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EP-4510935-B1 - SYSTEMS AND METHODS FOR POSITRON EMISSION TOMOGRAPHY IMAGING

EP4510935B1EP 4510935 B1EP4510935 B1EP 4510935B1EP-4510935-B1

Inventors

  • LYU, Yang

Dates

Publication Date
20260506
Application Date
20230530

Claims (15)

  1. A system (100) for positron emission tomography (PET) imaging, comprising: at least one storage device (150) including a set of instructions for medical imaging; and at least one processor (140) in communication with the at least one storage device (150), wherein when executing the set of instructions, the at least one processor (140) is configured to direct the system (100) to perform operations including: (410) obtaining scan data of an object (118) collected by a PET scan over a scan time period; characterised by (420) determining a plurality of target sets of scan data from the scan data based on a preset condition, wherein each of the plurality of target sets of scan data corresponds to a target sub-time period in the scan time period; (430) generating, based on the plurality of target sets of scan data, one or more intermediate images corresponding to one or more sub-time periods different from the plurality of target sub-time periods in the scan time period; and (440) generating a target image sequence of the object (118) based on the plurality of target sets of scan data and the one or more intermediate images.
  2. The system (100) of claim 1, wherein the determining a plurality of target sets of scan data from the scan data based on a preset condition includes: (510) dividing the scan data into a plurality of candidate sets of scan data; (520) for each of the plurality of candidate sets of scan data, determining a three-dimensional (3D) counting distribution map corresponding to the candidate set of scan data, wherein the 3D counting distribution map includes at least one pixel each of which corresponds to a pixel value indicating a count of coincidence events associated with the pixel; and (530) determining the plurality of target sets of scan data based on a plurality of 3D counting distribution maps corresponding to the plurality of candidate sets of scan data respectively.
  3. The system (100) of claim 2, wherein the determining the plurality of target sets of scan data based on a plurality of 3D counting distribution maps corresponding to the plurality of candidate sets of scan data respectively includes: arranging the plurality of 3D counting distribution maps in chronological order to form a map sequence; and determining the plurality of target sets of scan data by traversing the map sequence starting from the first 3D counting distribution map in the map sequence, wherein the traversing the map sequence starting from the first 3D counting distribution map includes: determining a difference between the latest 3D counting distribution map corresponding to the latest determined target set of scan data and each of the 3D counting distribution maps after the latest 3D counting distribution map in sequence until the difference between the latest 3D counting distribution map and one of the 3D counting distribution maps after the latest 3D counting distribution map is larger than or equal to a preset threshold, and designating a candidate set of scan data corresponding to the one of the 3D counting distribution maps as a target set of scan data.
  4. The system (100) of claim 1, wherein the determining a plurality of target sets of scan data from the scan data based on a preset condition includes: obtaining a time-activity curve associated with a tracer for the PET scan; and determining the plurality of target sets of scan data from the scan data based on the time-activity curve.
  5. The system (100) of claim 1, wherein the determining a plurality of target sets of scan data from the scan data based on a preset condition includes: obtaining a vital signal of the object (118) corresponding to the scan data; determining the plurality of target sets of scan data from the scan data based on the vital signal.
  6. The system (100) of any one of claims 1-5, wherein the generating one or more intermediate images correspond to one or more sub-time periods different from the plurality of target sub-time periods in the scan time period includes: generating a plurality of target images corresponding to the plurality of target sets of scan data respectively; and generating the one or more intermediate images based on the plurality of target images.
  7. The system (100) of claim 6, wherein the generating a plurality of target images corresponding to the plurality of target sets of scan data respectively includes: determining a plurality of preliminary images corresponding to the plurality of target sets of scan data respectively; and determining the plurality of target images by performing a de-noise operation on the plurality of preliminary images using a de-noise model and/or performing a resolution-improve operation on the plurality of preliminary images using a resolution-improve model.
  8. The system (100) of claims 6 or 7, wherein the generating one or more intermediate images correspond to one or more sub-time periods different from the plurality of target sub-time periods in the scan time period includes: for each pair of one or more pairs of target images among the plurality of target images, determining a motion field between the pair of target images using a motion field generation model; and generating one or more intermediate images corresponding to the pair of target images based on the motion field using an image generation model.
  9. The system (100) of claim 8, wherein the motion field generation model and the image generation model are integrated into a single model.
  10. The system (100) of any one of claims 6-9, wherein the generating a target image sequence of the object (118) based on the plurality of target sets of scan data and the one or more intermediate images includes: for each pair of one or more pairs of images among the plurality of target images and the one or more intermediate images, determining a secondary motion field between the pair of images using a motion field generation model; and generating one or more secondary intermediate images corresponding to the pair of images based on the secondary motion field using an image generation model; and generating the target image sequence of the object (118) based on the plurality of target images, the one or more intermediate images, one or more secondary intermediate images.
  11. The system (100) of claim 10, wherein a difference between the pair of images is larger than a preset difference threshold.
  12. A method for positron emission tomography (PET) imaging, the method being implemented on a computing device (200) having at least one storage device (220) and at least one processor (210), the method comprising: (410) obtaining scan data of an object (118) collected by a PET scan over a scan time period; characterised by (420) determining a plurality of target sets of scan data from the scan data based on a preset condition, wherein each of the plurality of target sets of scan data corresponds to a target sub-time period in the scan time period; (430) generating, based on the plurality of target sets of scan data, one or more intermediate images corresponding to one or more sub-time periods different from the plurality of target sub-time periods in the scan time period; and (440) generating a target image sequence of the object (118) based on the plurality of target sets of scan data and the one or more intermediate images.
  13. The method of claim 12, wherein the determining a plurality of target sets of scan data from the scan data based on a preset condition includes: (510) dividing the scan data into a plurality of candidate sets of scan data; (520) for each of the plurality of candidate sets of scan data, determining a three-dimensional (3D) counting distribution map corresponding to the candidate set of scan data, wherein the 3D counting distribution map includes at least one pixel each of which corresponds to a pixel value indicating a count of coincidence events associated with the pixel; and (530) determining the plurality of target sets of scan data based on a plurality of 3D counting distribution maps corresponding to the plurality of target sets of scan data respectively.
  14. The method of claim 13, wherein the determining the plurality of target sets of scan data based on a plurality of 3D counting distribution maps corresponding to the plurality of target sets of scan data respectively includes: arranging the plurality of 3D counting distribution maps in chronological order to form a map sequence; and determining the plurality of target sets of scan data by traversing the map sequence starting from the first 3D counting distribution map in the map sequence, wherein the traversing the map sequence starting from the first 3D counting distribution map includes: determining a difference between the latest 3D counting distribution map corresponding to the latest determined target set of scan data and each of the 3D counting distribution maps after the latest 3D counting distribution map in sequence until the difference between the latest 3D counting distribution map and one of the 3D counting distribution maps after the latest 3D counting distribution map is larger than or equal to a preset threshold, designating a candidate set of scan data corresponding to the one of the 3D counting distribution maps as a target set of scan data.
  15. A non-transitory computer readable medium, comprising at least one set of instructions for positron emission tomography (PET) imaging, wherein when executed by one or more processors (210) of a computing device (200), the at least one set of instructions causes the computing device (200) to perform a method, the method comprising: (410) obtaining scan data of an object (118) collected by a PET scan over a scan time period; characterised by (420) determining a plurality of target sets of scan data from the scan data based on a preset condition, wherein each of the plurality of target sets of scan data corresponds to a target sub-time period in the scan time period; (430) generating, based on the plurality of target sets of scan data, one or more intermediate images corresponding to one or more sub-time periods different from the plurality of target sub-time periods in the scan time period; and (440) generating a target image sequence of the object (118) based on the plurality of target sets of scan data and the one or more intermediate images.

Description

TECHNICAL FIELD The present disclosure relates to medical imaging technology, and in particular, to systems and methods for positron emission tomography (PET) imaging. BACKGROUND PET imaging has been widely used in clinical examination and disease diagnosis in recent years. In particular, dynamic PET imaging can provide a set of images over a dynamic scan time period and dynamic PET data can also provide rich information related to physiological parameters (e.g., perfusion pressure) that indicate the functional status of the imaged tissue(s) or organ(s). However, the dynamic PET imaging generally requires a relatively long scan time and a relatively long image reconstruction time, and also has a relatively low image quality. Therefore, it is desirable to provide systems and methods for PET imaging with improved imaging efficiency and image quality. Document US2013261440A1 may be considered to disclose the preamble of the independent claims. SUMMARY According to an aspect of the present disclosure, a system for PET imaging may be provided. The system may include at least one storage device including a set of instructions for medical imaging and at least one processor in communication with the at least one storage device. When executing the set of instructions, the at least one processor may be configured to direct the system to perform the following operations. The system may obtain scan data of an object collected by a PET scan over a scan time period. The system may determine a plurality of target sets of scan data from the scan data based on a preset condition, wherein each of the plurality of target sets of scan data corresponds to a target sub-time period in the scan time period. The system may also generate one or more intermediate images corresponding to one or more sub-time periods different from the plurality of target sub-time periods in the scan time period based on the plurality of target sets of scan data. The system may further generate a target image sequence of the object based on the plurality of target sets of scan data and the one or more intermediate images. In some embodiments, to determine a plurality of target sets of scan data from the scan data based on a preset condition, the system may perform the following operations. The system may divide the scan data into a plurality of candidate sets of scan data. For each of the plurality of candidate sets of scan data, the system may determine a three-dimensional (3D) counting distribution map corresponding to the candidate set of scan data. The 3D counting distribution map may include at least one pixel each of which corresponds to a pixel value indicating a count of coincidence events associated with the pixel. Further, the system may determine the plurality of target sets of scan data based on a plurality of 3D counting distribution maps corresponding to the plurality of candidate sets of scan data respectively. In some embodiments, to determine the plurality of target sets of scan data based on a plurality of 3D counting distribution maps corresponding to the plurality of candidate sets of scan data respectively, the system may perform the following operations. The system may arrange the plurality of 3D counting distribution maps in chronological order to form a map sequence. Further, the system may determine the plurality of target sets of scan data by traversing the map sequence starting from the first 3D counting distribution map in the map sequence. The traversing the map sequence starting from the first 3D counting distribution map may include determining a difference between the latest 3D counting distribution map corresponding to the latest determined target set of scan data and each of the 3D counting distribution maps after the latest 3D counting distribution map in sequence until the difference between the latest 3D counting distribution map and one of the 3D counting distribution maps after the latest 3D counting distribution map is larger than or equal to a preset threshold, and designating a candidate set of scan data corresponding to the one of the 3D counting distribution maps as a target set of scan data. In some embodiments, to determine a plurality of target sets of scan data from the scan data based on a preset condition, the system may perform the following operations. The system may obtain a time-activity curve associated with a tracer for the PET scan. The system may further determine the plurality of target sets of scan data from the scan data based on the time-activity curve. In some embodiments, to determining a plurality of target sets of scan data from the scan data based on a preset condition, the system may perform the following operations. The system may obtain a vital signal of the object corresponding to the scan data. Further, the system may determine the plurality of target sets of scan data from the scan data based on the vital signal. In some embodiments, to generate one or more intermediate images cor