Medical data processing device, medical data processing method, and program

Abstract

To improve compression efficiency of projection data.SOLUTION: A medical data processing device includes an acquisition unit, a classification unit, and a compression unit. The acquisition unit acquires a projection data set having a plurality of pieces of projection data in which a subject is projected by an X-ray. The classification unit classifies the projection data set into a plurality of regions for each category according to an appearance frequency of a pixel value of each pixel of the projection data. The compression unit generates a compression projection data set in which a data amount of each region is compressed by compression parameters corresponding to the category.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The embodiments disclosed in this specification and the drawings relate to a medical data processing device, a medical data processing method, and a program.

[0002] Conventionally, X-ray CT (Computed Tomography) devices collect a large amount of projection data using an X-ray tube and an X-ray detector that rotate around the subject, and the collected large amount of projection data becomes huge.

[0003] However, even if projection data is compressed using a general video or image compression method, it is difficult to achieve effective compression efficiency and processing speed. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2007-097977 A Summary of the Invention [Problem to be solved by the invention]

[0005] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to improve the compression efficiency of projection data. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0006] A medical data processing device according to an embodiment includes an acquisition unit, a classification unit, and a compression unit. The acquisition unit acquires a projection data set having a plurality of projection data obtained by projecting an object with X-rays. The classification unit classifies the projection data set into a plurality of regions for each category according to the frequency of occurrence of pixel values ​​of each pixel of the projection data. The compression unit generates a compressed projection data set by compressing the data amount of each of the regions using a compression parameter according to the category. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of an X-ray CT apparatus according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing an example of a classification result in which the projection data set is classified into three categories. [Diagram 3] FIG. 3 is a diagram showing an example of a classification method for classifying projection data sets into three categories. [Figure 4] FIG. 4 is a diagram showing an example of a sine wave represented by the formula (2). [Diagram 5] FIG. 5 is a flowchart showing an example of the category-based compression process executed by the X-ray CT apparatus 1 according to the first embodiment. [Figure 6] FIG. 6 is a diagram showing an example of a subject. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, a medical data processing device, a medical data processing method, and a program according to the present embodiment will be described with reference to the drawings. In the following embodiments, parts with the same reference numerals perform similar operations, and duplicated descriptions will be omitted as appropriate.

[0009] (First embodiment) 1 is a diagram showing an example of the configuration of an X-ray CT apparatus 1 according to the first embodiment. As shown in FIG. 1, the X-ray CT apparatus 1 according to the first embodiment includes a gantry device 10, a bed device 30, and a console device 40.

[0010] 1, the rotation axis of the rotating frame 13 in a non-tilted state or the longitudinal direction of the top board 33 of the bed device 30 is defined as the Z-axis direction. The axial direction that is perpendicular to the Z-axis direction and horizontal to the floor surface is defined as the X-axis direction. The axial direction that is perpendicular to the Z-axis and X-axis directions and perpendicular to the floor surface is defined as the Y-axis direction. For the sake of explanation, FIG. 1 illustrates the gantry device 10 from multiple directions, and shows a case where the X-ray CT device 1 has one gantry device 10.

[0011] The gantry 10 includes an X-ray tube 11, an X-ray detector 12, a rotating frame 13, an X-ray high voltage device 14, a control device 15, a wedge 16, a collimator 17, and a DAS (Data Acquisition System) 18. The gantry 10 is also called a gantry.

[0012] The X-ray tube 11 is a vacuum tube having a cathode (filament) that generates thermoelectrons and an anode (target) that generates X-rays upon impact of the thermoelectrons. The X-ray tube 11 generates X-rays to be irradiated onto the subject P by irradiating thermoelectrons from the cathode to the anode upon application of high voltage from the X-ray high voltage device 14. That is, the X-ray tube 11 generates X-rays according to the tube voltage and tube current applied from the X-ray high voltage device 14. For example, the X-ray tube 11 may be a rotating anode type X-ray tube that generates X-rays by irradiating a rotating anode with thermoelectrons.

[0013] The X-ray detector 12 detects X-rays irradiated from the X-ray tube 11 and passing through the subject P, and outputs a signal corresponding to the detected X-ray dose to the DAS 18. The X-ray detector 12 has, for example, a plurality of detection element rows in which a plurality of detection elements are arranged in a channel direction (channel direction) along an arc centered on the focal point of the X-ray tube 11. The X-ray detector 12 has, for example, a structure in which a plurality of detection element rows in which a plurality of detection elements are arranged in the channel direction are arranged in a row direction (slice direction, row direction).

[0014] For example, the X-ray detector 12 is an indirect conversion type detector having a grid, a scintillator array, and an optical sensor array. The scintillator array has a plurality of scintillators. The scintillator has a scintillator crystal that outputs light with a photon amount corresponding to the amount of incident X-rays. The grid is disposed on the X-ray incident side of the scintillator array, and has an X-ray shielding plate that absorbs scattered X-rays. The grid may also be called a collimator (one-dimensional collimator or two-dimensional collimator). The optical sensor array has a function of converting the amount of light from the scintillator into an electrical signal corresponding to the amount of light, and has an optical sensor such as a photodiode. The X-ray detector 12 may be a direct conversion type detector having a semiconductor element that converts the incident X-ray into an electrical signal.

[0015] The rotating frame 13 is an annular frame that supports the X-ray tube 11 and the X-ray detector 12 facing each other and rotates the X-ray tube 11 and the X-ray detector 12 by the control device 15. For example, the rotating frame 13 is a casting made of aluminum. The rotating frame 13 can further support the X-ray high voltage device 14, the wedge 16, the collimator 17, the DAS 18, and the like in addition to the X-ray tube 11 and the X-ray detector 12. The rotating frame 13 can further support various components not shown in FIG. 1. The various components supported by the rotating frame 13 will be described later. The rotating frame 13 is also referred to as a rotating base, a rotating body, and the like. In the gantry device 10, the rotating frame 13 and a portion that rotates together with the rotating frame 13 are also referred to as a rotating portion.

[0016] X-ray high voltage device 14 has electric circuits such as a transformer and a rectifier, and includes a high voltage generator that generates a high voltage to be applied to X-ray tube 11, and an X-ray control device that controls an output voltage according to the X-rays generated by X-ray tube 11. That is, X-ray high voltage device 14 controls the tube voltage and tube current to be applied to X-ray tube 11. The high voltage generator may be of a transformer type or an inverter type. X-ray high voltage device 14 may be provided on rotating frame 13, or on a fixed frame (not shown).

[0017] The control device 15 has a processing circuit having a CPU (Central Processing Unit) and the like, and a driving mechanism such as a motor and an actuator. The control device 15 receives an input signal from the input interface 43 and controls the operation of the gantry device 10 and the bed device 30. For example, the control device 15 controls the rotation of the rotating frame 13, the tilt of the gantry device 10, the operation of the bed device 30 and the tabletop 33, and the like. The control device 15 may be provided in the gantry device 10 or in the console device 40.

[0018] The wedge 16 is a filter for adjusting the amount of X-rays irradiated from the X-ray tube 11. Specifically, the wedge 16 is a filter that transmits and attenuates the X-rays irradiated from the X-ray tube 11 so that the distribution of X-rays irradiated from the X-ray tube 11 to the subject P becomes a predetermined distribution. For example, the wedge 16 is a wedge filter or a bow-tie filter, which is a filter made of processed aluminum or the like so as to have a predetermined target angle and a predetermined thickness.

[0019] The collimator 17 is a lead plate or the like for narrowing down the irradiation range of the X-rays transmitted through the wedge 16, and a slit is formed by combining a plurality of lead plates or the like. The collimator 17 may also be called an X-ray aperture. Although FIG. 1 shows a case where the wedge 16 is disposed between the X-ray tube 11 and the collimator 17, the collimator 17 may also be disposed between the X-ray tube 11 and the wedge 16. In this case, the wedge 16 transmits and attenuates the X-rays that are emitted from the X-ray tube 11 and whose irradiation range is limited by the collimator 17.

[0020] The DAS 18 collects X-ray signals detected by each detection element of the X-ray detector 12. For example, the DAS 18 has an amplifier that performs an amplification process on the electrical signal output from each detection element, and an A / D converter that converts the electrical signal into a digital signal, and generates detection data.

[0021] The data generated by the DAS 18 is transmitted by optical communication from a transmitter having a light emitting diode (LED) provided on the rotating frame 13 to a receiver having a photodiode provided on a non-rotating part of the gantry 10 (e.g., a fixed frame, etc., not shown in FIG. 1), and then transferred to the console device 40. Here, the non-rotating part is, for example, a fixed frame that rotatably supports the rotating frame 13. Note that the method of transmitting data from the rotating frame 13 to the non-rotating part of the gantry 10 is not limited to optical communication, and any non-contact data transmission method or a contact data transmission method may be adopted.

[0022] The bed device 30 is a device on which the subject P to be imaged is placed and moved, and includes a base 31, a bed driving device 32, a top plate 33, and a support frame 34. The base 31 is a housing that supports the support frame 34 so that it can move in the vertical direction. The bed driving device 32 is a drive mechanism that moves the top plate 33 on which the subject P is placed, in the longitudinal direction of the top plate 33, and includes a motor, an actuator, etc. The top plate 33, which is provided on the upper surface of the support frame 34, is a plate on which the subject P is placed. Note that the bed driving device 32 may move the support frame 34 in the longitudinal direction of the top plate 33 in addition to the top plate 33.

[0023] The console device 40 includes a memory 41, a display 42, an input interface 43, a NW interface 44, and a processing circuit 45. Although the console device 40 will be described as being separate from the gantry device 10, the gantry device 10 may include the console device 40 or some of the components of the console device 40.

[0024] The memory 41 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. The memory 41 stores, for example, projection data and CT image data. Also, for example, the memory 41 stores programs for causing circuits included in the X-ray CT apparatus 1 to realize various functions. The memory 41 may be realized by a server group (cloud) connected to the X-ray CT apparatus 1 via a network.

[0025] The display 42 displays various types of information. For example, the display 42 displays various images generated by the processing circuit 45, or displays a GUI (Graphical User Interface) for receiving various operations from an operator. For example, the display 42 is a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 42 may be a desktop type, or may be configured as a tablet terminal or the like capable of wireless communication with the console device 40 main body.

[0026] The input interface 43 is realized by a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touchpad that performs an input operation by touching an operation surface, a touch screen in which a display screen and a touchpad are integrated, a non-contact input circuit using an optical sensor, a voice input circuit, or the like. The input interface 43 may be provided in the pedestal device 10. The input interface 43 may also be configured by a tablet terminal or the like that can wirelessly communicate with the console device 40 main body. The input interface 43 is not limited to only those that have physical operation parts such as a mouse and a keyboard. For example, an example of the input interface 43 includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the console device 40 and outputs the electrical signal to the processing circuit 45.

[0027] The NW interface 44 is connected to the processing circuit 45, and controls the transmission and communication of various data between each device connected via the network. For example, the NW interface 44 is realized by a network card, a network adapter, a NIC (Network Interface Controller), etc.

[0028] The processing circuitry 45 controls the operation of the entire X-ray CT apparatus 1. For example, the processing circuitry 45 executes a system control function 451, a pre-processing function 452, a reconstruction processing function 453, an acquisition function 454, a classification function 455, a compression function 456, an expansion function 457, and a transmission function 458. For example, the processing functions executed by the system control function 451, the pre-processing function 452, the reconstruction processing function 453, the acquisition function 454, the classification function 455, the compression function 456, the expansion function 457, and the transmission function 458, which are components of the processing circuitry 45 shown in FIG. 1, are recorded in the memory 41 in the form of a program executable by a computer. The processing circuitry 45 is, for example, a processor, and realizes a function corresponding to each program read out by reading out each program from the memory 41 and executing it. In other words, the processing circuitry 45 in a state in which each program has been read out has each function shown in the processing circuitry 45 in FIG. 1.

[0029] 1, the processing functions performed by the system control function 451, the preprocessing function 452, the reconstruction processing function 453, the acquisition function 454, the classification function 455, the compression function 456, the expansion function 457, and the transmission function 458 are realized by a single processor, but a processing circuit 45 may be configured by combining a plurality of independent processors and each processor may execute a program to realize a function. Also, in FIG. 1, the single memory 41 is described as storing a program corresponding to each processing function, but a plurality of memories 41 may be distributed and arranged, and the processing circuit 45 may read out the corresponding program from each memory 41.

[0030] The term "processor" used in the above description refers to a circuit such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The processor achieves its function by reading and executing a program stored in memory 41. Note that instead of storing a program in memory 41, the processor may be configured to directly incorporate the program into its circuit. In this case, the processor achieves its function by reading and executing the program incorporated in the circuit.

[0031] The system control function 451 controls various functions of the processing circuit 45 based on an input operation received from an operator via the input interface 43. The system control function 451 is also an example of a control unit.

[0032] The pre-processing function 452 generates data by performing pre-processing such as logarithmic conversion processing, offset correction processing, inter-channel sensitivity correction processing, and beam hardening correction on the detection data output from the DAS 18. Note that the data before pre-processing (detection data) and the data after pre-processing may be collectively referred to as projection data. The pre-processing function 452 is an example of a pre-processing unit.

[0033] The reconstruction processing function 453 performs reconstruction processing using a filtered back projection method, an iterative reconstruction method, or the like on the projection data generated by the pre-processing function 452 to generate CT image data. The reconstruction processing function 453 is an example of a reconstruction processing unit. Based on an input operation received from an operator via the input interface 43, the reconstruction processing function 453 converts the CT image data generated by the reconstruction processing function 453 into tomographic image data of an arbitrary cross section or three-dimensional image data by a known method. The generation of the three-dimensional image data may be performed directly by the reconstruction processing function 453. The reconstruction processing function 453 is an example of an image processing unit.

[0034] The acquisition function 454 acquires a projection data set having a plurality of projection data pieces obtained by projecting the subject P with X-rays. The acquisition function 454 is an example of an acquisition unit. For example, the projection data set is a sinogram in which the projection data pieces are arranged in the order of the projection angles. That is, the acquisition function 454 acquires a sinogram in which the projection data pieces are arranged in the order of the projection angles as the projection data set. For example, the acquisition function 454 acquires a sinogram collected by a series of scans on the subject P, such as a Herculis scan.

[0035] The classification function 455 classifies the projection data set into a plurality of regions for each category according to the occurrence frequency of the pixel value of each pixel of the projection data. The classification function 455 is an example of a classification unit. A category is a type into which the projection data is classified according to the occurrence frequency of each pixel value in the region when the projection data is classified into a plurality of regions.

[0036] Fig. 2 is a diagram showing an example of a classification result in which projection data sets are classified into three categories. That is, Fig. 2 is a diagram showing an example of a classification method for classifying sinograms into three categories. In the sinogram shown in Fig. 2, the horizontal direction indicates the channel direction of the X-ray detector 12, and the vertical direction indicates the projection angle at which each piece of projection data was captured.

[0037] For example, the classification function 455 classifies the regions of each projection data included in the projection data set into three categories. As shown in Fig. 2, the classification function 455 classifies the regions into a first category region D1, a second category region D2, and a third category region D3 by calculating a first boundary line L1 and a second boundary line L2.

[0038] Here, the X-ray tube 11 and the X-ray detector 12 capture projection data while rotating around the subject P. Therefore, in the sinogram shown in Fig. 2, pixel values ​​indicating that X-rays that have passed through the subject P have been detected are concentrated at approximately the center in the channel direction. That is, in the first category region D1, pixel values ​​indicating that X-rays that have not passed through the subject P have been concentrated. In the second category region D2, pixel values ​​indicating that X-rays that have passed through the subject P have been concentrated. In the third category region D3, pixel values ​​indicating that X-rays that have not passed through the subject P have been detected are concentrated.

[0039] Therefore, the classification function 455 calculates a first boundary line L1 and a second boundary line L2. As a result, the classification function 455 classifies into a first category region D1 of the first category where pixel values ​​indicating that X-rays that have not passed through the subject P have been detected frequently, a second category region D2 of the second category where pixel values ​​indicating that X-rays that have passed through the subject P have been detected frequently, and a third category region D3 of the third category where pixel values ​​indicating that X-rays that have not passed through the subject P have been detected frequently.

[0040] Next, a description will be given of a classification method performed by the classification function 455. In other words, a method for calculating the first boundary line L1 and the second boundary line L2 will be described.

[0041] FIG. 3 is a diagram showing an example of a classification method for classifying a projection data set into three categories. Here, the X-ray tube 11 and the X-ray detector 12 capture projection data while rotating around the subject P. Therefore, the detection result of the X-rays that have passed through the subject P is a sine curve. Therefore, the classification function 455 classifies the projection data set into a plurality of regions based on the sine curve indicating the detection result of the X-rays included in the sinogram. For example, the classification function 455 can classify the sinogram according to the sine curve into a region with many pixels that have detected the X-rays that have passed through the subject P and a region with few pixels that have detected the X-rays that have passed through the subject P.

[0042] For example, the classification function 455 calculates a boundary line for classifying the sinogram into a plurality of category regions based on at least one sine curve locus among a plurality of sine curves included in the sinogram. For example, the classification function 455 calculates a first boundary line L1 and a second boundary line L2 based on at least one of a first locus C1 and a second locus C2, which are sine curves included in the sinogram shown in Fig. 3. Then, the classification function 455 classifies the sinogram into a first category region D1, a second category region D2, and a third category region D3 according to the first boundary line L1 and the second boundary line L2.

[0043] The first trajectory C1 is a trajectory of pixels whose pixel values ​​deviate from the average value of each pixel of one projection data between the first category region D1 and the second category region D2. The second trajectory C2 is a trajectory of pixels whose pixel values ​​deviate from the average value of each pixel of one projection data between the second category region D2 and the third category region D3. The first category region D1 is a region with a relatively large number of pixels that do not detect X-rays that have passed through the subject P. The second category region D2 is a region with a relatively large number of pixels that detect X-rays that have passed through the subject P. The third category region D3 is a region with a relatively large number of pixels that do not detect X-rays that have passed through the subject P.

[0044] 3 is a region to be calculated among the first trajectory C1 and the second trajectory C2. That is, the classification function 455 calculates the first boundary line L1 and the second boundary line L2 based on at least one of the first trajectory C1 and the second trajectory C2 of the calculation region E1 in the sinogram.

[0045] For example, the classification function 455 calculates the first boundary line L1 and the second boundary line L2 from the first trajectory C1 or the second trajectory C2 in the following manner.

[0046] Equation (2) can be derived from the sine wave equation shown in Equation (1). Fig. 4 is a diagram showing an example of the sine wave shown in Equation (2).

[0047] y(t) = A sin(ωt) = Asin(t 2π / T) Equation (1)

[0048] A indicates the maximum value, t indicates the phase, and T indicates the scan speed, in other words, the time it takes for the X-ray tube 11 and the X-ray detector 12 to make one revolution around the subject P.

[0049] A = ΔX / sin(2π / T t+α) = (X1-X0) / sin(2π / T t+α) Formula (2)

[0050] α indicates the phase between two points, in other words, the time between two points.

[0051] 3. The sine wave shown in Fig. 4 is also a sine wave extracted from a sinogram, that is, the sine wave shown in Fig. 4 is also the first locus C1 or the second locus C2 shown in Fig. 3.

[0052] Here, in the rotation angle at which the X-ray tube 11 and the X-ray detector 12 rotate around the subject P, if there is data for one degree, it is possible to calculate the maximum amplitude of the sine wave. However, in order to ensure accuracy, the classification function 455 classifies the projection data set into a plurality of regions based on a sine curve in a partial section of the sinogram. For example, the classification function 455 calculates the maximum amplitude of the sine wave using projection data for 30 degrees. Specifically, the classification function 455 calculates the maximum amplitude based on the first trajectory C1 or the second trajectory C2 of the calculation region E1 shown in FIG. 3.

[0053] The classification function 455 calculates the boundary line based on the maximum value of the amplitude of the sine wave. That is, the classification function 455 calculates the first boundary line L1 and the second boundary line L2 based on the maximum value of the amplitude of the first trajectory C1. Alternatively, the classification function 455 calculates the first boundary line L1 and the second boundary line L2 based on the maximum value of the amplitude of the second trajectory C2.

[0054] For example, the classification function 455 sets the line obtained by extending the maximum value of the amplitude of the sine wave in the direction of the projection angle of the sinogram as the boundary line. That is, the classification function 455 sets the lines obtained by extending the maximum value, which is the positive absolute value of the amplitude of the first trajectory C1, and the maximum value, which is the negative absolute value, in the direction of the projection angle of the sinogram as the first boundary line L1 and the second boundary line L2. Alternatively, the classification function 455 sets the lines obtained by extending the maximum value, which is the positive absolute value of the amplitude of the second trajectory C2, and the maximum value, which is the negative absolute value, in the direction of the projection angle of the sinogram as the first boundary line L1 and the second boundary line L2. In this way, the classification function 455 classifies the first category region D1, the second category region D2, and the third category region D3 by calculating the first boundary line L1 and the second boundary line L2.

[0055] The classification function 455 may also calculate the boundary line based on the maximum amplitudes of the multiple trajectories. For example, the classification function 455 may set the boundary line to a line obtained by extending the average value of the maximum amplitude on the positive side of the first trajectory C1 and the maximum amplitude on the positive side of the second trajectory C2 in the direction of the projection angle of the sinogram. Similarly, for example, the classification function 455 may set the boundary line to a line obtained by extending the average value of the maximum amplitude on the negative side of the first trajectory C1 and the maximum amplitude on the negative side of the second trajectory C2 in the direction of the projection angle of the sinogram. Note that the classification function 455 may calculate the boundary line using a median value, not limited to the average value, or may calculate the boundary line using other values. Furthermore, the classification function 455 may calculate the boundary line by selecting one maximum value from the maximum amplitude of the first trajectory C1 and the maximum amplitude of the first trajectory C1, such as the highest value or the lowest value.

[0056] The compression function 456 generates a compressed projection data set in which the data volume of each region classified by the classification function 455 is compressed using compression parameters according to the category classified by the classification function 455. The compression function 456 is an example of a compression unit. The compressed projection data set is data in which the data volume of all regions of the projection data set is compressed. For example, the compression function 456 compresses the data volume of the first category region D1, the second category region D2, and the third category region D3.

[0057] More specifically, the compression function 456 compresses the projection data set by entropy coding such as Huffman coding or arithmetic coding. For example, the compression function 456 determines a compression parameter for each area of ​​each category classified by the classification function 455 based on a standard distribution of pixel values ​​of each pixel. The compression parameter is a parameter indicating a correspondence relationship between codes before and after compression in compressing the amount of data. That is, the compression parameter is a parameter indicating a correspondence relationship between codes before and after compression according to a category according to the frequency of occurrence of pixel values. Then, the compression function 456 determines a compression parameter for each area of ​​each category such that a pixel value with the highest frequency of occurrence has the shortest code and a pixel value with the lowest frequency of occurrence has the longest code.

[0058] The compression function 456 compresses the first category region D1, the second category region D2, and the third category region D3 according to the determined compression parameters. In this way, the compression function 456 generates a compressed projection data set in which the amount of data is compressed. The compressed projection data set includes a compression parameter for each compressed region. For example, the compressed projection data set includes a compression parameter used to compress the first category region D1, a compression parameter used to compress the second category region D2, and a compression parameter used to compress the third category region D3.

[0059] The expansion function 457 expands the compressed projection data set compressed by the compression function 456. The expansion function 457 is an example of a expansion unit. That is, the expansion function 457 generates a projection data set from the compressed projection data set.

[0060] More specifically, the expansion function 457 expands each region based on a compression parameter for each region included in the compressed projection data set. For example, the expansion function 457 expands the first category region D1 using the compression parameter used to compress the first category region D1 included in the compressed projection data set. The expansion function 457 also expands the second category region D2 using the compression parameter used to compress the second category region D2 included in the compressed projection data set. The expansion function 457 also expands the third category region D3 using the compression parameter used to compress the third category region D3 included in the compressed projection data set. In this way, the expansion function 457 generates a projection data set from the compressed projection data set.

[0061] The transmission function 458 controls the NW interface 44 to transmit the compressed projection data set generated by compressing the data amount of the projection data set by the compression function 456. In other words, the transmission function 458 transmits the projection data set compressed by the compression function 456 and a compression parameter for each compression of each compressed region. The transmission function 458 is an example of a transmission unit.

[0062] Next, the processing executed by the X-ray CT apparatus 1 will be described.

[0063] FIG. 5 is a flowchart showing an example of the category-based compression process executed by the X-ray CT apparatus 1 according to the first embodiment.

[0064] The acquisition function 454 acquires a projection data set (step S1).

[0065] The classification function 455 classifies the projection data set into a plurality of regions for each category according to the frequency of occurrence of the pixel value of each pixel of the projection data (step S2).

[0066] The compression function 456 compresses each area classified by the classification function 455 using compression parameters according to the category classified by the classification function 455 (step S3).

[0067] With the above, the X-ray CT apparatus 1 ends the compression process by category.

[0068] As described above, the X-ray CT apparatus 1 according to the first embodiment acquires a projection data set having a plurality of projection data. The X-ray CT apparatus 1 also classifies the projection data set into a plurality of regions for each category according to the occurrence frequency of pixel values. For example, the X-ray CT apparatus 1 classifies into a region having many pixel values ​​that have passed through the subject P and a region having few pixel values ​​that have passed through the subject P. The X-ray CT apparatus 1 then generates a compressed projection data set by compressing the amount of data in each of the classified regions using a compression parameter according to the category.

[0069] In this way, the X-ray CT apparatus 1 compresses an area with many pixel values ​​having passed through the subject P with compression parameters suitable for an area with many pixel values ​​having passed through the subject P, and compresses an area with few pixel values ​​having passed through the subject P with compression parameters suitable for an area with few pixel values ​​having passed through the subject P. Therefore, the X-ray CT apparatus 1 can improve the compression efficiency of the projection data.

[0070] (Variation 1) The X-ray tube 11 and the X-ray detector 12 capture a projection data set while rotating around the subject P inserted in the body axis direction. The thickness of the subject P in the body axis direction varies depending on the part of the subject. FIG. 6 is a diagram showing an example of the subject P. For example, as shown in FIG. 6, the subject P may expand in the X-axis direction as it progresses in the Z-axis direction. In other words, the subject P may expand in the channel direction as it progresses in the projection angle direction in the sinogram.

[0071] In this case, as shown in Fig. 6, it is difficult to properly classify the entire sinogram using only the projection data for an arbitrary 30 degrees. Therefore, the classification function 455 may classify the projection data set into a plurality of regions based on the sine curves of a plurality of sections in the projection angle direction of the sinogram. For example, as shown in Fig. 6, the classification function 455 may calculate a boundary line for classifying into categories based on the calculation results of three points in the projection angle direction of the subject P.

[0072] Specifically, the classification function 455 may calculate a boundary line for classification into categories based on the first calculation region E21, the second calculation region E22, and the third calculation region E23. For example, in the first calculation region E21, the classification function 455 calculates the maximum amplitude value of the trajectory included in the sinogram by the same method as in the first embodiment. In addition, in the second calculation region E22, the classification function 455 calculates the maximum amplitude value of the trajectory included in the sinogram by the same method as in the first embodiment. In addition, in the third calculation region E23, the classification function 455 calculates the maximum amplitude value of the trajectory included in the sinogram by the same method as in the first embodiment.

[0073] The classification function 455 calculates a boundary line for classifying into categories based on the maximum value of the amplitude of the trajectories included in the multiple regions. For example, the classification function 455 calculates a boundary line for classifying into categories by connecting the maximum value of the first calculation region E21, the maximum value of the second calculation region E22, and the maximum value of the third calculation region E23. Note that the classification function 455 is not limited to the method of connecting the respective maximum values, and may calculate the boundary line by extending the average value of each maximum value in the direction of the projection angle, or may calculate the boundary line by extending the largest value of each maximum value in the direction of the projection angle, or may calculate the boundary line by other methods.

[0074] (Variation 2) The classification function 455 according to the first embodiment classifies regions into categories based on the sinogram. For example, the classification function 455 classifies regions into regions with a high frequency of pixel values ​​indicating that X-rays have been detected that have passed through the subject P and regions with a low frequency of pixel values ​​indicating that X-rays have been detected that have passed through the subject P, based on the sinogram.

[0075] Here, whether or not the X-rays irradiated from the X-ray tube 11 pass through the subject P can be estimated based on the body type of the subject P, the part of the subject P that is the projection target, and the posture of the subject P. Furthermore, it is possible to estimate the extent to which the X-rays are attenuated when they pass through the subject P. In other words, it is possible to estimate the pixel value of each pixel of the projection data included in the projection data set based on the body type of the subject P, the part of the subject P that is the projection target, and the posture of the subject P. Therefore, the classification function 455 can classify the projection data set into a plurality of regions based on the part of the subject P that is the projection target and the posture of the subject P.

[0076] For example, the classification function 455 classifies the projection data set into a plurality of regions based on list information of the body type of the subject P, the part of the subject P to be projected, the posture of the subject P, and the classification method of the regions of the projection data set. For example, the list information is generated by accumulating the classification results of the regions corresponding to the body type of the subject P, the part of the subject P to be projected, and the posture of the subject P. That is, the classification method of the regions of the projection data set is estimated from the classification results of the regions corresponding to the body type of the subject P, the part of the subject P to be projected, and the posture of the subject P. Then, the list information is generated by associating the estimated classification method with the body type of the subject P, the part of the subject P to be projected, and the posture of the subject P.

[0077] The classification function 455 may classify the projection data set into a plurality of regions by other methods than the method using the list information. For example, the classification function 455 may classify the projection data set into a plurality of regions based on a statistical result of the classification result of the regions corresponding to the body type of the subject P, the part of the subject P to be projected, and the posture of the subject P. The classification function 455 may also classify the projection data set into a plurality of regions by calculating a boundary line based on a sine curve estimated from the body type of the subject P, the part of the subject P to be projected, and the posture of the subject P.

[0078] (Variation 3) The classification function 455 according to variant example 3 may classify the projection data set into multiple regions using a learned model.

[0079] For example, the classification function 455 may classify the projection dataset into multiple regions by inputting the body type of the subject P, the part of the subject P to be projected, and the posture of the subject P into a trained model that has learned the correlation between the body type of the subject P, the part of the subject P to be projected, the posture of the subject P, and the classification of the regions of the projection dataset.

[0080] (Variation 4) The processing circuitry 45 of the console device 40 includes a system control function 451, a pre-processing function 452, a reconstruction processing function 453, an acquisition function 454, a classification function 455, an unpacking function 457, and a transmission function 458. However, the processing circuitry 45 does not need to have all of these functions. For example, if the processing circuitry 45 is installed in a device that does not execute reconstruction processing, the processing circuitry 45 does not need to have the pre-processing function 452 and the reconstruction processing function 453. Also, if the processing circuitry 45 is installed in a device that transmits but does not receive compressed projection data sets, the processing circuitry 45 does not need to have the unpacking function 457.

[0081] (Variation 5) It has been described that the processing circuit 45 of the console device 40 includes the system control function 451, the pre-processing function 452, the reconstruction processing function 453, the acquisition function 454, the classification function 455, the expansion function 457, and the transmission function 458. However, the system control function 451, the pre-processing function 452, the reconstruction processing function 453, the acquisition function 454, the classification function 455, the expansion function 457, and the transmission function 458 are not limited to the console device 40 and may be included in other devices.

[0082] For example, the acquisition function 454, the classification function 455, and the transmission function 458 may be included in the DAS 18. Furthermore, a device not shown in FIG. 1 may have all or part of the pre-processing function 452, the reconstruction processing function 453, the acquisition function 454, the classification function 455, the unfolding function 457, and the transmission function 458.

[0083] (Variation 6) In the first embodiment, a case has been described in which the medical data processing device is applied to an X-ray CT device 1 as an example. However, the medical data processing device is not limited to the X-ray CT device 1, and may be applied to a PET (Positron Emission Tomography) device or a CT-PET device that combines an X-ray CT device and a PET device.

[0084] According to at least one of the embodiments described above, the compression efficiency of the projection data can be improved.

[0085] Although some embodiments 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, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0086] 1 X-ray CT device 11 X-ray tube 12 X-ray detector 18 DAS (Data Acquisition System) 40 Console device 45 Processing circuit 451 System Control Functions 452 Pre-processing function 453 Reconstruction Processing Function 454 Acquisition Function 455 Classification function 456 Compression Function 457 Deployment Function 458 Sending Function L1 1st boundary line L2 2nd border D1 First category area D2 Second category area D3 Third Category Area C1 1st trajectory C2 2nd locus E1 calculation area E21 1st calculation area E22 2nd calculation area E23 Third calculation area P Subject

Claims

1. an acquisition unit that acquires a projection data set having a plurality of projection data obtained by projecting an object with X-rays; a classification unit that classifies the projection data set into a plurality of regions for each category according to an occurrence frequency of a pixel value of each pixel of the projection data; a compression unit for generating a compressed projection data set in which the amount of data of each of the regions is compressed using a compression parameter corresponding to the category; A medical data processing device comprising:

2. The projection data set is a sinogram in which the projection data is arranged in order of projection angle, The classification unit classifies the projection data set into a plurality of regions based on a sine curve of pixels included in the sinogram. The medical data processing device according to claim 1 .

3. The classification unit classifies the projection data set into a plurality of regions based on a sine curve of a partial section of the sinogram. The medical data processing device according to claim 2 .

4. The classification unit classifies the projection data set into a plurality of regions based on a sine curve of a plurality of sections in a projection angle direction of the sinogram. The medical data processing device according to claim 2 .

5. The classification unit classifies the projection data set into a plurality of regions based on a body type of the subject, a part of a projection target, and a posture of the subject. The medical data processing device according to claim 1 .

6. the classification unit classifies the projection data set into a plurality of regions by inputting the body type of the subject, the part of the subject to be projected, and the posture of the subject to a trained model that has trained correlations between the body type of the subject, the part of the subject to be projected, the posture of the subject, and the classification of regions; The medical data processing device according to claim 1 .

7. a transmission unit that transmits the compressed projection data set compressed by the compression unit and the compression parameters for each compressed region; The medical data processing device according to any one of claims 1 to 6.

8. a decompression unit that decompresses the compressed projection data set compressed by the compression unit, The medical data processing device according to any one of claims 1 to 6.

9. A projection data set is obtained, the projection data set including a plurality of projection data obtained by projecting an object with X-rays; classifying the projection data set into a plurality of regions for each category according to the frequency of occurrence of pixel values ​​of each pixel of the projection data; generating a compressed projection data set in which the amount of data of each of the regions is compressed according to a compression parameter corresponding to the category; A medical data processing method comprising:

10. On the computer, A projection data set is obtained, the projection data set including a plurality of projection data obtained by projecting an object with X-rays; classifying the projection data set into a plurality of regions for each category according to the frequency of occurrence of pixel values ​​of each pixel of the projection data; generating a compressed projection data set in which the amount of data of each of the regions is compressed according to a compression parameter corresponding to the category; A program to make this happen.

Images