JP4535795B2 - Image processing apparatus and X-ray CT system - Google Patents

Image processing apparatus and X-ray CT system Download PDF

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JP4535795B2
JP4535795B2 JP2004205079A JP2004205079A JP4535795B2 JP 4535795 B2 JP4535795 B2 JP 4535795B2 JP 2004205079 A JP2004205079 A JP 2004205079A JP 2004205079 A JP2004205079 A JP 2004205079A JP 4535795 B2 JP4535795 B2 JP 4535795B2
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JP2006025868A (en
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琴子 森川
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ジーイー・メディカル・システムズ・グローバル・テクノロジー・カンパニー・エルエルシー
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Description

  The present invention relates to an image processing apparatus, an image processing method, and an X-ray CT system for reconstructing an X-ray tomographic image from scanned X-ray projection data of a subject.

  An X-ray CT (Computerized Tomography) system has a doughnut-shaped cavity, and measures a projection data by irradiating a subject (subject) with X-rays (hereinafter referred to as “gantry”). In addition to providing various control signals to the gantry, an operation console for reconstructing an X-ray tomogram based on the X-ray projection data measured in the gantry and displaying it on the screen, etc. In order to fix and support the inside of the unit, the table unit is configured to transport a slidable table unit on which a subject is placed toward the cavity.

  Thus, the main purpose of the X-ray CT system is to collect projection data transmitted through the subject and reconstruct an X-ray tomographic image of a specific part of the subject from the data. In general, in an X-ray CT system, X-rays are irradiated toward a subject from different angles, and X-ray projection data transmitted through the subject at each angle is detected. In general, the series of X-ray irradiation / detection operations described above is called “scanning”. Then, the projection data detected by the gantry is received by the operation console, and after pre-processing, the X-ray tomogram is reconstructed. Normally, a filter back projection (FBP) method is used as a reconstruction method in this case. It is also known to improve the image quality of a reconstructed image at a predetermined site by performing post-processing on the reconstructed image using a successive approximation method (see, for example, Patent Document 1).

Further, as an image reconstruction method using the successive approximation method, a maximum likelihood estimation (ML) method (MLEM method) using an expectation maximization (EM) algorithm is known. As an improvement, an Ordered Subset Expectaion Maximization (OSEM) method by subsetting is also known.
JP 58-206726 A

  However, in the above-described successive approximation method, the MLEM method has a slow calculation speed, and the OSEM method has many repetitive operations, so that there is a problem that it takes a lot of calculation time compared to an analytical method such as the FBP method. Therefore, after the projection data acquired by the scan is reconstructed using the FBP method, a post-process using the OSEM method is further performed on a part that needs to be reconstructed in detail. However, in the conventional post-processing calculation using the OSEM method, the calculation end condition such as how much calculation should be performed using the OSEM method is not determined. For this reason, conventionally, it is determined only by the experience of the examiner and the like, and the reconstructed image after the post-processing cannot be quantitatively evaluated.

  The present invention has been made in consideration of such circumstances, and for the specific part of the CT image, while maintaining the sharpness of the edge part, other parts can be corrected more smoothly, An object of the present invention is to provide an image processing apparatus, an image processing method, and an X-ray CT system capable of quantitatively evaluating a reconstructed image.

In order to solve the above problems, the present invention is an image processing apparatus for reconstructing a CT image from X-ray projection data of a subject,
Preprocessing means for performing preprocessing of the X-ray projection data;
Reconstruction means for performing image reconstruction of a CT image from the preprocessed X-ray projection data;
Is it necessary to set a predetermined ROI on the CT image reconstructed by the reconstruction means, calculate an image measurement value in the ROI, and improve the image quality of the CT image based on the image measurement value? Determining means for determining whether or not;
When it is determined by the determination means that the CT image reconstruction parameter needs to be updated, image quality improvement means for improving the image quality of the CT image by changing the reconstruction parameter and reconstructing the image;
And an image output unit that outputs the CT image when it is determined by the determining unit that the image quality of the CT image does not need to be improved.

Further, the present invention is an image processing apparatus for reconstructing a CT image from X-ray projection data of a subject,
Reconstruction means for reconstructing a CT image from the X-ray projection data using a filtered back projection method;
An image quality improvement means for improving the image quality of the comparison image by a successive approximation method using the CT image and the predetermined comparison image;
Calculating means for setting a predetermined ROI on the comparative image whose image quality has been improved by the image quality improving means, and calculating a standard deviation in the ROI;
Determining means for determining whether or not further improvement in image quality of the comparison image is necessary based on the standard deviation;
If it is determined by the determining means that further improvement in image quality of the comparative image is necessary, the comparison image is re-imaged by a successive approximation method using the image quality improvement and the comparative image determined by the determining means. Image re-improvement means to improve,
And an output unit that outputs the comparison image as a CT image when it is determined by the determination unit that the image quality improvement of the comparison image is not necessary.

Furthermore, the present invention is an image processing method for reconstructing a CT image from X-ray projection data of a subject,
A preprocessing step for preprocessing the X-ray projection data;
A reconstruction step of reconstructing a CT image from the preprocessed X-ray projection data;
An image measurement value calculation step of setting a predetermined ROI on the CT image reconstructed by the reconstruction step and calculating an image measurement value in the ROI;
A determination step of determining whether image quality improvement of the CT image is necessary based on the image measurement value;
When it is determined that the image quality improvement of the CT image is necessary by the determination step, the image quality improvement step of improving the image quality of the CT image by changing the reconstruction parameter and reconstructing the image,
An image output step of outputting the CT image when it is determined that the image quality improvement of the CT image is not necessary in the determination step.

Furthermore, the present invention is an image processing method for reconstructing a CT image from X-ray projection data of a subject,
A reconstruction step of performing image reconstruction of a CT image using a filtered back projection method from the X-ray projection data;
An image quality improvement step of improving the image quality of the comparison image by a successive approximation method using the CT image and a predetermined comparison image;
A calculation step of setting a predetermined ROI on the comparative image whose image quality has been improved by the image quality improvement step, and calculating a standard deviation in the ROI;
A determination step of determining whether or not further improvement in image quality of the comparison image is necessary based on the standard deviation;
When it is determined that the image quality of the comparison image needs to be further improved by the determination step, the comparison image is re-imaged by a successive approximation method using the CT image and the comparison image determined by the determination step. Image quality re-improvement process to improve,
An image processing method comprising: an output step of outputting the comparison image as a CT image when it is determined that the image quality improvement of the comparison image is not necessary in the determination step.

Furthermore, the present invention relates to a gantry that collects X-ray projection data of a subject, an image processing device that reconstructs a CT image from the X-ray projection data, and the subject to the cavity of the gantry. An X-ray CT system comprising a transporting table device,
The image processing apparatus is
Preprocessing means for performing preprocessing of the X-ray projection data;
Reconstruction means for performing image reconstruction of a CT image from the preprocessed X-ray projection data;
Image measurement value calculation means for setting a predetermined ROI on the CT image reconstructed by the reconstruction means and calculating an image measurement value in the ROI;
Determination means for determining whether or not image quality improvement of the CT image is necessary based on the image measurement value;
When it is determined by the determination means that the image quality of the CT image needs to be improved, the image quality improvement means for improving the image quality of the CT image by changing the reconstruction parameter and reconstructing the image;
And an image output means for outputting the CT image when it is determined by the determining means that no improvement in the image quality of the CT image is necessary.

Furthermore, the present invention provides a gantry that collects X-ray projection data of a subject, an image processing device that reconstructs a CT image from the X-ray projection data, and transports the subject to a cavity of the gantry. An X-ray CT system comprising a table device for performing
The image processing apparatus is
Reconstruction means for performing image reconstruction of a CT image from the X-ray projection data using a filtered back projection method;
An image quality improvement means for improving the image quality of the comparison image by a successive approximation method using the CT image and the predetermined comparison image;
Calculating means for setting a predetermined ROI on the comparison image updated by the updating means and calculating a standard deviation in the ROI;
Determining means for determining whether or not further improvement in image quality of the comparison image is necessary based on the standard deviation;
If it is determined by the determination means that the comparison image needs further updating, the image quality of the comparison image is improved again by a successive approximation method using the CT image and the comparison image determined by the determination means. Image quality re-improvement means,
And an output unit that outputs the comparison image as a CT image when it is determined by the determination unit that the image quality improvement of the comparison image is not necessary.

  According to the present invention, it is possible to more smoothly correct other portions of a specific portion of a CT image while maintaining the sharpness of the edge portion, and to quantitatively evaluate the reconstructed image. it can.

  Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is an external view of an X-ray CT system according to an embodiment of the present invention. As shown in FIG. 1, the X-ray CT system according to this embodiment includes a gantry 100, a table device 200, and an operation console 300.

  The gantry 100 includes a donut-shaped rotating body 102 having a cavity 101. In the rotating body 102, an X-ray tube that is an X-ray generation source and an X-ray detection unit that includes an X-ray detection element group that detects X-rays generated from the X-ray tube are provided in the cavity 101. The X-ray tube and the X-ray detector rotate around the cavity 101 while maintaining the mutual positional relationship. Accordingly, the X-ray tube is driven and the X-ray detector is detected while rotating the rotating body 102 including the X-ray tube and the X-ray detector in a state where the subject is positioned in the cavity 101. It is possible to detect X-ray irradiation from different projection angles and X-rays transmitted through the subject.

  Further, the gantry 100 is provided with an operation panel 103, and using this operation panel 103, an operator can perform a slide operation of the table of the table device 200, execution of acquisition of projection data, and the like. .

  The operation console 300 has a function of communicating with the gantry 100, a function of receiving projection data transferred from the gantry 100, and reconstructing an X-ray tomogram.

  When an actual scan is performed using the X-ray CT system as shown in FIG. 1, the operator sets a scan condition and the like on the operation console 300 to make a scan plan. Then, a main scan is performed based on the scan plan. That is, using the table apparatus 200 in which the initial position is set, a table or the like on which the subject is placed is inserted into the cavity 101 of the gantry 100, the rotating body 102 is rotated and the X-ray tube is driven while the X-ray tube is driven. The X-ray transmitted through the subject is detected by the line detection unit. Then, transmission X-ray projection data at each rotation angle of the rotator 102 is transferred to the operation console 300. In the operation console 300, an X-ray tomographic image is reconstructed arithmetically.

  FIG. 2 is a block diagram showing a detailed configuration of the X-ray CT system according to the present embodiment.

  As shown in FIG. 2, the gantry 100 includes a main controller 104 that performs overall control and the following configuration.

  105a is an interface for communicating with the operation console 300, and 105b is an interface for communicating with the table device 200. Reference numeral 102 denotes a rotating body having a hollow portion 101 for positioning a subject lying on the table device 200. An X-ray tube 105 which is an X-ray generation source driven and controlled by an X-ray tube controller 106 and a collimator 107 having a slit for defining an X-ray irradiation range are provided inside the rotating body 102. .

  The rotating body 102 includes an X-ray detection unit 108 that detects X-rays transmitted through the subject, and a data collection unit 109 that collects projection data obtained from the transmitted X-rays detected by the X-ray detection unit 108. Also equipped. Note that the X-ray tube 105 and the collimator 107 and the X-ray detection unit 108 are provided at positions opposite to each other across the cavity 101 in the rotating body 102, that is, at positions facing each other across the subject. It rotates around the cavity 101 while being maintained. The rotating body 102 is rotated by a rotating motor 111 driven by a driving signal from a motor controller 110.

  Reference numeral 112 denotes a ROM that records various programs and the like. Reference numeral 113 denotes a RAM that is used as a work area and secures an area for holding position information and the like of the table unit sent from the table device 200.

  The main controller 104 analyzes various commands received via the interface 105a, and based on the analysis, various control signals are sent to the X-ray tube controller 106, the motor controller 110, the data collection unit 109, and the table device 200. Is output. Further, after the power is turned on, the main controller 104 loads a program according to the flowchart of FIG. 3 to be described later stored in the ROM 112 into the RAM 113 and executes the program. Further, the main controller 104 performs a process of sending the projection data collected by the data collection unit 109 and the data stored in the RAM 113 to the operation console 300 via the interface 105a.

  The operation console 300 is an image processing apparatus realized by a so-called workstation or the like, and as shown in FIG. 2, a CPU 51 that controls the entire apparatus, a ROM 52 that stores a boot program, and a RAM 53 that functions as a main storage device. The following configuration is provided.

  The HDD 54 is a hard disk device, in addition to the OS, a program for performing a scan plan, a diagnosis for giving various instructions to the gantry 100 and reconstructing an X-ray tomogram based on data received from the gantry 100 Stores programs, etc. The VRAM 55 is a memory for developing image data to be displayed, and can be displayed on the CRT 56 by developing the image data or the like here. Reference numerals 57 and 58 denote a keyboard and a mouse for performing various settings, respectively. Reference numeral 59 denotes an interface for communicating with the gantry 100.

  FIG. 3 is a flowchart for explaining image reconstruction processing by the operation console 300 in the X-ray CT system according to the present embodiment. As described above, the program according to the flowchart of FIG. 3 is stored in the ROM 52, loaded into the RAM 53 after the power is turned on, and executed by the CPU 51.

  First, projection data (scan data) of X-rays irradiated on the subject at every predetermined angle is acquired from the gantry 100 via the interface 59 (step S1). At this time, the acquired data is projection data at respective angles when rotating 180 degrees to 360 degrees with respect to the subject, for example, 984 pieces of projection data are acquired.

  Next, pre-processing such as noise removal is performed on each projection data (step S2). Then, image reconstruction processing using the FBP method is performed using all projection data (step S3). Then, in order to improve the image quality of the CT image (reconstructed image) for a region of interest (hereinafter referred to as “ROI”) corresponding to a specific part, image quality improvement processing using the OSEM method is performed as post-processing. (Step S4). Details of ROI image quality improvement processing using the OSEM method in the present embodiment will be described later. Then, a CT image after image quality improvement is acquired and displayed on the CRT 56 (step S5).

  FIG. 4 is a flowchart for explaining in detail the ROI image quality improvement processing procedure of the CT image reconstructed in the present embodiment.

First, re-projecting the reconstructed CT image I using the FBP method in step S3 described above, to produce a re-projected image i n (step S41). Here, i represents a reprojected image of the CT image I created using the FBP method, and n is a number for identifying a view (that is, corresponding to an angle or a phase) with respect to the CT image. When the projection is 100 views, it has any value from 1 to 100. The value of n can be an empirical value or an arbitrary value depending on the degree of improvement.

Next, a comparison image I ′ (m) to be compared with the reconstructed CT image I is created (step S42). Here, m represents the number of updates of the comparison image I ′, and is initially 0 (step S43). In the present embodiment, as the initial image I ′ (0) of the comparison image I ′ (m), an image having the same size as the CT image having a value of 1 for all the pixels is created. In addition, if this pixel value is a value other than 0, each pixel value may vary. Then, the initial image I ′ (0) is projected using the same view as in the case of reprojection of the CT image I reconstructed using the FBP method to generate a projection image i ′ n (0) ( Step S44). Here, i ′ represents a projected image of the comparative image I ′.

Then, the comparison image I ′ (m) is updated by comparing the reprojection image i n and the projection image i ′ n with the same angle (that is, the view with the same n), and the updated comparison image I ′. (M + 1) is generated (step S45). That is, the image I ′ (0) is updated to I ′ (1). Note that the update method using the successive approximation method in the present embodiment is performed using a known OSEM (expected value maximization by subsetting) method, and thus detailed description thereof is omitted. Next, the standard deviation SD of the pixels in the ROI set on the updated comparison image I ′ (m + 1) is calculated (step S46).

  Then, it is determined whether or not the calculated standard deviation SD is a value within a certain range (step S47). As a result, if the standard deviation SD is not within a certain range (No), it is determined that the comparison image needs to be updated again, and the process returns to step S44 to display the updated comparison image I ′ (m + 1) in the same view. Then, the above-described steps S45 to S47 are executed after reprojection. That is, the loop is repeated until it is determined that it is not necessary to update the comparison image, and the comparison image is updated each time. On the other hand, when the standard deviation SD is within a certain range (Yes), it is determined that the termination condition is satisfied, the post-processing is terminated, and the process proceeds to step S5. In step S5, the comparison image I ′ that is finally updated when the end condition is satisfied is displayed on the CRT 56 as a CT image with improved image quality.

  As described above, the operation console 300 functions as an image processing apparatus that reconstructs a CT image from X-ray projection data of a subject. That is, the image processing apparatus reconstructs a CT image from the X-ray projection data scanned by the gantry 100 using the FBP method, and performs the successive approximation method using the CT image and a predetermined comparison image. Update the comparison image. Next, a predetermined ROI is set on the updated comparison image, and a standard deviation in the ROI is calculated. Here, it is determined whether or not further updating of the comparative image is necessary based on the calculated standard deviation. As a result, when it is determined that the comparison image needs to be further updated, the comparison image is re-updated by the successive approximation method using the CT image and the comparison image. On the other hand, when it is determined that the comparison image does not need to be updated, the comparison image is output as a CT image.

  In the present embodiment, the standard deviation SD is used as the post-processing termination condition, but the same processing can be performed even when variance is used. That is, the variance within the ROI set on the comparison image is calculated, and it is determined whether or not the comparison image needs to be updated based on the variance.

  In addition, a method of previously determining the maximum value of the number of iterations known in the past, a method of ending when the rate of change of the expected value used in the OSEM method falls within a predetermined range, and the book described above The end condition may be determined in combination with the method using the standard deviation SD in the embodiment. For example, when a measurement means for measuring the number of updates of the comparison image is further provided and the predetermined number of updates is reached regardless of the determination result of whether or not the comparison image needs to be updated based on the standard deviation In this case, the comparison image update process is terminated, and the comparison image at that time is output as a CT image.

  Here, the process of calculating the standard deviation SD in step S46 will be described in detail. In the present embodiment, it is assumed that an ROI is provided for the CT image I reconstructed in step S3, and the standard deviation SD is calculated for the pixel values in the ROI. Therefore, as a ROI setting method, a method that uses a fixed ROI that is automatically set from the reconstructed image generated in step S3 and a dynamic that is set each time for the post-processed image in step S4. There is a method using a simple ROI.

  First, the method using the fixed ROI is a method in which the ROI is automatically detected in the image reconstructed in step S3, and the standard deviation SD is obtained by applying the ROI to the comparison image I ′ until the end. . FIG. 5 is a flowchart for explaining a detailed processing procedure for automatically detecting the ROI for calculating the standard deviation SD. First, the CT image is binarized in the range of CT values expected for the region of interest as ROI in the image reconstructed in step S3 of FIG. 3 (step S51). For example, when the imaging region is the abdomen and the region of interest as the ROI is the liver, the CT image I is binarized within the CT value range expected for the liver region in the CT image I (reconstructed image). .

  Next, by labeling the binary image obtained in step S51, each independent region existing in the binary image is labeled (step S52). And only the area | region which has an area more than fixed (the number of pixels more than a fixed number) among each labeled area | region is extracted (step S53). Next, by performing logical filtering on the extracted region, the region is contracted to remove the edge portion (step S54). As a result, it is possible to obtain the standard deviation SD and the variance that can quantitatively represent the image noise with the image measurement values of only the inner area surrounded by the edge portion. Then, the region of the comparison image I ′ at the position corresponding to the contracted region is set as the ROI, and post-processing using the OSEM method in step S4 is performed on the ROI. In this case, as described above, when the standard deviation SD or variance of the ROI in the updated comparison image I ′ falls within a predetermined range, the process is terminated thereafter.

  Further, in the method of setting the ROI for each comparison image I ′ after the post-processing in step S4, the comparison image I ′ updated by updating the image in step S44 in the flowchart of FIG. 4 described above. Each time, the ROI is set by the above procedure shown in FIG.

  As described above, in this embodiment, it is possible to suitably perform image reconstruction of a region of interest by calculating the standard deviation SD and variance only for a specific ROI, not the entire CT image (reconstructed image). It becomes possible. In addition, as described above, the ROI is automatically extracted from the CT image I (reconstructed image) or the updated comparison image I ′, and the update process is post-processing based on the standard deviation SD and variance value of the ROI. By setting the end condition, it is possible to quantitatively evaluate the updated comparison image, that is, the CT image finally obtained in step S5.

  The ROI setting may not be automatically detected from the CT image I or the comparison image I ′, but may use an area in a predetermined range as the ROI. That is, regardless of the type of image to be reconstructed, for example, a predetermined region in the image is set as the ROI from the beginning, and the standard deviation SD and variance of the pixel values within the range are obtained. According to this method, although it is not suitable for storing the edge portion, it is not necessary to perform an operation for setting the ROI, so that the update process can be performed at high speed using the same end condition.

<Other embodiments>
It is known that the reconstructed image can be improved with a relatively small number of calculations by changing the number of projections in the subset for each iteration of the operation using the OSEM method. Therefore, in the present embodiment, for example, in accordance with the standard deviation SD calculated in step S46 in FIG. 4 and the size of the variance, in the subset when the image I ′ is updated by the OSEM method in step S45 of the next loop. An example of changing the number of projections will be described.

  FIG. 6 is a diagram illustrating an example in which the image I ′ is updated by changing the number of projections in the subset based on the standard deviation SD and the variance calculated at each stage of the iterative calculation. For example, if the total projection data is 6 (6 views), in the first calculation, each pixel is projected with one projection number and 6 subsets as shown in (A), and the image I ′ is updated to update the ROI. A standard deviation SD1 is obtained. Then, in the second time, each pixel is projected with two projection numbers and three subsets based on the first standard deviation SD1, and the image I ′ is further updated to obtain the ROI standard deviation SD2. Further, in the third time, each pixel is projected with six projection numbers and one subset based on the second standard deviation SD2, and the image I 'is further updated to obtain the ROI standard deviation SD3.

  As described above, by changing the number of projections in the subset for each iteration of the operation using the OSEM method of the above-described embodiment, it is possible to accelerate the convergence of high-frequency components and obtain a good image with a small number of calculations. It becomes possible.

  Further, as a characteristic of the OSEM method, when the number of projections in the subset is small, the edge component is emphasized and noise cannot be reduced much. However, when the number of projections in the subset is large, the edge becomes smooth while the noise is smoothed. There is little degree to reduce. Accordingly, the number of projections in the subset may be changed according to the purpose, and the update process may be performed using the termination condition using the standard deviation SD described in the above embodiment.

  In addition to the MLEM method and the OSEM method, a maximum a posteriori (MAP-EM: Maximum A Posteriori) estimation method incorporating prior probabilities as image a priori knowledge is known as a successive approximation algorithm. The MAP-EM method is more stable against noise than the MLEM method because the image is estimated so as to maximize the posterior probability obtained by the likelihood function and the prior probability. Therefore, by combining the MAP-EM method using two parameters with the above-described embodiment, it is possible to determine an optimal combination of parameters and achieve both the sharpness of the edge portion and the smoothness of the image in the ROI.

  In the filtered back projection method, a reconstruction function can be considered as an image reconstruction parameter related to noise, and image noise can be similarly adjusted by adjusting the strength of each frequency band.

  In the present invention, a program for realizing the functions of the above-described embodiments by a computer is recorded on a computer-readable recording medium (storage medium), and the program recorded on the recording medium is stored in the system. A computer or the like may read and execute. Computer-readable recording media include portable recording media such as a flexible disk, CR-ROM, and DVD-ROM, a storage device (recording device) such as a hard disk or volatile memory (RAM) built in the system or computer. Device). The program may be downloaded to the system, computer, or the like via a network such as the Internet or a communication line such as a telephone line.

  Then, the functions of the above-described embodiments are realized by executing the read program in the system or the computer. In this case, the recording medium or the like stores a program corresponding to the flowchart described above.

1 is an external view of an X-ray CT system according to an embodiment of the present invention. It is a block diagram which shows the detailed structure of the X-ray CT system which concerns on this embodiment shown in FIG. It is a flowchart for demonstrating the image reconstruction process by the operation console 300 in the X-ray CT system which concerns on this embodiment. It is a flowchart for demonstrating in detail the image quality improvement processing procedure of ROI of the CT image reconfigure | reconstructed by this embodiment. It is a flowchart for demonstrating the detailed process sequence for automatically detecting ROI for calculating the standard deviation SD. It is a figure which shows the example in the case of updating the image I 'by changing the number of projections in a subset by the standard deviation SD calculated at each step of the iterative calculation.

Claims (15)

  1. An image processing apparatus for reconstructing a CT image from X-ray projection data of a subject,
    Preprocessing means for performing preprocessing of the X-ray projection data;
    Reconstruction means for performing image reconstruction of a CT image from the preprocessed X-ray projection data;
    Is it necessary to set a predetermined ROI on the CT image reconstructed by the reconstruction means, calculate an image measurement value in the ROI, and improve the image quality of the CT image based on the image measurement value? Determining means for determining whether or not;
    When it is determined by the determination means that the image quality of the CT image needs to be improved, the image reconstruction is performed by changing at least one reconstruction parameter of the number of projections, likelihood function, and posterior probability in the subset of the successive approximation method. An image quality improvement means for improving the image quality of the CT image,
    An image processing apparatus comprising: an image output unit that outputs the CT image when it is determined by the determination unit that the image quality of the CT image is not required to be improved.
  2.   The image processing apparatus according to claim 1, wherein the reconstruction unit reconstructs the CT image using a successive approximation method.
  3.   The image processing apparatus according to claim 1, wherein the reconstruction unit reconstructs the CT image using a filtered back projection method.
  4.   The image processing apparatus according to claim 1, wherein the image quality improving unit improves the image quality of the CT image by changing the number of projections in a subset of the successive approximation method.
  5.   The image processing apparatus according to claim 1, wherein the image quality improvement unit improves the image quality of the CT image using a reconstruction function of a filter-corrected back projection method as a reconstruction parameter.
  6.   The image processing apparatus according to claim 1, wherein a value representing image noise is used as the image measurement value.
  7.   The image processing apparatus according to claim 6, wherein a variance or standard deviation of each pixel value is used as the image measurement value.
  8.   8. The image processing apparatus according to claim 7, wherein the image measurement value is measured by setting the inside of a fixed ROI on the CT image regardless of the image.
  9.   The image processing apparatus according to claim 7, wherein an ROI set for each image is measured on the CT image as the image measurement value.
  10.   The image processing apparatus according to claim 7, wherein an ROI that is automatically set by image processing for each image is set and measured on the CT image as the image measurement value.
  11. An image processing apparatus for reconstructing a CT image from X-ray projection data of a subject,
    Reconstruction means for performing image reconstruction of a CT image from the X-ray projection data using a filtered back projection method;
    An image quality improvement means for improving the image quality of the comparison image by a successive approximation method using the CT image and the predetermined comparison image;
    Calculating means for setting a predetermined ROI on the comparative image whose image quality has been improved by the image quality improving means, and calculating a standard deviation in the ROI;
    Determining means for determining whether or not further improvement in image quality of the comparison image is necessary based on the standard deviation;
    When it is determined that the image quality improvement of the comparison image is further required by the determination means, the successive approximation using the CT image and the comparison image determined by the determination means that the image quality improvement is further required Image quality re-improvement means for re-improvement of the image quality of the comparison image by the method,
    An output unit that outputs the comparison image as a CT image when it is determined by the determination unit that image quality improvement of the comparison image is not necessary ;
    The image quality improvement means and the image quality again improved means, wherein a successive approximation, the image processing apparatus characterized that you use the expectation maximization method by subsetting.
  12. An image processing apparatus for reconstructing a CT image from X-ray projection data of a subject,
    Reconstruction means for performing image reconstruction of a CT image from the X-ray projection data using a filtered back projection method;
    An image quality improvement means for improving the image quality of the comparison image by a successive approximation method using the CT image and the predetermined comparison image;
    Calculating means for setting a predetermined ROI on the comparative image whose image quality has been improved by the image quality improving means, and calculating a standard deviation in the ROI;
    Determining means for determining whether or not further improvement in image quality of the comparison image is necessary based on the standard deviation;
    When it is determined that the image quality improvement of the comparison image is further required by the determination means, the successive approximation using the CT image and the comparison image determined by the determination means that the image quality improvement is further required Image quality re-improvement means for re-improvement of the image quality of the comparison image by the method,
    An output means for outputting the comparison image as a CT image when it is determined by the determination means that image quality improvement of the comparison image is not necessary ;
    An image processing apparatus , further comprising: parameter determination means for determining the number of projections and the number of subsets of the CT image and the comparison image in the successive approximation method based on the size of the standard deviation .
  13. The quality re improving means, said until it is determined not to be required image quality improvement of the comparative image by the determining means, further characterized in that the image quality again improve the comparison images quality improved just before claim 11 or 12. The image processing apparatus according to 12 .
  14.   An X-ray CT system comprising the image processing apparatus according to claim 1.
  15.   A program for causing a computer to function as the image processing apparatus according to any one of claims 1 to 13.
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