Medical imaging data pair generation method, medical image imaging method and medical imaging system

Abstract

The invention discloses a medical imaging data pair generation method, a medical image imaging method and a medical imaging system. A method for generating a medical imaging data pair includes: receiving an original three-dimensional projection dataset obtained by scanning an examination object by a medical imaging system; multiplying the original three-dimensional projection dataset in at least one dimension to obtain a multiplied projection dataset having a higher resolution than the original three-dimensional projection dataset; splitting the multiplied projection data set into a first projection data subset and a second projection data subset in at least one dimension; and generating at least one imaging data pair based on the first projection data subset and the second projection data subset, the imaging data pair includes an imaging data pair formed based on the first projection data subset and the second projection data subset and / or an imaging data pair formed of an image reconstructed from the first projection data subset and an image reconstructed from the second projection data subset.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing, and in particular, to a medical imaging data pair generating method, a medical image imaging method and a medical imaging system. Background Art

[0002] Imaging technology allows for the non-invasive acquisition of images of internal structures or features of a subject, such as a patient. Digital X-ray imaging systems generate digital data that can be reconstructed into radiographic images, such as in computed tomosynthesis (CT) or digital breast tomosynthesis (DBT) imaging processes. In a digital X-ray imaging system, radiation from a source is directed toward the subject. A portion of the radiation passes through the subject and strikes a detector. The detector includes an array of discrete picture elements or detector pixels, and is processed based on the amount or intensity of radiation striking each pixel area to obtain projection data. The complete projection data can reconstruct accurate slice images for diagnosis. These images are used to identify and / or examine internal structures and organs in a patient's body. If the image resolution is higher, the internal structures and organs can be more clearly distinguished, resulting in a more accurate diagnosis. Summary of the invention

[0003] It is to be understood that both the foregoing general description and the following detailed description of the present invention are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.

[0004] According to a first aspect of the present disclosure, a method for generating medical imaging data pairs is provided, comprising: receiving an original three-dimensional projection data set, wherein the original three-dimensional projection data set is obtained by scanning an examination object by a medical imaging system; multiplying the original three-dimensional projection data set in at least one dimension to obtain a multiplied projection data set having a higher resolution relative to the original three-dimensional projection data set; splitting the multiplied projection data set in the at least one dimension into a first projection data subset and a second projection data subset; and generating at least one imaging data pair based on the first projection data subset and the second projection data subset, the imaging data pair comprising an imaging data pair consisting of an imaging data pair based on the first projection data subset and the second projection data subset and / or an imaging data pair consisting of an image reconstructed from the first projection data subset and an image reconstructed from the second projection data subset.

[0005] In one embodiment, the original three-dimensional projection data set is acquired by the detector of the medical imaging system and includes three dimensions: a row direction, a channel direction, and a viewing angle direction. The row direction indicates the direction of the detector when the examination object moves toward or out of the medical imaging system, the channel direction indicates the extension direction of the detector that is perpendicular to the row direction and arranged locally around the examination object, and the viewing angle direction indicates the angle at which the detector acquires the original three-dimensional projection data set at different positions around the examination object.

[0006] In one embodiment, the first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in the viewing angle direction and / or the channel direction.

[0007] In one embodiment, the first projection data subset includes a data block at a first position in the viewing direction, the second projection data subset includes a data block at a second position in the viewing direction, and the first position and the second position alternate in the viewing direction; and / or the first projection data subset includes a data block at a third position in the channel direction, the second projection data subset includes a data block at a fourth position in the channel direction, and the third position and the fourth position alternate in the channel direction.

[0008] In one embodiment, it further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated part of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

[0009] In one embodiment, multiplying the original 3D projection dataset in at least one dimension includes upsampling the original 3D projection dataset in at least one direction of a channel direction, a row direction, and a viewing angle direction.

[0010] In one embodiment, upsampling the original three-dimensional projection data set in the viewing direction includes using a deep learning neural network, and the deep learning neural network includes a SwinIR transformer.

[0011] According to a second aspect of the present disclosure, a method for medical image imaging is provided, comprising: receiving a medical imaging data pair, wherein the medical imaging data pair is generated by the following steps: receiving an original three-dimensional projection data set, wherein the original three-dimensional projection data set is obtained by scanning an examination object by a medical imaging system; multiplying the original three-dimensional projection data set in at least one dimension to obtain a multiplied projection data set with a higher resolution relative to the original three-dimensional projection data set; splitting the multiplied projection data set in the at least one dimension into a first projection data subset and a second projection data subset; and generating at least one imaging data pair based on the first projection data subset and the second projection data subset, the imaging data pair comprising an imaging data pair consisting of an imaging data pair based on the first projection data subset and the second projection data subset and / or an imaging data pair consisting of an image reconstructed from the first projection data subset and an image reconstructed from the second projection data subset; and generating a medical image based on the medical imaging data pair.

[0012] In one embodiment, the original three-dimensional projection data set is acquired by the detector of the medical imaging system and includes three dimensions: a row direction, a channel direction, and a viewing angle direction. The row direction indicates the direction of the detector when the examination object moves toward or out of the medical imaging system, the channel direction indicates the extension direction of the detector that is perpendicular to the row direction and arranged locally around the examination object, and the viewing angle direction indicates the angle at which the detector acquires the original three-dimensional projection data set at different positions around the examination object.

[0013] In one embodiment, the first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in the viewing angle direction and / or the channel direction.

[0014] In one embodiment, the first projection data subset includes a data block at a first position in the viewing direction, the second projection data subset includes a data block at a second position in the viewing direction, and the first position and the second position alternate in the viewing direction; and / or the first projection data subset includes a data block at a third position in the channel direction, the second projection data subset includes a data block at a fourth position in the channel direction, and the third position and the fourth position alternate in the channel direction.

[0015] In one embodiment, it further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated part of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

[0016] In one embodiment, multiplying the original 3D projection dataset in at least one dimension includes upsampling the original 3D projection dataset in at least one direction of a channel direction, a row direction, and a viewing angle direction.

[0017] According to a third aspect of the present disclosure, a medical imaging system is provided, comprising: a scanning device configured to acquire an original three-dimensional projection data set of an examination object; and a processor configured to execute any of the aforementioned methods.

[0018] According to a fourth aspect of the present disclosure, a non-transitory computer-readable medium is provided, on which instructions are stored, and the instructions can be executed by a processor to implement any of the methods described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present disclosure may be better understood by describing exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, in which:

[0020] Figure 1 shows a schematic diagram of an exemplary CT system configured for CT imaging;

[0021] Figure 2 Shows something like Figure 1 An exemplary imaging system of a CT system;

[0022] Figure 3 A schematic diagram of a CT system when examining a patient is shown;

[0023] Figure 4 A flowchart of a method for generating a medical imaging data pair according to an embodiment of the present disclosure is shown;

[0024] Figure 5 A schematic diagram showing an original three-dimensional projection dataset and a multiplied projection dataset according to an embodiment of the present disclosure is shown;

[0025] Figure 6 A schematic diagram showing splitting of a multiplied projection data set in a viewing angle direction according to an embodiment of the present disclosure is shown;

[0026] Figure 7 A schematic diagram showing splitting of a multiplied projection data set in a channel direction according to an embodiment of the present disclosure is shown;

[0027] Figure 8 A schematic diagram showing splitting of a multiplied projection data set in a viewing direction and a channel direction according to an embodiment of the present disclosure is shown;

[0028] Fig. 9A flowchart of a method for model training according to an embodiment of the present disclosure is shown;

[0029] Fig.10 A schematic diagram of using medical imaging data pairs to train an image denoising model according to an embodiment of the present disclosure is shown;

[0030] Fig.11 A flowchart showing a method for medical imaging according to an embodiment of the present disclosure; and

[0031] Fig.12 An example block diagram of a computing device according to one embodiment of the present disclosure is shown.

[0032] In the accompanying drawings, similar components and / or features may have the same numerical reference number. In addition, components of the same type may be distinguished by following the reference number with a letter that distinguishes between similar components and / or features. If only the first numerical reference number is used in the specification, the description applies to any similar component and / or feature having the same first numerical reference number, regardless of the letter subscript. DETAILED DESCRIPTION

[0033] The specific implementation methods of the present disclosure will be described below. It should be noted that in the specific description of these implementation methods, in order to provide a concise description, it is impossible for this specification to provide a detailed description of all the features of the actual implementation methods. It should be understood that in the actual implementation of any implementation method, just as in the process of any engineering project or design project, in order to achieve the specific goals of the developer and to meet system-related or business-related restrictions, various specific decisions are often made, and this will also change from one implementation method to another. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for ordinary technicians in the field related to the content disclosed by the present disclosure, some changes in design, manufacturing or production based on the technical content disclosed in the present disclosure are just conventional technical means, and should not be understood as insufficient content of the present disclosure.

[0034] References in the specification to "one embodiment," "an embodiment," "an example embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment may include the particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it is considered to be within the knowledge of those skilled in the art to affect such feature, structure, or characteristic in conjunction with other embodiments, whether or not explicitly described.

[0035] For purposes of this disclosure, the phrase "A and / or B" means (A), (B), or (A and B). For purposes of this disclosure, the phrase "A, B, and / or C" means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C).

[0036] Unless otherwise defined, the technical terms or scientific terms used in the claims and the specification shall have the usual meanings understood by persons with ordinary skills in the technical field to which the present disclosure belongs. "Include" or "comprising" and similar words mean that the elements or objects appearing before "include" or "comprising" include the elements or objects listed after "include" or "comprising" and their equivalent elements, and do not exclude other elements or objects.

[0037] The following will take the example of Figures 1 to 12 To describe the embodiments of the present disclosure. Although the CT system is described by way of example, it should be understood that the technology of the present disclosure is widely applicable to various non-destructive detection fields. The technology of the present disclosure may also be useful when applied to images collected using other imaging modalities, such as X-ray imaging systems, magnetic resonance imaging (MRI) systems, positron emission tomography (PET) imaging systems, single photon emission computed tomography (SPECT) imaging systems, and combinations thereof (e.g., multimodal imaging systems, such as PET / CT, PET / MR or SPECT / CT imaging systems). Exemplarily, the embodiments of the present disclosure are described below in conjunction with X-ray computed tomography (CT) imaging. Those skilled in the art will appreciate that the embodiments of the present disclosure may also be applicable to other medical imaging.

[0038] Figure 1 A schematic diagram of an exemplary CT system 100 configured for CT imaging is shown. Specifically, the CT system 100 is configured to image a subject 112 (such as a patient, an inanimate object, one or more manufactured parts) and / or a foreign object (such as a dental implant, a stent, and / or a contrast agent present in the body). In one embodiment, the CT system 100 includes a gantry 102, which in turn may also include at least one X-ray source 104, which is configured to project an X-ray radiation beam 106 (see Figure 2 ) for imaging a subject 112 lying on an examination table 114. Specifically, the X-ray source 104 is configured to project an X-ray radiation beam 106 toward a detector array 108 positioned on an opposite side of the gantry 102. Figure 1A single X-ray source 104 is depicted, but in certain embodiments, multiple X-ray sources and detectors may be employed to project multiple X-ray radiation beams for acquiring projection data at different energy levels corresponding to the patient. In some embodiments, the X-ray source 104 may implement dual-energy gem spectrum imaging (GSI) by rapid peak kilovolt (kVp) switching. In some embodiments, the X-ray detector employed is a photon counting detector capable of distinguishing X-ray photons of different energies. In other embodiments, two sets of X-ray sources and detectors are used to generate dual-energy projections, wherein one set of X-ray sources and detectors is set to low kVp and the other set to high kVp. It should therefore be understood that the methods described herein may be implemented with single energy acquisition techniques as well as dual energy acquisition techniques.

[0039] In certain embodiments, the CT system 100 further includes an image processing unit 110 configured to reconstruct an image of a target volume of the subject 112 using an iterative or analytical image reconstruction method. For example, the image processing unit 110 may use an analytical image reconstruction method such as filtered back projection (FBP) to reconstruct an image of the target volume of the patient. As another example, the image processing unit 110 may use an iterative image reconstruction method (such as advanced statistical iterative reconstruction (ASIR), conjugate gradient (CG), maximum likelihood expectation maximization (MLEM), model-based iterative reconstruction (MBIR), etc.) to reconstruct an image of the target volume of the subject 112. As further described herein, in some examples, in addition to the iterative image reconstruction method, the image processing unit 110 may also use an analytical image reconstruction method (such as FBP).

[0040] In some CT imaging system configurations, an X-ray source projects a cone-shaped X-ray radiation beam that is collimated to lie in the XYZ plane of a Cartesian coordinate system and is often referred to as the "imaging plane". The X-ray radiation beam passes through an object being imaged, such as a patient or subject. The X-ray radiation beam, after being attenuated by the object, impinges upon an array of detector elements. The intensity of the attenuated X-ray radiation beam received at the detector array depends on the attenuation of the X-ray radiation beam by the object. Each detector element of the array produces a separate electrical signal that is a measurement of the X-ray beam attenuation at the detector location. The attenuation measurements from all detector elements are collected individually to produce a transmission profile.

[0041] In some CT systems, a gantry is used to rotate the X-ray source and the detector array around the object to be imaged in the imaging plane so that the angle at which the X-ray beam intersects the object is constantly changing. A set of X-ray radiation attenuation measurements (e.g., projection data) from the detector array at one gantry angle is called a "view". A "scan" of an object includes a set of views made at different gantry angles or viewing angles during one rotation of the X-ray source and the detector. It is conceivable that the benefits of the methods described herein are derived from medical imaging modalities other than CT, so as used herein, the term "view" is not limited to the use described above with respect to projection data from one gantry angle. The term "view" is used to mean a data acquisition whenever there are multiple data acquisitions from different angles (whether from CT, positron emission tomography (PET) or single photon emission CT (SPECT) acquisitions), and / or any other modality (including modalities yet to be developed) and their combination in fusion embodiments.

[0042] The projection data is processed to reconstruct an image corresponding to a two-dimensional slice acquired through the object, or in some examples where the projection data includes multiple views or scans, an image corresponding to a three-dimensional image of the object. A method for reconstructing an image from a set of projection data is known in the art as a filtered back projection technique. Transmission and emission tomography reconstruction techniques also include statistical iterative methods such as maximum likelihood expectation maximization (MLEM) and ordered subset expectation reconstruction techniques and iterative reconstruction techniques. The method converts attenuation measurements from the scan into an integer called a "CT number" or "Hou's unit", which is used to control the brightness of the corresponding pixel on the display device.

[0043] To reduce the total scan time, a "helical" scan can be performed. To perform a "helical" scan, the patient is moved while data for a specified number of slices is acquired. Such systems produce a single spiral from a cone beam helical scan. The spiral mapped out by the cone beam produces projection data from which an image in each specified slice can be reconstructed.

[0044] As used herein, the phrase "reconstructing an image" is not intended to exclude embodiments in which data representing an image is generated without generating a visible image. Thus, as used herein, the term "image" broadly refers to both a visible image and data representing a visible image. However, many embodiments generate (or are configured to generate) at least one visible image.

[0045] Figure 2 Shows something like Figure 1 1. An exemplary imaging system 200 of the CT system 100 of FIG. According to aspects of the present disclosure, the imaging system 200 is configured to image a subject 204 (eg, Figure 1In one embodiment, the imaging system 200 includes a detector array 108 (see Figure 1 ). The detector array 108 also includes a plurality of detector elements 202 that together sense the X-ray radiation beam 106 that passes through a subject 204 (such as a patient) (see Figure 2 ) to collect corresponding projection data. Therefore, in one embodiment, the detector array 108 is manufactured in a multi-row or row configuration including multiple rows or rows of units or detector elements 202. In such a configuration (e.g., multi-row or row detector CT or MDCT), another row or rows of detector elements 202 are arranged in a parallel configuration for acquiring projection data. The configuration may include 4, 8, 16, 32, 64, 128, or 256 rows or rows of detector elements. For example, a 64-row MDCT scanner may have 64 rows or rows of detector elements, while a 256-row MDCT scanner may have 256 rows or rows of detector elements. Therefore, four rotations of a spiral scan performed by a 64-row or row MDCT scanner may achieve detector coverage equal to a single rotation of a scan performed by a 256-row or row MDCT scanner.

[0046] In certain embodiments, the imaging system 200 is configured to traverse different angular positions around the subject 204 to collect the desired projection data. Thus, the gantry 102 and components mounted thereon can be configured to rotate about the rotation center 206 to collect, for example, projection data at different energy levels. Alternatively, in embodiments where the projection angle relative to the subject 204 changes over time, the mounted components can be configured to move along a generally curved line rather than along a section of a circle.

[0047] Thus, as the X-ray source 104 and the detector array 108 rotate, the detector array 108 collects data of the attenuated X-ray beam. The data collected by the detector array 108 then undergoes preprocessing and calibration to condition the data to represent the line integral of the attenuation coefficient of the scanned subject 204. The processed data is often referred to as a projection.

[0048] In some examples, individual detectors or detector elements 202 in detector array 108 may include photon counting detectors that register interactions of individual photons into one or more energy bins. It should be appreciated that the methods described herein may also be implemented using energy integrating detectors.

[0049] The acquired projection data sets may be used for basis material decomposition (BMD). During BMD, the measured projections are converted into a set of material density projections. The material density projections may be reconstructed to form a pair or set of material density maps or images for each corresponding basis material (such as bone, soft tissue, and / or contrast agent maps). The density maps or images may then be correlated to form a 3D volumetric image of the basis material (e.g., bone, soft tissue, and / or contrast agent) in the imaging volume.

[0050] Once reconstructed, the base material image produced by the imaging system 200 shows the internal features of the subject 204 represented by the density of the two base materials. The density image can be displayed to show these features. In traditional methods of diagnosing medical conditions (such as disease states), and more generally diagnosing medical events, a radiologist or physician will consider a hard copy or display of a density image to discern characteristic features of interest. Such features can include lesions, size and shape of specific anatomical structures or organs, as well as other features that should be discernible in the image based on the skill and knowledge of the individual practitioner.

[0051] In one embodiment, the imaging system 200 includes a control mechanism 208 to control movement of components, such as rotation of the gantry 102 and operation of the x-ray source 104. In certain embodiments, the control mechanism 208 also includes an x-ray controller 210 configured to provide power and timing signals to the x-ray source 104. Additionally, the control mechanism 208 includes a gantry motor controller 212 configured to control the rotational speed and / or position of the gantry 102 based on imaging requirements.

[0052] In certain embodiments, the control mechanism 208 also includes a data acquisition system (DAS) 214 that is configured to sample analog data received from the detector elements 202 and convert the analog data into digital signals for subsequent processing. The DAS 214 can also be configured to selectively aggregate analog data from a subset of the detector elements 202 into a so-called macro-detector, as further described herein. The data sampled and digitized by the DAS 214 is transmitted to a computer or computing device 216. In one example, the computing device 216 stores the data in a storage device or mass storage device 218. For example, the storage device 218 can include a hard drive, a floppy disk drive, a compact disk-read / write (CD-R / W) drive, a digital versatile disk (DVD) drive, a flash drive, and / or a solid-state storage drive.

[0053] In addition, the computing device 216 provides commands and parameters to one or more of the DAS 214, the X-ray controller 210, and the gantry motor controller 212 to control system operations, such as data acquisition and / or processing. In certain embodiments, the computing device 216 controls system operations based on operator input. The computing device 216 receives operator input, which includes, for example, commands and / or scan parameters, via an operator console 220 operatively coupled to the computing device 216. The operator console 220 may include a keyboard (not shown) or a touch screen to allow an operator to specify commands and / or scan parameters.

[0054] Although Figure 2 One operator console 220 is shown, but more than one operator console may be coupled to the imaging system 200, e.g., for inputting or outputting system parameters, requesting exams, plotting data, and / or viewing images. In addition, in certain embodiments, the imaging system 200 may be coupled to multiple displays, printers, workstations, and / or the like located locally or remotely, e.g., within an institution or hospital or at an entirely different location, via one or more configurable wired and / or wireless networks (such as the Internet and / or a virtual private network, a wireless telephone network, a wireless local area network, a wired local area network, a wireless wide area network, a wired wide area network, etc.).

[0055] In one embodiment, for example, the imaging system 200 includes or is coupled to a picture archiving and communication system (PACS) 224. In an exemplary implementation, the PACS 224 is further coupled to a remote system (such as a radiology department information system, a hospital information system) and / or to an internal or external network (not shown) to allow operators at different locations to supply commands and parameters and / or gain access to image data.

[0056] The computing device 216 uses operator-provided and / or system-defined commands and parameters to operate a table motor controller 226, which in turn can control the table 114, which can be a motorized table. Specifically, the table motor controller 226 can move the table 114 to properly position the subject 204 in the gantry 102 to acquire projection data corresponding to a target volume of the subject 204.

[0057] As previously described, the DAS 214 samples and digitizes the projection data collected by the detector elements 202. Subsequently, the image reconstructor 230 uses the sampled and digitized X-ray data to perform high-speed reconstruction. Figure 2The image reconstructor 230 is shown as a separate entity, but in certain embodiments, the image reconstructor 230 may form part of the computing device 216. Alternatively, the image reconstructor 230 may not be present in the imaging system 200, and instead the computing device 216 may perform one or more functions of the image reconstructor 230. Furthermore, the image reconstructor 230 may be located locally or remotely, and may be operably connected to the imaging system 200 using a wired or wireless network. In particular, one exemplary embodiment may use computing resources in a "cloud" network cluster for the image reconstructor 230.

[0058] In one embodiment, the image reconstructor 230 stores the reconstructed image in the storage device 218. Alternatively, the image reconstructor 230 may transmit the reconstructed image to the computing device 216 to generate usable patient information for diagnosis and evaluation. In certain embodiments, the computing device 216 may transmit the reconstructed image and / or the patient information to a display or display device 232 that is communicatively coupled to the computing device 216 and / or the image reconstructor 230. In some embodiments, the reconstructed image may be transmitted from the computing device 216 or the image reconstructor 230 to the storage device 218 for short-term or long-term storage.

[0059] Figure 3 FIG. 4 shows a schematic diagram of a CT system when detecting a patient. Figure 3As shown, the CT system 310 generally includes a rotatable gantry 312 and a support table 315 arranged in a hollow imaging area 314 of the rotatable gantry 312 for carrying a patient 330. The rotatable gantry 312 includes an X-ray source S and a detector 318 arranged opposite to the X-ray source S, wherein the detector 318 includes a plurality of independent detector units D arranged in an array. When the rotatable gantry 312 is located at a certain scanning position, the X-ray source S emits a fan-shaped X-ray beam 320 in the direction of the detector 318, and the plurality of detector units D respectively sense the X-rays attenuated by the patient 330, so that a group of projection data is sensed by the detector units D, and a corresponding frame of projection data is obtained. As the rotatable gantry 312 rotates, the X-ray source S and the detector 318 rotate around the rotation center O, and the CT system 310 performs multiple scans. In each scanning process, all the detector units D can sense and obtain each corresponding frame of projection data. When the detector units D are normal, the corresponding frame of projection data can be directly used to reconstruct one or more images. The direction of the detector 318 in which the inspection object moves toward or out of the medical imaging system is called the row direction, that is, the direction in which the inspection object on the support table 315 moves toward or out of the rotatable rack 312. The extending direction of the detector 318 perpendicular to the row direction and arranged along a part surrounding the inspection object is called the channel direction, that is, the direction in which the detector 318 is arranged in an arc along the rotatable rack 312. The angle at which the detector 318 acquires raw projection data at different positions around the inspection object is called the view direction, that is, the different angles of the detector 318 when it surrounds the inspection object along the rotatable rack 312.

[0060] In digital X-ray imaging systems, it is expected to obtain clearer and higher-resolution CT images to better identify tissue structures and features. Therefore, the image can be denoised by artificial intelligence. In the process of denoising CT images, if artificial intelligence is used for denoising, in order to train the image denoising model, clean image data is usually required as the true value during the training process. However, clean image data is difficult to achieve for CT images. Therefore, an image denoising model based on self-supervised learning can be used to achieve denoising of CT images. However, if the original image obtained by the imaging system is directly segmented as a data pair, due to the limited number of acquisitions in the row direction, channel direction and viewing angle direction during the X-ray imaging process, the original image density is limited, and the segmented image data will become more sparse, and the data volume is insufficient, which may cause the image output by the model to have defects such as stripes, artifacts, and unexpected patterns, which is unacceptable for medical images.

[0061] In view of the above problems, an embodiment of the present disclosure innovatively proposes a method for generating a medical imaging data pair.

[0062] Figure 4 A flow chart of a method 400 for generating a medical imaging data pair according to an embodiment of the present disclosure is shown. In step 402, an original three-dimensional projection data set is received. The original three-dimensional projection data set is obtained by scanning an examination object by a medical imaging system. Next, in step 404, the original three-dimensional projection data set is multiplied in at least one dimension to obtain a multiplied projection data set with a higher resolution relative to the original three-dimensional projection data set. Then, in step 406, the multiplied projection data set is split into a first projection data subset and a second projection data subset in at least one dimension. Finally, in step 408, at least one imaging data pair is generated based on the first projection data subset and the second projection data subset. The imaging data pair includes an imaging data pair consisting of a first projection data subset and a second projection data subset. Additionally or alternatively, the imaging data pair includes an imaging data pair consisting of an image reconstructed from the first projection data subset and an image reconstructed from the second projection data subset.

[0063] By multiplying the original three-dimensional projection data set in at least one dimension, a multiplied projection data set with a higher resolution than the original three-dimensional projection data set can be obtained. Compared with directly splitting the original three-dimensional projection data set, the first projection data subset and the second projection data subset obtained by splitting the multiplied projection data set have lower data sparsity, reducing the possibility of defects in the output of the model. The first projection data subset and the second projection data subset provided as a data pair help to improve the performance of the image denoising model.

[0064] Figure 5 A schematic diagram of an original three-dimensional projection dataset and a multiplied projection dataset according to an embodiment of the present disclosure is shown. The original three-dimensional projection dataset is a three-dimensional projection dataset acquired by a detector of a medical imaging system. For example, the original three-dimensional projection dataset is obtained by the aforementioned Figures 1 to 3The detectors 108, 318 of the CT systems 100, 200, 310 described herein acquire. In one embodiment, the medical imaging system may be a computed tomography (CT) medical imaging system, a positron emission tomography (PET-CT) medical imaging system, or a positron emission tomography (PET) medical imaging system. The original three-dimensional projection data set shown in part (a) includes three dimensions: a row direction (Z direction), a channel direction (X direction), and a viewing angle direction (Y direction). The row direction (Z direction) indicates the direction of the detector and the direction in which the object under examination moves toward or out of the medical imaging system, that is, the scanning translation direction of the medical imaging system. For example, the detectors 108, 318 of the CT systems 100, 200, 310 may be configured with different numbers of rows of detector units in the row direction (Z direction), for example, 8 rows, 16 rows, 32 rows, 64 rows, 256 rows, 512 rows, and the like. The channel direction (X direction) indicates the extension direction of the detectors arranged perpendicular to the row direction and partially around the examination object, that is, the width direction of the detectors 108, 318 of the medical imaging system. For example, there may be about 900 channels. The viewing angle direction (Y direction) indicates the angle at which the detector acquires the original three-dimensional projection data set at different positions around the examination object. For example, the aforementioned Figures 1 to 3 The described CT systems 100, 200, 310 acquire views at angles or viewing angles at which the detectors 108, 318 are rotated with the gantry. For example, in an axial scan, the medical imaging system may acquire raw projection data or views at approximately 1000 angular positions, each of which is referred to as a viewing angle.

[0065] In one embodiment, multiplying the original three-dimensional projection data set in at least one dimension includes upsampling the original three-dimensional projection data set in at least one direction of the channel direction, the row direction, and the viewing angle direction. The multiplication multiples in different dimensions may be the same or different. The multiplication multiples may be any suitable multiples such as 2 times, 4 times, 8 times, etc. In one embodiment, the multiplication in the channel direction and the row direction is performed using different algorithms than the multiplication in the viewing angle direction. As an example, in the channel and row planes, a neural network model suitable for single image super-resolution (SISR) technology may be used to perform multiplication. As an example, in the viewing angle direction, a deep learning neural network may be used for upsampling. For example, the original data may be interpolated, and then the interpolated data may be optimized using a neural network model. For example, a SwinIR transformer (Image Restoration Using Swin Transformer, image restoration using Swin (Shifted Windows) transformer) may be used to achieve multiplication in the viewing angle direction. By performing multiplication in at least one dimension on the original three-dimensional projection data set, a multiplied projection data set with a higher resolution may be obtained. The multiplied projection dataset shown in part (b) is multiplied in the channel direction, the row direction, and the viewing angle direction. By multiplying the original 3D projection dataset, artifacts such as streaks, undesired patterns, and other defects in the reconstructed image caused by the data sparsity of the split dataset can be eliminated.

[0066] In one embodiment, the first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in a viewing angle direction. Figure 6A schematic diagram of splitting a multiplied projection data set in the viewing direction according to an embodiment of the present disclosure is shown. Part (a) shows a multiplied projection data set. In this example, the multiplied projection data set is obtained by multiplying the original three-dimensional projection data set by 4 times in the channel direction, the row direction, and the viewing direction. Then, the multiplied projection data set is alternately split in the viewing direction to obtain a first projection data subset represented by a dotted line shown in part (b1) and a second projection data subset represented by a solid line shown in part (b2). Part (b1) is located at a first position in the viewing direction of part (a), and part (b2) is located at a second position in the viewing direction of part (a). The first position and the second position alternate in the viewing direction, so the multiplication factor in the viewing direction of parts (b1) and (b2) is reduced to 2 times. Although the splitting shown in the figure is equally spaced, in other embodiments, unequally spaced splitting can also be used. For example, a splitting with a smaller interval is used in the center of the image, a splitting with a larger interval is used at the edge of the image, and so on.

[0067] In one embodiment, method 400 further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated portion of the interpolated side of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

[0068] Continue to refer Figure 6 , the first projection data subset shown in part (b1) is interpolated to obtain part (c1), wherein the interpolation part (d1) is represented by a single dot-dashed line, and the multiplication factor of the interpolation part shown in part (d1) in the viewing direction is 2 times, so after interpolation, the multiplication factor of part (c1) in the viewing direction increases to 4 times. Since the interpolation part of part (d1) and the first projection data subset of part (b1) are arranged alternately, there is a corresponding relationship with the second projection data subset shown in part (b2), and the interpolation part shown in part (d1) is referred to as a pseudo second projection data subset. Therefore, the pseudo second projection data subset of part (d1) and the uninterpolated second projection data subset of part (b2) can constitute a medical imaging data pair.

[0069] Additionally or alternatively, similar processing can be performed on the second projection data subset shown in part (b2). The second projection data subset shown in part (b2) is interpolated to obtain part (c2), wherein the interpolation part (d2) is represented by a two-dot dash line, and the multiplication factor of the interpolation part shown in part (d2) in the viewing angle direction is 2 times, so after interpolation, the multiplication factor of part (c2) in the viewing angle direction increases to 4 times. Since the interpolation part of part (d2) and the second projection data subset of part (b2) are arranged alternately, there is a corresponding relationship with the first projection data subset shown in part (b1), and the interpolation part shown in part (d2) is referred to as a pseudo first projection data subset. Therefore, the pseudo first projection data subset of part (d2) and the uninterpolated first projection data subset of part (b1) can constitute a medical imaging data pair.

[0070] Additionally or alternatively, if the pseudo first projection data subset of part (d2) and the pseudo second projection data subset of part (d1) are alternately arranged to form a pseudo projection data set, the pseudo projection data set also corresponds to the multiplied projection data set of part (a). Therefore, the pseudo projection data set formed by the alternate arrangement of the pseudo first projection data subset of part (d2) and the pseudo second projection data subset of part (d1) and the non-interpolated multiplied projection data set of part (a) can constitute a medical imaging data pair.

[0071] In one embodiment, the imaging data pair includes an imaging data pair consisting of an image reconstructed from a first projection data subset and an image reconstructed from a second projection data subset. For example, the first projection data subset of part (b1) can be reconstructed into a first reconstructed image, and the second projection data subset of part (b2) can be reconstructed into a second reconstructed image, and then the first reconstructed image and the second reconstructed image are used as a medical imaging data pair.

[0072] Additionally or alternatively, part (c1) and part (c2) may be reconstructed into reconstructed images for use as a medical imaging data pair, or part (d1) and part (d2) may be reconstructed into reconstructed images for use as a medical imaging data pair, or part (b1) and part (d2) may be reconstructed into reconstructed images for use as a medical imaging data pair, or part (d1) and part (b2) may be reconstructed into reconstructed images for use as a medical imaging data pair, or a data set formed by alternating (d1) and (d2) may be reconstructed into reconstructed images with part (a) for use as a medical imaging data pair, and so on.

[0073] In one embodiment, the first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in a channel direction. Figure 7A schematic diagram of splitting a multiplied projection data set in the channel direction according to an embodiment of the present disclosure is shown. Part (a) shows a multiplied projection data set. In this example, the multiplied projection data set is obtained by multiplying the original three-dimensional projection data set by 4 times in the channel direction, the row direction, and the viewing angle direction. Then, the multiplied projection data set is split alternately in the channel direction to obtain a first projection data subset represented by a dotted line shown in part (b1) and a second projection data subset represented by a solid line shown in part (b2). Part (b1) is located at the third position in the channel direction of part (a), and part (b2) is located at the fourth position in the channel direction of part (a). The third position and the fourth position alternate in the channel direction, so the multiplication factor in the channel direction of parts (b1) and (b2) is reduced to 2 times. Although the splitting shown in the figure is equally spaced, in other embodiments, unequally spaced splitting can also be used. For example, a splitting with a smaller interval is used in the center of the image, a splitting with a larger interval is used at the edge of the image, and so on.

[0074] In one embodiment, method 400 further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated portion of the interpolated side of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

[0075] Continue to refer Figure 7 ,and Figure 6 The processing process is similar to that of , the first projection data subset shown in part (b1) is interpolated to obtain a pseudo second projection data subset, i.e., the interpolated part. Therefore, the pseudo second projection data subset and the second projection data subset of part (b2) can constitute a medical imaging data pair.

[0076] Additionally or alternatively, with Figure 6 The processing process is similar to that of , and the second projection data subset shown in part (b2) is interpolated to obtain a pseudo first projection data subset, that is, the interpolated part. Therefore, the pseudo first projection data subset and the first projection data subset of part (b1) can constitute a medical imaging data pair.

[0077] Additionally or alternatively, a pseudo projection data set formed by alternating the pseudo first projection data subset and the pseudo second projection data subset and the non-interpolated multiplied projection data set of part (a) may constitute a medical imaging data pair.

[0078] In one embodiment, the imaging data pair includes an imaging data pair consisting of an image reconstructed from a first projection data subset and an image reconstructed from a second projection data subset. For example, the first projection data subset of part (b1) can be reconstructed into a first reconstructed image, and the second projection data subset of part (b2) can be reconstructed into a second reconstructed image, and then the first reconstructed image and the second reconstructed image are used as a medical imaging data pair.

[0079] Additionally or alternatively, the interpolated (b1) part and the interpolated (b2) part may be reconstructed into reconstructed images for use as a medical imaging data pair, or the pseudo-first projection data subset and the pseudo-second projection data subset may be reconstructed into reconstructed images for use as a medical imaging data pair, or the first projection data subset and the pseudo-second projection data subset may be reconstructed into reconstructed images for use as a medical imaging data pair, or the pseudo-first projection data subset and the second projection data subset may be reconstructed into reconstructed images for use as a medical imaging data pair, or the data set formed by alternating the pseudo-first projection data subset and the pseudo-second projection data subset may be reconstructed into reconstructed images with part (a) for use as a medical imaging data pair, and so on.

[0080] In one embodiment, the first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in the viewing angle direction and the channel direction. Figure 8 A schematic diagram of splitting a multiplied projection data set in the viewing direction and the channel direction according to an embodiment of the present disclosure is shown. Part (a) shows a multiplied projection data set. In this example, the multiplied projection data set is obtained by multiplying the original three-dimensional projection data set by 4 times in the channel direction, the row direction, and the viewing direction. Then, the multiplied projection data set is split alternately in the viewing direction and the channel direction to obtain a first projection data subset represented by black squares shown in part (b1), and a second projection data subset represented by diagonal squares shown in part (b2), wherein white squares represent no data, part (b1) is located at the first position in the viewing direction of part (a) and at the third position in the channel direction, and part (b2) is located at the second position in the viewing direction of part (a) and at the fourth position in the channel direction, the first position and the second position alternate in the viewing direction, and the third position and the fourth position alternate in the channel direction. It should be understood that although Figure 8 The terms first position, second position, third position, and fourth position are used, but these positions are not Figure 6 and Figure 7 The positions in the image may be the same or different. Although the splitting shown in the figure is equally spaced, in other embodiments, unequally spaced splitting may also be used. For example, a splitting with smaller intervals may be used in the center of the image, a splitting with larger intervals may be used at the edge of the image, and so on.

[0081] In one embodiment, method 400 further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated portion of the interpolated side of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

[0082] Continue to refer Figure 8 ,and Figure 6 The processing process is similar to that of , the first projection data subset shown in part (b1) is interpolated to obtain a pseudo second projection data subset, i.e., the interpolated part. Therefore, the pseudo second projection data subset and the second projection data subset of part (b2) can constitute a medical imaging data pair.

[0083] Additionally or alternatively, with Figure 6 The processing process is similar to that of , and the second projection data subset shown in part (b2) is interpolated to obtain a pseudo first projection data subset, that is, the interpolated part. Therefore, the pseudo first projection data subset and the first projection data subset of part (b1) can constitute a medical imaging data pair.

[0084] Additionally or alternatively, a pseudo projection data set formed by alternating the pseudo first projection data subset and the pseudo second projection data subset and the non-interpolated multiplied projection data set of part (a) may constitute a medical imaging data pair.

[0085] In one embodiment, the imaging data pair includes an imaging data pair consisting of an image reconstructed from a first projection data subset and an image reconstructed from a second projection data subset. For example, the first projection data subset of part (b1) can be reconstructed into a first reconstructed image, and the second projection data subset of part (b2) can be reconstructed into a second reconstructed image, and then the first reconstructed image and the second reconstructed image are used as a medical imaging data pair.

[0086] Additionally or alternatively, the interpolated (b1) part and the interpolated (b2) part may be reconstructed into reconstructed images for use as a medical imaging data pair, or the pseudo-first projection data subset and the pseudo-second projection data subset may be reconstructed into reconstructed images for use as a medical imaging data pair, or the first projection data subset and the pseudo-second projection data subset may be reconstructed into reconstructed images for use as a medical imaging data pair, or the pseudo-first projection data subset and the second projection data subset may be reconstructed into reconstructed images for use as a medical imaging data pair, or the data set formed by alternating the pseudo-first projection data subset and the pseudo-second projection data subset may be reconstructed into reconstructed images with part (a) for use as a medical imaging data pair, and so on.

[0087] Therefore, the method for generating medical imaging data pairs proposed in the present invention multiplies the original data and then splits the multiplied data, so that the data density of the generated medical imaging data pairs is improved, which helps the image denoising model to generate higher resolution images and eliminate defects in the images.

[0088] Fig. 9 A flowchart of a method 900 for model training according to an embodiment of the present disclosure is shown. In step 902, a medical imaging data pair is generated. Method 400 can be used to generate the medical imaging data pair. In step 904, the image denoising model is trained using the medical imaging data pair to obtain a trained image denoising model. In one embodiment, the image denoising model is a Noise2Noise model. The Noise2Noise model is a model based on self-supervised learning, and if the image data used as input and the true value are noise-associated, the model can learn the denoised image data without the need for clean image data as supervision.

[0089] Fig.10 A schematic diagram of using medical imaging data pairs to train an image denoising model according to an embodiment of the present disclosure is shown. Part (a) shows the use of projection data as a medical imaging data pair to train an image denoising model. For example, a pseudo-second projection data subset and a second projection data subset can be used as a medical imaging data pair to train an image denoising model. In the left direction, the pseudo-second projection data subset can be used as an input to the image denoising model, and the second projection data subset can be used as the true value of the image denoising model to train the image denoising model. Conversely, in the right direction, the second projection data subset can be used as an input to the image denoising model, and the pseudo-second projection data subset can be used as the true value of the image denoising model to train the image denoising model. Similarly, a reference Figures 6 to 8 The image denoising model is trained by using other medical imaging data pairs generated based on the projection data as described. Finally, the image denoising model trained using the projection data as the medical imaging data pair can be used to generate denoised projection data based on the original projection data for reconstructing an image.

[0090] Part (b) shows the use of reconstructed images as medical imaging data pairs to train an image denoising model. For example, a first reconstructed image reconstructed from a first subset of projection data and a second reconstructed image reconstructed from a second subset of projection data can be used as a medical imaging data pair to train an image denoising model. In the left direction, the first reconstructed image can be used as an input to the image denoising model, and the second reconstructed image can be used as a true value of the image denoising model to train the image denoising model. Conversely, in the right direction, the second reconstructed image can be used as an input to the image denoising model, and the first reconstructed image can be used as a true value of the image denoising model to train the image denoising model. Similarly, a reference image can be used. Figures 6 to 8 The image denoising model is trained by using other medical imaging data pairs generated based on the reconstructed image discussed. Finally, the image denoising model trained by using the reconstructed image as the medical imaging data pair can be used to generate a denoised reconstructed image based on the original reconstructed image.

[0091] As an example, using the medical imaging data pair to train the image denoising model may include: using the image denoising model to generate a prediction result based on the input, calculating a loss function between the prediction result and the true value, and updating the parameters of the image denoising model based on the loss function to obtain a trained image denoising model. Other suitable methods for training the image denoising model using the medical imaging data pair are also optional.

[0092] Therefore, the method for model training proposed in the present disclosure can train an image denoising model with higher performance, so that the generated medical images have higher resolution and are free from defects such as artifacts, stripes, and unexpected patterns.

[0093] Fig.11A flowchart of a method 1100 for medical image imaging according to an embodiment of the present disclosure is shown. The method 1100 may be performed by an image denoising model. In step 1102, a medical imaging data pair is received. The medical imaging data pair is generated by the following steps: receiving an original three-dimensional projection data set, wherein the original three-dimensional projection data set is obtained by scanning an inspection object by a medical imaging system; multiplying the original three-dimensional projection data set in at least one dimension to obtain a multiplied projection data set with a higher resolution than the original three-dimensional projection data set; splitting the multiplied projection data set into a first projection data subset and a second projection data subset in at least one dimension; and generating at least one imaging data pair based on the first projection data subset and the second projection data subset, the imaging data pair including an imaging data pair consisting of an imaging data pair based on the first projection data subset and the second projection data subset and / or an imaging data pair consisting of an image reconstructed from the first projection data subset and an image reconstructed from the second projection data subset. In step 1104, a medical image is generated based on the medical imaging data pair. In one embodiment, the image denoising model may be trained using the method 900.

[0094] In one embodiment, the original three-dimensional projection data set is acquired by a detector of a medical imaging system and includes three dimensions: a row direction, a channel direction, and a viewing angle direction. The row direction indicates the direction of the detector when the examination object moves toward or out of the medical imaging system, the channel direction indicates the extension direction of the detector that is perpendicular to the row direction and arranged locally around the examination object, and the viewing angle direction indicates the angle at which the detector acquires the original three-dimensional projection data set at different positions around the examination object.

[0095] In one embodiment, the first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in a viewing angle direction and / or a channel direction.

[0096] In one embodiment, the first projection data subset includes a data block at a first position in the viewing direction, the second projection data subset includes a data block at a second position in the viewing direction, and the first position and the second position alternate in the viewing direction; and / or the first projection data subset includes a data block at a third position in the channel direction, the second projection data subset includes a data block at a fourth position in the channel direction, and the third position and the fourth position alternate in the channel direction.

[0097] In one embodiment, it further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated part of the interpolated side of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

[0098] In one embodiment, multiplying the original 3D projection dataset in at least one dimension includes upsampling the original 3D projection dataset in at least one of a channel direction, a row direction, and a viewing angle direction.

[0099] In one embodiment, upsampling the original three-dimensional projection dataset in the viewing direction includes using a deep learning neural network, the deep learning neural network including a SwinIR transformer.

[0100] In addition, the present disclosure further provides a medical imaging system, including: a scanning device configured to acquire an original three-dimensional projection data set of an inspection object; and a processor configured to execute any one of methods 400 , 900 , and 1100 .

[0101] In addition, the present disclosure also provides a non-transitory computer-readable medium having instructions stored thereon, where the instructions can be executed by a processor to implement any one of the methods 400 , 900 , and 1100 .

[0102] Fig.12 An example block diagram of a computing device 1200 according to an embodiment of the present disclosure is shown. The computing device 1200 may be used as Figure 2 An example implementation of a computing device 216 is shown. The computing device 1200 includes: one or more processors 1220; a storage device 1210 for storing one or more programs, which, when executed by the one or more processors 1220, cause the one or more processors 1220 to implement the processes described in the present disclosure. The processor is, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0103] Fig.12 The computing device 1200 shown is only an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0104] like Fig.12 As shown, computing device 1200 is in the form of a general computing device. Components of computing device 1200 may include, but are not limited to: one or more processors 1220, storage device 1210, and bus 1250 connecting different system components (including storage device 1210 and processor 1220).

[0105] Bus 1250 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0106] The computing device 1200 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 1200, including volatile and non-volatile media, removable and non-removable media.

[0107] Storage device 1210 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 1211 and / or cache memory 1212. Computing device 1200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 1213 may be used to read and write non-removable, non-volatile magnetic media ( Fig.12 not shown, usually called a "hard drive"). Although Fig.12 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 1250 via one or more data medium interfaces. The storage device 1210 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0108] A program / utility 1214 having a set (at least one) of program modules 1215 may be stored, for example, in a storage device 1210, such program modules 1215 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 1215 generally perform the functions and / or methods of any embodiment described in the present disclosure.

[0109] The computing device 1200 may also communicate with one or more external devices 1260 (e.g., keyboard, pointing device, display 1270, etc.), one or more devices that enable a user to interact with the computing device 1200, and / or any device that enables the computing device 1200 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 1230. Furthermore, the computing device 1200 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 1240. Fig.12 As shown, the network adapter 1240 communicates with other modules of the computing device 1200 via the bus 1250. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the computing device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0110] The processor 1220 executes the programs stored in the storage device 1210 to perform various functional applications and data processing, such as implementing the process described in the present disclosure.

[0111] The technology described herein can be implemented in hardware, software, firmware, or any combination thereof, unless specifically described as being implemented in a particular manner. Any features described as modules or components can also be implemented together in an integrated logic device, or separately implemented as discrete but interoperable logic devices. If implemented in software, the technology can be implemented at least in part by a non-transient processor-readable storage medium including instructions, and when the instructions are executed, one or more of the above methods are executed. The non-transient processor-readable data storage medium can form a part of a computer program product that may include packaging materials. The program code can be implemented in a high-level procedural programming language or an object-oriented programming language to communicate with a processing system. If necessary, the program code can also be implemented in assembly language or machine language. In fact, the mechanism described herein is not limited to the scope of any particular programming language. In any case, the language can be a compiled language or an interpreted language.

[0112] One or more aspects of at least some embodiments may be implemented by representative instructions stored on a machine-readable medium that represent various logic within a processor, which when read by a machine causes the machine to fabricate logic for performing the techniques described herein.

[0113] Such machine-readable storage media may include, but are not limited to, a non-transitory tangible arrangement of articles manufactured or formed by a machine or apparatus, including storage media such as: a hard disk; any other type of disk, including floppy disks, optical disks, compact disk-read only memory (CD-ROM), compact disk-rewritable (CD-RW), and magneto-optical disks; semiconductor devices, such as read-only memory (ROM), random access memory (RAM) such as dynamic random access memory (DRAM) and static random access memory (SRAM), erasable programmable read-only memory (EPROM), flash memory, electrically erasable programmable read-only memory (EEPROM); phase change memory (PCM); magnetic or optical cards; or any other type of medium suitable for storing electronic instructions.

[0114] Instructions may also be further sent or received via a communication network using a transmission medium via a network interface device that utilizes any of a number of transmission protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.).

[0115] Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), a mobile telephone network (e.g., a cellular network), a plain old telephone (POTS) network, and a wireless data network (e.g., a wireless network called The Institute of Electrical and Electronics Engineers (IEEE) 802.11 series of standards, known as ), IEEE 802.15.4 series of standards, peer-to-peer (P2P) networks, etc. In an example, the network interface device may include one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas for connecting to a communication network. In an example, the network interface device may include multiple antennas that communicate wirelessly using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies.

[0116] The term "transmission medium" shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and includes digital or analog communications signals or other intangible medium for facilitating communication of such software.

[0117] So far, a method for generating a medical imaging data pair and a method for medical imaging according to the present disclosure are described, as well as a medical imaging system and a computer-readable storage medium capable of implementing the method.

[0118] Some exemplary embodiments have been described above. However, it should be understood that various modifications may be made to the exemplary embodiments described above without departing from the spirit and scope of the present invention. For example, if the described techniques are performed in a different order and / or if the components in the described systems, architectures, devices, or circuits are combined in different ways and / or replaced or supplemented by other components or their equivalents, suitable results may be achieved, and accordingly, these modified other implementations also fall within the scope of protection of the claims.

Claims

1. A method for generating a medical imaging data pair, comprising: Receiving an original three-dimensional projection data set, wherein the original three-dimensional projection data set is obtained by scanning an examination object by a medical imaging system; multiplying the original three-dimensional projection dataset in at least one dimension to obtain a multiplied projection dataset having a higher resolution relative to the original three-dimensional projection dataset; splitting the multiplied projection data set into a first projection data subset and a second projection data subset in the at least one dimension; as well as At least one imaging data pair is generated based on the first projection data subset and the second projection data subset, the imaging data pair including an imaging data pair consisting of the first projection data subset and the second projection data subset and / or an imaging data pair consisting of an image reconstructed from the first projection data subset and an image reconstructed from the second projection data subset.

2. The method according to claim 1, characterized in that The original three-dimensional projection data set is acquired by the detector of the medical imaging system and includes three dimensions: a row direction, a channel direction, and a viewing angle direction. The row direction indicates the direction of the detector and the direction in which the examination object moves toward or out of the medical imaging system. The channel direction indicates the extension direction of the detector that is perpendicular to the row direction and arranged locally around the examination object. The viewing angle direction indicates the angle at which the detector acquires the original three-dimensional projection data set at different positions around the examination object.

3. The method according to claim 2, characterized in that The first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in the viewing angle direction and / or the channel direction.

4. The method according to claim 3, characterized in that The first projection data subset includes a data block at a first position in the viewing direction, and the second projection data subset includes a data block at a second position in the viewing direction, the first position and the second position alternate in the viewing direction; and / or The first projection data subset includes data blocks at a third position in the channel direction, and the second projection data subset includes data blocks at a fourth position in the channel direction, and the third position and the fourth position alternate in the channel direction.

5. The method according to claim 1, characterized in that It further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated part of the interpolated side of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

6. The method according to claim 2, characterized in that Multiplying the original three-dimensional projection data set in at least one dimension includes upsampling the original three-dimensional projection data set in at least one direction of a channel direction, a row direction, and a viewing angle direction.

7. The method according to claim 6, characterized in that Upsampling the original three-dimensional projection data set in the viewing direction includes using a deep learning neural network, and the deep learning neural network includes a SwinIR transformer.

8. A method for medical imaging, comprising: A medical imaging data pair is received, wherein the medical imaging data pair is generated by: Receiving an original three-dimensional projection data set, wherein the original three-dimensional projection data set is obtained by scanning an examination object by a medical imaging system; multiplying the original three-dimensional projection dataset in at least one dimension to obtain a multiplied projection dataset having a higher resolution relative to the original three-dimensional projection dataset; splitting the multiplied projection data set into a first projection data subset and a second projection data subset in the at least one dimension; and generating at least one imaging data pair based on the first projection data subset and the second projection data subset, the imaging data pair comprising an imaging data pair consisting of the first projection data subset and the second projection data subset and / or an imaging data pair consisting of an image reconstructed from the first projection data subset and an image reconstructed from the second projection data subset; and A medical image is generated based on the medical imaging data pair.

9. The method according to claim 8, characterized in that The original three-dimensional projection data set is acquired by the detector of the medical imaging system and includes three dimensions: a row direction, a channel direction, and a viewing angle direction. The row direction indicates the direction of the detector and the direction in which the examination object moves toward or out of the medical imaging system. The channel direction indicates the extension direction of the detector that is perpendicular to the row direction and arranged locally around the examination object. The viewing angle direction indicates the angle at which the detector acquires the original three-dimensional projection data set at different positions around the examination object.

10. The method according to claim 9, characterized in that The first projection data subset and the second projection data subset are obtained by alternately splitting the multiplied projection data set in the viewing angle direction and / or the channel direction.

11. The method according to claim 10, characterized in that The first projection data subset includes a data block at a first position in the viewing direction, and the second projection data subset includes a data block at a second position in the viewing direction, the first position and the second position alternate in the viewing direction; and / or The first projection data subset includes data blocks at a third position in the channel direction, and the second projection data subset includes data blocks at a fourth position in the channel direction, and the third position and the fourth position alternate in the channel direction.

12. The method according to claim 8, characterized in that It further includes interpolating at least one of the first projection data subset and the second projection data subset in the imaging data pair, and then forming at least one imaging data pair based on the interpolated part of the interpolated side of the first projection data subset and the second projection data subset and the projection data subset of the non-interpolated side.

13. The method according to claim 8, characterized in that Multiplying the original three-dimensional projection data set in at least one dimension includes upsampling the original three-dimensional projection data set in at least one direction of a channel direction, a row direction, and a viewing angle direction.

14. A medical imaging system comprising: A scanning device configured to acquire an original three-dimensional projection data set of an inspection object; as well as A processor configured to execute the method according to any one of claims 1 to 13.

15. A non-transitory computer readable medium having instructions stored thereon, wherein the instructions are executable by a processor to implement the method according to any one of claims 1 to 13.

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