JP7487367B2 - X-ray CT apparatus, detector correction method, and medical information system - Google Patents

Description

An embodiment of the present invention relates to an X-ray CT device and a detector unit.

The response characteristics of the detector used in the X-ray CT scanner include afterglow characteristics, charge sharing, pile-up, etc., and these multiple response characteristics occur simultaneously. Therefore, when performing image reconstruction using the output from a photon-counting detector, for example, it is necessary to correct the response characteristics of the detector.
The above-mentioned correction process for the multiple response characteristics involves constructing a model for each characteristic, calculating a correction coefficient, and obtaining a value obtained by correcting the response characteristic from the obtained detector output.
However, since the above-mentioned multiple response characteristics occur in a time-dependent manner as a series of responses, it is very difficult to simultaneously correct them, that is, to determine a response function and perform the correction.

U.S. Pat. No. 9,833,202 U.S. Pat. No. 9,854,656 US Patent Application Publication No. 2018/0192977

The problem that this invention aims to solve is to perform highly accurate correction.

The X-ray CT apparatus according to this embodiment includes an acquisition unit and a correction unit. The acquisition unit acquires a plurality of signals corresponding to a plurality of views, including a view to be corrected, obtained by the same detection element. The correction unit corrects the response characteristics of a detector including the detection element for the signal corresponding to the view to be corrected, based on the plurality of signals.

FIG. 1 is a block diagram showing an X-ray CT apparatus according to this embodiment. FIG. 2 is a diagram illustrating a method for generating a trained model according to this embodiment. FIG. 3 is a diagram showing a concept of learning a model according to this embodiment. FIG. 4 is a diagram illustrating a concept of using a trained model according to this embodiment. FIG. 5 is a diagram showing a first example of input data to a trained model during use in this embodiment. FIG. 6 is a diagram showing a first input example of input data to a trained model one time ahead from FIG. 5 . FIG. 7 is a diagram showing a second input example of input data for a trained model during use in this embodiment. FIG. 8 is a diagram showing a second input example of input data to the trained model one time ahead from FIG. 7 .

The X-ray CT (Computed Tomography) device and detector unit according to this embodiment will be described below with reference to the drawings. In the following embodiment, parts with the same reference numerals perform similar operations, and duplicated descriptions will be omitted as appropriate. One embodiment will be described below with reference to the drawings.

The X-ray CT device according to this embodiment will be described below with reference to the block diagram in FIG. 1. The X-ray CT device 1 shown in FIG. 1 has a gantry device 10, a bed device 30, and a console device 40 that realizes the processing of the X-ray CT device. For the sake of convenience of explanation, multiple gantry devices 10 are drawn in FIG. 1.

In this embodiment, the rotation axis of the rotating frame 13 in the non-tilted state or the longitudinal direction of the tabletop 33 of the bed device 30 is defined as the Z-axis direction, the axis direction perpendicular to the Z-axis direction and horizontal to the floor surface is defined as the X-axis direction, and the axis direction perpendicular to the Z-axis direction and perpendicular to the floor surface is defined as the Y-axis direction.

For example, the gantry device 10 and the bed device 30 are installed in a CT examination room, and the console device 40 is installed in a control room adjacent to the CT examination room. Note that the console device 40 does not necessarily have to be installed in a control room. For example, the console device 40 may be installed in the same room as the gantry device 10 and the bed device 30. In any case, the gantry device 10, the bed device 30, and the console device 40 are connected to each other by wire or wirelessly so that they can communicate with each other.

The gantry device 10 is a scanning device configured to perform X-ray CT imaging of the subject P. The gantry device 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 data acquisition device 18 (hereinafter also referred to as DAS (Data Acquisition System) 18).

The X-ray tube 11 is a vacuum tube that generates X-rays by irradiating thermoelectrons from a cathode (filament) to an anode (target) through the application of high voltage from the X-ray high voltage device 14 and the supply of filament current. Specifically, X-rays are generated when the thermoelectrons collide with the target. For example, the X-ray tube 11 is a rotating anode type X-ray tube that generates X-rays by irradiating a rotating anode with thermoelectrons. The X-rays generated by the X-ray tube 11 are shaped into a cone beam, for example, via a collimator 17, and irradiated to the subject P.

In this embodiment, the X-ray detector 12 is assumed to be a photon-counting detector, and a general X-ray detector, i.e., an integral type detector.

When the X-ray detector 12 is a photon-counting detector, it detects the X-rays emitted from the X-ray tube 11 and passing through the subject P in photon units. The X-ray detector 12 has, for example, multiple X-ray detection element rows in which multiple X-ray detection elements are arranged in the channel direction along one arc centered on the focal point of the X-ray tube 11. The X-ray detector 12 has, for example, a row structure in which multiple X-ray detection element rows in which multiple X-ray detection elements are arranged in the channel direction are arranged in the slice direction (row direction).

Specifically, the X-ray detector 12 is an indirect conversion type detector having, for example, a grid, a scintillator array, and a photosensor array. The X-ray detector 12 is an example of a detection unit.
The scintillator array includes a plurality of scintillators that convert incident X-rays into a number of photons that correspond to the intensity of the incident X-rays.
The grid is disposed on the X-ray incident surface of the scintillator array and has an X-ray shielding plate that has a function of absorbing scattered X-rays. The grid is also called a collimator (a one-dimensional collimator or a two-dimensional collimator).
The optical sensor array has a function of amplifying the light received from the scintillator, converting it into an electric signal, and generating an output signal (energy signal) having a peak value according to the energy of the incident X-ray, and has an optical sensor such as a photomultiplier tube (PMT). The generated energy signal is output to the DAS 18.

On the other hand, when the X-ray detector 12 is an integral type detector, it detects X-rays irradiated from the X-ray tube 11 and passing through the subject P, and outputs an electrical signal corresponding to the amount of X-rays to the DAS 18. The scintillator array has a plurality of scintillators. The scintillator has a scintillator crystal that receives incident X-rays and outputs light with a photon amount corresponding to the incident X-rays. The photosensor array has a function of amplifying light received from the scintillator and converting it into an electrical signal, and has a photosensor such as a photomultiplier tube (PMT).
Although the X-ray detector 12 described above is assumed to be an indirect conversion type detector, it may be a direct conversion type detector having a semiconductor element that converts incident X-rays into an electrical signal.

The rotating frame 13 supports the X-ray generating unit (X-ray tube 11, wedge 16, and collimator 17) and the X-ray detector 12 so that they can rotate around a rotation axis. Specifically, 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 using a control device 15 described below. The rotating frame 13 is rotatably supported by a fixed frame (not shown) made of a metal such as aluminum. More specifically, the rotating frame 13 is connected to the edge of the fixed frame via a bearing. The rotating frame 13 receives power from the drive mechanism of the control device 15 and rotates at a constant angular velocity around the rotation axis Z.

The rotating frame 13 further includes and supports the X-ray high voltage device 14 and the DAS 18 in addition to the X-ray tube 11 and the X-ray detector 12. Such a rotating frame 13 is housed in a substantially cylindrical housing in which an opening (bore) 19 forming an imaging space is formed. The opening substantially coincides with the FOV. The central axis of the opening coincides with the rotation axis Z of the rotating frame 13. The imaging data generated by the DAS 18 is transmitted, for example, from a transmitter having a light-emitting diode (LED) to a receiver (not shown) having a photodiode provided in a non-rotating part of the gantry (for example, a fixed frame; not shown in FIG. 1) by optical communication, and is then transferred to the console device 40. The method of transmitting imaging data from the rotating frame to the non-rotating part of the gantry is not limited to the optical communication described above, and any method of non-contact data transmission may be used.

The X-ray high voltage device 14 has electrical circuits such as a transformer and a rectifier, and includes a high voltage generator having the function of generating a high voltage to be applied to the X-ray tube 11 and a filament current to be supplied to the X-ray tube 11, and an X-ray control device that controls the output voltage according to the X-rays emitted by the X-ray tube 11. The high voltage generator may be of a transformer type or an inverter type. The X-ray high voltage device 14 may be provided on the rotating frame 13 described later, or on the fixed frame (not shown) side of the gantry device 10.

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 processing circuit has a processor such as a CPU or an MPU (Micro Processing Unit) and a memory such as a ROM (Read Only Memory) or a RAM (Random Access Memory) as hardware resources. The control device 15 may also be realized by an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), other Complex Programmable Logic Devices (CPLDs), or Simple Programmable Logic Devices (SPLDs). The control device 15 controls the X-ray high voltage device 14 and the DAS 18, etc., according to commands from the console device 40. The processor realizes the above control by reading and implementing the programs stored in the memory.

The control device 15 also has a function of controlling the operation of the gantry 10 and the bed 30 upon receiving an input signal from an input interface 43 (described later) attached to the console device 40 or the gantry 10. For example, the control device 15 receives an input signal and controls the rotation of the rotating frame 13, the tilt of the gantry 10, and the operation of the bed 30 and the tabletop 33. The control of tilting the gantry 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 the input interface 43 attached to the gantry 10. The control device 15 may be provided in the gantry 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. In this case, the processor realizes the above control by reading and executing the program incorporated in the circuit.

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. For example, the wedge 16 (wedge filter, bow-tie filter) is a filter made of processed aluminum to have a predetermined target angle and a predetermined thickness.

The collimator 17 is a lead plate or the like that narrows the irradiation range of the X-rays that have passed through the wedge 16, and a slit is formed by combining multiple lead plates or the like. The collimator 17 is sometimes called an X-ray aperture.

The DAS 18 is realized by, for example, an ASIC (Application Specific Integrated Circuit) equipped with circuit elements capable of generating imaging data. The DAS 18 and the X-ray detector 12 constitute a detector unit.
When the X-ray detector 12 is a photon-counting detector, the DAS 18 generates digital data (hereinafter also referred to as spectrum data) indicating the count of X-rays detected by the X-ray detector 12 for each of a plurality of energy bands (hereinafter also referred to as energy bins or simply bins). The spectrum data is a set of count value data identified by the channel number of the detector element that generated the spectrum data, the row number, the view number indicating the collected view (also referred to as the projection angle), and the energy bin number. The spectrum data is transferred to the console device 40.

On the other hand, when the X-ray detector 12 is an integral detector, the DAS 18 reads out an electrical signal from the X-ray detector 12, and generates projection data, which is digital data relating to the dose of X-rays detected by the X-ray detector 12, based on the read out electrical signal. The projection data is a set of data indicating the channel number and row number of the X-ray detection element from which the projection data was generated, the view number indicating the collected view (also called the projection angle), and the integral value of the detected dose of X-rays.
For example, the DAS 18 includes a preamplifier, a variable amplifier, an integration circuit, and an A/D converter for each detection element. The preamplifier amplifies the electrical signal from the connected detection element with a predetermined gain. The variable amplifier amplifies the electrical signal from the preamplifier with a variable gain. The integration circuit integrates the electrical signal from the preamplifier over one view period to generate an integrated signal. The peak value of the integrated signal corresponds to the X-ray dose value detected by the connected detection element over one view period. The A/D converter performs analog-to-digital conversion of the integrated signal from the integration circuit to generate projection data. Hereinafter, when there is no distinction between spectrum data and projection data, they are referred to as imaging data.

The bed device 30 is a device for placing and moving the subject P to be scanned, and is equipped with 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 the support frame 34 is movable in the vertical direction.
The bed driving device 32 is a motor or actuator that moves the tabletop 33 on which the subject P is placed in the longitudinal direction of the tabletop 33. The bed driving device 32 moves the tabletop 33 under the control of the console device 40 or the control of the control device 15. For example, the bed driving device 32 moves the tabletop 33 in a direction perpendicular to the subject P so that the body axis of the subject P placed on the tabletop 33 coincides with the central axis of the opening of the rotating frame 13. The bed driving device 32 may also move the tabletop 33 along the body axis direction of the subject P in accordance with the X-ray CT imaging performed using the gantry device 10. The bed driving device 32 generates power by driving at a rotation speed according to the duty ratio of a drive signal from the control device 15. The bed driving device 32 is realized by a motor such as a direct drive motor or a servo motor.

The tabletop 33 provided on the upper surface of the support frame 34 is a plate on which the subject P is placed. The bed driving device 32 may move the support frame 34 in the longitudinal direction of the tabletop 33 in addition to the tabletop 33.

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 via a bus (BUS). Note that although the console device 40 is 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.

The memory 41 is a storage device such as an HDD (Hard Disk Drive), SSD (Solid State Drive), or integrated circuit storage device that stores various information. The memory 41 stores, for example, imaging data and reconstructed image data. In addition to an HDD or SSD, the memory 41 may be a drive device that reads and writes various information between a portable storage medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a flash memory, or a semiconductor memory element such as a RAM (Random Access Memory). The storage area of the memory 41 may be in the X-ray CT device 1 or in an external storage device connected via a network. For example, the memory 41 stores data of CT images and display images. The memory 41 also stores a control program according to this embodiment.

The display 42 displays various information. For example, the display 42 outputs medical images (CT images) generated by the processing circuit 44, a GUI (Graphical User Interface) for receiving various operations from the operator, and the like. For example, the display 42 may be a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electroluminescence display (OELD), a plasma display, or any other display, as appropriate. The display 42 may also be provided on the pedestal device 10. The display 42 may also be a desktop type, or may be configured as a tablet terminal capable of wireless communication with the console device 40 main body.

The input interface 43 accepts various input operations from the operator, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 44. For example, the input interface 43 accepts from the operator the acquisition conditions for acquiring imaging data, the reconstruction conditions for reconstructing CT images, and the image processing conditions for generating post-processed images from CT images. 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 used as appropriate. 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 electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the processing circuit 44 is also included as an example of the input interface 43. The input interface 43 may be provided in the gantry device 10. The input interface 43 may also be configured as a tablet terminal capable of wireless communication with the console device 40 main body.

The processing circuitry 44 controls the operation of the entire X-ray CT apparatus 1 in response to the electrical signals of the 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, an acquisition function 442, a selection function 443, a correction function 444, a reconstruction processing function 445, and a display control function 446 by a processor that executes a program deployed in the memory. Note that each function (system control function 441, acquisition function 442, selection function 443, correction function 444, reconstruction processing function 445, and display control function 446) is not limited to being realized by a single processing circuit. A processing circuit may be configured by combining multiple independent processors, and each processor may execute a program to realize each function.

The system control function 441 controls each function of the processing circuit 44 based on the input operation received from the operator via the input interface 43. Specifically, the system control function 441 reads out the control program stored in the memory 41, expands it on the memory in the processing circuit 44, and controls each part of the X-ray CT device 1 according to the expanded control program. For example, the processing circuit 44 controls each function of the processing circuit 44 based on the input operation received from the operator via the input interface 43. For example, the system control function 441 acquires a two-dimensional positioning image of the subject P for determining the scan range, imaging conditions, etc. The positioning image is also called a scano image or a scout image. The system control function 441 is an example of a system control unit.

The acquisition function 442 acquires, in each detection element of the X-ray detector 12, a plurality of signals corresponding to a plurality of views obtained by the same detection element. The plurality of views includes a correction target view which is a view to be corrected. The plurality of signals corresponding to the plurality of views indicate history data of signal values along a time series. The acquisition function 442 is an example of an acquisition unit.
When a trained model is generated for each X-ray condition, the selection function 443 refers to the trained model generated for each X-ray condition and selects the trained model corresponding to the X-ray condition when acquiring the signal of the view to be processed.

The correction function 444 corrects the response characteristics of the X-ray detector 12 including detection elements for signals corresponding to the view to be corrected, based on the multiple signals acquired by the acquisition function 442. The correction function 444 generates a signal in which the response characteristics of the X-ray detector 12 have been corrected for signals corresponding to the view to be corrected, according to a learned model that receives multiple signals, for example, and outputs a signal in which the response characteristics of the X-ray detector 12 have been corrected. In this embodiment, the "signal in which the response characteristics of the X-ray detector have been corrected" means a signal that is estimated to be output from the X-ray detector in which the response characteristics have been corrected. The correction function 444 is an example of a processing unit.
The reconstruction processing function 445 performs reconstruction processing using a filtered back projection method (FBP method), an iterative reconstruction method, or the like on the imaging data corrected by the correction function 444 to generate CT image data.
The display control function 446 controls the display 42 to display information on the process or results of each function or process of the processing circuit 44 .

The processing circuit 44 also performs scan control processing and image processing.
The scan control process is a process for controlling various operations related to X-ray scanning, such as supplying a high voltage to the X-ray high voltage device 14 and causing the X-ray tube 11 to irradiate X-rays.

Image processing is a process of converting CT image data generated by the reconstruction processing function 445 into tomographic image data of an arbitrary cross section or three-dimensional image data based on input operations received from the operator via the input interface 43. Note that the generation of three-dimensional image data may be performed directly by the reconstruction processing function 445.

The processing circuitry 44 is not limited to being included in the console device 40, but may also be included in an integrated server that collectively processes data acquired by multiple medical image diagnostic devices.
Although the console device 40 has been described as a single console that executes multiple functions, the multiple functions may be executed by separate consoles. For example, the functions of the processing circuit 44, such as the acquisition function 442 and the correction function 444, may be distributed.

Next, a method for generating a trained model used by the correction function 444 will be described with reference to FIG.
2 is a block diagram showing an example of a medical information processing system for generating a trained model. The medical information processing system shown in FIG. 2 includes an X-ray CT apparatus 1, a training data storage device 20, and a model training device 22.

The learning data storage device 20 stores learning data including multiple learning samples. For example, the learning data storage device 20 is a computer with a built-in large-capacity storage device. The learning data storage device 20 may also be a large-capacity storage device communicatively connected to the computer via a cable or a communication network. As the storage device, a HDD, SSD, integrated circuit storage device, etc. can be used as appropriate.

The model learning device 22 generates a trained model 411 by having the model perform machine learning according to a model learning program based on the learning data stored in the training data storage device 20. In this embodiment, the machine learning algorithm is assumed to be a neural network, deep learning, a recurrent neural network (RNN) capable of handling time series data in particular, a long short-term memory (LSTM), etc., but is not limited to these and may be another machine learning algorithm such as a hidden Markov model (HMM). The RNN has a loop in an internal intermediate layer called a hidden layer, and is capable of sustaining information until processing at a later time. In addition, the LSTM is a network that can learn longer-term information dependencies than the RNN. The model learning device 22 is a computer such as a workstation having a processor such as a CPU and a GPU.

The model learning device 22 and the learning data storage device 20 may be communicatively connected via a cable or a communication network. The learning data storage device 20 may also be mounted on the model learning device 22. In these cases, learning data is supplied from the learning data storage device 20 to the model learning device 22. Note that the model learning device 22 and the learning data storage device 20 do not need to be communicatively connected. In this case, the learning data is supplied from the learning data storage device 20 to the model learning device 22 via a portable storage medium on which the learning data is stored.

The X-ray CT device 1 and the model learning device 22 may be communicatively connected via a cable or a communication network. The trained model 411 generated by the model learning device 22 is supplied to the X-ray CT device 1, and the trained model 411 is stored in the memory 41. Note that the X-ray CT device 1 and the model learning device 22 do not necessarily need to be communicatively connected. In this case, the trained model 411 is supplied from the model learning device 22 to the X-ray CT device 1 via a portable storage medium or the like in which the trained model 411 is stored.

The trained model 411 can also be considered a parameterized composite function in which multiple functions are combined. A parameterized composite function is defined by a combination of multiple adjustable functions and parameters. The trained model may be any parameterized composite function that meets the above requirements.

The trained model 411 according to this embodiment is generated by machine learning a pre-machine learning multi-layer network (simply referred to as a model) using an algorithm that handles time-series data, such as RNN or LSTM. The trained model 411 may be generated by training a model using the machine learning algorithm described above, without being limited to RNN and LSTM.

Next, the concept of model learning by the model learning device 22 will be described with reference to FIG.
The model learning device 22 uses the learning data to train the multi-layer network 410 by machine learning. To correct the response characteristics for each detection element of the X-ray detector 12, the learning data is created for each detection element. The response characteristics of the X-ray detector 12 also change depending on the X-ray conditions. For example, when the tube current value is large, the number of photons detected by the detection element is greater than when the tube current value is small, and the pile-up characteristics change depending on the tube current value. Therefore, the model learning device 22 trains a model including the X-ray conditions.

In the following example, it is assumed that the X-ray detector 12 is a photon-counting detector, and the multiple signals that serve as input data are spectral data for multiple views obtained by one detection element for each view. Note that, if the X-ray detector 12 is an integral type, the multiple signals that serve as input data are integral values of the signals detected by the detection elements.

When learning the model, for example, at the time of shipment from the factory, the input data is the spectrum data 61 of multiple views of the subject P actually photographed and the X-ray conditions 63 at the time of photographing, and the learning data is a single ideal spectrum data 65 simulated by setting the X-ray conditions 63 at the time of photographing, and the correct answer data (output data). The model learning device 22 uses the learning data to train the multi-layer network 410 and generate a trained model 411. Examples of the X-ray conditions 63 include tube voltage (kV), tube current (mA), view rate, filter information, and collimator information. The trained model 411 may be updated when the X-ray CT device 1 is repaired or the software is updated.

The subject P may be a patient, a healthy individual, a phantom, a deceased patient, or a living or dead animal.

When imaging the subject P and collecting input data, the material (air, water, element) and thickness are taken into consideration as transmission conditions. In particular, when the subject P is a phantom, it is preferable to use a phantom such as an elliptical cylinder in which the subject thickness through which the X-ray path passes varies with the rotation of the rotating frame 13.
In addition, when learning the model, the variation for each energy band may also be learned. For example, the spectrum data 61 is acquired by applying a filter made of a metal with a relatively small atomic number that affects the low energy side. Furthermore, the spectrum data 51 is acquired by applying a filter made of a metal with a large atomic number (Pb, W) that affects the high energy side, i.e., attenuates high energy components. Then, the acquired spectrum data 61 may be trained according to the X-ray conditions 63.

The ideal spectrum data 65 is assumed by simulation as imaging data itself of radiation transmitted through the subject P, and is therefore not influenced by the response characteristics of the X-ray detector 12 .
On the other hand, the spectrum data 61 obtained by actually imaging the subject P is affected by a plurality of response characteristics of the X-ray detector 12. The spectrum data 61 is affected by the interaction between these plurality of response characteristics. The plurality of response characteristics are time-dependent, in other words, change over time. The response characteristics of the X-ray detector 12 include, for example, a charge sharing characteristic, an afterglow characteristic, and a pile-up characteristic. The charge sharing characteristic is a characteristic in which the charge generated by the X-rays spreads over adjacent detection elements depending on the angle of incidence of the X-rays. The afterglow characteristic is a characteristic related to the decay time in which an image captured at a previous time point remains in an image captured at a next time point. The pile-up characteristic is a characteristic that depends on the short interval between incident photons on the detection elements.

In this embodiment, by learning the spectral data 61 of multiple views as input data, the response characteristics can be appropriately corrected without the need to separate and correct the interactions and time-dependent effects of the multiple response characteristics described above.

The spectrum data 61 of multiple views used as input data includes the spectrum of multiple views before as history data. The number of spectrum data, i.e., the number of views, is determined based on the X-ray conditions, the subject P, the rotation speed of the rotating frame 13, and the decay time related to the X-ray detector 12. For example, the number of views collected when the rotating frame 13 rotates once may be set as the maximum number of views, and may be appropriately designed to be less than or equal to the maximum number of views. As a design guideline, for example, taking into account the afterglow characteristics, the number of views acquired from the time when a certain view is captured to the time when the afterglow component generated at that view becomes less than or equal to a threshold value may be set as the number of views (number of spectrum data) used for input data.

The "decay time" in the afterglow characteristics is the fluorescence decay time of the scintillator when the X-ray detector 12 is an indirect conversion type. On the other hand, when the X-ray detector 12 is a direct conversion type, the "decay time" in the afterglow characteristics is the time it takes for the charge to return from a non-uniform state to a uniform state.

Next, the concept of using the trained model 411 trained by machine learning in FIG. 3 will be described with reference to FIG.
When the trained model 411 is used, the processing circuit 44 inputs, as input data, a plurality of spectral data 67 to be processed, captured using the X-ray CT device 1, and the X-ray conditions 63 at the time of capture, to the trained model 411 by the correction function 444. The trained model 411 outputs corrected spectral data 69 in which the response characteristics of the X-ray detector 12 have been corrected according to the input plurality of spectral data 67. The corrected spectral data 69 is spectral data in which components dependent on response characteristics related to signal degradation have been reduced compared to the input data. In other words, the corrected spectral data 69 is spectral data estimated to be generated by the X-ray detector 12 and DAS 18 having ideal response characteristics.

Next, a first example of input data to the trained model 411 during use will be described with reference to Figures 5 and 6.
Fig. 5 shows an example of using the trained model 411 at a certain point in time. The spectral data assumed in this embodiment is represented by count value data divided into energy bands (bins). In the example of Fig. 5, an RNN is assumed as the trained model 411, and the trained model 411 has an input layer 4111, an intermediate layer 4113, and an output layer 4115. As input data, spectral data of three views going back two views from the current time (T) and the X-ray conditions at the time of imaging are used. Each spectral data is represented by three bins.

In the first input example, three pieces of spectral data are input to the trained model 411 at once. That is, the spectral data 51 at time (T), the spectral data 52 at time (T-1), and the spectral data 53 at time (T-2) are input to the trained model 411 at once.

As shown in FIG. 5, the count values of each bin of each spectral data are input to the nodes of the input layer 4111 of the trained model 411. Specifically, the count values of the three bins E(1), E(2), and E(3) of the spectral data 51 are input to the nodes of the input layer 4111 as input data E(1,T), input data E(2,T), and input data E(3,T), respectively. Spectral data 52 and spectral data 53 are similarly input to the nodes.

In the trained model 411, the intermediate layer 4113 receives data from the input layer 4111, executes processing, and outputs the data to the output layer 4115. The output layer 4115 outputs corrected spectrum data in which the response characteristics of the X-ray detector 12 are corrected for the spectrum data at time (T).

Next, FIG. 6 shows a case where the time advances by one and the spectral data in the next view is processed. When the time advances by one, three spectral data items that have been advanced by one time are input. That is, the spectral data 55 at time (T+1), the spectral data 51 at time (T), and the spectral data 52 at time (T-1) are input to the trained model 411 at once. The trained model 411 executes the same process as in FIG. 5, and the output layer 4115 outputs corrected spectral data in which the response characteristics of the X-ray detector 12 have been corrected for the spectral data at time (T+1).

Next, a second example of input data to the trained model 411 during use will be described with reference to Figures 7 and 8.
In the second input example, the spectral data is input to the trained model one by one in chronological order.

In the trained model 411, processing is performed after the same number of views of spectral data as during training has been accumulated, and corrected spectral data is output from the trained model 411. Therefore, for example, if three views of spectral data are used as input data for training data during training, when the trained model 411 is used, three views of spectral data may be input at once for the initial input only, or the spectral data may be input in chronological order, and the trained model may not output corrected spectral data until three views of spectral data have been accumulated.

In FIG. 7, it is assumed that spectral data at time (T-2) and spectral data at time (T-1) have already been input to the trained model 411 (not shown), and that next, spectral data at time (T) is input.

When spectral data at time (T) is input, corrected spectral data is output from the trained model 411 based on the input data of three views of spectral data, including the spectral data at time (T-2) and time (T-1).

Next, FIG. 8 shows a case where the time advances by one and the spectral data in the next view is processed. When the time advances by one, the spectral data 55 at time (T+1) is input to the trained model 411. The trained model 411 has already received the spectral data 52 at time (T-1) and the spectral data 51 at time (T). Therefore, based on the three spectral data at times (T-1), (T), and (T+1), the intermediate layer 4113 performs processing similar to that in FIG. 7, and the output layer 4115 outputs corrected spectral data 54 in which the response characteristics of the X-ray detector 12 have been corrected for the spectral data at time (T+1).

In the above example, a trained model is assumed that outputs corrected spectrum data in which the detector response is corrected according to the X-ray conditions by learning including the X-ray conditions, but a trained model may be generated for each X-ray condition. In this case, when using the trained model, the selection function 443 causes the processing circuitry 44 to select a trained model according to the X-ray conditions when measuring the subject P, that is, when acquiring a signal of the target view. The correction function 444 causes the processing circuitry 44 to output corrected spectrum data in which the response characteristics included in the spectrum data are corrected according to the trained model corresponding to the X-ray conditions selected by the selection function 443.

In the above example, the spectral data at the current time is input to the trained model 411, and the spectral data at the current time with the response characteristics corrected is output, but this is not limiting. Data with corrected response characteristics for spectral data at a time prior to the current time may be output as the output. Specifically, the trained model 411 may be designed to receive, for example, three pieces of spectral data at time (T-2), time (T-1), and time T, and output data with corrected response characteristics for the spectral data at the time prior to time (T-1) rather than time T.

In the above example, the processing circuit 44 executes the acquisition function 442, the selection function 443, and the correction function 444, but this is not limiting, and the DAS 18 may include a processing circuit that executes the acquisition function 442, the selection function 443, and the correction function 444. In other words, the detector unit, rather than the console device 40, may execute the correction of the response characteristics of the X-ray detector 12 using the processing circuit.

According to the first embodiment described above, by inputting historical data including time series information as multiple signals from the X-ray detector and making it possible to output a signal in which the response characteristics of the X-ray detector have been corrected, it is possible to perform rapid and highly accurate correction processing without constructing a complex model that takes into account the time-series interactions between the response characteristics, thereby improving the reliability of the subsequent CT image reconstruction and spectral image reconstruction.

In addition, according to the X-ray CT apparatus of this embodiment, by using a trained model, it is possible to perform appropriate correction of response characteristics including the interaction between response characteristics without individually correcting each of the charge sharing characteristics, afterglow characteristics, and pile-up characteristics. Therefore, it is possible to generate a CT image with improved image quality (contrast, noise). Therefore, it is possible to obtain a high-quality image while suppressing costs.
In addition, since there is no need to perform separate software-based response characteristic correction processing for each step, the calculation time required for the software-based response characteristic correction processing can be omitted, and the overall calculation time required for processing can be shortened. As a result, the processing efficiency can be improved.

There are various types of X-ray CT devices, such as the Rotate/Rotate-Type (third generation CT) in which the X-ray tube and detector rotate as a single unit around the subject P, and the Stationary/Rotate-Type (fourth generation CT) in which a large number of X-ray detection elements are fixed in a ring-shaped array and only the X-ray tube rotates around the subject P, and any of these types can be applied to this embodiment.

The hardware that generates X-rays is not limited to the X-ray tube 11. For example, instead of the X-ray tube 11, X-rays may be generated using a fifth-generation method that includes a focus coil that focuses the electron beam generated from the electron gun, a deflection coil that electromagnetically deflects the beam, and a target ring that surrounds half of the subject P and generates X-rays when the deflected electron beam collides with the target ring.

Furthermore, this embodiment can be applied to both single-tube X-ray CT devices and so-called multi-tube X-ray CT devices in which multiple pairs of X-ray tubes and detectors are mounted on a rotating ring.

In addition, each function according to the embodiment can be realized by installing a program that executes the above-mentioned processes in a computer such as a workstation and expanding the program in memory. In this case, the program that can cause the computer to execute the above-mentioned methods can also be stored and distributed on a storage medium such as a magnetic disk (such as a hard disk), an optical disk (such as a CD-ROM or DVD), or a semiconductor memory.

Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are within the scope of the invention and its equivalents as set forth in the claims, as well as the scope and gist of the invention.

REFERENCE SIGNS LIST 1 X-ray CT device 10 Mount device 11 X-ray tube 12 X-ray detector 13 Rotating frame 14 X-ray high voltage device 15 Control device 16 Wedge 17 Collimator 18 Data acquisition device (DAS)
19 Bore
20 Learning data storage device 22 Model learning device 30 Bed device 31 Base 32 Bed driving device 33 Top plate 34 Support frame 40 Console device 41 Memory 42 Display 43 Input interface 44 Processing circuit 51, 52, 53, 55, 61, 67 Spectral data 54, 69 Corrected spectral data 63 X-ray conditions 65 Ideal spectral data 410 Multilayer network 411 Learned model 441 System control function 442 Acquisition function 443 Selection function 444 Correction function 445 Reconstruction processing function 446 Display control function 4111 Input layer 4113 Intermediate layer 4115 Output layer

Claims (12)

an acquisition unit that acquires a plurality of signals corresponding to a plurality of views including a correction target view obtained by a single detection element;
a correction unit that corrects a response characteristic of a detector including the detection element for a signal corresponding to the correction target view based on the plurality of signals;
Equipped with
the plurality of views are chronologically consecutive views,
the number of views is determined based on decay times and x-ray conditions for the detector ;
X-ray CT device. The X-ray CT apparatus according to claim 1, wherein the plurality of views are the view to be corrected and a view that precedes the view to be corrected in the chronological order. The X-ray CT apparatus according to claim 1, wherein the plurality of views are the view to be corrected and a view that is chronologically advanced from the view to be corrected. an acquisition unit that acquires a plurality of signals corresponding to a plurality of views including a correction target view obtained by a single detection element;
a correction unit that corrects a response characteristic of a detector including the detection element for a signal corresponding to the correction target view based on the plurality of signals;
Equipped with
the plurality of views are chronologically consecutive views,
the correction unit generates a signal in which a response characteristic of the detector including the detection element is corrected for a signal of the correction target view according to a trained model that receives input of a plurality of signals and outputs a signal in which a response characteristic of the detector including the detection element is corrected ;
X- ray CT device. The X-ray CT device according to claim 4, wherein the correction unit inputs the plurality of signals one by one in chronological order to the trained model. an acquisition unit that acquires a plurality of signals corresponding to a plurality of views including a correction target view obtained by a single detection element;
a correction unit that corrects a response characteristic of a detector including the detection element for a signal corresponding to the correction target view based on the plurality of signals;
Equipped with
the number of views is determined based on decay times and x-ray conditions for the detector;
X-ray CT device. The X-ray CT apparatus according to claim 6 , wherein the number of the plurality of views is further determined based on a rotation speed of a rotating frame. 7. The X-ray CT apparatus according to claim 1 , wherein the decay time is a time required for charges to return from a non-uniform state to a uniform state when the detector is a direct conversion type detector. acquiring a plurality of signals corresponding to a plurality of time-series consecutive views including a correction target view obtained by the same detection element;
correcting a response characteristic of a detector including the detection element for a signal corresponding to the view to be corrected based on the plurality of signals;
A detector correction method , wherein the number of views is determined based on decay times and x-ray conditions for the detector . acquiring a plurality of signals corresponding to a plurality of views including a view to be corrected, the plurality of signals being obtained by the same detector element;
a detector correction method for correcting a response characteristic of a detector including the detection element for a signal corresponding to the correction target view based on the plurality of signals,
A detector correction method in which the number of views is determined based on decay times and x-ray conditions for the detector. an acquisition unit that acquires a plurality of signals corresponding to a plurality of views including a correction target view obtained by a single detection element;
a correction unit that corrects a response characteristic of a detector including the detection element for a signal corresponding to the correction target view based on the plurality of signals;
Equipped with
the plurality of views are chronologically consecutive views,
the number of views is determined based on decay times and x-ray conditions for the detector ;
Medical information processing system. an acquisition unit that acquires a plurality of signals corresponding to a plurality of views including a correction target view obtained by a single detection element;
a correction unit that corrects a response characteristic of a detector including the detection element for a signal corresponding to the correction target view based on the plurality of signals;
Equipped with
the number of views is determined based on decay times and x-ray conditions for the detector;
Medical information processing system.