WO2009131109A1 - 医用画像処理装置、医用画像処理方法、プログラム - Google Patents
医用画像処理装置、医用画像処理方法、プログラム Download PDFInfo
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Definitions
- the present invention relates to a medical image processing apparatus that processes and displays medical image information acquired by a medical imaging apparatus such as an X-ray CT apparatus. Specifically, the present invention relates to a medical image processing apparatus that displays medical image information based on two or more different energy intensities.
- Medical images taken with an X-ray CT system do not have a unique CT value even if they are the same type of living tissue due to the effects of photon noise, system noise, beam hardening effects, etc. Exhibits a normal distribution centered on representative values such as average CT values.
- the X-ray absorption rate in each living tissue when irradiating the subject with X-rays depends on the energy intensity of the irradiated X-rays. Therefore, even if different types of biological tissues show similar X-ray absorption rates in X-ray irradiation with a certain energy intensity, they show different X-ray absorption rates in X-ray irradiation with another energy intensity.
- a multi-energy X-ray CT system (MECT: Multi-Energy Computed Tomography) rotates one or more X-ray sources and one or more X-ray detectors arranged opposite to the X-ray source, The subject placed between the X-ray detector and the X-ray detector is photographed by irradiating the source with X-rays.
- the multi-energy X-ray CT apparatus is a medical image capturing apparatus that detects transmitted X-rays of a subject having two or more different energy intensities using an X-ray detector and obtains transmitted X-ray information and reconstructed image information.
- the device that measures bone density uses the DEXA (Dual Energy X-ray Absorptionmergy) method to irradiate two different energy intensities and obtain the difference between them in order to obtain the bone density. Also in X-ray CT devices, there is a method to identify bone and contrast blood vessels, contrast blood vessels and calcification in blood vessels, etc. using subject transmitted X-ray information of two or more different energy intensities taken by MECT. (For example, see [Patent Document 1] and [Patent Document 2].)
- the image information obtained by multi-energy imaging create a map with the X axis as the CT value at high energy and the Y axis as the CT value at low energy, and refer to the map for the type of biological tissue (For example, see [Non-Patent Document 1].).
- the bone distribution area and the contrast medium distribution area on the map are separated by a straight line.
- the CT value of the reconstructed image information obtained by multi-energy imaging is projected onto the map, and the biological tissue in the bone region is identified as “bone”, and the biological tissue in the contrast agent region is identified as “contrast agent”.
- the identified biological tissue is displayed by being colored according to the biological tissue and superimposed on the original image.
- a first invention for achieving the above-described object is a medical image processing apparatus that displays on a display device medical image information based on two or more different energy intensities obtained by irradiating a subject with X-rays.
- a statistical information acquisition unit that acquires statistical information corresponding to the type of biological tissue of the subject for each energy intensity, and an identification map for identifying the type of biological tissue based on the statistical information
- An identification map creating means, a tissue identifying means for identifying the type of biological tissue in the obtained medical image information based on the identification map, and an identification result by the tissue identifying means is displayed on the display device as the medical image information.
- a medical image processing apparatus displays on a display device medical image information based on two or more different energy intensities obtained by irradiating a subject with X-rays.
- the medical image processing apparatus acquires statistics information corresponding to the type of biological tissue of a subject for each energy intensity, creates an identification map, and types of biological tissue in the obtained medical image information Are identified based on the identification map and displayed on the display device.
- the medical image information is reconstructed image information indicating transmitted X-ray information and tomographic images of a subject imaged by a medical image imaging apparatus such as an X-ray CT apparatus.
- the statistic information is an average CT value set as transmission X-ray information for a predetermined living tissue and a standard deviation of CT values.
- the statistics information is set for each combination of the type of biological tissue and the energy intensity to be irradiated.
- the types of biological tissues include blood vessel regions, bone regions, fat regions, and the like.
- the medical image processing apparatus identifies the type of biological tissue in the medical image information with high accuracy by using an identification map created based on statistical information corresponding to the type of biological tissue of the subject. be able to.
- the medical image processing apparatus creates an identification probability map indicating the degree of identification of the living tissue based on the statistical information and the identification map, and the tissue identification unit further determines the identification probability value of the biological tissue based on the identification probability map. To obtain the degree of identification of the living tissue.
- an identification map may be created by selecting the type of biological tissue having the highest existence probability value for each region corresponding to a combination of statistical information of two or more different energy intensities.
- an identifier for identifying the type of the selected living tissue is preferably set for each region.
- the identifier may be any of a flag for identifying the type, a numerical value, a symbol, and the like.
- an identification probability map may be created by setting an identification probability value based on the existence probability value of the living tissue and the distance from the type boundary obtained from the identification map to the region.
- the identification probability value for determining the identification degree of the living tissue is related to the existence probability values of a plurality of types in consideration of the distance from the boundary of the types, and is calculated with high accuracy.
- the tissue identification means may acquire medical image information, determine an identifier set for each region based on the identification map, and identify the type of biological tissue for each region.
- the tissue identification means acquires medical image information, identifies the type of biological tissue for each region based on the identification map, and further obtains the identification probability value of the biological tissue for each region based on the identification probability map. Also good.
- the type of biological tissue identified by the tissue identification means may be displayed by setting a different color for each type of biological tissue.
- the color of the biological tissue identified by the tissue identification means is set to a different color for each type of biological tissue, and the gradation corresponding to the identification probability value of the biological tissue for each region obtained by the tissue identification means is set. It may be set and displayed.
- a second invention is a medical image processing method in a medical image processing apparatus for causing a display device to display medical image information based on two or more different energy intensities obtained by irradiating a subject with X-rays.
- a statistical information acquisition step for acquiring statistical information corresponding to the type of biological tissue for each energy intensity, and an identification map for creating an identification map for identifying the type of biological tissue based on the statistical information
- a creation step for identifying the type of biological tissue in the obtained medical image information based on the identification map, and a display step for displaying the identification result of the tissue identification step on the display device as the medical image information
- a medical image processing method characterized by comprising:
- the second invention is an invention relating to a medical image processing method in the medical image processing apparatus of the first invention.
- the computer uses the statistic information acquisition means for acquiring the statistic information corresponding to the type of the biological tissue of the subject for each energy intensity, and the type of the biological tissue based on the statistic information.
- An identification map creating means for creating an identification map for identifying; a tissue identifying means for identifying the type of biological tissue in the obtained medical image information based on the identification map; and an identification result by the tissue identifying means
- a display unit configured to display the medical image information on the display device as a medical image processing apparatus.
- the third invention relates to a program that causes a computer to function as the medical image processing apparatus of the first invention.
- the present invention it is possible to provide a medical image processing apparatus that enables highly accurate identification of the type of biological tissue and display of the degree of identification of medical image information in multi-energy imaging.
- Hardware configuration diagram of the medical image processing apparatus 1 Hardware configuration diagram of medical image photographing device 23 Flow chart showing the overall operation of the medical image processing apparatus 1 Diagram showing organization information input screen 301 Diagram showing organization information input screen 302 Diagram explaining the correspondence between probability distribution data and statistics information Diagram explaining how to create probability distribution data when there is a range of CT values Diagram showing probability distribution data of organization Diagram showing identification map 81 Flow chart showing the creation process of the identification probability map 97 Diagram explaining boundary calculation processing Diagram showing identification boundary 85 Diagram showing distance map 87 Diagram showing maximum probability map 89 The figure which shows the position of attention coordinate 93 Diagram showing identification probability map 97 Diagram showing the projection of CT values on the identification map 81 The figure which shows the display screen 303 of a biological tissue Diagram showing recognition result display Diagram showing recognition probability setting screen The figure which shows the display screen of recognition probability and recognition result
- FIG. 1 is a hardware configuration diagram of the medical image processing apparatus 1.
- the medical image processing apparatus 1 includes a CPU 3, a main memory 5, a storage device 7, a display memory 9, a display device 11, a mouse 15, a keyboard 17, and a network adapter 19 connected to the controller 13 connected by a system bus 21.
- the medical image processing apparatus 1 is connected to the image database 25 via the network 27.
- the medical image processing apparatus 1 is connected to the medical image photographing apparatus 23 so that data can be transmitted and received.
- the medical image photographing device 23 may be connected to the medical image processing device 1 via the network 27.
- the CPU3 is a device that controls the operation of each component.
- the CPU 3 loads the program stored in the storage device 7 and data necessary for program execution into the main memory 5 and executes the program.
- the storage device 7 is a device that acquires and stores medical image information captured by the medical image capturing device 23 via a network 27 such as a LAN (local area network).
- the storage device 7 stores a program executed by the CPU 3 and data necessary for program execution.
- the main memory 5 stores programs executed by the CPU 3 and the progress of arithmetic processing.
- the mouse 15 and the keyboard 17 are operation devices for an operator to give an operation instruction to the medical image processing apparatus 1.
- the display memory 9 stores display data to be displayed on the display device 11 such as a liquid crystal display or a CRT.
- the controller 13 detects the state of the mouse 15, detects the position of the mouse pointer on the display device 11, and outputs a detection signal to the CPU 3.
- the network adapter 19 is for connecting the medical image processing apparatus 1 to a network 27 such as a LAN, a telephone line, or the Internet.
- the medical image photographing device 23 is a device for photographing medical image information such as a tomographic image of a subject.
- the medical imaging apparatus 23 is, for example, an X-ray CT apparatus, an X-ray fluoroscopic imaging apparatus, an MRI apparatus, or an ultrasonic imaging apparatus.
- the image database 25 is a database system that stores medical image information captured by the medical image capturing device 23.
- the image database 25 may store medical image information captured by a plurality of other medical image capturing apparatuses connected to the network 27.
- FIG. 2 is a hardware configuration diagram of the medical image photographing apparatus 23.
- a configuration of a multi-energy X-ray CT apparatus will be described as the medical image photographing apparatus 23.
- the medical imaging apparatus 23 includes a gantry 29, an X-ray source 31 and an X-ray detector 39 mounted on the gantry 29, a table 35 on which the subject 33 is placed, an X-ray controller 41 for controlling the irradiation X-ray 37, and a gantry 29.
- the reconstructed image information reconstructed by the reconstruction computing unit 49 is provided to the medical image processing apparatus 1.
- the reconstructed image information may be stored in the image database 25 via the storage device 7 of the medical image processing apparatus 1 or the network 25.
- the X-ray source 31 irradiates X-rays toward an X-ray detector 39 placed opposite to the subject 33.
- the X-ray detector 39 detects X-rays that have passed through the subject 33.
- the X-ray source 31 and the X-ray detector 39 rotate around the subject 33 during one scan.
- the multi-energy X-ray CT apparatus In the multi-energy X-ray CT apparatus, transmission X-ray information is obtained when the same subject is irradiated with two or more X-rays having different energy intensities.
- the multi-energy X-ray CT apparatus includes two or more X-ray sources 31 and an X-ray detector 39 in the gantry 29, and performs multi-energy imaging by applying different tube voltages to the respective X-ray sources 31. . Details of multi-energy imaging will be described later.
- the X-ray controller 41 controls the X-ray source 31.
- the X-ray controller 41 supplies the X-ray source 31 with a power signal for controlling the tube voltage, an X-ray generation timing signal, and the like.
- the gantry controller 43 controls the rotational speed and position of the X-ray source 31 and the X-ray detector 39 arranged in the gantry 29.
- the table controller 45 controls the moving speed and position of the table 35 on which the subject 33 is placed.
- the data collection circuit 47 collects the transmitted X-ray information detected by the X-ray detector 39, converts the analog signal into a digital signal, and provides it to the reconstruction calculator 49.
- the reconstruction calculator 49 performs an image reconstruction process on the transmission X-ray information sent from the data acquisition circuit 47, and creates a tomographic image (reconstructed image) of the subject.
- the reconstruction calculator 49 sends the reconstructed image to the medical image processing apparatus 1.
- FIG. 3 is a flowchart showing the overall operation of the medical image processing apparatus 1.
- the multi-energy imaging information will be described as imaging information when the tube voltage is 140 kV at high energy and imaging information when the tube voltage is 80 kV at low energy.
- the value of the tube voltage is not limited to this.
- the CPU 3 of the medical image processing apparatus 1 reads the statistical information corresponding to the living tissue, that is, the average CT value and the CT value, by inputting from the pointing device such as the mouse 15 or the keyboard 17 by the operator or reading from the external input device. A value such as a standard deviation indicating variation in the number is acquired (step 1001).
- FIG. 4 is a diagram showing a tissue information input screen 301 for an operator to input information related to a living tissue.
- the organization information input screen 301 is displayed on the display device 11.
- an input frame for a tissue name 51-1 of a living tissue, a display color 53-1, a tube voltage 55-1 at low energy, and a tube voltage 61-1 at high energy is arranged on the tissue information input screen 301.
- the statistical information of the living tissue input as the tissue name 51-1 that is, the average CT value 57-1 for each tube voltage, the average CT value 63-1, the standard deviation 59-1, and the standard deviation of the CT value 65-1 input frame is placed.
- the display color 53-1 at least one of a color and a pattern for displaying an area corresponding to the tissue name 51-1 of the medical image information captured by the multi-energy X-ray CT apparatus is set.
- the operator inputs a tissue name 51-1, a display color 53-1 when displaying for each living tissue, statistical information of the living tissue, and the like on the tissue information input screen 301.
- the “OK” button 67 is pressed, the input content is determined.
- the “Cancel” button 69 is pressed, the input content is cancelled.
- the CPU 3 stores the determined input content in the storage device 7 or the like.
- FIG. 5 is a diagram showing a tissue information input screen 302 when inputting tissue information having a wide average CT value depending on the concentration, such as a contrast agent.
- tissue information input screen 302 On the tissue information input screen 302, an input frame for a tissue name 51-2 of a living tissue, a display color 53-2, a tube voltage 55-2 at low energy, and a tube voltage 61-2 at high energy is arranged.
- the average CT value 57-2 at the tube voltage 55-2 at low energy and the standard deviation 59-2 of the CT value, and the average CT value 63-2 at the tube voltage 61-2 at high energy and the standard deviation 65- Input boxes for the upper and lower limit values of 2 are arranged.
- the tissue information input screen shown in FIGS. 4 and 5 is an example of a screen configuration for inputting information corresponding to a living tissue, and is not limited to this.
- the tissue information input screen may be a screen for inputting information such as the name of the living tissue to be identified, the average CT value, and the standard deviation of the CT value.
- the medical image processing apparatus 1 acquires statistical information such as a display color and an average CT value at the time of X-ray irradiation with a predetermined energy intensity and a standard deviation of CT values for each living tissue.
- the display color setting and the statistic information for each living tissue may be acquired in advance as information registered in the medical image processing apparatus 1 or may be acquired via the network 27.
- the medical image processing apparatus 1 creates an identification map based on the tissue information for each living tissue acquired in step 1001 (step 1002).
- the identification map is used when identifying the type of biological tissue from medical image information (transmission X-ray information or reconstructed image information) acquired from the medical image capturing apparatus 23.
- FIG. 6 is a diagram for explaining the correspondence between probability distribution data and statistic information.
- Probability distribution data is calculated based on the statistic information of the predetermined biological tissue acquired in step 1001.
- the CT value at the tube voltage of 80 kV is taken on the X axis
- the CT value at the tube voltage of 140 kV is taken on the Y axis.
- the probability distribution data is calculated by approximation with a two-dimensional Gaussian distribution as shown in the equation (1).
- i, j x coordinate and y coordinate on probability distribution data
- ⁇ 80 standard deviation of CT value at tube voltage 80kV
- ⁇ 140 standard deviation of CT value at tube voltage 140kV
- m 80 at tube voltage 80kV
- Average CT value m 140 Average CT value at a tube voltage of 140 kV
- the value calculated by Equation (1) is imaged to obtain probability distribution data shown in FIG.
- the average CT value (m 80 ) at a tube voltage of 80 kV and the coordinate value g (i, j) at the average CT value (m 140 ) at a tube voltage of 140 kV are the highest.
- the value of (i, j) decreases gradually.
- the coordinate value g (i, j) corresponds to the existence probability value.
- FIG. 6 the distribution situation is shown by connecting portions having the same existence probability value with a line.
- Fig. 6 shows probability distribution data when one average CT value is determined as in the organization information input screen 301 (Fig. 4).
- FIG. 7 is a diagram for explaining a method of creating probability distribution data when there is a range in CT values.
- the upper limit value and the lower limit value of the average CT value input on the organization information input screen 302 (FIG. 5) are taken on the drawing, and the average CT value (upper limit) 77-1 and the average CT value (lower limit) 77, respectively. -2.
- the average CT value (upper limit) 77-1 and average CT value (lower limit) 77-2 are connected by a straight line, and a predetermined number of interpolation points on this line are average CT value (interpolation) 77-3, average CT value (interpolation) ) Set as 77-4, average CT value (interpolation) 77-5.
- Probability distribution is calculated by the above equation (1), and data indicating the maximum probability value for each coordinate is selected to create probability distribution data.
- the obtained probability distribution data has a rod shape as shown in FIG. 8 (a), for example.
- FIG. 8 is a diagram showing the probability distribution data of the organization.
- 8A shows probability distribution data 79-1 for organization A
- FIG. 8B shows probability distribution data 79-2 for organization B
- FIG. 8C shows probability distribution data 79-3 for organization C.
- the organization A, the organization B, and the organization C represent different types of biological tissues.
- tissue B in which probability distribution data show a rod-shaped shape are structures
- the distribution situation is shown by connecting portions having the same existence probability value with a line.
- the medical image processing apparatus 1 calculates the probability distribution of each tissue for all the common coordinates of the probability distribution data 79-1, the probability distribution data 79-2 of the tissue B, and the probability distribution data 79-3 of the tissue C.
- the data is compared, and an identifier (flag) corresponding to the tissue having the highest probability (existence probability value) is set at each coordinate.
- Identifiers (flags) are set for all coordinates, and an identification map 81 is set.
- the identifier may be any flag, numerical value, character string, etc. for identifying the type of organization.
- FIG. 9 is a diagram showing an identification map 81.
- the identification map 81 for the organization exhibiting a combination of a predetermined CT value (tube voltage 80 kV) and CT value (tube voltage 140 kV)
- the corresponding tissue type (organization A or organization B or organization C) Can be identified.
- the medical image processing device 1 creates an identification probability map based on the statistic information (average CT value and standard deviation of CT values) obtained in step 1001 and the identification map 81 created in step 1002 (Step 1003).
- the identification probability map is a map that is referred to in order to obtain the degree of identification for a tissue exhibiting a combination of a predetermined CT value (tube voltage 80 kV) and CT value (tube voltage 140 kV). Details of the identification probability map creation process will be described with reference to FIGS.
- FIG. 10 is a flowchart showing a process for creating the identification probability map 97 shown in FIG.
- the medical image processing apparatus 1 calculates the identification boundary 85 of each tissue region based on the identification map 81 created in step 1002 (step 2001).
- FIG. 11 is a diagram for explaining the boundary calculation process.
- FIG. 11 is an enlarged view of the coordinates of a part of the area in the identification map 81 (FIG. 9). Paying attention to the predetermined coordinates, let it be the center coordinates (i, j) 83-1.
- the center coordinate (i, j) 83-1 Four coordinates near the top, bottom, left, and right of the center coordinate (i, j) 83-1 (near coordinates (i, j-1) 83-2, near coordinates left (i-1, j) 83-3, near coordinates
- FIG. 12 is a diagram showing the identification boundary 85. For example, a flag indicating a boundary is set in the coordinates corresponding to the identification boundary 85 in FIG.
- the medical image processing apparatus 1 calculates the distance from the identification boundary 85 in the same coordinate system as the probability distribution data (for example, FIGS. 6 and 8) based on the identification boundary 85 obtained in step 2001, and the distance map 87 is created (step 2002).
- the distance from the identification boundary 85 to the coordinates is obtained using, for example, Euclidean distance conversion.
- the distance from the identification boundary 85 is set for all coordinates, and a distance map 87 is created.
- FIG. 13 is a diagram showing a distance map 87. As shown in FIG. A distance “0” is set to the coordinates on the identification boundary 85. In FIG. 13, portions having the same distance from the identification boundary 85 are connected by a line.
- the medical image processing apparatus 1 creates a maximum probability map 89 with reference to the probability distribution data 79 (FIG. 8) (step 2003). That is, the medical image processing apparatus 1 compares the probability distribution data (tissue probability distribution data 79-1, tissue B probability distribution data 79-2, tissue C probability distribution data 79-3) for each living tissue. The existence probability value of the tissue showing the highest existence probability value at the same coordinates is selected as the maximum probability value of the coordinates. Regardless of the type of biological tissue, a maximum probability value is set for each coordinate, and a maximum probability map 89 is created.
- FIG. 14 is a diagram showing the maximum probability map 89.
- the maximum probability map 89 an existence probability value is set for each coordinate, and the type of living tissue cannot be identified.
- portions having the same existence probability value are connected by lines for each type of biological tissue.
- the medical image processing apparatus 1 creates an identification probability map 97 using the distance map 87 created in step 2002 and the maximum probability map 89 created in step 2003 (step 2004).
- a value indicating the identification degree of each coordinate is set. It is assumed that a value from “0” to “255” is set as the identification probability value r in each coordinate of the identification probability map 97 as in the color gradation.
- the value of the same coordinate is acquired from the distance map 87 and the maximum probability map 89.
- the distance value d (i, j) is acquired from the distance map 87, and the existence probability value p (i, j) is acquired from the maximum probability map 89.
- the distance value d (i, j) is a distance from the boundary, and the existence probability value p (i, j) is a probability value when the center value (maximum probability) of the living tissue is “1.0”.
- FIG. 15 is a diagram illustrating the position of the attention coordinate 93.
- the distance between the center value 91 and the target coordinate 93 is l 1
- the distance from the boundary 95 to the target coordinate 93 is l 2 .
- the center value 91 is obtained from the maximum probability map 89
- the boundary 95 is obtained from the distance map 87.
- the identification probability value r (i, j) of the attention coordinate 93 is calculated by the following equation (4).
- an identification probability value r is calculated and set, and an identification probability map 97 is obtained.
- FIG. 16 is a diagram showing an identification probability map 97.
- the identification probability value r in the identification probability map 97 has a maximum value “255” as the center value, and the value decreases as approaching the boundary indicating the identification probability value “0”.
- identification probability values (values of 0 to 255) are set for the respective coordinates, and the type of biological tissue cannot be identified.
- portions having the same identification probability value are connected by lines for each type of biological tissue.
- the method for creating the identification probability map 97 is not limited to the method described above. For example, it may be calculated based on a probability distribution based on the maximum probability map 89.
- the distance l 1 from the center value 91 of the attention coordinate 93 may be expressed by the following equation (5).
- the medical image processing apparatus 1 acquires display color settings and statistic information for each living tissue in advance before acquiring multi-energy imaging information, and refers to it when identifying the type of living tissue.
- An identification map 81 and an identification probability map 97 for determining the degree of identification are created (step 1001 to step 1003).
- the CPU 3 of the medical image processing apparatus 1 acquires multi-energy imaging information captured by the medical image capturing apparatus 23 from the storage device 7 or the image database 25 and reads it into the main memory 5 (step 1004).
- the multi-energy imaging information is transmitted X-ray information or reconstructed image information.
- the medical image capturing apparatus 23 will be described as acquiring, as multi-energy imaging information, imaging information when the tube voltage is 140 kV at high energy, and imaging information when the tube voltage is 80 kV at low energy.
- the multi-energy imaging method is not limited as long as the subject transmission X-ray information having two or more different energy intensities can be obtained.
- the medical image processing apparatus 1 identifies the type of biological tissue included in the multi-energy imaging information based on the multi-energy imaging information acquired in step 1004 and the identification map 81 created in step 1002 (step 1005).
- the respective CT values for the pixel of interest are acquired from the image information of the tube voltage 80 kV and the image information of the tube voltage 140 kV obtained as multi-energy imaging information.
- the CT value of the image information with the tube voltage of 80 kV is CT 80
- the CT value of the image information with the tube voltage of 140 kV is CT 140 .
- FIG. 17 is a diagram showing a projection of the CT value of the target pixel onto the identification map 81.
- the target pixel is projected onto the identification map 81 based on the CT value (CT 80 , CT 140 ), and an identifier (flag) set at the coordinates P of the projection position is obtained.
- the type of living tissue can be identified by the identifier.
- the coordinate P refers to the identifier and is identified as “tissue A”.
- the medical image processing apparatus 1 performs identification processing of the type of the biological tissue for all the pixels of the tube voltage 80 kV image information obtained as multi-energy imaging information and the tube voltage 140 kV image information.
- the medical image processing apparatus 1 displays the result identified in step 1005 on the display device 11.
- the identification result of the pixel of the image information indicates the display color 53 set for each living tissue at the time of inputting the tissue information (step 1001) and the type of living tissue acquired by the identification processing of the type of living tissue (step 1005).
- the identifier is displayed in association with it.
- the image for superimposing display colors may be either an image with a tube voltage of 80 kV or an image with a tube voltage of 140 kV, or may be superimposed on an image created by processing both images.
- FIG. 18 is a diagram showing a display screen 303 for living tissue.
- FIG. 18 shows a tomographic image of the subject 33, and is a display screen 303 in which the living tissue is identified by the types of tissue A, tissue B, and tissue C based on the identification map 81 and displayed in different display colors.
- the biological tissue shown in FIG. 18 is different from the biological tissue according to the description up to FIG. In FIG. 18, the difference in display color is expressed by the difference in pattern.
- the medical image processing apparatus 1 of the present invention identifies the tomographic image of the subject 33 obtained as multi-energy imaging information for each type of biological tissue, and overlays different colors for each type. Since it displays, a display screen with high visibility can be obtained. According to the present embodiment, the visibility of the display image is enhanced, which has the effect of speeding up and improving the accuracy of medical diagnosis by a medical worker and reducing diagnostic errors.
- the medical image processing apparatus 1 of the present invention can be used even when the imaging conditions and setting conditions of the medical imaging apparatus 23 and the like are changed, and the CT value and the distribution of CT values of the living tissue to be imaged are changed. By changing the information, the identification performance of the living tissue can be maintained, and the degradation of the identification accuracy can be prevented.
- the medical image processing apparatus 1 may further display an identification result reflecting the degree of identification based on the identification probability map 97 (FIG. 16) (step 1006).
- the medical image processing apparatus 1 acquires each CT value for the target pixel from the image information of the tube voltage 80 kV and the image information of the tube voltage 140 kV.
- the CT value of the image information with tube voltage 80 kV is CT 80 and the CT value of the image information with tube voltage 140 kV is CT 140, which is projected onto the identification probability map 97 and the coordinates of the projection destination in the identification probability map 97
- the identification probability value (value between 0 and 255) set to is obtained.
- the medical image processing apparatus 1 acquires the image information of the tube voltage 80 kV obtained as the multi-energy imaging information and the identification probability value (value of 0 to 255) of the living tissue for all the pixels of the image information of the tube voltage 140 kV. .
- the medical image processing apparatus 1 Based on the acquired identification probability value (value of 0 to 255), the medical image processing apparatus 1 changes the lightness and saturation of the display color 53 for each biological tissue acquired in Step 1001 to change the identification level of the biological tissue. Is reflected in the display method. There are the following methods as a method of expressing the degree of identification by reflecting it in the display method.
- (a) Brightness change according to identification probability value There is a method of changing the brightness of the display color according to the acquired identification probability value (value of 0 to 255). For example, when the display color is set to “red”, the display color is “red” when the identification probability value is the maximum value “255” in the method of decreasing the brightness as the identification probability value decreases. As the identification probability value decreases, the brightness of the display color decreases and approaches black. When the identification probability value is the minimum value “0”, the display color is “black”.
- the display color is “red” when the identification probability value is the maximum value “255”. As the identification probability value decreases, the brightness of the display color increases and approaches white. When the identification probability value is the minimum value “0”, the display color is “white”.
- (b) Saturation change according to identification probability value There is a method of changing the saturation of the display color according to the acquired identification probability value (value of 0 to 255). For example, when the display color is set to “red”, the display color is “red” when the identification probability value is the maximum value “255”. As the identification probability value decreases, the saturation of the display color decreases and approaches gray. When the identification probability value is the minimum value “0”, the display color is “gray”.
- (c) Change in transparency according to the identification probability value There is a method of changing the transparency of the display color according to the acquired identification probability value (value from 0 to 255). For example, when the display color is set to “red”, the display color is “red” when the identification probability value is the maximum value “255”. The transparency increases as the identification probability value decreases, and when the identification probability value is the minimum value “0”, the transparency is completely transparent.
- the above (a) to (c) are examples of a method of expressing the degree of identification by reflecting it in the display method, and the present invention is not limited to this method. Further, the degree of identification may be expressed by combining the above display methods. In addition, the display is not limited to the combination of the identification degree and the display color as described above, and only the identification degree is expressed using the same display color for all living tissues (expressing brightness, saturation, transparency, etc.). May be.
- the medical image processing apparatus 1 displays a region having a low identification probability such as noise and a region having a high identification probability in which the tissue is normally recognized in a stepwise manner depending on the identification degree. An accurate diagnosis of a living tissue by an operator and a rapid diagnosis are enabled.
- FIG. 19 (a) is a diagram showing a screen 304 when displayed regardless of the degree of identification of the living tissue. Due to the influence of noise or the like, an area with a low identification probability value (misrecognition area 99-1) is displayed on the screen in the same expression as the normally recognized area (normal recognition area 101-1).
- the erroneous recognition area 99-1 and the normal recognition area 101-1 are illustrated so that they can be distinguished, but when displayed on an actual display device The erroneous recognition area 99-1 and the normal recognition area 101-1 are not differentiated and displayed.
- FIG. 19 (b) is a diagram showing a screen 305 when the expression method is changed according to the identification probability value (identification degree) by the method of the present embodiment.
- the screen 305 displays an area with a low identification probability value (misrecognition area 99-2) with increased transparency, and is displayed differentiating from a normally recognized area (normal recognition area 101-2).
- the erroneous recognition area 99-1 and the normal recognition area 101-1 are differentiated and displayed.
- 20 and 21 are screens on which the operator sets a range of identification probability values.
- the identification probability map 97 (FIG. 16) calculated in step 2004, a value from “0” to “255” is set as the identification probability value for each coordinate, but the operator can set the identification probability value intuitively. Therefore, here, the identification probability value “0” by the identification probability map 97 is displayed as the recognition probability “0%” and the identification probability value “255” is displayed as the recognition probability “100%” in FIGS.
- FIG. 20A shows a recognition probability setting screen 306 that allows the operator to input the recognition probability center value 103 and the recognition probability width 105.
- the operator may input the recognition probability value directly from the keyboard 17 on the recognition probability setting screen 306, or may input the recognition probability value by operating the bar with the mouse 15.
- FIG. 20B shows a recognition probability setting screen 307 that allows the operator to input the recognition probability upper limit 107 and the recognition probability lower limit 109.
- FIG. 21 (a) shows a screen 308 on which the biological tissue of the subject 33 is displayed when the recognition probability upper limit value “100%” and the recognition probability lower limit value “0%” are set on the recognition probability setting screen 307. It is. On the screen 308, all recognized tissues including the low recognition area 111-1 and the high recognition area 113-1 are displayed in the same expression (the same brightness, saturation, transparency, etc.) regardless of the recognition probability.
- FIG. 21B shows a screen 309 on which the living tissue of the subject 33 is displayed when the recognition probability upper limit value “100%” and the recognition probability lower limit value “62%” are set on the recognition probability setting screen 307. It is. On the screen 309, the low recognition area 111-2 outside the recognition probability range is increased in transparency, and the high recognition area 113-2 in the recognition probability range is displayed in the same expression or the expression corresponding to the recognition probability regardless of the recognition probability. .
- medical image information based on multi-energy imaging may be displayed by appropriately combining display method discrimination according to the type of biological tissue, display method based on the degree of identification of the biological tissue, and display method based on the recognition probability setting by the operator. .
- display method discrimination according to the type of biological tissue
- display method based on the degree of identification of the biological tissue and display method based on the recognition probability setting by the operator.
- the X-ray CT image has been described.
- the present invention can be applied to diagnosis of medical image information acquired by a medical image capturing apparatus such as an X-ray fluoroscopic apparatus, an MRI apparatus, or an ultrasonic diagnostic apparatus.
- 1 medical image processing device 3 CPU, 5 main memory, 7 storage device, 9 display memory, 11 display device, 13 controller, 15 mouse, 17 keyboard, 19 network adapter, 21 system bus, 23 medical imaging device, 25 images Database, 27 network, 29 gantry, 31 X-ray source, 33 subject, 35 table, 37 irradiated X-ray, 39 X-ray detector, 41 X-ray controller, 43 gantry controller, 45 table controller, 47 data collection Circuit, 49 Reconstruction calculator, 51-1, 51-2 Organization name, 53-1, 53-2 Display color, 55-1, 55-2, 61-1, 61-2 Tube voltage, 57-1, 57-2, 63-1, 63-2, 75, 77-1 to 77-5 Average CT value, 59-1, 59-2, 65-1, 65-2 Standard deviation, 67 “OK” button, 69 "Cancel” button, 71 CT value (tube voltage 80 kV), 73 CT value (tube voltage 140 kV), 79-1 to 73-3 organization probability distribution data, 81 identification map, 83-1 to 8
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Abstract
Description
医用画像情報は、X線CT装置等の医用画像撮影装置によって撮影された被検体の透過X線情報及び断層像を示す再構成画像情報である。
統計量情報とは、所定の生体組織について透過X線情報として設定される平均CT値と、CT値の標準偏差である。統計量情報は、生体組織の種別及び照射するエネルギー強度の組み合わせ毎に設定される。
生体組織の種別とは、血管領域、骨領域、脂肪領域等である。
最初に、図1を参照しながら、医用画像処理装置1の構成について説明する。
図1は、医用画像処理装置1のハードウェア構成図である。
医用画像処理装置1は、CPU3、主メモリ5、記憶装置7、表示メモリ9、表示装置11、コントローラ13に接続されたマウス15やキーボード17、ネットワークアダプタ19がシステムバス21によって接続されて構成される。医用画像処理装置1は、ネットワーク27を介して画像データベース25に接続される。また医用画像処理装置1は、医用画像撮影装置23とデータの送受信可能に接続される。医用画像撮影装置23は、ネットワーク27を介して医用画像処理装置1に接続されるようにしてもよい。
次に、図2を参照しながら、医用画像撮影装置23の構成について説明する。
図2は、医用画像撮影装置23のハードウェア構成図である。
医用画像撮影装置23として、マルチエネルギー型X線CT装置の構成について説明する。医用画像撮影装置23は、ガントリ29、ガントリ29に搭載されたX線源31とX線検出器39、被検体33を載せるテーブル35、照射X線37を制御するX線制御器41、ガントリ29を制御するガントリ制御器43、テーブル35を制御するテーブル制御器45、X線検出器39が検出した透過X線情報を収集するデータ収集回路47、収集データの再構成演算を行う再構成演算器49等から構成される。再構成演算器49で再構成された再構成画像情報は医用画像処理装置1に提供される。再構成画像情報は医用画像処理装置1の記憶装置7、或いはネットワーク25を介して画像データベース25に蓄積されてもよい。
ガントリ制御器43は、ガントリ29に配置されるX線源31やX線検出器39の回転速度や位置等を制御する。
データ収集回路47は、X線検出器39が検出した透過X線情報を収集し、アナログ信号からデジタル信号に変換して再構成演算器49に提供する。
次に、図3を参照しながら、マルチエネルギー撮影情報に基づいて複数の生体組織の種別を識別し、識別度合を組織ごとに可視化して表示する医用画像処理装置1の動作について説明する。
医用画像処理装置1のCPU3は、操作者によるマウス15やキーボード17等のポインティングデバイスによる入力や外部入力装置から読み込むことにより、識別する生体組織に対応する統計量情報、即ち平均CT値やCT値のばらつきを示す標準偏差等の値を取得する(ステップ1001)。
操作者は、組織情報入力画面301に、組織名51-1、生体組織毎に表示する際の表示色53-1、生体組織の統計量情報等を入力する。
「OK」ボタン67が押下されると、入力内容が決定される。「キャンセル」ボタン69が押下されると、入力内容がキャンセルされる。CPU3は決定された入力内容を記憶装置7等に記憶する。
次に、医用画像処理装置1は、ステップ1001で取得した生体組織毎の組織情報に基づいて、識別マップを作成する(ステップ1002)。識別マップは、医用画像撮影装置23から取得する医用画像情報(透過X線情報又は再構成画像情報)の中から生体組織の種別を識別する際に利用される。
図6は、確率分布データと統計量情報との対応を説明する図である。ステップ1001で取得した所定の生体組織の統計量情報に基づいて、確率分布データが算出される。図6では、X軸に管電圧80kV時のCT値、Y軸に管電圧140kV時のCT値を取る。確率分布データは、式(1)に示すように2次元ガウス分布で近似して算出される。
б80 :管電圧80kV時のCT値の標準偏差
б140 :管電圧140kV時のCT値の標準偏差
m80 :管電圧80kV時の平均CT値
m140 :管電圧140kV時の平均CT値
式(1)によって算出された値を画像化し、図6に示す確率分布データを得る。管電圧80kV時の平均CT値(m80)、管電圧140kV時の平均CT値(m140)の時の座標の値g(i,j)が最も高く、周辺になるに従って、座標の値g(i,j)はなだらかに値が小さくなる。座標の値g(i,j)は、存在確率値に相当する。図6では、存在確率値の等しい部分を線で結ぶことによって、分布状況を示している。
次に、医用画像処理装置1は、ステップ1001で取得した生体組織の統計量情報(平均CT値とCT値の標準偏差)、及びステップ1002で作成した識別マップ81に基づいて識別確率マップを作成する(ステップ1003)。識別確率マップは、所定のCT値(管電圧80kV)とCT値(管電圧140kV)との組み合わせを呈する組織について、識別度合を求めるために参照するマップである。識別確率マップ作成処理の詳細について、図10から図16を参照しながら説明する。
医用画像処理装置1は、ステップ1002で作成した識別マップ81に基づいて、各組織領域の識別境界85を算出する(ステップ2001)。
次に、医用画像処理装置1は、ステップ2001で求めた識別境界85に基づき、確率分布データ(例えば図6、図8)と同じ座標系において、識別境界85からの距離を算出し、距離マップ87を作成する(ステップ2002)。識別境界85から座標までの距離は、例えばユークリッド距離変換を用いて求める。全ての座標に対して、識別境界85からの距離が設定され、距離マップ87が作成される。
識別境界85上にある座標には、距離「0」が設定される。図13では、識別境界85上からの距離が等しい部分を線で結んでいる。
次に、医用画像処理装置1は、確率分布データ79(図8)を参照して、最大確率マップ89を作成する(ステップ2003)。即ち、医用画像処理装置1は、生体組織毎の確率分布データ(組織Aの確率分布データ79-1、組織Bの確率分布データ79-2、組織Cの確率分布データ79-3)を比較し、同じ座標において最も高い存在確率値を示す組織の存在確率値を、その座標の最大確率値として選択する。生体組織の種別に係らず、各座標について最大確率値が設定され、最大確率マップ89が作成される。
次に、医用画像処理装置1は、ステップ2002で作成した距離マップ87と、ステップ2003で作成した最大確率マップ89とを用いて、識別確率マップ97を作成する(ステップ2004)。識別確率マップ97の各座標には、各座標の識別度合を示す値が設定される。識別確率マップ97の各座標には、色の階調と同様に「0」から「255」までの値が識別確率値rとして設定されるものとする。
l1=1/p(i,j)-1 ・・・・・(2)
l2=d(i,j) ・・・・・(3)
である。
=255×d(i、j)/[{(1/p(i,j))-1}+d(i,j)] ・・・・・(4)
全ての座標について、識別確率値rが算出され設定されて、識別確率マップ97が得られる。
以上述べたように、医用画像処理装置1は、マルチエネルギー撮影情報を取得する前に、予め生体組織毎の表示色設定や統計量情報を取得し、生体組織の種別を識別する際に参照する識別マップ81及び識別度合を求めるための識別確率マップ97を作成する(ステップ1001~ステップ1003)。
次に、医用画像処理装置1のCPU3は、医用画像撮影装置23によって撮影されたマルチエネルギー撮影情報を記憶装置7あるいは画像データベース25から取得して、主メモリ5に読み込む(ステップ1004)。マルチエネルギー撮影情報は、透過X線情報又は再構成画像情報である。
次に、医用画像処理装置1は、ステップ1004で取得したマルチエネルギー撮影情報と、ステップ1002で作成した識別マップ81とに基づいて、マルチエネルギー撮影情報に含まれる生体組織の種別を識別する(ステップ1005)。
医用画像処理装置1は、ステップ1005で識別した結果を表示装置11に表示する。画像情報の画素の識別結果は、組織情報入力時(ステップ1001)に生体組織毎に設定された表示色53と、生体組織の種別の識別処理(ステップ1005)で取得した生体組織の種別を示す識別子とが対応付けられて表示される。
図18は、被検体33の断層像を示し、生体組織が識別マップ81に基づいて組織A、組織B、組織Cの種別に識別され、それぞれ異なる表示色で表示された表示画面303である。尚、図18に示す生体組織は、図17までの説明に係る生体組織とは異なるものである。また、図18では、表示色の違いを模様の違いで表現している。
以上述べたように、本発明の医用画像処理装置1は、マルチエネルギー撮影情報として得られた被検体33の断層像画像を、生体組織の種別毎に識別し、種別毎に異なる色を重ねて表示するので、視認性の高い表示画面を得ることができる。本実施の形態によれば、表示画像の視認性が高まることで、医療従事者による医療診断の迅速化・高精度化を図り診断ミスを低減させる効果がある。
医用画像処理装置1は、更に識別確率マップ97(図16)に基づいて識別度合を反映させた識別結果を表示させてもよい(ステップ1006)。
医用画像処理装置1は、管電圧80kVの画像情報と、管電圧140kVの画像情報とから、注目画素についてそれぞれのCT値を取得する。
識別度合を表示方法に反映させて表現する方法として、以下の方法がある。
取得した識別確率値(0~255の値)に応じて、表示色の明度を変化させる方法がある。例えば、表示色が「赤」に設定されている場合、識別確率値が小さくなるほど明度を下げる方法では、識別確率値が最大値「255」の時は表示色が「赤」である。識別確率値が低くなるにつれて表示色の明度が下がって黒色に近づき、識別確率値が最小値「0」の時は表示色が「黒」となる。
取得した識別確率値(0~255の値)に応じて、表示色の彩度を変化させる方法がある。例えば、表示色が「赤」に設定されている場合、識別確率値が最大値「255」の時は表示色が「赤」である。識別確率値が低くなるにつれて表示色の彩度が下がって灰色に近づき、識別確率値が最小値「0」の時は表示色が「灰色」となる。
取得した識別確率値(0~255の値)に応じて、表示色の透明度を変化させる方法がある。例えば、表示色が「赤」に設定されている場合、識別確率値が最大値「255」の時は表示色が「赤」である。識別確率値が低くなるにつれて透明度を上げ、識別確率値が最小値「0」の時は完全に透明となる。
以上述べたように、医用画像処理装置1は、ノイズ等の識別確率の低い領域と、組織が正常に認識された識別確率の高い領域を識別度合に応じて段階的に区別して表示するので、操作者による生体組織の正確な診断、及び迅速な診断を可能にする。
次に、図19から図21を参照しながら、表示装置11に表示される画面について説明する。
図19(a)は、生体組織の識別度合に関係なく表示した場合の画面304を示す図である。ノイズ等の影響により、識別確率値が低い領域(誤認識領域99-1)が、正常に認識した領域(正常認識領域101-1)と同様な表現で画面上に表示される。尚、図19(a)では、視認性を高める為に、誤認識領域99-1と正常認識領域101-1とを区別できるように図示しているが、実際の表示装置に表示された場合、誤認識領域99-1と正常認識領域101-1とは差別化して表示されない。
ステップ2004で算出された識別確率マップ97(図16)は、各座標に識別確率値として「0」から「255」の値が設定されるが、操作者が直感的に識別確率値を設定可能にするため、ここでは識別確率マップ97による識別確率値「0」を、図20及び図21では認識確率「0%」、識別確率値「255」を認識確率「100%」と表示する。
図20(b)は、操作者が認識確率上限値107と、認識確率下限値109を入力することが可能な認識確率設定画面307である。
以上述べたように、認識確率を指定して生体組織表示の表現方法の変更が可能であるので、操作者は、注目する組織を好みに合わせて強調して表示することができる。従って、操作者は生体組織を正確に診断することができるので、生体組織の誤認識による診断ミスを低減することができる。
尚、生体組織の種別による表示方法の区別、生体組織の識別度合による表示方法、操作者による認識確率設定による表示方法を適宜組み合わせて、マルチエネルギー撮影による医用画像情報を表示させるようにしてもよい。異なる観点で医用画像情報を表示させることにより、生体組織の診断の迅速化を図り、より正確な診断を行うことができる。
Claims (10)
- X線を被検体に照射して得られる2以上の異なるエネルギー強度に基づく医用画像情報を、表示装置に表示させる医用画像処理装置であって、
被検体の生体組織の種別に対応する統計量情報を、エネルギー強度毎に取得する統計量情報取得手段と、
前記統計量情報に基づいて、生体組織の種別を識別するための識別マップを作成する識別マップ作成手段と、
得られる医用画像情報中の生体組織の種別を、前記識別マップに基づいて識別する組織識別手段と、
前記組織識別手段による識別結果を前記医用画像情報として表示装置に表示させる表示手段と、
を具備することを特徴とする医用画像処理装置。 - 前記統計量情報及び前記識別マップに基づいて生体組織の識別度合を示す識別確率マップを作成する識別確率マップ作成手段、を更に具備し、
前記組織識別手段は、更に、前記識別確率マップに基づいて、生体組織の識別確率値を求めることで、生体組織の識別度合を取得し、前記表示手段は、前記識別度合に基づき前記識別結果を表示させることを特徴とする請求項1に記載の医用画像処理装置。 - 前記識別マップ作成手段は、2以上の異なるエネルギー強度のそれぞれの統計量情報の組み合わせに対応する領域毎に、最も高い存在確率値を有する生体組織の種別を選択して識別マップを作成することを特徴とする請求項1に記載の医用画像処理装置。
- 前記識別マップは、前記領域毎に、選択された生体組織の種別を識別するための識別子が設定されることを特徴とする請求項3に記載の医用画像処理装置。
- 前記識別確率マップ作成手段は、前記識別マップ上の各生体組織に対応する領域毎に、生体組織の存在確率値及び前記識別マップから求められる生体組織の種別の境界から前記領域までの距離に基づいて前記識別確率値を設定して、前記識別確率マップを作成することを特徴とする請求項2に記載の医用画像処理装置。
- 前記表示手段は、前記組織識別手段によって識別された生体組織の種別を、生体組織の種別毎に異なる色を設定して表示させることを特徴とする請求項4に記載の医用画像処理装置。
- 前記表示手段は、前記組織識別手段によって識別された生体組織の種別を、生体組織の種別毎に異なる色を設定し、更に前記組織識別手段によって得られた前記領域毎の生体組織の識別確率値に応じた階調を設定して表示させることを特徴とする請求項5に記載の医用画像処理装置。
- X線を被検体に照射して得られる2以上の異なるエネルギー強度に基づく医用画像情報を、表示装置に表示させる医用画像処理装置における医用画像処理方法であって、
被検体の生体組織の種別に対応する統計量情報を、エネルギー強度毎に取得する統計量情報取得ステップと、
前記統計量情報に基づいて、生体組織の種別を識別するための識別マップを作成する識別マップ作成ステップと、
得られる医用画像情報中の生体組織の種別を、前記識別マップに基づいて識別する組織識別ステップと、
前記組織識別ステップによる識別結果を前記医用画像情報として表示装置に表示させる表示ステップと、
を含むことを特徴とする医用画像処理方法。 - 前記統計量情報及び前記識別マップに基づいて生体組織の識別度合を示す識別確率マップを作成する識別確率マップ作成ステップ、を更に具備し、
前記組織識別ステップは、更に、前記識別確率マップに基づいて、生体組織の識別確率値を求めることで、生体組織の識別度合を取得し、前記表示ステップは、前記識別度合に基づき前記識別結果を表示させることを特徴とする請求項8に記載の医用画像処理方法。 - コンピュータを、
被検体の生体組織の種別に対応する統計量情報を、エネルギー強度毎に取得する統計量情報取得手段と、
前記統計量情報に基づいて、生体組織の種別を識別するための識別マップを作成する識別マップ作成手段と、
得られる医用画像情報中の生体組織の種別を、前記識別マップに基づいて識別する組織識別手段と、
前記組織識別手段による識別結果を前記医用画像情報として表示装置に表示させる表示手段と、
を具備する医用画像処理装置として機能させるプログラム。
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