WO2018105493A1 - Dispositif de capture d'image radiographique, système de capture d'image radiographique, procédé de capture d'image radiographique et programme - Google Patents

Dispositif de capture d'image radiographique, système de capture d'image radiographique, procédé de capture d'image radiographique et programme Download PDF

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WO2018105493A1
WO2018105493A1 PCT/JP2017/043116 JP2017043116W WO2018105493A1 WO 2018105493 A1 WO2018105493 A1 WO 2018105493A1 JP 2017043116 W JP2017043116 W JP 2017043116W WO 2018105493 A1 WO2018105493 A1 WO 2018105493A1
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radiation
pixel
prior probability
average
information
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PCT/JP2017/043116
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English (en)
Japanese (ja)
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明 佃
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キヤノン株式会社
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Priority claimed from JP2016235548A external-priority patent/JP2020096646A/ja
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Publication of WO2018105493A1 publication Critical patent/WO2018105493A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment

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  • the present invention relates to a radiation imaging apparatus, a radiation imaging system, a radiation imaging method, and a program.
  • Patent Document 1 instead of individually measuring photons, the average energy and average of incident radiation are calculated from the average value and noise of a plurality of pixel values (values representing the amount of incident light output from the pixels of the radiation imaging apparatus). A method for estimating the number of photons has been proposed.
  • Patent Document 1 has a problem that the number of pixels necessary for estimating the energy or the average number of photons with high accuracy becomes enormous.
  • the radiographic apparatus is a radiographic apparatus that captures a radiographic image with radiation, and includes a determination unit that determines a prior probability based on a predetermined condition, and obtains a pixel value measured from the radiographic image. And means for calculating at least one of the average energy and the average number of photons of the radiation based on the prior probability and the pixel value.
  • the present invention can calculate the average energy or the average number of photons with high accuracy with fewer pixels than the prior art.
  • the figure which shows the method of determining the spatial distribution of the coefficient of a regularization term The figure which shows the method of determining the spatial distribution of the coefficient of a regularization term.
  • FIG. 1 is a diagram showing an example of the configuration of a radiation imaging system according to the present invention.
  • a radiation imaging system (or a radiation imaging apparatus) captures a radiation image with radiation.
  • a radiation tube (radiation generator) 101 generates radiation. Radiation irradiated from the radiation tube 101 passes through the subject 102 and enters an FPD (Flat Panel Detector) 103 that is a radiation detection unit.
  • the radiation tube 101 may be provided with a radiation filter for shielding low-energy radiation.
  • the FPD 103 includes a phosphor that converts radiation into visible light, and an image sensor that converts visible light into electric charge and voltage (a plurality of image sensors are provided, and one image sensor corresponds to one pixel). With.
  • the FPD 103 also includes an image generation unit that converts the converted voltage into a digital radiographic image, and an image correction unit that performs offset correction, gain correction, and defect correction on the radiographic image.
  • a method of directly converting radiation using cadmium telluride (CdTe) or amorphous selenium (a-Se) into a voltage can be applied.
  • the radiation conditions (irradiation conditions) to be irradiated are set by the operation panel 104.
  • the operation panel 104 includes a condition specifying unit 105, an imaging region specifying unit 106, and a condition display unit 107.
  • the condition designation unit 105 can designate irradiation conditions such as a tube voltage, a tube current, and an irradiation time with a button or the like.
  • the imaging region designating unit 106 specifies the imaging region, it is possible to set the radiation irradiation condition corresponding to the imaging region.
  • the irradiation instruction unit 108 includes a foot pedal, and the radiation tube 101 can emit radiation when the operator depresses the foot pedal.
  • the radiation image output from the FPD 103 is transferred to the computer 109.
  • the computer 109 includes an image acquisition unit 110, a prior information acquisition unit (determination unit) 111, an energy calculation unit 112, a prior information storage unit 113, an input device 114, and a display device 115.
  • the image correction unit included in the FPD 103 may be included in the computer 109.
  • the irradiation conditions of radiation are stored in advance in the computer 109 instead of the operation panel 104.
  • the image acquisition unit 110 has a function of capturing the radiation image generated by the FPD 103 into the computer 109.
  • the image acquisition unit (acquisition unit) 110 acquires a radiographic image from the FPD 103 and acquires a pixel value measured from the radiographic image.
  • the prior information acquisition unit 111 acquires prior information from the radiation tube 101, the FPD 103, the operation panel 104, the prior information storage unit 113, the input device 114, the C arm 118, and the gantry 119, and determines a prior probability described later.
  • the prior information acquisition unit (determination unit) 111 includes radiation generation information related to a radiation tube (radiation generation unit) 101 that generates radiation, radiation detection information related to an FPD (radiation detection unit) 103 that detects radiation, and a subject 102 to be photographed.
  • Subject information relating to the subject imaging part information relating to the imaging part of the subject 102 to be photographed, contrast agent information relating to a contrast agent injected into the subject 102, support relating to the C-arm (support unit) 118 that supports the radiation imaging apparatus or the radiation tube 101
  • a prior probability described later is determined.
  • the prior information acquisition unit (determination unit) 111 uses a radiographic image captured before a predetermined time among a plurality of captured radiographic images, to generate radiation generation information, radiation detection information, subject information, imaging region information, And at least one of the contrast agent information may be acquired.
  • Prior information acquired by the prior information acquisition unit 111 includes radiation generation information, radiation detection information, subject information, imaging region information, contrast agent information, support information, and gantry information, as well as a probability relating to the number of radiation (number of photons). There is probability distribution information of the distribution.
  • the probability distribution information includes functions of Poisson distribution and normal distribution, functions derived from these functions (functions represented by expressions (15), (16), (23), and (24) described later, and Poisson distribution and normal distribution parameters).
  • the radiation generation information includes at least one tube information of tube voltage of the radiation tube 101, tube current of the radiation tube 101, radiation irradiation time of the radiation tube 101, presence / absence of a radiation filter, and characteristics of the radiation filter. It is.
  • the radiation generation information includes the radiation spectrum of the radiation tube 101.
  • the radiation detection information regarding the FPD 103 includes an accumulation time and the like.
  • the imaging part information includes information for identifying the chest and head.
  • the subject information includes the identification number, name, sex, age, height, weight, and the like of the subject 102 input by the input device 114.
  • the contrast agent information includes the type and concentration of the contrast agent, the site where the contrast agent is injected, and the like.
  • the support information regarding the C arm 118 includes the material, position, angle, and the like of the C arm 118.
  • the gantry information regarding the gantry 119 includes the position of the gantry 119 and the like.
  • the prior information acquired by the prior information acquisition unit 111 is used for energy or photon number calculation, so that the average energy or average can be obtained with a smaller number of shots than the prior art (for example, the invention described in Patent Document 1). It becomes possible to calculate the number of photons with high accuracy.
  • the energy calculation unit 112 is based on the probability distribution regarding the number of radiations (number of photons) acquired by the prior information acquisition unit 111, the prior probability calculated by the prior information acquisition unit 111, and the radiation image acquired by the image acquisition unit 110. Calculate and output average energy or average number of photons.
  • the energy calculation unit (calculation unit) 112 calculates at least one of the average energy of radiation and the average number of photons by the Bayes' theorem described later based on the probability distribution, the prior probability, and the pixel value.
  • the probability distribution of this embodiment is a probability distribution related to the number of photons of radiation.
  • the image acquisition unit 110, the advance information acquisition unit 111, the energy calculation unit 112, and the advance information storage unit 113 are a central processing unit of a computer, a main storage device, a storage device such as a hard disk, a graphic processing for high-speed calculation, It can be configured by general hardware such as a unit and a LAN (Local Area Network) adapter.
  • the function of each component is implemented as software. Further, a circuit that executes the function of each component may be electrically mounted.
  • the input device 114 is, for example, a keyboard, a mouse, a touch panel, or the like.
  • the operator can use the input device 114 to input imaging part information and subject information.
  • the imaging part information and the subject information may be input through a LAN adapter mounted on the computer 109 in addition to the input by the input device 114.
  • the imaging part information and subject information are acquired as prior information by the prior information acquisition unit 111 and used to determine the prior probability.
  • the display device 115 is used to display a radiation image.
  • the display device 115 includes a color display, it is possible to express information on average energy by color (color or gradation), and improvement in diagnostic performance of radiographic images is expected.
  • Some of the functions of the operation panel 104 may be provided in the input device 114 and the display device (output device) 115.
  • the input of the radiation irradiation condition performed through the condition designating unit 105 or the imaging region designating unit 106 may be performed by the input device 114.
  • radiation conditions such as tube voltage, tube current, and irradiation time may be displayed on the display device 115 instead of the condition display unit 107.
  • the radiation tube 101, the FPD 103, the operation panel 104, the irradiation instruction unit 108, and the computer 109 are connected to the synchronization device 116.
  • the synchronization device 116 determines whether or not radiation exposure is possible from the state of the FPD 103, the pressing state of the irradiation instruction unit 108, and the processing state of the computer 109.
  • the radiographic system of the present embodiment includes a contrast medium injection device 117 for performing angiography.
  • the contrast medium injection device 117 injects a blood vessel contrast medium such as iodine into the subject 102 based on an instruction from the operator.
  • the contrast medium injection device 117 transmits contrast medium information as prior information to the prior information acquisition unit 111.
  • the contrast agent information includes the type and concentration of the contrast agent.
  • FIG. 2 is a flowchart showing an example of the operation of the radiation imaging system of the present embodiment.
  • the operator inputs radiation irradiation conditions.
  • the radiation irradiation condition is input through, for example, the condition specifying unit 105 or the imaging region specifying unit 106.
  • subject information such as the identification number of the subject 102 is input through the input device 114 together with the irradiation condition.
  • step 202 the operator instructs radiation irradiation through the irradiation instruction unit.
  • step 203 the synchronization device 116 determines whether or not radiation exposure is possible from the state of the radiation tube 101, the FPD 103, the operation panel 104, the irradiation instruction unit 108, and the computer 109. If it is determined that exposure is not possible, a warning is output to the display device 115 in step 204.
  • the prior information acquisition unit 111 determines a probability distribution (for example, a probability distribution related to the number of radiations) and a prior probability necessary for calculating the average energy based on the prior information.
  • the prior information is acquired from the radiation tube 101, the FPD 103, the operation panel 104, the prior information storage unit 113, the input device 114, the C arm 118, and the gantry 119.
  • the prior information acquisition unit (determination unit) 111 determines a probability distribution and a priori probability of Bayes' theorem described later.
  • the prior information acquisition unit 111 determines the number of pixel values necessary for estimating the energy or the average number of photons.
  • the number of radiographic images corresponding to is determined. Data in which the imaging region and the number of images are associated with each other is stored in advance in the pre-information storage unit 113.
  • the prior information acquisition unit 111 notifies the synchronization device 116 that radiation irradiation is possible, and the synchronization device 116 sends a radiation irradiation instruction signal to the radiation tube 101 and the FPD 103.
  • radiation (irradiation) acquisition is started by irradiating the FPD 103 with radiation.
  • step 207 the radiographic image capturing is terminated.
  • step 208 the average energy is calculated from the probability distribution and prior probabilities regarding the number of radiations obtained in step 205 and the pixel value obtained in step 206.
  • the energy calculation unit 112 calculates average energy. A method for calculating the average energy will be described later.
  • step 209 the information on the average energy calculated in step 208 is displayed and stored.
  • the information about the average energy can be displayed in color by the display device 115 by associating the obtained average energy with the color.
  • Equation (1) the relationship between the average pixel value m of the pixel values acquired in step 206, the average number of photons n incident on the pixel, and the proportionality coefficient k (E) that is a function of the average energy E is expressed by equation (1). It is represented by
  • K (E) is a pixel value per photon and is a function of energy E. Since the pixel value (light emission amount) per photon can be obtained as a function of the energy E, if k (E) can be calculated from the pixel value, the average energy E is calculated.
  • k (E) is expressed by a linear expression of average energy E.
  • k (E) E. That is, if the average pixel value m can be divided into the average photon number n and the proportional coefficient k (E), the average energy E can be calculated.
  • K is a normalization constant.
  • p 1 is the average pixel value m
  • p 2 is the reciprocal 1 / k (E) of the proportional coefficient k (E).
  • p 1 is the average pixel value m
  • p 2 is the pixel value variance ⁇ 2 (or standard deviation).
  • the first parameter p is at least one of a temporal average average pixel value at a predetermined pixel of the radiographic image and a spatial average average pixel value of a plurality of pixels of the radiographic image.
  • the second parameter is at least one of a function having the average energy E as a variable, a variance ⁇ 2 of the pixel values of the radiographic image, and a standard deviation of the pixel values of the radiographic image.
  • p, q) and the prior probability h (p, q) are calculated, the probability f (p, q
  • FIG. 3 is a diagram showing the effect of this embodiment.
  • the prior probability h (p, q) is determined by the equation (27).
  • the horizontal axis represents the number of radiographic images (that is, the number of measurements), and the vertical axis represents the pixel value k (E) per photon corresponding to the average energy E.
  • this embodiment calculates a value that approximates the true value of the average energy E with a smaller number of radiation images (number of measurements) than the prior art. can do.
  • p, q) a probability distribution relating to the number of radiations (photon number) can be used in consideration of physical phenomena relating to generation and absorption of radiation.
  • the prior probability h (p, q) can be calculated from information (that is, prior information) acquired separately from the measurement of the pixel value x.
  • Formula (2) is described as a product of functions, but in order to facilitate calculation, Formula (3) describing Formula (2) in logarithm is used hereinafter.
  • Equation (3) searching for a local maximum value according to the local maximum condition of Equation (6) based on the stationary point (p, q) that becomes Equation (4) and the Hessian matrix of Equation (5),
  • a method such as a steepest descent method may be used.
  • the energy calculation unit (calculation unit) 112 calculates the probability f of the event that the first parameter p 1 and the second parameter p 2 become the first value and the second value, respectively, when the pixel value x i is measured.
  • the parameter solution is calculated so that (p, q
  • the energy calculation unit (calculation unit) 112 has a probability distribution g (m i
  • FIG. 4 is a flowchart for calculating solutions of the first parameter and the second parameter.
  • the prior information acquisition unit 111 determines the prior probability log e h (p, q) from the prior information. A method for determining the prior probability log e h (p, q) will be described later.
  • step 304 as described above, the parameters p and q that maximize the probability log ef (p, q
  • step 305 the average pixel value m and 1 / k (E) or the variance ⁇ 2 of pixel values (or standard deviation) are determined from the parameters p and q determined in step 304.
  • the energy calculation unit (calculation unit) 112 determines the average photon number n and the proportional coefficient k (E), and calculates the average energy E from the relational expression of the proportional coefficient k (E) and the average energy E.
  • the probability distribution is at least one of a Poisson distribution and a normal distribution.
  • mi is the pixel value of a certain pixel in the i-th frame.
  • Equation 10 Substituting Equation (8) and Equation (9) into Equation (7) yields Equation (10).
  • Equation (10) is a function of the pixel values m i, and the average pixel value m, the reciprocal 1 / k of the proportional coefficient k (E) and (E). Therefore, the parameters p 1 and p 2 are the average pixel value m and the reciprocal 1 / k (E) of the proportionality coefficient, respectively, and the probability g (m i
  • the first parameter is an average pixel value m that is a time average of predetermined pixels of the radiation image.
  • the second parameter is a function 1 / k (E) having the average energy E as a variable.
  • the calculation proceeds with the second parameter as the reciprocal 1 / k (E) of k (E), but the calculation is performed with the second parameter as the proportional coefficient k (E) instead of the reciprocal 1 / k (E).
  • a similar result can be obtained by proceeding.
  • Equation (12) Since Equation (12), factorial ⁇ m i / k (E) ⁇ ! Is represented by a ⁇ function, Equation (12) becomes Equation (14).
  • ⁇ (x) ⁇ log e ⁇ (x) / ⁇ x is a digamma function.
  • the Newton method is used, and the average pixel value m as a solution and the reciprocal 1 / k (E) of the proportional coefficient can be obtained.
  • the average photon number n is calculated by substituting the average pixel value m obtained by the equations (15) and (16) and the inverse of the proportionality coefficient 1 / k (E) into the equation (1).
  • the energy calculation unit (calculation unit) 112 determines that the event probability f (p, q
  • the solution of the first parameter m and the second parameter 1 / k (E) is calculated as follows.
  • Expression (17) becomes Expression (18). Further, considering the probability normalization condition, Equation (18) becomes Equation (19).
  • the parameters p 1 and p 2 are the average pixel value m and the variance ⁇ 2 of the pixel values, respectively, and the probability g (m i
  • the first parameter is an average pixel value m that is a time average of predetermined pixels of the radiation image.
  • the second parameter is the variance ⁇ 2 of the pixel values of the radiation image.
  • the proportional coefficient k (E) can be calculated, and the average photon number n can be calculated by Equation (7).
  • Equations (23) and (24) can determine the average pixel value m, which is the solution, and the variance ⁇ 2 of pixel values, for example, by using the Newton method.
  • the energy calculation unit (calculation unit) 112 determines that the event probability f (p, q
  • the solution of the first parameter m and the second parameter ⁇ 2 is calculated as follows.
  • the prior probability h (p, q) is expressed in the form of a natural logarithm, for example, log e h (p, q) in equation (3). Therefore, the prior probability h (p, q) is represented by a canonical distribution as shown in Equation (25).
  • ⁇ is a coefficient.
  • Z ( ⁇ ) is a normalization constant.
  • e is a natural logarithm.
  • E (p, q) is a function of parameters p and q.
  • the function E (p, q) is a function having at least one extreme value.
  • E (p, q) By giving an extreme value to E (p, q), it is possible to make an assumption that the probability increases when the parameters p and q are predetermined values, and the number of pixel values is less than a predetermined threshold value. Even in this case, it is possible to converge to p or q with high accuracy.
  • E (p, q) may be expressed by a function of quadratic or higher (in the example described later, as shown in equation (29), E (p, q) is Expressed by a quartic function).
  • Equation (4) becomes Equation (23) and Equation (24)
  • Equation (p, q) is expressed by a simple algebraic equation, Equation ( By substituting Equation (25) into Equation (23) and Equation (24) for transformation, an equation representing the solution may be obtained. In that case, the solution can be calculated without using the Newton method.
  • L pixel values are acquired by one pixel by acquiring L radiation images.
  • the pixel values necessary for the calculation of the above parameters are set to a plurality of pixels. Can also be obtained.
  • the number of acquired radiological images is L / 4.
  • the number of radiographic images taken can be made smaller than L, and high-precision p or q can be calculated while shortening the radiographing time.
  • FIG. 5 is a diagram illustrating an example of a method for determining the prior probability h (p, q).
  • step 401 tube information is acquired.
  • the prior information acquisition unit 111 acquires the tube information from the operation panel 104 or the like.
  • the prior information acquisition unit 111 calculates the minimum energy (or minimum average energy) of radiation and the minimum proportional coefficient kmin . As the radiation passes through the object, the low energy photons of the radiation tend to be selectively absorbed by the object, so the average energy of the radiation after passing through the object is increased compared to before passing through the object.
  • the corresponding radiation spectrum is obtained from the set tube voltage of the radiation tube 101 and the presence or absence of the radiation filter, and the average energy of the radiation when not passing through the object such as the subject 102 is calculated. Then, find the minimum energy of radiation.
  • the energy calculation unit 112 calculates a corresponding proportionality coefficient kmin based on the calculated minimum energy of radiation, and the prior information acquisition unit 111 acquires the calculated proportionality coefficient kmin as prior information.
  • the radiation spectrum can be obtained by using a known model such as a TASMIP (Tungsten Anode Spectral Modeling Interpolating Polynomials) model.
  • TASMIP Tungsten Anode Spectral Modeling Interpolating Polynomials
  • the result of evaluating the characteristics of the radiation tube 101 in advance using a spectrometer may be stored in the prior information storage unit 113.
  • the minimum energy of radiation may be determined from the stored radiation spectrum.
  • the prior information storage unit 113 stores information on the mass attenuation coefficient of the filter as prior information, and after passing through the filter based on the stored information on the mass attenuation coefficient.
  • the radiation spectrum may be determined.
  • the method described in Patent Document 1 can also be used.
  • the method described in Patent Document 1 it is only necessary to obtain a radiographic image with the filter inserted and obtain the average energy.
  • the maximum energy (or maximum average energy) of radiation and the maximum proportionality coefficient k max are obtained.
  • the maximum value of photon (radiation photon) energy is determined by the set tube voltage. For example, when the set tube voltage is 100 kilovolts, the maximum energy of radiation photons may be considered as 100 kilovolts. Therefore, the prior information acquisition unit 111 calculates the maximum energy of photons from the set tube voltage of the radiation tube 101, and calculates the corresponding proportional coefficient k max from the calculated maximum energy.
  • step 404 prior probabilities log (p, q) are obtained from the proportional coefficients k min and k max calculated in steps 402 and 403.
  • the calculation of the prior probability h (p, q) is performed by the prior information acquisition unit (determination unit) 111.
  • the probability distribution of the number of photons is a Poisson distribution
  • the prior probability h (p, q) is expressed by Equation (26).
  • FIG. 6 is a diagram illustrating a distribution of prior probabilities h (p, q) when the number of photons is a normal distribution as shown in Expression (27).
  • FIG. 6A is a diagram illustrating an inner region and an outer region where the horizontal axis is the average pixel value m and the vertical axis is the variance ⁇ 2 .
  • FIG. 6B is a diagram illustrating the relationship between the variance ⁇ 2 and the prior probability h (m, ⁇ 2 ) when the average pixel value m is a.
  • the prior probability h (m, ⁇ 2 ) becomes maximum between the second parameters (inner regions) corresponding to the minimum energy and the maximum energy of radiation.
  • the prior probability h (m, ⁇ 2 ) is 0 in a range (outer region) other than between the second parameters corresponding to the minimum energy and the maximum energy of radiation.
  • the prior probability h (m, ⁇ 2 ) may be maximized between the first parameters (inner regions) corresponding to the minimum energy and the maximum energy of radiation.
  • the prior probability h (m, ⁇ 2 ) may be 0 in a range (outer region) other than between the first parameters corresponding to the minimum energy and the maximum energy of radiation.
  • the probability does not take the maximum value, and the obtained parameter (average pixel value m) is a parameter that always exists in the inner region.
  • the prior probability h (p, q) in the outer region of the equation (26) or the equation (27) is set to A instead of 0.
  • a finite number that is sufficiently smaller may be set.
  • FIG. 7 is a diagram showing another example of a method for determining the prior probability h (p, q).
  • the prior probability h (p, q) is calculated using information input before radiographic image capturing.
  • step 601 the prior information acquisition unit 111 acquires imaging part information.
  • step 602 subject information is acquired.
  • the subject information is information input through the input device 114 or the like and acquired by the prior information acquisition unit 111, and includes the identification number, name, sex, age, height, weight, and the like of the subject 102.
  • a prior probability is determined for each pixel.
  • the anatomical feature of the subject 102 captured as a radiographic image is estimated for each pixel. For example, it is estimated whether each pixel images a bone part, a muscle part, or a lung part. Then, based on the prior probabilities determined for each pixel, the average energy in each pixel is estimated.
  • the prior probability h (m, ⁇ 2 ) may be the maximum in the first parameter m corresponding to the energy of the radiation that has passed through the imaging region of the subject 102 to be imaged.
  • the proportionality coefficient k bone is a proportionality coefficient corresponding to the average energy of the radiation transmitted through the bone part.
  • the proportionality coefficient k muscle is a proportionality coefficient corresponding to the average energy of the radiation transmitted through the heart or myocardial part.
  • ⁇ is a parameter.
  • Z ( ⁇ ) is a normalization constant.
  • e is a natural logarithm.
  • E (m, ⁇ 2 ) is represented by Expression (29).
  • c 1 (m), c 2 (m), c 3 (m), c 4 (m), and c 5 (m) are functions of the average pixel value m.
  • subject information such as the identification number of the subject 102 may be considered.
  • the amount of radiation absorbed is larger than that of the thin type, and thus the observed average energy is high. Therefore, when photographing the obese body type subject 102, the prior probability of the position of higher average energy is set to be larger than that of the thin type body subject 102.
  • FIG. 9 is a diagram illustrating changes in the prior probability distribution of obesity type and lean type.
  • the prior probability h (m, ⁇ 2 ) is a first parameter m corresponding to the radiation absorption amount of the subject 102 to be imaged, and may be maximized.
  • BMI Body Mass Index
  • the body type may be determined according to the BMI.
  • the body type may be determined using the body fat percentage data.
  • the body shape may be determined according to the irradiation time of radiation from the radiation tube 101.
  • the irradiation time of radiation can also be controlled using an AEC (Automatic Exposure Control) device or the like.
  • Equation (30) represents a formula for calculating BMI.
  • the imaging region, size, and body shape of the subject 102 are determined based on information (imaging region information, subject information, etc.) input by the operation panel 104 or the input device 114.
  • the imaging region, size, and body shape of the subject 102 may be determined by a method such as pattern matching based on the captured radiographic image. In that case, in consideration of processing time such as pattern matching among a plurality of captured radiographic images, a determination is made using a radiographic image captured before a predetermined time, whereby the processing of the present embodiment Time can be shortened.
  • the prior information acquisition unit (determination unit) 111 acquires subject information or imaging region information using a radiographic image captured before a predetermined time among a plurality of captured radiographic images.
  • a prior probability distribution may be generated by using past information associated with imaging part information and subject information as prior information.
  • the prior information storage unit 113 stores energy information obtained when a radiation image has been captured in the past as prior information, so that a prior probability distribution can be generated.
  • a pixel value necessary for parameter determination is acquired using a certain pixel, but a pixel value required for parameter determination may be acquired using a plurality of pixels.
  • the prior probability is determined for each set of a plurality of pixels. For example, as described above, when four pixels are used to obtain L pixel values, the prior probabilities corresponding to the four pixels are determined from imaging part information, subject information, images, and the like.
  • FIG. 10 is a diagram illustrating a method of determining the prior probability h (m, ⁇ 2 ) according to contrast agent information such as the type and concentration of contrast agent.
  • the prior probability h (m, ⁇ 2 ) is the first parameter m corresponding to the energy of the radiation that has passed through the contrast agent injected into the object 102 to be imaged, and may be maximized.
  • step 901 the prior information acquisition unit 111 acquires information on contrast medium to be injected (contrast medium type, concentration, and the like).
  • contrast medium to be injected
  • the contrast agent is iodine or the like
  • the average energy of the radiation increases after the radiation passes through the subject 102.
  • the contrast agent is carbon dioxide or the like
  • the average energy of the radiation decreases after the radiation passes through the subject 102.
  • step 902 the prior information acquisition unit 111 and the energy calculation unit 112 calculate an expected average energy and a proportional coefficient k (E) corresponding thereto from the contrast agent information.
  • the prior probability h (m, ⁇ 2 ) at the pixel is set so that the prior probability becomes maximum at the position of the average energy obtained after the radiation passes through the contrast agent. Is done.
  • Proportionality factor k CM is a proportionality coefficient corresponding to the average energy of the radiation transmitted through the imaging moiety of the contrast agent.
  • step 903 a contrast medium is injected and a radiographic image is taken. Then, the average energy and the like are calculated from the radiation image (pixel value), the probability distribution information regarding the number of radiations, and the prior probability.
  • the prior information acquisition unit (determination unit) 111 may acquire contrast agent information using a radiographic image captured before a predetermined time among a plurality of radiographic images captured. For example, the position of the blood vessel may be determined in advance from the radiographic image using pattern matching, and the prior probability in the pixel of the contrast portion may be calculated using the blood vessel through which the contrast agent passes as the contrast portion.
  • the prior information acquisition unit (determination unit) 111 sets the first prior probability in the first pixel of the radiation image. Then, a second prior probability different from the first prior probability is set for the second pixel of the radiation image.
  • the energy calculation unit (calculation unit) 112 calculates at least one of the average energy of radiation and the average number of photons in the first pixel based on the probability distribution, the first prior probability, and the pixel value of the first pixel. calculate.
  • the energy calculation unit (calculation unit) 112 calculates at least one of the average energy of radiation and the average number of photons in the second pixel based on the probability distribution, the second prior probability, and the pixel value of the second pixel. calculate.
  • the prior information acquisition unit 111 can also use pixel information around the target pixel for the prior probability log (p, q) of the target pixel.
  • the prior information acquisition unit 111 acquires pixel information around the pixel of interest.
  • the prior information acquisition unit 111 introduces an amount related to the difference between the target pixel and surrounding pixels into the prior probability, and the larger the difference is, the smaller the value of the above-described formula (3) is.
  • FIG. 11 shows the relationship between the target pixel and surrounding pixels.
  • examples of prior probabilities in which pixel information around a pixel of interest is introduced include, for example, the square of the difference between parameters p and q (L2 norm) as shown in Equation (31).
  • (i, j) is the coordinate of the target pixel.
  • ⁇ (2) k, l , ⁇ (2) k, l are coefficients ( ⁇ (2) k, l , ⁇ (2) k, l > 0).
  • p i , j is the value of the parameter p at the coordinates (i, j).
  • q i , j is the value of the parameter q at the coordinates (i, j).
  • (2) of ⁇ (2) k, l means that the coefficient is related to the square of the difference.
  • An example of calculating the prior probability in which surrounding pixel information is introduced is, for example, a form using an absolute value of a difference (
  • the coefficients ⁇ k, l , ⁇ k, l are changed according to the distance from the target pixel.
  • the upper left, upper right, lower left, and lower right coefficients for the pixel of interest ( ⁇ ⁇ 1, ⁇ 1 , ⁇ 1, ⁇ 1 , ⁇ ⁇ if the coefficient ⁇ is related to the parameter p).
  • 1 , 1 , ⁇ 1,1 ) are the upper, lower, right, and left coefficients ( ⁇ 0, ⁇ 1 , ⁇ 0,1 , ⁇ 0,1 if the coefficient ⁇ is related to the parameter p).
  • ⁇ 0, ⁇ 1 That is, the relationship shown in Expression (36) is established.
  • the coefficients ⁇ k, l , ⁇ k, l and the number of surrounding pixels to be considered can be switched depending on the photographing conditions.
  • the coefficients ⁇ k, l , ⁇ k, l are defined as the intensity of the regularization term.
  • the coefficients ⁇ k, l , ⁇ k, l are increased and the surroundings are increased. Increase the number of pixels and give priority to noise reduction. For example, fluoroscopy is the case. In general shooting (still image shooting) that requires detailed description, the coefficients ⁇ k, l , ⁇ k, l are reduced, the number of surrounding pixels is reduced, and noise reduction is prioritized.
  • the prior information acquisition unit 111 may extract edge information from the pixel value of the image and determine a regularization term coefficient for each pixel. Since the boundary portions having greatly different pixel values generally differ greatly in energy, the prior probability term (regularization term) in which peripheral pixel information is introduced is reduced in the boundary portion to reduce the influence of adjacent pixels.
  • FIG. 12 is a flow showing details of step 301 (determination of prior probabilities) in FIG. 4 in the present embodiment.
  • the processing from step 501 to step 504 is processing of the prior information acquisition unit 111.
  • step 501 a plurality of images are averaged to generate an average image. Since one image is noisy and unsuitable for edge extraction, a plurality of images are averaged to generate an average image.
  • a boundary is extracted from the average image.
  • the prior information acquisition unit 111 includes a boundary extraction unit that extracts the boundary of the tissue in the image.
  • a generally used image filter such as a Laplacian filter may be used.
  • the boundary is, for example, a boundary between tissues in the subject such as the lungs and abdomen, and a boundary between the subject and a portion where there is no subject (elementary omission).
  • FIG. 13 is a diagram illustrating a method for determining the spatial distribution of the coefficient of the regularization term of each coordinate.
  • FIG. 13A shows the spatial distribution of the intensity of the regularization term when a subject (represented by a white dotted line) is photographed. The spatial distribution is determined for each pixel (the cell in FIG. 13A). When an edge of an image taken through a subject is extracted, it becomes like a black portion of a grid. For the boundary pixels and the non-boundary pixels, the coefficients of the regularization term are determined by different methods. The boundary is where the structure of the subject changes rapidly.
  • the prior information acquisition unit 111 holds a threshold value in advance, and the prior information acquisition unit 111 compares the pixel value of the edge with the threshold value, and determines a boundary that exceeds the threshold value.
  • a prior probability formula is determined in step 504 for each pixel.
  • the determination of the prior probability is performed using a prior probability formula that introduces surrounding pixel information such as Formula (31), Formula (34), and Formula (35).
  • a prior probability formula that introduces surrounding pixel information such as Formula (31), Formula (34), and Formula (35).
  • FIG. 13 only the first parameter p and the coefficient ⁇ are shown in order to avoid complexity. The same applies to the second parameter q and the coefficient ⁇ .
  • the form of the prior probability is the expression (31), the same applies to the expressions (34) and (35).
  • the method for determining the coefficient of the regularization term is changed depending on whether or not the target pixel is a boundary.
  • the regularization term is a coefficient of the regularization term of surrounding pixels to be compared (difference in this example).
  • the target pixel (i, j) of the pixel A in FIG. 13A is not a boundary.
  • the coefficient ⁇ relating to the comparison term ( pi, j ⁇ pi + 1, j ) 2 with the pixel (i + 1, j) on the right side of the target pixel is the coefficient ⁇ of the pixel (i + 1, j) on the right side. i + 1, j . Since the pixel (i + 1, j) is not a boundary, a relatively large value is set for the coefficient ⁇ i + 1, j .
  • the target pixel (i, j) is set so as to be easily influenced by the pixel (i + 1, j) on the right side.
  • the coefficient ⁇ applied to the comparison term (p i, j ⁇ p i ⁇ 1, j ) 2 with the pixel (i ⁇ 1, j ) on the left side of the target pixel is equal to the pixel (i ⁇ 1, j ) on the left side.
  • j) is the coefficient ⁇ i ⁇ 1, j .
  • the coefficient ⁇ i ⁇ 1, j is set to a relatively small value.
  • the pixel of interest (i, j) is set so as not to be affected by the pixel (i-1, j) on the left side.
  • coefficients ⁇ k, l , ⁇ k, l representing the size of the regularization term are used as the coefficients of the target pixel (i, j) in the boundary part.
  • the coefficient of the regularization term of the pixel value of the boundary having a low correlation with the upper, lower, left and right pixels needs to be relatively small.
  • FIG. 14 is a flow showing details of step 301 (determination of prior probabilities) in FIG. 4 in the present embodiment.
  • the processing from step 701 to step 704 is the processing of the prior information acquisition unit 111.
  • the prior information acquisition unit 111 acquires part information to be imaged from the operation panel 104.
  • the acquired part information is used in step 702 and step 703.
  • the part information is used to call a regularization term function associated with the imaging part.
  • the regularization term function is a function that represents the pixel value of a pixel and the strength of the regularization term. For example, if the acquired imaging region is a chest front image, a regularization term function for the chest front is called. Details of the regularization term function will be described later.
  • the regularization term function is stored in advance in the prior information storage unit 113.
  • step 501 an average image for edge extraction is generated, and in step 502, the boundary is extracted.
  • This step is the same as the step described in FIG.
  • Step 702 region extraction and regularization term determination.
  • Region extraction can be performed using, for example, a histogram.
  • An example of an image histogram of the front of the chest is shown in FIG.
  • the highest pixel value is a portion where there is no subject (element missing).
  • the next highest pixel value is in the lung field containing a lot of air, and the pixel value is the lowest in the mediastinum or the like. Therefore, by determining which peak (peak) of the histogram the pixel value of the pixel belongs to, it is possible to identify the site where the pixel is imaged.
  • the relationship between the pixel value and the part is stored in the prior information storage unit 113 and acquired in step 701.
  • the region extraction method is not limited to a method using a histogram, and other known methods can be used.
  • step 703 the coefficient of the regularization term of each coordinate is determined.
  • the coefficient is determined based on the area determined in step 702. For example, in the case of an image of the front of the chest, there are fine blood vessels. That is, the lung field has a small correlation with surrounding pixels.
  • the coefficient of the regularization term in the lung field needs to be set lower than the coefficient of the regularization term in other parts.
  • the determination of the regularization term coefficient is performed using a regularization term function.
  • the regularization term function is a function that represents the relationship between the pixel value and the regularization term, and is a function that differs for each imaging region. Therefore, it is called using the information on the imaging region acquired in step 701.
  • the regularization term function is prepared as a curve smoothed by a Bezier curve, for example.
  • FIG. 15B shows a regularization term function.
  • the regularization term based on boundary extraction is determined in this step.
  • the regularization term coefficient based on the boundary extraction is determined using the method described above. Note that the strength of the regularization term set at the boundary is set to be weaker than the strength of the regularization term determined based on the region. For example, in the lung field, the regularization term has a medium strength. For omissions and others, the strength of the regularization term is increased. The boundary makes the strength of the regularization term small.
  • a prior probability formula is determined.
  • the determination method is performed using the method described above.
  • the boundary uses a regularization term of the boundary, and uses a regularization term of surrounding pixels other than the boundary. Thereafter, the procedure after step 302 in FIG. 4 is executed.
  • the description is made assuming a radiation angiography apparatus and a radiography apparatus, but the present embodiment can also be applied to other radiography apparatuses (for example, a mammography apparatus).
  • the present embodiment can also be applied to a radiation imaging apparatus using radiation other than X-rays, for example, ⁇ rays, ⁇ rays, and ⁇ rays.
  • the present invention supplies software (program) that realizes the functions of the above-described embodiments to a system or apparatus via a network or various storage media, and a computer (CPU, MPU, etc.) of the system or apparatus reads the program. May be executed.
  • the present invention can also be realized by a process in which one or more processors in a computer of a system or apparatus read and execute a program, and can also be realized by a circuit (for example, an ASIC) that realizes one or more functions.

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Abstract

L'invention concerne un dispositif de capture d'image radiographique capable de calculer avec un degré élevé de précision l'énergie moyenne ou le nombre moyen de photons d'un rayonnement à l'aide d'un plus petit nombre de valeurs de pixel que dans l'état de la technique et qui capture une image radiographique au moyen d'un rayonnement. Ledit dispositif comprend : un moyen de détermination pour déterminer une probabilité antérieure sur la base de critères prédéterminés ; un moyen d'acquisition pour acquérir une valeur de pixel mesurée à partir de l'image radiographique ; et un moyen de calcul pour calculer au moins l'un parmi l'énergie moyenne et le nombre moyen de photons du rayonnement sur la base de la probabilité antérieure et de la valeur de pixel.
PCT/JP2017/043116 2016-12-05 2017-11-30 Dispositif de capture d'image radiographique, système de capture d'image radiographique, procédé de capture d'image radiographique et programme WO2018105493A1 (fr)

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Publication number Priority date Publication date Assignee Title
WO2020241030A1 (fr) * 2019-05-30 2020-12-03 キヤノン株式会社 Dispositif de traitement d'images, procédé de traitement d'images et programme

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Publication number Priority date Publication date Assignee Title
JP2004508124A (ja) * 2000-09-14 2004-03-18 ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー 組織特異的撮影のためのｘ線検出器及び方法
JP2009285356A (ja) * 2008-05-30 2009-12-10 Institute Of National Colleges Of Technology Japan 医療用撮影システム、画像処理装置、画像処理方法、およびプログラム
JP2011024773A (ja) * 2009-07-24 2011-02-10 National Institute Of Advanced Industrial Science & Technology Ｘ線成分計測装置

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004508124A (ja) * 2000-09-14 2004-03-18 ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー 組織特異的撮影のためのｘ線検出器及び方法
JP2009285356A (ja) * 2008-05-30 2009-12-10 Institute Of National Colleges Of Technology Japan 医療用撮影システム、画像処理装置、画像処理方法、およびプログラム
JP2011024773A (ja) * 2009-07-24 2011-02-10 National Institute Of Advanced Industrial Science & Technology Ｘ線成分計測装置

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020241030A1 (fr) * 2019-05-30 2020-12-03 キヤノン株式会社 Dispositif de traitement d'images, procédé de traitement d'images et programme

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