JP2012223227A - Image generating apparatus, x-ray ct apparatus, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To achieve the low exposure in the contrast examination by an X-ray CT photographing.SOLUTION: The dual energy photographing of a contrast medium administered subject is performed, and the water density image that shows the density distribution of water in the subject and the iodine density image that shows the density distribution of iodine in the subject are generated based on the acquired data. A virtual non-contrast image corresponding to an image obtained by the non-contrast photographing of the subject is generated based on the water density image and the iodine density image. As a result, the contrast image and an image that corresponds to the non-contrast image can be acquired by one photographing.

Description

  The present invention relates to an image generation apparatus, an X-ray CT (Computed Tomography) apparatus, and a program therefor, and more particularly, to a technique for reducing exposure using dual energy imaging in a contrast examination.

  Conventionally, when performing a contrast examination of a subject using an X-ray CT apparatus, a simple image obtained by simple imaging before administration of a contrast agent and a contrast image obtained by contrast imaging after administration of the contrast agent are obtained, and these Are often read and compared with each other (see, for example, the summary of Patent Document 1).

JP 2002-850401 A

  In order to acquire a simple image and a contrast image, it is usually necessary to separately perform simple imaging and contrast imaging of the subject, so that the exposure of the subject is larger than that of a general examination.

Under such circumstances, it is desired to reduce the exposure of the subject in the contrast examination.

  The invention according to the first aspect is a model in which the subject is represented by the synthesis of water and iodine based on data obtained by X-ray CT dual energy imaging of the subject to which a contrast medium containing iodine is administered. First generation means for generating a water density image representing a virtual water density distribution in the subject and an iodine density image representing a virtual iodine density distribution in the subject based on There is provided an image generation apparatus comprising: a second generation unit configured to generate a virtual non-contrast image corresponding to an image obtained by non-contrast imaging of the subject based on a density image and an iodine density image.

According to a second aspect of the present invention, the second generation means includes at least a part of the virtual non-contrast image.
(Virtual non-contrast image) = β × (water density image) + α × (iodine density image) −γ
However, 1.1 ≧ β ≧ 0.9, 0.30 ≧ α ≧ 0.24, 1100 ≧ γ ≧ 900
... (Formula 1)
An image generation apparatus according to the first aspect that is generated according to the following mathematical formula 1 is provided.

According to a third aspect of the present invention, the second generation means includes at least a part of the virtual non-contrast image.
(Virtual non-contrast image) =
β × {(water density image) + α × (iodine density image) + α ² × (iodine density image)} − γ
However, 1.1 ≧ β ≧ 0.9, 0.30 ≧ α ≧ 0.24, 1100 ≧ γ ≧ 900
... (Formula 2)
There is provided an image generation apparatus according to the first aspect which is generated according to the following mathematical formula 2.

  The invention according to a fourth aspect provides the image generating device according to the second aspect or the third aspect, wherein at least a part of the virtual non-contrast image is a part corresponding to a soft part of the subject.

  According to a fifth aspect of the invention, in the image generating apparatus according to the fourth aspect, the second generating unit specifies a portion corresponding to the soft part based on a pixel value of an image generated using the data. I will provide a.

  According to a sixth aspect of the invention, the second generating means uses an X-ray having a predetermined effective X-ray energy based on the data for a portion corresponding to a substance different from the soft part of the subject. The image generating apparatus according to the fourth aspect or the fifth aspect, which generates an image corresponding to an image obtained by line CT imaging, is provided.

According to a seventh aspect of the invention, the second generation means includes a portion corresponding to a substance different from the soft part of the subject,
(Virtual non-contrast image) = b × (water density image) + f (index relating to iodine concentration in the subject)
However, 1.0 ≧ b ≧ 0.9, f: function using the index as a parameter (Formula 3)
An image generation apparatus according to the fourth aspect or the fifth aspect, which is generated according to the following mathematical formula 3, is provided.

According to an eighth aspect of the invention, the second generation means includes a portion corresponding to a substance different from the soft part of the subject,
(Virtual non-contrast image) = b × (water density image) + c
However, 1.1 ≧ b ≧ 0.9, c: constant (Formula 4)
The image generation apparatus according to the fourth aspect or the fifth aspect, which is generated according to the following mathematical formula 4, is provided.

According to a ninth aspect of the present invention, in the mathematical formula 1,
1.05 ≧ β ≧ 0.95, 0.29 ≧ α ≧ 0.25, 1050 ≧ γ ≧ 950
An image generation apparatus according to any one of the second to eighth aspects that satisfies the following condition is provided.

  The tenth aspect of the present invention provides the image generation apparatus according to any one of the second to ninth aspects, in which α in Formula 1 is designated by the operator.

  According to an eleventh aspect of the present invention, the first generation means has a first projection data obtained by X-rays of the first energy obtained by the dual energy imaging and a second different from the first energy. The image obtained by subjecting the second projection data by X-rays of the energy of X and the data obtained by performing weighted subtraction processing so as to eliminate the iodine component to generate the water density image, The iodine density image is generated by performing image reconstruction processing on data obtained by performing weighted subtraction processing on the first projection data and the second projection data so that the water component is eliminated. An image generation apparatus according to any one of the first to tenth aspects is provided.

  An invention according to a twelfth aspect is an image corresponding to an image obtained when an X-ray CT image of a subject to which the contrast medium has been administered is obtained using X-rays having desired effective X-ray energy based on the data. An image generation apparatus according to any one of the first to eleventh aspects, further comprising a third generation unit for generating the image.

  An invention of a thirteenth aspect provides an X-ray CT apparatus provided with the image generating apparatus according to any one of the first to twelfth aspects.

  The invention according to a fourteenth aspect provides a program for causing a computer to function as the image generating apparatus according to any one of the first to twelfth aspects.

  The “density” mentioned here is not an actual density but an imaginary density. Two predetermined types of substances are used as a base substance, and an X-ray absorption coefficient of a certain substance is set to X of the two types of base substances. When expressed by the weighted addition value of the linear absorption coefficient, it corresponds to a weighting coefficient to be multiplied by the X-ray absorption coefficient of the two types of base materials. Therefore, for example, a water density image when water and iodine are a set of base materials is the X-ray absorption coefficient of the substance represented by each pixel in the image to be imaged, the X-ray absorption coefficient of water and the X-ray of iodine. When expressed by a weighted addition value with an absorption coefficient, a distribution diagram of values corresponding to the weighting coefficient multiplied by the water X-ray absorption coefficient is obtained.

  According to the invention of the above aspect, a virtual non-contrast image can be obtained with high accuracy by X-ray CT dual energy imaging of a subject to which a contrast medium containing iodine is administered. As a result, both the image corresponding to the contrast image and the image corresponding to the non-contrast image can be obtained by one dual energy imaging, and it is not necessary to perform contrast imaging and non-contrast imaging, respectively. Low exposure can be realized.

It is a figure which shows the structure of the X-ray CT apparatus by 1st embodiment. It is a figure which shows the flow of a process in the X-ray CT apparatus which concerns on 1st embodiment. It is a figure which shows the 1st example of the method of reconstructing an iodine density image and a water density image. It is a figure which shows the 2nd example of the method of reconstructing an iodine density image and a water density image. It is a figure which shows an example of the method of reconstructing a monochrome image (monochromatic). It is a figure which shows the relationship between the pixel value of an iodine density image when changing contrast density, and the pixel value of a water density image. It is a figure which shows the flow of a process in the X-ray CT apparatus which concerns on 2nd embodiment. It is a figure which shows the flow of a process in the X-ray CT apparatus which concerns on 3rd embodiment.

  Embodiments of the invention will be described below.

(First embodiment)
FIG. 1 is a diagram showing a configuration of an X-ray CT apparatus according to the first embodiment.

  The X-ray CT apparatus 100 includes an operation console 1, an imaging table 10, and a scanning gantry 20.

  The operation console 1 includes an input device 2 that receives input from an operator, a central processing unit 3 that performs control of various units and various calculations for scanning the subject 40, and data acquired by the scanning gantry 20. A data collection buffer (buffer) 5, a monitor 6 for displaying images, and a storage device 7 for storing programs and data.

  The imaging table 10 includes a cradle 12 on which the subject 40 is placed and carried into and out of the opening of the scanning gantry 20. The cradle 12 is moved up and down and horizontally moved by a motor built in the imaging table 10.

The scanning gantry 20 includes a rotating unit 15 and a support unit 16 that rotatably supports the rotating unit 15. The rotating unit 15 includes an X-ray tube 21, an X-ray controller (controller) 22 that controls the X-ray tube 21, and a collimator that collimates and shapes the X-ray 81 generated from the X-ray tube 21. ) 23, an X-ray detector 24 that detects X-rays 81 irradiated from the X-ray tube 21 and transmitted through the subject 40, and a data collection device that converts and collects the output of the X-ray detector 24 into projection data (DAS; Data
Acquisition System) 25 and a rotating unit controller 26 for controlling the X-ray controller 22, the collimator 23, and the DAS 25 are mounted. The support unit 16 includes a control controller 29 that communicates control signals and the like with the operation console 1 and the imaging table 10. The rotating portion 15 and the support portion 16 are provided with a slip ring (slip
ring) 30 and is electrically connected.

  Hereafter, the flow of processing in the X-ray CT apparatus according to the first embodiment will be described.

  FIG. 2 is a diagram showing a flow of processing in the X-ray CT apparatus according to the first embodiment.

  In step S1, the subject 40 to be imaged is subjected to dual energy imaging, first tube voltage projection data Pv1 that is projection data of a plurality of views by the first X-ray tube voltage, and a second X-ray tube. Second tube voltage projection data Pv2, which is projection data of a plurality of views by voltage, is collected.

  For example, the subject 40 is scanned while alternately switching the X-ray tube voltage between the first X-ray tube voltage and the second X-ray tube voltage in units of one or several views, and the 180 ° + X-ray beam (beam) is scanned. Collect projection data with a fan angle of α ° or more. In this case, the projection data of the missing view is obtained by performing weighted addition processing on the projection data of the view adjacent to the missing view.

  Also, for example, the X-ray tube voltage is first set to the first X-ray tube voltage and scanning is performed to collect projection data of 180 ° + fan angle α ° or more, and then the X-ray tube voltage is set to the second X-ray tube voltage. To scan and collect projection data of 180 ° + fan angle α ° or more.

  The first and second X-ray tube voltages are 80 kVp and 140 kVp, for example.

  In step S2, the subject 40 is represented based on the first and second tube voltage projection data Pv1 and Pv2 based on a predetermined model in which the subject 40 is represented by the synthesis of two types of base materials and water and iodine as the base materials. A water density image Gw representing a virtual water density distribution at 40 and an iodine density image Gio representing a virtual iodine density distribution in the subject 40 are reconstructed.

  FIGS. 3 and 4 are diagrams conceptually illustrating an example of a method for reconstructing an iodine density image and a water density image based on the first and second tube voltage projection data using water and iodine as a base material. .

  For example, as shown in FIG. 3, the first intermediate projection representing the water component is obtained by performing weighted subtraction processing on the first tube voltage projection data Pv1 and the second tube voltage projection data Pv2 so that the iodine component is eliminated. Data Pwa is generated. Further, the first tube voltage projection data Pv1 and the second tube voltage projection data Pv2 are weighted and subtracted so that the water component is eliminated, thereby generating second intermediate projection data Pio representing the iodine component. Next, the first intermediate projection data Pwa is subjected to image reconstruction processing to reconstruct the water density image Gw. Further, the second intermediate projection data Pio is subjected to image reconstruction processing by back projection processing or the like to reconstruct the iodine density image Gio.

  Further, for example, as shown in FIG. 4, the first tube voltage projection data Pv1 is subjected to image reconstruction processing to reconstruct the first tube voltage image Gv1. Further, the second tube voltage projection data Pv2 is subjected to image reconstruction processing to reconstruct the second tube voltage image Gv2. Next, a weighted subtraction process is performed on the first tube voltage image Gv1 and the second tube voltage image Gv2 so as to eliminate the iodine component, thereby reconstructing the water density image Gw. Further, the iodine density image Gio is reconstructed by performing weighted subtraction processing on the first tube voltage image Gv1 and the second tube voltage image Gv2 so that the water component is eliminated.

  In step S3, based on the water density image Gw and the iodine density image Gio, an image obtained when X-ray CT imaging is performed on the subject 40 to which the contrast medium has been administered using X-rays having a desired effective X-ray energy E1. The monochrome image Gm corresponding to is reconstructed. The monochrome image Gm is an image corresponding to a contrast image of the subject 40. The effective X-ray energy E1 is, for example, 120 keV.

  FIG. 5 is a diagram conceptually illustrating an example of a method for reconstructing a monochrome image based on a water density image and an iodine density image.

  For example, as shown in FIG. 5, the water density image Gw and the iodine density image Gio are weighted and added with a predetermined weighting factor corresponding to the effective X-ray energy E1, thereby reconstructing the monochromatic image Gm.

  For the method of generating each material density image and monochrome image, refer to Japanese Patent Application Laid-Open No. 2010-172590, US Patent Document: US Pat. No. 7724865 (US2009 / 0052612), and the like.

  In step S4, based on the water density image Gw and the iodine density image Gio, a virtual non-contrast image Gnc corresponding to an image obtained when X-ray CT imaging is performed on the subject 40 before the contrast agent is administered. It generates according to the following formula 1.

Gnc = β ・ Gw + α ・ Gio−γ
However, 1.1 ≧ β ≧ 0.9, 0.30 ≧ α ≧ 0.24, 1100 ≧ γ ≧ 900
... (Formula 1)

  That is, the pixel value of the virtual non-contrast image Gnc is obtained by multiplying the pixel value of the corresponding pixel in the water density image Gw by the coefficient β, and the value obtained by multiplying the pixel value of the corresponding pixel in the iodine density image Gio by the coefficient α. And -γ which is an offset component is added.

  Here, the meaning of Formula 1 will be described.

  When the water density image Gw and the iodine density image Gio are generated based on the data obtained by the dual energy imaging of the subject 40 to which the contrast agent is administered, the iodine density in the subject 40 is reflected in the iodine density image Gio. Therefore, it should hardly be reflected in the water density image Gio.

  The virtual non-contrast image Gnc to be obtained in the present embodiment is an image corresponding to a so-called simple image obtained when X-ray CT imaging of the subject 40 is performed without a contrast agent. Therefore, it can be considered that the virtual non-contrast image Gnc is basically an image close to the water density image Gw that should hardly reflect the density of iodine in the subject 40.

  Here, FIG. 6 shows the relationship between the pixel value in the iodine density image and the pixel value in the water density image obtained by dual energy imaging of a plurality of types of contrast agent phantoms having different concentrations. As can be understood from the graph of FIG. 6, when the concentration of the contrast agent changes, the pixel value of the iodine density image naturally changes, but the pixel value of the water density image also changes depending on the concentration of the contrast agent. . This is because iodine in the subject 40 is caused by the concentration and amount of the contrast agent administered to the subject 40, the degree of staining of the contrast agent (individual difference of subjects, imaging timing (timing) from administration of the contrast agent), and the like. This means that the pixel value of the water density image Gw changes with the pixel value of the iodine density image Gio depending on the density of the water. That is, the water density image Gw as it is means that the image is unstable as an image corresponding to a simple image.

  Therefore, the virtual non-contrast image Gnc is added to the water density image Gw with a small amount of the iodine density image Gio component so as to compensate for the change in the pixel value of the water density image Gw accompanying the change in iodine concentration in the subject 40. Like that.

  Thereafter, the coefficient β multiplied by the water density image Gw, the coefficient α multiplied by the iodine density image Gio, and −γ for the offset are set so that the pixel value in the virtual non-contrast image Gnc takes an appropriate value as the CT value. adjust. Formula 1 is derived by such an idea.

  By the way, the offset value -γ is appropriately -1000 so that the pixel value of the virtual non-contrast image Gnc becomes the CT value and the CT value becomes -1000HU when the imaging target is air. However, in practice, the specific gravity of water changes with temperature, and its X-ray absorption coefficient also changes. In addition, the data value obtained by the data acquisition system of the X-ray CT apparatus also has an error due to individual differences. Therefore, in consideration of these, the range of 1100 ≧ γ ≧ 900 is appropriate from experience, and there is a high possibility that the range of 1050 ≧ γ ≧ 950 is adjusted at the mounting level.

  The coefficients β and α can be obtained as follows, for example.

  First, the CT value Gnc of the virtual non-contrast image is defined as follows.

Gnc = β · {(Gw−OFFSETw) + α · (Gio−OFFSETio)} − γ (Formula A1)
Where OFFSETw: offset component in the water density image Gw,
OFFSETio: Offset component in iodine density image Gio

  Next, dual energy imaging is performed using air as an imaging target. Since the CT value of the theoretical water density image (Gw−OFFSETw) when air is photographed should be 0, the following equation is established when the measured value of the water density image at this time is Gw (air).

(Gw (air) −OFFSETw) = 0 (Formula A2)
∴ OFFSETw ＝ Gw (air)

  As described above, the offset OFFSETw can be obtained from the dual energy imaging using the air as the imaging target.

  Next, dual energy imaging is performed using water as an imaging target. Since the CT value of the theoretical iodine density image (Gio-OFFSETio) when water is photographed should be 0, the following equation is established when the measured value of the iodine density image at this time is Gio (water).

(Gio (water) −OFFSETio) = 0 (Formula A3)
∴ OFFSETio ＝ Gio (water)

  In addition, since the CT value Gnc (water) of the virtual non-contrast image at this time should be 0, the following equation is further established.

Gnc (water) = β ・ {(Gw (water) −OFFSETw) + α ・ (Gio (water) −OFFSETio)} − γ

= Β · {(Gw (water) −OFFSETw)} − γ = 0 (Formula A4)
∴ β = γ / (Gw (water) −OFFSETw)

  As described above, the offset OFFSETio and the coefficient β can be obtained from the dual energy imaging using water as an imaging target.

  According to actual experimental results, the coefficient β is obtained in the range of 1.1 ≧ β ≧ 0.9, and is adjusted in the range of 1.05 ≧ β ≧ 0.95 at the mounting level. Is preferred.

  Next, dual energy imaging is performed using the contrast agent phantom as an imaging target. Since the CT value of the virtual non-contrast image Gnc (iodine) when the contrast agent is imaged should be 0, the measured values of the water density image Gw and iodine density image at this time are Gw (iodine) and Gio (iodine), respectively. Then, the following equation holds.

Gnc (iodine) = β · {(Gw (iodine) −OFFSETw) + α · (Gio (iodine) −OFFSETio)} − γ
0 = β · {(Gw (iodine) −OFFSETw) + α · (Gio (iodine) −OFFSETio)} − γ
{(Gw (iodine) −OFFSETw) + α ・ (Gio (iodine) −OFFSETio)} = γ / β
α · (Gio (iodine) −OFFSETio) = γ / β− (Gw (iodine) −OFFSETw) (Formula A5)
∴ α ＝ {γ / β− (Gw (iodine) −OFFSETw)} / (Gio (iodine) −OFFSETio)

  In this way, the coefficient α can be obtained from the dual energy imaging using the contrast agent as the imaging target.

  Note that according to the actual experimental results of a plurality of patterns with different contrast agent concentrations, the coefficient α is obtained in the range of 0.30 ≧ α ≧ 0.24, and 0.28 ≧ α at the mounting level. It is preferable to adjust in the range of ≧ 0.26.

  The actual coefficient α may be determined by averaging the value of the coefficient α obtained for each contrast agent concentration. Alternatively, an option may be given to the operator to make a selection, or a recommended range may be shown to the operator and a value may be directly input.

  Incidentally, if β, α, and γ are fixed values, for example, β = 1, α = 0.27, and γ = 1000 are preferable.

  It should be noted that β, α, and γ are respectively determined as the above values, and the virtual non-contrast image artificially generated by the present embodiment is compared with the non-contrast image (simple image) actually captured. However, it has been confirmed that the CT values of the subject's easy-to-liver viscera and the like almost coincide with each other with a high accuracy within an error of −2 to 1.

  In step S5, the generated monochromatic image Gm and virtual non-contrast image Gnc are displayed.

  Accordingly, a contrast image and a non-contrast image of the subject 40 can be generated by one dual energy imaging without imaging with and without the contrast agent, thereby reducing the exposure of the subject 40. Can be planned.

  Note that, for example, a mathematical expression including a multi-order term such as the following expression can be used instead of the above mathematical expression 1.

Gnc = β · {Gw + α · Gio + α ² · Gio} −γ
However, 1.1 ≧ β ≧ 0.9, 0.30 ≧ α ≧ 0.24, 1100 ≧ γ ≧ 900
... (Formula 2)

(Second embodiment)
FIG. 7 is a diagram showing a flow of processing in the X-ray CT apparatus according to the second embodiment.

  Since steps T1 to T3 are the same as steps S1 to S3, description thereof is omitted.

  In step T4, the target pixel is selected in the image space of the virtual non-contrast image Gnc to be generated.

  In step T5, whether or not the pixel value (CT value) Gm (x, y) of the corresponding pixel of the target pixel in the monochrome image falls within a pixel value range corresponding to the soft part of the subject 40, for example, 0 to 300HU. Determine whether.

  Here, when it is determined that the pixel value Gm (x, y) of the corresponding pixel is within the pixel value range corresponding to the soft part, the target pixel is likely to be stained with the contrast agent, and the change due to the presence or absence of the contrast agent is large. It judges that it represents a soft tissue, and proceeds to step T6.

  On the other hand, when it is determined that the pixel value Gm (x, y) of the corresponding pixel is not within this pixel value range, the target pixel is difficult to be stained with the contrast agent, and the change due to the presence or absence of the contrast agent is small. It judges that it represents a substance, and proceeds to step T7.

  In step T6, the pixel value Gnc (x, y) of the target pixel in the virtual non-contrast image is calculated according to the following formula 1.

Gnc (x, y) = β · Gw (x, y) + α · Gio (x, y) −γ
However, 1.05 ≧ β ≧ 0.95, 0.29 ≧ α ≧ 0.25, 1050 ≧ γ ≧ 950
... (Formula 1)

  That is, the pixel value Gnc (x, y) of the target pixel is multiplied by the coefficient β to the pixel value Gw (x, y) of the corresponding pixel in the water density image Gw, and the pixel value of the corresponding pixel in the iodine density image Gio. A value obtained by multiplying Gio (x, y) by a coefficient α and −γ is added.

  After calculating the pixel value Gnc (x, y) of the target pixel in step T6, the process proceeds to step T8.

  In step T7, the pixel value Gm (x, y) of the corresponding pixel in the monochrome image is adopted as the pixel value Gnc (x, y) of the target pixel.

  Substances other than soft tissue are less likely to stain the contrast medium, and change due to the presence or absence of the contrast medium is originally small. For the image portion representing such a substance, the soft tissue with the contrast medium is expressed by Equation 1 or Equation 2. It is expected that a monochrome image, which is general and proven as an image corresponding to a simple image, is closer to a simple image than an image generated by a mathematical formula derived with a focus on. Therefore, for pixels representing substances other than soft tissue, the pixel value of the corresponding pixel in the monochrome image is adopted.

  In step T8, it is determined whether or not there is still another pixel to be selected as the target pixel in the image space of the generated virtual non-contrast image. When it is determined that there is still a pixel to be selected as the target pixel, the process returns to step T4 to select a new pixel as the target pixel and continue the processing. If it is determined that there are no more pixels to be selected as the target pixel, the process proceeds to step T9.

  In step T9, the generated monochrome image and virtual non-contrast image are displayed.

  According to the second embodiment, since the portion representing the substance other than the soft tissue in the virtual non-contrast image is a monochrome image, the portion that originally changed little due to the presence or absence of the contrast agent is changed to a simple image. A more general and proven image can be adopted as the corresponding image, and the virtual non-contrast image can be expected to be an image closer to the actual simple image.

(Third embodiment)
FIG. 8 is a diagram showing a flow of processing in the X-ray CT apparatus according to the third embodiment.

  Since steps U1 to U6 and U8 are the same as steps T1 to T6 and T8, description thereof is omitted.

  In step U7, the pixel value Gnc (x, y) of the target pixel in the virtual non-contrast image Gnc is calculated according to the following Equation 3.

Gnc (x, y) = b · Gw (x, y) + f (ρ)
However, 1.0 ≧ b ≧ 0.9,
ρ: an index related to iodine concentration in the subject, f (ρ): function of ρ (Equation 3)

  That is, the pixel value Gnc (x, y) of the target pixel in the virtual non-contrast image Gnc is multiplied by the coefficient b to the pixel value Gw (x, y) of the corresponding pixel in the water density image Gw. The value of the function f (ρ) having the parameter ρ related to iodine concentration as a parameter is added and calculated.

  Substances other than soft tissue are less likely to stain the contrast medium, and change due to the presence or absence of the contrast medium is originally small. For the image portion representing such a substance, the soft tissue with the contrast medium is expressed by Equation 1 or Equation 2. Compared to the image generated by the formula derived by focusing on the image, it is basically simpler to add an offset component determined by the iodine concentration in the subject based on the water density image Gw. It is expected. Therefore, for pixels representing substances other than soft tissue, the pixel value of the corresponding pixel in an image generated by a mathematical expression including a function value having an index related to iodine concentration in the subject as a parameter, as shown in mathematical expression 3, is used. I will adopt it.

  As an index relating to the iodine concentration in the subject, for example, the concentration of the contrast agent administered to the subject, the imaging timing from the start of the administration of the contrast agent, and the like can be considered.

  As the function f (ρ), for example, f (ρ) = n · ρ (n = constant) can be considered.

  In addition, instead of Equation 3, for example, an equation such as the following equation can also be used.

Gnc (x, y) = b · Gw (x, y) + c
However, 1.1 ≧ b ≧ 0.9, C = constant (Formula 4)

  Substances other than soft tissue are naturally less subject to changes due to the presence or absence of a contrast agent, so that even if an offset component is added based on the water density image Gw, it may become closer to a simple image.

  In the above formulas 3 and 4, the coefficient b is 1.1 ≧ b ≧ 0.9, but according to the idea that this is based on a water density image, the coefficient b is 1.1. This is because it is preferable to adjust in the range of ≧ b ≧ 0.9.

  As described above, according to each of the above embodiments, a virtual non-contrast image can be obtained with high accuracy by X-ray CT dual energy imaging of a subject to which a contrast medium containing iodine is administered. As a result, both a monochrome image corresponding to a contrast image and a virtual non-contrast image corresponding to a non-contrast image can be obtained by one dual energy imaging, and it is necessary to perform contrast imaging and non-contrast imaging, respectively. In addition, low exposure in contrast examination can be realized.

  As mentioned above, although embodiment of invention was described, embodiment of invention is not limited to said thing, In the range which does not deviate from the meaning of invention, various forms can be taken.

  For example, in each of the above embodiments, the monochromatic image is generated based on the water density image and the iodine density image, but may be generated based on a material density image using another substance as a base substance.

  Further, for example, in each of the above embodiments, a monochrome image is adopted as an image corresponding to the contrast image, but an iodine density image may be adopted.

  Further, for example, in each of the above-described embodiments, the pixel value determination as to whether or not the target pixel in the virtual non-contrast image corresponds to the soft part of the subject is performed using an image other than the monochrome image, such as the first X-ray tube voltage image or the first image. A 2X-ray tube voltage image, a water density image, an iodine density image, or the like may be used.

  Further, for example, each of the above embodiments is an X-ray CT apparatus, but an image generation apparatus having a virtual non-contrast image generation function as described above is also an embodiment of the invention.

Further, for example, a program for causing a computer to function as the above-described image generation apparatus is also an embodiment of the invention.

DESCRIPTION OF SYMBOLS 1 Operation console 2 Input device 3 Central processing unit 5 Data collection buffer 6 Monitor 7 Storage device 10 Imaging table 12 Cradle 15 Rotation part 16 Support part 20 Scanning gantry 21 X-ray tube 22 X-ray tube controller 23 Collimator 24 X-ray detector 25 Data collection device (DAS)
26 Rotating part controller 29 Control controller 30 Slip ring 40 Subject 81 X-ray

Claims (14)

Based on data obtained by X-ray CT dual energy imaging of a subject to which a contrast medium containing iodine is administered, virtual water in the subject is based on a model representing the subject by synthesis of water and iodine. A first density generating unit that generates a water density image representing a density distribution of the sample and an iodine density image representing a virtual density distribution of iodine in the subject;
An image generation apparatus comprising: a second generation unit configured to generate a virtual non-contrast image corresponding to an image obtained by non-contrast imaging of the subject based on the water density image and the iodine density image. The second generation means generates at least a part of the virtual non-contrast image,
(Virtual non-contrast image) = β × (water density image) + α × (iodine density image) −γ
However, 1.1 ≧ β ≧ 0.9, 0.30 ≧ α ≧ 0.24, 1100 ≧ γ ≧ 900
... (Formula 1)
The image generation apparatus according to claim 1, wherein the image generation apparatus is generated according to the following mathematical formula 1. The second generation means generates at least a part of the virtual non-contrast image,
(Virtual non-contrast image) =
β × {(water density image) + α × (iodine density image) + α ² × (iodine density image)} − γ
However, 1.1 ≧ β ≧ 0.9, 0.30 ≧ α ≧ 0.24, 1100 ≧ γ ≧ 900
... (Formula 2)
The image generation apparatus according to claim 1, wherein the image generation apparatus is generated in accordance with Formula 2 below   The image generating apparatus according to claim 2, wherein at least a part of the virtual non-contrast image is a part corresponding to a soft part of the subject.   The image generation apparatus according to claim 4, wherein the second generation unit specifies a portion corresponding to the soft portion based on a pixel value of an image generated using the data.   The second generation means converts a portion corresponding to a substance different from the soft part of the subject into an image obtained by X-ray CT imaging using X-rays having a predetermined effective X-ray energy based on the data. The image generation apparatus according to claim 4, wherein the image generation apparatus is generated as a corresponding image. The second generation means may include a portion corresponding to a substance different from the soft part of the subject.
(Virtual non-contrast image) = b × (water density image) + f (index relating to iodine concentration in the subject)
However, 1.0 ≧ b ≧ 0.9, f: function using the index as a parameter (Formula 3)
The image generation apparatus according to claim 4, wherein the image generation apparatus is generated according to the following mathematical formula 3. The second generation means may include a portion corresponding to a substance different from the soft part of the subject.
(Virtual non-contrast image) = b × (water density image) + c
However, 1.1 ≧ b ≧ 0.9, c: constant (Formula 4)
The image generation apparatus according to claim 4, wherein the image generation apparatus is generated according to the following mathematical formula 4. In Equation 1,
1.05 ≧ β ≧ 0.95, 0.29 ≧ α ≧ 0.25, 1050 ≧ γ ≧ 950
The image generation device according to any one of claims 2 to 8, which satisfies the following condition.   The image generation apparatus according to claim 2, wherein α in the formula 1 is specified by an operator.   The first generation means includes first projection data obtained by X-rays of the first energy obtained by the dual energy imaging, and second of X-rays of a second energy different from the first energy. The data obtained by performing weighted subtraction processing on the projection data so that the iodine component is eliminated is subjected to image reconstruction processing, thereby generating the water density image, and the first projection data and the first projection data. The iodine density image is generated by performing image reconstruction processing on data obtained by performing weighted subtraction processing on the projection data of 2 so that the water component is eliminated. An image generation apparatus according to claim 1.   Third generation means for generating an image corresponding to an image obtained when X-ray CT imaging is performed on the subject to which the contrast medium is administered using X-rays having desired effective X-ray energy based on the data. The image generation apparatus according to claim 1, further comprising:   An X-ray CT apparatus provided with the image generation apparatus according to any one of claims 1 to 12.   The program for functioning a computer as an image generation apparatus as described in any one of Claims 1-12.

Images