CN111584034A - Radiation therapy implementation quality control method and system based on artificial intelligence - Google Patents
Radiation therapy implementation quality control method and system based on artificial intelligence Download PDFInfo
- Publication number
- CN111584034A CN111584034A CN202010294371.0A CN202010294371A CN111584034A CN 111584034 A CN111584034 A CN 111584034A CN 202010294371 A CN202010294371 A CN 202010294371A CN 111584034 A CN111584034 A CN 111584034A
- Authority
- CN
- China
- Prior art keywords
- information
- patient
- module
- treatment
- radiotherapy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001959 radiotherapy Methods 0.000 title claims abstract description 180
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 58
- 238000003908 quality control method Methods 0.000 title claims abstract description 39
- 238000004458 analytical method Methods 0.000 claims abstract description 32
- 238000013441 quality evaluation Methods 0.000 claims abstract description 31
- 238000012795 verification Methods 0.000 claims abstract description 27
- 238000007689 inspection Methods 0.000 claims abstract description 8
- 210000000056 organ Anatomy 0.000 claims description 16
- 230000001815 facial effect Effects 0.000 claims description 12
- 238000009472 formulation Methods 0.000 claims description 10
- 239000000203 mixture Substances 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000007408 cone-beam computed tomography Methods 0.000 claims description 9
- 238000012384 transportation and delivery Methods 0.000 claims description 9
- 238000000034 method Methods 0.000 abstract description 43
- 230000008569 process Effects 0.000 description 16
- 230000001988 toxicity Effects 0.000 description 10
- 231100000419 toxicity Toxicity 0.000 description 10
- 230000002159 abnormal effect Effects 0.000 description 9
- 206010028980 Neoplasm Diseases 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 238000001514 detection method Methods 0.000 description 7
- 230000005855 radiation Effects 0.000 description 7
- 241000282414 Homo sapiens Species 0.000 description 6
- 238000010801 machine learning Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 6
- 230000008447 perception Effects 0.000 description 6
- 201000011510 cancer Diseases 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 210000000920 organ at risk Anatomy 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 206010027476 Metastases Diseases 0.000 description 1
- 241001123605 Peru tomato mosaic virus Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000002512 chemotherapy Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000000275 quality assurance Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 210000004881 tumor cell Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1031—Treatment planning systems using a specific method of dose optimization
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/103—Treatment planning systems
- A61N5/1039—Treatment planning systems using functional images, e.g. PET or MRI
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1048—Monitoring, verifying, controlling systems and methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
- A61N5/00—Radiation therapy
- A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
- A61N5/1048—Monitoring, verifying, controlling systems and methods
- A61N5/1071—Monitoring, verifying, controlling systems and methods for verifying the dose delivered by the treatment plan
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Pathology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Radiology & Medical Imaging (AREA)
- Veterinary Medicine (AREA)
- Medical Informatics (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Surgery (AREA)
- Urology & Nephrology (AREA)
- Radiation-Therapy Devices (AREA)
Abstract
The invention discloses a radiation therapy implementation quality control method and system based on artificial intelligence, and the method comprises the following steps: setting a morning inspection quality control flow of the radiotherapy equipment according to the Daily QA standard of the radiotherapy implementation stage, and initializing the equipment; acquiring mechanical information of equipment in real time, analyzing and generating equipment debugging information; inputting basic information of a patient, setting a treatment plan of the patient, and storing the basic information and the treatment plan of the patient into a database; acquiring real-time treatment information of a patient, and performing identity matching association; acquiring real-time body position information and treatment irradiation information of a patient, performing body position analysis and verification according to a set treatment plan of the patient, and calculating an irradiation dose error value; analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient; and generating and sending a treatment early warning control instruction according to the radiotherapy quality evaluation information. The invention can effectively ensure the safety and the accuracy of radiotherapy.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a radiation therapy implementation quality control method and system based on artificial intelligence.
Background
The malignant tumor is a serious disease seriously threatening the health of human beings, the 2015 Chinese cancer statistical data shows that the incidence rate of cancer in China is about 312/10 ten thousand, the three major treatment means of the malignant tumor published by WHO combines the total cure rate to be about 45 percent, wherein, the operation accounts for 22 percent, the radiotherapy accounts for 18 percent, and the chemotherapy accounts for 5 percent; in 55% of patients who are incurable, 18% are local uncontrolled and 37% are distant metastases, and most of these patients need to be treated with radiation at some stage of the disease process, which is one of the important means for treating malignant tumors. In the field of radiation therapy, radiation therapists, physicists and radiation therapists need to work in close cooperation to safely and accurately complete radiation therapy for a tumor patient. No matter how accurate the tumor target area is delineated by the radiotherapy physician, how perfect the plan designed by the physicist is, ultimately, the radiotherapy technician is required to perform precisely to ensure the quality of the radiotherapy. The basic goal of radiotherapy is to maximize the radiotherapy gain ratio, i.e., maximize the concentration of radiation to the tumor Planning Target Volume (PTV), while killing tumor cells while protecting surrounding normal tissues and Organs At Risk (OAR) from unnecessary exposure. To ensure safe achievement of this medical goal requires not only precise delineation of PTVs and OARs by the radiation oncologist, precise planning of radiation therapy by the medical physicist, but also precise implementation of precise radiation therapy plans by the radiation therapist. At the present stage, the basic reality of the radiotherapy clinical practice in China is as follows: 1, the tumor patient base number is large; 2, the resources of radiotherapy are relatively insufficient; 3, professional practitioners of radiotherapy are relatively rare, and professional quality levels are different; and 4, a comprehensive and perfect quality control and quality assurance standardization process is not established. Therefore, the establishment of a perfect standardized QC process in the radiation therapy implementation stage and the strict management and control are important guarantees for ensuring the safety and the accuracy of precise radiation therapy. Artificial Intelligence (AI) is a highly comprehensive cross discipline developed on the basis of multidisciplinary studies such as computer science, cybernetics, information theory, neuropsychology, philosophy, linguistics and the like, and is also a rapidly developing antecedent discipline, and has a very promising application prospect in the implementation of radiotherapy technology by simulating, extending and expanding the human intelligence. By applying the elements of knowledge representation, machine perception, machine thinking, machine learning and the like of the AI to the standardized QC process of the radiotherapy implementation stage, the AI monitoring of the radiotherapy equipment, the AI checking of the physiological characteristics of the patient, the real-time radiotherapy posture verification and tracking, the real-time radiotherapy dose tracking, the radiotherapy toxicity prediction and the like can be realized. By applying AI to the radiation therapy to implement the standardized QC process, the safety and the accuracy of the radiation therapy can be ensured, the workload of a radiation therapy technician can be reduced, the radiation therapy can be carried out orderly, and the optimization, the reasonability and the scientific use of radiation therapy resources are promoted. The existing radiotherapy implementation stage does not have a standardized QC program, the body position of radiotherapy and auxiliary appliances are checked by medical staff in the implementation stage, and the safety and the accuracy of radiotherapy are difficult to guarantee comprehensively.
Disclosure of Invention
In view of the above problems, the present invention provides a radiation therapy implementation quality control method and system based on artificial intelligence, which has the advantage of effectively ensuring the safety and accuracy of radiation therapy through the AI full-process management and control established on the standardized QC procedure.
Aiming at the problems, the invention provides a radiation therapy implementation quality control method based on artificial intelligence, which comprises the following steps:
s1, setting a morning inspection quality control flow of the radiotherapy equipment according to the Daily QA standard of the radiotherapy implementation stage, and initializing the equipment;
s2, acquiring the mechanical information of the equipment in real time, analyzing and generating equipment debugging information, feeding back and storing the equipment debugging information to the central data processing center;
s3, inputting basic information of the patient, setting a treatment plan of the patient, and storing the basic information of the patient and the treatment plan into a database;
s4, acquiring real-time treatment information of the patient, and performing identity matching association according to the input basic information of the patient;
s5, acquiring real-time body position information and treatment irradiation information of the patient, performing body position analysis and verification according to a set treatment plan of the patient, and calculating an irradiation dose error value;
s6, analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient;
and S7, generating and sending a treatment early warning control instruction according to the radiotherapy quality evaluation information.
Setting morning inspection quality control flow of radiotherapy equipment according to Daily QA standard of radiotherapy implementation stage, aligning with ISO9001, initializing equipment, acquiring mechanical information of the equipment in real time, analyzing and generating equipment debugging information, performing AI quality control detection on the performance of the radiotherapy equipment, inputting basic information of a patient, setting a treatment plan of the patient according to the condition of the patient, storing the basic information and the treatment plan of the patient into a database for subsequent inquiry, acquiring real-time treatment information of the patient, performing identity matching association according to the input basic information of the patient, identifying the current patient by biological identifiers such as human face and fingerprint, and simultaneously adopting a photo identification method or other methods to ensure that the selected patient is the correct patient, if the correct patient indicates that the association is successful, then acquiring real-time body position information and treatment irradiation information of the patient, and performing posture analysis and verification according to a set treatment plan of the patient, performing real-time AI posture verification and tracking on the patient subjected to radiotherapy, realizing real-time PTV and OARs radiotherapy dose tracking through a machine algorithm, calculating an irradiation dose error value, analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient, performing radiotherapy toxicity prediction, generating and sending a treatment early warning control command according to the radiotherapy quality evaluation information, sending an early warning control command when the quality is evaluated to be abnormal, performing early warning prompt, and stopping treatment so as to ensure the safety and accuracy of treatment.
The method is expected to realize AI monitoring of radiotherapy equipment, AI checking of physiological characteristics of patients, real-time radiotherapy posture verification and tracking, real-time radiotherapy dose tracking, radiotherapy toxicity prediction and the like by applying the elements of knowledge representation, machine perception, machine thinking, machine learning and the like of AI to the standardized QC process of the radiotherapy implementation stage. By applying AI to the radiation therapy to implement the standardized QC process, the safety and the accuracy of the radiation therapy can be ensured, the workload of a radiation therapy technician can be reduced, the radiation therapy can be carried out orderly, and the optimization, the reasonability and the scientific use of radiation therapy resources are promoted.
In a further technical scheme, the mechanical information of the equipment comprises frame angle in-place precision information, laser lamp and other center precision information, collimator angle indication information, distance indicator precision information, MLC in-place precision information, treatment couch position precision information, radiotherapy environment information and beam quality information output by radiotherapy equipment.
By acquiring various relevant information of the equipment, including the geometric parameters of the equipment and the physical parameters of the equipment, comprehensive and accurate analysis and debugging can be performed subsequently, and effective treatment is ensured.
In a further aspect, step S2 includes the following steps:
s21, acquiring the mechanical information of the equipment in real time;
s22, analyzing the obtained mechanical information of the equipment, judging whether the equipment meets the treatment standard, if so, generating normal equipment debugging information and entering the step S3; if not, go to step S23;
and S23, generating equipment debugging information and sending an alarm prompt.
And (3) actively early warning abnormal or early warning data, reminding technicians to take further measures, and gathering all the collected data and processing conditions to a uniform data platform, so that the collected data and the processing conditions are convenient for the staff or managers to read and check and report the data at any time.
In a further technical solution, the basic information of the patient includes identity information of the patient, facial image information of the patient, and fingerprint information of the patient.
The method comprises the steps of obtaining various information of a patient, and carrying out identity matching correlation on the patient through facial image features and fingerprint features of the patient, so that the accuracy of identity recognition is ensured.
In a further aspect, step S5 includes the following steps:
s51, acquiring real-time posture information and treatment irradiation information of the patient through CBCT or OBI scanning;
s52, performing posture analysis and verification according to the set treatment plan of the patient, judging whether the normal irradiation dose of the organ of the patient is exceeded, and if so, entering the step S53; if not, go to step S54;
s53, generating alarm information, and entering the step S54;
and S54, calculating an irradiation dose error value.
The body position information and the treatment irradiation information of the patient are acquired in real time, and the dosage error of the organ irradiation treatment is calculated in real time by adjusting the body position (positioning) of the patient so as to be convenient for subsequent adjustment, ensure the effectiveness of the treatment and improve the treatment precision.
In view of the above problems, the present invention also provides an artificial intelligence-based radiotherapy implementation quality control system, which comprises a standard formulation module, an equipment debugging module, a plan formulation module, an identity association module, a body position analysis module, a quality evaluation module and an early warning control module, wherein:
the standard formulation module is used for setting a morning inspection quality control flow of the radiotherapy equipment according to the Daily QA standard of the radiotherapy implementation stage and initializing the equipment;
the device debugging module is used for acquiring mechanical information of the device in real time, analyzing and generating device debugging information, feeding back and storing the device debugging information to the central data processing center;
the plan making module is used for inputting basic information of the patient, setting a treatment plan of the patient and storing the basic information and the treatment plan of the patient into the database;
the identity correlation module is used for acquiring real-time treatment information of the patient and performing identity matching correlation according to the input basic information of the patient;
the body position analysis module is used for acquiring real-time body position information and treatment irradiation information of the patient, performing body position analysis verification according to a set treatment plan of the patient and calculating an irradiation dose error value;
the quality evaluation module is used for analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient;
and the early warning control module is used for generating and sending a treatment early warning control instruction according to the radiotherapy quality evaluation information.
The morning examination quality control flow of the radiotherapy equipment is set through a standard setting module according to the Daily QA standard of a radiotherapy implementation stage, the morning examination quality control flow is aligned with ISO9001, equipment initialization is carried out, the mechanical information of the equipment is obtained in real time through an equipment debugging module, equipment debugging information is analyzed and generated, AI quality control detection is carried out on the performance of the radiotherapy equipment, the basic information of a patient is input through a plan setting module, the treatment plan of the patient is set according to the condition of the patient, the basic information and the treatment plan of the patient are stored in a database for subsequent inquiry, the real-time treatment information of the patient is obtained through an identity correlation module, identity matching correlation is carried out according to the input basic information of the patient, the current patient is identified through biological identifiers such as human faces and fingerprints, and a photo identification method or other methods are adopted to ensure that the selected patient is the correct patient, and the correlation success is indicated if the patient, then the body position analysis module obtains real-time body position information and treatment irradiation information of a patient, body position analysis and verification are carried out according to a set treatment plan of the patient, real-time AI body position verification and tracking are carried out on the patient subjected to radiotherapy, real-time PTV and OARs radiotherapy dose tracking is realized through a machine algorithm, an irradiation dose error value is calculated, a quality evaluation module analyzes and generates radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient, radiotherapy toxicity prediction is carried out, an early warning control module generates and sends a treatment early warning control command according to the radiotherapy quality evaluation information, and when the quality is evaluated to be abnormal, an early warning control command is sent, early warning prompt is carried out, treatment is stopped, and safety and accuracy of treatment are guaranteed.
The system can realize AI monitoring of radiotherapy equipment, AI checking of physiological characteristics of patients, real-time radiotherapy posture verification and tracking, real-time radiotherapy dose tracking, radiotherapy toxicity prediction and the like by applying the elements of knowledge representation, machine perception, machine thinking, machine learning and the like of AI to the standardized QC process of the radiotherapy implementation stage. By applying AI to the radiation therapy to implement the standardized QC process, the safety and the accuracy of the radiation therapy can be ensured, the workload of a radiation therapy technician can be reduced, the radiation therapy can be carried out orderly, and the optimization, the reasonability and the scientific use of radiation therapy resources are promoted.
In a further technical scheme, the mechanical information of the equipment comprises frame angle in-place precision information, laser lamp and other center precision information, collimator angle indication information, distance indicator precision information, MLC in-place precision information, treatment couch position precision information, radiotherapy environment information and beam quality information output by radiotherapy equipment.
By acquiring various relevant information of the equipment, including the geometric parameters of the equipment and the physical parameters of the equipment, comprehensive and accurate analysis and debugging can be performed subsequently, and effective treatment is ensured.
In a further technical scheme, the device debugging module comprises an information acquisition submodule, a standard reaching judgment submodule and an information generation submodule, wherein:
the information acquisition submodule is used for acquiring the mechanical information of the equipment in real time;
the standard-reaching judgment sub-module is used for analyzing the acquired mechanical information of the equipment, judging whether the equipment reaches a treatment standard, if so, generating normal equipment debugging information, and working the plan making module; if not, the information generation submodule works;
and the information generation submodule is used for generating equipment debugging information and sending an alarm prompt.
And (3) actively early warning abnormal or early warning data, reminding technicians to take further measures, and gathering all the collected data and processing conditions to a uniform data platform, so that the collected data and the processing conditions are convenient for the staff or managers to read and check and report the data at any time.
In a further technical solution, the basic information of the patient includes identity information of the patient, facial image information of the patient, and fingerprint information of the patient.
The method comprises the steps of obtaining various information of a patient, and carrying out identity matching correlation on the patient through facial image features and fingerprint features of the patient, so that the accuracy of identity recognition is ensured.
In a further technical scheme, the body position analysis module comprises a treatment information submodule, a dose judgment submodule, an alarm submodule and an error calculation submodule, wherein:
the treatment information submodule is used for acquiring real-time body position information and treatment irradiation information of the patient through CBCT or OBI scanning;
the dose judgment submodule is used for carrying out body position analysis and verification according to a set treatment plan of the patient and judging whether the normal irradiation dose of the organ of the patient exceeds, and if so, the alarm submodule works; if not, the error calculation submodule works;
the alarm submodule is used for generating alarm information;
and the error calculation submodule is used for calculating an irradiation dose error value.
The body position information and the treatment irradiation information of the patient are acquired in real time, and the dosage error of the organ irradiation treatment is calculated in real time by adjusting the body position (positioning) of the patient so as to be convenient for subsequent adjustment, ensure the effectiveness of the treatment and improve the treatment precision.
The invention has the beneficial effects that:
1. by applying the elements of knowledge representation, machine perception, machine thinking, machine learning and the like of the AI to the standardized QC process of the radiotherapy implementation stage, the AI monitoring of the radiotherapy equipment, the AI checking of the physiological characteristics of the patient, the real-time radiotherapy posture verification and tracking, the real-time radiotherapy dose tracking, the radiotherapy toxicity prediction and the like can be realized. By applying AI to the radiation therapy to implement the standardized QC process, the safety and the accuracy of the radiation therapy can be ensured, the workload of a radiation therapy technician can be reduced, the radiation therapy can be carried out orderly, and the optimization, the reasonability and the scientific use of radiation therapy resources are promoted;
2. by acquiring the relevant information of various devices, comprehensive and accurate analysis and debugging can be performed subsequently, and effective treatment is ensured;
3. the technical personnel is reminded to take further measures when abnormal or early warning data are actively early warned, and all the collected data and the processing conditions are gathered to a uniform data platform, so that the data can be conveniently read and checked and data can be conveniently reported by the working personnel or managers at any time;
4. acquiring various information of a patient, and performing identity matching association on the patient through facial image features and fingerprint features of the patient to ensure the accuracy of identity identification;
5. the body position information and the treatment irradiation information of the patient are acquired in real time, and the dosage error of the organ irradiation treatment is calculated in real time by adjusting the body position (positioning) of the patient so as to be convenient for subsequent adjustment, ensure the effectiveness of the treatment and improve the treatment precision.
Drawings
FIG. 1 is a flow chart of a method for performing quality control in artificial intelligence based radiation therapy in accordance with an embodiment of the present invention;
fig. 2 is a schematic diagram of an artificial intelligence based radiation therapy delivery quality control system according to an embodiment of the present invention.
Description of reference numerals:
10. a standard formulation module; 20. a device debugging module; 21. an information acquisition submodule; 22. a standard reaching judgment submodule; 23. an information generation submodule; 30. a plan making module; 40. an identity correlation module; 50. a body position analysis module; 51. a treatment information submodule; 52. a dose judgment submodule; 53. an alarm submodule; 54. an error calculation submodule; 60. a quality evaluation module; 70. and an early warning control module.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example (b):
as shown in fig. 1, a radiation therapy implementation quality control method based on artificial intelligence comprises the following steps:
s1, setting a morning inspection quality control flow of the radiotherapy equipment according to the Daily QA standard of the radiotherapy implementation stage, and initializing the equipment;
s2, acquiring the mechanical information of the equipment in real time, analyzing and generating equipment debugging information, feeding back and storing the equipment debugging information to the central data processing center;
s3, inputting basic information of the patient, setting a treatment plan of the patient, and storing the basic information of the patient and the treatment plan into a database;
s4, acquiring real-time treatment information of the patient, and performing identity matching association according to the input basic information of the patient;
s5, acquiring real-time body position information and treatment irradiation information of the patient, performing body position analysis and verification according to a set treatment plan of the patient, and calculating an irradiation dose error value;
s6, analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient;
and S7, generating and sending a treatment early warning control instruction according to the radiotherapy quality evaluation information.
Setting morning examination quality control flow of radiotherapy equipment according to Daily QA standard of radiotherapy implementation stage, aligning with ISO9001, initializing equipment, acquiring mechanical information of the equipment in real time, analyzing and generating equipment debugging information, performing AI quality control detection on the performance of the radiotherapy equipment, inputting basic information of a patient, setting a treatment plan of the patient according to the condition of the patient, storing the basic information and the treatment plan of the patient into a database for subsequent query, acquiring real-time treatment information of the patient, performing identity matching association according to the input basic information of the patient, identifying the current patient by biological identifiers such as human face and fingerprint, and simultaneously adopting a photo identification method or other methods to ensure that the selected patient is the correct patient, if the correct patient is radiotherapy treatment, indicating that the association is successful, inputting facial features or fingerprint features of the patient and corresponding to the plan and a body position fixing device through a machine sensing function, the information is completely matched, so that the positioning before treatment can be carried out. Therefore, the false irradiation caused by the duplicate names or communication obstacles or the negligence of working personnel can be effectively avoided. The face recognition technology comprises three parts of face detection, face tracking and face comparison, wherein the face detection is to judge whether a face exists in a dynamic scene with a complex background and isolate the face like the face, and generally, the following methods are adopted: a reference template method, a face rule method, a sample learning method and a skin color model method; the face tracking refers to tracking a detected dynamic target based on a model and a method combining motion and the model, and then carrying out a series of preprocessing on a face model; the comparison of the faces is to determine the identity of the person, i.e. to perform a target search in the library, which in practice means sampling and inventorying of the face images, in order to match the best object; the method comprises the steps that when a patient registers, face information is collected through a camera and stored, according to a big data analysis theory, the face data is collected, imported, preprocessed and statistically analyzed and stored in a database, when the patient enters a treatment room to be treated, a treatment technician opens a treatment information window of the patient through a bar code and calls out picture information, the patient carries out identity verification through face recognition, the collected face image features are compared with face image features prestored in the database, the best matching object is found, and finally the identity is determined, and if the verification fails, an alarm is given; then acquiring real-time body position information and treatment irradiation information of a patient, analyzing and verifying the body position according to a set treatment plan of the patient, verifying and tracking the AI body position of the patient subjected to radiotherapy in real time, realizing real-time PTV and OARs radiotherapy dose tracking through a machine algorithm, calculating an irradiation dose error value, analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient, predicting the toxicity of radiotherapy, generating and sending a treatment early warning control command according to the radiotherapy quality evaluation information, sending an early warning control command when the quality is evaluated to be abnormal, giving an early warning prompt, and stopping treatment so as to ensure the safety and accuracy of treatment.
The method is expected to realize AI monitoring of radiotherapy equipment, AI checking of physiological characteristics of patients, real-time radiotherapy posture verification and tracking, real-time radiotherapy dose tracking, radiotherapy toxicity prediction and the like by applying the elements of knowledge representation, machine perception, machine thinking, machine learning and the like of AI to the standardized QC process of the radiotherapy implementation stage. By applying AI to the radiation therapy to implement the standardized QC process, the safety and the accuracy of the radiation therapy can be ensured, the workload of a radiation therapy technician can be reduced, the radiation therapy can be carried out orderly, and the optimization, the reasonability and the scientific use of radiation therapy resources are promoted.
In one embodiment, the mechanical information of the device comprises geometric parameters of the device and physical parameters of the device, wherein the geometric parameters of the device comprise frame angle in-place precision information, laser lamp and other center precision information, collimator angle indication information, distance indicator precision information, MLC in-place precision information and treatment bed position precision information; the physical parameters of the device comprise radiotherapy environment information and beam quality information output by the radiotherapy device.
By acquiring various relevant information of the equipment, including the geometric parameters of the equipment and the physical parameters of the equipment, comprehensive and accurate analysis and debugging can be performed subsequently, and effective treatment is ensured.
In one embodiment, step S2 includes the following steps:
s21, acquiring the mechanical information of the equipment in real time;
s22, analyzing the obtained mechanical information of the equipment, judging whether the equipment meets the treatment standard, if so, generating normal equipment debugging information and entering the step S3; if not, go to step S23;
and S23, generating equipment debugging information and sending an alarm prompt.
The AI technology is adopted to carry out performance detection on the treatment equipment and automatically detect the mechanical precision of the radiotherapy equipment, wherein the mechanical precision comprises the items of the frame angle in-place precision, the laser lamp and other center precision, the collimator angle indication, the distance indicator precision and the like; the radiation therapy environmental pressure, the temperature, the humidity and other natural information are recognized in real time through various sensors in the machine, data acquisition and active operation are carried out in the processes of detecting the beam stability, the output flatness and the symmetry of radiation of the radiation therapy equipment, the ray quality standard reaching degree is distinguished, active early warning is carried out on abnormal or early warning data, technical personnel are reminded to take further measures, all acquired data and processing conditions are gathered to a unified data platform, and the radiation therapy environmental pressure, the temperature, the humidity and other natural information can be conveniently read and checked and reported by working personnel or managers at any time.
In one embodiment, the basic information of the patient includes identity information of the patient, facial image information of the patient and fingerprint information of the patient.
The method comprises the steps of obtaining various information of a patient, and carrying out identity matching correlation on the patient through facial image features and fingerprint features of the patient, so that the accuracy of identity recognition is ensured.
In one embodiment, step S5 includes the following steps:
s51, acquiring real-time posture information and treatment irradiation information of the patient through CBCT or OBI scanning;
s52, performing posture analysis and verification according to the set treatment plan of the patient, judging whether the normal irradiation dose of the organ of the patient is exceeded, and if so, entering the step S53; if not, go to step S54;
s53, generating alarm information, and entering the step S54;
and S54, calculating an irradiation dose error value.
The method comprises the steps that CBCT or OBI scanning is carried out on a patient after the positioning of the patient is finished, real-time body position information and treatment irradiation information of the patient are obtained, CT data in the real-time treatment irradiation information are matched with CT data when a radiotherapy doctor in a treatment plan of the patient delineates a target area, positioning errors of an image structure and possible residual errors are calculated through AI, dosage errors received by PTV and OARs due to positioning are calculated according to the positioning errors, and error alarming and limiting are set. The specific method comprises the following steps:
analyzing the CBCT image at each time, and comparing the CBCT image with a plan; the content of comparison is as follows: by image analysis, a distance check generated by tumor deformation is sketched, and when a tumor area is reduced, a normal tissue enters the volume of an irradiation field; analyzing the names of organs of surrounding groups by using organ identification through AI technology, and searching the tolerance condition of the organs according to the names; accumulating the irradiation dose of the normal organ every time to obtain the total irradiation dose; judging whether an organ tolerance extreme value is reached or not according to the obtained total irradiation dose; and then give evaluation, early warning, reminding and the like.
The body position information and the treatment irradiation information of the patient are acquired in real time, and the dosage error of the organ irradiation treatment is calculated in real time by adjusting the body position (positioning) of the patient so as to be convenient for subsequent adjustment, ensure the effectiveness of the treatment and improve the treatment precision.
As shown in fig. 2, the present invention further provides an artificial intelligence-based radiotherapy implementation quality control system, which comprises a standard formulation module, an equipment debugging module, a plan formulation module, an identity association module, a body position analysis module, a quality evaluation module and an early warning control module, wherein:
the standard formulation module is used for setting a morning inspection quality control flow of the radiotherapy equipment according to the Daily QA standard of the radiotherapy implementation stage and initializing the equipment;
the device debugging module is used for acquiring mechanical information of the device in real time, analyzing and generating device debugging information, feeding back and storing the device debugging information to the central data processing center;
the plan making module is used for inputting basic information of the patient, setting a treatment plan of the patient and storing the basic information and the treatment plan of the patient into the database;
the identity correlation module is used for acquiring real-time treatment information of the patient and performing identity matching correlation according to the input basic information of the patient;
the body position analysis module is used for acquiring real-time body position information and treatment irradiation information of the patient, performing body position analysis verification according to a set treatment plan of the patient and calculating an irradiation dose error value;
the quality evaluation module is used for analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient;
and the early warning control module is used for generating and sending a treatment early warning control instruction according to the radiotherapy quality evaluation information.
The morning examination quality control flow of the radiotherapy equipment is set through a standard setting module according to the Daily QA standard of a radiotherapy implementation stage, the morning examination quality control flow is aligned with ISO9001, equipment initialization is carried out, the mechanical information of the equipment is obtained in real time through an equipment debugging module, equipment debugging information is analyzed and generated, AI quality control detection is carried out on the performance of the radiotherapy equipment, the basic information of a patient is input through a plan setting module, the treatment plan of the patient is set according to the condition of the patient, the basic information and the treatment plan of the patient are stored in a database for subsequent inquiry, the real-time treatment information of the patient is obtained through an identity correlation module, identity matching correlation is carried out according to the input basic information of the patient, the current patient is identified through biological identifiers such as human faces and fingerprints, and a photo identification method or other methods are adopted to ensure that the selected patient is the correct patient, and the correlation success is indicated if the patient, then the body position analysis module obtains real-time body position information and treatment irradiation information of a patient, body position analysis and verification are carried out according to a set treatment plan of the patient, real-time AI body position verification and tracking are carried out on the patient subjected to radiotherapy, real-time PTV and OARs radiotherapy dose tracking is realized through a machine algorithm, an irradiation dose error value is calculated, a quality evaluation module analyzes and generates radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient, radiotherapy toxicity prediction is carried out, an early warning control module generates and sends a treatment early warning control command according to the radiotherapy quality evaluation information, and when the quality is evaluated to be abnormal, an early warning control command is sent, early warning prompt is carried out, treatment is stopped, and safety and accuracy of treatment are guaranteed.
The system can realize AI monitoring of radiotherapy equipment, AI checking of physiological characteristics of patients, real-time radiotherapy posture verification and tracking, real-time radiotherapy dose tracking, radiotherapy toxicity prediction and the like by applying the elements of knowledge representation, machine perception, machine thinking, machine learning and the like of AI to the standardized QC process of the radiotherapy implementation stage. By applying AI to the radiation therapy to implement the standardized QC process, the safety and the accuracy of the radiation therapy can be ensured, the workload of a radiation therapy technician can be reduced, the radiation therapy can be carried out orderly, and the optimization, the reasonability and the scientific use of radiation therapy resources are promoted.
In one embodiment, the mechanical information of the device comprises geometric parameters of the device and physical parameters of the device, wherein the geometric parameters of the device comprise frame angle in-place precision information, laser lamp and other center precision information, collimator angle indication information, distance indicator precision information, MLC in-place precision information and treatment bed position precision information; the physical parameters of the device comprise radiotherapy environment information and beam quality information output by the radiotherapy device.
By acquiring various relevant information of the equipment, including the geometric parameters of the equipment and the physical parameters of the equipment, comprehensive and accurate analysis and debugging can be performed subsequently, and effective treatment is ensured.
In one embodiment, as shown in fig. 2, the device debugging module includes an information obtaining sub-module, a standard reaching judgment sub-module, and an information generating sub-module, where:
the information acquisition submodule is used for acquiring the mechanical information of the equipment in real time;
the standard-reaching judgment sub-module is used for analyzing the acquired mechanical information of the equipment, judging whether the equipment reaches a treatment standard, if so, generating normal equipment debugging information, and working the plan making module; if not, the information generation submodule works;
and the information generation submodule is used for generating equipment debugging information and sending an alarm prompt.
And (3) actively early warning abnormal or early warning data, reminding technicians to take further measures, and gathering all the collected data and processing conditions to a uniform data platform, so that the collected data and the processing conditions are convenient for the staff or managers to read and check and report the data at any time.
In one embodiment, the basic information of the patient includes identity information of the patient, facial image information of the patient and fingerprint information of the patient.
The method comprises the steps of obtaining various information of a patient, and carrying out identity matching correlation on the patient through facial image features and fingerprint features of the patient, so that the accuracy of identity recognition is ensured.
In one embodiment, as shown in fig. 2, the body position analyzing module includes a treatment information sub-module, a dose judging sub-module, an alarm sub-module, and an error calculating sub-module, wherein:
the treatment information submodule is used for acquiring real-time body position information and treatment irradiation information of the patient through CBCT or OBI scanning;
the dose judgment submodule is used for carrying out body position analysis and verification according to a set treatment plan of the patient and judging whether the normal irradiation dose of the organ of the patient exceeds, and if so, the alarm submodule works; if not, the error calculation submodule works;
the alarm submodule is used for generating alarm information;
and the error calculation submodule is used for calculating an irradiation dose error value.
The body position information and the treatment irradiation information of the patient are acquired in real time, and the dosage error of the organ irradiation treatment is calculated in real time by adjusting the body position (positioning) of the patient so as to be convenient for subsequent adjustment, ensure the effectiveness of the treatment and improve the treatment precision.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (10)
1. A radiation therapy implementation quality control method based on artificial intelligence is characterized by comprising the following steps:
s1, setting a morning inspection quality control flow of the radiotherapy equipment according to the Daily QA standard of the radiotherapy implementation stage, and initializing the equipment;
s2, acquiring the mechanical information of the equipment in real time, analyzing and generating equipment debugging information, feeding back and storing the equipment debugging information to the central data processing center;
s3, inputting basic information of the patient, setting a treatment plan of the patient, and storing the basic information of the patient and the treatment plan into a database;
s4, acquiring real-time treatment information of the patient, and performing identity matching association according to the input basic information of the patient;
s5, acquiring real-time body position information and treatment irradiation information of the patient, performing body position analysis and verification according to a set treatment plan of the patient, and calculating an irradiation dose error value;
s6, analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient;
and S7, generating and sending a treatment early warning control instruction according to the radiotherapy quality evaluation information.
2. The artificial intelligence based radiation therapy delivery quality control method of claim 1, wherein the mechanical information of the apparatus comprises gantry angle in-place accuracy information, laser light isocenter accuracy information, collimator angle indication information, distance indicator accuracy information, MLC in-place accuracy information, couch position accuracy information, radiation therapy environment information, beam quality information output by the radiation therapy apparatus.
3. The artificial intelligence based radiation therapy delivery quality control method as claimed in claim 2, wherein the step S2 includes the steps of:
s21, acquiring the mechanical information of the equipment in real time;
s22, analyzing the obtained mechanical information of the equipment, judging whether the equipment meets the treatment standard, if so, generating normal equipment debugging information and entering the step S3; if not, go to step S23;
and S23, generating equipment debugging information and sending an alarm prompt.
4. The artificial intelligence based radiation therapy delivery quality control method of claim 3, wherein the patient's basic information includes patient's identity information, patient's facial image information, and patient's fingerprint information.
5. The artificial intelligence based radiation therapy delivery quality control method as claimed in claim 4, wherein the step S5 includes the steps of:
s51, acquiring real-time posture information and treatment irradiation information of the patient through CBCT or OBI scanning;
s52, performing posture analysis and verification according to the set treatment plan of the patient, judging whether the normal irradiation dose of the organ of the patient is exceeded, and if so, entering the step S53; if not, go to step S54;
s53, generating alarm information, and entering the step S54;
and S54, calculating an irradiation dose error value.
6. The utility model provides a quality control system is implemented in radiotherapy based on artificial intelligence which characterized in that, includes standard formulation module, equipment debugging module, plan formulation module, identity correlation module, position analysis module, quality evaluation module and early warning control module, wherein:
the standard formulation module is used for setting a morning inspection quality control flow of the radiotherapy equipment according to the Daily QA standard of the radiotherapy implementation stage and initializing the equipment;
the device debugging module is used for acquiring mechanical information of the device in real time, analyzing and generating device debugging information, feeding back and storing the device debugging information to the central data processing center;
the plan making module is used for inputting basic information of the patient, setting a treatment plan of the patient and storing the basic information and the treatment plan of the patient into the database;
the identity correlation module is used for acquiring real-time treatment information of the patient and performing identity matching correlation according to the input basic information of the patient;
the body position analysis module is used for acquiring real-time body position information and treatment irradiation information of the patient, performing body position analysis verification according to a set treatment plan of the patient and calculating an irradiation dose error value;
the quality evaluation module is used for analyzing and generating radiotherapy quality evaluation information according to the irradiation dose error value and the set treatment plan of the patient;
and the early warning control module is used for generating and sending a treatment early warning control instruction according to the radiotherapy quality evaluation information.
7. The artificial intelligence based radiation therapy delivery quality control system of claim 6, wherein the mechanical information of the device includes gantry angle in-place accuracy information, laser light isocenter accuracy information, collimator angle indication information, distance indicator accuracy information, MLC in-place accuracy information, couch position accuracy information, radiation therapy environment information, beam quality information output by the radiation therapy device.
8. The artificial intelligence based radiation therapy delivery quality control system of claim 7, wherein the device commissioning module comprises an information acquisition sub-module, a compliance determination sub-module, and an information generation sub-module, wherein:
the information acquisition submodule is used for acquiring the mechanical information of the equipment in real time;
the standard-reaching judgment sub-module is used for analyzing the acquired mechanical information of the equipment, judging whether the equipment reaches a treatment standard, if so, generating normal equipment debugging information, and working the plan making module; if not, the information generation submodule works;
and the information generation submodule is used for generating equipment debugging information and sending an alarm prompt.
9. The artificial intelligence based radiation therapy delivery quality control system of claim 8, wherein the patient's basic information includes patient identity information, patient facial image information, and patient fingerprint information.
10. The artificial intelligence based radiation therapy delivery quality control system of claim 9, wherein the body position analysis module comprises a treatment information sub-module, a dose judgment sub-module, an alarm sub-module, and an error calculation sub-module, wherein:
the treatment information submodule is used for acquiring real-time body position information and treatment irradiation information of the patient through CBCT or OBI scanning;
the dose judgment submodule is used for carrying out body position analysis and verification according to a set treatment plan of the patient and judging whether the normal irradiation dose of the organ of the patient exceeds, and if so, the alarm submodule works; if not, the error calculation submodule works;
the alarm submodule is used for generating alarm information;
and the error calculation submodule is used for calculating an irradiation dose error value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010294371.0A CN111584034A (en) | 2020-04-14 | 2020-04-14 | Radiation therapy implementation quality control method and system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010294371.0A CN111584034A (en) | 2020-04-14 | 2020-04-14 | Radiation therapy implementation quality control method and system based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111584034A true CN111584034A (en) | 2020-08-25 |
Family
ID=72112549
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010294371.0A Pending CN111584034A (en) | 2020-04-14 | 2020-04-14 | Radiation therapy implementation quality control method and system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111584034A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112382371A (en) * | 2020-10-16 | 2021-02-19 | 北京全域医疗技术集团有限公司 | Radiotherapy dose analysis method and device, storage medium and computer equipment |
| CN112885425A (en) * | 2021-02-25 | 2021-06-01 | 中科超精(南京)科技有限公司 | Radiotherapy dose online reconstruction and verification system |
| CN113491845A (en) * | 2021-08-02 | 2021-10-12 | 上海联影医疗科技股份有限公司 | Radiotherapy system, radiotherapy device and storage medium |
| CN114334085A (en) * | 2021-12-29 | 2022-04-12 | 上海联影医疗科技股份有限公司 | Radiotherapy information display method, radiotherapy information adjustment method, electronic device and storage medium |
| CN115223683A (en) * | 2022-08-19 | 2022-10-21 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Radiotherapy plan evaluation method, radiotherapy plan evaluation device, computer equipment and medium |
| CN115300811A (en) * | 2022-08-08 | 2022-11-08 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Dose distribution determination method and device based on machine learning |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101015723A (en) * | 2006-02-09 | 2007-08-15 | 吴大怡 | Robot radiation therapy system |
| CN104971437A (en) * | 2015-07-06 | 2015-10-14 | 谭庭强 | Automatic patient identification method based on biological characteristics |
| CN105031833A (en) * | 2015-08-28 | 2015-11-11 | 瑞地玛医学科技有限公司 | Dosage verification system for radiotherapy apparatus |
| CN105488330A (en) * | 2015-11-23 | 2016-04-13 | 北京全域医疗技术有限公司 | Remote quality control method of cloud radiotherapy |
| CN106730412A (en) * | 2016-12-23 | 2017-05-31 | 北京精诚聚信科技发展有限公司 | Radiotherapy homogeneity method and platform |
| CN107358607A (en) * | 2017-08-13 | 2017-11-17 | 强深智能医疗科技（昆山）有限公司 | Tumour radiotherapy visual monitoring and visual servo intelligent control method |
| CN109771842A (en) * | 2017-11-10 | 2019-05-21 | 北京连心医疗科技有限公司 | Cloud radiotherapy method of quality control, equipment and storage medium based on machine learning |
| CN110841205A (en) * | 2019-10-21 | 2020-02-28 | 温州医科大学附属第一医院 | Accurate dose verification method, device and equipment for tumor patient |
| CN110929575A (en) * | 2019-10-22 | 2020-03-27 | 苏州雷泰智能科技有限公司 | Radiotherapy patient identity verification method and device and radiotherapy equipment |
-
2020
- 2020-04-14 CN CN202010294371.0A patent/CN111584034A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101015723A (en) * | 2006-02-09 | 2007-08-15 | 吴大怡 | Robot radiation therapy system |
| CN104971437A (en) * | 2015-07-06 | 2015-10-14 | 谭庭强 | Automatic patient identification method based on biological characteristics |
| CN105031833A (en) * | 2015-08-28 | 2015-11-11 | 瑞地玛医学科技有限公司 | Dosage verification system for radiotherapy apparatus |
| CN105488330A (en) * | 2015-11-23 | 2016-04-13 | 北京全域医疗技术有限公司 | Remote quality control method of cloud radiotherapy |
| CN106730412A (en) * | 2016-12-23 | 2017-05-31 | 北京精诚聚信科技发展有限公司 | Radiotherapy homogeneity method and platform |
| CN107358607A (en) * | 2017-08-13 | 2017-11-17 | 强深智能医疗科技（昆山）有限公司 | Tumour radiotherapy visual monitoring and visual servo intelligent control method |
| CN109771842A (en) * | 2017-11-10 | 2019-05-21 | 北京连心医疗科技有限公司 | Cloud radiotherapy method of quality control, equipment and storage medium based on machine learning |
| CN110841205A (en) * | 2019-10-21 | 2020-02-28 | 温州医科大学附属第一医院 | Accurate dose verification method, device and equipment for tumor patient |
| CN110929575A (en) * | 2019-10-22 | 2020-03-27 | 苏州雷泰智能科技有限公司 | Radiotherapy patient identity verification method and device and radiotherapy equipment |
Non-Patent Citations (1)
| Title |
|---|
| 李益民等: "人工智能：肿瘤放疗的机遇与挑战", 《中华放射肿瘤学杂志》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112382371A (en) * | 2020-10-16 | 2021-02-19 | 北京全域医疗技术集团有限公司 | Radiotherapy dose analysis method and device, storage medium and computer equipment |
| CN112382371B (en) * | 2020-10-16 | 2024-03-22 | 北京全域医疗技术集团有限公司 | Radiotherapy dosage analysis method and device, storage medium and computer equipment |
| CN112885425A (en) * | 2021-02-25 | 2021-06-01 | 中科超精(南京)科技有限公司 | Radiotherapy dose online reconstruction and verification system |
| CN113491845A (en) * | 2021-08-02 | 2021-10-12 | 上海联影医疗科技股份有限公司 | Radiotherapy system, radiotherapy device and storage medium |
| CN114334085A (en) * | 2021-12-29 | 2022-04-12 | 上海联影医疗科技股份有限公司 | Radiotherapy information display method, radiotherapy information adjustment method, electronic device and storage medium |
| CN115300811A (en) * | 2022-08-08 | 2022-11-08 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Dose distribution determination method and device based on machine learning |
| CN115300811B (en) * | 2022-08-08 | 2024-01-05 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Dose distribution determining method and device based on machine learning |
| CN115223683A (en) * | 2022-08-19 | 2022-10-21 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Radiotherapy plan evaluation method, radiotherapy plan evaluation device, computer equipment and medium |
| CN115223683B (en) * | 2022-08-19 | 2023-08-25 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Radiation treatment plan evaluation method, radiation treatment plan evaluation device, computer equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111584034A (en) | Radiation therapy implementation quality control method and system based on artificial intelligence | |
| US11191979B2 (en) | Portal dosimetry systems, devices, and methods | |
| US11266858B2 (en) | Systems, devices, and methods for quality assurance of radiation therapy | |
| US7570738B2 (en) | Four-dimensional (4D) image verification in respiratory gated radiation therapy | |
| US9179982B2 (en) | Method and system for automatic patient identification | |
| US8238516B2 (en) | Radiotherapy support apparatus | |
| WO2004000120A1 (en) | Patient representation in medical machines | |
| KR20080049716A (en) | Method and system for evaluating quality assurance criteria in delivery of a treament plan | |
| Park et al. | Automatic marker detection and 3D position reconstruction using cine EPID images for SBRT verification | |
| KR101968671B1 (en) | Intelligent automated method and system for setting radiation therapy plan | |
| CN102989086B (en) | Method and device for verifying irradiation field | |
| JP6278483B2 (en) | Treatment support system, operation method of treatment support system, and treatment support program | |
| Tonigan | Evaluation of intensity modulated radiation therapy (IMRT) delivery error due to IMRT treatment plan complexity and improperly matched dosimetry data | |
| CN111228656A (en) | Quality control system and method for applying radiotherapy external irradiation treatment based on virtual intelligent medical platform | |
| CN117323584B (en) | Electronic device for radiation treatment plan adjustment, radiation treatment system and related apparatus | |
| KR102409284B1 (en) | Automatic Evaluation Apparatus for Tracking Tumors and Radiation Therapy System using the same | |
| Fang et al. | Automatic detection of graticule isocenter and scale from kV and MV images | |
| CN109670384A (en) | A kind of quick biopsy method of iris | |
| CN116983561A (en) | Patient transferring and lying auxiliary device for gamma knife treatment | |
| Rajasekar | Optimising the use of an EPID-based in-vivo dose verification system for 3DCRT breast treatments. | |
| Esposito et al. | Quality Assurance of Epid Transit Dosimetry Software: Multicentric Implementation of an Independent Evaluation Protocol | |
| LOW et al. | QUALITY ASSURANCE PROCESSES |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200825 |