CN116912239B - Medical imaging full-flow quality control management method and system based on industrial Internet - Google Patents
Medical imaging full-flow quality control management method and system based on industrial Internet Download PDFInfo
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Abstract
The invention relates to the technical field of medical image processing, in particular to a medical imaging full-flow quality control management method and system based on industrial Internet, comprising the following steps: and performing quality detection and calibration on the medical imaging equipment by using an adaptive noise reduction algorithm and a deep learning technology, wherein the quality detection and calibration comprises imaging quality and parameter setting, and generating an equipment quality control detection result. According to the invention, the accuracy of medical imaging is improved through a self-adaptive noise reduction algorithm and a deep learning technology, the scanning parameters can be automatically adjusted through a model predictive control technology and an operation guide algorithm, the flow efficiency is improved, the high dynamic range technology and a color correction algorithm are adopted, the visibility and the readability of images are optimized, the deep neural network is used for image analysis, the diagnosis quality is improved, the quality control of each link is integrated through the statistical process control, the optimization and the quality assurance of the whole flow are realized, the standardization and the mutual recognition of data are promoted, and a plurality of mechanisms can share and utilize medical imaging data more easily.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to a medical imaging full-flow quality control management method and system based on an industrial Internet.
Background
Medical image processing refers to a technical field of processing, analyzing and improving medical images by applying computer science and engineering technology. These images include images generated by various medical imaging techniques such as X-ray, computed Tomography (CT), magnetic Resonance Imaging (MRI), ultrasound, magnetic resonance imaging (NMR), and the like. The primary goal of medical image processing is to improve the quality, visualization, analysis, and diagnostic capabilities of medical images to assist doctors in making more accurate diagnostic and therapeutic decisions.
The medical imaging whole-process quality control management method is a management technology and aims to ensure quality control in the whole medical imaging process. The main purpose of this approach is to ensure quality control of the entire medical imaging procedure, its main purposes include quality assurance, consistency and compliance. To achieve these goals, standardized operational procedures, equipment calibration and maintenance, data management, training and certification, and automated tools are commonly employed. The implementation of the method is beneficial to improving the accuracy and reliability of medical images, thereby improving the quality of medical diagnosis and treatment and reducing the potential medical risks.
In the existing full-process quality control management method for medical imaging, although the field of medical imaging is quite mature, the accuracy still needs to be improved. Under high noise or special conditions, the image quality may be affected. The existing method requires medical staff to manually adjust scanning parameters and equipment settings, is time-consuming and is prone to introducing errors. The device standard or display is not always optimal, especially in terms of contrast and color. The current quality control is focused on a single link (such as equipment or image), and lacks full-flow coverage. Medical image data has limited applications, lack of uniform standards and sharing mechanisms, resulting in data being limited to specific scenes or within an organization. Improvements are needed to improve the quality and reliability of medical images.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a medical imaging full-flow quality control management method and system based on the industrial Internet.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the medical imaging full-flow quality control management method based on the industrial Internet comprises the following steps:
s1: performing quality detection and calibration on medical imaging equipment by using an adaptive noise reduction algorithm and a deep learning technology, wherein the quality detection and calibration comprises imaging quality and parameter setting, and generating equipment quality control detection results;
S2: based on the quality control detection result of the equipment, calibrating the scanning parameter setting and the operation method by using a model predictive control technology and an operation guide algorithm to generate an operation technology quality control result;
s3: based on the quality control result of the operation technology, adopting a high dynamic range technology and a color correction algorithm to perform window adjustment calibration on the print film and the screen display, and generating a display storage quality control result;
s4: based on the display storage quality control result, performing image analysis by using a deep neural network, and simultaneously evaluating and calibrating the diagnosis level of a basic diagnostician to generate an image diagnosis quality control result;
s5: integrating the quality control detection result of the equipment, the quality control result of the operation technology, the quality control result of the display storage and the quality control result of the image diagnosis, and performing full-flow quality control by using a statistical process control method to generate a full-flow quality control management result;
s6: based on the full-flow quality control management result, the medical imaging data is subjected to standardized processing by using a data cleaning and conversion technology and a distributed data storage scheme, and mutual recognition and sharing of the data are performed to generate standardized and shared medical imaging data.
As a further scheme of the invention, the quality detection and calibration of the medical imaging equipment are carried out by applying an adaptive noise reduction algorithm and a deep learning technology, the method comprises the steps of imaging quality and parameter setting, and the step of generating an equipment quality control detection result is specifically as follows:
S101: preprocessing original image data of medical imaging equipment by adopting an image enhancement and noise filtering algorithm to generate preprocessed image data;
s102: based on the preprocessed image data, detecting the imaging quality of the equipment by using a self-adaptive noise reduction algorithm to obtain equipment quality detection data;
s103: based on the equipment quality detection data, performing preliminary parameter calibration by using a linear regression algorithm to generate preliminary parameter calibration data;
s104: based on the preliminary parameter calibration data, performing optimization of parameter calibration by using a deep learning algorithm to obtain equipment parameter calibration data;
s105: and integrating the equipment quality detection data and the equipment parameter calibration data to generate an equipment quality control detection result.
As a further scheme of the invention, based on the quality control detection result of the equipment, the scanning parameter setting and the operation method are calibrated by using a model predictive control technology and an operation guide algorithm, and the step of generating the quality control result of the operation technology comprises the following steps:
s201: based on the quality control detection result of the equipment, performing preliminary calibration of scanning parameter setting by using a model predictive control technology to obtain preliminary scanning parameter setting;
S202: based on the preliminary scanning parameter setting, performing parameter optimization by using a model predictive control technology to obtain an optimized scanning parameter setting;
s203: generating an operation method guide by using an operation guide algorithm based on the optimized scanning parameter setting;
s204: integrating the optimized scanning parameter setting and the operating method guide to generate an operating technology quality control result.
As a further scheme of the invention, based on the quality control result of the operation technology, the high dynamic range technology and the color correction algorithm are adopted to perform window adjustment calibration on the print film and the screen display, and the steps for generating and storing the quality control result are specifically as follows:
s301: based on the quality control result of the operation technology, window adjustment calibration of the printing film is performed by utilizing a high dynamic range technology, and the printing film after the high dynamic range application is generated;
s302: based on the printing film after the high dynamic range application, adjusting the color balance to obtain the printing film after the color balance adjustment;
s303: based on the color balance adjusted printing film, applying a color correction algorithm to carry out window adjustment of screen display, and generating screen display after window adjustment;
s304: and integrating the screen display after window adjustment and the printing film to generate a display storage quality control result.
As a further scheme of the invention, based on the display and storage quality control result, the deep neural network is applied to carry out image analysis, and meanwhile, the diagnosis level of a basic diagnostician is estimated and calibrated, and the step of generating the image diagnosis quality control result specifically comprises the following steps:
s401: based on the display and storage quality control result, performing image feature extraction by using a deep neural network to obtain a feature extraction result;
s402: based on the feature extraction result, performing image analysis to obtain an image analysis result;
s403: based on the image analysis result, performing diagnosis level evaluation of a basic diagnostician to generate diagnosis level evaluation data;
s404: and integrating the image analysis result and the diagnosis level evaluation data to generate an image diagnosis quality control result.
As a further scheme of the present invention, the steps of integrating the quality control detection result of the device, the quality control result of the operation technology, the display storage quality control result and the image diagnosis quality control result, and performing the full-flow quality control by using the statistical process control method, and generating the full-flow quality control management result specifically include:
s501: integrating the quality control detection result of the equipment, the quality control result of the operation technology, and the quality control result of the display storage and the quality control result of the image diagnosis to obtain full-flow quality control data;
S502: based on the full-flow quality control data, applying a statistical model to perform quality control evaluation to obtain quality control evaluation data;
s503: based on the quality control evaluation data, a quality control feedback mechanism is established, the evaluation data is fed back to the submodule for optimization, and a quality control feedback report is generated;
s504: and generating a full-flow quality control management result based on the quality control evaluation data and the quality control feedback report.
As a further scheme of the invention, based on the full-flow quality control management result, the data cleaning and conversion technology and the distributed data storage scheme are applied to perform standardized processing on medical imaging data, and the mutual recognition and sharing of the data are performed, so that the standardized and shared medical imaging data are generated by the steps of:
s601: evaluating the data quality of the medical imaging data, identifying abnormal values, repeated values and missing values, and cleaning to obtain cleaned medical imaging data;
s602: according to the suggestion of the full-flow quality control management result, converting the cleaned medical imaging data into a uniform format by using a data conversion technology, and carrying out data normalization and quantization processing to obtain medical imaging data in a standard format;
s603: storing the standardized medical imaging data in a distributed environment using a distributed data storage scheme;
S604: and establishing a protocol or platform for data access and sharing, sorting and summarizing medical imaging data in a standard format, and generating standardized and shared medical imaging data.
The medical imaging full-flow quality control management system based on the industrial Internet is used for executing the medical imaging full-flow quality control management method based on the industrial Internet, and comprises an equipment quality control module, an operation technology quality control module, a display storage quality control module, an image diagnosis quality control module, a full-flow quality control management module and a data standardization and sharing module.
As a further scheme of the invention, the equipment quality control module preprocesses the original image data of the medical imaging equipment, detects the imaging quality of the equipment and calibrates parameters to generate an equipment quality control detection result;
the operation technology quality control module performs calibration and optimization of scanning parameter setting based on equipment quality control detection results to generate operation technology quality control results;
the display storage quality control module performs window adjustment calibration and color balance adjustment on the print film based on the quality control result of the operation technology, performs window adjustment of screen display, and generates a display storage quality control result;
The image diagnosis quality control module is used for extracting and analyzing image characteristics based on the display and storage quality control result, evaluating diagnosis level and generating an image diagnosis quality control result;
the full-flow quality control management module integrates the quality control results to perform quality control evaluation, and then establishes a quality control feedback mechanism to generate a full-flow quality control management result;
the data standardization and sharing module evaluates and cleans the medical imaging data, performs data conversion and storage, realizes data sharing, and generates standardized and shared medical imaging data.
As a further scheme of the invention, the equipment quality control module comprises an image preprocessing sub-module, an equipment quality detection sub-module, a preliminary parameter calibration sub-module and a parameter optimization sub-module;
the operation technology quality control module comprises a scanning parameter setting sub-module, a parameter optimizing sub-module and an operation method guide sub-module;
the display storage quality control module comprises a print film window adjusting sub-module, a color balance adjusting sub-module and a screen display window adjusting sub-module;
the image diagnosis quality control module comprises an image feature extraction sub-module, an image analysis sub-module and a diagnosis level evaluation sub-module;
The full-flow quality control management module comprises a quality control data integration sub-module, a quality control evaluation sub-module and a quality control feedback sub-module;
the data standardization and sharing module comprises a data quality evaluation sub-module, a data cleaning sub-module, a data conversion sub-module, a data storage sub-module and a data sharing sub-module.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the invention, the accuracy of medical imaging is improved through the self-adaptive noise reduction algorithm and the deep learning technology, so that the reliability of diagnosis is enhanced. The model predictive control technology and the operation guide algorithm can automatically adjust scanning parameters, reduce manual intervention and improve flow efficiency. The visibility and readability of the image are optimized using high dynamic range techniques and color correction algorithms. The deep neural network is used for image analysis, and can automatically detect lesions possibly ignored by human eyes, so that the diagnosis quality is improved. And the quality control of each link is integrated through statistical process control, so that the optimization and quality assurance of the whole process are realized. The method facilitates standardization and mutual recognition of data so that multiple institutions can more easily share and utilize medical imaging data.
Drawings
FIG. 1 is a schematic workflow diagram of the present invention;
FIG. 2 is a S1 refinement flowchart of the present invention;
FIG. 3 is a S2 refinement flowchart of the present invention;
FIG. 4 is a S3 refinement flowchart of the present invention;
FIG. 5 is a S4 refinement flowchart of the present invention;
FIG. 6 is a S5 refinement flowchart of the present invention;
FIG. 7 is a S6 refinement flowchart of the present invention;
FIG. 8 is a system flow diagram of the present invention;
fig. 9 is a system block diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1, the present invention provides a technical solution: the medical imaging full-flow quality control management method based on the industrial Internet comprises the following steps:
s1: performing quality detection and calibration on medical imaging equipment by using an adaptive noise reduction algorithm and a deep learning technology, wherein the quality detection and calibration comprises imaging quality and parameter setting, and generating equipment quality control detection results;
s2: based on the equipment quality control detection result, calibrating the scanning parameter setting and the operation method by using a model predictive control technology and an operation guide algorithm to generate an operation technology quality control result;
s3: based on the quality control result of the operation technology, adopting a high dynamic range technology and a color correction algorithm to perform window adjustment calibration on the printing film and the screen display, and generating a display storage quality control result;
s4: based on the display and storage quality control result, performing image analysis by using a deep neural network, and simultaneously evaluating and calibrating the diagnosis level of a basic diagnostician to generate an image diagnosis quality control result;
s5: integrating the quality control detection result of the equipment, the quality control result of the operation technology, the quality control result of the display storage and the quality control result of the image diagnosis, and carrying out full-flow quality control by using a statistical process control method to generate a full-flow quality control management result;
S6: based on the full-flow quality control management result, the medical imaging data is subjected to standardized processing by using a data cleaning and conversion technology and a distributed data storage scheme, and mutual recognition and sharing of the data are performed to generate standardized and shared medical imaging data.
Firstly, through equipment quality detection and calibration, quality and parameter setting of the imaging equipment are effectively controlled, and accuracy and reliability of an imaging result are ensured. Secondly, the calibration and standardization of the operation technology promote the consistency of operators, reduce human errors and repeated scanning, and improve the imaging quality. Through window adjustment and color correction, the accuracy and consistency of the image are ensured, and more reliable image results are provided. The depth neural network is adopted to analyze the image and evaluate the diagnosis level, so that the accuracy and consistency of diagnosis are improved. By the statistical process control method of the whole-flow quality control management, problems and risks can be found and corrected early, and the stability and controllability of the system are improved. Finally, through data standardization and sharing, medical imaging data can be exchanged and shared among different systems and institutions, facilitating the development and collaboration of medical image science. By combining the beneficial effects, the method is beneficial to improving the quality and consistency of medical imaging, promoting the support of clinical decisions and the improvement of patient care experience, and promoting the progress and development of the medical imaging field.
Referring to fig. 2, the quality detection and calibration of the medical imaging device by using the adaptive noise reduction algorithm and the deep learning technology, including imaging quality and parameter setting, specifically, the steps of generating the device quality control detection result are as follows:
s101: preprocessing original image data of medical imaging equipment by adopting an image enhancement and noise filtering algorithm to generate preprocessed image data;
s102: based on the preprocessed image data, detecting the imaging quality of the equipment by using a self-adaptive noise reduction algorithm to obtain equipment quality detection data;
s103: based on the equipment quality detection data, performing preliminary parameter calibration by using a linear regression algorithm to generate preliminary parameter calibration data;
s104: based on the preliminary parameter calibration data, performing optimization of parameter calibration by using a deep learning algorithm to obtain equipment parameter calibration data;
s105: integrating the equipment quality detection data and the equipment parameter calibration data to generate an equipment quality control detection result.
Firstly, through image preprocessing and the application of a noise filtering algorithm, the quality and definition of an image can be improved, and the interference of noise on an imaging result is reduced, so that a medical image is clearer and more reliable. And secondly, detecting the imaging quality of the equipment through an adaptive noise reduction algorithm, and timely finding and evaluating quality problems in the image, thereby providing valuable data for subsequent calibration and optimization. And then, the equipment parameters are calibrated through linear regression and a deep learning algorithm, so that the imaging parameters can more accurately meet the requirements, and the consistency and comparability of imaging are improved. And finally, integrating the quality detection data and the parameter calibration data of the equipment to generate a quality control detection result, so that comprehensive quality evaluation and calibration data can be provided, and an important basis is provided for accurate diagnosis and treatment decision of medical images. In combination, the method can improve the quality and reliability of medical imaging, improve the accuracy and effect of medical services, and improve the treatment experience and result of patients.
Referring to fig. 3, based on the device quality control detection result, the scan parameter setting and operation method are calibrated by using a model predictive control technology and an operation guidance algorithm, and the steps for generating the operation technology quality control result are specifically as follows:
s201: based on the quality control detection result of the equipment, performing preliminary calibration of the scanning parameter setting by using a model predictive control technology to obtain preliminary scanning parameter setting;
s202: based on the preliminary scanning parameter setting, performing parameter optimization by using a model predictive control technology to obtain an optimized scanning parameter setting;
s203: generating an operation method guide by using an operation guide algorithm based on the optimized scanning parameter setting;
s204: integrating the optimized scanning parameter setting and the operation method guide to generate an operation technology quality control result.
Firstly, in the steps S201 and S202, the scanning parameters are initially calibrated and optimized by a model predictive control technology, so that the scanning accuracy and consistency can be effectively improved, and a more reliable imaging result can be obtained. In the step S203, an operation method guide is generated through an operation guide algorithm, so that the operation flow and the technical points are standardized, the consistency of operators is improved, personal errors and unnecessary repeated scanning are reduced, and the imaging quality is further improved. Finally, in step S204, the optimized scan parameter setting and the operation method guidance are integrated, and an operation technology quality control result is generated, so as to provide a basis for operation quality management. In combination, the method can improve the quality and consistency of medical imaging, improve the technical level of operators, reduce errors and repeated work, thereby providing more reliable and accurate imaging results for clinical decisions and nursing of patients and improving the quality of medical service.
Referring to fig. 4, based on the quality control result of the operation technology, the window adjustment calibration is performed on the print film and the screen display by adopting the high dynamic range technology and the color correction algorithm, and the steps for generating the display storage quality control result are specifically as follows:
s301: based on the quality control result of the operation technology, window adjustment calibration of the printing film is performed by utilizing the high dynamic range technology, and the printing film after the high dynamic range application is generated;
s302: based on the printing film after the high dynamic range application, adjusting the color balance to obtain the printing film after the color balance adjustment;
s303: based on the color balance adjusted printing film, window adjustment of screen display is carried out by applying a color correction algorithm, and screen display after window adjustment is generated;
s304: and integrating the screen display after window adjustment and the printing film to generate a display storage quality control result.
First, in steps S301 and S302, the print film is calibrated by high dynamic range technology and color balance adjustment, and the sharpness, contrast and visual perception range of the image are improved during display. This will make the medical image more visible, highlight the details, improve the diagnostic reliability and accuracy of the image. Next, in step S303, a color correction algorithm is applied to window the screen display. By appropriate color and brightness adjustment of the screen according to the color information and window adjustment requirements of the print film, it is ensured that an image consistent with the print film is displayed on the screen. This helps the medical image to be consistently presented on different display devices, providing accurate image interpretation and diagnostic results. Finally, in step S304, the windowed screen display and the print film are integrated to generate a display storage quality control result. By comprehensively considering quality control indexes and adjustment parameters of the print film and the screen display, the display quality and the calibration effect can be evaluated, and accurate and reliable basis is provided for diagnosis and interpretation of medical images.
Referring to fig. 5, based on the display and storage quality control result, the image analysis is performed by using the deep neural network, and the diagnostic level of the basic diagnostician is evaluated and calibrated at the same time, so as to generate an image diagnosis quality control result, which specifically includes the following steps:
s401: based on the display and storage quality control result, performing image feature extraction by using a deep neural network to obtain a feature extraction result;
s402: based on the feature extraction result, performing image analysis to obtain an image analysis result;
s403: based on the image analysis result, performing diagnosis level evaluation of a basic diagnostician to generate diagnosis level evaluation data;
s404: and integrating the image analysis result and the diagnosis level evaluation data to generate an image diagnosis quality control result.
First, in step S401, image feature extraction is performed through a deep neural network, so that key feature information can be extracted from a medical image. This helps the physician to accurately find lesions, abnormal areas and other important visual features, providing a reliable basis for further analysis of the image. Next, in step S402, image analysis is performed based on the feature extraction result. The deep neural network can effectively process the extracted features and automatically execute tasks such as lesion localization, key index quantification and the like. This will increase the accuracy and precision of the image analysis, providing the doctor with a more comprehensive and reliable image analysis result. Next, in step S403, the diagnostic level of the primary diagnosing doctor is evaluated. By combining the image diagnosis quality control result and the deep neural network analysis result, the diagnosis capability of a doctor can be quantitatively evaluated, and personalized feedback and guidance can be provided. This will help doctors to increase the level of diagnosis, reduce the risk of misdiagnosis and missed diagnosis, and provide training and support to enhance the diagnostic capabilities of the entire medical team. Finally, in step S404, the image analysis result and the diagnosis level evaluation data are integrated to generate an image diagnosis quality control result. This will provide comprehensive image analysis results and physician assessment data for quality management, performance assessment, and medical education. Through timely diagnosis quality control and personalized doctor calibration, the accuracy, consistency and efficiency of image diagnosis can be improved, and finally the treatment result and the quality of medical service of a patient are improved.
Referring to fig. 6, the steps of integrating the device quality control detection result, the operation technology quality control result, the display storage quality control result and the image diagnosis quality control result, performing the full-flow quality control by using the statistical process control method, and generating the full-flow quality control management result are specifically as follows:
s501: integrating the quality control detection result of the equipment, the quality control result of the operation technology, and the quality control result of the display storage and the quality control result of the image diagnosis to obtain full-flow quality control data;
s502: based on the full-flow quality control data, applying a statistical model to perform quality control evaluation to obtain quality control evaluation data;
s503: based on the quality control evaluation data, a quality control feedback mechanism is established, the evaluation data is fed back to the submodule to be optimized, and a quality control feedback report is generated;
s504: and generating a full-flow quality control management result based on the quality control evaluation data and the quality control feedback report.
Firstly, in step S501, a plurality of quality control results are integrated to obtain full-process quality control data, which provides comprehensive quality control information to help discover and solve problems such as equipment failure, improper operation technology, storage distortion and diagnosis error, and further optimize operation and quality of the full process. Next, in step S502, statistical models are applied to evaluate the overall process quality control data, which helps identify potential variations and anomalies. Through statistical analysis and modeling, reliable quality evaluation indexes and thresholds can be established, quality problems can be found in time, and measures can be taken to intervene and improve. This will reduce errors and inconsistencies, improving stability and consistency of the quality control, and thus improving overall quality levels. Next, in step S503, a quality control feedback mechanism is established, and the evaluation data is fed back to the corresponding sub-module. This will facilitate the implementation of timely problem solving and improving measures. The quality control feedback report provides specific guidance and advice, helps to optimize equipment maintenance, operation training, a storage system and an image diagnosis process, and further improves the quality control effect of the whole process. Finally, in step S504, a full-process quality control management result is generated based on the quality control evaluation data and the quality control feedback report. This will provide a comprehensive quality management guideline and decision basis for the medical institution. The quality control management result of the whole process covers the aspects of quality improvement planning, training measures, equipment updating and the like, so that the improvement and continuous improvement of the quality of the whole process are promoted.
Referring to fig. 7, based on the overall process quality control management result, the data cleaning and conversion technology and the distributed data storage scheme are applied to perform standardization processing on medical imaging data, and perform mutual recognition and sharing of data, and the steps of generating the standardized and shared medical imaging data specifically include:
s601: evaluating the data quality of the medical imaging data, identifying abnormal values, repeated values and missing values, and cleaning to obtain cleaned medical imaging data;
s602: according to the suggestion of the full-flow quality control management result, the medical imaging data after cleaning is converted into a unified format by using a data conversion technology, and data normalization and quantization processing are carried out to obtain medical imaging data in a standard format;
s603: storing standardized medical imaging data in a distributed environment using a distributed data storage scheme;
s604: and establishing a protocol or platform for data access and sharing, sorting and summarizing medical imaging data in a standard format, and generating standardized and shared medical imaging data.
First, in step S601, medical imaging data is subjected to quality evaluation and cleaning. By identifying and processing the problems of abnormal values, repeated values, missing values and the like, the quality and the accuracy of medical imaging data can be improved, and the effectiveness and the reliability of subsequent data processing are ensured. Next, in step S602, according to the advice of the overall-process quality control management result, the cleaned medical imaging data is converted into a unified format by using the data conversion technology. This includes data normalization and quantization processes to ensure that data from different sources and devices all meet the same criteria, thereby improving consistency and comparability of the data. Medical imaging data in a standard format will provide a uniform basis for subsequent analysis and application. Next, in step S603, standardized medical imaging data is stored in a distributed environment using a distributed data storage scheme. The storage mode can disperse the pressure of data storage, improve the reliability and availability of data, support remote access and sharing of data, and facilitate data exchange and cooperation among different institutions or research teams. Finally, in step S604, a protocol or platform for data access and sharing is established, and medical imaging data in a standard format is collated and summarized. This will provide a convenient data access and sharing mechanism for researchers, doctors and decision makers, facilitating cross-institutional, cross-domain collaboration and research. The shared medical imaging data will greatly promote the progress of medical research and diagnosis, accelerate understanding of diseases and formulation of treatment schemes, and provide better medical services for patients.
Referring to fig. 8, the medical imaging full-process quality control management system based on the industrial internet is configured to execute the medical imaging full-process quality control management method based on the industrial internet, where the medical imaging full-process quality control management system based on the industrial internet is composed of an equipment quality control module, an operation technology quality control module, a display storage quality control module, an image diagnosis quality control module, a full-process quality control management module, and a data standardization and sharing module.
The equipment quality control module preprocesses the original image data of the medical imaging equipment, detects the imaging quality of the equipment and calibrates parameters to generate an equipment quality control detection result;
the operation technology quality control module performs calibration and optimization of scanning parameter setting based on the equipment quality control detection result to generate an operation technology quality control result;
the display storage quality control module performs window adjustment calibration and color balance adjustment on the print film based on the operation technology quality control result, performs window adjustment of screen display, and generates a display storage quality control result;
the image diagnosis quality control module is used for extracting and analyzing image characteristics based on the display storage quality control result, evaluating diagnosis level and generating an image diagnosis quality control result;
The full-flow quality control management module integrates the quality control results, performs quality control evaluation, and then establishes a quality control feedback mechanism to generate a full-flow quality control management result;
the data standardization and sharing module evaluates and cleans the medical imaging data, performs data conversion and storage, realizes data sharing, and generates standardized and shared medical imaging data.
Firstly, by applying the equipment quality control module and the operation technology quality control module, the accuracy and stability of the medical imaging equipment are improved, and the consistency and standardization of the operation technology are ensured, so that more reliable and accurate medical imaging data are obtained. This will help the physician make more accurate diagnostic and therapeutic decisions, improving the patient's medical outcome and therapeutic outcome.
And secondly, by displaying the application of the storage quality control module and the image diagnosis quality control module, the accuracy and consistency of the watching and displaying of the images are ensured, and doctors or professionals can obtain clear and accurate image information, so that the accuracy and reliability of image diagnosis are improved. This helps to reduce the risk of misdiagnosis and missed diagnosis, improving the diagnostic accuracy and therapeutic effect of the patient.
In addition, the application of the full-flow quality control management module can monitor and improve the full flow of medical imaging, improve the efficiency and the precision of quality control and reduce the occurrence of quality problems. Through a timely quality control evaluation and feedback mechanism, the quality problem can be rapidly found and solved, and the quality and flow of medical imaging can be continuously optimized.
Finally, through the application of the data standardization and sharing module, the consistency and comparability of medical imaging data are improved, and the data communication, cooperation and sharing between different institutions and research teams are facilitated. This will promote the progress of medical research and the sharing of knowledge, accelerate understanding of the disease and formulation of treatment regimens, and provide better medical services for patients.
Referring to fig. 9, the device quality control module includes an image preprocessing sub-module, a device quality detection sub-module, a preliminary parameter calibration sub-module, and a parameter optimization sub-module;
the operation technology quality control module comprises a scanning parameter setting sub-module, a parameter optimizing sub-module and an operation method guide sub-module;
the display storage quality control module comprises a print film window adjusting sub-module, a color balance adjusting sub-module and a screen display window adjusting sub-module;
the image diagnosis quality control module comprises an image feature extraction sub-module, an image analysis sub-module and a diagnosis level evaluation sub-module;
The full-flow quality control management module comprises a quality control data integration sub-module, a quality control evaluation sub-module and a quality control feedback sub-module;
the data standardization and sharing module comprises a data quality evaluation sub-module, a data cleaning sub-module, a data conversion sub-module, a data storage sub-module and a data sharing sub-module.
Firstly, the quality and stability of the imaging equipment can be improved by using the equipment quality control module, and high-quality medical image data is ensured. The application of the operation technology quality control module can realize technical standardization, reduce human errors and improve imaging quality. The application of the display and storage quality control module can ensure the accurate display of the images and improve the reliability of interpretation and diagnosis of the images by doctors. The application of the image diagnosis quality control module can improve the accuracy of image analysis and diagnosis and assist doctors to make correct diagnosis decisions. The application of the full-flow quality control management module strengthens quality control management and continuous improvement, and ensures the quality and reliability of medical imaging. The application of the data standardization and sharing module promotes the consistency and comparability of data, and facilitates the data exchange and cooperation between different institutions. By combining the effects, the application of the medical imaging full-flow quality control management system based on the industrial Internet can improve the quality and accuracy of medical imaging, strengthen the reliability and efficiency of medical image diagnosis, promote the development of medical research and clinical practice and promote the medical service and health achievement of patients.
Working principle: in the equipment quality control module, the system uses an adaptive noise reduction algorithm and a deep learning technology to perform quality detection and calibration on the medical imaging equipment. First, raw image data of the device is preprocessed, including image enhancement and noise filtering. Then, the self-adaptive noise reduction algorithm is used for detecting the imaging quality of the equipment, and equipment quality detection data are obtained. And according to the detection data, the system performs preliminary parameter calibration by using a linear regression algorithm to generate preliminary parameter calibration data. Further, the parameters are optimized by using a deep learning algorithm, and the equipment parameter calibration data are obtained. And finally, integrating the equipment quality detection data and the equipment parameter calibration data to generate an equipment quality control detection result.
In the operation technology quality control module, the system uses the equipment quality control detection result to calibrate the scanning parameter setting and operation method by using a model predictive control technology and an operation guidance algorithm. And performing preliminary calibration on the scan parameter setting according to the quality control detection result of the equipment, and performing parameter optimization by using a model predictive control technology to obtain the optimized scan parameter setting. Meanwhile, an operation method guide is generated according to the optimized parameter setting and the operation guide algorithm. And finally, integrating parameter setting and an operation method guide to generate an operation technology quality control result.
In the display storage quality control module, the system adopts a high dynamic range technology and a color correction algorithm to calibrate window adjustment of the print film and the screen display based on the quality control result of the operation technology. And window adjustment calibration is carried out on the printing film by utilizing a high dynamic range technology, so that the printing film after the high dynamic range application is obtained. And then, carrying out color balance adjustment to obtain the print film after the color balance adjustment. And window adjusting the screen display by using a color correction algorithm based on the color balance adjusted printing film to generate the window-adjusted screen display. And finally, integrating the screen display after window adjustment and the printing film to generate a display storage quality control result.
In the image diagnosis quality control module, the system performs image analysis by applying a deep neural network based on the display and storage quality control result, and evaluates and calibrates the diagnosis level of a basic diagnostician. Firstly, extracting image features by using a deep neural network to obtain feature extraction results. Then, image analysis is carried out to obtain an image analysis result. Based on the image analysis result, the diagnostic level of the primary diagnosing doctor is evaluated, and diagnostic level evaluation data is generated. And finally, integrating the image analysis result and the diagnosis level evaluation data to generate an image diagnosis quality control result.
In the full-flow quality control management module, the system synthesizes the quality control detection result of the equipment, the quality control result of the operation technology, the display storage quality control result and the image diagnosis quality control result, and performs full-flow quality control by using a statistical process control method. First, integrating the quality control result to obtain the full-flow quality control data. And then, carrying out quality control evaluation by using a statistical model to obtain quality control evaluation data. Based on the quality control evaluation data, a quality control feedback mechanism is established, the evaluation data is fed back to the submodule to be optimized, and a quality control feedback report is generated. And finally, integrating the quality control evaluation data and the quality control feedback report to generate a full-flow quality control management result.
In the data standardization and sharing module, the system evaluates and cleans medical imaging data, and adopts a data conversion and distributed data storage scheme to realize standardization and sharing of the data. Firstly, performing quality evaluation on medical imaging data, identifying abnormal values, repeated values and missing values, and performing data cleaning to obtain cleaned medical imaging data. According to the suggestion of the full-flow quality control management result, the cleaned data are converted into a uniform format by utilizing a data conversion technology, and data normalization and quantization processing are carried out to obtain medical imaging data in a standard format. Standardized medical imaging data is stored in a distributed environment using a distributed data storage scheme. And finally, establishing a protocol or platform for data access and sharing, and sorting and summarizing medical imaging data in a standard format to realize mutual recognition and sharing of the data.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (8)
1. The medical imaging full-flow quality control management method based on the industrial Internet is characterized by comprising the following steps of:
performing quality detection and calibration on medical imaging equipment by using an adaptive noise reduction algorithm and a deep learning technology, wherein the quality detection and calibration comprises imaging quality and parameter setting, and generating equipment quality control detection results;
based on the quality control detection result of the equipment, calibrating the scanning parameter setting and the operation method by using a model predictive control technology and an operation guide algorithm to generate an operation technology quality control result;
based on the quality control result of the operation technology, adopting a high dynamic range technology and a color correction algorithm to perform window adjustment calibration on the print film and the screen display, and generating a display storage quality control result;
Based on the display storage quality control result, performing image analysis by using a deep neural network, and simultaneously evaluating and calibrating the diagnosis level of a basic diagnostician to generate an image diagnosis quality control result;
integrating the quality control detection result of the equipment, the quality control result of the operation technology, the quality control result of the display storage and the quality control result of the image diagnosis, and performing full-flow quality control by using a statistical process control method to generate a full-flow quality control management result;
based on the full-flow quality control management result, the medical imaging data is subjected to standardized processing by using a data cleaning and conversion technology and a distributed data storage scheme, and mutual recognition and sharing of the data are performed to generate standardized and shared medical imaging data;
based on the quality control detection result of the equipment, the scanning parameter setting and the operation method are calibrated by using a model predictive control technology and an operation guide algorithm, and the step of generating the quality control result of the operation technology comprises the following steps:
based on the quality control detection result of the equipment, performing preliminary calibration of scanning parameter setting by using a model predictive control technology to obtain preliminary scanning parameter setting;
based on the preliminary scanning parameter setting, performing parameter optimization by using a model predictive control technology to obtain an optimized scanning parameter setting;
Generating an operation method guide by using an operation guide algorithm based on the optimized scanning parameter setting;
integrating the optimized scanning parameter setting and the operating method guide to generate an operating technology quality control result;
based on the quality control result of the operation technology, the high dynamic range technology and the color correction algorithm are adopted to carry out window adjustment calibration on the print film and the screen display, and the steps of generating and storing the quality control result are specifically as follows:
based on the quality control result of the operation technology, window adjustment calibration of the printing film is performed by utilizing a high dynamic range technology, and the printing film after the high dynamic range application is generated;
based on the printing film after the high dynamic range application, adjusting the color balance to obtain the printing film after the color balance adjustment;
based on the color balance adjusted printing film, applying a color correction algorithm to carry out window adjustment of screen display, and generating screen display after window adjustment;
and integrating the screen display after window adjustment and the printing film to generate a display storage quality control result.
2. The full-flow quality control management method for medical imaging based on industrial internet according to claim 1, wherein the quality detection and calibration of the medical imaging device are performed by using an adaptive noise reduction algorithm and a deep learning technology, the steps of including imaging quality and parameter setting, and generating a device quality control detection result specifically include:
Preprocessing original image data of medical imaging equipment by adopting an image enhancement and noise filtering algorithm to generate preprocessed image data;
based on the preprocessed image data, detecting the imaging quality of the equipment by using a self-adaptive noise reduction algorithm to obtain equipment quality detection data;
based on the equipment quality detection data, performing preliminary parameter calibration by using a linear regression algorithm to generate preliminary parameter calibration data;
based on the preliminary parameter calibration data, performing optimization of parameter calibration by using a deep learning algorithm to obtain equipment parameter calibration data;
and integrating the equipment quality detection data and the equipment parameter calibration data to generate an equipment quality control detection result.
3. The full-flow quality control management method for medical imaging based on the industrial internet according to claim 1, wherein based on the display and storage quality control result, performing image analysis by applying a deep neural network, and simultaneously evaluating and calibrating the diagnostic level of a basic diagnostician, the step of generating an image diagnosis quality control result is specifically as follows:
based on the display and storage quality control result, performing image feature extraction by using a deep neural network to obtain a feature extraction result;
Based on the feature extraction result, performing image analysis to obtain an image analysis result;
based on the image analysis result, performing diagnosis level evaluation of a basic diagnostician to generate diagnosis level evaluation data;
and integrating the image analysis result and the diagnosis level evaluation data to generate an image diagnosis quality control result.
4. The method for managing quality control of a full-process medical imaging system based on the industrial internet according to claim 1, wherein the steps of integrating the quality control detection result of the device, the quality control result of the operation technology, the quality control result of the display storage and the quality control result of the image diagnosis, and performing the full-process quality control by using the statistical process control method, and generating the full-process quality control management result are specifically as follows:
integrating the quality control detection result of the equipment, the quality control result of the operation technology, and the quality control result of the display storage and the quality control result of the image diagnosis to obtain full-flow quality control data;
based on the full-flow quality control data, applying a statistical model to perform quality control evaluation to obtain quality control evaluation data;
based on the quality control evaluation data, a quality control feedback mechanism is established, the evaluation data is fed back to the submodule for optimization, and a quality control feedback report is generated;
and generating a full-flow quality control management result based on the quality control evaluation data and the quality control feedback report.
5. The industrial internet-based medical imaging full-flow quality control management method according to claim 1, wherein the step of performing standardized processing on medical imaging data and performing mutual recognition and sharing of the data by applying a data cleaning and conversion technology and a distributed data storage scheme based on a full-flow quality control management result to generate standardized and shared medical imaging data is specifically as follows:
evaluating the data quality of the medical imaging data, identifying abnormal values, repeated values and missing values, and cleaning to obtain cleaned medical imaging data;
according to the suggestion of the full-flow quality control management result, converting the cleaned medical imaging data into a uniform format by using a data conversion technology, and carrying out data normalization and quantization processing to obtain medical imaging data in a standard format;
storing the standardized medical imaging data in a distributed environment using a distributed data storage scheme;
and establishing a protocol or platform for data access and sharing, sorting and summarizing medical imaging data in a standard format, and generating standardized and shared medical imaging data.
6. The medical imaging full-flow quality control management system based on the industrial Internet is characterized by being used for executing the medical imaging full-flow quality control management method based on the industrial Internet according to any one of claims 1-5, and comprises an equipment quality control module, an operation technology quality control module, a display storage quality control module, an image diagnosis quality control module, a full-flow quality control management module and a data standardization and sharing module.
7. The full-flow quality control management system for medical imaging based on the industrial internet as claimed in claim 6, wherein the equipment quality control module performs preprocessing on raw image data of the medical imaging equipment, performs detection and parameter calibration on equipment imaging quality, and generates an equipment quality control detection result;
the operation technology quality control module performs calibration and optimization of scanning parameter setting based on equipment quality control detection results to generate operation technology quality control results;
the display storage quality control module performs window adjustment calibration and color balance adjustment on the print film based on the quality control result of the operation technology, performs window adjustment of screen display, and generates a display storage quality control result;
the image diagnosis quality control module is used for extracting and analyzing image characteristics based on the display and storage quality control result, evaluating diagnosis level and generating an image diagnosis quality control result;
the full-flow quality control management module integrates the quality control results to perform quality control evaluation, and then establishes a quality control feedback mechanism to generate a full-flow quality control management result;
the data standardization and sharing module evaluates and cleans the medical imaging data, performs data conversion and storage, realizes data sharing, and generates standardized and shared medical imaging data.
8. The full-flow quality control management system for medical imaging based on the industrial internet according to claim 7, wherein the equipment quality control module comprises an image preprocessing sub-module, an equipment quality detection sub-module, a preliminary parameter calibration sub-module and a parameter optimization sub-module;
the operation technology quality control module comprises a scanning parameter setting sub-module, a parameter optimizing sub-module and an operation method guide sub-module;
the display storage quality control module comprises a print film window adjusting sub-module, a color balance adjusting sub-module and a screen display window adjusting sub-module;
the image diagnosis quality control module comprises an image feature extraction sub-module, an image analysis sub-module and a diagnosis level evaluation sub-module;
the full-flow quality control management module comprises a quality control data integration sub-module, a quality control evaluation sub-module and a quality control feedback sub-module;
the data standardization and sharing module comprises a data quality evaluation sub-module, a data cleaning sub-module, a data conversion sub-module, a data storage sub-module and a data sharing sub-module.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110581948A (en) * | 2018-06-07 | 2019-12-17 | 三星电子株式会社 | electronic device for providing quality customized image, control method thereof and server |
| CN113748443A (en) * | 2019-04-24 | 2021-12-03 | 普罗热尼奇制药公司 | System and method for interactively adjusting intensity window settings in nuclear medicine images |
| CN114343555A (en) * | 2021-12-24 | 2022-04-15 | 中尚医疗仪器(深圳)有限公司 | Colposcope artificial intelligence auxiliary quality control management method and device |
| CN114519705A (en) * | 2022-02-16 | 2022-05-20 | 中国人民解放军空军特色医学中心 | Ultrasonic standard data processing method and system for medical selection and identification |
| CN114795915A (en) * | 2022-05-05 | 2022-07-29 | 中国人民解放军陆军军医大学第二附属医院 | Metering and calibrating device of integrated cardio-pulmonary resuscitation machine |
| CN115862801A (en) * | 2022-11-22 | 2023-03-28 | 四川大学华西医院 | Method and system for evaluating diagnosis ability of examining and verifying doctor based on image structured report |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7082993B2 (en) * | 2018-01-24 | 2022-06-09 | 富士フイルム株式会社 | Medical image processing equipment, methods and programs, diagnostic support equipment, methods and programs, and medical support systems and methods |
-
2023
- 2023-09-11 CN CN202311160084.0A patent/CN116912239B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110581948A (en) * | 2018-06-07 | 2019-12-17 | 三星电子株式会社 | electronic device for providing quality customized image, control method thereof and server |
| CN113748443A (en) * | 2019-04-24 | 2021-12-03 | 普罗热尼奇制药公司 | System and method for interactively adjusting intensity window settings in nuclear medicine images |
| CN114343555A (en) * | 2021-12-24 | 2022-04-15 | 中尚医疗仪器(深圳)有限公司 | Colposcope artificial intelligence auxiliary quality control management method and device |
| CN114519705A (en) * | 2022-02-16 | 2022-05-20 | 中国人民解放军空军特色医学中心 | Ultrasonic standard data processing method and system for medical selection and identification |
| CN114795915A (en) * | 2022-05-05 | 2022-07-29 | 中国人民解放军陆军军医大学第二附属医院 | Metering and calibrating device of integrated cardio-pulmonary resuscitation machine |
| CN115862801A (en) * | 2022-11-22 | 2023-03-28 | 四川大学华西医院 | Method and system for evaluating diagnosis ability of examining and verifying doctor based on image structured report |
Non-Patent Citations (1)
| Title |
|---|
| 江苏省妇女" 两癌" 检查质量控制的实施与思考;刘 娜 等;中国妇幼卫生杂志;全文 * |
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