Medical image processing apparatus, medical image diagnostic apparatus, magnetic resonance imaging apparatus, and medical image processing method

Abstract

To provide a medical image processing apparatus capable of saving a storage capacity.SOLUTION: A medical image processing apparatus according to the present embodiment includes a generation unit and a reconstruction processing unit. When a second reconstruction condition different from a first reconstruction condition is set for first medical image data reconstructed based on first raw data and the first reconstruction condition, the generation unit generates second raw data corresponding to reproduction of the first raw data based on the first medical image data. The reconstruction processing unit reconstructs second medical image data based on the second raw data and the second reconstruction condition.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The embodiments disclosed in this specification and the drawings relate to a medical image processing apparatus, a medical image diagnostic apparatus, a magnetic resonance imaging apparatus, and a medical image processing method. [Background technology]

[0002] In recent years, in the medical field, not only medical images but also the data on which medical images are generated (hereinafter referred to as raw data) are increasingly stored in storage devices such as external storage. The total amount of raw data used to generate one medical image is larger than the data amount of one medical image. For this reason, storing raw data in a storage device can put extreme strain on storage capacity.

[0003] For example, in the field of X-ray computed tomography devices, high-definition X-ray detectors (hereinafter referred to as high-definition detectors) have recently appeared in order to obtain higher-definition images. The size of an X-ray detection element in a high-definition detector is, for example, 1 / 4 the size of a normal X-ray detection element. If an X-ray detector (hereinafter referred to as a normal detector) composed of normal-sized X-ray detection elements and a high-definition detector have the same detection area, the total amount of raw data collected by the high-definition detector is four times the total amount of raw data collected by the normal detector. For this reason, in an X-ray computed tomography device equipped with a high-definition detector, the problem of extremely straining memory capacity can become more serious. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2016-168323 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 provide a medical image processing device capable of saving memory capacity. 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] The medical image processing apparatus according to the present embodiment includes a generating unit and a reconstruction processing unit. When a second reconstruction condition different from the first reconstruction condition is set for first medical image data reconstructed based on first raw data and a first reconstruction condition, the generating unit generates second raw data corresponding to a reproduction of the first raw data based on the first medical image data. The reconstruction processing unit reconstructs the second medical image data based on the second raw data and the second reconstruction condition. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram showing an example of the arrangement of an X-ray CT apparatus 1 according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing an overview of a reconstruction retry process according to the first embodiment. [Diagram 3] FIG. 3 is a flowchart showing an example of a procedure of a reconstruction retry process according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing a display image of the first medical image data displayed on a display and a display image of the second medical image data in the case where Yes is selected in step S302 of FIG. 3 according to the first embodiment. [Diagram 5] FIG. 5 is a diagram showing a display image of the first medical image data displayed on a display and a display image of the second medical image data in the case where Yes is selected in step S304 of FIG. 3 according to the first embodiment. [Figure 6] FIG. 6 is a diagram showing an example of a CNN used as means for realizing a reproduction filter according to the first embodiment. [Figure 7]FIG. 7 is a diagram showing an example of a process for offline training in generating a reproduction filter according to the first embodiment. [Figure 8] FIG. 8 is a flow diagram of step 130 of the method for training a network using training data according to the first embodiment. [Figure 9] FIG. 9 is a diagram showing an overview of a filter generation process according to the first embodiment. [Figure 10] FIG. 10 is a flowchart showing an example of a procedure of a filter generation process according to the first embodiment. [Figure 11] FIG. 11 is a flowchart showing an example of a procedure of a reconstruction retry process according to an application example of the first embodiment. [Figure 12] 12 is a diagram showing a display image of the first medical image data displayed on a display according to an application example of the first embodiment, and a display image of the second medical image data in the case where the answer is Yes in step S113 of FIG. [Figure 13] FIG. 13 is a schematic diagram showing an outline of iterative reconstruction according to the second embodiment. [Figure 14] FIG. 14 is a diagram showing an overview of a reconstruction retry process according to the second embodiment. [Figure 15] FIG. 15 is a configuration diagram showing an example of a medical image processing apparatus in the case where the technical idea of ​​the embodiment is realized by the medical image processing apparatus. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, with reference to the drawings, embodiments of a medical image processing device, a medical image diagnostic device, and a medical image processing method will be described. In the following embodiments, parts with the same reference numerals perform the same operation, and duplicated descriptions will be omitted as appropriate. For concrete explanation, the medical image processing device according to the embodiment will be described as being mounted on an X-ray computed tomography device (hereinafter referred to as an X-ray CT (computed tomography) device) as an example of a medical image diagnostic device. Note that the medical image diagnostic device on which the medical image processing device is mounted is not limited to an X-ray CT device, and may be a nuclear medicine diagnostic device such as PET (Positron Emission Tomography) or SPECT (Single Photon Emission Computed Tomography) that involves reconstruction for generating medical images, or a magnetic resonance imaging device.

[0009] (First embodiment) FIG. 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 includes a gantry 10, a bed 30, and a console 40. In this embodiment, the longitudinal direction of the rotation axis of the rotating frame 13 in a non-tilted state is defined as the Z-axis direction, the direction perpendicular to the Z-axis direction and from the center of rotation toward the support column supporting the rotating frame 13 is defined as the X-axis, and the direction perpendicular to the Z-axis and the X-axis is defined as the Y-axis. For convenience of explanation, a plurality of gantry devices 10 are depicted in FIG. 1, but the actual configuration of the X-ray CT apparatus 1 includes only one gantry device 10.

[0010] The gantry device 10 and the bed device 30 operate based on operations by an operator via a console device 40, or operations by an operator via an operation unit provided on the gantry device 10 or the bed device 30. The gantry device 10, the bed device 30, and the console device 40 are connected to each other by wire or wirelessly so as to be able to communicate with each other.

[0011] The gantry device 10 is an apparatus having an imaging system that irradiates an object P with X-rays and collects projection data from detection data of the X-rays that have passed through the object P. The gantry device 10 has 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 bow-tie filter 16, a collimator 17, and a DAS (Data Acquisition System) 18.

[0012] The X-ray tube 11 is a vacuum tube that generates X-rays by irradiating thermoelectrons from a cathode (filament) to an anode (target) by application of a high voltage from an X-ray high voltage device 14 and supply of a filament current. X-rays are generated when the thermoelectrons collide with the target. X-rays generated at the tube focus in the X-ray tube 11 pass through an X-ray radiation window in the X-ray tube 11 and are shaped, for example, into a cone beam via a collimator 17, and are irradiated onto the subject P. The X-ray tube 11 may be, for example, 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 an electric signal corresponding to the 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 the channel direction along one arc with the focus of the X-ray tube 11 as the center. The X-ray detector 12 has, for example, a structure in which a plurality of the detection element rows are arranged in the slice direction (row direction). Note that there are various types of X-ray CT devices 1, such as a Rotate / Rotate-Type (third generation CT) in which the X-ray tube 11 and the X-ray detector 12 rotate together around the subject P, and a Stationary / Rotate-Type (fourth generation CT) in which a large number of X-ray detection elements arrayed in a ring shape are fixed, and only the X-ray tube 11 rotates around the subject P, and any of these types can be applied to this embodiment.

[0014] The X-ray detector 12 is an indirect conversion type detector having, for example, a grid, a scintillator array, and an optical sensor array. The scintillator array has a plurality of scintillators, and 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 surface of the scintillator array on the X-ray incident side, and has an X-ray shielding plate having a function of absorbing 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 into an electric signal corresponding to the amount of light from the scintillator, and has an optical sensor such as a photomultiplier tube (PMT). The X-ray detector 12 may be a direct conversion type detector having a semiconductor element that converts the incident X-ray into an electric signal. The X-ray detector 12 may be a photon counting type X-ray detector. The X-ray detector 12 is an example of an X-ray detection unit.

[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 a control device 15 described later. In addition to the X-ray tube 11 and the X-ray detector 12, the rotating frame 13 further supports an X-ray high voltage device 14 and a DAS 18. The rotating frame 13 is rotatably supported by a non-rotating part of the gantry device 10 (e.g., a fixed frame; not shown in FIG. 1). The rotation mechanism includes, for example, a motor that generates a rotational driving force and a bearing that transmits the rotational driving force to the rotating frame 13 to rotate it. The motor is provided in the non-rotating part, for example, and the bearing is physically connected to the rotating frame 13 and the motor, and the rotating frame rotates according to the rotational force of the motor.

[0016] The rotating frame 13 and the non-rotating part are provided with a non-contact or contact communication circuit, which allows communication between the unit supported by the rotating frame 13 and the non-rotating part or an external device of the gantry 10. For example, when optical communication is used as the non-contact communication method, the detection 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 the non-rotating part of the gantry 10, and is further transferred from the non-rotating part to the console device 40 by the transmitter. As the communication method, in addition to the above, a non-contact data transmission method such as a capacitive coupling method or an electromagnetic wave method, or a contact data transmission method using a slip ring and an electrode brush may be used. The rotating frame 13 is an example of a rotating part.

[0017] X-ray high voltage device 14 has electric circuits such as a transformer and a rectifier, and includes a high voltage generator having a function of generating a high voltage to be applied to X-ray tube 11 and a filament current to be supplied to X-ray tube 11, and an X-ray control device that controls an output voltage according to the X-rays irradiated by 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 the rotating frame 13, or on the fixed frame side of gantry device 10. X-ray high voltage device 14 is an example of an X-ray high voltage unit.

[0018] 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 has a function of receiving an input signal from an input interface attached to the console device 40 or the gantry device 10 and controlling the operation of the gantry device 10 and the bed device 30. For example, the control device 15 receives an input signal and controls the rotation of the rotating frame 13, the tilt of the gantry device 10, and the operation of the bed device 30 and the tabletop 33. The control of tilting the gantry device 10 is realized by the control device 15 rotating the rotating frame 13 around an axis parallel to the X-axis direction based on inclination angle (tilt angle) information input by an input interface attached to the gantry device 10. The control device 15 may be provided in the gantry device 10 or in the console device 40. The control device 15 may be configured to directly incorporate a program into the circuit of the processor instead of storing the program in the memory. The control device 15 is an example of a control unit.

[0019] 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 X-rays irradiated from the X-ray tube 11 to the subject P have a predetermined distribution. The wedge 16 is, for example, a wedge filter or a bow-tie filter, and is a filter made of processed aluminum to have a predetermined target angle and a predetermined thickness.

[0020] The collimator 17 is a lead plate or the like for constricting the X-rays transmitted through the wedge 16 to an X-ray irradiation range, 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.

[0021] The DAS 18 has an amplifier that performs an amplification process on the electrical signals output from each X-ray detection element of the X-ray detector 12, and an A / D converter that converts the electrical signals into digital signals, and generates detection data. The detection data generated by the DAS 18 is transferred to the first processing circuit 36. The detection data may be referred to as pure raw data. The DAS 18 is also an example of a data acquisition unit.

[0022] The bed device 30 is a device on which the subject P to be scanned 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 motor or actuator that moves the top plate 33, on which the subject P is placed, in the longitudinal direction of the top plate 33. 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 has a memory 41, a display 42, an input interface 43, and a processing circuit 44. Data communication between the memory 41, the display 42, the input interface 43, and the processing circuit 44 is performed, for example, via a bus (BUS). Note that the console device 40 will be described as being separate from the gantry device 10, but 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 random access memory (RAM), a flash memory, a hard disk, an optical disk, a solid state drive (SSD), or the like. The memory 41 stores, for example, detection data output from the DAS 18, projection data generated by the pre-processing function 442, and reconstructed image data reconstructed by the reconstruction processing function 443. The reconstructed image data is three-dimensional CT image data, and is hereinafter referred to as medical image data. Medical images are also referred to as volume data. Moreover, data before pre-processing by the pre-processing function 442 (detection data or pure raw data) and projection data are collectively referred to as first raw data. That is, the first raw data may be pure raw data or may be projection data.

[0025] The memory 41 stores the forward projection condition, the reproduction filter, and the first medical image data in association with each other. The first medical image data is volume data reconstructed by the reconstruction processing function 443 based on the first raw data and the first reconstruction condition. The reproduction filter is a filter that reproduces the first raw data from the forward projection data obtained by forward projecting the first medical image data. The reproduction filter is generated by using the forward projection data and the first raw data so as to bring the forward projection data closer to the first raw data. The reproduction filter is generated in advance according to, for example, a part of the subject P related to the first raw data. The forward projection condition is a forward projection condition related to the generation of the forward projection data used in the generation of the reproduction filter. The process related to the generation of the reproduction filter (hereinafter referred to as a filter generation process) will be described later.

[0026] The memory 41 stores programs related to the execution of each of a system control function 441, a preprocessing function 442, a reconstruction processing function 443, an image processing function 444, a generation function 445, and a determination function 446 executed by the processing circuitry 44. The memory 41 is an example of a storage unit.

[0027] The display 42 displays various information. For example, the display 42 outputs a medical image (CT image) generated by the processing circuit 44, a GUI (Graphical User Interface) for receiving various operations from an operator, and the like. For example, as the display 42, a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electroluminescence display (OELD), a plasma display, or any other arbitrary display can be used as appropriate. The display 42 may be provided on the pedestal device 10. 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. The display 42 is an example of a display unit.

[0028] The input interface 43 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuit 44. For example, the input interface 43 receives from the operator collection conditions for collecting projection data, reconstruction conditions for reconstructing CT image data, image processing conditions for post-processing of CT image data, and the like. The post-processing may be performed by either the console 40 or an external workstation. Also, the post-processing may be performed simultaneously by both the console 40 and the workstation. The post-processing defined here is a concept that refers to processing of an image reconstructed by the reconstruction processing function 443. For example, it includes a Multi Planar Reconstruction (MPR) display of the reconstructed image and rendering of volume data. As the input interface 43, for example, a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touchpad, a touch panel display, and the like can be appropriately used.

[0029] In this embodiment, the input interface 43 is not limited to one having physical operation parts such as a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touchpad, and a touch panel display. For example, an example of the input interface 43 includes an electric signal processing circuit that receives an electric signal corresponding to an input operation from an external input device provided separately from the device and outputs the electric signal to the processing circuit 44. The input interface 43 is also an example of an input unit. The input interface 43 may be provided in the pedestal device 10. The input interface 43 may be configured with a tablet terminal or the like capable of wireless communication with the console device 40 main body. The input interface 43 is an example of an input unit.

[0030] The processing circuitry 44 controls the operation of the entire X-ray CT apparatus 1 in response to, for example, an electrical signal of an input operation output from the input interface 43. For example, the processing circuitry 44 has a processor such as a CPU, MPU, or GPU (Graphics Processing Unit) and a memory such as a ROM or RAM as hardware resources. The processing circuitry 44 executes a system control function 441, a pre-processing function 442, a reconstruction processing function 443, an image processing function 444, a generation function 445, and a determination function 446 by a processor that executes a program expanded in the memory. Note that each of the functions 441 to 446 is not limited to being realized by a single processing circuit. A processing circuit may be configured by combining a plurality of independent processors, and each processor may execute a program to realize each of the functions 441 to 446.

[0031] The system control function 441 controls each function of the processing circuitry 44 based on an input operation received from an operator via the input interface 43. The system control function 441 also reads out a control program stored in the memory 41, expands it on the memory in the processing circuitry 44, and controls each part of the X-ray CT apparatus 1 according to the expanded control program. The system control function 441 is an example of a control unit.

[0032] The pre-processing function 442 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. As described above, data before pre-processing is called pure raw data, and data after pre-processing is called projection data. The pre-processing function 442 is an example of a pre-processing unit.

[0033] The reconstruction processing function 443 performs reconstruction processing using a filtered back projection method (FBP method) or the like on the projection data generated by the pre-processing function 442 to generate CT image data (medical image data). The reconstruction processing includes various types of processing such as various correction processing such as scattering correction and beam hardening correction, and application of a reconstruction function under the reconstruction conditions. The reconstruction processing function 443 stores the reconstructed CT image data in the memory 41. The reconstruction processing function 443 is an example of a reconstruction processing unit.

[0034] The image processing function 444 converts the CT image data generated by the reconstruction processing function 443 into tomographic image data of an arbitrary cross section or three-dimensional image data by a known method based on an input operation received from an operator via the input interface 43. Note that the generation of the three-dimensional image data may be directly performed by the reconstruction processing function 443. The image processing function 444 is an example of an image processing unit.

[0035] When a second reconstruction condition different from the first reconstruction condition is set for the first medical image data, the generating function 445 generates second raw data corresponding to the reproduction of the first raw data based on the first medical image data. The second raw data is data obtained by reproducing the first raw data, and corresponds to pseudo first raw data. Specifically, the generating function 445 generates the second raw data by applying forward projection data obtained by forward projecting the first medical image data to a reproduction filter. More specifically, when the second reconstruction condition is set (input) via the input interface 43 (hereinafter, referred to as a reconstruction retry instruction), the generating function 445 reads out the forward projection condition and the reproduction filter associated with the first medical image data from the memory 41. Next, the generating function 445 generates forward projection data from the first medical image data using the forward projection condition read out from the memory 41. Next, the generating function 445 applies the forward projection data to the reproduction filter read out from the memory 41 to generate the second raw data. The reconstruction processing function 443 reconstructs the second medical image data by using the generated second raw data and the second reconstruction conditions. The reconstructed second medical image data is stored in the memory 41. The generation function 445 is an example of a generation unit.

[0036] The determination function 446 determines whether or not the second raw data is generated based on the characteristics of the first medical image data. The characteristics of the first medical image data are, for example, at least one of the element spacing of the multiple detection elements of the X-ray detector 12 mounted on the X-ray CT device 1 and the resolution of the first medical image data. The characteristics are attached to the first medical image data when the first medical image data is generated by the reconstruction processing function 443. The attached information attached to the first medical image data includes, in addition to the above characteristics, for example, imaging conditions related to the collection of the first raw data. The determination function 446 is an example of a determination unit.

[0037] The determination function 446 determines that the second raw data can be generated if the element spacing is equal to or less than a predetermined spacing or if the resolution is equal to or greater than the predetermined resolution. Also, the determination function 446 determines that the second raw data cannot be generated if the element spacing exceeds a predetermined spacing or if the resolution is less than the predetermined resolution. If the determination function 446 determines that the second raw data can be generated, that is, if reconstruction retry is possible, it determines that it is unnecessary to store the first raw data in the memory 41. At this time, the first raw data is not stored in the memory 41. Also, if the first raw data is stored in the memory 41 at this time, the system control function 441 deletes the first raw data from the memory 41. Also, if the determination function 446 determines that the second raw data cannot be generated, that is, if reconstruction retry is impossible, it determines that it is necessary to store the first raw data in the memory 41.

[0038] In the X-ray CT apparatus 1 according to this embodiment configured as described above, the process of generating second raw data from first medical image data in response to a reconstruction retry instruction and reconstructing the second medical image data based on the generated second raw data (hereinafter referred to as reconstruction retry process) will be described with reference to Figures 2 to 5.

[0039] FIG. 2 is a diagram showing an overview of the reconstruction retry process. FIG. 3 is a flowchart showing an example of the procedure of the reconstruction retry process. As shown in FIG. 2, prior to the reconstruction retry process, the memory 41 stores the forward projection conditions, the reproduction filter, and the first medical image data in association with each other. Also, the first raw data that is the source of the first medical image data is assumed to be unsaved at the time of setting the second reconstruction conditions. For example, the first raw data is held in the memory 41 for a certain period of time after the scan for generating the first raw data. That is, the first raw data is deleted from the memory 41 after the certain period of time. Note that the first raw data is not limited to the memory 41, and may be held by an image archiving server such as a picture archiving and communication system (hereinafter referred to as PACS). In other words, during the certain period of time, the reconstruction retry using the second reconstruction conditions for the first medical image data is executed by the first raw data.

[0040] (Reconfiguration retry process) (Step S301) A reconstruction retry instruction is input for the first medical image data by an instruction from the operator via the input interface 43. For example, a second reconstruction condition different from the first reconstruction condition is input for the first medical image data displayed on the display 42 by an instruction from the operator via the input interface 43. At this time, the generation function 445 identifies a portion of the first medical image data related to the reconstruction retry instruction under the second reconstruction condition. The generation function 445 identifies a reproduction filter related to the identified portion.

[0041] (Step S302) The determination function 446 determines whether a reconstruction retry is possible based on the characteristics of the first medical image data. For example, if the element interval is equal to or less than a predetermined interval or the resolution is equal to or greater than a predetermined resolution, the determination function 446 determines that the second raw data can be generated from the first medical image data (reconstruction retry possible) (Yes in step S302). At this time, the process of step S305 is executed. Also, if the element interval exceeds a predetermined interval or the resolution is equal to or less than the predetermined resolution, the determination function 446 determines that the second raw data cannot be generated (reconstruction retry not possible). At this time, the process of step S303 is executed.

[0042] The predetermined interval is, for example, 0.25 mm, but is not limited to this value. The predetermined resolution is, for example, the resolution of reconstruction data reconstructed based on the output from an X-ray detector having an element interval of 0.25 mm. The predetermined interval and the predetermined resolution are stored in advance in the memory 41.

[0043] (Step S303) The display 42 displays a predetermined warning. The predetermined warning is, for example, a character string indicating that the first raw data is not stored in the memory 41 and that the image quality of the image reconstructed by the reconstruction retry is not guaranteed to have a predetermined resolution. That is, according to the processing of steps S301 to S303, when it is determined that the second raw data cannot be generated, the display 42 displays the predetermined warning, for example, by a pop-up message, in response to the setting of the second reconstruction condition.

[0044] (Step S304) When an instruction to retry the reconstruction is input via the input interface 43 (Yes in step S304), the process of step S305 is executed. When an instruction to retry the reconstruction is not input via the input interface 43 (No in step S305), the reconstruction retry process ends.

[0045] (Step S305) The generation function 445 reads out the forward projection condition and the reproduction filter related to the first medical image data from the memory 41 according to the specified region.

[0046] (Step S306) The generating function 445 executes forward projection on the first medical image data using the forward projection condition, thereby generating forward projection data corresponding to the first medical image data.

[0047] (Step S307) The generation function 445 applies the forward projection data to a reproduction filter. The generation function 445 generates second raw data by the reproduction filter. The second raw data is data obtained by reproducing the first raw data, and corresponds to pseudo first raw data.

[0048] (Step S308) The reconstruction processing function 443 reconstructs the second medical image data based on the second raw data and the second reconstruction conditions. The reconstruction processing function 443 attaches, to the second medical image data, identification information indicating that the second raw data is different from the first raw data. The reconstruction processing function 443 stores the second medical image data with the identification information attached in the memory 41.

[0049] If the answer is Yes in step S302, the identification information is, for example, a character string that reads "reproduced." The identification information is not limited to the character string as long as it is information that indicates that the second raw data that is the source of reconstruction of the second medical image data is not the first raw data acquired by scanning the subject P, that is, that it is pseudo-generated raw data (pseudo pure raw data or pseudo projection data).

[0050] The first medical image data may be accompanied by identification information indicating that the first raw data from which the first medical image data is reconstructed is data (pure raw data or projection data) acquired by scanning the subject P. In this case, the identification information is, for example, a character string that reads "genuine." The identification information is not limited to the character string as long as it indicates that the first raw data is data acquired by scanning the subject P, that is, data that is truly generated by scanning.

[0051] If the answer is Yes in step S304, the identification information is, for example, a character string that reads, “Low-resolution reproduction.” The identification information is not limited to the character string as long as it is information that indicates that the second raw data that is the source of reconstruction of the second medical image data is not the first raw data acquired by scanning the subject P, that is, that the data is artificially generated, and that the image quality of the second medical image data cannot be guaranteed as a predetermined image quality.

[0052] (Step S306) The display 42 displays the second medical image data together with the identification information. Specifically, the image processing function 444 generates a display image related to the second medical image data based on the second medical image data. The display 42 displays the display image of the second medical image data together with the identification information.

[0053] FIG. 4 is a diagram showing a display image MI1 of the first medical image data displayed on the display 42, and a display image RI2 of the second medical image data in the case of Yes in step S302. As shown in FIG. 4, both the display image MI1 and the display image RI2 are axial images. Also, as shown in FIG. 4, the character string "genuine" is displayed as identification information II near the display image MI1 of the first medical image data. In addition, as shown in FIG. 4, the character string "reproduced" is displayed as identification information II near the display image RI2 of the second medical image data. As shown in FIG. 4, the display image RI2 generated by the reconstruction retry process according to this embodiment is also displayed with identification information.

[0054] 5 is a diagram showing a display image MI1 of the first medical image data displayed on the display 42, and a display image LRI2 of the second medical image data in the case where the result of step S304 is Yes. As shown in FIG. 5, the display image MI1 and the display image LR2 are both axial images. As shown in FIG. 5, a character string "Low-resolution reproduction" is displayed as identification information II near the display image LRI2 of the second medical image data. As shown in FIG. 5, the display image LRI2 generated by the reconstruction retry process according to this embodiment is also displayed with the identification information.

[0055] Hereinafter, a procedure of a filter generation process for generating a reproduction filter used in the reconstruction retry process will be described. The reproduction filter is a filter that reproduces the first raw data from the forward projection data obtained by forward projecting the first medical image data. For this reason, the reproduction filter is a filter that has a function of removing the influence of various correction processes and reconstruction functions used in the reconstruction process from the forward projection data. In other words, the reproduction filter is a filter that outputs (reproduces) second raw data corresponding to the first raw data by applying the forward projection data. The reproduction filter is realized by, for example, a general-purpose filter using deep learning (hereinafter referred to as DL), a dedicated filter in which the general-purpose filter is additionally learned using the forward projection data acquired according to the subject P and the first raw data related to the forward projection data, and a mathematical inverse filter (deconvolution). Instead of the above example, the reproduction filter may generate pseudo raw data to which a predetermined process, for example, scattered radiation correction process, has been applied. In this case, by associating information indicating what processing has been applied to the generated pseudo-raw data as identification information, various types of processing that are applied to raw data obtained by imaging can also be applied in an appropriate manner to the pseudo-raw data.

[0056] To make the explanation more specific, first, an example of applying a Convolutional Neural Network (CNN) as a general-purpose filter trained for each part will be explained. Next, a processing procedure of generating a reproduction filter by additionally training the general-purpose filter using first raw data and first medical image data related to the subject P will be explained.

[0057] FIG. 6 shows an example of a CNN used as an implementation of a reconstruction filter. A CNN is a type of neural network (NN) that has beneficial properties for image processing and is therefore particularly relevant for image denoising and sinogram restoration applications. A CNN uses a feedforward NN where the connectivity patterns between neurons can represent convolutions in image processing. For example, a CNN can be used for image processing optimization by using multiple layers of small neuron collections that process parts of the input image, called receptive fields. The outputs of these collections can then be tiled so that the outputs of these collections overlap to obtain a better representation of the original image. This processing pattern can be repeated over multiple layers with alternating convolutional and pooling layers.

[0058] In addition, Figure 6 shows an example of a fully connected network in which all nodes in the previous layer are used to determine the nodes in the next layer. This example is merely an example of a deep neural network (DNN). In CNN, it is common for the network to be a loosely connected (partially connected) network in which some nodes in the previous layer are used to determine the nodes in the next layer.

[0059] FIG. 7 illustrates an example of an offline training process 110 for generating a reproduction filter. The offline training process 110 is implemented, for example, in a training server or a medical image processing device. In step 130 of the process 110, the noisy data 115 and the optimized data 120 are used as training data to train a DL network. The DL network output from step 130 corresponds to the reproduction filter. More generally, the data 115 can be referred to as defect-presenting data. The "defects" in the defect-presenting data can be any undesired features, such as effects of various correction processes and reconstruction functions used in the reconstruction process, and noise caused by the reconstruction process and forward projection process on the first raw data.

[0060] In this embodiment, the data 115 corresponds to forward projection data obtained by performing forward projection on medical image data reconstructed for each part. The data 120 may be called "defect-reduced data", "defect-minimized data", or "optimized data" if the data has fewer defects than the defects in the data 115. In this embodiment, the data 120 corresponds to first measured raw data that is the source of the medical image data. In this embodiment, the offline training process trains the DL network 135 using a large number of forward projection data 115. The large number of forward projection data 115 are paired with a large number of first measured raw data in order to train the DL network 135 to generate second raw data similar to the first measured raw data from the forward projection data.

[0061] FIG. 8 shows an example flow diagram of an embodiment of step 130 of method 100 for training a network using training data.

[0062] In step 210 of step 130, an initial guess is generated for the coefficients of DL network 135. For example, the initial guess may be based on a priori knowledge of the region being imaged, or based on one or more denoising, edge detection, and / or blob detection techniques. Additionally, the initial guess may be based on DL network 135 being trained on training data associated with different noise levels, or using different CT scan techniques, as described above.

[0063] Exemplary denoising methods include linear smoothing filters, anisotropic diffusion, non-local means, and nonlinear filters. Linear smoothing filters remove noise by convolving the original image with a mask that represents a low-pass filter or smoothing operation. For example, a Gaussian mask has elements determined by a Gaussian function. The convolution attempts to make each pixel's value closer to the values ​​of its neighbors. Anisotropic diffusion removes noise while preserving sharp boundaries by evolving the image under a smooth partial differential equation similar to the heat equation. A median filter is an example of a nonlinear filter that, if properly designed, can also preserve boundaries and avoid blurring. A median filter is an example of a rank-conditioned rank-selection (RCRS) filter that can be applied to remove salt and pepper noise from an image without introducing obvious blurring artifacts. Furthermore, filters using a total variation (TV) minimization regularization term can be used when the imaged region supports the assumption that the region is homogeneous over large regions separated by sharp boundaries between homogeneous areas. A TV filter is another example of a nonlinear filter. Additionally, non-local means filtering is an exemplary method that uses a weighted average over similar patches in the image to determine the denoised pixels.

[0064] In step 220 of step 130, an error (e.g., a cost function) is calculated between the network processed noisy data (output from the CNN during training) 115 and the optimized data 120. The error can be calculated using any known cost function or distance measure between image (sinogram) data, including cost functions such as those described above.

[0065] In step 230 of step 130, a change in error can be calculated as a function of the changes in the network (e.g., an error gradient), and such change in error can be used to select the direction and step size for subsequent changes to the weights / coefficients of DL network 135. Calculating the error gradient in this manner is consistent with certain embodiments of gradient descent optimization. In certain other embodiments, as will be appreciated by those skilled in the art, this step may be omitted and / or replaced with another step following a different optimization algorithm (e.g., a non-gradient descent optimization algorithm such as simulated annealing or a genetic algorithm).

[0066] In step 240 of step 130, a new set of coefficients is determined for DL ​​network 135. For example, the weights / coefficients can be updated, such as in a gradient descent optimization method or an over-relaxation acceleration method, using the changes calculated in step 230.

[0067] In step 250 of step 130 , new error values ​​are calculated using the updated weights / coefficients of the DL network 135 .

[0068] In step 260 of step 130, a predetermined stopping criterion is used to determine whether the training of the network is complete. For example, the predetermined stopping criterion can determine whether the new error and / or the total number of iterations performed exceeds a threshold. For example, the stopping criterion can be met when either the new error falls below a predetermined threshold or the maximum number of iterations has been reached. If the stopping criterion is not met, the process 130 will continue back to the beginning of the iterative loop using new weights and coefficients by repeating back to step 230 (such iterative loop includes steps 230, 240, 250, 260). If the stopping criterion is met, the process 130 is complete. This generates a generic filter for each part.

[0069] In addition to the embodiment for error minimization shown in FIG. 8, process 130 can use one of many other known minimization methods, including, for example, local minimization methods, convex optimization methods, and global optimization methods.

[0070] When a cost function (e.g., error) has local minima different from the global minimum, a robust stochastic optimization process is useful to find the global minimum of the cost function. An example of an optimization method for finding a local minimum can be the Nelder-Mead simplex, gradient descent, Newton's method, conjugate gradient methods, shooting methods, or one of the other known local optimization methods. There are also many known methods for finding a global minimum, including genetic algorithms, simulated annealing, exhaustive search, interval methods, and other traditional deterministic, stochastic, heuristic, and metaheuristic methods. Any of these methods can be used to optimize the weights and coefficients of the DL network. Additionally, neural networks can be optimized using backpropagation.

[0071] Although the generic filter shown in FIG. 6 is generated according to the part, the generation of the generic filter is not limited to this. For example, in the activation function in each node shown in FIG. 6, the threshold of the activation function may be changed as a parameter according to the part. In this case, the threshold of the activation function is set according to the part in the learning process. When the generic filter generated in this way or a dedicated filter additionally learned from the generic filter is used as a reproduction filter, the generation function 445 adjusts the threshold of the activation function in the reproduction filter according to the part of the subject P under the second reconstruction condition, for example, in step S305.

[0072] Hereinafter, a procedure of a filter generation process for generating a dedicated filter corresponding to the first medical image data of the subject P as a reproduction filter using a general-purpose filter will be described. Fig. 9 is a diagram showing an overview of the filter generation process. Fig. 10 is a flowchart showing an example of the procedure of the filter generation process.

[0073] (Filter generation process) (Step S101) First raw data is acquired by scanning the subject P. The reconstruction processing function 443 generates first medical image data based on the first raw data.

[0074] (Step S102) The generation function 445 executes forward projection on the first medical image data using the forward projection conditions to generate forward projection data.

[0075] (Step S103) The generating function 445 additionally learns the generic filter so that the difference between the data output by applying the forward projection data to the generic filter and the first raw data is minimized, and generates a reproduction filter. The additional learning is similar to the process shown in Fig. 8, so a description thereof will be omitted. Note that the additional learning is not limited to being performed by the generating function, and may be performed by various servers such as PACS or a separate learning device.

[0076] (Step S104) The generation function 445 or the device that generated the reproduction filter stores the forward projection condition and the reproduction filter in association with the first medical image data in the memory 41. This step completes the generation of the reproduction filter. Note that the first raw data may be deleted from the memory 41 by the system control function 441 after a certain period of time has elapsed since the end of this step.

[0077] Furthermore, after the process of step S104, the determination function 446 may determine whether or not the second raw data can be generated, that is, whether or not a reconstruction retry using the first medical image data and the reproduction filter can be performed, based on the characteristics of the first medical image data. At this time, the display 42 displays whether or not the first raw data needs to be stored, depending on whether or not a reconstruction retry is possible. Specifically, when a reconstruction retry is possible, the determination function 446 determines that it is not necessary to store the first raw data in the memory 41. At this time, the first raw data is not stored in the memory 41, or is deleted from the memory 41.

[0078] Furthermore, if reconstruction retry is not possible, the determination function 446 determines that it is necessary to store the first raw data in the memory 41. At this time, the first raw data is stored in the memory 41, or is not deleted from the memory 41.

[0079] According to the X-ray CT apparatus 1 of the first embodiment described above, when a second reconstruction condition different from the first reconstruction condition is set for the first medical image data reconstructed based on the first raw data and the first reconstruction condition, the second raw data corresponding to a reproduction of the first raw data is generated based on the first medical image data, and the second medical image data is reconstructed based on the second raw data and the second reconstruction condition. Moreover, according to the X-ray CT apparatus 1, the first raw data is not yet saved at the time when the second reconstruction condition is set.

[0080] Specifically, according to the present X-ray CT apparatus 1, the second raw data is generated using a reproduction filter that reproduces the first raw data from forward projection data obtained by forward projecting the first medical image data. Also, according to the present X-ray CT apparatus 1, the reproduction filter is generated using the forward projection data and the first raw data so as to bring the forward projection data closer to the first raw data. At this time, according to the present X-ray CT apparatus 1, the forward projection conditions for generating the forward projection data used in generating the reproduction filter are stored in association with the reproduction filter and the first medical image data, and the forward projection data is generated from the first medical image data using the forward projection conditions associated with the first medical image data, and the forward projection data is applied to the reproduction filter associated with the first medical image data to generate the second raw data. From these points, the reproducibility of the first raw data by the second raw data can be improved by the reproduction filter.

[0081] Moreover, according to the present X-ray CT apparatus 1, whether or not the second raw data can be generated is determined based on the characteristics of the first medical image data. Specifically, the present X-ray CT apparatus 1 determines that the second raw data can be generated when the element interval is equal to or less than a predetermined interval or the resolution of the first medical image data is equal to or greater than a predetermined resolution, and determines that the second raw data cannot be generated when the element interval exceeds the predetermined interval or the resolution is less than or equal to the predetermined resolution. When it is determined that the second raw data can be generated, the second raw data is generated in response to the setting of the second reconstruction condition, and when it is determined that the second raw data cannot be generated, a predetermined warning is displayed in response to the setting of the second reconstruction condition. Moreover, according to the present X-ray CT apparatus 1, whether or not the first raw data needs to be stored is displayed based on the result of the determination of whether or not the second raw data can be generated.

[0082] From these facts, according to the present X-ray CT apparatus 1, even if the first raw data is collected by a high-resolution detector, the capacity of the memory 41 can be saved compared to when the first raw data related to the reconstructed medical image data is stored. Also, even if the first raw data is not stored, the second raw data that is substantially equivalent to the first raw data can be reproduced by using a reproduction filter in response to an instruction for retrying the reconstruction from a radiologist or a clinician, and medical image data related to the retry of the reconstruction can be generated with image quality equivalent to that of the first raw data. From the above facts, according to the present X-ray CT apparatus 1, by saving the capacity of the memory 41, it is possible to reduce, for example, the data management cost in a hospital.

[0083] (Application example) In this application example, when the element interval exceeds a predetermined interval or the resolution of the first medical image data is less than a predetermined resolution, a super-resolution filter that improves the resolution of the forward projection data is applied to the forward projection data to generate super-resolution data, and the super-resolution data is applied to a reproduction filter to generate second raw data. The super-resolution filter is generated in advance and stored in the memory 41. The generation of the super-resolution filter will be described later.

[0084] FIG. 11 is a flowchart showing an example of a procedure for a reconfiguration retry process in this application example.

[0085] (Reconfiguration retry process) (Step S111) A reconstruction retry instruction for the first medical image data is input by an operator via the input interface 43. The processing content of this step is similar to that of step S301, and therefore a description thereof will be omitted.

[0086] (Step S112) The generating function 445 reads out the forward projection condition and the reproduction filter related to the first medical image data from the memory 41 according to the specified part. The generating function 445 executes forward projection on the first medical image data using the forward projection condition. As a result, the generating function 445 generates forward projection data corresponding to the first medical image data.

[0087] (Step S113) The determination function 446 determines whether the element interval exceeds a predetermined interval or whether the resolution of the first medical image data is less than a predetermined resolution. If the element interval is less than the predetermined interval or the resolution of the first medical image data is greater than or equal to the predetermined resolution, the process of step S114 is executed. If the element interval exceeds the predetermined interval or the resolution of the first medical image data is less than the predetermined resolution (step S113 Yes), the process of step S115 is executed.

[0088] (Step S114) The generation function 445 applies the forward projection data to a reproduction filter. The generation function 445 generates second raw data by the reproduction filter. The process in this step is the same as that in step S307.

[0089] (Step S115) The generation function 445 reads the super-resolution filter from the memory 41. The generation function 445 applies the forward projection data to the super-resolution filter to generate super-resolution data.

[0090] (Step S116) The generating function 445 applies the super-resolution data to a reproduction filter, and generates second raw data by the reproduction filter.

[0091] (Step S117) The reconstruction processing function 443 reconstructs the second medical image data based on the second raw data and the second reconstruction conditions. The processing in this step will not be described for the same content as in step S308. If the result in step S113 is Yes, the identification information is, for example, a character string "super-resolution". The identification information is not limited to the character string as long as it indicates that the second raw data from which the second medical image data is reconstructed is not the first raw data acquired by scanning the subject P, that is, that the data is artificially generated, and indicates that the second raw data is data whose resolution has been improved by a super-resolution filter.

[0092] (Step S118) The display 42 displays the second medical image data together with the identification information. Specifically, the image processing function 444 generates a display image related to the second medical image data based on the second medical image data. The display 42 displays the display image of the second medical image data together with the identification information.

[0093] FIG. 12 is a diagram showing a display image MI1 of the first medical image data displayed on the display 42, and a display image HRI2 of the second medical image data in the case of Yes in step S113. As shown in FIG. 12, both the display image MI1 and the display image RI2 are axial images. Also, as shown in FIG. 12, the character string "true" is displayed as identification information II near the display image MI1 of the first medical image data. In addition, as shown in FIG. 12, the character string "super-resolution" is displayed as identification information II near the display image HRI2 of the second medical image data. As shown in FIG. 12, the display image RI2 generated by the reconstruction retry process according to this embodiment is also displayed with identification information.

[0094] The generation of the super-resolution filter will be briefly described below. The super-resolution filter is generated, for example, by executing learning on a CNN as shown in FIG. 6. The flow of generating the super-resolution filter is the same as that in FIG. 7. As an example, the resolution of the forward projection data input to the super-resolution filter (hereinafter referred to as input resolution) corresponds to an element interval of 0.5 mm, and the resolution of the super-resolution data output from the super-resolution filter (hereinafter referred to as output resolution) corresponds to an element interval of 0.25 mm. In addition, the super-resolution filter is generated for each part.

[0095] A super-resolution filter is generated for each part and for each combination of input resolution and output resolution. That is, a plurality of super-resolution filters may be generated according to the combination of input resolution and output resolution (hereinafter, referred to as resolution combination) and the part. At this time, the super-resolution filter used in step S115 is selected by the generation function 445 according to the resolution of the first medical image data and the part and output resolution in the second reconstruction condition.

[0096] In Fig. 7, data 120 relating to this application example corresponds to forward projection data having an output resolution (hereinafter referred to as output projection data). Data 115 relating to this application example corresponds to pseudo forward projection data having an input resolution obtained by downsampling the output projection data (hereinafter referred to as input projection data). Super-resolution data is generated by training a CNN using the input projection data and the output projection data in the flow shown in Figs. 7 and 8. The processing procedure relating to the generation of a super-resolution filter is similar to that shown in Figs. 7 and 8, and therefore will not be described.

[0097] In addition, the super-resolution filter may change the activation function threshold in each node shown in Fig. 6 according to the part and the resolution combination as a parameter. In this case, the threshold in the activation function is set according to the part and the resolution combination in the learning process. When the super-resolution filter generated in this way is used, the generation function 445 adjusts the threshold of the activation function in the super-resolution filter according to the part of the subject P and the resolution in the first medical image data under the second reconstruction condition, for example, before the execution of the process of step S115.

[0098] According to the X-ray CT apparatus 1 according to the application example of the first embodiment described above, when the element interval exceeds a predetermined interval or the resolution of the first medical image data is less than a predetermined resolution, a super-resolution filter that improves the resolution of the forward projection data related to the first medical image data is applied to the forward projection data to generate super-resolution data, and the super-resolution data is applied to the reproduction filter to generate second raw data. As a result, even if the resolution of the first medical image data is low, the second medical image data of a predetermined output resolution can be generated by using the super-resolution filter. Therefore, even if the first raw data related to the predetermined resolution is not stored, a reconstruction retry can be performed, and the efficiency of the examination, etc. can be improved. Other effects are the same as those of the first embodiment, so a description thereof will be omitted.

[0099] Second embodiment In the second embodiment, the first medical image data reconstructed by iterative reconstruction on the first raw data is forward projected to generate second raw data, and the second raw data is generated by executing the iterative reconstruction on the second raw data. That is, the method of reconstructing medical image data in this embodiment is iterative reconstruction. The iterative reconstruction is, for example, a method of repeatedly reducing the difference between pseudo forward projection data obtained by forward projecting a reconstructed image reconstructed from the first raw data and the first raw data, thereby minimizing the difference and reconstructing medical image data. The iterative reconstruction is realized by algebraic calculation or a learned model. The configuration in this embodiment corresponds to one in which the determination function 446 in FIG. 1 is omitted.

[0100] 13 is a schematic diagram showing an outline of iterative reconstruction. As shown in FIG. 13, the reconstruction processing function 443 reconstructs first medical image data (FULL-IR (Iterative Reconstruction) image) by algebraic iterative reconstruction of the first raw data. When a learned model (Deep-Learning Reconstruction) is used as the iterative reconstruction, the first medical image data becomes a pseudo Full-IR image. When the reconstruction of the first medical image data is completed, the reconstruction processing function 443 stores the first medical image data in the memory 41 and deletes the first raw data from the memory 41.

[0101] Fig. 14 is a diagram showing an overview of the reconstruction retry process. As shown in Fig. 14, the generation function 444 reads out the first medical image data from the memory 41. The generation function 444 executes forward projection on the read out first medical image data to generate second raw data. The generation function 444 may apply a reproduction filter to the forward projection data forward projected onto the first medical image data to generate the second raw data. Next, the reconstruction processing function 443 generates the second medical image data by iterative approximation reconstruction using the second reconstruction condition.

[0102] According to the X-ray CT apparatus 1 according to the second embodiment described above, the first medical image data reconstructed by iterative reconstruction on the first raw data is forward projected to generate second raw data, and iterative reconstruction is performed on the second raw data to generate second medical image data. The medical image data generated by iterative reconstruction is a process that minimizes the difference between the first raw data and the forward projection data by forward projection, so that the reproducibility of the first raw data by the second raw data can be improved. Other effects are similar to those of the first embodiment, so a description thereof will be omitted.

[0103] As a modification of each of the above embodiments, the process related to the reconstruction retry may be executed not only by a medical image diagnostic device such as the X-ray CT device 1 but also by an image processing server independent of a scanner such as a medical image diagnostic device. The image processing server is not limited to an in-hospital server such as a PACS. In this case, the first raw data is held in the image processing server or a data server communicatively connected to the image processing server.

[0104] When the technical idea of ​​the present embodiment is realized by a medical image processing device, when a second reconstruction condition different from the first reconstruction condition is set for the first medical image data reconstructed based on the first raw data and the first reconstruction condition, the medical image processing device generates second raw data corresponding to the reproduction of the first raw data based on the first medical image data, and reconstructs the second medical image data based on the second raw data and the second reconstruction condition. FIG. 15 is a configuration diagram showing an example of a medical image processing device 5. The medical image processing device 5 has, for example, a memory 51, a display 52, an input interface 53, and a processing circuit 54. In addition, the processing circuit 54 has, for example, a reconstruction processing function 543, an image processing function 544, a generation function 545, and a determination function 546. The medical image processing device 5 executes a reconstruction retry process based on the configuration shown in FIG. 15. Regarding the reconstruction retry processing executed by the medical image processing apparatus, the processing contents in the various components and various functions, and the procedure and effects of the reconstruction retry processing are similar to those in the first and second embodiments, and therefore a description thereof will be omitted.

[0105] When the technical idea of ​​the embodiment is realized in a medical image processing method, the medical image processing method sets a second reconstruction condition different from the first reconstruction condition for first medical image data reconstructed based on the first raw data and the first reconstruction condition, generates second raw data corresponding to a reproduction of the first raw data based on the first medical image data in response to the setting of the second reconstruction condition, and reconstructs the second medical image data based on the second raw data and the second reconstruction condition. The procedure and effect of the reconstruction retry process executed by the medical image processing method are similar to those of the first and second embodiments, and therefore will not be described.

[0106] When the technical ideas in the embodiments are realized by a medical image processing program, the medical image processing program causes a computer to set second reconstruction conditions different from the first reconstruction conditions for first medical image data reconstructed based on the first raw data and the first reconstruction conditions, generate second raw data corresponding to a reproduction of the first raw data based on the first medical image data in response to the setting of the second reconstruction conditions, and reconstruct the second medical image data based on the second raw data and the second reconstruction conditions.

[0107] For example, the reconstruction retry process can be realized by installing a medical image processing program in various server devices (processing devices) related to medical image processing and expanding the program in memory. In this case, the program that can cause a computer to execute the method can be stored in a storage medium such as a magnetic disk (hard disk, etc.), an optical disk (CD-ROM, DVD, etc.), or a semiconductor memory and distributed. The processing procedure and effects of the medical image processing program are similar to those of the first and second embodiments, so a description thereof will be omitted.

[0108] When the technical idea of ​​the embodiment is realized by a medical image diagnostic device, the medical image diagnostic device corresponds to the above-mentioned X-ray CT device 1, nuclear medicine diagnostic device, or magnetic resonance imaging device. The first raw data is acquired by the X-ray CT device 1, nuclear medicine diagnostic device, or magnetic resonance imaging device. When the medical image diagnostic device is a PET (Positron Emission Tomography) device in the nuclear medicine diagnostic device, the first raw data corresponds to, for example, list mode data or sinogram mode data. Also, when the medical image diagnostic device is a magnetic resonance imaging device, the first raw data corresponds to k-space data.

[0109] In another embodiment, the X-ray CT apparatus 1 does not need to have the bed apparatus 30. For example, when the opening of the gantry apparatus 10 of the X-ray CT apparatus 1 has a substantially cylindrical shape extending vertically, the subject is photographed in an upright position, and therefore the bed apparatus 30 is not necessary.

[0110] According to at least one of the embodiments and modifications described above, it is possible to provide a medical image processing device capable of saving storage capacity.

[0111] 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]

[0112] 1 X-ray CT device 5 Medical image processing equipment 10 Mounting device 11 X-ray tube 12 X-ray detector 13 Rotating Frame 14 High voltage X-ray device 15 Control device 16 Wedge 17 Collimator 18 DAS(Data Acquisition System) 30 Sleeping device 31 Foundation 32 Bed drive unit 33 Tabletop 34 Support frame 40 Console device 41 Memory 42 Display 43 Input Interface 44 Processing circuit 51 Memory 52 Display 53 Input Interface 54 Processing circuit 441 System Control Functions 442 Pre-processing function 443 Reconstruction Processing Function 444 Image Processing Function 445 Generation function 446 Judgment Function 543 Reconstruction Processing Function 544 Image Processing Function 545 Generation function 546 Judgment Function

Claims

1. a generating unit that generates, when a second reconstruction condition different from the first reconstruction condition is set for first medical image data reconstructed based on the first raw data and the first reconstruction condition, second raw data corresponding to a reproduction of the first raw data based on the first medical image data; a reconstruction processing unit that reconstructs second medical image data based on the second raw data and the second reconstruction conditions; A medical image processing device comprising:

2. The generating unit generates the second raw data using a reproduction filter that reproduces the first raw data from forward projection data obtained by forward projecting the first medical image data. The medical image processing device according to claim 1 .

3. the reproduction filter is generated by using the forward projection data and the first raw data so as to approximate the forward projection data to the first raw data; The medical image processing device according to claim 2 .

4. a storage unit that stores a forward projection condition related to generation of the forward projection data used in generating the reproduction filter in association with the reproduction filter and the first medical image data; The generation unit is generating the forward projection data from the first medical image data using the forward projection condition associated with the first medical image data; applying the forward projection data to the reproduction filter associated with the first medical image data to generate the second raw data. The medical image processing device according to claim 2 or 3.

5. The generating unit generates second raw data by forward projecting the first medical image data reconstructed by iterative reconstruction on the first raw data; The reconstruction processing unit performs the iterative reconstruction on the second raw data to generate the second medical image data. The medical image processing device according to claim 1 .

6. The first raw data is unstored at the time of setting the second reconstruction condition. The medical image processing apparatus according to claim 1 .

7. A determination unit that determines whether or not the second raw data can be generated based on characteristics of the first medical image data. The medical image processing apparatus according to claim 1 .

8. The first raw data is acquired by an X-ray computed tomography apparatus or a nuclear medicine diagnostic apparatus; The characteristic is at least one of an element interval between a plurality of detection elements of a radiation detector mounted on the X-ray computed tomography apparatus or the nuclear medicine diagnosis apparatus and a resolution of the first medical image data, the determination unit determines that the second raw data can be generated when the element spacing is equal to or smaller than a predetermined spacing or when the resolution is equal to or larger than a predetermined resolution, and determines that the second raw data cannot be generated when the element spacing exceeds the predetermined spacing or when the resolution is smaller than the predetermined resolution, When it is determined that the second raw data can be generated, the generating unit generates the second raw data in response to setting of the second reconstruction condition; a display unit that displays a predetermined warning in response to the setting of the second reconstruction condition when it is determined that the second raw data cannot be generated, The medical image processing device according to claim 7 .

9. The first raw data is acquired by an X-ray computed tomography apparatus or a nuclear medicine diagnostic apparatus; At least one of an element interval in a plurality of detection elements of a radiation detector mounted on the X-ray computed tomography apparatus or the nuclear medicine diagnosis apparatus and a resolution in the first medical image data is added to the first medical image data, The generation unit is If the element spacing exceeds a predetermined spacing or if the resolution of the first medical image data is less than a predetermined resolution, a super-resolution filter that improves the resolution of the forward projection data is applied to the forward projection data to generate super-resolution data; applying the super-resolution data to the reconstruction filter to generate the second raw data; The medical image processing apparatus according to claim 2 .

10. and a display unit that displays the second medical image data together with identification information indicating that the second raw data is different from the first raw data. The medical image processing apparatus according to claim 1 .

11. The determination unit is If it is determined that the second raw data can be generated, it is determined that it is unnecessary to store the first raw data; If it is determined that the second raw data cannot be generated, it is determined that the first raw data needs to be stored; Further comprising a display unit that displays whether or not the first raw data needs to be stored.

9. The medical image processing device according to claim 7 or 8.

12. A medical image diagnostic apparatus comprising the medical image processing apparatus according to any one of claims 1 to 11, The medical image diagnostic apparatus is an X-ray computed tomography apparatus or a nuclear medicine diagnostic apparatus. Medical imaging diagnostic equipment.

13. A magnetic resonance imaging apparatus comprising the medical image processing apparatus according to any one of claims 1 to 4.

14. setting a second reconstruction condition different from the first reconstruction condition for the first medical image data reconstructed based on the first raw data and the first reconstruction condition; generating second raw data corresponding to a reproduction of the first raw data based on the first medical image data in response to the setting of the second reconstruction condition; reconstructing second medical image data based on the second raw data and the second reconstruction condition; A medical image processing method comprising:

Images