JP2008272014A - Quantitative measurement apparatus, quantitative evaluation apparatus and image processing program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To highly accurately extract desired tissue with high reproducibility even when there is a partial volume effect and to quantitatively evaluate it. <P>SOLUTION: On the basis of a pixel value range between a first pixel value range defining first tissue and a second pixel value range defining second tissue adjacent to the first tissue, intermediate pixels for which the first tissue and the second tissue are included in one pixel are extracted from a medical image. On the basis of a distribution coefficient indicating the ratio of the first tissue included in one pixel and the pixel value of the intermediate pixels, the first tissue included in all the intermediate pixels in the medical image is separated. The amount of the first tissue separated on the basis of the first pixel value range from the medical image and the amount of the first tissue separated from the intermediate pixels are added to calculate the amount of all the first tissue included in the medical image. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a quantitative measurement device, a quantitative evaluation device, and an image processing program for performing quantitative evaluation by accurately separating images taken by a medical imaging device for each tissue.

  By using an imaging apparatus such as an X-ray CT apparatus or an MRl apparatus, three-dimensional voxel data in which tomographic images of a subject are stacked can be acquired. The acquired image is subjected to image processing in accordance with purposes such as observation and analysis and presented to the operator.

  For example, image processing for observation includes a function (segmentation) for extracting only a target tissue so that interpretation can be easily performed, and volume rendering so that the positional relationship between a lesion and other tissues can be easily grasped. There is a dimension display function. Further, as the image processing for analysis, there is a function of obtaining the area and volume of the region extracted using the segmentation function. These functions often include threshold processing, and the stability of the pixel value affects the extraction accuracy.

  Further, even when an advanced segmentation technique such as a region expansion method is used instead of simple threshold processing, the connectivity with neighboring pixels is often determined by a pixel value, so that the extraction accuracy also decreases.

In order to cope with this, Japanese Patent Application Laid-Open No. H10-228667 corrects the boundary between substances having different positional relations and areas of unknown measurement objects by correcting the image data acquired by the X-ray CT apparatus based on the blur distribution. Describes the method of extraction.
JP-A-11-56841

  However, in the method described in Patent Document 1, when a weighted average CT value of muscle and fat is measured by the partial volume effect, a pixel indicating the weighted average CT value of muscle and fat is displayed. There remains an unresolved issue of being unable to cope with an isolated extraction.

  Further, by measuring the volume of a lung cancer lesion or the like multiple times over a period of time, the time required for the volume to double (doubling time) may be estimated and used to determine the grade of malignancy. However, there are solid nodules, light shadows called GGO (ground glass shading), and hybrid types of solidity and light shading, so the method to determine the volume of lung cancer lesions etc. Is not established.

  The present invention has been made in view of the above circumstances, and even when there is a partial volume effect, a quantitative measurement apparatus and a quantitative evaluation that can extract a desired tissue with high accuracy and high reproducibility and perform quantitative evaluation An object is to provide an apparatus and an image processing program.

  In order to solve the above problem, the quantitative measurement apparatus according to claim 1 is based on an image acquisition unit that acquires a medical image and a first pixel value range that defines a first tissue included in the medical image. First separation means for separating a first tissue from the medical image, and a first pixel value range and a second pixel value range defining a second tissue adjacent to the first tissue Intermediate pixel extraction means for extracting, from the medical image, an intermediate pixel in which the first tissue and the second tissue are included in one pixel based on a pixel value range between the first pixel value range, A distribution coefficient setting means for setting a distribution coefficient indicating a ratio of the first tissue included in the intermediate pixel using a pixel value between the second pixel value range as a variable, and included in the intermediate pixel in the medical image The first tissue is classified based on the pixel value of each intermediate pixel and the distribution coefficient. And a second separating means, and an adding means for adding the amount of the first tissue separated by the first separating means and the amount of the first tissue separated by the second separating means. It is characterized by.

  According to the quantitative measurement device according to claim 1, between the first pixel value range that defines the first tissue and the second pixel value range that defines the second tissue adjacent to the first tissue. Based on the pixel value range, an intermediate pixel in which the first tissue and the second tissue are included in one pixel is extracted from the medical image. Next, the first tissue included in the intermediate pixel in the medical image is separated based on the distribution coefficient and the pixel value of the intermediate pixel. Then, the first tissue is separated from the medical image based on the first pixel value range, and the amount of the separated first tissue and the amount of the first tissue separated from the intermediate pixel are added. Thus, the amounts of all the first tissues included in the medical image are calculated. Here, the distribution coefficient indicates a ratio of the first tissue included in the intermediate pixel using a pixel value between the first pixel value range and the second pixel value range as a variable.

  Thereby, even when there is a partial volume effect, a desired analysis tissue can be extracted with high accuracy and high reproducibility. That is, an intermediate pixel having a pixel value that is not the pixel value of the analysis tissue is also analyzed, and the amount of analysis tissue included in the intermediate pixel can be obtained by using a distribution coefficient without overestimating. Further, since it is not necessary for the operator to set a threshold value used for calculating the volume, the difference between the operators can be eliminated and the reproducibility can be improved.

  The quantitative measurement apparatus according to claim 1 may include an output unit that outputs the amount of the first tissue added by the addition unit.

  According to the present invention, even when there is a partial volume effect, a desired tissue can be extracted with high accuracy and high reproducibility, and quantitative evaluation can be performed.

  The best mode for carrying out the present invention will be described below with reference to the accompanying drawings.

  FIG. 1 is a hardware configuration diagram showing the overall configuration of the quantitative evaluation apparatus 10 according to the first embodiment of the present invention.

  The quantitative evaluation apparatus 10 is an apparatus that measures and evaluates the amount of a desired tissue included in an acquired medical image, and is imaged by the medical image capturing apparatus 1 and the medical image capturing apparatus 1 that capture an image of a subject. Are connected to an image database 2 in which stored images are stored by a network 3 such as a LAN. The medical image photographing apparatus 1 is configured by an apparatus capable of photographing a subject image (preferably a three-dimensional image) such as an X-ray CT apparatus, an MR apparatus, or an ultrasonic imaging apparatus.

  The quantitative evaluation apparatus 10 mainly includes a central processing unit (CPU) 11, a main memory 12, a data recording device 13, a display memory 14, a display 15, a controller 16, a pointing device 17, and an external input device 18. And a common bus 19.

  The CPU 11 is connected to the above components by a common bus 19, and controls the operation of each component.

  The main memory 12 stores a control program for the apparatus and serves as a work area when the program is executed.

  The data recording device 13 stores an operating system (OS) and various application software including device drives of peripheral devices. These programs stored in the data recording device 13 are read from the data recording device 13 by the CPU 11, loaded into the main memory 12, and executed.

  The display memory 14 temporarily stores the produced display data.

  The display 15 is a display device such as a CRT monitor or a liquid crystal monitor, and displays an image based on data output from the display memory 14.

  The controller 16 detects the state of the pointing device 17 and outputs signals such as the position of the mouse pointer on the display 15 and the state of the pointing device 17 to the CPU 11.

  The pointing device 17 is an input device such as a mouse, a trackball, or a touch panel, and is used for operating a soft switch on the display 15.

  The external input device 18 is a keyboard or the like for an operator to input an instruction to the CPU 11.

  In the present embodiment, the data recording device 13 is connected as a storage device other than the main memory 12. However, the data recording device 13 is a memory such as a memory or a magnetic disk incorporated in or attached to the quantitative evaluation device 10. A device, a device for writing / reading data to / from a removable external medium, a device for transmitting / receiving data to / from an external storage device via a network, and the like may be applied.

  Next, a method for quantitative evaluation of a desired tissue from the acquired image in the quantitative evaluation apparatus 10 configured as described above will be described.

  In performing quantitative evaluation of a desired tissue from the acquired image, it is necessary to extract the desired tissue with high accuracy and high reproducibility. Hereinafter, the necessity of extracting a desired tissue with high accuracy and high reproducibility will be described.

  In a medical X-ray CT apparatus, factors that affect the accuracy of CT values include nonlinear factors such as beam hardening, detector energy characteristics, slice thickness, and the like. Among these factors, beam hardening is basically not a problem because it has a dedicated correction. In addition, the characteristics of the detector are basically not a problem because of the calibration process.

  In contrast, slice thickness results in a loss of accuracy known as the partial volume effect. As shown in FIG. 2, the partial volume effect occurs when the tissue of the subject changes in the slice direction within the measurement slice opening. Hereinafter, the reason why the partial volume effect causes a decrease in accuracy will be described.

  As shown in FIG. 3, consider a case where the boundary between two tissues crosses diagonally within a slice section. In the pixel J-1, the first tissue occupies 100%, and in the pixel J + 4, the second tissue occupies 100%. Therefore, these pixels can obtain correct pixel values. On the other hand, since the pixels J to J + 3 include the first tissue and the second tissue, the pixel values are intermediate between the pixel values of the first tissue and the pixel values of the second tissue. It is measured.

  FIG. 4 shows a result of extracting muscles and fats by threshold processing in CT images of the lower limbs. The pixel indicated by the arrow in FIG. 4A is not extracted as muscle as shown in FIG. 4A, and is not extracted as fat as shown in FIG. 4B. Such a pixel is a non-analysis target pixel having an intermediate value different from the CT value of the original tissue due to the partial volume effect.

  If the threshold processing is performed with a small threshold value in order to extract the muscle region without deficiency by reducing the number of non-analysis target pixels, even fat is extracted as a muscle region. On the other hand, in order not to include fat, the threshold value must be considerably increased, and in this case, all the regions that are considered to be muscles are excluded. For this reason, conventionally, an optimum threshold value has been examined by a phantom experiment. However, clinical data is more complex and difficult to optimize, and when the area and volume are measured at different facilities, the results may vary depending on the threshold used, and exact comparison may not be possible.

  Note that the partial volume effect becomes a problem particularly when a portion such as a bone and a soft tissue, in which the density and pixel value of the first tissue and the second tissue are greatly different is included.

  Therefore, in order to perform quantitative evaluation of a desired tissue such as a muscle volume, it is necessary to extract the desired tissue with high accuracy and high reproducibility even when there is a partial volume effect.

  FIG. 5 is a flowchart showing a flow of processing for performing quantitative evaluation of a desired tissue. The following processing is performed by the CPU 11 after the tomographic image of the subject is acquired from the medical image capturing apparatus 1 or the image database 2. Hereinafter, when a tomographic image in which muscles and fats are mixed is acquired by an X-ray CT apparatus, a case where muscles and fats are separated and a muscle volume is obtained will be described.

  First, two target tissues are defined by the pointing device 17 and the external input device 18 (step S10). For example, muscle and fat, bone and bone marrow fluid, alveoli and solid nodule (or GGO). In the present embodiment, muscle and fat are defined as two target tissues. Of the two target tissues, muscle is set as the analysis tissue.

  Next, two target tissues are extracted from the tomographic image by a known method such as threshold processing (step S20). The threshold for extracting the two target tissues is determined by the two kinds of tissues defined in step S10. As a result, as shown in FIG. 6, the region including the first tissue (muscle, pixel P) and the second tissue (fat, pixel R) is intermediate between the muscle and fat contained in the muscle. A pixel having a pixel value (intermediate pixel, pixel Q) is extracted.

  Next, a process (tissue distribution process) for distributing the intermediate pixel to each tissue (muscle and fat) is performed by weighting the intermediate pixel with a distribution coefficient (step S30).

  Hereinafter, the tissue distribution process (step S30, that is, steps S31 to S34) will be described.

  First, a pixel (analysis target pixel) including a muscle that is an analysis tissue is extracted from the region extracted by the threshold processing (step S31). As shown in FIG. 7, when the muscle CT value is α ± A (CT value 1 to CT value 1 ′) and the fat CT value is β ± B (CT value 2 to CT value 2 ′), The threshold for muscle extraction used at this time is β + B (CT value 2) (α> β). As a result, pixels other than pixels that can be clearly determined to be fat, such as pixels that are not analyzed in FIG. Here, it is considered that the muscle CT value and the fat CT value are shown in a range due to individual differences, and therefore, there is a possibility that the threshold cannot be fixed. However, by setting the threshold value to β−B (CT value 2 ′), individual differences can be absorbed and all adipose tissues can be analyzed.

  Next, a distribution coefficient that determines a division ratio when one pixel (unit volume) having a predetermined CT value is distributed to fat and muscle is determined (steps S32 to S33).

  First, a pixel value to which a distribution coefficient is assigned is determined (step S32). Here, as shown by the dotted line in FIG. 7, the above-described muscle CT values (CT value 1, CT value 1 ′) and fat CT values (CT value 2, CT value 2 ′) are pixel values to which distribution coefficients are assigned. It becomes.

  Thereafter, a distribution coefficient corresponding to the pixel value is determined (step S33). First, the distribution coefficient from CT value 2 'to CT value 2 is set to 1.0, that is, it is treated as complete fat. Similarly, the distribution coefficient from CT value 1 'to CT value 1 is 1.0, that is, it is treated as a complete muscle. The CT value 2 and CT value 1 are connected by a line. Thus, the distribution coefficient table or function (which can be functionalized and calculated so that the distribution coefficient to fat increases as the CT value becomes closer to the CT value of fat, and the distribution coefficient to muscle increases as the CT value of muscle becomes closer. If time is not a problem, it is not necessary to make a table).

  The CT value 2 and the CT value 1 may be linear as shown in FIG. 8 or non-linear (Partial), and an algorithm may be selected depending on the purpose and target organ. Actually, the ratio and the projection value are measured in a slightly non-linear relationship (Fig. 8, Partial), but there are many cases where there is no practical problem even if treated as linear (Fig. 8, Linear). is there.

  When the distribution coefficient table as shown in FIG. 7 is determined in steps S32 to S33, the distribution coefficient Wi determined for each pixel is multiplied by the pixel volume ΔV, and the sum of the target pixels is obtained. The volume (ΣWi · ΔV) of the muscle among the target pixels distributed to the muscle and the muscle is calculated (step S34). The volume calculated in step S34 is a more accurate analysis value because the pixel having a low CT value due to the partial volume effect also contributes.

  Thereby, the tissue distribution process (step S30, that is, steps S31 to S34) is completed.

  When the tissue distribution process (step S30) is completed, an analysis value calculation process for finally calculating an analysis value required by the user is performed (step S40). The analysis value is calculated by adding the muscle volume (ΣWi · ΔV) of the target pixel calculated in step S34 to the value obtained by multiplying the number of muscle pixels P extracted in step S10 by the unit volume of the voxel size. The final muscular volume can be used.

  Other examples of the analysis value include a time required for doubling the volume (doubling time) calculated by performing processing on a plurality of tomographic images having different imaging timings, and after a predetermined time has elapsed. For example, the volume of the tumor after one month may be used as the analysis value. The doubling time and the like can be calculated as follows.

  Since the cells multiply twice such that one is two and two are four, the volume (d1) of the tumor, etc. at a certain point in time, and the tumor, etc. after a certain period of time from a certain point in time If the volume (d2) is known, the time (doubling time: DT) required to double the volume of the tumor or the like can be calculated therefrom. If the doubling time is known, the volume (d3) of the tumor or the like at another time point can be estimated by calculation. In the following equation, t is the time from d1 to d2, and t 'is the time from d3 to d2.

[Equation 1]
DT = t × 1/10 (logd2-logd1)
= T '* 1/10 (logd3-logd2)
As another example, in the case where there is a GGO (ground glass-like shadow) as shown in FIG.

  Information such as numerical values of analysis values and graphs is output to the request source via the network 3 and the like (step S50).

  According to this embodiment, even when there is a partial volume effect, a desired analysis tissue can be extracted with high accuracy and high reproducibility. In other words, an intermediate pixel having a pixel value that is not within the pixel value range of the analysis tissue is also analyzed, and the amount of analysis tissue contained in the intermediate pixel is distributed with high accuracy by using a distribution coefficient, and is overestimated. You can ask without.

  Further, since it is not necessary for the operator to set a threshold value used for calculating the volume of the analysis tissue, a difference between operators can be eliminated and reproducibility can be improved. Moreover, since it is not influenced by the threshold value, a tissue amount closer to the true value can be calculated. Thereby, useful information at the time of transplantation, such as the volume of a desired organ (such as the liver), can be obtained.

  In addition, according to the present embodiment, since the accuracy does not decrease even if the slice thickness is increased, the efficiency and accuracy can be increased by increasing the slice thickness in a medical examination or the like.

  In addition, according to the present embodiment, the total sum of the analysis tissues can be calculated with high accuracy with a value close to the true value, so that the area, volume, doubling time (doubling time) of the analysis tissues, It is possible to output information desired by the operator, such as a ratio with other organizations.

  In the present embodiment, the case where fat and muscle are defined as two target tissues has been described. However, the present invention can also be applied to muscle and fat, bone and bone marrow fluid, alveoli and solid nodules, and the like. That is, the present invention can be applied anywhere as long as it is known in advance that two materials are mixed, such as lung (ie, air) and bone, cerebrospinal fluid (ie, water), and bone.

  In the present embodiment, the case where the partial volume is composed of two tissues of muscle and fat has been described. However, the present invention can also be applied to the case where the partial volume is composed of three or more tissues. For example, when another tissue is included outside the second tissue in FIG. 6, this can be dealt with by increasing the number of times the above processing is performed. Further, it is possible to cope with this by applying the above method to these two materials by classifying them into two materials such as bone and soft tissue by regarding a plurality of soft tissues as the same.

  In the present embodiment, an example in which threshold processing is used in the processing (step S20) in which two target tissues are extracted from a tomographic image has been described. However, region expansion processing and expansion / contraction processing are performed in region division processing. For example, a tissue may be extracted by a known method, or a means for specifying a region of interest using a GUI may be provided. For example, as shown in FIG. 10, when a tomographic image includes AB muscle, CD muscle, and EF muscle, two target tissues are defined when quantitative evaluation is performed only on the CD muscle. In step S10, the C muscle and the D muscle are set as target tissues and the C muscle is set as an analysis tissue, and the above processing is performed on an image obtained by extracting a CD muscle from a tomographic image by a region extraction method or the like. Thus, quantitative evaluation of the C muscle can be performed. In addition, when the lung field region is targeted, it is possible to avoid the analysis of even the air outside the body.

  Further, in the present embodiment, the distribution coefficient as shown by the dotted line in FIG. 6 is used in order to obtain the accumulation of muscle which is the first tissue, but the distribution coefficient is as shown by the solid line and the broken line in FIG. You may prepare two types, the case where 1 structure | tissue is set as an analysis structure | tissue, and the case where 2nd structure | tissue is set as an analysis structure | tissue. That is, the sum of the first organization and the second organization may be arithmetically 1.0.

  In this embodiment, muscles and fats contained in a unit volume (voxel) of one slice are distributed, but a plurality of slices can be added by adding the results of similar processing performed on a plurality of slices. It can also be applied to the distribution of muscle and fat contained in the slices.

  In this embodiment, a case where a tomographic image is acquired as a cross-sectional image of a subject has been described. However, an acquired image such as an axial image, a sagittal image, a coronal image, or an MPR image is not limited to a tomographic image. Any two-dimensional image reconstructed from data can be applied.

  In this embodiment, the analysis value is calculated and output. However, an image in which the pixel density is changed based on the distributed result may be displayed.

It is a hardware block diagram which shows the whole structure of the quantitative evaluation apparatus 10 which concerns on this invention. It is explanatory drawing explaining the partial volume effect. It is explanatory drawing explaining the partial volume effect. It is an example of an image from which an intermediate pixel is not extracted. It is explanatory drawing about the flow of a process of the said quantitative evaluation apparatus. It is an example by which the analysis object of the said quantitative evaluation apparatus 10 was extracted. It is explanatory drawing about the distribution coefficient of the said quantitative evaluation apparatus. It is a graph which shows the projection value used as the basis which determines the distribution coefficient of the said quantitative evaluation apparatus 10, and the mixing ratio of two structures | tissues. It is an example used as the object of the present invention. It is an example used as the object of the present invention.

Explanation of symbols

1: medical imaging device, 2: image database, 3: network, 10: quantitative evaluation device, 11: central processing unit (CPU), 12: main memory, 13 magnetic disk :, 14: display memory, 15: display, 16: Controller, 17: Pointing device, 18: External input device, 19: Common bus

Claims (6)

Image acquisition means for acquiring medical images;
First separation means for separating the first tissue from the medical image based on a first pixel value range defining the first tissue included in the medical image;
The first tissue and the second tissue based on a pixel value range between the first pixel value range and a second pixel value range defining a second tissue adjacent to the first tissue; Intermediate pixel extraction means for extracting an intermediate pixel included in one pixel from the medical image;
A distribution coefficient setting means for setting a distribution coefficient indicating a ratio of the first tissue included in the intermediate pixel using a pixel value between the first pixel value range and the second pixel value range as a variable;
Second separation means for separating a first tissue included in an intermediate pixel in the medical image based on a pixel value of each intermediate pixel and the distribution coefficient;
Adding means for adding the amount of the first tissue separated by the first separating means and the amount of the first tissue separated by the second separating means;
A quantitative measurement apparatus comprising:   The quantitative measurement apparatus according to claim 1, wherein the medical image is a tomographic image of a subject or an image of a predetermined region extracted from the tomographic image.   The quantitative measurement apparatus according to claim 1, further comprising a first output unit that outputs the amount of the first tissue added by the addition unit. Tissue setting means for setting two predetermined tissues included in the medical image;
A second output means for outputting a ratio of the amount of each tissue measured by the quantitative measurement device according to claim 1 or 2 when each of the set two tissues is the first tissue;
The quantitative measurement device according to claim 1, further comprising: A first tissue amount acquisition means for acquiring the amount of the first tissue at a certain point in time, measured by the quantitative measurement device according to claim 1;
A second tissue amount acquisition means for acquiring the amount of the first tissue at a time after a desired time has elapsed from a certain time, as measured by the quantitative measurement device according to claim 1 or 2;
The amount of the first tissue is doubled from the amount of the first tissue acquired by the first tissue amount acquisition unit and the amount of the first tissue acquired by the second tissue amount acquisition unit. Doubling time calculating means for calculating the time (doubling time) required to become,
Third output means for outputting the doubling time calculated by the doubling time calculating means;
A quantitative evaluation device characterized by comprising: Obtaining a medical image;
Separating the first tissue from the medical image based on a first pixel value range defining the first tissue included in the medical image;
The first tissue and the second tissue based on a pixel value range between the first pixel value range and a second pixel value range defining a second tissue adjacent to the first tissue; Extracting an intermediate pixel included in one pixel from the medical image;
Setting a distribution coefficient indicating a ratio of a first tissue included in the intermediate pixel using a pixel value between the first pixel value range and the second pixel value range as a variable;
Separating a first tissue included in intermediate pixels in the medical image based on a pixel value of each intermediate pixel and the distribution coefficient;
Adding the amount of the first tissue separated by the first separation means and the amount of the first tissue separated by the second separation means;
An image processing program for causing an arithmetic device to execute the above.

Images