CN115713526A - Image quality control system based on artificial intelligence - Google Patents
Image quality control system based on artificial intelligence Download PDFInfo
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- CN115713526A CN115713526A CN202211504315.0A CN202211504315A CN115713526A CN 115713526 A CN115713526 A CN 115713526A CN 202211504315 A CN202211504315 A CN 202211504315A CN 115713526 A CN115713526 A CN 115713526A
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- 238000003908 quality control method Methods 0.000 title claims abstract description 46
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 11
- 238000004088 simulation Methods 0.000 claims abstract description 13
- 230000005540 biological transmission Effects 0.000 claims abstract description 12
- 238000000605 extraction Methods 0.000 claims abstract description 10
- 238000011156 evaluation Methods 0.000 claims description 13
- 238000003384 imaging method Methods 0.000 claims description 10
- 239000013589 supplement Substances 0.000 claims description 6
- 238000002601 radiography Methods 0.000 claims description 5
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims 1
- 230000001502 supplementing effect Effects 0.000 claims 1
- 238000000034 method Methods 0.000 description 5
- 230000001678 irradiating effect Effects 0.000 description 3
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Abstract
The invention discloses an artificial intelligence-based image quality control system, which comprises a 3D camera and an image quality control system, wherein the 3D camera comprises a posture characteristic extraction module, an image registration module, a 3D virtual modeling module and a simulation transmission module, the 3D camera and the image quality control system can identify a specific posture of a patient before scanning, and a virtual projection contour is obtained through the 3D virtual modeling module and the simulation transmission module, and the image quality control system carries out AI registration before scanning and AI scoring on the image after scanning on the image, so that the quality of the image is improved, the problems that the quality of the image is evaluated subjectively in the current hospital, objective quantitative indexes are lacked, and the opportunity for explaining the image to have the state is lacked for the current operator are solved, the feedback of the image to a radiology technician is enhanced, and the radiology technician is allowed to carry out supplementary explanation on the image.
Description
Technical Field
The invention relates to the field of medical images, in particular to an image quality control system based on artificial intelligence.
Background
In the daily workflow of imaging, the evaluation of imaging quality is a very missing option, and the evaluation of imaging quality is equivalent to giving a radiology technician a clear feedback mechanism. At present, hospitals basically begin to be aware of the importance of a quality control system, and subjective evaluation on the quality of images is adopted in many cases. There are several problems with this evaluation system: subjectivity evaluates the quality of the image and lacks objective quantitative indexes; a passive evaluation state; there is no opportunity for the operator at that time to explain why the image will appear to be so.
Therefore, there is a need for an image quality control system based on artificial intelligence.
Disclosure of Invention
In order to solve the technical problems, the invention provides an image quality control system based on artificial intelligence.
The technical scheme of the invention is realized as follows:
an image quality control system based on artificial intelligence comprises a 3D camera and an image quality control system, wherein the 3D camera comprises a posture characteristic extraction module, an image registration module, a 3D virtual modeling module and a simulation transmission module.
The posture feature extraction module comprises a contour scanning unit and an imaging unit.
The image registration module comprises an image registration unit and an adjustment instruction output unit.
The image quality control system comprises a network virtual comparison module, an AI scoring module, an AI indicating mark module, a feedback module, an uploading module and an artificial supplement module.
The AI scoring module comprises an AI evaluation unit and an AI scoring unit.
Has the beneficial effects that: the invention can identify the specific body position of a patient before scanning through the 3D camera and the image quality control system, obtains a virtual projection outline through the 3D virtual modeling module and the simulation transmission module, and scores the AI of the image before scanning and the AI of the image after scanning through the image quality control system, thereby improving the quality of the image, solving the problems that the current hospital evaluates the quality of the image subjectively, lacks objective quantitative indexes and lacks the opportunity for the current operator to explain the condition of the image, enhancing the feedback of the image to a radiological technician, and allowing the radiological technician to perform supplementary explanation on the image for the comprehensive evaluation of the image quality control and realizing a closed-loop system of shooting-image quality control.
Drawings
Fig. 1 is a schematic view of the working process of the present invention.
Detailed Description
The image quality control system based on artificial intelligence comprises a 3D camera and an image quality control system, wherein the 3D camera comprises a posture characteristic extraction module, an image registration module, a 3D virtual modeling module and a simulation transmission module. Before emitting X-ray for imaging, a patient with a chest radiography can be shot according to needs through a 3D camera at the upper end of an X-ray bulb or irradiating a fixed area of a room, a 3D virtual model is constructed, an image contour is simulated and transmitted to enter a simulated electronic film to form a virtual projection contour, the formed virtual projection contour is transmitted to an image quality control system to be subjected to network virtual contrast through a network virtual contrast module, and on the premise that the contrast of the scanning body position of the patient and the virtual projection contour reaches the standard, an image technician is prompted to confirm exposure, and finally X-ray is generated for shooting. The body state feature extraction module comprises a contour scanning unit and an imaging unit, the body state contour of the patient is obtained in real time through the contour scanning unit, the body state feature image of the patient is generated through the imaging unit, and the body state feature image is transmitted to the simulation transmission module to generate the virtual projection contour.
The image registration module comprises an image registration unit and an adjustment instruction output unit, AI scanning correction is carried out on the malposed patient through the image registration unit, and an adjustment instruction is made on the X-ray scanning equipment through the adjustment instruction output unit.
The image quality control system comprises a network virtual comparison module, an AI scoring module, an AI indicating mark module, a feedback module, an uploading module and a manual supplement module, wherein the AI scoring module comprises an AI evaluation unit and an AI scoring unit, the virtual projection outline and the scanning body position of a patient are compared through the network virtual comparison module, X-ray photography is carried out after the X-ray photography reaches the standard, the X-ray photography result is evaluated and scored through the AI scoring module, left and right R/L indicating marks are placed on a film through the AI indicating mark module, the mark of organism tissues in the film is not shielded, a feedback module is used for timely giving feedback to a technician, and the technician is asked to finally decide whether to repeat photography or complete photography of a chest film, and the technician can manually supplement and explain the X-ray photography after AI evaluation through the manual supplement module. The timely feedback and scoring are uploaded to the system through an uploading module, corresponding feedback evaluation and quality control scoring are given to the image and loaded into a file, and finally the X-ray photography is transmitted back to the image quality control system for deep learning.
Example one
The invention relates to an image quality control system based on artificial intelligence, which comprises a 3D camera and an image quality control system, wherein the 3D camera comprises a posture characteristic extraction module, an image registration module, a 3D virtual modeling module and a simulation transmission module.
When the image obtained by the image equipment needs to be identified, the working process of the invention is as follows:
step 1, obtaining the chest posture of a patient in real time through a 3D camera, generating a posture characteristic image, and transmitting the image to a simulation transmission module to generate a virtual projection outline;
step 2, before emitting X-ray for imaging, the upper end of an X-ray bulb or a 3D camera irradiating a fixed area of a room can shoot a patient with a chest film according to needs, 3D virtual model construction is carried out, and an image contour is simulated and transmitted to enter a simulated electronic film to form a virtual projection contour;
step 3, simultaneously, performing network virtual contrast on the formed virtual projection outline and an image quality control index, prompting an image technician on the premise that the contrast of the scanning body position of the patient and the virtual projection outline reaches the standard, confirming exposure, and finally generating X-ray for shooting;
and 4, in addition, after the X-ray image is generated, a left R/L indicator mark and a right R/L indicator mark are placed on the film through the AI indicator mark module, and the mark of organism tissues in the film is not shielded.
And 5, finally finishing the whole process of the AI + chest radiography X-ray photography after passing through an image quality control system in which the AI participates.
Example two
The invention relates to an image quality control system based on artificial intelligence, which comprises a 3D camera and an image quality control system, wherein the 3D camera comprises a posture characteristic extraction module, an image registration module, a 3D virtual modeling module and a simulation transmission module.
When the image quality control system needs to be deeply trained, the working process of the invention is as follows:
step 1, obtaining chest body state of a patient in real time through a 3D camera, generating body state characteristic images, and transmitting the body state characteristic images to a simulation transmission module to generate a virtual projection outline;
step 2, before emitting X-ray for imaging, the upper end of an X-ray bulb or a 3D camera irradiating a fixed area of a room can shoot a patient with a chest film according to needs, 3D virtual model construction is carried out, and an image contour is simulated and transmitted to enter a simulated electronic film to form a virtual projection contour;
step 3, simultaneously, performing network virtual contrast on the formed virtual projection outline and an image quality control index, prompting an image technician on the premise that the contrast of the scanning body position of the patient and the virtual projection outline reaches the standard, confirming exposure, and finally generating X-ray for shooting;
step 4, after the X-ray image is generated, a left and a right R/L indicating marks are placed on the film through an AI indicating mark module, and the marks of organism tissues in the film are not shielded;
and 5, finally completing the whole process of X-ray photography of AI + chest radiography after passing through the image quality control system with AI participation, and transmitting the X-ray photography to the image quality control system for deep learning.
Claims (7)
1. An image quality control system based on artificial intelligence comprises a 3D camera and an image quality control system, wherein the 3D camera comprises a posture characteristic extraction module, an image registration module, a 3D virtual modeling module and a simulation transmission module, and the image quality control system comprises a network virtual comparison module, an AI scoring module, an AI indication mark module, a feedback module, an uploading module and an artificial supplement module;
the posture characteristic extraction module is used for acquiring the posture contour of the patient and generating a posture characteristic image of the patient; the image registration module is used for carrying out AI scanning correction on the patient with the malposture and making an adjustment instruction on the X-ray scanning equipment;
the 3D virtual modeling module is used for shooting a patient of the chest radiography according to the requirement and constructing a 3D virtual model;
the simulation transmission module simulates and transmits an image contour into the simulation electronic film to form a virtual projection contour;
the network virtual contrast module is used for virtually contrasting the formed virtual projection outline network, prompting an image technician on the premise that the contrast of the scanning body position of the patient and the virtual projection outline reaches the standard, confirming exposure and finally generating X-ray for shooting;
the AI scoring module is used for evaluating and scoring the X-ray shooting result;
an AI indication mark module, which is used for placing the indication marks of left and right R/L on the film;
the feedback module is used for giving the X-ray photography result subjected to AI scoring to a technician in time for feedback;
the uploading module is used for timely feeding back and scoring the X-ray photography results to the system, loading corresponding feedback evaluation and quality control scoring of the images into a file, and finally returning the X-ray photography results to the image quality control system for deep learning;
and the manual supplement module is used for manually supplementing and explaining the X-ray films subjected to AI evaluation by a technician.
2. The system of claim 1, wherein the image quality control system comprises: the posture feature extraction module comprises a contour scanning unit and is used for acquiring the posture contour of the patient in real time; and the imaging unit is responsible for generating a posture characteristic image of the patient.
3. The system of claim 1, wherein the image quality control system comprises: the image registration module comprises an image registration unit and is used for carrying out AI scanning correction on the patient with the malposition; and the adjusting instruction output unit is responsible for making adjusting instructions for the X-ray scanning equipment.
4. The system of claim 1, wherein the image quality control system comprises: the AI scoring module comprises an AI evaluation unit which is responsible for evaluating the X-ray film; and the AI scoring unit is responsible for scoring the X-ray films.
5. The system of claim 1, wherein the image quality control system comprises: the 3D camera is located at the upper end of a transmitting bulb of the X-ray equipment.
6. The system of claim 1, wherein the image quality control system comprises: the image quality control system synchronously controls the quality of the image while generating the X-ray image.
7. The system of claim 1, wherein the image quality control system comprises: the image quality control system predicts and manages the real-time X-ray chest radiography of the target object through the convolutional neural network.
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| CN110897651A (en) * | 2019-12-13 | 2020-03-24 | 四川大学华西医院 | Intelligent physical examination X-ray chest radiography body position tutoring method and system |
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| CN113647967A (en) * | 2021-09-08 | 2021-11-16 | 上海联影医疗科技股份有限公司 | Control method, device and system of medical scanning equipment |
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111062947A (en) * | 2019-08-14 | 2020-04-24 | 深圳市智影医疗科技有限公司 | Deep learning-based X-ray chest radiography focus positioning method and system |
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| CN111528879A (en) * | 2020-05-06 | 2020-08-14 | 上海联影医疗科技有限公司 | Method and system for acquiring medical image |
| CN113035329A (en) * | 2021-03-22 | 2021-06-25 | 杭州联众医疗科技股份有限公司 | Medical image quality control system |
| CN113180716A (en) * | 2021-05-11 | 2021-07-30 | 深圳市深图医学影像设备有限公司 | Medical digital X-ray system capable of realizing intelligent positioning |
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Application publication date: 20230224 |