Medical image quality detection method

Abstract

The invention relates to the technical field of medical image quality detection, and discloses a medical image quality detection method. According to the method, patient body data of a patient before medical image detection and motion data of the patient in the medical image detection process are collected, and a patient comprehensive quality influence coefficient is obtained through comprehensive calculation based on a patient body influence coefficient and a patient motion influence coefficient; the method comprises the following steps: collecting equipment data of a patient in medical image detection, constructing an equipment influence coefficient, after equipment outputs a medical image, respectively reading a comprehensive quality influence coefficient and the equipment influence coefficient of the patient, and when any one of the comprehensive quality influence coefficient and the equipment influence coefficient exceeds a corresponding threshold value, executing a quality early warning instruction. And when the comprehensive quality influence coefficient and the equipment influence coefficient do not exceed the corresponding threshold values, a quality qualification instruction is sent out, so that the quality evaluation can be directly carried out on the medical image on the shooting site, and detection personnel can find problems in time.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image quality detection, and particularly to a medical image quality detection method. Background Art

[0002] Medical images mainly include X-rays, computed tomography (CT), and magnetic resonance. By taking internal images of the human body for physical examinations, they play an important role in the field of disease detection. Medical images can help doctors quickly lock in the cause of the disease and formulate treatment plans by presenting lesions through images. Therefore, high-quality and clear medical images are required. In the actual process of radiological examinations, due to factors such as the proficiency of the detection personnel's shooting techniques, the quality of medical images may deviate. The existing quality control methods for medical images generally evaluate by random sampling during the later stage and then provide shooting guidance to the detection personnel. Such a quality control method is not real-time for the randomly sampled medical images. When it is found that the quality of the randomly sampled medical image is unqualified, the corresponding subject has already left, and it is time-consuming and laborious to notify them to come back for shooting. There are few existing technologies that can directly evaluate the quality of medical images on-site during shooting, so that the detection personnel can timely discover problems and adjust and reshoot. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a medical image quality detection method, which has the advantages of being able to timely evaluate the quality of medical images, thereby being able to timely discover problems such as shooting defects, and solves the above technical problems.

[0004] To achieve the above object, the present invention provides the following technical solution: A medical image quality detection method, including the following steps: S1: Collect the patient's body data before the medical image detection and the patient's movement data during the medical image detection; S2: Based on the comprehensive analysis of the patient's body data and the image data during the medical image detection, obtain the patient's body influence coefficient and the patient's movement influence coefficient respectively; S3: Based on the patient's body influence coefficient and the patient's movement influence coefficient, comprehensively calculate to obtain the patient's comprehensive quality influence coefficient; S4: Collect the equipment data during the medical image detection of the patient, and construct an equipment influence coefficient based on the comprehensive analysis of the equipment data during the medical image detection of the patient; S5: After the device outputs a medical image, read the patient comprehensive quality influence coefficient and the device influence coefficient respectively. When either the comprehensive quality influence coefficient or the device influence coefficient exceeds the corresponding threshold, execute the quality warning instruction. When both the comprehensive quality influence coefficient and the device influence coefficient do not exceed the corresponding threshold, issue a quality qualified instruction.

[0005] As a preferred technical solution of the present invention, the specific expression for collecting the patient's physical data before the medical image detection in S1 is as follows: Among them, represents the patient's physical data, respectively represent the height values at different times in the same day recorded by the patient himself, respectively represent the weight values at different times in the same day recorded by the patient himself, and calculate the patient's , the specific expression is as follows: Among them, represents the sum of , represents the patient's BMI value, represents the rd height value in the same day recorded by the patient himself, represents the th weight value in the same day recorded by the patient himself.

[0006] As a preferred technical solution of the present invention, the specific steps for collecting the patient's movement data during the medical image detection in S1 are as follows: S1.1: Obtain the image of the patient lying flat and construct the patient body type proportion factor; S1.2: Monitor the patient's breathing frequency during the medical image detection and obtain the breathing fluctuation factor; S1.3: Monitor the patient's limb movement during the medical image detection and obtain the limb movement factor; S1.4: Store the patient's breathing frequency and limb movement factor into the patient movement data set, the specific expression is as follows: Among them, represents the breathing fluctuation factor, represents the limb movement factor, represents the patient movement data set.

[0007] As a preferred technical solution of the present invention, the specific expression for obtaining the image of the patient after lying flat in S1.1 and constructing the patient body shape ratio factor is as follows: Wherein, represents the patient body shape ratio factor, represents the patient area, represents the examination table area; In S1.2, the respiratory rate of the patient during the medical imaging detection process is monitored, and the specific expression for obtaining the respiratory fluctuation factor is as follows: Wherein, represents the average respiratory rate of the patient during the medical imaging detection process, represents the maximum respiratory rate of the patient during the medical imaging detection process, represents the respiratory fluctuation factor; In S1.3, the limb movement condition of the patient during the medical imaging detection process is monitored, and the specific expression for obtaining the limb movement factor is as follows: Wherein, represents the limb movement factor, represents the area of the patient's limb movement area, obtained by image comparison, represents the patient area.

[0008] As a preferred technical solution of the present invention, the expressions for obtaining the patient body influence coefficient and the patient movement influence coefficient based on the comprehensive analysis of the patient body data and the image data of the patient during the medical imaging detection process in S2 are as follows: Wherein, represents the patient's BMI value, represents the patient body shape ratio factor, represents the maximum value of the patient's BMI value stored in the current database, represents the patient body influence coefficient, represents the patient movement influence coefficient, represents the respiratory fluctuation factor, represents the limb movement factor.

[0009] As a preferred technical solution of the present invention, the expression for obtaining the patient comprehensive quality influence coefficient based on the comprehensive calculation of the patient body influence coefficient and the patient movement influence coefficient in S3 is as follows: Among them, represents the patient's physical impact coefficient, represents the patient's exercise impact coefficient, respectively represent weight coefficients whose sum is 1, represents the patient's comprehensive quality impact coefficient.

[0010] As a preferred technical solution of the present invention, the specific expression for collecting the device data of the patient during medical imaging detection in S4 is as follows: Among them, represents the device data set, respectively represent the first device impact parameters, obtained through device logs.

[0011] As a preferred technical solution of the present invention, the specific expression for constructing the device impact coefficient based on the comprehensive analysis of the device data of the patient during medical imaging detection in S4 is as follows: Among them, represents the device impact coefficient, represents the th device impact parameter, represents the summation of a total of J device impact parameters, represents the absolute value of, represents the th standard value corresponding to the device impact parameter.

[0012] As a preferred technical solution of the present invention, when the comprehensive quality impact coefficient exceeds the comprehensive quality impact threshold , then a quality warning instruction is executed. The quality warning instruction is specifically: transmitting the currently collected medical image through data to the doctor for viewing, and the doctor judges whether a reshoot is required. If the comprehensive quality impact coefficient does not exceed the comprehensive quality impact threshold , then a comprehensive quality qualified result is issued; When the device impact coefficient exceeds the device impact coefficient threshold , then a quality warning instruction is executed. When the device impact coefficient does not exceed the device impact coefficient threshold , then a device quality qualified result is issued.

[0013] As a preferred technical solution of the present invention, when the comprehensive quality qualification result and the equipment quality qualification result are sent out simultaneously, a quality qualification instruction is sent out to determine that the current medical image is qualified; When the comprehensive quality influence coefficient exceeds the comprehensive quality influence threshold and when the equipment influence coefficient exceeds the equipment influence coefficient threshold then a reshoot instruction is directly sent out.

[0014] Compared with the prior art, the present invention provides a method for detecting the quality of medical images, having the following beneficial effects: The present invention collects the patient's body data before the medical image detection and the patient's movement data during the medical image detection, comprehensively calculates the patient's comprehensive quality influence coefficient based on the patient's body influence coefficient and the patient's movement influence coefficient, collects the equipment data during the patient's medical image detection, and comprehensively analyzes and constructs the equipment influence coefficient based on the equipment data during the patient's medical image detection. After the equipment outputs the medical image, the patient's comprehensive quality influence coefficient and the equipment influence coefficient are respectively read. When either the comprehensive quality influence coefficient or the equipment influence coefficient exceeds the corresponding threshold, a quality warning instruction is executed. When both the comprehensive quality influence coefficient and the equipment influence coefficient do not exceed the corresponding threshold, a quality qualification instruction is sent out, so as to ensure a method for directly evaluating the quality of medical images at the shooting site, enabling the detection personnel to timely discover problems and make adjustments for reshooting. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Please refer to Figure 1 , a method for detecting the quality of medical images, including the following steps: S1: Collect the patient's body data before the medical image detection and the patient's movement data during the medical image detection. Combining the movement data during the detection helps to timely discover detection abnormalities caused by the patient's own factors, so as to take corresponding measures for adjustment or remedy; The specific expression for collecting the patient's physical data before medical imaging detection in S1 is as follows: Among them, represents the patient's physical data, respectively represent the height values at different times on the same day recorded by the patient himself, respectively represent the weight values at different times on the same day recorded by the patient himself, and the patient's is calculated, and the specific expression is as follows: Among them, represents the sum of , represents the patient's BMI value, represents the th height value on the same day recorded by the patient himself, represents the th weight value on the same day recorded by the patient himself.

[0018] The specific steps for collecting the patient's motion data during medical imaging detection in S1 are as follows: S1.1: Obtain the image of the patient lying flat and construct the patient's body shape ratio factor; S1.2: Monitor the patient's breathing frequency during medical imaging detection and obtain the breathing fluctuation factor; S1.3: Monitor the patient's limb movement during medical imaging detection and obtain the limb movement factor; S1.4: Store the patient's breathing frequency and limb movement factor in the patient's motion data set, and the specific expression is as follows: Among them, represents the breathing fluctuation factor, represents the limb movement factor, represents the patient's motion data set.

[0019] The specific expression for obtaining the image of the patient lying flat and constructing the patient's body shape ratio factor in S1.1 is as follows: Among them, represents the patient's body shape ratio factor, represents the patient's area, represents the area of the examination table area, which can reflect the influence of the patient's body shape; During S1.2, monitor the patient's respiratory rate during the medical imaging detection process, and the specific expression of the respiratory fluctuation factor is as follows: Among them, represents the average respiratory rate of the patient during the medical imaging detection process, represents the maximum respiratory rate of the patient during the medical imaging detection process, represents the respiratory fluctuation factor; During S1.3, monitor the patient's limb movement during the medical imaging detection process, and the specific expression of the limb movement factor is as follows: Among them, represents the limb movement factor, represents the area of the patient's limb movement area, which is obtained by image comparison, that is, the movement of the rest of the limbs before and after the patient's medical imaging is taken, and is used to reflect the impact of the movement of the rest of the limbs on the medical imaging, represents the patient's area; S2: Based on the patient's body data and the image data during the patient's medical imaging detection process, comprehensively analyze the patient's body impact coefficient and the patient's movement impact coefficient. Considering the body data and the image data comprehensively, analyze the patient's situation from multiple perspectives. The body data provides the patient's static physiological information, while the image data reflects the actual imaging situation during the detection process. The combination of the two can more comprehensively evaluate the impact of patient factors on medical imaging detection, and separately obtain the body impact coefficient and the movement impact coefficient, which can clearly distinguish the respective impacts of body factors and movement factors on the detection; S3: Based on the patient's body impact coefficient and the patient's movement impact coefficient, comprehensively calculate the patient's comprehensive quality impact coefficient. By comprehensively calculating a unified coefficient, in subsequent quality control and early warning judgments, there is no need to consider multiple complex influencing factors separately, and only need to make a decision based on the comparison result of this comprehensive coefficient and the threshold, which greatly simplifies the decision-making process and improves work efficiency; S4: Collect the equipment data during the patient's medical imaging detection, and comprehensively analyze and construct the equipment impact coefficient based on the equipment data during the patient's medical imaging detection. The equipment impact coefficient, as an important indicator for evaluating the impact of the equipment on the detection result, can help the operator timely discover potential problems of the equipment and take corresponding maintenance or adjustment measures to ensure that the equipment is always in the best working state, thereby ensuring the quality of medical imaging detection; S5: After the device outputs a medical image, read the patient's comprehensive quality impact coefficient and the device impact coefficient respectively. When either the comprehensive quality impact coefficient or the device impact coefficient exceeds the corresponding threshold, execute the quality warning instruction. When both the comprehensive quality impact coefficient and the device impact coefficient do not exceed the corresponding threshold, issue a quality qualified instruction.

[0020] In S2, the expressions for the patient's body impact coefficient and the patient's movement impact coefficient obtained through comprehensive analysis of the patient's body data and the image data during the medical imaging detection process are as follows: Among them, represents the patient's BMI value, represents the patient's body type proportion factor. Patients with heavier weights or more fat distribution may cause a decrease in tissue contrast in the image, affecting the visibility of details. For example, abdominal obesity may affect the clarity of abdominal CT or MRI images. Obese patients may increase the probability of generating motion artifacts during imaging, especially during CT scans, increasing image noise and distortion, and reducing image quality and accuracy. represents the maximum value of the patient's BMI value stored in the current database, represents the patient's body impact coefficient, represents the patient's movement impact coefficient, represents the respiratory fluctuation factor, represents the limb activity factor.

[0021] In S3, the expression for calculating the patient's comprehensive quality impact coefficient based on the patient's body impact coefficient and the patient's movement impact coefficient is as follows: Among them, represents the patient's body impact coefficient, represents the patient's movement impact coefficient, respectively represent weight coefficients whose sum is 1, represents the patient's comprehensive quality impact coefficient.

[0022] In S4, the specific expression for collecting the device data of the patient during the medical imaging detection is as follows: Among them, represents the device data set, respectively represent the first device impact parameter, obtained through the device log.

[0023] In S4, the specific expression for constructing the device influence coefficient based on the comprehensive analysis of device data during medical imaging detection of patients is as follows: Wherein, represents the device influence coefficient, represents the th device influence parameter, represents the summation of a total of J device influence parameters, represents the absolute value of, represents the th standard value corresponding to the device influence parameter.

[0024] When the comprehensive quality influence coefficient exceeds the comprehensive quality influence threshold , the quality warning instruction is executed. The quality warning instruction is specifically: transmit the currently collected medical image through data to the doctor for viewing, and let the doctor determine whether a reshoot is required. If the comprehensive quality influence coefficient does not exceed the comprehensive quality influence threshold , a comprehensive quality qualified result is issued; When the device influence coefficient exceeds the device influence coefficient threshold , the quality warning instruction is executed. When the device influence coefficient does not exceed the device influence coefficient threshold , a device quality qualified result is issued.

[0025] After the comprehensive quality qualified result and the device quality qualified result are simultaneously issued, a quality qualified instruction is issued to determine that the current medical image is qualified; When the comprehensive quality influence coefficient exceeds the comprehensive quality influence threshold and when the device influence coefficient exceeds the device influence coefficient threshold , a reshoot instruction is directly issued.

[0026] Embodiment The data recorded in this embodiment is as shown in Table 1 below: Table 1 At this time, it is calculated that 0.342, 0.0515, = 0.18513 is less than 0.65 and does not exceed the comprehensive quality influence threshold A comprehensive quality qualified result is issued; The device data of the patient during medical imaging detection is shown in Table 2 below: Table 2 At this time, it is calculated that 0.058 does not exceed the device impact coefficient threshold Issue the result of qualified device quality.

[0027] After the comprehensive quality qualified result and the device quality qualified result are issued simultaneously, then issue the quality qualified instruction to determine that the current medical image is qualified.

[0028] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A medical image quality detection method, characterized in that: It includes the following steps: S1: Collect the patient's physical data before medical imaging detection and the patient's movement data during the medical imaging detection process; S2: Based on the comprehensive analysis of the patient's physical data and the image data during the medical imaging detection process, obtain the patient's body influence coefficient and the patient's movement influence coefficient respectively; S3: Based on the patient's body influence coefficient and the patient's movement influence coefficient, comprehensively calculate the patient's comprehensive quality influence coefficient; S4: Collect the equipment data during the patient's medical imaging detection, and construct the equipment influence coefficient based on the comprehensive analysis of the equipment data during the patient's medical imaging detection; S5: After the equipment outputs the medical image, read the patient's comprehensive quality influence coefficient and the equipment influence coefficient respectively. When either the comprehensive quality influence coefficient or the equipment influence coefficient exceeds the corresponding threshold, execute the quality warning instruction. When both the comprehensive quality influence coefficient and the equipment influence coefficient do not exceed the corresponding threshold, issue a quality qualified instruction.

2. The medical image quality detection method according to claim 1, characterized in that: The specific expression for collecting the patient's physical data before medical imaging detection in S1 is as follows: Among them, represents the patient's body data, respectively represent the height values at different times on the same day recorded by the patient himself, respectively represent the weight values at different times on the same day recorded by the patient himself, and the patient's is calculated. The specific expression is as follows: Among them, represents the sum of and represents the patient's BMI value, represents the th height value on the same day recorded by the patient himself / herself, represents the th weight value on the same day recorded by the patient himself / herself.

3. The medical image quality detection method according to claim 2, wherein: The specific steps for collecting the patient's movement data during the medical imaging detection process in S1 are as follows: S1.1: Obtain the image of the patient after lying flat and construct the patient's body type ratio factor; S1.2: Monitor the patient's breathing frequency during the medical imaging detection process and obtain the breathing fluctuation factor; S1.3: Monitor the patient's limb movement during the medical imaging detection process and obtain the limb movement factor; S1.4: Store the patient's breathing frequency and limb movement factor in the patient's movement dataset. The specific expression is as follows: Among them, represents the respiratory fluctuation factor, represents the limb movement factor, represents the patient's movement data set.

4. A medical image quality detection method according to claim 3, characterized in that: The specific expression for obtaining the image of the patient after lying flat and constructing the patient's body type ratio factor in S1.1 is as follows: Among them, represents the patient body type ratio factor, represents the patient area, represents the examination table area; The specific expression for monitoring the patient's breathing frequency during the medical imaging detection process and obtaining the breathing fluctuation factor in S1.2 is as follows: Among them, represents the average respiratory rate of the patient during the medical imaging detection process, represents the maximum respiratory rate of the patient during the medical imaging detection process, represents the respiratory fluctuation factor; The specific expression for monitoring the patient's limb movement during the medical imaging detection process and obtaining the limb movement factor in S1.3 is as follows: Among them, represents the limb movement factor, represents the area of the patient's limb movement region, which is obtained by image comparison, represents the area of the patient region.

5. A medical image quality detection method according to claim 4, characterized in that: The expressions for obtaining the patient's body influence coefficient and the patient's movement influence coefficient respectively based on the comprehensive analysis of the patient's physical data and the image data during the medical imaging detection process in S2 are as follows: Among them, represents the patient's BMI value, represents the patient's body type proportion factor, represents the maximum value of the patient's BMI value stored in the current database, represents the patient's body influence coefficient, represents the patient's exercise influence coefficient, represents the respiratory fluctuation factor, represents the limb activity factor.

6. A medical image quality detection method according to claim 5, characterized in that: The expression for comprehensively calculating the patient's comprehensive quality influence coefficient based on the patient's body influence coefficient and the patient's movement influence coefficient in S3 is as follows: Among them, represents the patient's physical influence coefficient, represents the patient's exercise influence coefficient, respectively represent weight coefficients whose sum is 1, represents the patient's comprehensive quality influence coefficient.

7. A medical image quality detection method according to claim 6, characterized in that: The specific expression for collecting the equipment data during the patient's medical imaging detection in S4 is as follows: Among them, represents the device data set, respectively represent the 1st device impact parameter, obtained through device logs.

8. A medical image quality detection method according to claim 7, characterized in that: The specific expression for constructing the equipment influence coefficient based on the comprehensive analysis of the equipment data during the patient's medical imaging detection in S4 is as follows: Among them, represents the equipment influence coefficient, represents the th equipment influence parameter, represents the summation of the J equipment influence parameters, represents the absolute value of represents the th standard value corresponding to the equipment influence parameter.

9. The medical image quality detection method according to claim 8, characterized in that: When the comprehensive quality impact coefficient exceeds the comprehensive quality impact threshold , a quality warning instruction is executed. The quality warning instruction is specifically as follows: Transmit the currently collected medical image to the doctor via data for viewing, and let the doctor determine whether a reshoot is required. If the comprehensive quality impact coefficient does not exceed the comprehensive quality impact threshold , a comprehensive quality qualified result is issued; When the equipment influence coefficient exceeds the equipment influence coefficient threshold , the quality warning instruction is executed. When the equipment influence coefficient does not exceed the equipment influence coefficient threshold , the equipment quality qualified result is issued.

10. A medical image quality detection method according to claim 9, characterized in that: When the comprehensive quality qualified result and the equipment quality qualified result are issued simultaneously, then issue a quality qualified instruction to determine that the current medical image is qualified; When the comprehensive quality impact coefficient exceeds the comprehensive quality impact threshold and when the equipment impact coefficient exceeds the equipment impact coefficient threshold then a reshoot instruction is directly issued.

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