image processing program can be distributed via a network such as the Internet. Further, this medical image processing program is recorded in a computer-readable non-transitory recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, an MO, a DVD, etc., and is read from the recording medium by the computer. It can also be run by.

According to at least one embodiment described above, the visibility of a lesion can be improved in a synthetic 2D image obtained by tomosynthesis imaging.

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 modifications thereof are included in the invention described in the claims and equivalents thereof, as well as included in the scope and spirit of the invention.

1 Medical Image Processing System 10 X-ray Diagnostic Device 112 Memory 114 Processing Circuit 114a Control Function 114b Display Control Function 114c Detection Function 114d Image Processing Function 30 Medical Image Processing Device 33 Memory 34 Processing Circuit 341 Control Function 342 Detection Function 343 Image Processing Function 344 Display control function

Claims (15)

A detection unit that detects the position of the breast lesion in a plurality of tomographic images included in the three-dimensional medical data obtained by tomosynthesis imaging of the breast of the subject;
An image processing unit that generates two-dimensional image data by synthesizing the plurality of tomographic images based on the detected position of the lesion. The image processing unit determines a weight for each pixel in the plurality of tomographic images based on the position of the lesion detected by the detection unit, and synthesizes the plurality of tomographic images based on the weight. The medical image processing apparatus according to claim 1, wherein the two-dimensional image data is generated. The detection unit further detects the type of the lesion in the plurality of tomographic images,
The medical image processing apparatus according to claim 2, wherein the image processing unit further determines the weight by further using the type of the lesion. The image processing unit determines a weight for each pixel in the plurality of tomographic images by assigning a weight corresponding to a type of the lesion to a pixel at the lesion position detected by the detection unit. Item 3. The medical image processing apparatus according to Item 3. The image processing unit, when a disordered structure is detected as the lesion, gives a pixel at a position of the disordered structure a weight greater than a weight given to a pixel at a position of the lesion of another type, The medical image processing apparatus according to claim 4. When the lesion is detected over a plurality of positions overlapping in the slice direction of the three-dimensional medical data, the image processing unit determines that the pixel weight of the position having the smallest pixel value among the plurality of positions is The medical image processing apparatus according to claim 4, wherein the weight for each pixel in the plurality of tomographic images is determined so as to be larger than the weight of the pixel at the position. A storage unit that stores a distribution of weights in the slice direction of the three-dimensional medical data for each type of the lesion;
The detection unit further detects the type of the lesion in the plurality of tomographic images,
The image processing unit determines the weight for each pixel in the plurality of tomographic images by applying the distribution according to the type of the lesion to the position of the lesion detected by the detection unit, and the weight The medical image processing apparatus according to claim 1, wherein the two-dimensional image data is generated by synthesizing the plurality of tomographic images based on the. When the detection unit detects a plurality of lesions in a position overlapping in the slice direction, the image processing unit, the distribution according to the type of each of the plurality of lesions, depending on the position of each of the plurality of lesions. The medical image processing apparatus according to claim 7, wherein a composite distribution is calculated by combining, and a weight for each pixel in the plurality of tomographic images is determined based on the composite distribution. The medical image processing apparatus according to claim 7, wherein the storage unit stores the distribution set so that the kurtosis increases as the size of the lesion decreases. The detection unit further detects the type of the lesion in the plurality of tomographic images,
The image processing unit performs image processing according to the type of the lesion on the pixel corresponding to the position of the lesion detected by the detection unit among the pixels in the plurality of tomographic images, and after the image processing. Determining the weight for each pixel in the plurality of tomographic images based on the position and type of the lesion, and synthesizing the plurality of tomographic images based on the weight to generate the two-dimensional image data. Item 1. The medical image processing apparatus according to Item 1. The image processing unit performs image processing for emphasizing the contrast on a pixel corresponding to the position of the structural disorder among the pixels in the plurality of tomographic images when the structural disorder is detected as the lesion. The medical image processing apparatus according to claim 10. The image processing unit executes weighted averaging processing based on the determined weight when the weighted pixel is present in the slice direction, and is minimum when the weighted pixel is not present in the slice direction. The medical image processing apparatus according to claim 2, wherein the two-dimensional image data is generated by executing a value projection process. Further comprising a display control unit for displaying the two-dimensional image data,
The image processing unit generates the two-dimensional image data for each of the plurality of lesions when the plurality of lesions are detected at positions where the detection unit overlaps in the slice direction,
The medical image processing apparatus according to claim 1, wherein the display control unit displays the plurality of generated two-dimensional image data. A control unit for collecting three-dimensional medical data by executing tomosynthesis imaging on the breast of the subject;
A detection unit for detecting the position of the breast lesion in a plurality of tomographic images included in the three-dimensional medical data;
An image processing unit that generates two-dimensional image data by synthesizing the plurality of tomographic images based on the detected position of the lesion. The position of the breast lesion is detected in a plurality of tomographic images included in the three-dimensional medical data obtained by tomosynthesis imaging of the breast of the subject,
A medical image processing program that causes a computer to execute each process of generating two-dimensional image data by synthesizing the plurality of tomographic images based on the detected position of the lesion.

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