Medical image quality evaluation method, device, equipment and storage medium

Abstract

The embodiment of the invention discloses a medical image quality evaluation method, a device, equipment and a storage medium. The method comprises the following steps: acquiring a reference image anda target image which are scanned and reconstructed by a subject and a trained complete image quality evaluation machine learning model, the reference image and the target image have the same fault information and position information, and the reference image does not have image quality defects to be evaluated corresponding to the image quality evaluation machine learning model; and inputting thereference image and the target image into an image quality evaluation machine learning model to obtain an evaluation result of the image quality of the target image. According to the medical image quality evaluation method, by taking the reference image without the to-be-evaluated image quality defect as a gold standard, whether the target image has the image quality defect is evaluated, so that adoctor can make a scanning decision according to a quality evaluation result, the workload of the doctor can be effectively reduced, and subsequent diagnosis is prevented from being influenced by a medical image with poor quality.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of medical image processing, and in particular, to a method, device, device, and storage medium for evaluating the quality of medical images. Background technique [0002] In medical imaging systems, the image quality depends on many factors, such as spatial resolution, tissue contrast, signal-to-noise ratio, etc. The defects in image quality caused by these factors can be improved by optimizing hardware and scanning parameters. This results in better image quality; however, in some cases, for example, the defect of metal artifacts caused by metal devices such as surgical instruments, motion artifacts caused by the subject's movement during the scanning process such as breathing, heartbeat, etc. The defect of imaging is that better image quality cannot be obtained through optimization of hardware and scanning parameters, and poor image quality cannot meet the requirements of clinical di...

AI Technical Summary

Problems solved by technology

However, each subject may correspond to multiple reconstructed images. Take the whole body MRI scan as an example. On the one hand, due to the limitation of scanning hardware, the whole body scan can only be completed by scanning several beds separately; on the other hand, each A routine MRI scan on a bed should include images with different weights such as T1, T2, and DWI, and images with the same weight should include different acquisition orientations such as T2 transverse and T2 coronal, acquisitions with different parameters, and / or, some diseases will More targeted MRI scan sequences are added in some parts, therefore, there may be at least twenty MRI scan sequences, that is, there may be at least twenty reconstructed images for each subject, which allows physicians to observe the images The quality process is quite time-consuming and laborious, increasing the workload of physicians

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