JP5283882B2 - X-ray diagnostic apparatus, image processing apparatus, and filter coefficient calculation program used for image reconstruction processing - Google Patents

Description

  The present invention relates to a filter coefficient calculation program used for image reconstruction processing used in X-ray tomographic image generation.

  In recent years, various methods have been proposed as an imaging processing method for digital tomography using an X-ray diagnostic apparatus or the like (see, for example, Non-Patent Document 1). A typical example is called a filtered backprojection (FBP) method as shown in FIG. In the FBP method, a filter is applied to a plurality of images obtained by performing processing such as logarithmic calculation on an X-ray image taken from a plurality of directions, and a multi-tomographic image is obtained by back projecting them. . As a filter, for example, a filter called a Shepp-Logan filter (see the equation in FIG. 15) is known. The FBP method is characterized in that the same filter coefficient is applied to all X-ray images taken from multiple directions, and the same filter is applied without changing the coefficient depending on the position in one image. The filtered backprojection method is originally a reconstruction method for a CT scanner capable of complete reconstruction, and is not an appropriate imaging processing method in digital tomography where complete reconstruction is impossible. However, due to the short processing time, there are many research reports applying the filtered backprojection method to digital tomography.

In addition, several imaging processing methods that can obtain high image quality in application to digital tomography have been proposed. For example, a result of comparing an iterative imaging method called Maximum Likelihood algorithm (ML method) and a filtered backprojection method has been reported (for example, see Non-Patent Document 2). This report shows that the ML method has better image quality than the filtered backprojection, but the processing time of the filtered backprojection method is 15 minutes or less, whereas the processing time of the ML method is 3 hours or more. . Furthermore, for example, an imaging processing method called matrix inversion tomosynthesis is known (see, for example, Patent Document 1), and according to this method, it has been reported that higher image quality can be obtained than the filtered backprojection method (for example, Non-Patent Document 3).
U.S. Pat. No. 4,903,204 James T Dobbins III, and Devon J Godfrey: Digital x-ray tomosynthesis: current state of the art and clinical potential, Physics in Medicine and Biology Vol. 48 pp.R65-R106, 2003 Tao Wu, Richard H. Moore, Elizabeth A. Rafferty, and Daniel B. Kopans, A comparison of reconstruction algorithms for breast tomosynthesis, Medical Physics Vol. 31, No. 9, September 2004 Ying Chen, Joseph Y. Lo, James T. Dobbins III, Noise power spectrum analysis for several digital breast tomosynthesis reconstruction algorithms, Proc.SPIE vol. 6142, pp. 1677-1684, 2006

  When an X-ray tomographic image is generated for examination at a clinical site, it is essential to provide an image quality sufficient to interpret the presence or absence and state of an abnormality. In addition, the fact that an inspection image can be provided in a short processing time, for example, within a few minutes, is as important as good image quality in practice.

  However, as described above, the conventional filtered backprojection method has a problem that the processing time is short but the image quality is inferior. Another imaging processing method with good image quality takes a lot of processing time and has a problem that it cannot be put into practical use.

  The present invention has been made in view of the above circumstances, and in digital tomography, an X-ray diagnostic apparatus, an image processing apparatus, an X-ray diagnostic apparatus, or the like that can generate and display a high-quality formed image at high speed. An object of the present invention is to provide a filter coefficient calculation program used for image formation processing.

  In order to achieve the above object, the present invention takes the following measures.

An image processing apparatus according to an embodiment includes an X-ray image for each frame acquired by exposing an X-ray to a subject while moving an X-ray tube along a predetermined scan trajectory, A storage unit that stores different filter coefficients for each frame of the line image and each pixel position of the detector, and each X based on a combination of the predetermined scan trajectory, the frame of the X-ray image, and the pixel position of the detector A filter processing unit that determines a filter coefficient for each pixel of the line image and performs a filtering process on the X-ray image for each frame using the determined filter coefficient, and an X-ray image for each frame after the filtering process used, provided the tomographic image generator unit for generating a tomographic image, the filter coefficients the memory unit stores the scan track, the frame of the X-ray image, a pixel position of the detector For each combination, an equivalent blur function is calculated as a two-dimensional image, a deconvolution function of the equivalent blur function is calculated, and a combination of a scan trajectory, an X-ray image frame, and a detector pixel position is calculated using the deconvolution function. It is determined for each .
An X-ray diagnostic apparatus according to an embodiment is an imaging in which X-rays are exposed to a subject while moving an X-ray tube along a predetermined scan trajectory, and X-rays incident on a detection surface of a detector are detected. A unit, an image generation unit that generates an X-ray image for each frame based on the detected X-rays, and a different filter coefficient for each scan trajectory, each frame of the X-ray image, and each pixel position of the detector Determining a filter coefficient for each pixel of each X-ray image based on a combination of the storage unit, the predetermined scan trajectory, the frame of the X-ray image, and the pixel position of the detector, and using the determined filter coefficient A filter processing unit that performs a filtering process on the X-ray image for each frame, and a tomographic image generation unit that generates a tomographic image using the X-ray image for each frame after the filtering process. And Bei, filter coefficients said storage unit stores a scan trajectory, and calculates the frame of the X-ray image, an equivalent blurring function for each combination of pixel positions of the detector as a two-dimensional image, deconvolution function of the equivalent blurring function And is determined for each combination of scan trajectory, X-ray image frame, and detector pixel position using the deconvolution function .
A filter coefficient calculation program according to an embodiment is used for digital tomography reconstruction processing in an X-ray diagnostic apparatus, and the computer stores each scan trajectory, each frame of an X-ray image, and each pixel of a detector. A function for calculating an equivalent blur function as a two-dimensional image for each combination of positions, a function for calculating a deconvolution function of the equivalent blur function, a scan trajectory, a frame of an X-ray image, and detection using the deconvolution function And a function of calculating a filter coefficient for each combination of pixel positions of the unit.
A filter coefficient calculation program according to another embodiment is used for reconstruction processing of digital tomography in an X-ray diagnostic apparatus, and performs back projection, projection, back projection, and double integration of projection on a computer. A function for calculating an equivalent blur function, a function for calculating a deconvolution function of the equivalent blur function, and a scan trajectory, an X-ray image frame, and an X-ray image on each X-ray image using the deconvolution function. And a function of calculating filter coefficients for each combination of pixel positions.

  As described above, according to the present invention, in digital tomography, an X-ray diagnostic apparatus, an image processing apparatus, and an X-ray diagnostic apparatus that can generate and display a high-quality formed image at high speed are used. A coefficient calculation program can be realized.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following description, components having substantially the same function and configuration are denoted by the same reference numerals, and redundant description will be given only when necessary.

  In the present embodiment, for the sake of specific explanation, the case where the technical idea of the present invention is applied to an X-ray diagnostic apparatus for mammography is taken as an example. However, the present invention is not limited to mammography, and can be applied to any apparatus that performs reconstruction processing such as digital tomography using X-rays (for example, an X-ray diagnostic apparatus for cardiovascular imaging or the like). .

  FIG. 1 is an external view of a mammography X-ray diagnostic apparatus 1 according to this embodiment. As shown in the figure, the mammography X-ray diagnostic apparatus 1 includes an arm unit 10 and a column unit 35. The arm unit 10 is provided with an X-ray source device 13, a flat detector 20, a compression plate 15, and the like that are installed facing each other. Further, the support column 35 is provided with a display panel 27a, foot pedals 31a and 31b, a touch panel 31c, and the like. The arm unit 10 can move up and down with respect to the support column 35 and rotate around the shaft 36. The arm unit 10 is set in a predetermined posture, and the breast is fixed by the detection surface of the flat detector 20 and the compression plate 15, so that an image can be acquired by a desired scan orbit when performing, for example, tomosynthesis (tomography). it can.

  FIG. 2 shows a block diagram of the mammography X-ray diagnostic apparatus 1. As shown in the figure, the mammography X-ray diagnostic apparatus 1 includes an X-ray control unit 11, an X-ray source device 13, a compression plate 15, a compression plate control unit 16, a compression plate drive unit 17, and a flat detector 20. , A memory 21, a preprocessing unit 23, an imaging processing unit 24, an image processing unit 25, a D / A converter 26, a display unit 27, a central control unit 30, an operation unit 31, and a storage unit 33.

  The X-ray control unit 11 controls the X-ray source device 13 according to an instruction from the central control unit 30 so that X-rays with a predetermined intensity are emitted at a predetermined rate.

  The X-ray source device 13 includes an X-ray tube 130 and an X-ray irradiation field limiter 131. The X-ray tube 130 is a vacuum tube that generates X-rays, and generates X-rays by accelerating electrons with a high voltage from a high-voltage generator and colliding with a target. The X-ray irradiation field limiter 131 shapes X-rays emitted from the X-ray tube 130 into a predetermined shape.

  The compression plate 15 is for fixing the breast flat and thin at the time of imaging by sandwiching and compressing the breast with the detection surface of the flat detector 20. By compressing and fixing the breast in this way (positioning), the scattered X-rays from the subject can be reduced, the overlap of mammary tissue can be reduced, image contrast can be improved, and noise caused by body movement can be prevented. Reduction of the irradiation dose can be realized.

  The compression plate control unit 16 controls the compression plate drive unit 17 in order to position the compression plate 15 based on an instruction from the central control unit 30. Further, the compression plate control unit 16 measures the compression thickness (thickness at the time of compression) of the fixed breast and sends it to the central control unit 30.

  The compression plate drive unit 17 drives the compression plate 15 under the control of the compression plate control unit 16.

  The flat detector 20 has a scintillator and a photodiode array, generates electron holes by applying X-rays transmitted through the subject to the photoelectric film, accumulates them in a semiconductor switch, and reads them as electrical signals. X-rays are detected by converting them into electrical signals. The conversion method may be direct conversion from X-rays to electric signals or indirect conversion from X-rays to electric signals via light.

  The memory 21 temporarily stores the digital signal supplied from the flat detector 20.

  The preprocessing unit 23 reads a digital signal from the memory 21 for each frame to generate a digital X-ray image, and executes preprocessing (for example, offset correction processing, blood vessel pixel correction processing, logarithmic calculation processing, etc.). In this pre-processing, pixel bundling may be performed as necessary.

  The imaging processing unit 24 executes imaging processing by the Projection Inversion method described later under the control of the central control unit 30.

  The image processing unit 25 performs predetermined image processing such as subtraction processing as necessary using an X-ray image for each frame.

  The D / A converter 26 converts the digital signal sequence of the image data input from the image processing unit 23 into an analog signal sequence.

  The display unit 27 displays a CRT, a plasma display, a liquid crystal display, etc. for displaying an X-ray image such as a digital tomographic image obtained by tomosynthesis based on a signal received from the D / A converter 26, and a display indicating an operation state of the apparatus. A panel 27a is provided. In addition, the display unit 27 displays a screen for inputting conditions relating to the scan trajectory in an imaging process based on the Projection Inversion method described later.

  The central control unit 30 is a central processing unit that performs control related to collection of image data and control related to image processing and image reproduction processing of the collected image data. In particular, the central control unit 30 executes control related to image formation processing by the Projection Inversion method described later.

  The operation unit 31 is an input device provided with a keyboard, various switches, a mouse, foot pedals 31a and 31b, a touch panel 31c, etc., and inputs patient information (patient ID, examination site, examination purpose, etc.), and the upper and lower sides of the compression plate 15 It is used when inputting conditions related to movement, imaging instructions, pulse rate selection, image selection, scan trajectory, and the like.

  The storage unit 33 stores image data or the like before or after image processing by the image processing unit 23. In addition, the storage device 33 stores a dedicated program for realizing an imaging process by the Projection Inversion method described later. Further, the storage device 33 stores filter coefficients (that is, filter coefficients determined for each scan trajectory, each image frame, and each position on the image) used in the image formation processing by the Projection Inversion method.

(Image formation by Projection Inversion method)
Next, imaging processing by the Projection Inversion method (PI method) will be described. This process can be roughly classified into a filter coefficient calculation process and a tomographic image generation process. Hereinafter, each processing will be described.

[1. Tomographic image generation processing]
FIG. 3 is a diagram conceptually showing a flow of image forming processing by the PI method. As shown in the figure
The tomographic image generation process is a process of obtaining a tomographic image by capturing a plurality of X-ray images along a scan trajectory, pre-processing and filtering the X-ray image, and performing back projection. For the filtering, a filter coefficient calculated by a filter coefficient calculation process described later is used. This filter coefficient is different for each scan trajectory, each image frame, and each position on the image. Accordingly, an appropriate filter coefficient is selected according to the scan trajectory used for imaging. Further, the filter coefficient is selected so that different filter coefficients are applied to each of a plurality of X-ray images obtained by one imaging. Furthermore, since the filter coefficient varies depending on the detector pixel position of the X-ray image, different filter coefficients are configured to be applied depending on the detector pixel position.

The background theory of the imaging process by the PI method is as follows. That is, when the voxel data obtained as a result of the imaging process is expressed as y in vector format, the projection image is expressed as g in vector format, and the projection operation is expressed as Wy in matrix format, the projection process is expressed by the following equation (1). I can write.

  ε is noise added to the X-ray image. Here, each component of the projection image g is a logarithm of the ratio (attenuation ratio) of the X-ray dose attenuated by the absorption distribution of the subject.

According to minimum-mean-square-error estimation, it is known that the solution of equation (1) is obtained by the following equation (2).

g ^meas is a projection image obtained by photographing, and σ ² _y and σ ² _ε are a standard deviation of the pixel value of the volume and a standard deviation of noise included in the projection image, respectively. Here, a blur correction projection image x defined by the following equation (3) is introduced.

Then, equation (2) can be expressed by the following equation (4), and if x is known, a solution can be obtained by back projection calculation.

Note that W ^T x represents performing a back projection operation of x. From equation (3), x is the solution of equation (5) below.

X can be obtained by solving the simultaneous linear equations expressed by Equation (5), but since this equation is large, it cannot be solved by the direct method. However, a solution can be obtained by minimizing the objective function of the following equation (6) using an iterative method.

In order to minimize Equation (6), an optimization method such as a conjugate gradient method is used. To obtain the solution of equation (5) using a conjugate gradient method, it is sufficient to prepare a processing routine for calculating the Ax, A, W, without holding the large dimensions of the matrix, such as W ^T, calculated It can be executed.

If the solution of the equation (5) is obtained by the conjugate gradient method (this is the blur correction projection image x), the back projection is performed by the equation (4), and the tomographic image y ^est can be obtained.

  One important point of this method is that inversion is performed in the projection image area. That is, equation (5) is an image of the projection area on both the right and left sides. Matrix inversion tomosynthesis and many other repetitive releases are in contrast to inversion in tomographic areas.

  Equation (2) and equation (5) obtained by transforming equation (2) are equations for the optimum solution in digital tomography, and the method of solving this is the best in terms of image quality. In particular, there is a significant improvement in image quality compared with the simple backprojection method and the filtered backprojection method. However, the above method using the conjugate gradient method also requires a lot of processing time because several iterations are executed. That is, all the methods described above directly solve equation (5) in order to obtain the blur corrected X-ray image x. Therefore, there is a problem that it takes a lot of processing time to actually execute this.

  However, considering that equation (5) is a linear equation and both the right side and the left side are images of the projection region, there is a possibility that there is a filter operation for obtaining the blur-corrected X-ray image x. Filter operations can be executed much faster than solving equations. The inventors of the present invention derived the filter by a method described later and confirmed that there is a filter operation for solving the equation (5). In addition, it was confirmed that an equivalent tomographic image was obtained by the filter operation and that the processing time could be greatly reduced.

[2. Filter coefficient calculation process]
Next, the filter coefficient calculation process will be described. In this process, according to the scan trajectory at the time of X-ray image capturing (representing how many images are to be captured and at which focal position, detector position, and detector orientation each image is captured), An appropriate filter coefficient to be applied to the line image is calculated. This process can be performed after the X-ray image is captured, but can be performed before the X-ray image is captured if the scan trajectory to be captured is known in advance. In addition, if there are a small number of scan trajectories used for imaging, filter coefficients for the small number of scan trajectories are calculated in advance, and the filter coefficients calculated in advance are applied to the tomographic image generation processing after imaging. can do.

  The filter coefficient differs for each scan trajectory, but further, for each image frame (that is, for each condition such as focus position, detector position, detector orientation, etc.) or for each pixel position in the same image (for each region in the image). It depends on the situation. Accordingly, different filter coefficients are calculated for each scan trajectory, each image frame, and each pixel position (or detector pixel position) in the image.

FIG. 4 is a diagram conceptually showing a flow of calculation processing of filter coefficients used for image forming processing by the PI method. As shown in the figure, the seek blur correction X-ray image, once calculated the equivalent blurring function p _k of the projection area, then, constitutes a deconvolution filter response function p _k using FFT ( Two steps called inverse filter calculation are adopted.

[2-1. Equivalent blur function calculation process]
5 to 8 are diagrams showing a procedure for calculating the equivalent blur function. Assume that a detection pixel h in a certain frame of the projection image is 1 and all other pixels are 0 ((1) in FIG. 5). Since it is sufficient processing performed equivalent integral calculation in the subsequent need not produce an image set, for explanation, it is assumed that making such image set p _1. Further, this image is back-projected to the imaging region to obtain a back-projection image b ₁ ((2) in FIG. 5).

Then projection of the backprojection image _{b 1} for all frames, obtaining a projection image _{p 2} (in FIG. 6 (3)). Then, the projection image _{p 2} and back projection, to obtain a _{b 2} (in FIG. 7 (4)). The backprojection image b ₂ to the projection on one frame pixel h is present (in FIG. 8 (5)). An equivalent blur function pk is _obtained by extracting an image of an image region around the pixel h from one projection image obtained by the back projection.

Such a back projection-projection-back projection-projection operation can be expressed by a single double integral formula. Therefore, in terms of implementation, the equivalent blur function _ph is calculated by executing this double integration.

Integral equation of the equivalent blurring function p _h, said backprojection - projection - backprojection - obtained by the process of the projection will follow each step. Only the calculation method is shown here.

d _h is the coordinate value of the filter center, and d is the position where the value of the blur function on the detector is to be examined. The above integration is not an explicit expression for d, and d is a variable of s and t. However, in terms of numerical values, it can be calculated by the above equation, and d may be determined within a loop of t and s, and integration may be accumulated in that pixel.

L is a parameter (integration region size) representing the size of the integration region, and specifies the length of the subject thickness. t ₁ and t ₂ are obtained by the following (a3) and (a4) using L in the integration range of t.

r _st and r _sl are the coordinates of the focus at the projections k and l, respectively. r _k (s), r _l (s, t) are coordinates on the projection line of the projection k, l, and are expressed by the following equations (a5) and (a6) using s and t.

m _k (s) and m _l (s, t) are geometric enlargement ratios and are obtained in the following (a7).

θ _k and θ _l are angles formed by the projected straight line and the detection surface, and are obtained by the following (a8).

w _k and w _l are vectors representing the normal direction of the detector at the projections k and l. d is the intersection of the detection line and the projection line connecting the focal point of projection k and r _l (s, t). Using the geometric enlargement ratio m _k (s, t), it can be easily calculated as the following (a9).

[2-2. Inverse filter calculation process]
First, a vector _ak is obtained first. a _k is the vector only added value _{^{γ 2 = σ 2 ε / σ}} 2 y pixels corresponding to the pixel h equivalent blurring function _{p k.} Note that both a _k and p _k represent two-dimensional images.

Of blurring correction projection image x, when the image of the k-th frame as _{x k,} the image of the k-th frame of the captured projection images ^{g meas} and _g ^{k meas,} equation (5) This expression (8) It is expressed approximately as follows.

In the formula (8), because _{a k} and _g ^{k meas} that known by _{x k} is unknown, it is possible to determine the _{x k} by deconvolution operation. The deconvolution operation is executed using the FFT algorithm as shown in the following equation (9).

Note that F [. ] Represents a two-dimensional Fourier transform. The inverse filter to be obtained here is obtained by the following equation (10).

[2-3. Tomographic image generation processing by PI method]
If the filter coefficient f is used, the tomographic image generation processing is executed in two steps: filtering represented by Expression (11) and back projection represented by Expression (4).

[2-4. Characteristics of calculated filter coefficients]
FIG. 9 shows an example in which a filter coefficient at a certain pixel position in a certain frame is calculated and imaged. The filter coefficient calculated by such a projection inversion method has a value for each pixel position and generally has the following properties.

-It has a high value at the center pixel (pixel h).

A value close to 0 at a pixel far from the center pixel (pixel h).

A region having a negative value in two opposite directions from the central pixel (pixel h) extends radially.

  Note that the two opposing directions are directions of lines where the detection plane intersects with a plane including the projection straight line (line connecting the pixel h and the focal point) and the moving direction of the focal point shown in FIG. As shown in FIG. 10, such a filter coefficient is obtained for the method of calculating the equivalent blur function from the scan trajectory. In a region having a negative value, the absolute value has a large value at a point close to the central pixel, and a value close to 0 at a far point. Further, the above property is due to the use of an equivalent blur function calculated using the scan trajectory for the calculation of the filter coefficient. On the contrary, if it has the above properties, similar image quality can be obtained regardless of the exact configuration of filter coefficient calculation.

[2-5. Determination of filter coefficients in this mammography X-ray diagnostic apparatus]
The filter coefficient calculated by the method described above may be calculated every time a tomographic image is generated. However, from the viewpoint of reducing the turn around time at the time of tomographic image generation, the mammographic X-ray diagnostic apparatus 1 is provided for each scan trajectory and image frame calculated in advance using a computer or the like (for example, when the apparatus is shipped from the factory). The filter coefficient for each pixel position is stored in advance, and an appropriate filter coefficient is selected to execute tomographic image generation processing.

  The filter coefficient is selected using a filter coefficient identification character string. The filter coefficient identification character string is for specifying a scan trajectory identification character string, a scan trajectory calculation parameter, an image frame number, and a pixel position on the image.

  The scan trajectory identification character string includes the number of shots (number of frames), scan angle (for example, which angle range is shot in the case of arc scan), frame rate (how many X-ray images are shot per second) , Focus / detector moving speed, and SID (focus, distance between detectors). For example, the scan trajectory identification character string in the case of 41 shots, scan angle (arc scan angle range) 90 degrees, frame rate 0.5 [frame / sec], and SID 900 mm is “SP90_4frms_05dfps_sid900”.

  In this embodiment, for example, a scan trajectory identification character string is input and set by selecting a condition related to the scan trajectory from preset values using an interface described later. However, regardless of this, the scan trajectory identification character string may be set by directly inputting an arbitrary value or condition.

  The scan trajectory calculation parameter includes a parameter such as γ in equation (7) and the number of filter coefficient representative points taken in the x and y directions. Therefore, the scan trajectory identification character string is “SP90_41frms_05dfps_sid900”. For example, the filter coefficient of the second detector pixel position in the horizontal direction (for example, x direction) and the second direction (for example, y direction) in the second frame in FIG. The identification character string is defined as “SP90_41frms_05dfps_sid900 / gamma10_n5_3 / 2_3_2”. Note that “n5_3” in the character string indicates that five representative points in the horizontal direction and three representative points in the vertical direction are used for the filter calculation. The filter coefficient identification character string in this embodiment is expressed as “scan trajectory identification character string +“ / ”+ filter coefficient calculation parameter +“ / ”+ character string indicating the detector pixel position”.

  The imaging processing unit 24 reads out the filter coefficient corresponding to the set filter coefficient identification character string from the storage unit 33 and manages the file in association with each filter coefficient identification character string (for example, the filter coefficient identification character string is stored in the file). Save as part of the name)

(Operation)
Next, the operation in the imaging process by the PI method of the mammography X-ray diagnostic apparatus 1 will be described.

  FIG. 12 is a flowchart showing a flow of operations in imaging processing by the PI method of the mammography X-ray diagnostic apparatus 1. In the figure, first, before the image forming process is executed, a condition relating to a scan trajectory (that is, a condition for determining a scan trajectory identification character string), a scan trajectory calculation parameter, and other image forming process conditions ( The center position of the tomographic image, the size of the tomographic image (field of view), the pixel interval of the tomographic image, etc.) are input (step S1).

  FIG. 13 is a diagram illustrating an example of an interface for inputting conditions related to the scan trajectory. By selecting a desired value from the input screen as shown in the figure, a condition relating to the scan trajectory is input, and a scan trajectory identification character string corresponding to this is set. In the example of FIG. 13 (that is, when the number of shots is 72, the scan angle is 30 degrees, the frame rate is 7.2 [frame / sec], and SID is 700 mm), the scan trajectory identification character string is “SP90_72frms_7.2dfps_sid700”. It becomes a character string.

  Next, when an instruction to start imaging is given from the operation unit 31, the central control unit 30 executes imaging of the designated number of X-ray images along the designated scanning trajectory, and acquires a plurality of X-ray images. (Step S2).

  Next, the preprocessing unit 23 performs preprocessing on the plurality of captured X-ray images (step S3). This preprocessing includes offset correction, defective pixel correction processing, logarithmic calculation, and the like, and pixel bundling may be performed as necessary.

Next, the central control unit 30 generates a filter coefficient identification character string for each pixel of each image (step S4). The imaging processing unit 24 selects a filter coefficient using the generated filter coefficient identification character string, and executes a filtering process (step S5). That is, the imaging processing unit 24 acquires the total number of representative points for which the filter coefficient is calculated from the filter coefficient identification character string, and reads all the filter coefficients from the file. Thereafter, the imaging processing unit 24 performs the following processes (1) to (3) for all detector pixels (i, j) for every frame k. (1) Detector pixels ( It is determined to which representative point (I, J) i, j) is close (that is, the representative point position (I, J) is calculated from the filter coefficient identification character string).

(2) The related filter coefficient is specified by the filter coefficient identification character string (that is, the related filter coefficient is selected from the storage unit 33 based on the filter coefficient identification character string). In this case, the filter coefficient identification character string is defined as “scan trajectory identification character string +“ / ”+ filter coefficient calculation parameter +“ / ”+“ k_I_J ””.

(3) Filter by the filter coefficient associated with frame k and detector pixel (i, j).

  Next, a tomographic image is generated by executing a back projection process using the image that has been subjected to the filtering process (step S6). This back projection processing is executed based on the scan trajectory specified by the scan trajectory identification character string and other designated imaging processing conditions. The generated tomographic image is displayed on the display unit 27 and automatically stored in the storage unit 33 (step S7).

  The method described above is an example in which the filter coefficient at the representative point closest to each pixel of the detector is used, but there is also a method of determining the filter coefficient by interpolation. For example, four representative points close to the pixel (i, j) of the detector are selected. At that time, the representative points are selected so that (i, j) is included in the quadrilateral formed by the four representative points. From the filter coefficients of the four representative points, the filter coefficient at the pixel (i, j) is obtained by bilinear interpolation. In the case of bilinear interpolation, four representative points are selected. However, when other interpolation methods are used, the number of representative points to be selected is different.

  In addition, in the filter coefficient calculation process, the filter coefficient can be obtained by a filter coefficient sampling method in addition to the above-described method (direct configuration method). The filter coefficient calculation process by this method is performed as follows. First, a pseudo projection image is generated. This projection image is a projection image when it is assumed that dots and strips exist in the imaging region (FIG. 14A). The projected images of the points are arranged in a lattice pattern, and the projected image of the strip is inserted into the gap. The direction of the strip is the direction in which the strip is projected as a point on one of all 11 projection images. Based on this projection image, a solution x of Equation (5) is obtained by a method of repetitively solving using Equation (6) or another method. As a result, a response to the point projection image is obtained (FIG. 14B). The filter coefficient sampling method collects this response as a filter coefficient. By filtering the projection image using the obtained filter coefficient and performing back projection, processing equivalent to the iterative method according to Equation (6) can be executed at higher speed.

  According to the configuration described above, the following effects can be obtained.

  In this X-ray diagnostic apparatus, a pre-processing is applied to each image obtained by imaging a plurality of X-ray images at a plurality of X-ray tube positions, and then a tomographic image is generated by performing filtering processing and back projection. At this time, the filter coefficient used for filtering is determined according to each scan trajectory, each image frame, and each pixel position in the image, and this filter coefficient is determined optimally in digital tomography. This corresponds to finding a solution equation (see equations (2) and (5)) by a filter operation. Therefore, it is possible to generate a tomographic image with higher image quality than in the past, and to contribute to improving the quality of diagnosis.

  In addition, the X-ray diagnostic apparatus does not require repetitive calculation processing in tomographic image generation, stores a previously calculated filter coefficient, and uses a filter coefficient identification character string from the filter coefficient for each pixel position. Since the filter coefficient is selected, it is not necessary to calculate the filter coefficient from the beginning each time a tomographic image is generated. Accordingly, it is possible to shorten the Turn around time from imaging to tomographic image display while realizing a tomographic image with high image quality.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. Specific examples of modifications are as follows.

  Each function according to the present embodiment can also be realized by installing a program for executing the processing in a computer such as a workstation and developing the program on a memory. At this time, a program capable of causing the computer to execute the technique is stored in a recording medium such as a magnetic disk (floppy (registered trademark) disk, hard disk, etc.), an optical disk (CD-ROM, DVD, etc.), or a semiconductor memory. It can also be distributed.

  In addition, various inventions can be formed by appropriately combining a plurality of components disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

  As described above, according to the present invention, in digital tomography, a filter used for imaging processing in an X-ray diagnostic apparatus, an image processing apparatus, an X-ray diagnostic apparatus, and the like capable of generating and displaying a high-quality formed image at high speed. A coefficient calculation program can be realized.

FIG. 1 is an external view of a mammography X-ray diagnostic apparatus 1 according to this embodiment. FIG. 2 is a block diagram of the mammography X-ray diagnostic apparatus 1 according to this embodiment. FIG. 3 is a diagram conceptually showing a flow of image forming processing by the PI method. FIG. 4 is a diagram conceptually showing a flow of calculation processing of filter coefficients used for image forming processing by the PI method. FIG. 5 is a diagram illustrating a procedure for calculating an equivalent blur function. FIG. 6 is a diagram illustrating a procedure for calculating an equivalent blur function. FIG. 7 is a diagram illustrating a procedure for calculating an equivalent blur function. FIG. 8 is a diagram illustrating a procedure for calculating an equivalent blur function. FIG. 9 shows an example in which a filter coefficient at a certain pixel position in a certain frame is calculated and imaged. FIG. 10 is a diagram for explaining the characteristics of the filter coefficient obtained by the filter coefficient calculated by the projection inversion method. FIG. 11 is a diagram for explaining a method of generating a filter coefficient identification character string. FIG. 12 is a flowchart showing a flow of operations in imaging processing by the PI method of the mammography X-ray diagnostic apparatus 1. FIG. 13 is a diagram illustrating an example of an interface for inputting conditions related to the scan trajectory. FIGS. 14A and 14B are diagrams for explaining the filter coefficient sampling method. FIG. 15 is a diagram for explaining the prior art.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Mammography X-ray diagnostic apparatus, 10 ... Arm part, 11 ... X-ray control part, 13 ... X-ray source apparatus, 15 ... Compression plate, 16 ... Compression plate control part, 17 ... Compression plate drive part, 20 ... Flat detector, 21 ... Memory, 23 ... Pre-processing unit, 24 ... Imaging processing unit, 25 ... Image processing unit, 26 ... D / A converter, 27 ... Display unit, 30 ... Central control unit, 31 ... Operation unit 33 ... Storage part, 35 ... Supporting part, 36 ... Axis

Claims (16)

An X-ray image for each frame acquired by irradiating the subject with X-rays while moving the X-ray tube along a predetermined scan trajectory, a scan trajectory, an X-ray image frame, and a detector pixel A storage unit for storing different filter coefficients for each position;
A filter coefficient for each pixel of each X-ray image is determined based on a combination of the predetermined scan trajectory, the X-ray image frame, and the pixel position of the detector, and the determined filter coefficient is used for each frame. A filter processing unit for performing filter processing on the X-ray image;
A tomographic image generation unit that generates a tomographic image using the X-ray image for each frame after the filter processing;
The filter coefficient stored in the storage unit is calculated as an equivalent blur function as a two-dimensional image for each combination of scan trajectory, X-ray image frame, and pixel position of the detector,
Calculate a deconvolution function of the equivalent blur function,
Using the deconvolution function, determined for each combination of scan trajectory, X-ray image frame, detector pixel position,
An image processing apparatus.   The image processing apparatus according to claim 1, further comprising an input unit for inputting the predetermined scan trajectory. The image processing apparatus according to claim 1, wherein the filter processing unit uses an interpolation process for a filter coefficient of each pixel.   Each of the filter coefficients has a high value in the central pixel, a value close to 0 in the pixel far from the central pixel, and regions having negative values in two opposite directions determined with the central pixel as a reference extend radially. The image processing apparatus according to claim 1, wherein the image processing apparatus is one.   The image processing apparatus according to claim 4, wherein the two opposite directions are directions of a line where a plane including a projection straight line and a moving direction of a focal point intersects with a detection surface.   5. The image processing apparatus according to claim 4, wherein in the area having the negative value, the absolute value has a large value at a point close to the central pixel, and a value close to 0 at a far point. An imaging unit that emits X-rays to the subject while moving the X-ray tube along a predetermined scan trajectory, and detects X-rays incident on the detection surface of the detector;
An image generation unit for generating an X-ray image for each frame based on the detected X-ray;
A storage unit that stores different filter coefficients for each scan trajectory, each frame of the X-ray image, and each pixel position of the detector;
A filter coefficient for each pixel of each X-ray image is determined based on a combination of the predetermined scan trajectory, the X-ray image frame, and the pixel position of the detector, and the determined filter coefficient is used for each frame. A filter processing unit for performing filter processing on the X-ray image;
A tomographic image generation unit that generates a tomographic image using the X-ray image for each frame after the filter processing;
The filter coefficient stored in the storage unit is calculated as an equivalent blur function as a two-dimensional image for each combination of scan trajectory, X-ray image frame, and pixel position of the detector,
Calculate a deconvolution function of the equivalent blur function,
Using the deconvolution function, determined for each combination of scan trajectory, X-ray image frame, detector pixel position,
X-ray diagnostic apparatus characterized by the above.   The X-ray diagnostic apparatus according to claim 7, further comprising an input unit for inputting the predetermined scan trajectory. 9. The X-ray diagnosis apparatus according to claim 7, wherein the filter processing unit uses an interpolation process for a filter coefficient of each pixel.   Each of the filter coefficients has a high value in the central pixel, a value close to 0 in the pixel far from the central pixel, and regions having negative values in two opposite directions determined with the central pixel as a reference extend radially. The X-ray diagnostic apparatus according to claim 7, wherein the X-ray diagnostic apparatus is a device.   The X-ray diagnostic apparatus according to claim 10, wherein the two opposite directions are directions of a line where a plane including a projection straight line and a moving direction of a focal point and a detection surface intersect.   The X-ray diagnostic apparatus according to claim 10, wherein in the region having a negative value, the absolute value has a large value at a point close to the central pixel, and a value close to 0 at a far point. A filter coefficient calculation program used for digital tomography reconstruction processing in an X-ray diagnostic apparatus,
On the computer,
A function for calculating an equivalent blur function as a two-dimensional image for each scan trajectory, for each frame of the X-ray image, and for each combination of pixel positions of the detector;
A function for calculating a deconvolution function of the equivalent blur function;
A function for calculating a filter coefficient for each combination of scan trajectory, X-ray image frame, and pixel position of the detector using the deconvolution function;
A filter coefficient calculation program characterized by realizing the above.   14. The filter coefficient calculation program according to claim 13, further causing a function of causing the computer to store the calculated filter coefficient in association with scan trajectory information in a storage unit. A filter coefficient calculation program used for digital tomography reconstruction processing in an X-ray diagnostic apparatus,
On the computer,
A function to calculate an equivalent blur function by performing back projection, projection, back projection, double integration of projection,
A function for calculating a deconvolution function of the equivalent blur function;
A function of calculating a filter coefficient for each combination of scan trajectory, X-ray image frame, and pixel position on each X-ray image using the deconvolution function;
A filter coefficient calculation program characterized by realizing the above.   16. The filter coefficient calculation program according to claim 15, further comprising a function of causing the storage unit to store the calculated filter coefficient in association with scan trajectory information.

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