US-12626439-B2 - Methods and systems for correcting projection data

Abstract

The present disclosure provides a method and system for correction projection data. The method includes: obtaining the entity projection data and the transformed projection data related to the imaging object, the transformed projection data includes the first projection data related to the pixel shading generated by the detector components; inputting the entity projection data, the transformed projection data to the trained correction model and obtaining the correction projection data. The correction model obtained through training realizes the efficient and accurate correction of multi-energy imaging/spectral radiography, so that the obtained correction projection data may be more in line with the complexity of the actual system, and the correction effect may be better.

Inventors

• Yanyan LIU

Assignees

• SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

Dates

Publication Date

20260512

Application Date

20230629

Priority Date

20210113

Claims (20)

1. 1 . A method, implemented on a computing device having one or more processors and one or more storage devices, comprising: obtaining raw projection data by scanning an object using an imaging device including a detector; determining, based on the raw projection data, transformed projection data, wherein the transformed projection data includes at least one of: first projection data relating to radiation obstructed by the imaging device, or a portion thereof, or second projection data relating to a position of the detector, or a portion thereof; obtaining corrected projection data by processing the raw projection data and the transformed projection data using a correction model, wherein the correction model includes a trained machine learning model; and generating a medical image of the object by reconstructing the corrected projection data.
2. 2 . The method of claim 1 , further comprising: determining the first projection data based on exponential transformation of the raw projection data.
3. 3 . The method of claim 1 , further comprising: determining the second projection data based on gradient information of the raw projection data, the gradient information relating to the position of the detector, or a portion thereof.
4. 4 . The method of claim 1 , further comprising: obtaining the second projection data by performing a differential operation on the raw projection data based on the position of the detector, or a portion thereof.
5. 5 . The method of claim 1 , wherein the imaging device includes a multi-energy computed tomography (CT) scanner, and the object is scanned by using the multi-energy CT scanner to emit a multi-energy ray for performing multi-energy imaging on the object.
6. 6 . The method of claim 1 , wherein the correction model is obtained by a training process, the training process comprising: obtaining a plurality of training samples each of which includes sample raw projection data, sample transformed projection data obtained based on the sample raw projection data, and standard projection data of a sample standard energy ray corresponding to the sample raw projection data; and determining the correction model by training a preliminary correction model using the plurality of training samples.
7. 7 . The method of claim 6 , wherein the sample raw projection data of a training sample is obtained by a multi-energy imaging of a sample object, or a simulation thereof.
8. 8 . The method of claim 6 , wherein the standard energy ray is a single energy ray.
9. 9 . The method of claim 6 , wherein the standard projection data is obtained by polynomial fitting of the sample raw projection data.
10. 10 . The method of claim 1 , wherein the corrected projection data is obtained by processing the raw projection data and the transformed projection data based on a correction coefficient group.
11. 11 . The method of claim 10 , wherein the corrected projection data is obtained by performing polynomial fitting on the raw projection data and the transformed projection data based on the correction coefficient group.
12. 12 . The method of claim 10 , wherein the correction coefficient group is determined based on one or more parameters determined from a correction model.
13. 13 . The method of claim 12 , wherein the correction coefficient group includes a weight corresponding to each input of the correction model.
14. 14 . The method of claim 1 , wherein the obtaining corrected projection data by processing the raw projection data and the transformed projection data comprises: obtaining the corrected projection data by inputting the raw projection data and the transformed projection data into a correction model, wherein the correction model is a trained machine learning model.
15. 15 . The method of claim 1 , wherein the transformed projection data further includes at least one term of an N-th power of a polynomial representation of the raw projection data, wherein N is an integer greater than or equal to 2.
16. 16 . A system for correcting projection data, comprising: at least one storage device including a set of instructions or programs; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions or programs, the at least one processor is configured to cause the system to perform operations including: obtaining raw projection data by scanning an object using an imaging device including a detector; determining, based on the raw projection data, transformed projection data, wherein the transformed projection data includes at least one of: first projection data relating to radiation obstructed by the imaging device, or a portion thereof, second projection data relating to a position of the detector, or a portion thereof, or at least one term of an N-th power of a polynomial representation of the raw projection data, wherein N is an integer greater than or equal to 2; obtaining corrected projection data by processing the raw projection data and the transformed projection data; and generating a medical image of the object by reconstructing the corrected projection data.
17. 17 . The system of claim 16 , wherein the operations further include: determining the first projection data based on exponential transformation of the raw projection data.
18. 18 . The system of claim 16 , wherein the operations further include: determining the second projection data based on gradient information of the raw projection data, the gradient information relating to the position of the detector, or a portion thereof.
19. 19 . The system of claim 16 , wherein the imaging device includes a multi-energy computed tomography (CT) scanner, and the object is scanned by using the multi-energy CT scanner to emit a multi-energy ray for performing multi-energy imaging on the object.
20. 20 . A non-transitory computer-readable storage medium embodying a computer program product, the computer program product comprising instructions configured to cause a computing device to: obtaining raw projection data-by scanning an object using an imaging device including a detector; determining, based on the raw projection data, transformed projection data, wherein the transformed projection data includes at least one of: first projection data relating to radiation obstructed by the imaging device, or a portion thereof, or second projection data relating to a position of the detector, or a portion thereof; obtaining corrected projection data by processing the raw projection data and the transformed projection data using a correction model, wherein the correction model includes a trained machine learning model; and generating a medical image of the object by reconstructing the corrected projection data.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of International Application No. PCT/CN2022/071843, filed on Jan. 13, 2022, which claims priority to Chinese Patent Application No. 202110045078.5, filed on Jan. 13, 2021, the contents of each of which are hereby incorporated by reference. TECHNICAL FIELD The present disclosure generally relates to systems and methods for image processing, and in particular, to systems and methods for correcting projection data. BACKGROUND Multi-energy imaging/spectral radiography exploits different absorption rates of radiation of different ray energies by tissue, organs, and/or materials to generate an image that may allow differentiation of such tissue, organs, and/or material compositions. In multi-energy imaging/spectral radiography, as the rays emitted are in a broadband spectrum, beam hardening may occur during the ray transmission process, resulting in different attenuation at different positions, and further appearing in the projection data, as well as in an image as an artifact (e.g., a cup-shaped artifact). In addition, one or more additional artifacts may occur caused by a deviation of an installation position of a component of an imaging device (e.g., a detector, or a portion thereof) used to acquire the projection data relative to a reference position (e.g., an ideal position), a ray obstructed by a component of the imaging device (e.g., detector, or a portion thereof), etc. Therefore, methods and systems for artifact correction in multi-energy imaging/spectral radiography is needed. SUMMARY According to an aspect of the present specification, it provides a method for correcting projection data. The method includes: obtaining transform projection data and entity projection data related to the imaging object, the transform projection data includes the first projection data related to the pixel shading generated by the detector components; inputting the entity projection data, the transform projection data to the trained correction model and obtaining the correction projection data. According to another aspect of the present specification, it provides a system for correcting projection data. The system includes: data obtaining module, configured to obtain the transform projection data and the entity projection data related to the imaging object, the transform projection data includes the first projection data related to the pixel shading generated by the detector components; correction module, configured to put the entity projection data and the transform data into a trained correction model and obtain the correction projection data. According to another aspect of the present specification, it provides a device for correcting projection data, including a processor, the processor is configured to execute the method for correcting projection data. Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure is further describable in terms of exemplary embodiments. These exemplary embodiments are describable in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein: FIG. 1 is a schematic diagram illustrating an exemplary system for correcting projection data according to some embodiments of the present disclosure; FIGS. 2A and 2B are block diagrams illustrating an exemplary first processing device configured to correct projection data and an exemplary second processing device configured to generate a correction model according to some embodiments of the present disclosure, respectively; FIG. 3 is a flowchart illustrating an exemplary process for correcting projection data according to some embodiments of the present disclosure; FIG. 4 is a flowchart illustrating an exemplary process for training a preliminary correction model according to some embodiments of the present disclosure; and FIG. 5 is a schematic diagram illustrating an exemplary correction model according to some embodiments of the present disclosure. DETAILED DESCRIPTION In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure. However, it should be apparent to those skilled in the art that the present disclosure may be practiced without such details. In other instances, well-known methods, procedures, systems, components, and/or circuitry