JP7179479B2 - X-ray CT device - Google Patents

Description

An embodiment of the present invention relates to an X-ray CT apparatus.

Photon-counting CT (Computed Tomography) counts individual incident X-rays and measures the energy of the incident X-rays to obtain an X-ray spectrum after passing through the subject. Photon counting CT then identifies the substances contained in the subject from the obtained spectrum and generates an image of each substance.

Moreover, in recent years, there is an increasing need to reduce the amount of radiation exposure in examinations. Photon-counting CT has no contribution from circuit noise compared to conventional integral CT, so it is possible to reconstruct a high-quality image. For this reason, photon counting CT is expected to reduce the dose required to obtain an image of image quality equivalent to that of conventional integral CT. In this way, commercialization of photon counting CT is required also from the viewpoint of reducing the amount of radiation exposure.

U.S. Patent Application Publication No. 2015/178958

A problem to be solved by the present invention is to provide an X-ray CT apparatus having high material discrimination ability.

An X-ray CT apparatus according to an embodiment includes a photon counting detector, a first generator, a second generator, and a reconstruction processor. A photon-counting detector consists of a plurality of detection elements and outputs a signal corresponding to the number of photons counted. The first generation unit generates first projection data based on signals from the plurality of detection elements. The second generation unit generates second projection data by bundling the first projection data from the predetermined number of detection elements in spatial units. The reconstruction processing unit reconstructs a material discrimination image based on the second projection data.

FIG. 1 is a diagram showing a configuration example of an X-ray CT apparatus according to the first embodiment. FIG. 2A is a diagram for explaining a sinogram according to the first embodiment; FIG. 2B is a diagram for explaining a sinogram according to the first embodiment; FIG. 3 is a flow chart showing processing procedures by the X-ray CT apparatus according to the first embodiment. FIG. 4A is a diagram for explaining the first embodiment. FIG. 4B is a diagram for explaining the first embodiment. FIG. 4C is a diagram for explaining the first embodiment. FIG. 5 is a diagram for explaining the first embodiment. FIG. 6 is a diagram for explaining a comparative example. FIG. 7 is a diagram for explaining the first embodiment. FIG. 8 is a diagram for explaining a modification of the first embodiment. FIG. 9 is a diagram for explaining the second embodiment. FIG. 10 is a diagram for explaining an example of processing when the number of photons is less than the second threshold. FIG. 11 is a flowchart showing an example flow of second projection data generation processing according to the modification.

An X-ray CT apparatus according to an embodiment will be described below with reference to the drawings.

An X-ray CT (Computed Tomography) apparatus described in the following embodiments is an apparatus capable of performing photon counting CT. That is, the X-ray CT apparatus described in the following embodiments counts the X-rays transmitted through the subject using a photon counting type detector instead of a conventional integrating type (current mode measurement type) detector. Thus, the apparatus can reconstruct X-ray CT image data with a high SN ratio. In principle, the contents described in one embodiment are similarly applied to other embodiments.

(First embodiment)
FIG. 1 is a diagram showing a configuration example of an X-ray CT apparatus according to the first embodiment. As shown in FIG. 1, the X-ray CT apparatus 1 according to the first embodiment has a gantry 10, a bed 20, and a console 30. As shown in FIG.

The gantry 10 is a device that irradiates the subject P with X-rays and collects data on the X-rays that have passed through the subject P, and includes an X-ray high-voltage device 11, an X-ray generator 12, and an X-ray detector. 13 , a data collection circuit 14 , a rotating frame 15 and a gantry controller 16 . Further, in the gantry 10, as shown in FIG. 1, an orthogonal coordinate system consisting of X, Y and Z axes is defined. That is, the X-axis indicates the horizontal direction, the Y-axis indicates the vertical direction, and the Z-axis indicates the rotation center axis direction of the rotating frame 15 when the gantry 10 is not tilted.

The rotating frame 15 supports the X-ray generator 12 and the X-ray detector 13 so as to face each other with the subject P interposed therebetween. It is an annular frame that rotates to

The X-ray generator 12 is a device that generates X-rays and irradiates the subject P with the generated X-rays. The X-ray generator 12 has an X-ray tube 12a, a wedge 12b, and a collimator 12c.

The X-ray tube 12 a is a vacuum tube that receives a high voltage supply from the X-ray high voltage device 11 and irradiates thermal electrons from a cathode (sometimes called a filament) toward an anode (target). rotates, the subject P is irradiated with the X-ray beam. That is, the X-ray tube 12a uses the high voltage supplied from the X-ray high voltage device 11 to generate X-rays.

Also, the X-ray tube 12a generates an X-ray beam that spreads with a fan angle and a cone angle. For example, under the control of the X-ray high-voltage device 11, the X-ray tube 12a continuously emits X-rays all around the subject P for full reconstruction, or half-reconfigurable exposure for half-reconstruction. It is possible to continuously irradiate X-rays in an irradiation range (180 degrees + fan angle). Further, the X-ray tube 12a can intermittently emit X-rays (pulse X-rays) at a preset position (tube position) under the control of the X-ray high-voltage device 11 . The X-ray high-voltage device 11 can also modulate the intensity of X-rays emitted from the X-ray tube 12a. For example, the X-ray high-voltage device 11 increases the intensity of the X-rays emitted from the X-ray tube 12a at a specific tube position, and increases the intensity of the X-rays emitted from the X-ray tube 12a at ranges other than the specific tube position. Decrease the intensity of the emitted X-rays.

The wedge 12b is an X-ray filter for adjusting the dose of X-rays emitted from the X-ray tube 12a. Specifically, the wedge 12b transmits X-rays emitted from the X-ray tube 12a so that the X-rays emitted from the X-ray tube 12a to the subject P have a predetermined distribution. It is an attenuating filter. For example, the wedge 12b is a filter made of aluminum so as to have a predetermined target angle and a predetermined thickness. A wedge is also called a wedge filter or a bow-tie filter.

The collimator 12c is made of a lead plate or the like and has a slit in part. For example, the collimator 12c narrows down the irradiation range of X-rays with the X-ray amount adjusted by the wedge 12b by a slit under the control of the X-ray high-voltage device 11, which will be described later.

Note that the X-ray source of the X-ray generator 12 is not limited to the X-ray tube 12a. For example, instead of the X-ray tube 12a, the X-ray generator 12 includes a focus coil for converging the electron beam generated from the electron gun and a deflection coil for electromagnetically deflecting the electron beam, which surrounds the half circumference of the subject P and collides with the deflected electron beam. and a target ring for generating X-rays.

The X-ray high-voltage device 11 is composed of electric circuits such as a transformer and a rectifier. It is composed of an X-ray control device for controlling the output voltage according to the X-rays to be emitted. The high voltage generator may be of a transformer type or an inverter type. For example, the X-ray high-voltage device 11 adjusts the X-ray dosage with which the subject P is irradiated by adjusting the tube voltage and tube current supplied to the X-ray tube 12a. Also, the X-ray high voltage device 11 is controlled by the processing circuit 37 of the console 30 .

The gantry control device 16 includes a processing circuit configured by a CPU (Central Processing Unit) and the like, and drive mechanisms such as motors and actuators. The gantry controller 16 has a function of receiving an input signal from an input interface 31 attached to the console 30 or an input interface attached to the gantry 10 and controlling the operation of the gantry 10 . For example, the gantry controller 16 receives an input signal and rotates the rotating frame 15 to control the rotation of the X-ray tube 12a and the X-ray detector 13 on a circular orbit centered on the subject P, Control for tilting the gantry 10 and control for operating the bed 20 and the top plate 22 are performed. The gantry controller 16 is controlled by the processing circuitry 37 of the console 30 .

The X-ray detector 13 is an example of a photon-counting detector which consists of a plurality of detection elements and outputs a signal corresponding to the number of photons counted. The X-ray detector 13 has, for example, a plurality of X-ray detection elements (also referred to as "sensors" or simply "detection elements") arranged in the channel direction along one circular arc with the focal point of the X-ray tube 12a as the center. It is composed of a plurality of X-ray detection element arrays. The X-ray detector 13 has a structure in which a plurality of X-ray detection element arrays each having a plurality of X-ray detection elements arranged in the channel direction are arranged in the slice direction. Each X-ray detection element of the X-ray detector 13 detects X-rays emitted from the X-ray generator 12 and passed through the subject P, and outputs an electrical signal (pulse) corresponding to the X-ray dose to the data collection circuit 14. output to The crest value of this electrical signal (pulse) has a correlation with the energy value of the X-ray photons. An electric signal output by each X-ray detection element is also called a detection signal.

The X-ray detector 13 is, for example, a direct conversion type detector composed of a grid and a semiconductor element array that converts incident X-rays into electrical signals. The semiconductor element array is composed of a plurality of compound semiconductors such as cadmium zinc telluride (CdZnTe) and cadmium telluride (CdTe), or single semiconductors such as silicon, and outputs electrical signals corresponding to the amount of incident X-rays. The grid is arranged on the surface of the semiconductor element array on the X-ray incident side and is composed of an X-ray shielding plate having a function of absorbing scattered X-rays. The X-ray detector 13 may be an indirect conversion type detector composed of a grid, a scintillator array, and a photosensor array. The scintillator array is composed of a plurality of scintillators, and the scintillators are composed of scintillator crystals that output a photon amount of light corresponding to the amount of incident X-rays. The grid is arranged on the surface of the scintillator array on the X-ray incident side and is composed of an X-ray shielding plate having a function of absorbing scattered X-rays. The photosensor array has a function of converting the amount of light from the scintillator into an electrical signal, and is composed of photosensors such as photomultiplier tubes. Here, the optical sensor is, for example, SiPM (Silicon photomultiplier).

The data acquisition circuit 14 (DAS: Data Acquisition System) includes an amplifier that amplifies the electrical signal output from each X-ray detection element of the X-ray detector 13, and an A/D that converts the electrical signal into a digital signal. and a D (Analog-to-Digital) converter, and generates detection data which is the result of counting processing using the detection signal of the X-ray detector 13 . Here, the detected data is, for example, a sinogram. A sinogram is data obtained by arranging the results of counting processing incident on each detection element at each position of the X-ray tube 12a. 2A and 2B are diagrams for explaining sinograms according to the first embodiment.

An example of a sinogram is shown in FIG. 2A. As shown in FIG. 2A, the sinogram is data obtained by arranging the results of the counting process in a two-dimensional orthogonal coordinate system having axes in the view direction and the channel direction. The data acquisition circuit 14 generates a sinogram, for example, for each row in the slice direction of the X-ray detector 13 .

FIG. 2B shows an example of the result of the counting process. As shown in FIG. 2B, the result of the counting process is data in which the number of X-ray photons is assigned to each energy bin. For example, the data acquisition circuit 14 counts photons (X-ray photons) derived from X-rays emitted from the X-ray tube 12a and transmitted through the subject P, discriminates the energy of the counted photons, and calculates the result of the counting process. and In the example of FIG. 2B, the energy is divided into 7 bins and the number of photons in each energy bin is shown. Note that the number of energy bins is not limited to seven, and can be set arbitrarily. The data collection circuit 14 transfers the generated detection data to the console 30 . Also, the data collection circuit 14 is an example of a first generator.

The data output from the data acquisition circuit 14 is referred to as detection data, and logarithmic conversion processing, offset correction processing, inter-channel sensitivity correction processing, inter-channel gain correction processing, pile-up correction processing, beam Data that has undergone preprocessing such as hardening correction is referred to as raw data. Further, detection data and raw data are collectively referred to as projection data.

The bed 20 is a device for placing and moving the subject P to be scanned, and includes a bed driving device 21 , a top plate 22 , a base 23 , and a base (support frame) 24 .

The top plate 22 is a plate on which the subject P is placed. A base 24 supports the top plate 22 . The base 23 is a housing that supports the base 24 so as to be vertically movable. The bed driving device 21 is a motor or actuator that moves the table 22 on which the subject P is placed in the longitudinal direction of the table 22 to move the subject P into the rotating frame 15 . The bed driving device 21 can also move the top plate 22 in the X-axis direction.

The method of moving the top plate may be to move only the top plate 22 or to move the entire base 24 of the bed 20 . Also, in the case of standing CT, a method of moving a patient moving mechanism corresponding to the top board 22 may be used.

Note that the gantry 10 performs helical scanning in which the subject P is spirally scanned by, for example, rotating the rotating frame 15 while moving the top plate 22 . Alternatively, after moving the tabletop 22, the gantry 10 rotates the rotation frame 15 while fixing the position of the subject P to perform conventional scanning in which the subject P is scanned in a circular orbit. In the following embodiment, it is assumed that the change in the relative position between the pedestal 10 and the top plate 22 is realized by controlling the top plate 22, but the embodiment is not limited to this. For example, if the gantry 10 is self-propelled, the relative position between the gantry 10 and the top plate 22 may be changed by controlling the movement of the gantry 10 . Further, by controlling the traveling of the gantry 10 and the top plate 22, the change in the relative position between the gantry 10 and the top plate 22 may be realized.

The console 30 is a device that receives an operator's operation of the X-ray CT apparatus 1 and reconstructs X-ray CT image data using the counting results collected by the gantry 10 . The console 30 has an input interface 31, a display 32, a memory circuit 35, and a processing circuit 37, as shown in FIG.

The input interface 31 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuit 37 . For example, the input interface 31 receives acquisition conditions for acquiring projection data, reconstruction conditions for reconstructing CT images, image processing conditions for generating post-processed images from CT images, and the like from the operator. . For example, the input interface 31 is implemented by a mouse, keyboard, trackball, switch, button, joystick, or the like. Also, the input interface 31 is an example of an input unit.

The display 32 displays various information. For example, the display 32 outputs a medical image (CT image) generated by the processing circuit 37, a GUI (Graphical User Interface) for accepting various operations from the operator, and the like. For example, the display 32 is configured by a liquid crystal display, a CRT (Cathode Ray Tube) display, or the like.

The storage circuit 35 is implemented by, for example, a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk, an optical disk, or the like. The storage circuit 35 stores projection data and reconstructed image data, for example.

The processing circuitry 37 executes, for example, a system control function 371, a second projection data generation function 372, a preprocessing function 373, a reconstruction processing function 374, an image processing function 375, a scan control function 376, and a display control function 377. . Here, for example, a system control function 371, a second projection data generation function 372, a preprocessing function 373, a reconstruction processing function 374, an image processing function 375, and a scan control function, which are components of the processing circuit 37 shown in FIG. Each processing function executed by the display control function 376 and the display control function 377 is recorded in the storage circuit 35 in the form of a computer-executable program. The processing circuit 37 is, for example, a processor, and reads each program from the storage circuit 35 and executes the read program to implement the function corresponding to the read program. In other words, the processing circuit 37 with each program read has each function shown in the processing circuit 37 of FIG.

The system control function 371 controls various functions of the processing circuit 37 based on input operations received from the operator via the input interface 31 .

The second projection data generation function 372 generates detection data by bundling detection data from a predetermined number of detection elements in spatial units. Hereinafter, for convenience of explanation, the detection data generated by the data acquisition circuit 14 will be referred to as “first projection data”, and the detection data bundled in spatial units by the second projection data generation function 372 will be referred to as “first projection data”. This is called "second projection data". Details of the second projection data generation function 372 will be described later. Also, the second projection data generation function 372 is an example of a second generation unit.

The preprocessing function 373 performs logarithmic transformation processing, offset correction processing, inter-channel sensitivity correction processing, inter-channel gain correction processing, pile-up Preprocessing such as correction processing and beam hardening correction is performed to generate raw data. The preprocessing function 373 performs logarithmic conversion processing, offset correction processing, inter-channel sensitivity correction processing, inter-channel gain correction processing, and Preprocessing such as pile-up correction processing and beam hardening correction is performed to generate raw data. Raw data generated by preprocessing the first projection data is referred to as first raw data, and raw data generated by preprocessing the second projection data is referred to as second raw data.

A reconstruction processing function 374 performs reconstruction processing using a filtered back projection method, an iterative reconstruction method, or the like on the projection data generated by the preprocessing function 373 to generate CT image data. The reconstruction processing function 374 stores the reconstructed CT image data in the storage circuit 35 . Note that CT image data reconstructed from data containing all energy information by adding information of all bins for each pixel is also called a "base image".

Here, the projection data generated from the counting results obtained by the photon counting CT contains information on the energy of X-rays attenuated by passing through the subject P. FIG. Therefore, the reconstruction processing function 374 can, for example, reconstruct X-ray CT image data of a specific energy component. Also, the reconstruction processing function 374 can, for example, reconstruct X-ray CT image data of each of a plurality of energy components.

The reconstruction processing function 374 also assigns, for example, a color tone according to the energy component to each pixel of the X-ray CT image data of each energy component, and superimposes a plurality of X-ray CT image data color-coded according to the energy component. generated image data. In addition, the reconstruction processing function 374 can generate image data that enables identification of the substance, for example, using the substance-specific K absorption edge. Other image data generated by the reconstruction processing function 374 include monochromatic X-ray image data, density image data, effective atomic number image data, and the like.

Also, as an application of X-ray CT, there is a technique of discriminating the types, abundances, densities, etc. of substances contained in a subject by utilizing the fact that the absorption characteristics of X-rays differ from substance to substance. This is called material discrimination. For example, the reconstruction processing function 374 performs material discrimination on the projection data to obtain material discrimination information. Then, the reconstruction processing function 374 reconstructs the material discrimination image using the material discrimination information that is the result of the material discrimination.

The reconstruction processing function 374 can apply full-scan reconstruction methods and half-scan reconstruction methods to reconstruct CT images. For example, the reconstruction processing function 374 requires projection data for 360 degrees around the subject in the full-scan reconstruction method. Further, the reconstruction processing function 374 requires projection data for 180°+fan angle in the half-scan reconstruction method. To simplify the explanation, the reconstruction processing function 374 uses a full-scan reconstruction method for reconstruction using projection data for 360° around the object.

The image processing function 375 converts the CT image data generated by the reconstruction processing function 374 into a tomographic image of an arbitrary cross section or a rendering process by a known method based on an input operation received from the operator via the input interface 31. Convert to image data such as a three-dimensional image. The image processing function 375 stores the converted image data in the storage circuit 35 .

A scan control function 376 controls CT scans performed on the gantry 10 . For example, the scan control function 376 controls the operations of the X-ray high-voltage device 11, the X-ray detector 13, the gantry controller 16, the data acquisition circuit 14, and the bed driving device 21, thereby starting scanning in the gantry 10, Controls the execution of scans and the end of scans. Specifically, the scan control function 376 controls acquisition processing of projection data in imaging for acquiring positioning images (scano images) and main imaging (scanning) for acquiring images used for diagnosis.

The display control function 377 controls various image data stored in the storage circuit 35 to be displayed on the display 32 .

The configuration of the X-ray CT apparatus 1 according to the first embodiment has been described above. With this configuration, the X-ray CT apparatus 1 according to the first embodiment performs material discrimination on projection data, and reconstructs a material-discriminating image using the result of the material discrimination. By the way, in order to perform material discrimination, it is necessary to accurately detect minute differences in spectra. For this reason, when performing material discrimination, imaging with a higher dose is required than when generating a base image. However, if imaging is performed with a dose higher than that for generating the base image in order to perform material discrimination, the amount of exposure to the subject increases. Since such an increase in exposure dose to the subject is clinically unacceptable, it is not always desirable to carry out imaging at a high dose just for material discrimination in clinical practice. As a result, even when material discrimination is performed, imaging is performed with the same dose as when generating the base image, and there is a problem that the accuracy of material discrimination is lowered.

For this reason, the X-ray CT apparatus 1 according to the first embodiment executes the following material discrimination processing in order to achieve high material discrimination performance. That is, the X-ray CT apparatus 1 bundles the first projection data from the predetermined number of detection elements in spatial units to generate the second projection data. Then, the X-ray CT apparatus 1 reconstructs a material discrimination image based on the second projection data.

FIG. 3 is a flow chart showing a processing procedure by the X-ray CT apparatus 1 according to the first embodiment. FIG. 3 shows a flowchart for explaining the operation of the X-ray CT apparatus 1, and explains which step in the flowchart each component corresponds to.

Step S 1 is a step corresponding to the scan control function 376 . In this step, the scan control function 376 is realized by the processing circuit 37 calling a predetermined program corresponding to the scan control function 376 from the storage circuit 35 and executing it. At step S1, the scan control function 376 executes a scan.

Step S<b>2 is a step implemented by the data collection circuit 14 . At step S2, the data acquisition circuit 14 generates first projection data. For example, the data acquisition circuit 14 generates detection data, which is the result of counting processing using detection signals from the X-ray detector 13, as the first projection data. In other words, data acquisition circuit 14 generates the first projection data based on signals from the plurality of detector elements.

Step S<b>3 is a step corresponding to the reconstruction processing function 374 . In this step, the reconstruction processing function 374 is realized by the processing circuit 37 calling a predetermined program corresponding to the reconstruction processing function 374 from the storage circuit 35 and executing it. In step S3, the reconstruction processing function 374 reconstructs the base image. For example, reconstruction processing function 374 generates a base image based on the first projection data.

Step S<b>4 is a step corresponding to the display control function 377 . This is a step in which the display control function 377 is realized by the processing circuit 37 calling a predetermined program corresponding to the display control function 377 from the storage circuit 35 and executing it. In step S<b>4 , the display control function 377 displays the base image on the display 32 .

Steps S5 and S6 are steps started in the background of steps S3 and S4 following the start of step S3. Step S<b>5 is a step corresponding to the second projection data generation function 372 . This is a step in which the second projection data generation function 372 is realized by the processing circuit 37 calling a predetermined program corresponding to the second projection data generation function 372 from the storage circuit 35 and executing it. In step S5, the second projection data generation function 372 generates second projection data. Here, the second projection data generating function 372 starts the process of generating the second projection data when the reconstruction processing function 374 starts the process of reconstructing the base image. Alternatively, the second projection data generating function 372 may start the process of generating the second projection data upon receipt of the first projection data from the data acquisition circuit 14 . Then, for example, the second projection data generation function 372 generates second projection data by bundling the first projection data from the predetermined number of detection elements in spatial units.

4A, 4B, and 4C are diagrams for explaining the first embodiment. 4A and 4B show part of the detection elements of the X-ray detector 13. FIG. 4A and 4B illustrate the channel direction and column direction, respectively. For convenience of explanation, FIGS. 4A and 4B show a 14×8 detection element group in which 14 detection elements are arranged in the channel direction and 8 detection elements are arranged in the column direction. However, the arrangement pattern of the detection elements in the channel direction and the row direction in the detection element group according to the embodiment is not limited to this, and can be arbitrarily changed.

The second projection data generating function 372 adds, for example, the first projection data from a plurality of adjacent detection elements in the channel direction and column direction of the photon counting detector as bundles in spatial units.

As an example, the second projection data generation function 372 groups four detector elements of 2×2 pixels as shown in FIG. 4B in the 14×8 detector element group shown in FIG. 4A. That is, the second projection data generation function 372 bundles the 14×8 detection element groups in spatial units so that there are seven groups in the channel direction and four groups in the column direction.

More specifically, the second projection data generation function 372 bundles the detection element r1c1, detection element r1c2, detection element r2c1, and detection element r2c2 shown in FIG. 4A as one group. Similarly, the second projection data generation function 372 bundles the detector elements r1c3, r1c4, r2c3, and r2c4 shown in FIG. 4A into one group.

Then, the second projection data generating function 372 adds each first projection data in the bundled group. For example, the second projection data generation function 372 adds the first projection data from detector elements r1c1, r1c2, r2c1, and r2c2 shown in FIG. 4A. That is, the second projection data generation function 372 generates first projection data from the detection element r1c1, first projection data from the detection element r1c2, first projection data from the detection element r2c1, and Add the number of x-ray photons for each energy bin with the first projection data from r2c2. Processing for adding the number of X-ray photons for each energy bin in each first projection data will be described with reference to FIG. 4C.

On the left side of FIG. 4C, from top to bottom, the first projection data from the detection element r1c1, the first projection data from the detection element r1c2, the first projection data from the detection element r2c1, and the first projection data from the detection element r2c2. 1 projection data. In FIG. 4C, the first projection data shows the number of photons in each of seven energy bins. The second projection data generation function 372 adds the number of X-ray photons for each energy bin in each first projection data to generate the second projection data shown on the right side of FIG. 4C.

More specifically, in the first projection data, the number of photons from each detection element in each energy bin is added, when each energy bin is E1 to E7 in order from low energy to high energy. For example, the second projection data generating function 372 simply calculates the number of photons in the detector element r1c1, the number of photons in the detector element r1c2, the number of photons in the detector element r2c1, and the number of photons in the detector element r2c2 in the energy bin E1. to add. The second projection data generation function 372 similarly adds the number of photons for the energy bins E2 to E7 to generate the second projection data shown on the right side of FIG. 4C. As shown on the right side of FIG. 4C, the number of photons in each energy bin in the second projection data increases over the number of photons in each energy bin in the first projection data. As a result, the statistic increases in the second projection data. That is, the second projection data generation function 372 sacrifices spatial resolution (resolution) to increase the statistic. Note that the second projection data generation function 372 can obtain statistics by grouping 16 detection elements of 4×4 pixels, for example, when it is desired to further improve the material discrimination accuracy even at the expense of resolution. may be increased.

Return to FIG. Step S 6 is a step corresponding to the reconstruction processing function 374 . In this step, the reconstruction processing function 374 is realized by the processing circuit 37 calling a predetermined program corresponding to the reconstruction processing function 374 from the storage circuit 35 and executing it. In step S6, the reconstruction processing function 374 reconstructs a material discrimination image based on the second projection data. For example, the reconstruction processing function 374 performs material discrimination on the second projection data, and uses the result of the material discrimination to reconstruct a material discriminated image (first material discriminated image). For convenience of description, the reconstruction processing function 374 will be described below as performing material discrimination of water, calcium, and iodine.

Here, the reconstruction processing function 374 may use the material discrimination image reconstructed using the material discrimination result for the second projection data as the final material discrimination image. However, the second projection data is a bundle of the first projection data, and has reduced spatial resolution. Therefore, material discrimination information obtained as a result of material discrimination for the second projection data is material discrimination information with low resolution. For this reason, the reconstruction processing function 374 combines a high-resolution base image and a material discrimination image (the first material discrimination image described above) reconstructed from low-resolution material discrimination information, and finally A material-discriminating image (second material-discriminating image) may be generated. That is, the reconstruction processing function 374 further generates a base image based on the first projection data, and uses the base image and an image for each of the plurality of energy bins based on the second projection data to generate a second Generate an image for each energy bin with higher spatial resolution than the image for each of the plurality of energy bins based on the projection data of No. 2, and use the result of material discrimination of the generated image for each energy bin to obtain a second material Reconstruct the discrimination image. Note that the second material-discrimination image has higher spatial resolution than the above-described first material-discrimination image. Here, for example, the reconstruction processing function 374 uses hybrid reconstruction disclosed in Japanese Unexamined Patent Application Publication No. 2015-33581.

More specifically, the reconstruction processing function 374 reconstructs a first material discrimination image for each energy bin from the low resolution material discrimination information. The reconstruction processing function 374 then generates a final second material discrimination image by minimizing the objective functional between the first material discrimination image for each energy bin and the high-resolution base image. .

Note that when the number of pixels of the base image is 1024×1024 and the first projection data are bundled in units of 2×2 pixels, the number of pixels of the material discrimination image is 512×512, and the number of pixels of the material discrimination image is 512×512. It will be less than the number of pixels in the base image. For this reason, when returning the second projection data to the image information, a constant value is returned to each bundled pixel based on the number of photons after addition. may have the same number of pixels as Note that even if the bundled pixels are restored at a constant value, the spatial resolution of the material discrimination image is lower than that of the base image.

FIG. 5 is a diagram for explaining the first embodiment. FIG. 5 shows an example of a material discrimination image when three types of materials of material A (water), material B (calcium), and material C (iodine) are discriminated. The reconstruction processing function 374, as shown in FIG. 5, generates a material discrimination image in which different color tones are assigned to material A (IM1), material B (IM2), and material C (IM3).

Return to FIG. Step S7 is a step corresponding to the display control function 377. FIG. This is a step in which the display control function 377 is realized by the processing circuit 37 calling a predetermined program corresponding to the display control function 377 from the storage circuit 35 and executing it. In step S<b>7 , the display control function 377 displays the material discrimination image generated in step S<b>6 on the display 32 .

As described above, in the first embodiment, the X-ray CT apparatus 1 generates the second projection data by bundling the first projection data from the predetermined number of detection elements in spatial units. A material discrimination image is reconstructed based on the projection data of . Here, since the second projection data bundles the first projection data in spatial units, the statistic increases more than the first projection data. As described above, in the first embodiment, by using the second projection data whose statistical amount is larger than that of the first projection data, high material discrimination performance can be achieved. Here, the effect obtained by the first embodiment will be described as a comparative example in which a material discrimination image is generated without bundling the first projection data in spatial units. FIG. 6 is a diagram for explaining a comparative example, and FIG. 7 is a diagram for explaining the first embodiment.

FIG. 6 shows an example of the material discrimination image in the comparative example, and FIG. 7 shows an example of the material discrimination image in the first embodiment. 6 and 7 show material-discrimination images of bones (calcium) and blood vessels (iodine) contrasted with a contrast agent by material-discriminating calcium and iodine. In the comparative example, the first projection data from each detector element is used, and the statistic of the number of photons in each energy bin is small, so the material discrimination accuracy is low. Therefore, in the comparative example, calcium and iodine cannot be accurately discriminated. As a result, in the comparative example, for example, as shown in FIG. 6, a material discrimination image in which the same color tone is assigned to bone 61 and blood vessel 62 is generated.

On the other hand, in the example shown in FIG. 7, the first projection data from a predetermined number of detection elements are bundled in spatial units to generate the second projection data, and the statistic of the number of photons in each energy bin is As it increases, the accuracy of material discrimination improves. Therefore, in the first embodiment, it is possible to accurately perform substance discrimination between calcium and iodine. As a result, in the first embodiment, for example, as shown in FIG. 7, a material discrimination image is generated in which bones 71 and blood vessels 72 are assigned different color tones.

In the above-described embodiment, the reconstruction processing function 374 has explained the case where water, calcium, and iodine are subjected to substance discrimination, but the embodiment is not limited to this. For example, the reconstruction processing function 374 may further material discriminate substances other than water, calcium and iodine, or may discriminate substances for any combination of water, calcium and iodine. Furthermore, the reconstruction processing function 374 may accept selection of substances to be discriminated from the operator via the input interface 31 . In such a case, for example, in step S4 shown in FIG. 3, when the base image is displayed on the display 32, a list of substances that can be distinguished is further displayed to accept selection of a substance to be distinguished from the operator.

(Modification of the first embodiment)
In the above-described embodiment, the case of adding the first projection data from a plurality of detection elements adjacent to each other in the channel direction and the column direction of the photon counting detector as bundling in spatial units has been described. Embodiments are not so limited. For example, the second projection data generation function 372 generates first projection data from a plurality of detector elements adjacent in the channel direction of the photon counting detector and a plurality of detector elements adjacent in the column direction as bundles in spatial units. At least one of the first projection data from the detection elements may be added.

More specifically, the second projection data generation function 372 adds together the first projection data from a plurality of detector elements adjacent in the channel direction of the photon counting detector as bundles in spatial units. Alternatively, the second projection data generation function 372 adds the first projection data from a plurality of detector elements adjacent in the column direction of the photon counting detector as bundles in spatial units.

Also, in the above-described embodiment, the case of simple addition has been described, but the embodiment is not limited to this. For example, the second projection data generation function 372 may generate the second projection data by taking the moving average of the first projection data. FIG. 8 is a diagram for explaining a modification of the first embodiment.

FIG. 8 illustrates part of the detection elements of the X-ray detector 13 . Also, FIG. 8 shows the channel direction and the column direction, respectively. For convenience of explanation, FIG. 8 shows a 14×2 detection element group in which 14 detection elements are arranged in the channel direction and two detection elements are arranged in the column direction. However, the arrangement pattern of the detection elements in the channel direction and the row direction in the detection element group according to the embodiment is not limited to this, and can be arbitrarily changed.

In the example shown in FIG. 8, the second projection data generation function 372 generates first projection data from a plurality of detector elements adjacent in the channel direction and column direction of the photon counting detector as bundles in spatial units. A case of adding is explained. The second projection data generation function 372 groups four detector elements of 2×2 pixels in the 14×2 detector element group shown in FIG. Here, the second projection data generation function 372 performs moving average so that the first projection data from some detection elements overlap. That is, the second projection data generation function 372 bundles the 14×2 detection element groups in spatial units such that 13 groups are formed in the channel direction and one group is formed in the column direction.

More specifically, the second projection data generation function 372 adds the first projection data from the detection elements r1c1, r1c2, r2c1, and r2c2 shown in FIG. Subsequently, the second projection data generation function 372 adds the first projection data from the detection elements r1c2, r1c3, r2c2, and r2c3 shown in FIG. Then, the second projection data generation function 372 adds the respective first projection data from the detection element r1c3, detection element r1c4, detection element r2c3, and detection element r2c4 shown in FIG. In this way, the second projection data generation function 372 generates the second projection data by taking a moving average so that the first projection data from some detection elements overlap.

Note that the second projection data generation function 372 may generate the second projection data by weighting and adding the first projection data in units of a predetermined number of detector elements. For example, the second projection data generation function 372 performs weighted addition using a PSF (Point Spread Function).

(Second embodiment)
In the above-described embodiments, the first projection data from a plurality of detector elements adjacent in the channel direction of the photon counting detector and the first projection data from a plurality of detector elements adjacent in the column direction are bundled in spatial units. The case of adding at least one of the projection data of . In the second embodiment, a case will be described where the first projection data from each detection element at the same position in the photon counting detector is added in units of adjacent views as bundling in spatial units.

The configuration of the X-ray CT apparatus 1 according to the second embodiment is the same as the configuration of the X-ray CT apparatus 1 shown in FIG. 1, except that a part of the second projection data generation function 372 is different. be. Therefore, in the second embodiment, only functions executed by the second projection data generation function 372 will be described.

The second projection data generation function 372 adds the first projection data from each co-located detector element in the photon counting detector in units of adjacent views as a bundle in spatial units. FIG. 9 is a diagram for explaining the second embodiment.

FIG. 9 illustrates part of the detection elements of the X-ray detector 13 . The upper part of FIG. 9 shows the detection element group in the i-th view, and the lower part of FIG. 9 shows the detection element group in the (i+1)-th view. Also, FIG. 9 shows the channel direction and the column direction, respectively. For convenience of explanation, FIG. 9 shows a 14×4 detection element group in which 14 detection elements are arranged in the channel direction and 4 detection elements are arranged in the column direction. However, the arrangement pattern of the detection elements in the channel direction and the row direction in the detection element group according to the embodiment is not limited to this, and can be arbitrarily changed.

The second projection data generation function 372 bundles the detection element r1c1 in the i-th view and the detection element r1c1 in the (i+1)-th view as one group, as shown in FIG. Similarly, the second projection data generation function 372 bundles the detection element r1c2 in the i-th view and the detection element r1c2 in the (i+1)-th view as one group, as shown in FIG.

Then, the second projection data generating function 372 adds each first projection data in the bundled group. For example, the second projection data generation function 372 generates the first projection data from the detection element r1c1 in the i-th view shown in FIG. 9 and the first projection data from the detection element r1c1 in the i+1-th view. The number of x-ray photons for each energy bin is summed to generate second projection data. Here, the second projection data generation function 372 simply adds the number of photons from each detector element in each energy bin. Then, the reconstruction processing function 374 reconstructs the material discrimination image based on the second projection data.

As described above, in the second embodiment, the first projection data from each detection element at the same position in the photon counting detector is added in units of adjacent views as a bundle in spatial units. . As a result, according to the second embodiment, the statistic of the number of photons in each energy bin is increased, so the accuracy of material discrimination is improved.

In the second embodiment, the case of bundling the first projection data in units of adjacent views has been described, but the embodiment is not limited to this. For example, the second projection data generation function 372 uses both the method of bundling in spatial units described in the first embodiment and the method of bundling in spatial units described in the second embodiment. may More specifically, the second projection data generation function 372 may bundle in the channel direction and bundle the first projection data in units of adjacent views. Alternatively, the second projection data generation function 372 may bundle the first projection data in units of adjacent views while bundling in the column direction. Alternatively, the second projection data generation function 372 may bundle the first projection data in units of adjacent views while bundling in the channel direction and the column direction.

Also, the second projection data generating function 372 has explained the case of simple addition when bundling the first projection data, but the embodiment is not limited to this. For example, the second projection data generation function 372 may generate the second projection data by taking the moving average of the first projection data. Alternatively, the second projection data generation function 372 may generate the second projection data by weighted addition of the first projection data in units of a predetermined number of detector elements. For example, the second projection data generation function 372 adds weights so that the middle view is the heaviest when three views are weighted and added.

(Other embodiments)
Embodiments are not limited to the embodiments described above.

Further, in the above-described embodiment, the second projection data generation function 372 is triggered by the start of processing for reconstructing the base image by the reconstruction processing function 374 or reception of the first projection data from the data acquisition circuit 14 . , and the second projection data is generated, the embodiment is not limited to this. For example, the second projection data generation function 372 may generate the second projection data only if the statistics of the first projection data are less than the reference. More specifically, the second projection data generation function 372 generates second projection data when the number of photons is less than a threshold (first threshold) in all energy bins in the first projection data. You may make it Here, a common threshold may be set for all energy bins, or a different threshold may be set for each energy bin. Note that the second projection data generation function 372 performs the second projection when the number of energy bins in which the number of photons is less than the threshold is equal to or greater than a predetermined number, or when the number of photons in a specific energy bin is less than the threshold. Data may be generated. That is, the second projection data generation function 372 generates second projection data when the number of photons in the first projection data is less than the threshold.

Further, when the number of photons in the first projection data is less than a second threshold which is smaller than the first threshold, the second projection data generation function 372 sets the X photons belonging to one group as a bundling unit. The number of line detection elements may be increased. FIG. 10 is a diagram for explaining an example of processing when the number of photons is less than the second threshold. For example, as shown in FIG. 10, the second projection data generation function 372, when the number of photons in the first projection data is less than the first threshold and equal to or greater than the second threshold, A group of four detector elements is assumed. Further, when the number of photons in the first projection data is less than the second threshold, the second projection data generation function 372 groups 16 detection elements of 4×4 pixels. That is, the second projection data generation function 372 may change the bundling unit according to the number of photons in the first projection data.

In addition, there is a tendency that the number of photons derived from X-rays transmitted through a thin portion of the object tends to be larger than the number of photons derived from X-rays transmitted through a thick portion of the object. The amount of X-ray attenuation differs depending on the X-ray transmission position. Therefore, the second projection data generation function 372 uses the first projection data output from the X-ray detection element that detects X-rays that pass through a portion of the object through which the X-rays pass, the thickness of which is equal to or less than a threshold. may be bundled in spatial units to generate the second projection data. Alternatively, the second projection data generation function 372 detects X-rays that pass through a portion of the subject through which the X-rays pass, where the amount of X-ray attenuation predicted from the thickness of the subject is equal to or greater than a threshold. The second projection data may be created by spatially bundling only the first projection data output from the X-ray detection elements. A second projection data generation process according to such a modification will be described with reference to FIG.

FIG. 11 is a flowchart showing an example flow of second projection data generation processing according to the modification. Note that the second projection data generation process according to the modification is performed for each view. For example, as shown in FIG. 11, the second projection data generation function 372 acquires subject information indicating the body shape of the subject stored in the storage circuit 35 (step S20). The subject whose body type is indicated by the subject information acquired in step S20 is the subject from whom the first projection data to be processed has been collected. Then, the second projection data generation function 372 uses the subject information to derive the thickness of the portion of the subject through which X-rays pass (step S21).

Then, the second projection data generation function 372 selects an X-ray detection element that detects X-rays that pass through a portion whose derived thickness is equal to or greater than a threshold (third threshold) among the plurality of X-ray detection elements. Identify (step S22). Then, the second projection data generating function 372 generates second projection data by bundling only the first projection data output from the specified X-ray detection elements in spatial units (step S23). end the projection data generation process.

Through such second projection data generation processing, the second projection data is generated for each view based on the first projection data only from the X-ray detection elements assumed to have a small number of photons from the object information. be. Then, the reconstruction processing function 374 detects, for example, projection data for one round (for 360°) (second projection data for one round, and X-rays passing through a portion whose thickness is less than the threshold value). CT image data is reconstructed using the projection data for one round from the X-ray detection element which

In addition, the second projection data generation function 372 uses the projection data acquired when the positioning image is captured as the first projection data, and sets the number of photons to the threshold value in all energy bins in the first projection data. You may determine whether it is less than. Then, when the number of photons in all energy bins is less than the threshold, the second projection data generation function 372 converts the projection data collected in the actual imaging into spatial unit data into a second projection data. It may be generated as projection data.

Further, when the reconstruction processing function 374 obtains the material discrimination information, the target for which the material discrimination is performed may be projection data or image data.

In the above-described embodiment, the Rotate/Rotate-Type (third generation CT) X-ray CT apparatus 1 in which the X-ray tube 12a and the X-ray detector 13 are integrally rotated around the subject has been described. The form is not limited to this. For example, in the X-ray CT apparatus, in addition to the third-generation CT, an X-ray detector having a plurality of X-ray detection elements is dispersed and fixed in a ring shape, and only the X-ray tube rotates around the subject. There is Stationary/Rotate-Type (4th generation CT). The embodiments described above are also applicable to 4th generation CT. Also, the above-described embodiments are applicable to a hybrid type X-ray CT apparatus combining third generation CT and fourth generation CT.

In addition, the above-described embodiments can also be applied to a conventional single-tube type X-ray CT apparatus, and a so-called multi-tube CT apparatus in which a plurality of pairs of X-ray tubes and detectors are mounted on a rotating ring. type X-ray CT apparatus.

Further, in the above-described embodiment, the processing circuit 37 is described as performing a plurality of functions, but the embodiment is not limited to this. For example, multiple functions may be provided as independent circuits within console 30, each circuit performing its own function. For example, the second projection data generation function 372 executed by the processing circuit 37 may be provided as the second projection data generation circuit, and the second projection data generation circuit may execute the second projection data generation function. Further, the reconstruction processing function 374 executed by the processing circuit 37 may be provided as a reconstruction processing circuit, and the reconstruction processing circuit may execute the reconstruction processing function.

Also, in the above-described embodiment, the second projection data generation function 372 and the reconstruction processing function 374 are executed within the console 30, but the embodiment is not limited to this. For example, an external workstation may execute the second projection data generation function 372 and the reconstruction processing function 374 .

Also, the substance discrimination processing described in the above-described embodiments can also be realized by software. For example, the material discrimination processing is realized by causing a computer to execute a material discrimination program that defines the processing procedure described as being performed by the second projection data generation function 372 and the reconstruction processing function 374 in the above embodiment. be. This material discrimination program is stored in, for example, a hard disk or a semiconductor memory device, and read and executed by a processor such as a CPU or MPU. Also, this material discrimination program can be recorded on a computer-readable recording medium such as a CD-ROM (Compact Disc-Read Only Memory), MO (Magnetic Optical disk), DVD (Digital Versatile Disc) and distributed.

The term "processor" used in the above description is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an application specific integrated circuit (ASIC), a programmable logic device (e.g., Circuits such as Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), and Field Programmable Gate Array (FPGA)). A processor achieves its functions by reading and executing a program embedded in the circuitry of the processor. It should be noted that the program may be stored in the memory circuit of the console 30 instead of being embedded in the circuit of the processor. In this case, the processor implements its functions by reading and executing the program stored in the memory circuit. Note that each processor of the present embodiment is not limited to being configured as a single circuit for each processor, and may be configured as one processor by combining a plurality of independent circuits to realize its function. good. Furthermore, a plurality of components in FIG. 1 may be integrated into one processor to realize its functions.

In the description of the above embodiments, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the illustrated one, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured. Further, each processing function performed by each device may be implemented in whole or in part by a CPU and a program analyzed and executed by the CPU, or implemented as hardware based on wired logic.

Further, the control method described in the above embodiment can be realized by executing a prepared control program on a computer such as a personal computer or a workstation. This control program can be distributed via a network such as the Internet. In addition, this control program is recorded on a computer-readable recording medium such as a hard disk, flexible disk (FD), CD-ROM, MO, DVD, etc., and can be executed by being read from the recording medium by a computer.

According to at least one of the embodiments described above, it is possible to achieve high material discrimination performance.

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

REFERENCE SIGNS LIST 10 gantry 13 X-ray detector 14 data acquisition circuit 30 console 37 processing circuit 372 second projection data generation function 374 reconstruction processing function

Claims (10)

a photon counting detector that consists of a plurality of detection elements and outputs a signal corresponding to the number of photons counted;
a first generation unit that generates first projection data based on signals from the plurality of detection elements;
a second generation unit that generates second projection data by bundling the first projection data from the predetermined number of detection elements in spatial units;
a reconstruction processing unit that reconstructs a material discrimination image based on the second projection data;
with
The second generation unit generates the second projection data by adding the number of photons for each energy bin in each of the first projection data of the predetermined number of detection elements,
The reconstruction processing unit further generates a base image based on the first projection data, and uses the base image and an image for each of the plurality of energy bins based on the second projection data, generating an image for each of the energy bins having higher spatial resolution than the image for each of the plurality of energy bins based on the second projection data, and using a result of material discrimination of the generated image for each of the energy bins , an X-ray CT apparatus for reconstructing said material discrimination image . The second generation unit adds the first projection data from each detection element at the same position in the photon counting detector in units of adjacent views as the bundling in the spatial unit. The X-ray CT apparatus according to claim 1. 2. The second generation unit adds the first projection data from a plurality of the detection elements adjacent in the channel direction of the photon counting detector as a bundle in the spatial unit. 2. The X-ray CT apparatus according to 2 above. 2. The second generation unit adds the first projection data from a plurality of the detection elements adjacent in the column direction of the photon counting detector as the spatial unit bundling. 2. The X-ray CT apparatus according to 2 above. wherein the second generation unit adds the first projection data from a plurality of the detection elements adjacent in the channel direction and the row direction of the photon counting detector as the bundle in the spatial unit; 3. The X-ray CT apparatus according to Item 1 or 2. 6. The X-ray CT apparatus according to any one of claims 1 to 5, wherein said second generation unit generates second projection data by taking a moving average of said first projection data. 6. The second generation unit according to any one of claims 1 to 5, wherein the second generation unit generates the second projection data by weighted addition of the first projection data in units of a predetermined number of the detection elements. X-ray CT equipment. a photon counting detector that consists of a plurality of detection elements and outputs a signal corresponding to the number of photons counted;
a first generation unit that generates first projection data based on signals from the plurality of detection elements;
a second generation unit that generates second projection data by bundling the first projection data from the predetermined number of detection elements in spatial units;
a reconstruction processing unit that reconstructs a material discrimination image based on the second projection data;
with
When the number of photons in the first projection data is less than a threshold, the second generation unit adds the number of photons for each energy bin in each of the first projection data of the predetermined number of detection elements. to generate the second projection data . a photon counting detector that consists of a plurality of detection elements and outputs a signal corresponding to the number of photons counted;
a first generation unit that generates first projection data based on signals from the plurality of detection elements;
a second generation unit that generates second projection data by bundling the first projection data from the predetermined number of detection elements in spatial units;
a reconstruction processing unit that reconstructs a material discrimination image based on the second projection data;
with
The second generation unit generates the second projection data by adding the number of photons for each energy bin in each of the first projection data of the predetermined number of detection elements,
The X -ray CT apparatus, wherein the second generator changes a bundling unit according to the number of photons in the first projection data. a photon counting detector that consists of a plurality of detection elements and outputs a signal corresponding to the number of photons counted;
a first generation unit that generates first projection data based on signals from the plurality of detection elements;
a second generation unit that generates second projection data by bundling the first projection data from the predetermined number of detection elements in spatial units;
a reconstruction processing unit that reconstructs a material discrimination image based on the second projection data;
with
The second generation unit generates the second projection data by adding the number of photons for each energy bin in each of the first projection data of the predetermined number of detection elements,
The second generation unit uses subject information indicating the body shape of the subject to calculate an X-ray attenuation amount when passing through the subject for each of the plurality of detection elements. Of the elements, only the first projection data output from the detection elements that detect the X-rays passing through the portions where the calculated X-ray attenuation amount is equal to or greater than the threshold value are bundled in spatial units to generate the second projection data. X -ray CT equipment.

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