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.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a continuation of U.S. patent application Ser. No. 16 / 902,330, filed on Jun. 16, 2020, which claims the benefit of Chinese Patent Application No. 201910519964.X, filed on Jun. 17, 2019, the disclosures of which are incorporated herein by reference in their entireties.BACKGROUND[0002]The present disclosure relates to a medical imaging technology, and more specifically, to a medical imaging method and system and a non-transitory computer-readable storage medium.[0003]In an X-ray imaging system, radiation from a source irradiates upon a subject, where the subject is usually a patient in medical diagnosis applications. Part of the radiation passes through the patient and impacts a detector, where the detector is divided into a matrix of discrete elements (for example, pixels). Detector elements are read to generate output signals based on the quantity or intensity of radiation impacting each pixel region. The signals may t...

AI Technical Summary

Benefits of technology

The patent describes a method for improving medical images by using a network trained on deep learning techniques. The method can process X-ray images and produce an optimized image that has improved clarity and reduced noise. This can be useful for medical professionals in diagnosing and treating diseases. The main technical effect is to enhance the quality of medical images for better interpretation and diagnosis.

Problems solved by technology

The image quality of the medical image obtained based on the signals acquired by the detector is usually undesirable, and post-processing (such as noise reduction, detail enhancement, and contrast adjustment) needs to be performed on the medical image to improve image quality.

In this process, however, image processing parameters are mutually dependent, and the adjustment of a specific post-processing parameter may affect the post-processing effect of other parameters.

For example, the contrast of the medical image is adjusted based on a contrast parameter, but the adjustment of this parameter may affect some details of the medical image (for example, obscure some key details); accordingly, processing of background noise of the medical image may affect the details (or contrast or other aspects) of the medical image.

As a result, obtaining an ideal optimized image by adjusting the corresponding parameter in each post-processing process is difficult and requires repeated attempts.

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