JP2023074285A - Medical image processing apparatus and x-ray diagnostic apparatus - Google Patents

Abstract

To improve accuracy of truncation correction.SOLUTION: A medical image processing apparatus according to an embodiment includes an acquisition unit and a generation unit. The acquiring unit acquires a plurality of projection data sets respectively acquired by irradiating a subject with X-rays from a plurality of angles. The generation unit generates synthesized projection data by synthesizing model projection data based on human body model data having an internal structure with corresponding projection data for each of the plurality of projection data sets.SELECTED DRAWING: Figure 1

Description

The embodiments disclosed in the specification and drawings relate to a medical image processing apparatus and an X-ray diagnostic apparatus.

Conventionally, an X-ray diagnostic apparatus has a CBCT (Cone-Beam Computed Tomography) function of collecting projection data at each angle while rotating a C-arm and reconstructing three-dimensional volume data from the collected projection data. In imaging using the CBCT function, truncation may occur in which the subject protrudes from the imaging field of view depending on the imaging geometry (the positional relationship between the subject, the X-ray tube, and the X-ray detector) and the size of the subject. When truncation occurs, artifacts (false signals generated near the edge), cupping effect in which the signal value at the edge is lower than the center in the axial cross section, and signal at the edge compared to the center in the axial cross section A high-value capping effect may occur, making diagnosis difficult.

Therefore, as a countermeasure against such truncation, truncation correction (correction for extrapolating pixel values outside the imaging field of view) is performed to estimate and correct the portion protruding outside the field of view. For example, as a method of truncation correction, there is known a method of estimating and extrapolating pixel values outside the field of view using a function that assumes the shape (body type) and density of a subject. The truncation correction amount is determined by a method of estimating from the collected signal values within the field of view, a method of setting with a fixed width, or the like.

Japanese Patent Publication No. 2007-521905

One of the problems to be solved by the embodiments disclosed in the specification and drawings is to improve the accuracy of truncation correction. However, the problems to be solved by the embodiments disclosed in this specification and drawings are not limited to the above problems. A problem corresponding to each effect of each configuration shown in the embodiments described later can be positioned as another problem.

A medical image processing apparatus according to an embodiment includes an acquisition unit and a generation unit. The acquiring unit acquires a plurality of projection data respectively acquired by irradiating an object with X-rays from a plurality of angles. The generation unit generates synthesized projection data by synthesizing model projection data based on human body model data having an internal structure with corresponding projection data for each of the plurality of projection data.

FIG. 1 is a block diagram showing an example configuration of a medical image processing apparatus according to the first embodiment. FIG. 2 is a diagram for explaining the human body model according to the first embodiment. FIG. 3 is a diagram for explaining processing by the extraction function according to the first embodiment. FIG. 4 is a diagram for explaining an example of processing by an extraction function according to the first embodiment. FIG. 5A is a diagram for explaining an example of processing by a generation function according to the first embodiment; 5B is a diagram for explaining an example of processing by a generation function according to the first embodiment; FIG. FIG. 6 is a flow chart showing processing procedures by the medical image processing apparatus according to the first embodiment. FIG. 7A is a diagram for explaining an example of processing by an extraction function according to the third embodiment; FIG. 7B is a diagram for explaining an example of processing by an extraction function according to the third embodiment; FIG. 7C is a diagram for explaining an example of processing by an extraction function according to the third embodiment; FIG. 8 is a diagram for explaining an example of processing by an extraction function according to the fourth embodiment. FIG. 9A is a diagram for explaining an example of processing by a generation function according to the fifth embodiment; FIG. 9B is a diagram for explaining an example of processing by a generation function according to the fifth embodiment; FIG. 10 is a block diagram showing an example configuration of an X-ray diagnostic apparatus according to another embodiment.

Hereinafter, embodiments of a medical image processing apparatus and an X-ray diagnostic apparatus will be described in detail with reference to the drawings. The medical image processing apparatus and X-ray diagnostic apparatus according to the present application are not limited to the embodiments described below. In addition, in the following description, common reference numerals are assigned to similar components, and duplicate descriptions are omitted.

(First embodiment)
FIG. 1 is a block diagram showing an example configuration of a medical image processing apparatus 3 according to the first embodiment. As shown in FIG. 1 , the medical image processing apparatus 3 is connected to the X-ray diagnostic apparatus 1 via the network 2 . Although FIG. 1 shows a case where only the X-ray diagnostic apparatus 1 and the medical image processing apparatus 3 are connected to the network 2, the embodiment is not limited to this, and other apparatuses can be connected to the network 2. (For example, other medical image diagnostic apparatus, medical image storage apparatus, etc.) may be connected.

The X-ray diagnostic apparatus 1 acquires an X-ray image (projection data) by irradiating an object with X-rays and detecting the X-rays that have passed through the object. Here, the X-ray diagnostic apparatus 1 can perform rotational imaging in which the subject is imaged at each rotation angle while rotating the X-ray tube and the X-ray detector. That is, the X-ray diagnostic apparatus 1 collects a plurality of X-ray images by irradiating the subject with X-rays from a plurality of angles. By performing this rotation imaging, it is possible to reconstruct three-dimensional image data (volume data) using images at each rotation angle.

The medical image processing apparatus 3 acquires X-ray images via the network 2 and performs various image processing on the acquired X-ray images. Specifically, the medical image processing apparatus 3 acquires a plurality of X-ray images acquired by rotational imaging, and performs truncation correction on the acquired plurality of X-ray images.

As shown in FIG. 1 , the medical image processing apparatus 3 has a communication interface 31 , an input interface 32 , a display 33 , a memory 34 and a processing circuit 35 . For example, the medical image processing apparatus 3 is realized by an information processing apparatus such as a tablet terminal or a workstation.

The communication interface 31 is connected to the processing circuit 35 and controls transmission and communication of various data with the X-ray diagnostic apparatus 1 and the like connected via the network. For example, the communication interface 31 is implemented by a network card, network adapter, NIC (Network Interface Controller), or the like.

The input interface 32 includes a trackball, a switch button, a mouse, a keyboard, a touchpad for performing input operations by touching the operation surface, a touch monitor in which the display screen and the touchpad are integrated, It is realized by a non-contact input circuit using an optical sensor, an audio input circuit, and the like. The input interface 32 is connected to the processing circuit 35 , converts an input operation received from an operator into an electric signal, and outputs the electric signal to the processing circuit 35 . It should be noted that the input interface 32 in this specification is not limited to having physical operation components such as a mouse and a keyboard. For example, an example of an input interface includes a processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to a control circuit.

The display 33 is connected to the processing circuit 35 and displays various information and various images output from the processing circuit 35 . For example, the display 33 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch monitor, or the like. For example, the display 33 displays a UI (User Interface) for receiving instructions from the operator, various images, and various processing results by the processing circuit 35 .

The memory 34 is connected to the processing circuit 35 and stores various data. For example, the memory 34 is implemented by a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk, an optical disk, or the like. In this embodiment, the memory 34 stores X-ray images received from the X-ray diagnostic apparatus 1 (a plurality of X-ray images acquired by rotational imaging) and the like. In addition, the memory 34 stores various information (human body model data) used for processing by the processing circuit 35, processing results by the processing circuit 35, and the like. The memory 34 also stores programs corresponding to various functions read and executed by the processing circuit 35 .

The processing circuit 35 executes a control function 351, an extraction function 352, a generation function 353, and a reconstruction function 354 according to an input operation received from an operator via the input interface 32, thereby generating a medical image. It controls the processing device 3 . Each processing function executed by the control function 351, the extraction function 352, the generation function 353, and the reconstruction function 354 is recorded in the memory 34 in the form of a computer-executable program. The processing circuit 35 is, for example, a processor, and reads each program from the memory 34 and executes the read program to implement the function corresponding to the read program. In other words, the processing circuit 35 with each program read has each function shown in the processing circuit 35 of FIG. Note that the control function 351 is an example of an acquisition unit. Also, the extraction function 352 is an example of an extraction unit. Also, the generation function 353 is an example of a generation unit. Also, the reconstruction function 354 is an example of a reconstruction unit.

The control function 351 controls transmission/reception of data with the X-ray diagnostic apparatus 1 via the communication interface 31, storage of received data in the memory 34, display of various information on the display 33, and the like. For example, the control function 351 acquires a plurality of projection data (X-ray images) acquired by irradiating the subject with X-rays from a plurality of angles.

The extracting function 352 extracts, from the human body model data having the internal structure, a region of the subject outside the field of view of the projection data for each of a plurality of pieces of projection data. Processing by the extraction function 352 will be described in detail later.

The generation function 353 generates synthesized projection data by synthesizing model projection data based on human body model data having an internal structure with corresponding projection data for each of a plurality of pieces of projection data. Specifically, the generation function 353 generates model projection data by projecting regions extracted from the human body model data for each of a plurality of pieces of projection data, and combines the generated model projection data with the corresponding projection data. Generate synthetic projection data respectively. Processing by the generation function 353 will be described in detail later.

A reconstruction function 354 reconstructs three-dimensional image data (volume data) using a plurality of projection data. For example, the reconstruction function 354 reconstructs three-dimensional image data using a plurality of synthetic projection data each generated from a plurality of projection data. Here, the reconstruction function 354 can appropriately use a known reconstruction method. For example, the reconstruction function 354 reconstructs three-dimensional image data from a plurality of projection data by Filtered Back Projection (FBP), Iterative Reconstruction, or the like.

The overall configuration of the medical image processing apparatus 3 has been described above. With such a configuration, the medical image processing apparatus 3 improves the correction accuracy of truncation through processing by the processing circuit 35 .

In CBCT imaging, in which volume data is reconstructed based on a plurality of projection data acquired by rotational imaging, if truncation occurs in which the subject protrudes from the imaging field of view, artifacts, cupping effects, and capping effects occur, making diagnosis difficult. Therefore, truncation correction is performed by estimating and extrapolating pixel values outside the field of view.

However, since the effect of truncation differs depending on which part of the subject is the boundary between the inside and outside of the imaging field of view (bone or soft tissue, etc.), it is common to perform uniform correction for truncation. A simple truncation correction may not be able to adequately respond to truncation conditions and may not be able to adequately compensate for the effects of truncation.

Therefore, in the medical image processing apparatus 3 according to the present embodiment, by performing truncation correction using a human body model having an internal structure, it is possible to extrapolate pixel values according to the boundaries inside and outside the range of the imaging field of view. , to improve the correction accuracy of the truncation. Details of the processing by the medical image processing apparatus 3 will be described below.

First, the data of the human body model according to this embodiment will be described. The human body model according to this embodiment is data having information on the internal structure of the human body. Specifically, the human body model includes information on the shape and position of each constituent element that constitutes the human body, and information on the substance contained in the constituent element. For example, data of a human body model has a structure in which a human body model having a standard structure of the human body is subdivided into a plurality of voxels, and each voxel contains information on substances (information on X-ray attenuation such as density). retained.

FIG. 2 is a diagram for explaining the human body model according to the first embodiment. The human body model is composed of a plurality of voxels, and holds information on the structure and position inside the human body and information on substances, as shown in FIG. 2, for example. That is, in the human body model, each voxel having coordinate information holds information about a part and information about a substance. For example, as shown in FIG. 2, a human body model consists of upper layers composed of "head", "chest", "abdomen", "waist", "legs", etc., and parts ( organ, etc.), and is managed as data in which material information is held in voxels corresponding to each part of the lower layer.

For example, the upper layer ``thorax'' includes lower layers classified from ``1st rib'' to ``12th rib'', and the voxel corresponding to each rib holds the information of the material ``bone''. be. In addition, the upper layer "thorax" includes other parts such as "lung fields", and material information (eg, organs/muscles, fat, air, etc.) is held by corresponding voxels for each part. . Positional information (coordinate information) in the three-dimensional space is attached to these voxels, and the human body model defines which position corresponds to which part in the three-dimensional space and what kind of material it is composed of. It is

In the human body model, information is similarly managed in other upper layers, and includes information on the shape and position of each constituent element of the entire human body and information on substances contained in the constituent elements. The parts included in the upper layer can be arbitrarily classified, but it is desirable to classify at least parts such as soft tissues, bones, and lung fields where the amount of X-ray attenuation changes greatly. Further, with respect to retention of material information in voxels, for example, each voxel may have a channel for each material, and management may be performed so that the numerical value of the corresponding material channel is increased.

As described above, the human body model holds information on the structure and position inside the human body and information on materials. Here, the human body model data is created in advance for each body type (height, weight) and stored in the memory 34, for example.

The control function 351 acquires a plurality of X-ray images acquired by rotational imaging according to operations via the input interface 32 . For example, the control function 351 acquires multiple X-ray images from the X-ray diagnostic apparatus 1 or an image storage device (not shown).

The extraction function 352 extracts, from the human body model, regions outside the visual field range for each of the plurality of X-ray images acquired by the control function 351 . Specifically, the extracting function 352 extracts, from the human body model, regions of the subject outside the field of view in the direction perpendicular to the axis of rotation in rotational imaging in each X-ray image.

Here, the extraction function 352 extracts a human body model based on imaging geometry that indicates the positional relationship between the subject, an X-ray tube that emits X-rays, and an X-ray detector that detects X-rays that have passed through the subject. A region outside the field of view of the X-ray image is extracted from the data of . That is, the extraction function 352 extracts the area outside the field of view based on the imaging geometry during rotational imaging.

FIG. 3 is a diagram for explaining processing by the extraction function 352 according to the first embodiment. For example, as shown in the upper diagram of FIG. 3, the extraction function 352 first selects a human body model to be used from a plurality of human body model data based on the body type information (height, weight, etc.) of the subject. Here, the extraction function 352 can also generate a human body model that is closer to the subject by transforming the selected human body model in accordance with the body shape information of the subject.

Then, the extraction function 352 acquires information including the position of the bed (tabletop), the position of the X-ray focal point, and the position of the X-ray detector during rotational imaging, as shown in the lower diagram of FIG. and generate a simulation model in which the selected human body model is placed at those positions. In other words, the extraction function 352 generates a simulation model that reproduces the actual state of rotation photography using a human body model. For example, the human body model is arranged in the center of the bed and is associated with the positional relationship.

Here, in the above simulation model, the shape of the human body model and the position of the internal structure do not match exactly, so the extraction function 352 corrects the human body model to match the actual state of the subject, and then adjusts the visual field range. Extract the outer region. Specifically, the extraction function 352 corrects the human body model data based on subject information included in the X-ray image, and extracts a region outside the field of view of the X-ray image from the corrected human body model data. . For example, the extraction function 352 corrects the human body model based on the result of comparison between the X-ray image and the model projection data obtained by projecting the entire human body model from the angle at which the X-ray image was acquired, and extracts the A region outside the field of view of the X-ray image is extracted. That is, the extraction function 352 corrects the human body model so that the actually acquired X-ray image and the image created by the simulation match, and extracts a region outside the visual field range from the corrected human body model.

FIG. 4 is a diagram for explaining an example of processing by the extraction function 352 according to the first embodiment. For example, as shown in FIG. 4, the extraction function 352 acquires an X-ray image at a certain angle acquired during rotational imaging (eg, an X-ray image at the angle at which rotational imaging is started). Then, the extraction function 352 generates a simulation image based on the simulation model at the same angle as the acquired X-ray image. That is, as shown in FIG. 4, the extraction function 352 generates a simulation image projected at the position of the X-ray detector when the human body model is irradiated with X-rays in the simulation model in which the human body model is arranged. .

Here, in the human body model, each voxel holds information on substances (information on X-ray attenuation). The extraction function 352 generates a simulation image by calculating the pixel value of each pixel based on the information on the substance held in the voxel on the X-ray path connecting the X-ray focus and the X-ray detector pixels. do.

Then, the extraction function 352 compares the acquired X-ray image with the generated simulation image, and modifies the human body model in the simulation model based on the comparison result to transform it into the human body model to be used. That is, the extraction function 352 compares the structure included in the X-ray image with the structure included in the simulation image, and extracts the structure included in the simulation model so that the structure included in the simulation image matches the structure included in the X-ray image. Modify the position and shape of the human body model in Here, the extraction function 352 first corrects the position and shape of the structure of the upper layer, and then corrects the position and shape of the structure of the lower layer in order.

The extraction function 352 corrects the human body model described above from one direction or from multiple directions. For example, when performing from a plurality of directions, the extraction function 352 compares the X-ray image and the simulation image at a plurality of predetermined angles or a plurality of angles specified by the operator, and based on the comparison result Then, correct the human body model from each angle.

As described above, the extraction function 352 corrects the position and shape of the human body model in the simulation model to reproduce the actual state at the time of rotational imaging with the human body model. As a result, the extraction function 352 can extract, in the simulation model, the region of the subject that is out of the field of view during the actual rotation imaging. That is, the extracting function 352 extracts the area outside the field of view in the human body model of the simulation model as the area of the subject outside the field of view during the actual rotation imaging.

The generation function 353 generates a synthetic X-ray image (composite projection data) by extrapolating the data outside the visual field range of the X-ray image based on the area outside the visual field range extracted from the human body model by the extraction function 352. do. Specifically, the generating function 353 forward-projects the entire human body model of the simulation model after correcting the position and shape of the human body model, thereby projecting a simulation image (model projection data) is generated, and a synthesized X-ray image is generated by synthesizing the generated simulation image (model projection data) with the X-ray image.

5A and 5B are diagrams for explaining an example of processing by the generation function 353 according to the first embodiment. Here, FIG. 5A shows an example of generating a simulation image (model projection data). Also, FIG. 5B shows an example of generation of a composite X-ray image.

For example, as shown in FIG. 5A, the generation function 353 projects the entire human body model in the simulation model after correcting the human body model to generate a simulation image (model projection data) in which an area outside the visual range is projected. Generate. In the human body model, each voxel holds information on substances (information on X-ray attenuation). That is, the extraction function 352 sets a virtual detector that projects an area outside the field of view on the same plane as the X-ray detector, and an X-ray path connecting pixels of the virtual detector from the X-ray focus. A simulation image (model projection data) is generated by calculating the pixel value of each pixel based on the material information held in the upper voxels.

Here, since the simulation model after correcting the human body model reproduces the state in which the subject was actually rotated and photographed, a simulation image projected outside the field of view of the human body model (model projection data ) is a projection of the structure protruding out of the field of view on the subject during rotational imaging.

Then, the generating function 353 generates a composite X-ray image by combining the generated simulation image (model projection data) with the X-ray image acquired in the rotation imaging. As a result, the generation function 353 can generate a composite X-ray image including a region protruding outside the field of view in rotational imaging.

FIG. 5B shows the composite X-ray image profile generated by the generation function 353 . Here, FIG. 5B shows the pixel value profile at the position indicated by the straight line L1 in the upper X-ray image (X-ray image acquired by rotational imaging). For example, in the composite X-ray image, the pixel values in the visual field range are the values indicated by the curve L2 (the pixel values at the positions indicated by the straight line L1), and the pixel values outside the visual field range are the values indicated by the curve L3 (projection outside the visual field range of the human body model). (pixel value obtained by As shown in FIG. 5B, in the synthesized X-ray image using the human body model, the boundary between the curve L2 and the curve L3 has an appropriate value, and the state of the pixel value outside the visual field range has a value corresponding to the material. as shown. On the other hand, when general truncation correction is performed, pixel values outside the field of view may become as indicated by curve L4, and there is a certain limit to the accuracy of truncation correction.

The generating function 353 generates the composite X-ray image described above for each angle in rotational imaging. In other words, the generation function 353 reproduces each angle of the rotation photographing in the simulation model to generate each simulation image (model projection data). Then, the generation function 353 synthesizes the simulation image (model projection data) generated at the same angle with the X-ray image at each angle acquired by the rotational imaging, thereby synthesizing the X-ray image for each angle in the rotational imaging. Generate an X-ray image.

A reconstruction function 354 reconstructs volume data using the synthetic X-ray image for each angle generated by the generation function 353 . As a result, the reconstruction function 354 can reconstruct volume data with less influence of truncation.

FIG. 6 is a flow chart showing a processing procedure by the medical image processing apparatus 3 according to the first embodiment. Here, step S101 in FIG. 6 is realized by the processing circuit 35 reading a program corresponding to the control function 351 from the memory 34 and executing it. Further, steps S102 to S104 are implemented by the processing circuit 35 reading and executing a program corresponding to the extraction function 352. FIG. Further, steps S105 and S106 are realized by the processing circuit 35 reading and executing a program corresponding to the generating function 353. FIG. Further, step S107 is realized by the processing circuit 35 reading out and executing a program corresponding to the reconstruction function 354 .

In the medical image processing apparatus 3 according to the first embodiment, for example, as shown in FIG. 6, the processing circuit 35 acquires an X-ray image acquired by rotation imaging (step S101). Then, the processing circuit 35 selects a human body model based on the subject information (step S102), and generates a simulation model using the selected human body model (step S103).

After that, the processing circuit modifies the simulation model based on the X-ray image acquired by rotational imaging (step S104), and generates a simulation image (model projection data) using the modified simulation model ( step S105).

Then, the processing circuit 35 performs truncation correction using the generated simulation image (model projection data) (step S106), and reconstructs volume data using the X-ray image after truncation correction (composite X-ray image). (step S107).

As described above, according to the first embodiment, the control function 351 obtains a plurality of projection data acquired by irradiating the subject with X-rays from a plurality of angles. The generation function 353 generates synthesized projection data by synthesizing model projection data based on human body model data having an internal structure with corresponding projection data for each of a plurality of pieces of projection data. Therefore, the medical image processing apparatus 3 according to the first embodiment can perform correction using a human body model having an internal structure, making it possible to improve the correction accuracy of truncation.

Further, according to the first embodiment, the extraction function 352 extracts, from the human body model data having the internal structure, the region of the subject that is outside the field of view of the projection data for each of a plurality of pieces of projection data. The generation function 353 generates model projection data by projecting regions extracted from human body model data for each of a plurality of pieces of projection data. Therefore, the medical image processing apparatus 3 according to the first embodiment makes it possible to generate highly accurate model projection data.

Further, according to the first embodiment, the extraction function 352 indicates the positional relationship between the subject, the X-ray tube that emits X-rays, and the X-ray detector that detects the X-rays that have passed through the subject. A region outside the field of view of the projection data is extracted from the human body model data based on the imaging geometry. Therefore, the medical image processing apparatus 3 according to the first embodiment makes it possible to extract a region outside the field of view in consideration of the state during rotational imaging.

Further, according to the first embodiment, the extraction function 352 corrects the human body model data based on the subject information included in the projection data, and the corrected human body model data is outside the field of view of the projection data. Extract regions. Therefore, the medical image processing apparatus 3 according to the first embodiment makes it possible to extract a region outside the field of view considering the structure of the subject.

Further, according to the first embodiment, the extraction function 352 extracts the human body model data based on the comparison result between the projection data and the model projection data obtained by projecting the entire human body model data from the angle at which the projection data was collected. After correction, the area outside the field of view of the projection data is extracted from the human body model data after correction. Therefore, the medical image processing apparatus 3 according to the first embodiment can reflect the state of the subject at the time of rotational imaging on the human body model, and can more accurately extract the area outside the visual field range. enable.

Further, according to the first embodiment, the human body model data includes information on the shape and position of each constituent element that constitutes the human body, and information on the substance contained in the constituent element. Therefore, the medical image processing apparatus 3 according to the first embodiment can perform truncation correction (generate a composite X-ray image) using pixel values corresponding to substances contained in regions outside the field of view, It is possible to improve the correction accuracy of truncation.

Further, according to the first embodiment, the reconstruction function 354 reconstructs three-dimensional image data using a plurality of synthetic projection data generated respectively from a plurality of projection data. Therefore, the medical image processing apparatus 3 according to the first embodiment makes it possible to reconstruct volume data in which the influence of truncation is further reduced.

(Second embodiment)
In the above-described first embodiment, a case has been described in which a human body model for each body type is generated from a standard human body model, and information on substances (information on X-ray attenuation) is held in each voxel. In the second embodiment, a case where CT (Computed Tomography) image data is used as a human body model will be described. That is, the extraction function 352 according to the second embodiment extracts, from the CT image data as the human body model data, a region outside the field of view range of the projection data acquired by rotational imaging. The second embodiment differs from the first embodiment only in the human body model used. This will be mainly described below.

In such a case, the CT image data for each body type is stored in the memory 34, and the extraction function 352 reads the corresponding CT image data based on the body type information of the subject, and extracts the corresponding CT image data as in the human body model in the first embodiment. Generate a simulation model to extract the area outside the field of view. The generating function 353 generates a composite X-ray image using the area outside the field of view extracted from the simulation model using CT image data.

Here, the CT image data has a CT value as information indicating the amount of X-ray attenuation, and the extraction function 352 and generation function 353 execute each process using the CT value in the CT image data. The CT image data as the human body model may be stored as one piece of data for the whole body, or may be divided for each part.

As described above, according to the second embodiment, the extraction function 352 extracts a region outside the field of view of projection data from CT image data as human body model data. Therefore, the medical image processing apparatus 3 according to the second embodiment can perform truncation correction using a human body model without generating a new human body model, and easily realizes improvement in truncation correction accuracy. make it possible.

(Third embodiment)
In the third embodiment, a case will be described in which a plurality of X-ray images acquired by rotational imaging are segmented for each material that constitutes the human body, and the human body model is corrected based on the segmentation results. The third embodiment differs from the first embodiment only in that the human body model is further corrected. This will be mainly described below.

The extraction function 352 according to the third embodiment extracts target substances included in a plurality of projection data, and extracts target substances in the collection space where the plurality of projection data are collected based on the positions of the target substances in each projection data. Identify the location of the material and modify the human body model data based on the identified location. Specifically, the extracting function 352 extracts the same target substance from each of a plurality of X-ray images acquired by rotational imaging, based on pixel values in the X-ray images. Then, the extraction function 352 specifies the position of the target substance in the three-dimensional space during rotational imaging based on the extracted position of the target substance in the X-ray image, and corrects the human body model based on the specified position. .

7A to 7C are diagrams for explaining an example of processing by the extraction function 352 according to the third embodiment. For example, as shown in FIG. 7A, the extraction function 352 extracts ribs based on pixel values from multiple X-ray images acquired by rotational imaging.

Then, as shown in the left diagram of FIG. 7B, the extraction function 352 generates a sinogram based on the pixel values of the multiple X-ray images and extracts a sine curve in the sinogram. That is, the extraction function 352 sets the acquisition angle (view direction) of the X-ray image in rotational imaging as the first direction (vertical axis of the sinogram in the drawing), and the direction orthogonal to the rotation axis in rotational imaging as the second direction. A sinogram is generated by assigning pixel values obtained by rotational imaging to a two-dimensional orthogonal coordinate system (horizontal axis of the sinogram in the figure). Then, the extraction function 352 associates the same part between the images based on the extracted pixel values of the ribs, and extracts the sine curve "rsin(φ+θ)" drawn by the same part on the sinogram.

Furthermore, the extraction function 352 calculates the position of the rib based on the extracted sine curve in the reconstruction coordinate system, as shown in the right diagram of FIG. 7B.

After that, as shown in FIG. 7C, the extraction function 352 performs coordinate transformation of the human body model in the simulation model and deformation of the human body model so as to match the positions of the ribs determined in the reconstruction coordinate system.

The extraction function 352 according to the third embodiment corrects the human body model in the simulation model by the above-described correction processing in addition to the correction processing described in the first embodiment, and then, similarly to the first embodiment. , to extract regions outside the field of view.

As described above, according to the third embodiment, the extraction function 352 extracts target substances included in a plurality of projection data, and based on the positions of the target substances in each projection data, the plurality of projection data. is specified, and the human body model data is corrected based on the specified position. Therefore, the medical image processing apparatus 3 according to the third embodiment can further correct the human body model based on the position of the substance, and can further improve the truncation correction accuracy.

(Fourth embodiment)
In the fourth embodiment, a case will be described in which the human body model is modified using information on the external shape of the subject. The fourth embodiment differs from the first embodiment only in that the human body model is further corrected. This will be mainly described below.

An extraction function 352 according to the fourth embodiment further corrects the human body model data based on a plurality of projection data collected from the same direction so as to include the entire subject. Specifically, the extraction function 352 uses the contour information in the X-ray images acquired to include the contour of the subject to further modify the contour of the human body model.

FIG. 8 is a diagram for explaining an example of processing by the extraction function 352 according to the fourth embodiment. For example, as shown in the upper diagram of FIG. 8, the extraction function 352 uses a plurality of fluoroscopic images acquired while moving the C-arm holding the X-ray tube and X-ray detector to determine the outline of the human body model. to fix. Here, as for the collection of fluoroscopic images, as shown in the lower diagram of FIG. 8, the number of fluoroscopic images having a size that covers the width of the subject is collected.

The extraction function 352 extracts the position of the outer shape (end) of the human body model placed in the simulation model from the imaging geometry when a plurality of fluoroscopic images are acquired and the position of the outer shape (end) of the subject in the fluoroscopic image. to fix.

The extraction function 352 according to the fourth embodiment modifies the human body model in the simulation model by the above-described modification processing in addition to the modification processing described in the first embodiment, and then, similarly to the first embodiment, , to extract regions outside the field of view.

As described above, according to the fourth embodiment, the extraction function 352 further modifies the human body model data based on multiple projection data collected from the same direction to include the entire subject. Therefore, the medical image processing apparatus 3 according to the fourth embodiment can further correct the human body model based on the external shape of the subject, making it possible to further improve the truncation correction accuracy.

(Fifth embodiment)
In the fifth embodiment, a case will be described in which the pixel values of an X-ray image acquired by rotational imaging are matched with the pixel values of a projected human body model. It should be noted that the fifth embodiment differs from the first embodiment in the content of processing by the generation function 353 . This will be mainly described below.

The generation function 353 according to the fifth embodiment generates the model projection data based on the comparison result between the pixel value in the projection data and the pixel value at the position corresponding to the projection data in the model projection data obtained by projecting the entire human body model data. The overall pixel values are modified, and the modified pixel values of the model projection data are used to generate synthetic projection data.

9A and 9B are diagrams for explaining an example of processing by the generation function 353 according to the fifth embodiment. For example, the generation function 353 generates a simulation image in which the entire human body model is projected in the simulation model modified by the extraction function 352, as shown in FIG. 9A. That is, the generation function 353 generates a simulation image including pixel values inside and outside the visual field range in the simulation model, as indicated by the profile curve L3 in FIG. 9A.

Then, the generation function 353 corrects the pixel values in the simulation image so that the pixel values are the same as the pixel values of the X-ray image acquired by rotational imaging. For example, as shown in FIG. 9B, the generating function 353 approximates the value of the curve L3 representing the profile in the simulation image in which the entire human body model is projected to the curve L2 representing the profile in the X-ray image acquired by rotation imaging. to modify the pixel values in the simulated image.

The generation function 353 generates a composite X-ray image by combining the pixel values outside the field of view in the simulation image after the pixel value correction described above with the X-ray image acquired by rotational imaging.

As described above, according to the fifth embodiment, the generation function 353 compares the pixel value in the projection data with the pixel value at the position corresponding to the projection data in the model projection data obtained by projecting the entire human body model data. pixel values of the entire model projection data are corrected based on, and synthetic projection data is generated using the corrected pixel values of the model projection data. Therefore, the medical image processing apparatus 3 according to the fifth embodiment can suppress the deviation between the pixel values in the X-ray image acquired by rotation imaging and the pixel values estimated from the human body model, and correct the truncation. Allows for further improvement in accuracy.

(Other embodiments)
Although the first to fifth embodiments have been described so far, the above-described first to fifth embodiments can be arbitrarily combined and carried out.

Moreover, in the above-described embodiment, the case where the medical image processing apparatus 3 executes various processes has been described. However, the embodiment is not limited to this, and each of the processes described above may be executed by the X-ray diagnostic apparatus 1 .

FIG. 10 is a block diagram showing an example configuration of an X-ray diagnostic apparatus 1 according to another embodiment. As shown in FIG. 10, the X-ray diagnostic apparatus 1 includes an X-ray high voltage device 11, an X-ray tube 12, an X-ray restrictor 13, a top plate 14, a C-arm 15, and an X-ray detector 16. , a memory 17 , a display 18 , an input interface 19 and a processing circuit 20 .

The X-ray high voltage device 11 applies a high voltage to the X-ray tube 12 under the control of the processing circuit 20 . For example, the X-ray high-voltage device 11 has electric circuits such as a transformer and a rectifier, and includes a high-voltage generator that generates a high voltage to be applied to the X-ray tube 12 and a and an X-ray control device for controlling an output voltage according to X-rays.

The X-ray tube 12 is a vacuum tube having a cathode (filament) that generates thermoelectrons and an anode (target) that generates X-rays upon collision with thermoelectrons. The X-ray tube 12 uses a high voltage applied from the X-ray high voltage device 11 to irradiate thermal electrons from the cathode to the anode, thereby generating X-rays.

The X-ray diaphragm 13 has an X-ray diaphragm that narrows down the irradiation range of X-rays generated by the X-ray tube 12 and a filter that adjusts the X-rays emitted from the X-ray tube 12 .

The X-ray diaphragm in the X-ray diaphragm 13 has, for example, four slidable diaphragm blades. The X-ray diaphragm narrows down the X-rays generated by the X-ray tube 12 by sliding diaphragm blades, and irradiates the subject P with the X-rays.

The filter in the X-ray restrictor 13 changes the quality of the transmitted X-rays depending on its material and thickness for the purpose of reducing the exposure dose to the subject P and improving the image quality of the X-ray image data. It reduces soft ray components that are likely to be depleted, and reduces high-energy components that cause deterioration in the contrast of X-ray image data. In addition, the filter changes the X-ray dose and irradiation range depending on its material, thickness, position, etc., and X-rays are arranged so that the X-rays irradiated from the X-ray tube 12 to the subject P have a predetermined distribution. attenuate

The top board 14 is a bed on which the subject P is placed, and is arranged on a bed (not shown). For example, the bed has a drive mechanism such as a motor and an actuator, and controls the movement and inclination of the tabletop 14 by operating the drive mechanism under the control of the processing circuit 20 .

The C-arm 15 holds the X-ray tube 12, the X-ray restrictor 13, and the X-ray detector 16 so as to face each other with the subject P interposed therebetween. For example, the C-arm 15 has a driving mechanism such as a motor and an actuator, and by applying a driving voltage to the driving mechanism according to a control signal received from the processing circuit 20, the X-ray tube 12 and the X-ray diaphragm 13 are driven. , the X-ray detector 16 is rotated and moved with respect to the subject P, and the X-ray irradiation position and irradiation angle are controlled.

The X-ray detector 16 is, for example, an X-ray flat panel detector (FPD) having detection elements arranged in a matrix. The X-ray detector 16 detects X-rays emitted from the X-ray tube 12 and transmitted through the subject P, and outputs a detection signal corresponding to the detected X-ray dose to the processing circuit 20 .

The memory 17 is implemented by, for example, a semiconductor memory device such as a RAM. The memory 17 temporarily stores the results of processing by the processing circuit 20 . For example, the memory 17 receives and temporarily stores various data such as X-ray image data collected by the processing circuit 20 . The memory 17 also stores a human body model. The memory 17 also stores programs corresponding to various functions read and executed by the processing circuit 20 .

The display 18 displays various information. For example, the display 18 displays a GUI for accepting operator's instructions and various X-ray images under the control of the processing circuit 20 . Also, the display 18 displays the results of processing by the processing circuit 20 .

The input interface 19 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuit 20 . For example, the input interface 19 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad that performs input operations by touching the operation surface, a touch screen that integrates a display screen and a touch pad, and an optical sensor. It is realized by the used non-contact input circuit, voice input circuit, or the like. Note that the input interface 19 may be composed of a tablet terminal or the like capable of wireless communication with the device main body. Moreover, the input interface 19 is not limited to those having physical operation parts such as a mouse and a keyboard. For example, the input interface 19 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the processing circuit 20. .

The processing circuit 20 controls the overall operation of the X-ray diagnostic apparatus 1 by executing an acquisition function 201 , a control function 202 , an extraction function 203 , a generation function 204 and a reconstruction function 205 . Specifically, the processing circuit 20 controls the operation of the X-ray diagnostic apparatus 1 by executing programs corresponding to various functions from the memory 17 .

Acquisition function 201 acquires X-ray images. For example, the acquisition function 201 controls the X-ray high voltage device 11 and adjusts the voltage applied to the X-ray tube 12, thereby controlling the X-ray dose irradiated to the subject P and ON/OFF. The collection function 201 also rotates and moves the C-arm 15 by controlling the operation of the C-arm 15 . Also, for example, the collection function 201 moves or tilts the tabletop 14 by controlling the motion of the bed. Here, the collection function 201 can be controlled to perform rotational imaging.

The acquisition function 201 also generates projection data based on the detection signal received from the X-ray detector 16 and stores the generated projection data in the memory 17 . The acquisition function 201 also generates an X-ray image by performing various image processing on the projection data stored in the memory 17 .

A control function 202 causes the display 18 to display a GUI and an X-ray image. For example, the control function 202 reads an X-ray image from the memory 17 and displays it on the display 18 according to an operation via the input interface 19 .

The extraction function 203 performs the same processing as the extraction function 352 described above. Also, the generation function 204 executes processing similar to that of the generation function 353 described above. Also, the reconstruction function 205 executes processing similar to that of the reconstruction function 354 described above.

In the medical image processing apparatus and X-ray diagnostic apparatus described in each embodiment, each processing function is stored in memory in the form of a computer-executable program. The processing circuit is a processor that implements a function corresponding to each program by reading and executing the program from memory. In other words, the processing circuit that has read each program has a function corresponding to the read program. In each of the above-described embodiments, each processing function is realized by a single processing circuit, but the embodiments are not limited to this. For example, the processing circuit may be configured by combining a plurality of independent processors, and each processor may implement each processing function by executing each program. Moreover, each processing function of the processing circuit may be appropriately distributed or integrated in a single or a plurality of processing circuits and implemented.

The term "processor" used in the above description includes, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), 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)). The processor implements functions by reading and executing programs stored in the storage 111 .

It should be noted that in each of the above-described embodiments, the memory has been described as storing a program corresponding to each processing function. However, a configuration may be adopted in which a plurality of memories are distributed and the corresponding programs are read out from the processing circuits and individual memories. Also, instead of storing the program in the memory, the program may be configured to be directly embedded in the circuitry of the processor. In this case, the processor realizes its function by reading and executing the program embedded in the circuit.

Each component of each device according to the above-described embodiments 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. Furthermore, all or any part of each processing function performed by each device can be implemented by a CPU and a program analyzed and executed by the CPU, or implemented as hardware based on wired logic.

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

According to at least one embodiment described above, it is possible to improve the correction accuracy of truncation.

While several embodiments have been described, these embodiments are provided 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, changes, and combinations of embodiments 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.

1 X-ray diagnostic apparatus 3 medical image processing apparatus 20, 35 processing circuit 202, 351 control function 203, 352 extraction function 204, 353 generation function 205, 354 reconstruction function

Claims (12)

an acquisition unit that acquires a plurality of projection data respectively acquired by irradiating an object with X-rays from a plurality of angles;
a generator for generating synthetic projection data by synthesizing model projection data based on human body model data having an internal structure with corresponding projection data for each of the plurality of projection data;
A medical image processing apparatus comprising: further comprising an extraction unit for extracting, from the human body model data, a region outside the field of view of the projection data on the subject for each of the plurality of projection data;
2. The medical image processing apparatus according to claim 1, wherein the generation unit generates the model projection data by projecting regions extracted from the human body model data for each of the plurality of projection data. The extraction unit extracts the human body based on an imaging geometry that indicates the positional relationship among the subject, an X-ray tube that irradiates the X-ray, and an X-ray detector that detects X-rays that have passed through the subject. 3. The medical image processing apparatus according to claim 2, wherein a region outside the field of view of said projection data is extracted from model data. 4. The extraction unit corrects the human body model data based on subject information included in the projection data, and extracts from the corrected human body model data a region outside the visual field range of the projection data. 3. The medical image processing apparatus according to . The extraction unit corrects the human body model data based on a comparison result between the projection data and model projection data obtained by projecting the entire human body model data from the angle at which the projection data was collected, and extracts the human body model data after the correction. 5. The medical image processing apparatus according to claim 4, wherein a region outside the field of view of said projection data is extracted from said projection data. The extraction unit extracts each of the target substances included in the plurality of projection data, and based on the position of the target substance in each projection data, the position of the target substance in the collection space where the plurality of projection data were collected. and correcting the human body model data based on the identified position. 7. The human body model data according to any one of claims 4 to 6, wherein the extraction unit further corrects the human body model data based on a plurality of projection data collected from the same direction so as to include the entire subject. Medical image processing equipment. The generator generates pixel values of the entire model projection data based on comparison results between pixel values in the projection data and pixel values at positions corresponding to the projection data in the model projection data obtained by projecting the entire human body model data. 8. The medical image processing apparatus according to any one of claims 1 to 7, wherein the synthesized projection data is generated by modifying values and using the modified pixel values of the model projection data. 8. The medical image processing according to any one of claims 2 to 7, wherein said extraction unit extracts a region outside the field of view of said projection data from CT (Computed Tomography) image data as said human body model data. Device. The medical image processing according to any one of claims 1 to 9, wherein the human body model data includes information on the shape and position of each component that constitutes the human body, and information on substances contained in the component. Device. The medical image processing according to any one of claims 1 to 10, further comprising a reconstruction unit that reconstructs three-dimensional image data using a plurality of synthetic projection data generated from the plurality of projection data. Device. an acquisition unit that acquires a plurality of projection data by irradiating an object with X-rays from a plurality of angles;
a generator for generating synthetic projection data by synthesizing model projection data based on human body model data having an internal structure with corresponding projection data for each of the plurality of projection data;
An X-ray diagnostic apparatus comprising:

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