CROSS REFERENCE TO RELATED APPLICATIONS

This patent application claims the benefit of and is a U.S. national filing of Chinese Patent Application Invention No. 202010311001.3, filed Apr. 20, 2020, in the name of Sun et al., and entitled METHOD AND SYSTEM FOR AUTOMATIC QUALITY CONTROL FOR DR IMAGES, which is hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates to the technical field of digital X-ray imaging, and more particularly, to an X-ray image quality control system.

TECHNICAL BACKGROUND

In digital X-ray imaging, images with poor quality are often unacceptable. The reasons may include incorrectly positioning of the imaged body part, patient movement, artifacts, and poor exposure. Among them, positioning errors can be divided into two types: center positioning errors and errors caused by improper projection, such as the rotation of the camera. The positioning error may also include errors such as the body part to be imaged is not completely imaged (or is blocked or truncated).

Quality control for X-ray images (such as digital radiography images or computed radiography images) is usually implemented based on the visual observation of a technician, which appears to be relatively subjective. Some image quality defects are relatively obvious, such as necklace shadows (which will block the observation of the chest or spine) or visual truncation of the soft tissue region of the knee. Some image quality defects, such as the area of the overlap region between the scapula and lungs exceeds a predetermined threshold, are relatively difficult to be identified and often require careful observation. In general, the quality control of X-ray images requires manual operation, and is subjective and highly depends on the professional experience of technicians.

An automated image quality assurance system includes motion detection, anatomical integrity detection, low exposure detection, and artifact detection. The image quality assurance system of the prior art may have the following defects: no special image quality standard is adopted in the solution for the detection of incorrectly positioning, and the solution for the detection of poor exposure may depend on the exposure index or a global or local histogram analysis, and the image quality assurance system does not consider whether the DR imaged body parts, tissues or bone structure can be identified. In another image quality assessment solution, the DR image is uploaded to the cloud service, and the image quality assessment is performed by the server of the cloud service. However, the technicians at the image acquisition side cannot get feedback in time to adjust the image acquisition solution.

SUMMARY OF THE INVENTION

According to one aspect of the present disclosure, an X-ray image quality control system is provided, which overcomes at least one of the above-mentioned defects of the prior art.

In order to achieve the above object, the present disclosure provides a technical solution as follows.

An X-ray image quality control system includes: an image acquisition unit configured to acquire an X-ray image and determine the type of the imaged body part and the type of the projection mode; a quality detection unit configured to detect an image defect in X-ray image with regard to the type of the imaged body part and/or the type of the projection mode.

Alternatively, the quality control system further comprises a quality feedback unit configured to feed back image defects to the image acquisition unit and/or the image acquisition side.

Alternatively, the quality detection unit is configured to extract a feature set of the position and orientation for a bone structure and/or a soft tissue structure of the imaged body part; locate or segment a region of interest of the imaged body part based on the calculation of the feature set.

Alternatively, the quality detection unit is configured to be trained using a machine learning algorithm to determine and/or adjust at least a first detection parameter.

Alternatively, the quality detection unit is configured to, for a first combination of the type of the imaged body part and the type of the projection mode, detect the region of interest based on the first detection parameter; for a second combination of the type of the imaged body part and the type of the projection mode, detect the region of interest based on a second detection parameter different from the first detection parameter.

Alternatively, the quality detection unit is further configured to detect the region of interest associated with a first image defect type by using a histogram analysis algorithm.

Alternatively, the image acquisition unit is configured to perform pre-processing on the X-ray image, the pre-processing comprises dividing the X-ray image into a bone structure region, a soft tissue region and a background region.

Alternatively, the quality feedback unit comprises an interactive unit for selecting image defects and/or setting an automatic selection parameters.

The present disclosure further provides an X-ray imaging apparatus, which includes the above-mentioned X-ray image quality control system.

According to another aspect of the present disclosure, an X-ray image quality detection method is provided, comprising: acquiring an X-ray image; determining the type of the imaged body part and the type of the projection mode; detecting an image defect of the X-ray images with regard to the type of the imaged body part and/or the type of the projection mode.

Alternatively, the quality detection method further includes feeding the image defect back to an X-ray image acquisition side.

Alternatively, detecting the image defect of the X-ray image comprises: extracting a feature set of the position and orientation for a bone structure and/or a soft tissue structure of the imaged body part; locating or segmenting a region of interest of the imaged body part based on the calculation of the feature set.

Alternatively, detecting the image defect of the X-ray image further comprises: for a first combination of the type of the imaged body part and the type of the projection mode, detecting the region of interest based on a first detection parameter; for a second combination of the type of the imaged body part and the type of the projection mode, detecting the region of interest based on a second detection parameter different from the first detection parameter; wherein the first detection parameter and the second detection parameter are determined by using a machine learning algorithm.

Alternatively, detecting the image defect of the X-ray image further comprises detecting the region of interest associated with a first image defect type by using a histogram analysis algorithm.

Alternatively, the method further comprises: performing pre-processing on the X-ray image, wherein the pre-processing comprises dividing the X-ray image into a bone structure region, a soft tissue region, and a background region.

The present disclosure provides an automated image quality control system, which automatically and comprehensively evaluates the quality of X-ray images, and identifies image defects including inaccurately positioning, patient movement, external object artifacts, and poor exposure. The image quality control system further provides feedback to technicians at the image acquisition point, which is beneficial for a timely adjustment of the image acquisition solution. In addition, the system can also use machine learning techniques to evaluate X-ray image quality in real time.

