Medical imaging method and system

Abstract

The present disclosure provides a medical imaging method and system and a non-transitory computer-readable storage medium. The medical imaging method comprises obtaining an original image acquired by an X-ray imaging system, and post-processing the original image based on a trained network to obtain an optimized image after processing. The medical imaging system comprises a control module, configured to obtain an original image of an object; a learning network module, configured to post-process the original image to obtain a post-processed image based on user preferences selected by one or more users; and an optimized module, configured to optimize the learning network based on generated images sent to the learning network module, wherein the generated images comprise the post-processed image previously obtained by the learning network module.

What is claimed is: 
     
         1 . A medical imaging system, comprising:
 a control module, configured to obtain an original image of an object;   a learning network module, configured to post-process the original image to obtain a post-processed image based on user preferences selected by one or more users; and   an optimized module, configured to optimize the learning network based on generated images sent to the learning network module, wherein the generated images comprise the post-processed image previously obtained by the learning network module.   
     
     
         2 . The system according to  claim 1 , wherein the system further comprises a display module comprising a display interface, configured to show one or more options corresponding to the one or more user preferences. 
     
     
         3 . The system according to  claim 2 , wherein the learning network module comprises one or more post-process units being corresponding to the one or more user preferences, wherein the one or more post-process units is configured to process the original image based on the one or more options correspondingly. 
     
     
         4 . The system according to  claim 3 , wherein based on a selection of one of the options, the original image is inputted into the post-process unit to obtain the post-processed image based on the corresponding user preference, and the obtained post-processed image is displayed in the display module. 
     
     
         5 . The system according to  claim 1 , wherein the one or more user preference is determined based on identity information of users. 
     
     
         6 . The system according to  claim 1 , wherein the one or more user preference is determined based on user's input. 
     
     
         7 . The system according to  claim 1 , wherein the learning network module is trained based on a sample original image set as an input and one or more sample target image set as an output, the sample original image set comprises a plurality of original images acquired by the control module, the one or more sample target image set comprise post-processed images obtained by post-processing the original images based on preferences of one or more users. 
     
     
         8 . The system according to  claim 7 , wherein the generated images are further trained as the sample target image set to optimize the learning network module. 
     
     
         9 . The system according to  claim 1 , wherein the optimized module comprises a transferring learning model, the generated images comprise the images sent to a Picture Archiving and Communication System (PACS), and the optimized module is further configured to select the generated image and send it to the learning network. 
     
     
         10 . An optimizing method for a post-process module, comprising:
 training a learning network to obtain the post-processing module based on preferences of one or more users; and   optimizing the post-processing module based on generated images sent to the post-processing module, wherein the generated images comprise the post-processed image obtained by the post-processing module.   
     
     
         11 . The method according to  claim 10 , wherein training the learning network comprises:
 obtaining a plurality of original images to obtain a sample original image set;   post-processing the original images based on preferences of one or more users to obtain a plurality of target image sets corresponding to preferences of each user; and   training the learning network by using the sample original image set as an input and each of the plurality of target image sets as an output, so as to obtain one or more post-process unit corresponding to the preferences of each user.   
     
     
         12 . The method according to  claim 11 , wherein optimizing the post-processing module comprises: inputting the post-processed image obtained by the post-processing module as the sample target image set to optimize the learning network. 
     
     
         13 . A non-transitory computer-readable storage medium for storing a computer program, wherein when executed by a computer, the computer program causes the computer to perform:
 training a learning network to obtain a post-processing module based on preferences of one or more users; and   optimizing the post-processing module based on generated images sent to the post-processing module, wherein the generated images comprise the post-processed image obtained by the post-processing module.