JP2014094229A - Medical image analyzer, and medical image capturing apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a medical image analyzer, reducing the influence of delay time, and performing perfusion analysis with favorable accuracy.SOLUTION: A medical image analyzer 1 includes: a blood inflow region setting part 10; a blood inflow transition information acquiring part 20; a biological tissue transition information acquiring part 30; a first perfusion parameter calculating part 40; a delay time calculating part 50; a second perfusion parameter calculating part 60; an image generating part 70; and a display part 80. The blood inflow region setting part 10 sets a blood inflow region. The blood inflow transition information acquiring art 20 acquires blood inflow transition information. The biological tissue transition information acquiring part 30 acquires biological tissue transition information. The first perfusion parameter calculating part 40 calculates a first perfusion parameter. The delay time calculating part 50 calculates a delay time Ti. The second perfusion parameter calculating part 60 calculates a second perfusion parameter. The image generating part 70 generates a hemodynamics image on the basis of the second perfusion parameter.

Description

  Embodiments described herein relate generally to a medical image analysis apparatus and a medical image photographing apparatus.

  A time series image of a subject to which a contrast medium has been administered is taken with a medical image taking apparatus such as an X-ray CT (Computed Tomography) apparatus or an MRI (Magnetic Resonance Imaging) apparatus, and the image is analyzed to analyze tissue blood. Information on flow dynamics is being obtained. This is called perfusion analysis and utilizes the fact that the concentration of the contrast agent in the tissue corresponding to the pixel can be obtained from the value of the pixel in the image.

  In general perfusion analysis, a deconvolution method is employed. The deconvolution method uses the time concentration curve in the nearest artery of the tissue as an input function, performs deconvolution (deconvolution integration) with the tissue time concentration curve, obtains the tissue impulse response function, and from this impulse response function The blood flow volume, the average transit time, the blood volume, etc. representing the blood flow dynamics of the tissue are calculated. The time density curve is a graph of transition information of the measured contrast agent concentration. In the present specification, the transition information indicates the transition of the contrast agent concentration.

  Since time is required for blood to move from the artery to the tissue, the contrast timing of the tissue is slower than that of the artery. This delay time depends on the position of the tissue. If the arterial transition information is used as it is in the deconvolution method, an error occurs due to the influence of the delay time. This error depends on the position of the tissue. In order to perform a suitable analysis, it is necessary to correct this delay time and reduce the error.

  For this delay time correction, an SVD (Single Value Deposition) method and a block circuit SVD method have been proposed. This method determines the delay time from the rise time of the tissue time concentration curve, and uses the delay time to shift the time concentration curve of the artery or the tissue time concentration curve in the time axis direction to reduce the influence of the delay time. To do.

Further, a method of performing model fitting on the assumption that the impulse response function can be expressed by the equation R (t) = a · exp {− (t−t ₀ ) / b} has been proposed. Here, a, b are model parameters, _{t 0} is the delay time. This method corrects the delay time by incorporating the delay time into the model and determining the delay time and model parameters.

Ona Wu et al, "Tracer Arrival Timing-Insensitive Technique for Estimating Flow in MR Perfusion-Weighted Imaging Using Singular Value Decomposition With a Block-Circulant Deconvolution Matrix", Magnetic Resonance in Medicine 50: 164-174 (2003) T. Y. Lee et al, “CT imaging of angiogenesis”, QJ Nucl. Med. 2003, vol. 41, p171-87.

  However, since the rise time of a general tissue time density curve is slow, it is difficult to measure the rise time by the SVD method and the block circuit SVD method. In addition, since the tissue time density curve contains a lot of noise, there is a problem that an erroneous delay time is obtained by recognizing the noise as a rise time, and a suitable analysis accuracy cannot be obtained.

  In addition, the method of incorporating the delay time into the model and performing model fitting has a problem that a suitable analysis accuracy cannot be obtained because many parameters are determined at a time.

  The problem to be solved by the present invention is to provide a medical image analysis apparatus and a medical image photographing apparatus capable of reducing the influence of delay time and performing perfusion analysis with suitable accuracy.

  The medical image analysis apparatus according to the embodiment obtains blood flow dynamics of a biological tissue by applying model fitting to information obtained from a time series image of the biological tissue. The medical image analysis apparatus includes a blood inflow region setting unit, a blood inflow transition information acquisition unit, a second perfusion parameter calculation unit, a biological tissue transition information acquisition unit, a first perfusion parameter calculation unit, and a delay time calculation. Part, an image generation part, and a display part. The blood inflow region setting unit sets a predetermined blood inflow region in the time series image. The blood inflow transition information acquisition unit acquires blood inflow transition information indicating time-series changes in pixel values in the blood inflow region. The biological tissue transition information acquisition unit acquires biological tissue transition information indicating time-series changes in pixel values in the biological tissue. The first perfusion parameter calculation unit calculates a first perfusion parameter indicating blood flow dynamics in the biological tissue by performing a first model fitting based on the blood inflow transition information and the biological tissue transition information. Based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter, the delay time calculation unit calculates a time until the blood that has passed through the blood inflow region reaches a position corresponding to each pixel of the time-series image. The delay time shown is calculated. The second perfusion parameter calculation unit calculates the second perfusion parameter for each pixel by performing the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time. The image generation unit generates a blood flow dynamic image based on the second perfusion parameter. The display unit displays a blood flow dynamic image.

  In addition, the medical imaging apparatus according to the embodiment obtains blood flow dynamics of the biological tissue by applying model fitting to information obtained from the time series image of the biological tissue. The medical imaging apparatus includes an imaging unit, a storage unit, a blood inflow region setting unit, a blood inflow transition information acquisition unit, a biological tissue transition information acquisition unit, a first perfusion parameter calculation unit, and a delay time calculation unit. And a second perfusion parameter calculation unit, an image generation unit, and a display unit. The imaging unit captures time-series images of living tissue. The storage unit stores time series images. The blood inflow region setting unit sets a predetermined blood inflow region in the time series image. The blood inflow transition information acquisition unit acquires blood inflow transition information indicating time-series changes in pixel values in the blood inflow region. The biological tissue transition information acquisition unit acquires biological tissue transition information indicating time-series changes in pixel values in the biological tissue. The first perfusion parameter calculation unit calculates a first perfusion parameter indicating blood flow dynamics in the biological tissue by performing a first model fitting based on the blood inflow transition information and the biological tissue transition information. Based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter, the delay time calculation unit calculates a time until the blood that has passed through the blood inflow region reaches a position corresponding to each pixel of the time-series image. The delay time shown is calculated. The second perfusion parameter calculation unit calculates the second perfusion parameter for each pixel by performing the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time. The image generation unit generates a blood flow dynamic image based on the second perfusion parameter. The display unit displays a blood flow dynamic image.

It is a functional block diagram showing the example of composition of the medical image analysis device concerning an embodiment. 1 is a schematic diagram illustrating an outline of a medical image analysis apparatus according to an embodiment. It is a flowchart showing the operation example of the medical image analysis apparatus which concerns on embodiment. It is a functional block diagram showing the example of composition of the medical image analysis device concerning an embodiment. It is a flowchart showing the operation example of the medical image analysis apparatus which concerns on embodiment. It is a functional block diagram showing the structural example of the medical image imaging device which concerns on embodiment. It is a functional block diagram showing the structural example of the medical image imaging device which concerns on embodiment. It is a flowchart showing the operation example of the medical image imaging device which concerns on embodiment.

<Embodiment of Medical Image Analysis Device>
A medical image analysis apparatus according to an embodiment will be described with reference to the drawings.

[Constitution]
A configuration example of a medical image analysis apparatus 1 according to the embodiment will be described with reference to FIG.

  The medical image analysis apparatus 1 obtains blood flow dynamics of a biological tissue by applying model fitting to information obtained from a time-series image of the biological tissue. The medical image analysis apparatus 1 includes a blood inflow region setting unit 10, a blood inflow transition information acquisition unit 20, a biological tissue transition information acquisition unit 30, a first perfusion parameter calculation unit 40, a delay time calculation unit 50, A second perfusion parameter calculation unit 60, an image generation unit 70, a display unit 80, and a control unit 90 are included.

  A time-series image of a living tissue is an image in which a plurality of still images (frames) of a living tissue acquired at predetermined time intervals are associated in time series. It is assumed that the time-series image of the living tissue is stored in the storage unit 2 existing inside or outside the medical image analysis apparatus 1. When a time-series image is stored in the external storage unit 2, the medical image analysis apparatus 1 obtains information from the storage unit 2 via a general communication unit.

(Blood inflow area setting part)
The blood inflow region setting unit 10 sets a predetermined blood inflow region in the time series image for each frame. The blood inflow region is a region in a time-series image corresponding to an artery which is a path for supplying blood to a target biological tissue. For example, if the biological tissue is the brain, the region in the time series image corresponding to the cerebral artery is the blood inflow region, and if the biological tissue is the liver, the region in the time series image corresponding to the hepatic artery and portal vein This area is the blood inflow area. As described above, the blood inflow region setting unit 10 sets one or more blood inflow regions corresponding to the living tissue. In this setting, the user designates a blood inflow region through a general user interface while viewing a certain frame, and the blood inflow region setting unit 10 can set the blood inflow region based on the designation. . Further, the blood inflow region setting unit 10 may automatically set the blood inflow region setting with reference to clinical statistical data.

(Blood inflow transition information acquisition unit)
The blood inflow transition information acquisition unit 20 acquires blood inflow transition information indicating time-series changes in pixel values in the blood inflow region set by the blood inflow region setting unit 10. That is, the blood inflow transition information acquisition unit 20 acquires contrast medium concentration transition information at a position in the real space corresponding to the blood inflow region. As an example of this processing, the blood inflow transition information acquisition unit 20 calculates an average value of pixel values included in the blood inflow region in each frame, and acquires transition information in which the average value has changed in time series.

(Biological tissue transition information acquisition unit)
The biological tissue transition information acquisition unit 30 acquires biological tissue transition information indicating time-series changes in pixel values in the biological tissue. The biological tissue transition information acquisition unit 30 associates pixels between a plurality of frames, and acquires transition information in which the pixel value of each pixel changes in time series. That is, the transition information of the concentration of the contrast agent in the living tissue is acquired.

(First perfusion parameter calculation unit)
In the following description, perfusion means blood flow in blood vessels (for example, capillaries) in living tissue. The 1st perfusion parameter calculation part 40 calculates the 1st perfusion parameter which shows the blood flow dynamics in a biological tissue by performing a 1st model fitting based on blood inflow transition information and biological tissue transition information. As a specific example of this calculation process, a case where the following model formula is used in the first model fitting for the liver will be described. As described above, there are two paths for supplying blood to the liver. The two pathways are the hepatic artery and the portal vein.

  The first model fitting is performed for each pixel of the time series image. The model parameters in [Formula 1] are the total blood flow f of the living tissue, the ratio α of the blood flow from the artery to the total blood flow f of the living tissue, the time scale parameter θ of the impulse response function, and the shape of the impulse response function. There are four parameters nk. Here, if a predetermined provisional value is substituted into the shape parameter nk, the model parameters are three, that is, the total blood flow f, the ratio α, and the time scale parameter θ. Then, the first perfusion parameter calculation unit 40 determines these three model parameters by the first model fitting. The first model fitting is model fitting in which a deconvolution method is applied to biological tissue transition information based on a model [Equation 1] in which no delay time is incorporated.

  The first perfusion parameter calculation unit 40 calculates the first perfusion parameter using the determined model parameter. This calculation is performed for each pixel of the time-series image. The first perfusion parameter includes at least one of blood flow, average transit time, and distribution volume. The blood flow rate is the volume of blood passing through a predetermined area per unit time. The average transit time is an average time required for blood to pass through a predetermined region. The distribution volume is an equivalent volume of a region where the contrast medium penetrates and is distributed. When calculating the blood flow rate, the blood flow rate fa from the artery and the blood flow rate fp from the portal vein are calculated by the following equations.

(Delay time calculation unit)
The delay time calculation unit 50 determines the time until the blood that has passed through the blood inflow region reaches a position corresponding to each pixel of the time series image based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter. Is calculated. As an example of this process, the delay time calculation unit 50 first calculates a delay approximate value Si in each pixel based on the blood inflow transition information, the living tissue transition information, and the first perfusion parameter. The delay approximate value Si is an approximate value of the delay time Ti. Here, i indicates a pixel corresponding to the i-th pixel out of the total number N of pixels. That is, the approximate delay value Si is an approximate value of the delay time Ti in the i-th pixel.

  The calculation of the delay approximate value Si will be described with reference to FIG. FIG. 2 shows a blood inflow time concentration curve C0 that graphs blood inflow transition information and a biological tissue time concentration curve Ci that graphs biological tissue transition information in a certain pixel. In FIG. 2, the horizontal axis represents time, and the vertical axis represents contrast density.

The delay time calculation unit 50 includes the time t _m when the blood inflow time concentration curve C0 becomes maximum, the maximum contrast value d1 of the biological tissue time concentration curve Ci, and the biological tissue time concentration curve Ci has a predetermined concentration d2 ( <seek and time _{t i} that exceed d1). For example, a value obtained by multiplying the maximum value d1 by 0.2 may be used as the density d2. The delay time calculation unit 50 obtains the time T0 from the contrast start time _{t 0} of the blood draining to time _{t m,} and a time T1 from the contrast start time _{t 0} to time _{t i.} The delay time calculation unit 50 subtracts the time T0 from the time T1 to obtain a delay approximate value Si. When time t _i <time t _m on the time axis, the delay approximation value Si is a negative value. In this case, the delay approximation value Si is 0. The user may set the density d2 as appropriate so that time t _m <time t _i .

  The delay time calculation unit 50 calculates the delay time Ti using the delay approximate value Si based on the negative correlation between the first perfusion parameter and the delay time. The calculation of the delay time when the blood flow rate fa is used as the first perfusion parameter will be described.

  It can be considered that as the blood flow amount fa from the artery increases, the time during which blood moves to the living tissue, that is, the delay time Ti decreases. Similarly, there is a positive or negative phase in the average transit time Tm and the distribution volume Vd. This correlation can be expressed as the following equation, for example.

  As the stabilization constant g, an arbitrary value larger than 0 is used in order to stabilize the calculation. Since the influence of the delay time Ti is not considered in the first model fitting, the blood flow rate fai may be 0 or a very small value. In this case, the delay time Ti is infinite or very large. Since this makes the calculation unstable, the calculation is stabilized by the stabilization constant g.

  As described above, since the delay approximate value Si is an approximate value of the delay time Ti, the relationship between the delay approximate value Si and the constant q can be expressed by the following equation.

  Since the blood flow rate fai, the delay approximation value Si, and the stabilization constant g are known, the constant q can be obtained from this equation. And the delay time Ti is calculated | required using following Formula.

(Second perfusion parameter calculation unit)
The second perfusion parameter calculation unit 60 calculates the second perfusion parameter for each pixel by performing the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time Ti. In the second model fitting, the following model can be used.

  The second model fitting is performed for each pixel of the time-series image. The model parameters in Equation 6 are the total blood flow f of the living tissue, the ratio α of the blood flow from the artery to the total blood flow f of the living tissue, the time scale parameter θ of the impulse response function, and the shape parameter nk of the impulse response function. It is. In the first model fitting, a provisional value is substituted for the shape parameter nk. However, in the second model fitting, the shape parameter nk may be a variable. That is, in the second model fitting, the model fitting can be performed using the total blood flow f, the ratio α, the time scale parameter θ, and the shape parameter nk as variables. The second model fitting is a model fitting in which the deconvolution method is applied to the biological tissue transition information based on the model [Equation 6] in which the delay time Ti is incorporated. That is, the second model fitting is different from the first model fitting in that it is a model that takes into account the influence of the delay time Ti.

  The second perfusion parameter calculation unit 60 uses the total blood flow f, the ratio α, the time scale parameter θ, and the shape parameter nk obtained by the second model fitting, for example, according to the following formula, for example, the blood flow fa from the artery, Four types of blood flow volume fp from the portal vein, average transit time Tm, and distribution volume Vd are calculated for each pixel.

(Image generator)
The image generation unit 70 generates a blood flow dynamic image based on the second perfusion parameter. The image generation unit 70 generates a blood flow dynamic image for each type of the second perfusion parameter. The image generation unit 70 generates a blood flow dynamic image by determining the pixel value in the pixel based on the value of the second perfusion parameter in each pixel.

(Display unit, control unit)
The display unit 80 displays a blood flow dynamic image. The display unit 80 is configured by a display device such as an LCD (Liquid Crystal Display). The control unit 90 controls the operation of each unit. The control unit 90 includes, for example, a processing unit and a storage unit. As the processing unit, for example, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), or an ASIC (Application Specific Integrated Circuit) is used. The storage unit is configured to include, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), and an HDD (Hard Disc Drive). The storage unit stores a computer program for controlling each unit of the medical image analysis apparatus 1.

[Operation]
The operation of the medical image analysis apparatus 1 according to this embodiment will be described with reference to the flowchart of FIG.

(S01: Blood inflow area setting)
The control unit 90 controls the blood inflow region setting unit 10 to set a predetermined blood inflow region in the time series image.

(S02: Acquire blood flow transition information)
The control unit 90 controls the blood inflow transition information acquisition unit 20 to acquire blood inflow transition information indicating time-series changes in pixel values in the blood inflow region.

(S03: Acquisition of biological tissue transition information)
The control unit 90 controls the biological tissue transition information acquisition unit 30 to acquire biological tissue transition information indicating time-series changes in pixel values in the biological tissue.

(S04: First perfusion parameter calculation)
The control unit 90 controls the first perfusion parameter calculation unit 40 to perform the first model fitting based on the blood inflow transition information and the biological tissue transition information, and the first perfusion indicating the blood flow dynamics in the biological tissue. Let the parameters be calculated.

(S05: Delay time calculation)
The control unit 90 controls the delay time calculation unit 50 to calculate the delay approximate value Si in each pixel based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter. Further, the control unit 90 controls the delay time calculation unit 50 so that the first perfusion parameter and the delay time have a negative correlation, and the delay approximate value Si is an approximate value of the delay time Ti. Based on the above, the delay time Ti in each pixel is calculated.

(S06: Calculation of second perfusion parameter)
The control unit 90 controls the second perfusion parameter calculation unit 60 to perform the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time Ti, and the second perfusion for each pixel. Let the parameters be calculated.

(S07: Blood flow dynamic image generation)
The control unit 90 controls the image generation unit 70 to generate a blood flow dynamic image based on the second perfusion parameter.

(S08: Blood flow dynamic image display)
The control unit 90 controls the display unit 80 to display a blood flow dynamic image. This is the end of the operation shown in FIG.

[Action / Effect]
The operation and effect of the medical image analysis apparatus 1 according to this embodiment will be described.

  The medical image analysis apparatus 1 obtains blood flow dynamics of a biological tissue by applying model fitting to information obtained from a time-series image of the biological tissue. The medical image analysis apparatus 1 includes a blood inflow region setting unit 10, a blood inflow transition information acquisition unit 20, a biological tissue transition information acquisition unit 30, a first perfusion parameter calculation unit 40, a delay time calculation unit 50, and a second perfusion parameter. A calculation unit 60, an image generation unit 70, and a display unit 80 are included. The blood inflow region setting unit 10 sets a predetermined blood inflow region in the time series image. The blood inflow transition information acquisition unit 20 acquires blood inflow transition information indicating time-series changes in pixel values in the blood inflow region. The biological tissue transition information acquisition unit 30 acquires biological tissue transition information indicating time-series changes in pixel values in the biological tissue. The 1st perfusion parameter calculation part 40 calculates the 1st perfusion parameter which shows the blood flow dynamics in a biological tissue by performing a 1st model fitting based on blood inflow transition information and biological tissue transition information. The delay time calculation unit 50 determines the time until the blood that has passed through the blood inflow region reaches a position corresponding to each pixel of the time series image based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter. Is calculated. The second perfusion parameter calculation unit 60 calculates the second perfusion parameter for each pixel by performing the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time Ti. The image generation unit 70 generates a blood flow dynamic image based on the second perfusion parameter. The display unit 80 displays a blood flow dynamic image. As described above, the delay time Ti is obtained based on the first model fitting, and the second model fitting is performed using the delay time Ti, thereby reducing the influence of the delay time and performing perfusion analysis with suitable accuracy. Can be performed.

  In addition, the first perfusion parameter can include at least one of the blood flow fa, the average transit time Tm, and the distribution volume Vd. Thereby, it is possible to provide a medical image analysis apparatus capable of performing perfusion analysis with more suitable accuracy.

  In addition, the delay time calculation unit 50 can calculate the approximate delay value Si in each pixel based on the blood inflow transition information, the living tissue transition information, and the first perfusion parameter. Further, the delay time calculation unit 50 can calculate the delay time Ti using the delay approximation value Si based on the negative correlation between the first perfusion parameter and the delay time Ti. This calculation method is not a calculation method based only on the rise time of biological tissue transition information, that is, measured transition information, but a calculation method based on measured transition information and a calculated model parameter. Thereby, the delay time calculation unit 50 can calculate the delay time Ti without being affected by noise included in the biological tissue information. Therefore, it is possible to provide a medical image analysis apparatus capable of performing perfusion analysis while suitably reducing the influence of the delay time Ti.

<First Modification of Medical Image Analysis Device>
The medical image analysis apparatus according to the modified example is different in the configuration of the delay time calculation unit 50, the second perfusion parameter calculation unit 60, and the image generation unit 70 from the above-described embodiment, and further includes a comparison unit 61. Other configurations are the same as those of the above-described embodiment. FIG. 4 is a functional block diagram of this modification.

[Constitution]
The delay time calculation unit 50 calculates a new delay time Ti using the second perfusion parameter as the first perfusion parameter. That is, the second perfusion parameter calculated by the second perfusion parameter calculation unit is fed back as the first perfusion parameter, and the delay time calculation unit 50 calculates a new delay time Ti. The delay time calculation unit 50 can calculate a new delay time Ti by the above-described equations [Equation 3] to [Equation 5].

  The second perfusion parameter calculation unit 60 calculates a new second perfusion parameter by performing the second model fitting again using the new delay time Ti. The second perfusion parameter calculation unit 60 calculates the second perfusion parameter by performing the second model fitting using the models and equations of [Expression 6] and [Expression 7] described above.

  The comparison unit 61 obtains a difference between the approximate information indicating the time-series change of the pixel value in the living tissue obtained by the new second perfusion parameter and the living tissue transition information, and compares this difference with a predetermined threshold value. . The approximate information is transition information calculated based on the model parameter included in the new second perfusion parameter, that is, calculation information obtained by substituting the model parameter into [Equation 6]. Therefore, the approximate information is biological tissue transition information acquired from the time series image, that is, transition information different from the measured information. The calculated transition information is obtained when the second perfusion parameter calculation unit 60 performs the second model fitting. Since the second model parameter is obtained by the second model fitting, there is a difference between the transition information calculated based on the second perfusion parameter and the transition information acquired by the biological tissue transition information acquisition unit 30. It is common. This difference is obtained by a general method such as a least square method. The comparison unit 61 compares this difference with a predetermined threshold value. The threshold value is set in advance. As another comparison method, it is possible to compare the value of the second perfusion parameter at the previous execution with the latest value, or compare whether the number of repetitions reaches a predetermined value.

  When the difference is equal to or smaller than the threshold, the image generation unit 70 generates a blood flow dynamic image based on the new second perfusion parameter.

  That is, in this modified example, the new delay time Ti and the new second perfusion parameter are repeatedly calculated until the difference becomes equal to or less than the threshold value. In this iterative process, the value of the stabilization constant g can be reduced. The stabilization constant g is for stabilizing the calculation, and is preferably close to 0. Therefore, by decreasing the stabilization constant g for each repetition, the second perfusion parameter calculation unit 60 can sequentially calculate the second perfusion parameter with better accuracy. When this calculation process is repeated a predetermined number of times, or when the stabilization constant g becomes equal to or less than a predetermined value, the image generation unit 70 generates a blood flow dynamic image based on the new second perfusion parameter. You may comprise as follows.

[Operation]
The operation of the medical image analysis apparatus 1 according to this modification will be described with reference to the flowchart of FIG.

(S11: Blood inflow region setting)
The control unit 90 controls the blood inflow region setting unit 10 to set a predetermined blood inflow region in the time series image.

(S12: Acquire blood flow transition information)
The control unit 90 controls the blood inflow transition information acquisition unit 20 to acquire blood inflow transition information indicating time-series changes in pixel values in the blood inflow region.

(S13: Acquisition of biological tissue transition information)
The control unit 90 controls the biological tissue transition information acquisition unit 30 to acquire biological tissue transition information indicating time-series changes in pixel values in the biological tissue.

(S14: First perfusion parameter calculation)
The control unit 90 controls the first perfusion parameter calculation unit 40 to perform the first model fitting based on the blood inflow transition information and the biological tissue transition information, and the first perfusion indicating the blood flow dynamics in the biological tissue. Let the parameters be calculated.

(S15: Delay time calculation)
The control unit 90 controls the delay time calculation unit 50 to calculate the delay approximate value Si in each pixel based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter. Further, the control unit 90 controls the delay time calculation unit 50 so that the first perfusion parameter and the delay time have a negative correlation, and the delay approximate value Si is an approximate value of the delay time Ti. Based on the above, the delay time Ti in each pixel is calculated.

(S16: Second perfusion parameter calculation)
The control unit 90 controls the second perfusion parameter calculation unit 60 to perform the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time Ti, and the second perfusion for each pixel. Let the parameters be calculated.

(S17: Comparison between difference and threshold)
The control unit 90 controls the comparison unit 61 to obtain the difference between the approximate information indicating the time-series change of the pixel value in the biological tissue obtained from the second perfusion parameter and the biological tissue transition information, and the difference and the predetermined value To the threshold value. When the difference exceeds the threshold value, the control unit 90 controls the delay time calculation unit 50 to calculate a new delay time Ti using the second perfusion parameter as the first perfusion parameter.

(S18: Blood flow dynamic image generation)
When the difference is equal to or smaller than the threshold value, the control unit 90 controls the image generation unit 70 to generate a blood flow dynamic image based on the second perfusion parameter.

(S19: Blood flow dynamic image display)
The control unit 90 controls the display unit 80 to display a blood flow dynamic image. This completes the operation shown in FIG.

[Action / Effect]
The operation and effect of the medical image analysis apparatus 1 according to this modification will be described.

  The delay time calculation unit 50 calculates a new delay time Ti using the second perfusion parameter as the first perfusion parameter, and the second perfusion parameter calculation unit 60 newly uses the new delay time Ti to generate a second second time. A new second perfusion parameter is calculated by performing the model fitting. The comparison unit 61 obtains a difference between the transition information calculated based on the new second perfusion parameter and the biological tissue transition information, and compares the difference with a predetermined threshold value. When the difference is equal to or smaller than the threshold value, the image generation unit 70 generates a blood flow dynamic image based on the new second perfusion parameter. Thereby, the second perfusion parameter calculation unit 60 can repeatedly calculate the second perfusion parameter while increasing its accuracy. Therefore, it is possible to provide a medical image analysis apparatus capable of performing perfusion analysis with suitable accuracy.

<Second Modification of Medical Image Analysis Device>
FIG. 6 is a functional block diagram illustrating a configuration of the second modification. The medical image analysis apparatus according to this modification is not a single component of the first perfusion parameter calculation unit 40 and the second perfusion parameter calculation unit 60 as in the first modification, but a single perfusion parameter calculation. The unit 41 is configured. Further, the perfusion parameter calculation unit 41 includes a delay time calculation unit 50. Other configurations are the same as those of the first modification. In the model fitting by the perfusion parameter calculation unit 41, for example, the following model can be used.

  [Equation 8] is obtained by substituting [Equation 3] into the delay time Ti of [Equation 6]. Based on [Equation 8], the perfusion parameter calculation unit 41 performs model fitting using the total blood flow f, the ratio α, the time scale parameter θ, and the shape parameter nk as variables related to each pixel, and the variable q as a variable common to all pixels. I do. The process performed by the delay time calculation unit 50 included in the perfusion parameter calculation unit 41 corresponds to the process related to “q / (αf + g)” in [Equation 8].

<Embodiment of Medical Image Shooting Apparatus>
A medical image photographing apparatus according to an embodiment will be described with reference to the drawings.

[Constitution]
FIG. 7 is a functional block diagram showing a configuration example of the medical image photographing apparatus 3 according to the embodiment. The medical image photographing device 3 has a photographing unit 11 (and a storage unit 12) as compared with the embodiment of the medical image analyzing device 1 described above. Other configurations are the same as those in the embodiment of the medical image analysis apparatus 1 described above.

  The imaging unit 11 captures time-series images of living tissue. The imaging unit 11 is configured by a general apparatus such as an X-ray CT apparatus or an MRI apparatus.

  The storage unit 12 stores time series images. The storage unit 12 includes a storage device (such as a memory or a hard disk drive).

[Operation]
FIG. 8 is a flowchart showing an operation example of the medical image photographing apparatus 3 according to the embodiment.

(S21: Image shooting)
The control unit 90 controls the photographing unit 11 to photograph time-series images of living tissue. In addition, the control unit 90 controls the storage unit 12 to store the time series image.

(S22: Blood inflow region setting)
The control unit 90 controls the blood inflow region setting unit 10 to set a predetermined blood inflow region in the time series image.

(S23: Blood flow transition information acquisition)
The control unit 90 controls the blood inflow transition information acquisition unit 20 to acquire blood inflow transition information indicating time-series changes in pixel values in the blood inflow region.

(S24: Acquisition of biological tissue transition information)
The control unit 90 controls the biological tissue transition information acquisition unit 30 to acquire biological tissue transition information indicating time-series changes in pixel values in the biological tissue.

(S25: First perfusion parameter calculation)
The control unit 90 controls the first perfusion parameter calculation unit 40 to perform the first model fitting based on the blood inflow transition information and the biological tissue transition information, and the first perfusion indicating the blood flow dynamics in the biological tissue. Let the parameters be calculated.

(S26: Delay time calculation)
The control unit 90 controls the delay time calculation unit 50 to calculate the delay approximate value Si in each pixel based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter. Further, the control unit 90 controls the delay time calculation unit 50 so that the first perfusion parameter and the delay time have a negative correlation, and the delay approximate value Si is an approximate value of the delay time Ti. Based on the above, the delay time Ti in each pixel is calculated.

(S27: Second perfusion parameter calculation)
The control unit 90 controls the second perfusion parameter calculation unit 60 to perform the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time Ti, and the second perfusion for each pixel. Let the parameters be calculated.

(S28: Blood flow dynamic image generation)
The control unit 90 controls the image generation unit 70 to generate a blood flow dynamic image based on the second perfusion parameter.

(S29: Blood flow dynamic image display)
The control unit 90 controls the display unit 80 to display a blood flow dynamic image. Thus, the operation shown in FIG.

[Action / Effect]
The operation and effect of the medical image photographing apparatus 3 according to this embodiment will be described.

  The medical image photographing apparatus 3 obtains the blood flow dynamics of the biological tissue by applying model fitting to information obtained from the time series image of the biological tissue. The medical imaging apparatus 3 includes an imaging unit 11, a storage unit 12, a blood inflow region setting unit 10, a blood inflow transition information acquisition unit 20, a living tissue transition information acquisition unit 30, a first perfusion parameter calculation unit 40, and a delay time calculation. Unit 50, second perfusion parameter calculation unit 60, image generation unit 70, and display unit 80. The imaging unit 11 captures time-series images of living tissue. The storage unit 12 stores time series images. The blood inflow region setting unit 10 sets a predetermined blood inflow region in the time series image. The blood inflow transition information acquisition unit 20 acquires blood inflow transition information indicating time-series changes in pixel values in the blood inflow region. The biological tissue transition information acquisition unit 30 acquires biological tissue transition information indicating time-series changes in pixel values in the biological tissue. The 1st perfusion parameter calculation part 40 calculates the 1st perfusion parameter which shows the blood flow dynamics in a biological tissue by performing a 1st model fitting based on blood inflow transition information and biological tissue transition information. The delay time calculation unit 50 determines the time until the blood that has passed through the blood inflow region reaches a position corresponding to each pixel of the time series image based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter. Is calculated. The second perfusion parameter calculation unit 60 calculates the second perfusion parameter for each pixel by performing the second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time Ti. The image generation unit 70 generates a blood flow dynamic image based on the second perfusion parameter. The display unit 80 displays a blood flow dynamic image. As described above, the delay time Ti is obtained based on the first model fitting, and the second model fitting is performed using the delay time Ti, thereby reducing the influence of the delay time and performing perfusion analysis with suitable accuracy. Can be performed.

  Note that any configuration described as the embodiment and the modification of the medical image analysis apparatus may be applied to the medical image photographing apparatus.

  In this specification, the example in which the perfusion analysis is applied to the liver has been described in detail. However, the embodiment is not limited thereto, and the present invention may be applied to the brain, heart, kidney, lung, and other biological tissues. Further, in the present specification, an example in which the blood flow rate is used for calculating the delay time is shown, but the embodiment is not limited thereto, and other parameters such as an average transit time and a distribution volume may be applied. [Equation 3] is given as an example of the relationship between the perfusion parameter and the delay time, but other relational expressions may be used. In addition to the blood flow rate fai or in place of the blood flow rate fai, other parameters may be included in the formula. These modifications can be applied to both the medical image analysis apparatus and the medical image photographing apparatus.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 1 Medical image analyzer 2 Memory | storage part 3 Medical image imaging device 10 Blood inflow area | region setting part 11 Imaging | photography part 12 Memory | storage part 20 Blood inflow transition information acquisition part 30 Biological tissue transition information acquisition part 40 1st perfusion parameter calculation part 41 Perfusion parameter Calculation unit 50 Delay time calculation unit 60 Second perfusion parameter calculation unit 61 Comparison unit 70 Image generation unit 80 Display unit 90 Control unit C0 Blood inflow time concentration curve Ci biological tissue time concentration curve Si delay approximate value T0 time T1 time Ti delay Time Tm Average transit time Vd Distribution volume d1 Maximum value d2 Concentration f Total blood flow fa Blood flow fai Blood flow fp Blood flow g Stabilization constant nk Shape parameter q Constant t ₀ Contrast start time t _i time t _m time α rate θ time Scale parameter

Claims (7)

A medical image analysis apparatus for obtaining blood flow dynamics of a living tissue by applying model fitting to information obtained from a time series image of the living tissue,
A blood inflow region setting unit for setting a predetermined blood inflow region in the time series image;
A blood inflow transition information acquisition unit for acquiring blood inflow transition information indicating a time-series change in pixel values in the blood inflow region;
A biological tissue transition information acquisition unit that acquires biological tissue transition information indicating time-series changes in pixel values in the biological tissue;
A first perfusion parameter calculation unit that calculates a first perfusion parameter indicating blood flow dynamics in the biological tissue by performing a first model fitting based on the blood inflow transition information and the biological tissue transition information;
Based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter, the time until the blood that has passed through the blood inflow region reaches a position corresponding to each pixel of the time series image is indicated. A delay time calculation unit for calculating the delay time;
A second perfusion parameter calculator that calculates a second perfusion parameter for each pixel by performing a second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time; ,
An image generation unit that generates a blood flow dynamic image based on the second perfusion parameter;
A medical image analysis apparatus comprising: a display unit that displays the blood flow dynamic image.   The medical image analysis apparatus according to claim 1, wherein the first perfusion parameter includes at least one of blood flow, average transit time, and distribution volume.   The delay time calculation unit calculates a delay approximate value in each pixel based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter. Medical image analysis device.   The delay time calculation unit calculates the delay time using the approximate delay value based on a negative correlation between the first perfusion parameter and the delay time. Item 4. The medical image analysis apparatus according to Item 3. The delay time calculation unit calculates a new delay time using the second perfusion parameter as the first perfusion parameter,
The second perfusion parameter calculation unit calculates a new second perfusion parameter by newly performing a second model fitting using the new delay time. The medical image analysis apparatus according to any one of the above. A comparison unit that obtains a difference between approximate information indicating a time-series change of a pixel value in the living tissue obtained by the new second perfusion parameter and the living tissue transition information, and compares the difference with a predetermined threshold value. Further comprising
The medical image analysis apparatus according to claim 5, wherein when the difference is equal to or less than the threshold, the image generation unit generates the blood flow dynamic image based on the new second perfusion parameter. A medical imaging apparatus for obtaining blood flow dynamics of a living tissue by applying model fitting to information obtained from a time series image of the living tissue,
An imaging unit for imaging time-series images of biological tissue;
A storage unit for storing the time-series images;
A blood inflow region setting unit for setting a predetermined blood inflow region in the time series image;
A blood inflow transition information acquisition unit for acquiring blood inflow transition information indicating a time-series change in pixel values in the blood inflow region;
A biological tissue transition information acquisition unit that acquires biological tissue transition information indicating time-series changes in pixel values in the biological tissue;
A first perfusion parameter calculation unit that calculates a first perfusion parameter indicating blood flow dynamics in the biological tissue by performing a first model fitting based on the blood inflow transition information and the biological tissue transition information;
Based on the blood inflow transition information, the biological tissue transition information, and the first perfusion parameter, the time until the blood that has passed through the blood inflow region reaches a position corresponding to each pixel of the time series image is indicated. A delay time calculation unit for calculating the delay time;
A second perfusion parameter calculator that calculates a second perfusion parameter for each pixel by performing a second model fitting based on the blood inflow transition information, the biological tissue transition information, and the delay time; ,
An image generation unit that generates a blood flow dynamic image based on the second perfusion parameter;
A medical image photographing apparatus comprising: a display unit that displays the blood flow dynamic image.

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