JP2025023667A - IMAGE PROCESSING APPARATUS, RADIATION IMAGING SYSTEM, IMAGE PROCESSING METHOD, AND PROGRAM - Google Patents

Abstract

To acquire high-quality images.SOLUTION: An image processing device includes a processing unit that performs material separation processing to perform separation into thickness images of multiple materials using multiple pieces of information corresponding to mutually-different radiation energies obtained by performing imaging by irradiating a subject with radiation. The processing unit performs material separation processing by using the thickness image subjected to noise reduction processing by using different parameters between partial pixels and other pixels specified on the basis of the information on the difference in the multiple pieces of information and the multiple pieces of information.SELECTED DRAWING: Figure 10

Description

The disclosed technology relates to an image processing device, a radiation imaging system, an image processing method, and a program. More specifically, the technology relates to an image processing device, a radiation imaging system, an image processing method, and a program used for still image capture such as general imaging in medical diagnosis and for video capture such as fluoroscopy.

Currently, radiation imaging devices using flat panel detectors (FPDs) made of semiconductor materials are widely used as imaging devices for medical image diagnosis and non-destructive testing using X-rays.

In energy subtraction, an imaging method using an FPD, multiple images of different energies are acquired by irradiating X-rays with different tube voltages, and then processed to obtain material-separated images, such as bone images and soft tissue images (Patent Document 1).

JP 2019-162358 A

By removing the contrast of soft tissues and bones acquired using energy subtraction processing, the visibility of contrast agents injected into blood vessels and medical devices such as stents and coils can be improved during interventional radiology (IVR). However, energy subtraction processing can increase image noise. Furthermore, if the subject moves when acquiring multiple images with different energies, motion artifacts can occur. To acquire images of high quality, it is necessary to reduce motion artifacts while suppressing the increase in noise.

The disclosed technology aims to provide image processing technology that can obtain images with excellent image quality.

According to one aspect of the disclosed technique, an image processing device includes a processing unit that performs a material separation process for separating an image into thickness images of a plurality of materials using a plurality of pieces of information corresponding to a plurality of different radiation energies obtained by irradiating a subject with radiation and capturing the image;
The processing means performs the material separation process using the thickness image which has been subjected to noise reduction processing using parameters which differ between a portion of pixels identified based on information regarding the difference between the plurality of pieces of information and other pixels, and the plurality of pieces of information.

The disclosed technology makes it possible to obtain images with excellent image quality.

1 is a diagram showing an example of the arrangement of an X-ray imaging system according to a first embodiment. FIG. 2 is an equivalent circuit diagram of a pixel of the X-ray imaging device according to the first embodiment. 4 is a timing chart of the X-ray imaging apparatus according to the first embodiment. 4 is a timing chart of the X-ray imaging apparatus according to the first embodiment. FIG. 4 is a block diagram illustrating a correction process according to the first embodiment. FIG. 2 is a block diagram illustrating signal processing according to the first embodiment. FIG. 2 is a block diagram illustrating image processing according to the first embodiment. FIG. 4 is a block diagram illustrating a correction process for acquiring a file image according to the first embodiment. 3A and 3B are diagrams illustrating an example of a stored image A and a bone image B according to the first embodiment. FIG. 2 is a block diagram illustrating signal processing according to the first embodiment. 5A to 5C are diagrams illustrating examples of motion artifacts in energy subtraction according to the first embodiment. 5A to 5C are views for explaining a filter parameter changing process according to the first embodiment; FIG. 4 is a diagram showing a motion artifact detection image according to the first embodiment. 13A to 13C are diagrams for explaining a filter parameter changing process according to the second embodiment. FIG. 13 is a diagram showing a motion artifact detection image according to the second embodiment. FIG. 13 is a diagram showing a motion artifact detection image according to the third embodiment. 13A to 13C are views for explaining a motion artifact detection process according to the third embodiment. FIG. 11 is a block diagram illustrating signal processing in a modified example.

The following embodiments are described in detail with reference to the attached drawings. Note that the following embodiments do not limit the invention according to the claims. Although the embodiments describe multiple features, not all of these multiple features are necessarily essential to the invention, and multiple features may be combined in any manner. Furthermore, in the attached drawings, the same reference numbers are used for the same or similar configurations, and duplicate explanations are omitted.

Note that radiation in the disclosed technology includes alpha rays, beta rays, gamma rays, and the like, which are beams made of particles (including photons) emitted by radioactive decay, as well as beams having the same or greater energy, such as X-rays, particle beams, and cosmic rays. In the following embodiment, an apparatus using X-rays as an example of radiation will be described. Therefore, hereinafter, a radiation imaging apparatus and a radiation imaging system will be described as an X-ray imaging apparatus and an X-ray imaging system, respectively.

First Embodiment
1 is a block diagram showing an example of the configuration of an X-ray imaging system as an example of a radiation imaging system according to an embodiment. The X-ray imaging system according to the embodiment includes an X-ray generating device 101 (radiation source), an X-ray control device 102, an imaging control device 103 (image processing device), and an X-ray imaging device 104 (radiation imaging device).

The X-ray generating device 101 generates X-rays and irradiates the subject with the X-rays. The X-ray control device 102 controls the generation of X-rays in the X-ray generating device 101. The imaging control device 103 acquires and controls image information. The imaging control device 103 has, for example, one or more processors (CPUs) and a memory (storage unit 136), and the processor executes a program stored in the memory (storage unit 136) to acquire X-ray images and perform image processing. Note that each process including image processing by the imaging control device 103 may be realized by dedicated hardware, or may be realized by a combination of hardware and software. The X-ray imaging device 104 has a phosphor 105 that converts X-rays into visible light, and a two-dimensional detector 106 that detects visible light. The two-dimensional detector is a sensor in which pixels 20 that detect X-ray quanta are arranged in an array of X columns x Y rows, and outputs image information.

The imaging control device 103 functions as an image processing device that processes radiation images using the above-mentioned processor. The acquisition unit 131, correction unit 132, signal processing unit 133, image processing unit 134, display control unit 135, and storage unit 136 show an example of the functional configuration of the image processing device. The imaging control device 103 (image processing device) acquires an image that has been subjected to noise reduction processing using multiple pieces of information corresponding to multiple radiation energies detected by the two-dimensional detector.

The acquisition unit 131 acquires multiple radiation images with different energies obtained by irradiating a subject with radiation and capturing an image. The acquisition unit 131 acquires multiple radiation images obtained by performing sample holds multiple times during the exposure of one shot of radiation.

The correction unit 132 corrects the multiple radiation images acquired by the acquisition unit 131 to generate multiple images to be used in the energy subtraction process.

The signal processing unit 133 acquires multiple pieces of information corresponding to multiple different radiation energies obtained by irradiating the subject with radiation and capturing an image.

Here, the multiple pieces of information include information corresponding to a first radiation energy among the multiple radiation energies, and information corresponding to a second radiation energy different from the first radiation energy. The information corresponding to the first radiation energy includes a first radiation image, and the information corresponding to the second radiation energy includes a second radiation image.

The first radiographic image captured by the X-ray imaging device 104 includes data in which the signal values (pixel values) of the electrical signals (image signals) obtained by imaging correspond to position information indicating the two-dimensional arrangement of the signal values. The second radiographic image captured by the X-ray imaging device 104 includes data in which the signal values (pixel values) of the electrical signals (image signals) obtained by imaging correspond to position information indicating the two-dimensional arrangement of the signal values. The multiple images (first radiographic image, second radiographic image) include a thickness image of a first material (first image) and a thickness image of a second material different from the first material (second image). The multiple images include an image (material separation image) that shows materials separated, such as bone and soft tissue.

The signal processing unit 133 generates, for example, a first image showing the thickness of a first material and a second image showing the thickness of a second material different from the first material, based on a plurality of radiation images captured with mutually different radiation energies. Here, the first material includes at least calcium, hydroxyapatite, or bone, and the second material includes at least water, or soft material that does not contain fat or calcium. Details of the signal processing unit 133 will be described later.

The image processing unit 134 generates an image for display using the first image and the second image acquired by the signal processing unit 133 through signal processing. The display control unit 135 displays the thickness image of the first substance and the thickness image of the second substance acquired by the signal processing unit 133 on a display device 137 consisting of, for example, a liquid crystal display or an organic EL display. The operation device 138 consists of, for example, a mouse and a keyboard, and inputs various instructions from an operator. The operation device 138 may be realized as a touch panel integrated with the display device 137.

The storage unit 136 stores various programs, databases (tables), and filter parameters (hereinafter also referred to as "parameters") for the filter process F1 (noise reduction process). The tables include various tables used during energy subtraction processing. The filter parameters are filter parameters optimized according to the imaging conditions (exposure conditions). One or more processors (CPUs) execute the programs stored in the storage unit 136 to realize the functions of the acquisition unit 131, correction unit 132, signal processing unit 133, image processing unit 134, and display control unit 135.

FIG. 2 is an equivalent circuit diagram of a pixel 20 according to an embodiment. The pixel 20 includes a photoelectric conversion element 201 and an output circuit section 202. The photoelectric conversion element 201 can typically be a photodiode. The output circuit section 202 includes an amplifier circuit section 204, a clamp circuit section 206, a sample-and-hold circuit 207, and a selection circuit section 208.

The photoelectric conversion element 201 includes a charge storage section, which is connected to the gate of the MOS transistor 204a of the amplifier circuit section 204. The source of the MOS transistor 204a is connected to a current source 204c via a MOS transistor 204b. The MOS transistor 204a and the current source 204c form a source follower circuit. The MOS transistor 204b is an enable switch that turns on when an enable signal EN supplied to its gate becomes active level, putting the source follower circuit into an operating state.

In the example shown in FIG. 2, the charge storage section of the photoelectric conversion element 201 and the gate of the MOS transistor 204a form a common node, and this node functions as a charge-voltage conversion section that converts the charge stored in the charge storage section into a voltage. That is, a voltage V (=Q/C) appears in the charge-voltage conversion section, which is determined by the charge Q stored in the charge storage section and the capacitance value C of the charge-voltage conversion section. The charge-voltage conversion section is connected to a reset potential Vres via a reset switch 203. When the reset signal PRES becomes active level, the reset switch 203 turns on and the potential of the charge-voltage conversion section is reset to the reset potential Vres.

The clamp circuit unit 206 clamps the noise output by the amplifier circuit unit 204 according to the potential of the reset charge-voltage conversion unit using the clamp capacitance 206a. In other words, the clamp circuit unit 206 is a circuit for canceling this noise from the signal output from the source follower circuit according to the charge generated by photoelectric conversion in the photoelectric conversion element 201. This noise includes the kTC noise at the time of reset. The clamp is performed by setting the clamp signal PCL to an active level to turn on the MOS transistor 206b, and then setting the clamp signal PCL to an inactive level to turn off the MOS transistor 206b. The output side of the clamp capacitance 206a is connected to the gate of the MOS transistor 206c. The source of the MOS transistor 206c is connected to the current source 206e via the MOS transistor 206d. The MOS transistor 206c and the current source 206e form a source follower circuit. MOS transistor 206d is an enable switch that turns on when the enable signal EN0 supplied to its gate becomes active, putting the source follower circuit into an operational state.

The signal output from the clamp circuit unit 206 according to the charge generated by photoelectric conversion in the photoelectric conversion element 201 is written as an optical signal to the capacitance 207Sb via the switch 207Sa when the optical signal sampling signal TS becomes active level. The signal output from the clamp circuit unit 206 when the MOS transistor 206b is turned on immediately after resetting the potential of the charge-voltage conversion unit is a clamp voltage. The noise signal is written to the capacitance 207Nb via the switch 207Na when the noise sampling signal TN becomes active level. This noise signal includes an offset component of the clamp circuit unit 206. The switch 207Sa and the capacitance 207Sb form a signal sample-and-hold circuit 207S, and the switch 207Na and the capacitance 207Nb form a noise sample-and-hold circuit 207N. The sample-and-hold circuit unit 207 includes a signal sample-and-hold circuit 207S and a noise sample-and-hold circuit 207N.

When the driving circuit unit drives the row selection signal to an active level, the signal (optical signal) held in the capacitance 207Sb is output to the signal line 21S via the MOS transistor 208Sa and the row selection switch 208Sb. At the same time, the signal (noise) held in the capacitance 207Nb is output to the signal line 21N via the MOS transistor 208Na and the row selection switch 208Nb. The MOS transistor 208Sa constitutes a constant current source (not shown) and a source follower circuit provided on the signal line 21S. Similarly, the MOS transistor 208Na constitutes a constant current source (not shown) and a source follower circuit provided on the signal line 21N. The signal selection circuit unit 208S is constituted by the MOS transistor 208Sa and the row selection switch 208Sb, and the noise selection circuit unit 208N is constituted by the MOS transistor 208Na and the row selection switch 208Nb. The selection circuit section 208 includes a signal selection circuit section 208S and a noise selection circuit section 208N.

The pixel 20 may have an addition switch 209S that adds the optical signals of adjacent pixels 20. In the addition mode, the addition mode signal ADD becomes active and the addition switch 209S is turned on. This causes the capacitances 207Sb of adjacent pixels 20 to be connected to each other by the addition switch 209S, and the optical signals are averaged. Similarly, the pixel 20 may have an addition switch 209N that adds the noises of adjacent pixels 20. When the addition switch 209N is turned on, the capacitances 207Nb of adjacent pixels 20 are connected to each other by the addition switch 209N, and the noises are averaged. The adder 209 includes an addition switch 209S and an addition switch 209N.

The pixel 20 may also have a sensitivity change unit 205 for changing the sensitivity. The pixel 20 may include, for example, a first sensitivity change switch 205a and a second sensitivity change switch 205'a, as well as circuit elements associated therewith. When the first change signal WIDE becomes active, the first sensitivity change switch 205a is turned on, and the capacitance value of the first additional capacitance 205b is added to the capacitance value of the charge-voltage conversion unit. This reduces the sensitivity of the pixel 20. When the second change signal WIDE2 becomes active, the second sensitivity change switch 205'a is turned on, and the capacitance value of the second additional capacitance 205'b is added to the capacitance value of the charge-voltage conversion unit. This further reduces the sensitivity of the pixel 20. By adding a function to reduce the sensitivity of the pixel 20 in this way, it becomes possible to receive a larger amount of light, and the dynamic range can be expanded. When the first change signal WIDE becomes active level, the enable signal ENw may be set to active level, and the MOS transistor 204'a may be changed to a source follower operation.

The X-ray imaging device 104 reads the output of the pixel circuits described above from the two-dimensional detector 106, converts it to a digital value using an AD converter (not shown), and then transfers the image to the imaging control device 103.

Next, the operation of the X-ray imaging system of the embodiment having the above-mentioned configuration will be described. FIG. 3 shows the drive timing of the X-ray imaging device 104 when energy subtraction is performed in the X-ray imaging system of the embodiment. The waveform in FIG. 3 shows the timing of X-ray exposure, synchronization signal, resetting of the photoelectric conversion element 201, sample hold circuit 207, and image readout from signal line 21, with time on the horizontal axis.

The photoelectric conversion element 201 is reset by a reset signal before X-rays are emitted. Ideally, the X-ray tube voltage is a square wave, but it takes a finite amount of time for the tube voltage to rise and fall. In particular, when the exposure time is short with pulsed X-rays, the tube voltage is no longer considered a square wave, and takes a waveform like that shown by X-rays 301 to 303. The X-rays in the rising phase 301, the X-rays in the stable phase 302, and the X-rays in the falling phase 303 each have different X-ray energies. Therefore, by obtaining X-ray images corresponding to the radiation in the periods separated by sample and hold, multiple types of X-ray images with different energies can be obtained.

The X-ray imaging device 104 performs sampling in the noise sample hold circuit 207N after the X-rays 301 in the rising phase are irradiated, and performs sampling in the signal sample hold circuit 207S after the X-rays 302 in the stable phase are irradiated. After that, the X-ray imaging device 104 reads out the difference between the signal line 21N and the signal line 21S as an image. At this time, the noise sample hold circuit 207N holds the signal (R ₁ ) of the X-rays 301 in the rising phase, and the signal sample hold circuit 207S holds the sum (R ₁ +B) of the signal of the X-rays 301 in the rising phase and the signal (B) of the X-rays 302 in the stable phase. Therefore, an image 304 corresponding to the signal of the X-rays 302 in the stable phase is read out.

Next, after completing the exposure of the X-ray 303 in the falling period and the readout of the image 304, the X-ray imaging device 104 performs sampling again in the signal sample-and-hold circuit 207S. After that, the X-ray imaging device 104 resets the photoelectric conversion element 201, performs sampling again in the noise sample-and-hold circuit 207N, and reads out the difference between the signal line 21N and the signal line 21S as an image. At this time, the noise sample-and-hold circuit 207N holds a signal in a state where no X-rays are irradiated, and the signal sample-and-hold circuit 207S holds the sum (R _{1 +} B+R ₂ ) of the signal of the X-ray 301 in the rising period, the _signal of the X-ray 302 in the stable period, and the signal of the X-ray 303 in the falling period. Therefore, an image 306 corresponding to the signal of the X-ray 301 in the rising period, the signal of the X-ray 302 in the stable period, and the signal of the X-ray 303 in the falling period is read out. Thereafter, by calculating the difference between the image 306 and the image 304, an image 305 corresponding to the sum of the X-rays 301 in the rising edge and the X-rays 303 in the falling edge is obtained. This calculation may be performed by the X-ray imaging device 104 or the imaging control device 103.

The timing for resetting the sample-and-hold circuit 207 and the photoelectric conversion element 201 is determined using a synchronization signal 307 indicating that X-ray exposure has started from the X-ray generating device 101. As a method for detecting the start of X-ray exposure, a configuration for measuring the tube current of the X-ray generating device 101 and determining whether or not the current value exceeds a preset threshold can be used, but is not limited to this. For example, a configuration for detecting the start of X-ray exposure by repeatedly reading out the pixel 20 after the resetting of the photoelectric conversion element 201 is completed and determining whether or not the pixel value exceeds a preset threshold can be used.

Alternatively, for example, an X-ray imaging device 104 may have an X-ray detector built in that is different from the two-dimensional detector 106, and the start of X-ray exposure may be detected by determining whether or not the measured value exceeds a preset threshold value. In either case, sampling of the signal sample-and-hold circuit 207S, sampling of the noise sample-and-hold circuit 207N, and resetting of the photoelectric conversion element 201 are performed after a pre-specified time has elapsed since the input of a synchronization signal 307 indicating the start of X-ray exposure.

In this manner, an image 304 corresponding to the stable period of the pulsed X-rays and an image 305 corresponding to the sum of the rising and falling periods are obtained. Since the energies of the X-rays irradiated when forming these two X-ray images are different, energy subtraction processing can be performed by performing calculations between these X-ray images.

Figure 4 shows the drive timing of the X-ray imaging device 104 when energy subtraction is performed in the X-ray imaging system according to the embodiment. The drive timing shown in Figure 4 differs from the drive timing in Figure 3 in that the tube voltage of the X-ray generator 101 is actively switched.

First, after the photoelectric conversion element 201 is reset, the X-ray generation device 101 irradiates low-energy X-rays 401. In this state, the X-ray imaging device 104 performs sampling using the noise sample-and-hold circuit 207N. Then, the X-ray generation device 101 switches the tube voltage to irradiate high-energy X-rays 402. In this state, the X-ray imaging device 104 performs sampling using the signal sample-and-hold circuit 207S. Then, the X-ray generation device 101 switches the tube voltage to irradiate low-energy X-rays 403. The X-ray imaging device 104 reads out the difference between the signal line 21N and the signal line 21S as an image. At this time, the noise sample-and-hold circuit 207N holds a signal (R ₁ ) of the low-energy X-ray 401, and the signal sample-and-hold circuit 207S holds the sum (R ₁ +B) of the signal of the low-energy X-ray 401 and the signal (B) of the high-energy X-ray 402. Therefore, an image 404 corresponding to the signal of the high-energy X-ray 402 is read out.

Next, after the X-ray imaging device 104 completes the exposure of the low-energy X-rays 403 and the readout of the image 404, it again performs sampling in the signal sample-and-hold circuit 207S. After that, the X-ray imaging device 104 resets the photoelectric conversion element 201, performs sampling again in the noise sample-and-hold circuit 207N, and reads out the difference between the signal line 21N and the signal line 21S as an image. At this time, the noise sample-and-hold circuit 207N holds a signal in a state where no X-rays are irradiated, and the signal sample-and-hold circuit 207S holds the sum (R ₁ +B+R _{2 ) of the signal of the low-energy X-rays 401, the signal of the high-energy X-rays 402, and the signal of the low-energy X-rays 403 (R 2 )} . Therefore, an image 406 corresponding to the signal of the low-energy X-rays 401, the signal of the high-energy X-rays 402, and the signal of the low-energy X-rays 403 is read out.

Then, by calculating the difference between image 406 and image 404, image 405 corresponding to the sum of low-energy X-rays 401 and low-energy X-rays 403 is obtained. This calculation may be performed by the X-ray imaging device 104 or the imaging control device 103. The synchronization signal 407 is the same as in FIG. 3. In this way, by acquiring images while actively switching the tube voltage, it is possible to make the energy difference between low-energy and high-energy radiation images larger than in the method of FIG. 3.

Next, the energy subtraction processing by the imaging control device 103 will be described. In this embodiment, the energy subtraction processing is divided into three stages: correction processing by the correction unit 132, signal processing by the signal processing unit 133, and image processing by the image processing unit 134. Each process will be described below.

The correction process processes multiple radiation images acquired from the X-ray imaging device 104 to generate multiple images to be used in the signal processing described below in the energy subtraction process.

Figure 5 shows a block diagram of the correction process for the energy subtraction process according to the embodiment. First, the acquisition unit 131 causes the X-ray imaging device 104 to perform imaging without emitting X-rays, and acquires an image by the drive shown in Figure 3 or Figure 4. This drive reads out two images. Hereinafter, the first image (image 304 or image 404) is F_ODD, and the second image (image 306 or image 406) is F_EVEN. F_ODD and F_EVEN are images that correspond to the fixed pattern noise (FPN: Fixed Pattern Noise) of the X-ray imaging device 104.

Next, the acquisition unit 131 causes the X-ray imaging device 104 to irradiate X-rays in the absence of a subject to perform imaging, and acquires an image for gain correction output from the X-ray imaging device 104 by the drive shown in FIG. 3 or FIG. 4. This drive reads out two images in the same manner as above. Hereinafter, the first image for gain correction (image 304 or image 404) is referred to as W_ODD, and the second image for gain correction (image 306 or image 406) is referred to as W_EVEN. W_ODD and W_EVEN are images corresponding to the sum of the FPN of the X-ray imaging device 104 and the signal due to X-rays. The correction unit 132 obtains images WF_ODD and WF_EVEN in which the FPN of the X-ray imaging device 104 has been removed, by subtracting F_ODD from W_ODD and F_EVEN from W_EVEN. This is called offset correction.

WF_ODD is an image corresponding to the X-rays 302 in the stable period, and WF_EVEN is an image corresponding to the sum of the X-rays 301 in the rising period, the X-rays 302 in the stable period, and the X-rays 303 in the falling period. Therefore, the correction unit 132 obtains an image corresponding to the sum of the X-rays 301 in the rising period and the X-rays 303 in the falling period by subtracting WF_ODD from WF_EVEN. The process of obtaining an image corresponding to the X-rays in a specific period separated by sample hold by subtracting multiple images in this way is called color correction. The energy of the X-rays 301 in the rising period and the X-rays 303 in the falling period is lower than the energy of the X-rays 302 in the stable period. Therefore, by subtracting WF_ODD from WF_EVEN through color correction, a low-energy image W_Low when there is no subject is obtained. Also, a high-energy image W_High when there is no subject is obtained from WF_ODD.

Next, the acquisition unit 131 causes the X-ray imaging device 104 to irradiate X-rays and perform imaging when a subject is present, and acquires images output from the X-ray imaging device 104 by the driving shown in FIG. 3 or FIG. 4. At this time, two images are read out. Hereinafter, the first image (image 304 or image 404) is referred to as X_ODD, and the second image (image 306 or image 406) is referred to as X_EVEN. The correction unit 132 performs offset correction and color correction similar to those performed when no subject is present, thereby obtaining a low-energy image X_Low when a subject is present, and a high-energy image X_High when a subject is present.

Here, if the thickness of the subject is d, the linear attenuation coefficient of the subject is μ, the output of pixel 20 when there is no subject is I ₀ , and the output of pixel 20 when there is a subject is I, the following formula 1 holds.

Transforming equation [1] gives the following equation [2]. The right-hand side of equation [2] indicates the transmittance of the subject. The transmittance of the subject is a real number between 0 and 1.

Therefore, the correction unit 132 obtains an image L of transmittance at low energy (hereinafter also referred to as "low energy image L or second radiation image L") by dividing the low energy image X_Low when a subject is present by the low energy image W_Low when no subject is present. Similarly, the correction unit 132 obtains an image H of transmittance at high energy (hereinafter also referred to as "high energy image H or first radiation image H") by dividing the high energy image X_High when a subject is present by the high energy image W_High when no subject is present. The process of obtaining an image (L, H) of transmittance at low energy or transmittance at high energy by dividing an image obtained based on a radiation image obtained in a state with a subject by an image obtained based on a radiation image obtained in a state without a subject is called gain correction.

Figure 6 shows a block diagram of signal processing for energy subtraction processing according to the embodiment. The signal processing unit 133 generates a material separation image using multiple images obtained from the correction unit 132. Below, a process for generating a material separation image consisting of a bone thickness image B (hereinafter also referred to as bone thickness B or bone image B) and a soft tissue thickness image S (hereinafter also referred to as soft tissue thickness S or soft tissue image S) will be described. The signal processing unit 133 obtains the bone thickness image B and the soft tissue thickness image S from the low energy transmittance image L and the high energy transmittance image H obtained by the correction shown in Figure 5 through the following process.

First, assuming that the energy of an X-ray photon is E, the number of photons at energy E is N(E), the thickness in the bone thickness image is B, the thickness in the soft tissue thickness image is S, the linear attenuation coefficient of bone at energy E is μ _B (E), the linear attenuation coefficient of soft tissue at energy E is μ _S (E), and the transmittance is I/I ₀ , the following equation (3) holds.

The number of photons N(E) at energy E is the spectrum of X-rays. The spectrum of X-rays is obtained by simulation or actual measurement. The linear attenuation coefficient μ _B (E) of bone at energy E and the linear attenuation coefficient μ _S (E) of soft tissue at energy E are obtained from databases such as NIST (National Institute of Standards and Technology). Therefore, according to the formula 3, it is possible to calculate the thickness B in an arbitrary bone thickness image, the thickness S in an arbitrary soft tissue thickness image, and the transmittance I/I ₀ in the spectrum N(E) of X-rays.

Here, if the spectrum of low-energy X-rays is N _L (E) and the spectrum of high-energy X-rays is N _H (E), the following equations [Equation 4] hold for the transmittance in image L and the transmittance in image H.

By solving the nonlinear simultaneous equations of [Equation 4], the thickness B in the bone thickness image and the thickness S in the soft tissue thickness image are obtained. As a representative method for solving the nonlinear simultaneous equations, the case where the Newton-Raphson method is used will be described here. First, when the number of iterations of the Newton-Raphson method is m, the bone thickness after the mth iteration is ^Bm , and the soft tissue thickness after the mth iteration is ^Sm , the high energy transmittance image ^Hm after the mth iteration and the low energy transmittance image ^Lm after the mth iteration are expressed by the following [Equation 5].

The rate of change in transmittance when the thickness is changed slightly is expressed by the following formula [6].

In this case, the bone thickness B ^m+1 and the soft tissue thickness S ^m+1 after the m+1th iteration are expressed by the following formula (7) using the high-energy transmittance image H and the low-energy transmittance image L.

The inverse of a 2x2 matrix, with its determinant det, is expressed by the following equation (8) using Cramer's rule.

Therefore, by substituting [Equation 8] into [Equation 7], we obtain the following [Equation 9].

By repeating the above calculations, the difference between the high energy transmittance image ^Hm after the mth iteration and the measured high energy transmittance image H approaches 0. The same is true for the low energy transmittance image L. As a result, the bone thickness ^Bm after the mth iteration converges to the bone thickness B, and the soft tissue thickness ^Sm after the mth iteration converges to the soft tissue thickness S. In this manner, the nonlinear simultaneous equations shown in [Equation 4] can be solved. Therefore, by calculating [Equation 4] for all pixels, the bone thickness image B and the soft tissue thickness image S can be obtained from the low energy transmittance image L and the high energy transmittance image H.

In the embodiment, the Newton-Raphson method is used to solve the nonlinear simultaneous equations. However, the disclosed technology is not limited to this form. For example, iterative solving methods such as the least squares method and the bisection method may be used.

In the embodiment, the nonlinear simultaneous equations are solved by an iterative solution method, but the disclosed technology is not limited to this form. Numerical integration is required in the process of solving the nonlinear simultaneous equations by an iterative solution method. Moreover, since recalculation is required every m iterations, the processing load in the signal processing of the energy subtraction shown in FIG. 6 may become high. Therefore, the bone thickness B and the soft tissue thickness S for various combinations of high-energy transmittance images H and low-energy transmittance images L may be calculated in advance to generate a table and stored in the storage unit 136, and the bone thickness B and the soft tissue thickness S may be calculated quickly by referring to this table.

(Explanation of image processing)
A block diagram of image processing according to this embodiment is shown in Fig. 7. The image processing unit 134 generates an image for display by performing post-processing on the bone thickness image B obtained by the signal processing of the energy subtraction shown in Fig. 6. As the post-processing, the image processing unit 134 can use logarithmic conversion, dynamic range compression, etc. The content of the processing may be switched by inputting the type and strength of the post-processing as parameters.

Figure 8 is a block diagram showing an example of a configuration for realizing correction processing for acquiring a stored image according to this embodiment. As an image to be displayed on the display device 137, a stored image A, which is an image that does not have energy resolution, i.e., an image compatible with an image captured by an existing radiation imaging system, is also preferably used. The stored image A is generated by dividing the image XF_EVEN by the image WF_EVEN, for example, as shown in Figure 8. The images XF_EVEN and WF_EVEN are as described in Figure 5. That is, the image XF_EVEN is an image corresponding to the sum of the X-rays 301 in the rising phase, the X-rays 302 in the stable phase, and the X-rays 303 in the falling phase when there is a subject. Also, the image WF_EVEN is an image corresponding to the sum of the X-rays 301 in the rising phase, the X-rays 302 in the stable phase, and the X-rays 303 in the falling phase when there is no subject.

The signal processing unit 133 may generate a stored image A as a display image by multiplying an image H with transmittance at high energy and an image L with transmittance at low energy by a coefficient and adding them together. For example, the stored image A may be generated using the formula [10]. In addition, in calculating the stored image A, one coefficient may be 0 and the other coefficient may be 1, and the image H or image L itself may be used as the stored image A. In other words, an image that has been captured at substantially the same time as the image to be subjected to the energy subtraction process and has not been subjected to the energy subtraction process may be used as the stored image A.

[Number 10]
A=XF_EVEN/WF_EVEN
=X_High/WF_EVEN+X_Low/WF_EVEN
=H*(W_High/WF_EVEN)+L*(W_Low/WF_EVEN)
FIG. 9 is a diagram showing an example of a stored image A and a bone image B according to the present embodiment. A normal human body is composed of only soft tissues and bones. However, when performing interventional radiology (IVR) using the radiation imaging system shown in FIG. 1, a contrast agent is injected into blood vessels. In addition, a catheter or a guide wire is inserted into a blood vessel, and a stent or a coil is placed. The interventional radiology (IVR) is performed while checking the positions and shapes of the contrast agent and the medical device. Therefore, there is a possibility that visibility can be improved by isolating only the contrast agent or the medical device, or removing the background such as the soft tissue or the bone.

As shown in FIG. 9, in an image compatible with a normal radiation imaging system, i.e., accumulated image A, contrast medium, stent, bone, and soft tissue are displayed. On the other hand, in bone image B in the radiation imaging system according to this embodiment, the contrast of the soft tissue can be removed (reduced).

On the other hand, the main component of the contrast agent is iodine, and the main component of the medical device is a metal such as stainless steel. Both have a higher atomic number than calcium, the main component of bone, so bone image B shows the bone, contrast agent, and medical device.

The inventors of the present application conducted an investigation and found that even when separating a water image W and a contrast agent image I based on a high-energy image H and a low-energy image L, the bone, contrast agent, and medical device were still displayed in the contrast agent image I. The same was true for other combinations of two substances. The same was also true when the tube voltages and filters for low-energy X-rays and high-energy X-rays were changed. That is, in all cases, it was confirmed that the bone image B displayed the bone, contrast agent, and medical device, and it was not possible to separate the contrast agent or medical device from the bone image B.

In cases where the contrast of soft tissue reduces visibility, such as in the lung field during imaging-guided radiology (IVR) of the chest, displaying bone image B in the radiation imaging system according to this embodiment may improve the visibility of the contrast agent and medical devices. However, bone image B may have greater noise than accumulated image A, and image quality may deteriorate. Therefore, in this embodiment, a processing example of noise reduction processing of bone image B will be described as a configuration for performing noise reduction processing on an image separated by energy subtraction processing. Note that in the following description, an example of obtaining a noise-reduced bone thickness image will be described, but this embodiment can also be applied to obtaining a noise-reduced soft tissue thickness image.

(Noise reduction processing)
The signal processing unit 133 performs a material separation process for separating the image into thickness images of a plurality of materials using a plurality of pieces of information corresponding to a plurality of different radiation energies obtained by irradiating a subject with radiation and performing imaging. The signal processing unit 133 performs a material separation process using a thickness image that has been subjected to noise reduction processing using different parameters for a portion of pixels and other pixels specified based on information regarding the difference between the plurality of pieces of information, and the plurality of pieces of information.

The signal processing unit 133 obtains information regarding the differences between the multiple pieces of information by comparing information obtained by subtracting the first radiographic image H and the second radiographic image L with a threshold value. Alternatively, the signal processing unit 133 obtains information regarding the differences between the multiple pieces of information by comparing information obtained by dividing the first radiographic image H and the second radiographic image L with a threshold value. Alternatively, the signal processing unit 133 obtains information regarding the differences between the multiple pieces of information by comparing the transmittance of the first radiographic image H with the transmittance of the second radiographic image L.

Figure 10 is a block diagram illustrating signal processing for implementing noise reduction processing according to an embodiment. This explains a configuration for reducing noise in an image (material separation image) obtained by the signal processing shown in Figure 6 by utilizing the thickness continuity shown in Figure 9.

Similar to the signal processing of energy subtraction shown in FIG. 6, the signal processing unit 133 generates a material separation image in block MD1 using multiple images obtained from the correction unit 132. That is, the signal processing unit 133 generates a bone thickness image B and a soft tissue thickness image S from an image L with low energy transmittance and an image H with high energy transmittance.

Then, in block C2, the signal processing unit 133 generates a thickness image T by adding the bone thickness image B and the soft tissue thickness image S. The thickness image T has more noise than the low-energy transmittance image L and the high-energy transmittance image H, and the image quality may deteriorate. For this reason, noise in the thickness image T is reduced by filtering. In block F1, the signal processing unit 133 applies filtering to the thickness image T to generate a thickness image T' with reduced noise.

In block C1, the signal processing unit 133 generates an accumulated image A from the low-energy transmittance image L and the high-energy transmittance image H in a manner similar to that described in FIG. 8. The signal processing unit 133 generates the accumulated image A by multiplying the high-energy transmittance image H and the low-energy transmittance image L by a coefficient and adding them. Next, the signal processing unit 133 performs re-separation in block MD2. That is, the signal processing unit 133 generates a noise-reduced bone thickness image B' from the filtered thickness image T' and the accumulated image A.

If the spectrum of an image of the sum of low-energy and high-energy X-rays, i.e., accumulated image A, is N _A (E), the thickness of the soft tissue is S, and the thickness of the bone is B, the following formula (11) holds.

Here, if the thickness of the soft tissue S and the thickness of the bone B are T, then T = B + S, and by transforming [Equation 11], the following [Equation 12] holds true.

By substituting pixel value A of the accumulated image at a certain pixel and thickness image T into equation (12) and solving the nonlinear equation, it is possible to obtain bone thickness image B at a certain pixel. In this case, by substituting thickness image T' after filtering into equation (12) instead of thickness image T and solving the nonlinear equation, a noise-reduced bone thickness image B' is obtained. Compared to accumulated image A, thickness image T has high continuity and does not contain high-frequency components. Therefore, even if noise is removed by filtering, signal components are unlikely to be lost. In this way, by using thickness image T' that has been subjected to noise reduction processing and accumulated image A, which originally has little noise, a noise-reduced bone thickness image B' can be obtained.

Note that, although an example of obtaining a noise-reduced bone thickness image B' has been described in equation (12), the same applies to obtaining a noise-reduced soft tissue thickness image S'. That is, the signal processing unit 133 can obtain a noise-reduced soft tissue thickness image S' based on the thickness image T' after filtering and the accumulated image A obtained based on the addition of multiple radiation images (H, L).

That is, by performing processing using the signal processing block diagram of FIG. 10 and the formula of FIG. 12, the signal processing unit 133 can generate a material separation image of the first material (bone thickness image B') that has been subjected to noise reduction processing compared to the first material separation image (bone thickness image B) based on the thickness image T' after filtering and the accumulated image A, and the signal processing unit 133 can generate a material separation image of the second material (soft tissue thickness image S') that has been subjected to noise reduction processing compared to the second material separation image (soft tissue thickness image S).

As the filter processing of the signal processing block F1 described in FIG. 10, the signal processing unit 133 can use a Gaussian filter, an epsilon filter, or the like.

The signal processing unit 133 performs noise reduction processing using parameters that are different between some pixels and other pixels, which are set by changing the kernel size of the Gaussian filter. For example, when using a Gaussian filter for filtering, the signal processing unit 133 first uses a Gaussian distribution function to calculate weights according to the distance from the center of the filter, and generates a Gaussian filter kernel. Furthermore, it normalizes the sum of all weights of the Gaussian filter kernel to be 1. Then, the Gaussian filter kernel is applied to the thickness image T to generate a thickness image T' after noise reduction.

The signal processing unit 133 performs noise reduction processing using parameters that are different between some pixels and other pixels, which are set by changing the threshold value of the epsilon filter. For example, when using an epsilon filter for filtering, the signal processing unit 133 first uses a Gaussian distribution function to calculate a weight according to the distance from the center, and generates a Gaussian filter kernel. In addition, a pixel to which the filter is applied (a pixel of interest) is selected, and a difference d between the pixel of interest and its surrounding pixels in the thickness image T is calculated. If the difference d exceeds a predetermined threshold ε, it is determined that there is a structure of the subject, and the weight of the edge determination kernel is set to 0. If the difference d is below a predetermined threshold ε, it is determined that there is no structure of the subject, and the weight of the edge determination kernel is set to 1. Next, the kernel of the Gaussian filter and the kernel of the edge determination are multiplied for each element of the filter to generate an epsilon filter kernel. Furthermore, the weights of all the epsilon filter kernels are normalized so that the sum of the weights is 1. Then, the kernel of the epsilon filter is applied to the thickness image T to generate a thickness image T' after noise reduction.

The signal processing unit 133 stores the calculation results of equation (12) in a table in its internal memory in advance, and by referring to the table during calculation of equation (12), it is possible to obtain the thickness image T' after filtering and the bone thickness image B' (soft tissue thickness image S') corresponding to the accumulated image A. This makes it possible for the signal processing unit 133 to obtain noise-reduced material separation images (B', S') of each material in a short time, not only for still image capture, but also for video capture such as imaging-guided therapy (IVR).

The display control unit 135 performs display control to display at least one of the noise-reduced thickness image of the first material (bone thickness image B') and the thickness image of the second material different from the first material (soft tissue thickness image S') on the display device 137. Based on an instruction input via the operation device 138, the display control unit 135 performs display control to switch the image to be displayed on the display device 137 (bone thickness image B' or soft tissue thickness image S'), or to display both images (bone thickness image B' and soft tissue thickness image S') side by side.

The storage unit 136 stores in advance the filter parameters used in the filter process F1 in the block diagram of FIG. 10. The filter parameters stored in the storage unit 136 are filter parameters optimized according to the imaging conditions (exposure conditions). In the filter process F1, the filter parameters are acquired from the storage unit 136 according to the imaging conditions.

When imaging a subject for imaging-guided therapy (IVR), the operator can adjust filter parameters by issuing instructions via the operation device 138 while viewing the display on the display device 137. Parameters for noise reduction processing set according to imaging conditions can be adjusted by issuing instructions via the operation device 138. The display control unit 135 performs display control to display on the display device 137 an image that has been subjected to noise reduction processing based on the adjusted parameters.

(Description of Motion Artifact Reduction Processing)
FIG. 11 is a diagram for illustratively explaining a motion artifact (motion artifact of energy subtraction) that is to be solved in this embodiment. Here, an example will be described in which a bone image B is generated from an image H with high energy transmittance and an image L with low energy transmittance. As a premise, the image H with high energy transmittance and the image L with low energy transmittance are acquired at the drive timing of FIG. 4. Here, an example is taken of an image of a subject in interventional radiology (IVR), where the subject is bone, soft tissue, and wire. The soft tissue is separated from the bone image B, and the image is only of the bone and wire.

In this example, if the wire moves at a certain speed or faster during image acquisition, the wire area may change between the high-energy transmittance image H and the low-energy transmittance image L. In the drive timing of FIG. 4, the accumulation times of the low-energy X-rays 401 and 403 are set to be longer before and after compared to the accumulation time of the high-energy X-rays 402, so that the low-energy transmittance image L appears as an image with a blurred (thickened) wire, as shown in FIG. 11. In other words, a mismatch in the coordinates of the subject may occur between the high-energy transmittance image H and the low-energy transmittance image L.

In the signal processing of energy subtraction shown in Figure 6, calculations are performed by referencing the pixel values of the same coordinates for all pixels in the high-energy image H and the low-energy image L. Therefore, the signal processing in Figure 6 is not performed correctly for pixels with coordinates where a mismatch occurs. Pixels for which signal processing is not performed correctly can result in motion artifacts such as bone image B in Figure 11. Motion artifacts indicate high pixel output, and appear as bright spots along the contours of the subject on the image. This can result in an unnatural image, such as flickering, which can reduce image quality.

The inventors of the present application have found that adjusting the filter parameters of the filter process F1 described in Figure 10 reduces motion artifacts.

Examples of filter parameters include the kernel size and a threshold value ε for determining the structure of the subject. Increasing the kernel size, i.e., raising the threshold value for determining the structure, reduces motion artifacts. However, changing the filter parameters for all pixels for motion artifacts can result in a deterioration in image quality because the noise reduction effect for the entire image is too low. This is because filter parameters are typically set to values that are optimal for image quality and suitable for noise reduction. Therefore, in this embodiment, pixels in which motion artifacts have occurred are detected, and the filter parameters for the detected pixels are changed.

Figure 12 is a diagram explaining the process flow for changing pixel filter parameters according to this embodiment, and Figure 13 is a diagram illustrating a motion artifact detection image M according to this embodiment.

In S1201, the signal processing unit 133 generates (acquires) a detection image M indicating the coordinates (position) of a pixel where a motion artifact has occurred. As an example, the signal processing unit 133 can identify a pixel where a motion artifact has occurred by comparing information obtained by subtracting the first radiographic image H with a high energy transmittance and the second radiographic image L with a low energy transmittance with a threshold value. Alternatively, the signal processing unit 133 can identify a pixel where a motion artifact has occurred by comparing information obtained by dividing the first radiographic image H and the second radiographic image L with a threshold value. Alternatively, the signal processing unit 133 can identify a pixel where a motion artifact has occurred by comparing the transmittance of the first radiographic image H with the transmittance of the second radiographic image L. The signal processing unit 133 acquires a detection image M in which some of the pixels identified by the above process are set as pixels where a motion artifact has been detected.

Next, in S1202, the signal processing unit 133 determines whether or not a motion artifact has occurred for each pixel in the filter process F1 in the signal processing based on the detection image M. The signal processing unit 133 identifies the position of a pixel in the thickness image T where a motion artifact has been detected by comparing each pixel of the thickness image T input to the filter process F1 (noise reduction process) with the pixel (pixel position information) set in the detection image M. That is, the input image of the filter process F1 is the thickness image T, and the signal processing unit 133 determines whether or not a motion artifact has been detected for each pixel of the thickness image T. The signal processing unit 133 determines whether or not a motion artifact has been detected based on the pixel value of the detection image M corresponding to each pixel of the thickness image T.

In the following processing, the signal processing unit 133 performs noise reduction processing using a first filter parameter on some pixels (detected pixels) identified as pixels in which motion artifacts are detected, and performs noise reduction processing using a second filter parameter smaller than the first filter parameter on the other pixels (undetected pixels).

If a motion artifact is detected in the determination process of S1202 (S1202-detected), in S1203 the signal processing unit 133 performs filtering on the relevant pixel using filter parameters for reducing motion artifacts (first filter parameters). On the other hand, if a motion artifact is not detected in the determination process of S1202 (S1202-not detected), in S1204 the signal processing unit 133 performs filtering on the relevant pixel using filter parameters (second filter parameters) used in normal filtering.

A noise-reduced thickness image T' is obtained by applying the determination process using the detection image M to all pixels of the thickness image T (S1204). Furthermore, a noise-reduced thickness image B' of the bone is obtained by substituting the filtered thickness image T' into equation (12) and solving the nonlinear equation while reducing motion artifacts.

As shown in FIG. 13, the motion artifact detection image M is an image showing the coordinates (position information) where the motion artifact occurred. The pixel values of the motion artifact detection image M are assigned binary values of "1" to identify a detected pixel and "0" to identify an undetected pixel, to clearly distinguish between detected pixels and undetected pixels. Note that the assignment of pixel values in the detection image M is not limited to the binary values of "1" and "0", and may be any identification information that can identify a detected pixel from an undetected pixel.

The motion artifact detection image M may be generated by, for example, performing subtraction (difference) between the image H with high energy transmittance and the image L with low energy transmittance, and comparing the information obtained by the subtraction with a threshold (threshold judgment). In addition, the motion artifact detection image M may be generated by, for example, performing division between the image H with high energy transmittance and the image L with low energy transmittance, and comparing the information obtained by the division with a threshold (threshold judgment), to generate an image similar to the motion artifact detection image M in FIG. 13. In the threshold judgment, pixels that exceed a predetermined threshold are registered as motion artifact occurrence pixels. The predetermined threshold can be set via the operation device 138. The transmittance of the high energy image H may be higher than that of the low energy image L. When performing division between the image H with high energy transmittance and the image L with low energy transmittance, division by image H/image L or image L/image H may be performed by switching between setting an upper limit threshold or a lower limit threshold.

In this embodiment, an abnormal pixel refers to a pixel that exhibits an impossible value (absolute value). The transmittance of the high-energy image H (first radiographic image) and the transmittance of the low-energy image L (second radiographic image) are compared, and a pixel exhibiting an abnormal pixel value, for example, where the transmittance (pixel value after gain correction) of the high-energy image H is lower than the transmittance (pixel value after gain correction) of the low-energy image L, may be registered as a pixel in which a motion artifact is detected (motion artifact detection pixel).

Normally, the transmittance of the high-energy image H is higher than that of the low-energy image L. However, this is not the case when a mismatch occurs in the coordinates of the subject, and the transmittance of the low-energy image L may be higher than that of the high-energy image H. Such pixels that show abnormal pixel values that are likely to indicate the occurrence of motion artifacts may be registered as motion artifact detection pixels.

The detection image M for detecting motion artifacts may be obtained (generated) using an image other than the high-energy transmittance image H and the low-energy transmittance image L. For example, the bone image B before noise reduction or the soft tissue thickness image S in FIG. 10 may be used. Alternatively, the bone image B' after noise reduction may be generated once without adjusting the filter parameters, and the motion artifact detection image M may be generated. In this case, the motion artifact detection method may include performing a threshold judgment on the pixel difference or performing an abnormal pixel value judgment. Here, in the abnormal pixel value judgment, for example, the bone image B before noise reduction, the soft tissue thickness image S, and the bone image B' after noise reduction may be focused on as an image alone, and a pixel showing an abnormal value, such as a negative value for the thickness of the bone or soft tissue, may be judged as an abnormal pixel. In the threshold judgment using the pixel difference, there may be cases where an abnormality cannot be detected due to the threshold setting, etc., but according to the abnormal pixel value judgment, a pixel showing an impossible value (absolute value) can be detected using information that can be obtained from the image alone.

According to this embodiment, it is possible to obtain energy subtraction images with reduced motion artifacts while suppressing an increase in noise, making it possible to obtain images with excellent image quality.

Second Embodiment
In the second embodiment, a configuration will be described in which the filter parameters of the filter processing are expanded to a plurality of patterns (e.g., three patterns) or more, thereby reducing motion artifacts and noise, and generating a bone image (energy subtraction image) with reduced image discomfort. The second embodiment is similar to the first embodiment up to the signal processing of Fig. 10. However, in the second embodiment, the content of the filter processing of block F1 in the signal processing of Fig. 10 is different.

In the following processing, the signal processing unit 133 performs noise reduction processing using a first filter parameter for some pixels (detection pixels), and performs noise reduction processing using a second filter parameter smaller than the first filter parameter for other pixels (undetection pixels). In this embodiment, noise reduction processing is performed using a third filter parameter smaller than the first filter parameter and larger than the second filter parameter for peripheral pixels located within a predetermined range based on the position of the some pixels (detection pixels) among the other pixels (undetection pixels).

FIG. 14 is a diagram explaining the flow of processing for changing pixel filter parameters according to the second embodiment, and FIG. 15 is a diagram illustrating an example of a motion artifact detection image M according to the second embodiment.

In S1401, the signal processing unit 133 generates a detection image M showing the coordinates (position) of a pixel where a motion artifact has occurred and the coordinates (positions) of surrounding pixels located therearound. In this embodiment, the coordinates (positions) of the surrounding pixels are identified by, for example, dividing the pixels into detected pixels and undetected pixels as described in FIG. 13, and then using the position of the detected pixel as a reference, identifying surrounding pixels located within a predetermined range from the detected pixel, for example, using pixel coordinate information and position information for the detected pixel.

Here, the surrounding pixels may include adjacent pixels adjacent to the coordinates (position) of the pixel where the motion artifact occurs, or pixels located within a predetermined range including the adjacent pixels. The predetermined range can be adjusted via the operation device 138 as position information including at least pixels adjacent to the detection pixel (part of the pixels). Information indicating the predetermined range including the adjacent pixels (coordinate information or position information for the detection pixel) may be stored in advance in the storage unit 136 via the operation device 138, for example, and when generating the detection image M, the storage unit 136 may be referenced to identify the coordinates (positions) of the surrounding pixels located around the pixel where the motion artifact was detected (detection pixel). Alternatively, the operator can view the image displayed on the display device 137 and adjust the information indicating the predetermined range (coordinate information or position information for the detection pixel) via the operation device 138. The range of the surrounding pixels can be adjusted by changing the setting of the predetermined range including the adjacent pixels.

Identification of pixels where motion artifacts have occurred is similar to the process (S1201) of the first embodiment, and the signal processing unit 133 can, for example, identify pixels where motion artifacts have occurred by comparing information obtained by subtracting the first radiographic image H with a high energy transmittance and the second radiographic image L with a low energy transmittance with a threshold value. Alternatively, the signal processing unit 133 can identify pixels where motion artifacts have occurred by comparing information obtained by dividing the first radiographic image H and the second radiographic image L with a threshold value. Alternatively, the signal processing unit 133 can identify pixels where motion artifacts have occurred by comparing the transmittance of the first radiographic image H with the transmittance of the second radiographic image L.

The signal processing unit 133 sets a pixel value of "2" for pixels where a motion artifact is detected (detection pixels) as information for identifying the detection pixels. In addition, by comparing the difference with a predetermined threshold, the signal processing unit 133 sets a pixel value of "0" for pixels where a motion artifact is not detected (undetected pixels) as information for identifying the undetected pixels. The signal processing unit 133 then identifies the coordinates (positions) of surrounding pixels located around the pixel where a motion artifact is detected (detection pixels), and sets a pixel value of "1" as information for identifying the surrounding pixels.

Next, in S1402, the signal processing unit 133 determines whether or not a motion artifact occurs at each pixel in the filter process F1 in the signal processing based on the detection image M. The signal processing unit 133 identifies the position of a pixel in the thickness image T where a motion artifact is detected by comparing each pixel of the thickness image T input to the filter process F1 (noise reduction process) with the pixel (pixel position information) set in the detection image M.

The input image for the filter process F1 is a thickness image T, and the signal processing unit 133 determines whether or not a motion artifact is detected for each pixel of the thickness image T. In the second embodiment, the determination process using the motion artifact detection image M branches into three patterns. In the examples of Figures 14 and 15, the first pattern is a pattern (S1403) in which a filter process is performed on a detection pixel in which a motion artifact is detected. The second pattern is a pattern (S1406) in which a filter process is performed on surrounding pixels, and the third pattern is a pattern (S1407) in which a filter process is performed on other pixels.

If a motion artifact is detected in the determination process of S1402 (S1402-detected), in S1403, the signal processing unit 133 performs filtering on the corresponding pixel using filter parameters for reducing motion artifacts (first filter parameters).

On the other hand, if a motion artifact is not detected in the determination process of S1402 (S1402-not detected), in S1205, the signal processing unit 133 determines whether the corresponding pixel is a surrounding pixel located in the vicinity of the detected pixel.

If the determination process of S1405 determines that the pixel in question is a peripheral pixel located in the vicinity of the detected pixel (S1405-YES), then in S1406, the signal processing unit 133 performs filtering on the pixel in question using the intermediate filter parameters (third filter parameters).

Here, the intermediate filter parameter (third filter parameter) is a filter parameter having a noise reduction characteristic lower than that of the filter parameter for motion artifact reduction (first filter parameter) and higher than that of the filter parameter for normal filter processing (second filter parameter). The intermediate filter parameter (third filter parameter) is a filter parameter having a value different from that of the filter parameter for motion artifact reduction (first filter parameter) and the filter parameter for normal filter processing (second filter parameter), and it is preferable to use a value between that of the parameter for motion artifact reduction (first filter parameter) and the filter parameter for normal filter processing (second filter parameter).

On the other hand, if it is determined in the determination process of S1405 that the pixel in question is not a surrounding pixel located in the vicinity of the detected pixel (S1405-NO), in S1407, the signal processing unit 133 performs filtering on the pixel in question using the filter parameters (second filter parameters) used in normal filtering.

As shown in FIG. 15, the motion artifact detection image M is an image showing the coordinates (position information) where the motion artifact occurred. The pixel values of the motion artifact detection image M are assigned three values: the pixel value of the detected pixel is "2", the pixel value of the undetected surrounding pixels around the detected pixel is "1", and the pixel value of the other pixels is "0", to clearly distinguish between the detected pixel, the undetected surrounding pixels around the detected pixel, and the other pixels.

The signal processing unit 133 applies the determination process using the detection image M to all pixels of the thickness image T to obtain a noise-reduced thickness image T' (S1404).

Furthermore, by substituting the filtered thickness image T' into equation [12] and solving the nonlinear equation, a bone thickness image B' is obtained that has reduced noise and motion artifacts, and further reduces the sense of incongruity in the image.

Note that in the explanation of Figures 14 and 15, an example in which three filter parameters are used is shown, but the disclosed technology is not limited to this example, and the number of filter parameters may be increased to four or more. In this embodiment, pixels in which motion artifacts are not detected are branched into two patterns (S1406, S1407), but the disclosed technology is not limited to this example. For example, if the occurrence of a motion artifact is detected for the pixel in question (S1402-detected), the processing may be branched for the pixel in which a motion artifact is detected, for example, by using multiple detection thresholds.

According to this embodiment, by expanding the filter parameters to multiple patterns (e.g., three or more patterns) using the position information of the detection pixel where the occurrence of a motion artifact is detected, it is possible to obtain an energy subtraction image that reduces motion artifacts, noise, and image discomfort. This makes it possible to obtain an image with excellent image quality.

Third Embodiment
In this embodiment, a configuration in which the techniques disclosed in the first and second embodiments are applied to a moving image will be described.

Intra-frame processing Fig. 16 is a diagram showing a motion artifact detection image of the third embodiment. Fig. 16 shows a set of a high-energy transmittance image H (H_ti), a low-energy transmittance image L (L_ti), and a motion artifact detection image M (M_ti) arranged in the time direction (horizontal axis direction) when the first to nth frames are captured with the drive timing of Fig. 4.

In this embodiment, the signal processing unit 133 performs noise reduction processing using different parameters for some pixels (detection pixels) identified based on information regarding the differences between multiple pieces of information and other pixels (undetection pixels) in each frame of video capture. For example, the signal processing unit 133 performs motion artifact detection processing using a high-energy image H (first radiographic image) and a low-energy image L (second radiographic image) in the same frame. The signal processing unit 133 acquires (generates) a detection image in which some pixels (detection pixels) identified based on information regarding the differences between multiple pieces of information are set as pixels in which motion artifacts are detected.

In FIG. 16, image 1601 is a high-energy image H_t1 in the first frame (time t1), and image 1602 is a low-energy image L_t1 in the first frame (time t2). A motion artifact detection image M_t1 in the first frame (time t1) is generated using the images (H_t1, L_t1) obtained within the frame. The signal processing unit 133 performs the same processing for each frame, thereby generating a detection image M for each frame in the video capture.

In the processing example of FIG. 16, an example is described in which the high-energy image H (H_ti) and the low-energy image L (L_ti) in each frame are used to generate the detection image M (M_ti), but this is not limited to the example. For example, the detection image M (M_ti) may be generated by using abnormal pixel value judgment on the bone image B (B_ti) before noise reduction and the soft tissue thickness image S (S_ti) obtained in each frame.

In this embodiment, the thickness images T in each frame obtained by energy subtraction processing are denoted as T_t1, T_t2, ... T_tn, and the noise-reduced bone thickness images B' are denoted as B'_t1, B'_t2, ... B'_tn.

The signal processing unit 133 performs filter processing F1 (noise reduction processing) using a detection image M generated using information acquired in the frame to be processed. For example, if the frame to be processed is the first frame, filter processing F1 (noise reduction processing) is performed using a detection image M (M_t1) generated using information acquired in the first frame, and if the frame to be processed is the nth frame, filter processing F1 (noise reduction processing) is performed using a detection image M (M_tn) generated using information acquired in the nth frame.

The signal processing unit 133 performs noise reduction processing by comparing each pixel of the thickness image T input to the filter processing F1 (noise reduction processing) with the pixels set in the detection image M to identify the positions of pixels in the thickness image T where motion artifacts are detected.

The signal processing unit 133 applies a determination process using the detection image M acquired by using the information of each frame to all pixels of the thickness image (T_t1, T_t2, ... T_tn) of each frame to acquire a noise-reduced thickness image T' (T'_t1, T'_t2, ... T'_tn). Furthermore, by substituting the filtered thickness image T' into equation [12] and solving the nonlinear equation, it is possible to acquire a noise-reduced bone thickness image B' (B'_t1, B'_t2, ... B'_tn), i.e., an energy subtraction video, while reducing motion artifacts.

Inter-frame processing In FIG. 16, an example is described in which a detection image M in each frame is obtained by using the difference between an image H with high energy transmittance and an image L with low energy transmittance in the same frame as an image obtained within the frame, but when detecting motion artifacts in a video, information between previous and next frames (between one frame and another frame following the first frame) may be used in addition to the detection image M obtained based on information within the same frame. When information changes between previous and next frames, for example, a pixel whose pixel value has changed significantly between frames is likely to have caused a large movement of the subject. Such a pixel may be added to the detection image M (detection pixel) of the motion artifact obtained by processing within the same frame.

Figure 17 is a diagram illustrating the motion artifact detection process in this embodiment. Note that the disclosed technology may combine the method of acquiring the detection image M described in Figure 16 and the method of acquiring the detection image M described in Figure 17, or may acquire the detection image M by each of the acquisition methods independently.

When processing between frames, the signal processing unit 133 obtains the difference in information using the same type of image. For example, when using a high-energy image H (first radiation image), the signal processing unit 133 obtains information regarding the difference in multiple pieces of information by comparing the information obtained by the difference between a first radiation image corresponding to a first radiation energy obtained in one frame (e.g., the first frame) in video capture and a first radiation image obtained in another frame (e.g., the second frame) following the one frame (e.g., the first frame) with a threshold value.

In addition, when a low-energy image L (second radiation image) is used, the signal processing unit 133 obtains the information obtained by subtracting the second radiation image corresponding to the second radiation energy obtained in one frame (e.g., the first frame) from the second radiation image obtained in another frame (e.g., the second frame) by comparing the information with a threshold value.

The signal processing unit 133 adds some pixels (detection pixels) identified between previous and next frames (e.g., between one frame and another frame following the first frame) to the detection image M (detection pixels) acquired in at least one of the first frame and the other frame. In other words, the signal processing unit 133 adds some pixels (detection pixels) identified between previous and next frames (between one frame and the other frame) to some pixels (detection pixels) identified in at least one of the first frame and the other frame.

In the method of acquiring the detection image M described in FIG. 17, a pixel position determined to have a motion artifact by threshold determination using the difference in information between frames (between previous and next frames) is added to the detection image M of the previous and next frames. Note that the frame to which the pixel position is added may be only the detection image M acquired by processing in the previous frame (one frame), or only the detection image M acquired by processing in the subsequent frame (another frame) following the previous frame (one frame). Alternatively, it may be the detection image M acquired by processing each of the previous frame (one frame) and the subsequent frame (another frame). The frame to which the pixel position is added can be set via the operation device 138.

In FIG. 17, T1-T2, T2-T3, and T(n-1)-Tn indicate the difference between information of previous and next frames. Here, the difference between information of previous and next frames is the difference between images of the same type. For example, T1-T2 may be the difference between the high-energy image H_t1 of the first frame and the high-energy image H_t2 of the second frame, or the difference between the low-energy image L_t1 of the first frame and the low-energy image L_t2 of the second frame.

The signal processing unit 133 adds the detection pixel identified using the difference between the information of the temporally consecutive previous and next frames to the detection image M (detection pixel) of at least one of the previous and next frames. For example, taking the first and second frames in FIG. 17 as an example, the detection pixel identified using the difference (T1-T2) between the information of the temporally consecutive previous and next frames may be added to the detection image (detection pixel) acquired by processing in the previous frame (first frame). Alternatively, the identified detection pixel may be added to the detection image (detection pixel) acquired by processing in the subsequent frame (second frame). Alternatively, the identified detection pixel may be added to the detection image M (detection pixel) acquired by processing each of the previous frame (first frame) and the subsequent frame (second frame 2).

The signal processing unit 133 performs a filter process F1 for each frame using the detection image M acquired by using the information obtained within the frame and the information of the previous and next frames. The signal processing unit 133 applies a judgment process using the detection image M acquired by using the information obtained within the frame and the information of the previous and next frames to all pixels of the thickness image (T_t1, T_t2, ... T_tn) of each frame to acquire a noise-reduced thickness image T' (T'_t1, T'_t2, ... T'_tn). Furthermore, by substituting the filtered thickness image T' into [Equation 12] and solving the nonlinear equation, it is possible to acquire a noise-reduced bone thickness image B' (B'_t1, B'_t2, ... B'_tn), i.e., an energy subtraction video, while reducing motion artifacts.

(Modification)
Fig. 18 is a block diagram for explaining signal processing for realizing noise reduction processing according to a modified example. The symbols used in the block diagram for explaining the signal processing in Fig. 18 are the same as those in Fig. 10, but differ in that the input image of the filter processing F1 is a soft tissue thickness image S, and the image output by the filter processing F1 is a soft tissue thickness image S' after noise reduction.

In the first embodiment, a configuration was described in which the nonlinear equation of [Equation 12], which is a modification of [Equation 11], utilizes the thickness continuity shown in FIG. 9 to substitute the filtered thickness image T' into [Equation 12] instead of the thickness image T (= B + S) and solve the nonlinear equation to obtain a noise-reduced bone thickness image B'.

In the configuration of this modification, the pixel value A of the accumulated image at a pixel and the thickness S of the soft tissue are substituted into the formula [11] to solve the nonlinear equation, and the bone thickness B at a pixel can be obtained. At this time, instead of the soft tissue thickness S, the soft tissue thickness S' after the filter processing shown in FIG. 18 is substituted into the formula [11] to solve the nonlinear equation, and a noise-reduced bone thickness image B' is obtained. In the description of this modification, an example of obtaining a noise-reduced bone thickness image B' has been described, but this modification can also be applied to the case of obtaining a noise-reduced soft tissue thickness image S'. According to the configuration of the modification, it is possible to reduce the calculation load compared to the processing using the nonlinear equation of [12], and compared to the configuration of the first embodiment, the processing speed is prioritized over the image quality, and a noise-reduced bone thickness image B' (or a noise-reduced soft tissue thickness image S') can be obtained simply.

In this embodiment, the X-ray imaging device 104 is an indirect X-ray sensor that uses a phosphor. However, the disclosed technology is not limited to this form. For example, a direct X-ray sensor that uses a direct conversion material such as CdTe may be used.

In addition, in this embodiment, the tube voltage of the X-ray generating device 101 is changed. However, the disclosed technology is not limited to this form. The energy of the X-rays irradiated to the X-ray imaging device 104 may be changed by, for example, switching the filter of the X-ray generating device 101 over time.

In addition, in this embodiment, images of different energies were obtained by changing the energy of the X-rays. However, the disclosed technology is not limited to this form. By stacking multiple phosphors 105 and two-dimensional detectors 106, images of different energies may be obtained from the two-dimensional detectors on the front and back sides relative to the direction of incidence of the X-rays. The two-dimensional detector 106 is not limited to medical use, and may be an industrial two-dimensional detector.

In addition, in this embodiment, energy subtraction processing is performed using the imaging control device 103 of the radiation imaging system. However, the disclosed technology is not limited to this form. The image acquired by the imaging control device 103 may be transferred to another computer and energy subtraction processing may be performed thereon. For example, the acquired image may be transferred to another image processing device (image viewer) via a medical PACS, and displayed after energy subtraction processing.

The disclosure of this specification includes the following image processing device, radiation imaging system, image processing method, and program.
(Item 1) A processing unit is provided for performing a material separation process for separating a plurality of material thickness images using a plurality of pieces of information corresponding to a plurality of different radiation energies obtained by irradiating a subject with radiation and performing imaging,
The processing means is an image processing device that performs the material separation processing using the thickness image that has been subjected to noise reduction processing using parameters that differ between some pixels identified based on information regarding the differences between the multiple pieces of information and other pixels, and the multiple pieces of information.
(Item 2) The image processing device according to item 1, wherein the information corresponding to a first radiation energy among the plurality of radiation energies includes a first radiation image, and the information corresponding to a second radiation energy different from the first radiation energy includes a second radiation image.
(Item 3) The image processing device according to item 2, wherein the first radiographic image includes data in which signal values of electrical signals obtained by the imaging correspond to position information indicating a two-dimensional arrangement of the signal values, and the second radiographic image includes data in which signal values of electrical signals obtained by the imaging correspond to position information indicating a two-dimensional arrangement of the signal values.
(Item 4) The image processing device according to item 2 or 3, wherein the processing means acquires information relating to a difference between the plurality of pieces of information by comparing information obtained by subtracting the first radiographic image and the second radiographic image with a threshold value.
(Item 5) The image processing device according to item 2 or 3, wherein the processing means acquires information relating to a difference between the plurality of pieces of information by comparing information obtained by dividing the first radiographic image and the second radiographic image with a threshold value.
(Item 6) The image processing device according to item 2 or 3, wherein the processing means acquires information relating to the difference between the plurality of pieces of information by comparing a transmittance of the first radiographic image with a transmittance of the second radiographic image.
(Item 7) The image processing device according to any one of items 1 to 6, wherein the processing means performs the noise reduction process using different parameters for the part of pixels identified based on information relating to differences in the plurality of pieces of information and the other pixels in each frame of moving image shooting.
(Item 8) The processing means is configured to:
The information obtained by subtracting a first radiation image corresponding to a first radiation energy obtained in one frame during the video capture from the first radiation image obtained in another frame subsequent to the first frame is compared with a threshold value, or
8. The image processing device according to item 7, wherein the image is acquired by comparing information obtained by subtracting a second radiographic image corresponding to a second radiation energy different from the first radiation energy acquired in the one frame and the second radiographic image acquired in the other frame with the threshold value.
(Item 9) The image processing device according to item 8, wherein the processing means adds the portion of pixels identified between the one frame and the other frame to the portion of pixels identified in at least one of the one frame and the other frame.
(Item 10) The processing means acquires a detection image in which the part of pixels specified based on information regarding the difference between the plurality of pieces of information is set as pixels in which a motion artifact is detected,
An image processing device according to any one of items 1 to 9, which identifies the position of a pixel in the thickness image where the motion artifact is detected by comparing each pixel of the thickness image input to the noise reduction process with the pixel set in the detection image.
(Item 11) The image processing device according to any one of items 1 to 10, wherein the processing means performs the noise reduction process using a Gaussian filter or an epsilon filter.
(Item 12) The processing means performs the noise reduction process using different parameters for the some pixels and the other pixels, the parameters being set by changing a kernel size of the Gaussian filter; or
12. The image processing device according to item 11, wherein the noise reduction process is performed using different parameters for the some pixels and the other pixels, the parameters being set by changing a threshold value of the epsilon filter.
(Item 13) The image processing device according to any one of items 1 to 12, wherein the processing means performs the noise reduction process on the some of the pixels using a first filter parameter, and performs the noise reduction process on the other pixels using a second filter parameter that is smaller than the first filter parameter.
(Item 14) The image processing device according to item 13, wherein the processing means performs the noise reduction process on surrounding pixels located within a predetermined range from the position of the partial pixel by using a third filter parameter that is smaller than the first filter parameter and larger than the second filter parameter.
(Item 15) The image processing device according to item 14, wherein the predetermined range is adjustable via an operation unit as position information including at least pixels adjacent to the certain pixels.
(Item 16) The device further includes a display control unit for causing the display unit to display an image,
the display control means performs display control to cause the display means to display at least one of the noise-reduced thickness image of a first material and the thickness image of a second material different from the first material;
An image processing device described in any one of items 1 to 15, which performs display control to switch between the thickness image of the first substance and the thickness image of the second substance, or display control to display the thickness image of the first substance and the thickness image of the second substance side by side, based on instructions input via an operation means.
(Item 17) The parameters in the noise reduction process that are set according to the shooting conditions can be adjusted by an instruction via the operation means,
17. The image processing device according to item 16, wherein the display control means performs display control to cause the display means to display the image on which the noise reduction process has been performed based on the adjusted parameters.
(Item 18) A radiation imaging device including a two-dimensional detector;
18. The image processing device according to any one of claims 1 to 17, which acquires an image that has been subjected to noise reduction processing using a plurality of pieces of information corresponding to a plurality of radiation energies detected by the two-dimensional detector;
A radiation imaging system comprising:
(Item 19) A step of performing a material separation process for separating a plurality of material thickness images using a plurality of pieces of information corresponding to a plurality of different radiation energies obtained by irradiating a subject with radiation and performing imaging;
performing the material separation process using the thickness image that has been subjected to noise reduction processing using parameters that differ between a portion of pixels identified based on information regarding the difference between the plurality of pieces of information and the other pixels, and the plurality of pieces of information;
An image processing method comprising the steps of:
(Item 20) A program for causing a computer to execute the image processing method according to item 19.

[Other embodiments]
The disclosed technology can also be realized by a process in which a program for realizing one or more of the functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and one or more processors in a computer of the system or device read and execute the program. Also, the disclosed technology can be realized by a circuit (e.g., ASIC) for realizing one or more of the functions.

The disclosed technology is not limited to the above-described embodiments, and various modifications and variations are possible without departing from the spirit and scope of the invention. Therefore, the following claims are appended to disclose the scope of the invention.

101: X-ray generator, 102: X-ray control device, 103: Imaging control device (image processing device), 104: X-ray imaging device, 106: Two-dimensional detector

Claims (20)

a processing means for performing a material separation process for separating a plurality of material thickness images using a plurality of pieces of information corresponding to a plurality of different radiation energies obtained by irradiating a subject with radiation and performing imaging,
The processing means is an image processing device that performs the material separation processing using the thickness image that has been subjected to noise reduction processing using parameters that differ between some pixels identified based on information regarding the differences between the multiple pieces of information and other pixels, and the multiple pieces of information. The image processing device according to claim 1, wherein the information corresponding to a first radiation energy among the plurality of radiation energies includes a first radiation image, and the information corresponding to a second radiation energy different from the first radiation energy includes a second radiation image. The image processing device according to claim 2, wherein the first radiographic image includes data in which the signal values of the electrical signals obtained by the imaging correspond to position information indicating a two-dimensional arrangement of the signal values, and the second radiographic image includes data in which the signal values of the electrical signals obtained by the imaging correspond to position information indicating a two-dimensional arrangement of the signal values. The image processing device according to claim 2, wherein the processing means acquires information regarding the difference between the plurality of pieces of information by comparing information obtained by subtracting the first radiographic image and the second radiographic image with a threshold value. The image processing device according to claim 2, wherein the processing means acquires information regarding the difference between the plurality of pieces of information by comparing information obtained by dividing the first radiographic image and the second radiographic image with a threshold value. The image processing device according to claim 2, wherein the processing means acquires information regarding the difference between the plurality of pieces of information by comparing the transmittance of the first radiographic image with the transmittance of the second radiographic image. The image processing device according to claim 1, wherein the processing means performs the noise reduction process using different parameters for the part of pixels identified based on information relating to the differences in the plurality of pieces of information and the other pixels in each frame of the video capture. The processing means processes information regarding the difference between the plurality of pieces of information as follows:
The information obtained by subtracting a first radiation image corresponding to a first radiation energy obtained in one frame during the video capture from the first radiation image obtained in another frame subsequent to the first frame is compared with a threshold value, or
8. The image processing device according to claim 7, wherein the image is acquired by comparing information obtained by subtracting a second radiographic image corresponding to a second radiation energy different from the first radiation energy acquired in the one frame and the second radiographic image acquired in the other frame with the threshold value. The image processing device according to claim 8, wherein the processing means adds the portion of pixels identified between the one frame and the other frame to the portion of pixels identified in at least one of the one frame and the other frame. The processing means acquires a detection image in which the part of pixels identified based on information regarding the difference between the plurality of pieces of information is set as pixels in which a motion artifact is detected,
The image processing device according to claim 1 , wherein the position of a pixel where the motion artifact is detected in the thickness image is identified by comparing each pixel of the thickness image input to the noise reduction processing with the pixel set in the detection image. The image processing device according to claim 1, wherein the processing means performs the noise reduction process using a Gaussian filter or an epsilon filter. The processing means performs the noise reduction process using different parameters for the part of pixels and the other pixels, the parameters being set by changing a kernel size of the Gaussian filter; or
The image processing device according to claim 11 , wherein the noise reduction process is performed using different parameters for the some pixels and the other pixels, the parameters being set by changing a threshold value of the epsilon filter. The image processing device according to claim 1, wherein the processing means performs the noise reduction process using a first filter parameter for the part of pixels, and performs the noise reduction process using a second filter parameter smaller than the first filter parameter for the other pixels. The image processing device according to claim 13, wherein the processing means performs the noise reduction process on surrounding pixels located within a predetermined range from the position of the partial pixel using a third filter parameter that is smaller than the first filter parameter and larger than the second filter parameter. The image processing device according to claim 14, wherein the predetermined range is adjustable via an operation means as position information including at least pixels adjacent to the portion of pixels. Further comprising a display control means for causing the display means to display an image,
the display control means performs display control to cause the display means to display at least one of the noise-reduced thickness image of a first material and the thickness image of a second material different from the first material;
2. The image processing device according to claim 1, further comprising: display control for switching between the thickness image of the first substance and the thickness image of the second substance, or display control for displaying the thickness image of the first substance and the thickness image of the second substance side by side, based on instructions input via an operating means. the parameters in the noise reduction process, which are set according to the shooting conditions, can be adjusted via the operation means;
17. The image processing apparatus according to claim 16, wherein the display control means performs display control to cause the display means to display the image that has been subjected to the noise reduction process based on the adjusted parameters. a radiation imaging device including a two-dimensional detector;
18. The image processing device according to claim 1 , further comprising: a processor for processing the image by a noise reduction process using a plurality of pieces of information corresponding to a plurality of radiation energies detected by the two-dimensional detector;
A radiation imaging system comprising: performing a material separation process for separating the image into a plurality of material thickness images using a plurality of pieces of information corresponding to a plurality of different radiation energies obtained by irradiating a subject with radiation and taking an image;
performing the material separation process using the thickness image that has been subjected to noise reduction processing using parameters that differ between a portion of pixels identified based on information regarding the difference between the plurality of pieces of information and the other pixels, and the plurality of pieces of information;
An image processing method comprising the steps of: A program for causing a computer to execute the image processing method according to claim 19.

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