CN114255296A - CT image reconstruction method and device based on single X-ray image - Google Patents

CT image reconstruction method and device based on single X-ray image Download PDF

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CN114255296A
CN114255296A CN202111587397.5A CN202111587397A CN114255296A CN 114255296 A CN114255296 A CN 114255296A CN 202111587397 A CN202111587397 A CN 202111587397A CN 114255296 A CN114255296 A CN 114255296A
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image
reconstruction
reconstructed
generating
breathing
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CN114255296B (en
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刘博�
江玲
张孟希
周付根
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Beihang University
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    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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Abstract

The invention discloses a CT image reconstruction method and a device based on a single X-ray image, wherein the method comprises the following steps: preprocessing training data, acquiring 4D CT images of N patients, constructing a breathing model for each patient, and generating 3D CT images under M breathing time phases for each breathing model; generating a digital reconstruction image under the angle K for the 3D CT image; model training, namely inputting a digital reconstructed image and a 3D CT image as a learning target, and sending the image into a deep learning neural network for training to obtain a CT image reconstructed network; and (4) CT image reconstruction, wherein a single X-ray image is sent into a CT image reconstruction network, and a 3D CT image reconstructed according to the single X-ray image is obtained. According to the technical scheme provided by the invention, a deep learning-based method is introduced to learn the mapping from the single X-ray image to the 3D CT image according to the characteristic that the human anatomy structure is strictly limited, so that the technical problem that the 3D CT image cannot be obtained in real time in the operation is solved.

Description

CT image reconstruction method and device based on single X-ray image
Technical Field
The invention relates to the technical field of digital image processing, in particular to a CT image reconstruction method and device based on a single X-ray image.
Background
During the operation of a patient, the change of the internal anatomical structure of the human body can be observed in real time and non-invasively based on the 2D image of the X-ray imaging. In single-view 2D images, the 3D anatomy of the patient is projected onto a 2D plane, resulting in human tissue structures that overlap each other, reducing the visibility of structures within the body. 3D CT (Computed Tomography) images can obtain patient images with high spatial resolution and generate three-dimensional images of internal anatomical structures of human bodies, and the problem that human tissues are overlapped in X-ray projection can be solved.
A 3D CT image is reconstructed clinically from a set of X-ray projection data acquired with X-rays that are rotated completely around the patient through a reconstruction algorithm. To obtain artifact-free reconstructed images, such algorithms require more sampling angles, which bring more radiation dose to the patient. Furthermore, during surgery, it is very difficult to perform X-ray projections that rotate around the patient.
In view of the above, a CT image reconstruction method based on single X-ray image is needed to alleviate the problems of the prior art.
Disclosure of Invention
The invention provides a CT image reconstruction method based on a single X-ray image, which introduces a deep learning neural network and alleviates the defects of the prior art through a single X-ray image fast 3D CT image.
In a first aspect, the present invention provides a CT image reconstruction method based on a single X-ray image, including: preprocessing training data, acquiring 4D CT images of N patients, constructing a breathing model for each patient, and generating 3D CT images under M breathing time phases for each breathing model; generating a digital reconstruction image under the angle K for the 3D CT image; model training, namely inputting a digital reconstructed image and a 3D CT image as a learning target, and sending the image into a deep learning neural network for training to obtain a CT image reconstructed network; and (4) CT image reconstruction, wherein a single X-ray image is sent into a CT image reconstruction network, and a 3D CT image reconstructed according to the single X-ray image is obtained.
Optionally, the breathing model is built from the breathing cycle of the patient, which is fitted from the displacement of points on the patient's thorax.
Optionally, the step of generating a digital reconstructed image under K angles for the 3D CT image includes: setting the distance between the 3D CT image and the virtual point light source and the radial cone angle of the virtual point light source; in the radial cone angle, T rays are led out from the virtual point light source, the T rays penetrate through the 3D CT image and then are projected on the virtual panel to form a digital reconstruction image, the rays simulate X rays, and when the rays penetrate through the 3D CT image, the simulated X rays are attenuated when penetrating through human tissues represented by the 3D CT image; and changing the positions of the virtual point light sources and the virtual panel for K times to generate a digital reconstruction image under K angles.
Optionally, before generating the digital reconstructed image at K angles for the 3D CT image, the method includes: and cutting the 3D CT image into a rectangular area.
Optionally, before generating the digital reconstructed image at the K angles for the 3D CT image, the method further includes: the 3D CT image is resampled to the same resolution.
Optionally, the CT image reconstruction network includes: a pooling component and a transposed convolution component.
In a second aspect, the present invention provides a CT image reconstruction apparatus based on a single X-ray image, including: the system comprises a preprocessing module, a data processing module and a data processing module, wherein the preprocessing module is used for acquiring 4D CT images of N patients, constructing a breathing model for each patient and generating 3D CT images under M breathing time phases for each breathing model; generating a digital reconstruction image under the angle K for the 3D CT image; the model training module is used for inputting the digital reconstructed image, taking the 3D CT image as a learning target, and sending the 3D CT image into a deep learning neural network for training to obtain a CT image reconstruction network; and the CT image reconstruction module is used for sending the single X-ray image into a CT image reconstruction network and acquiring a 3D CT image reconstructed according to the single X-ray image.
In a third aspect, the invention provides a computing device comprising: a processor and a memory storing a program, the processor implementing the method of the first aspect when executing the program.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a program which, when executed, performs the method of the first aspect.
The invention has the following beneficial effects:
the technical scheme provided by the invention can have the following beneficial effects: according to the characteristic that the human anatomy structure is strictly limited, a deep learning-based method is introduced to learn the mapping from a single X-ray image to a 3D CT image, so that the technical problem that the 3D CT image cannot be acquired in real time in an operation is solved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are one embodiment of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flowchart of a CT image reconstruction method based on a single X-ray image according to a first embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a CT image reconstruction apparatus based on a single X-ray image according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computing device according to a third embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and the described embodiments are some, but not all embodiments of the present invention.
The first embodiment is as follows:
fig. 1 is a flowchart illustrating a CT image reconstruction method based on a single X-ray image according to a first embodiment of the present invention, as shown in fig. 1, the method includes the following three steps.
Step S101: data for training is pre-processed. Specifically, training data are preprocessed, 4D CT images of N patients are obtained, a breathing model is built for each patient, 3D CT images under M breathing time phases are generated for each breathing model, and digital reconstruction images under K angles are generated for the 3D CT images.
The 4D CT image is a concept of adding time to the 3D CT image, and can dynamically show the state of the patient's body structure changing with time. Illustratively, as the patient breathes, the thorax fluctuates, and some of the patient's organs in the 3D CT image change position or morphology as the breathing moves. The patient's breathing exhibits a periodic variation, the breathing model describes an alternating variation of the patient's inspiration and expiration, and the M breathing phases can be selected at different points in time within a breathing cycle. Illustratively, the digitally reconstructed image at the K angle may be a digitally reconstructed image projected at 4 angles of 0 degrees, 30 degrees, 60 degrees, and 90 degrees from the 3D CT image.
In some embodiments, the breathing model is built from the patient's breathing cycle, which is fitted from the displacement of points on the patient's thorax. Illustratively, during the patient's breathing, the displacement of the patient's chest with the above points varies as a sine or cosine curve over time. And (3) extracting the displacement of each point on the chest of the patient from the 4D CT image, and fitting a sine curve or a cosine curve to obtain the respiratory cycle of the patient.
In some embodiments, the step of generating a digitally reconstructed image at K angles for the 3D CT image comprises: setting the distance between the 3D CT image and the virtual point light source and the radial cone angle of the virtual point light source; in the radial cone angle, T rays are led out from the virtual point light source, the T rays penetrate through the 3D CT image and then are projected on the virtual panel to form a digital reconstruction image, the rays simulate X rays, and when the rays penetrate through the 3D CT image, the simulated X rays are attenuated when penetrating through human tissues represented by the 3D CT image; and changing the positions of the virtual point light sources and the virtual panel for K times to generate a digital reconstruction image under K angles.
It should be noted that, according to the human tissue represented by the 3D CT image, the T rays are attenuated during the T rays pass through the 3D CT image. Illustratively, the attenuation is calculated for T rays based on the CT values of the pixels in the 3D CT image traversed by the ray.
In some embodiments, before generating the digitally reconstructed image at K angles for the 3D CT image, the method includes: and cutting the 3D CT image into a rectangular area. The 3D CT image is cut in a cuboid region, the learning target of the deep learning neural network is reduced, and the learning difficulty is reduced. Illustratively, a rectangular parallelepiped is set according to a human tissue envelope of a region of interest in the 3D CT image, thereby concentrating the learning target in the region of interest.
In some embodiments, before generating the digitally reconstructed image at K angles for the 3D CT image, the method further includes: the 3D CT image is resampled to the same resolution. Illustratively, the 3D CT image is resampled to 1 × 1 × 1mm3And the resolution ratio is reduced, so that the learning difficulty caused by the difference of the resolution ratios of the data samples is reduced.
Step S102: and training the CT image reconstruction network. Specifically, model training is carried out by taking a digital reconstructed image as input and a 3D CT image as a learning target, and then the input is sent into a deep learning neural network to be trained to obtain a CT image reconstruction network.
In some embodiments, a CT image reconstruction network includes: a pooling component and a transposed convolution component. It should be noted that the pooling component may be used for down-sampling, and the transposed convolution component, i.e., the deconvolution component, may be used for up-sampling.
Exemplarily, 80% of the data generated in step S101 is input into the deep learning neural network as a training set, data features are extracted through operations such as convolution pooling, and a reconstructed 3D CT image is generated after transposition convolution, and a loss function between a prediction result and an actual 3D CT image is calculated; and (5) back propagation and updating network parameters. The classification accuracy is verified under the current network using the remaining data generated in step S101 as a verification set. The loss function is reduced and converged through repeated iterations.
Step S103: and carrying out CT image reconstruction by using the single X-ray image. Specifically, in the CT image reconstruction, a single X-ray image is sent to a CT image reconstruction network, and a 3D CT image reconstructed according to the single X-ray image is obtained.
Example two:
the embodiment of the present invention provides a CT image reconstruction apparatus based on a single X-ray image, which is mainly used for executing the CT image reconstruction method based on a single X-ray image provided in the foregoing content of the embodiment of the present invention, and the following describes the CT image reconstruction apparatus provided in the embodiment of the present invention in detail.
Fig. 2 is a schematic structural diagram of a CT image reconstruction apparatus according to a second embodiment of the present invention. As shown in fig. 2, the CT image reconstruction apparatus 200 includes the following modules:
the preprocessing module 201 is configured to acquire 4D CT images of N patients, construct a respiratory model for each patient, and generate 3D CT images under M respiratory phases for each respiratory model; and generating a digital reconstruction image under the K angles for the 3D CT image.
And the model training module 202 is used for inputting the digital reconstructed image and inputting the 3D CT image as a learning target into a deep learning neural network for training to obtain a CT image reconstruction network.
And a CT image reconstruction module 203, configured to send the single X-ray image to a CT image reconstruction network, and obtain a 3D CT image reconstructed from the single X-ray image.
Example three:
the embodiment of the invention also provides the computing equipment. As shown in fig. 3, the computing apparatus 300 of this embodiment includes: a processor 301, a memory 302, and programs stored in the memory 302 and executable on the processor 301. The processor 301 executes a program to implement the steps of the above-mentioned CT image reconstruction method based on single X-ray image, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 301 executes programs to implement the functions of the modules in the above-described embodiments of the apparatuses, such as the modules in fig. 3, to implement the CT image reconstruction apparatus.
Illustratively, the program may be partitioned into one or more modules that are stored in the memory 302 and executed by the processor 301 to implement the present invention. The one or more modules may be a series of program instruction segments capable of performing certain functions, which are used to describe the execution of the program in a computing device. For example, the program may be partitioned into a model training module and an object recognition module.
The specific functions of each module are as follows: the preprocessing module 201 is configured to acquire 4D CT images of N patients, construct a respiratory model for each patient, and generate 3D CT images under M respiratory phases for each respiratory model; and generating a digital reconstruction image under the K angles for the 3D CT image. And the model training module 202 is used for inputting the digital reconstructed image and inputting the 3D CT image as a learning target into a deep learning neural network for training to obtain a CT image reconstruction network. And a CT image reconstruction module 203, configured to send the single X-ray image to a CT image reconstruction network, and obtain a 3D CT image reconstructed from the single X-ray image. .
The computing device can be a single chip microcomputer system, a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The computing device may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the schematic diagrams are merely examples and do not constitute a limitation of computing devices, and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the computing devices may also include input-output devices, etc.
The Processor may be a Micro Control Unit (MCU), a Central Processing Unit (CPU), or other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is the control center for the computing device and that connects the various parts of the overall computing device using various interfaces and lines.
The memory can be used for storing the programs and/or modules, and the processor can realize various functions of the CT image reconstruction method and device based on single X-ray images by operating or executing the programs and/or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Example four:
the modules integrated with the CT image reconstruction apparatus based on a single X-ray image may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as independent products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (9)

1. A CT image reconstruction method based on a single X-ray image is characterized by comprising the following steps:
the pre-processing of the training data is performed,
acquiring 4D CT images of N patients, constructing a breathing model for each patient, and generating 3D CT images under M breathing time phases for each breathing model;
generating a digital reconstruction image under the angle K for the 3D CT image;
the training of the model is carried out,
inputting the digital reconstructed image as an input, and inputting the 3D CT image as a learning target into a deep learning neural network for training to obtain a CT image reconstructed network;
the reconstruction of the CT image is carried out,
and sending the single X-ray image into a CT image reconstruction network to obtain a 3D CT image reconstructed according to the single X-ray image.
2. The method of claim 1, wherein the breathing model is constructed from a patient's breathing cycle that is fit to the displacement of points on the patient's thorax.
3. The method of claim 1, wherein the step of generating a digital reconstructed image at K angles for the 3D CT image comprises:
setting the distance between the 3D CT image and the virtual point light source and the radial cone angle of the virtual point light source;
in the radial cone angle, T rays are led out from the virtual point light source, the T rays pass through the 3D CT image and then are projected on a virtual panel to form a digital reconstruction image, the rays simulate X rays, and when the rays pass through the 3D CT image, the simulated X rays are attenuated when passing through human tissues represented by the 3D CT image;
and changing the positions of the virtual point light source and the virtual panel for K times to generate a digital reconstruction image under the K angles.
4. The method of claim 1, wherein generating the 3D CT image before generating the digitally reconstructed image at K angles comprises: and cutting the 3D CT image into a cuboid region.
5. The method of claim 1, further comprising, prior to generating a digitally reconstructed image at K angles for the 3D CT image: resampling the 3D CT image to the same resolution.
6. The method of claim 1, wherein the CT image reconstruction network comprises: a pooling component and a transposed convolution component.
7. A CT image reconstruction apparatus based on a single X-ray image, comprising:
a pre-processing module for
Acquiring 4D CT images of N patients, constructing a breathing model for each patient, and generating 3D CT images under M breathing time phases for each breathing model;
generating a digital reconstruction image under the angle K for the 3D CT image;
model training module for
Inputting the digital reconstructed image as an input, and inputting the 3D CT image as a learning target into a deep learning neural network for training to obtain a CT image reconstructed network;
CT image reconstruction module for
And sending the single X-ray image into a CT image reconstruction network to obtain a 3D CT image reconstructed according to the single X-ray image.
8. A computing device, comprising: processor and memory storing a program, wherein the processor implements the method of any one of claims 1 to 6 when executing the program.
9. A computer-readable storage medium having a program stored thereon, wherein the program when executed implements the method of any of claims 1-6.
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