A data processing device

Abstract

The present invention discloses a data processing device, and relates to the field of data processing technology. In the data processing device, in addition to using the maximum diameter as a treatment indication, the device also uses morphological parameters for characterizing the state of blood vessels based on at least the expansion rate of the lumen, the expansion rate of the blood vessel wall, and the relative expansion rate between the lumen and the blood vessel wall, that is, through rich morphological parameters combined with current clinical treatment indicators, the state of hemangioma can be personalized and accurately detected; in addition, compared with the complex pre-processing process and time-consuming calculation process of biomechanical analysis, the new morphological parameters proposed by the present invention can efficiently complete the measurement and calculation, and at the same time, these parameters contain some information on the mechanical characteristics of the blood vessel wall, which further improves the efficiency and accuracy of hemangioma state detection.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data processing device. Background Art

[0002] In order to alert users to the risk of aneurysm rupture, related technologies use computed tomography angiography (CTA) images for evaluation. In the process of CTA image evaluation, image data of the target area at a certain time phase is collected. The CTA image at each time phase can reconstruct a complete three-dimensional model of the target area. By recording image data at multiple time phases during the cardiac cycle, the dynamic process of blood vessel deformation under the action of pulsating blood pressure can be captured.

[0003] Since the conclusions of the CTA image-based parameters used to indicate the risk of aneurysm rupture come from statistical studies, such as the current clinical practice of using the maximum diameter of the aortic aneurysm as a preset value as a treatment indication. However, in clinical practice, cases of aneurysms smaller than the treatment indication rupturing are not uncommon, and cases of giant aneurysms (such as abdominal aortic aneurysms larger than 8 cm) that are much larger than the treatment indication but not rupturing are also common. Therefore, in clinical applications, it cannot meet the needs of patients for personalized and accurate assessment of aneurysm risk.

[0004] It can be seen that how to meet the needs of patients for personalized and accurate assessment of aneurysm risk is a technical problem that people in this field urgently need to solve. Summary of the invention

[0005] The purpose of the present invention is to provide a data processing device to solve the technical problem that the morphological parameters simply measured based on single-phase CTA imaging technology cannot meet the needs of patients for personalized and accurate assessment of aneurysm risk.

[0006] In order to solve the above technical problems, the present invention provides a data processing device, comprising:

[0007] A first acquisition module is used to acquire images of the hemangioma at the target site in multiple phases;

[0008] A second acquisition module is used to acquire the target section corresponding to the maximum diameter of the aneurysm from all images and acquire the maximum diameter and minimum diameter of the blood vessel part in the target section; wherein the blood vessel part includes the flow cavity and the blood vessel wall;

[0009] A first determination module is used to determine the value of the morphological parameter used to characterize the state of the blood vessel according to the maximum diameter and the minimum diameter of the blood vessel portion; wherein the morphological parameter used to characterize the state of the blood vessel at least includes the lumen diameter, the vascular wall diameter, the expansion rate of the lumen, the expansion rate of the vascular wall, and the relative expansion rate between the lumen and the vascular wall;

[0010] The second determination module is used to determine the state of the hemangioma according to the value of the morphological parameter used to characterize the state of the blood vessel and the current clinical standard for diagnosis and treatment of the maximum diameter of the aneurysm.

[0011] Exemplarily, the first determining module specifically includes:

[0012] The third acquisition module is used to obtain the target section corresponding to the maximum diameter of the aneurysm from all images, and obtain the minimum diameter average value and the maximum diameter average value of the blood vessel portion at the initial phase of the systolic period and the peak phase of the systolic period;

[0013] The third determination module is used to determine the value of the morphological parameter used to characterize the blood vessel state according to the minimum diameter average value and the maximum diameter average value of the blood vessel portion.

[0014] Exemplarily, the third acquisition module specifically includes:

[0015] A fourth acquisition module is used to acquire the target section corresponding to the maximum diameter of the aneurysm from all images, and to acquire a first average value between the maximum diameter of the lumen and the minimum diameter of the lumen at the phase when the blood vessel diameter is minimum;

[0016] The first module is used to use the first average value as the average value of the minimum diameter of the flow cavity at the initial phase of the systolic period and the peak phase of the systolic period of the blood vessel;

[0017] A fifth acquisition module, used to acquire a second average value between the maximum diameter of the lumen and the minimum diameter of the lumen at the phase when the blood vessel diameter is the largest;

[0018] The second module is used to use the second average value as the average value of the maximum diameter of the lumen at the initial phase of the systolic period and the peak phase of the systolic period of the blood vessel;

[0019] a sixth acquisition module, configured to acquire a third average value between the maximum diameter of the blood vessel wall and the minimum diameter of the blood vessel wall at a time phase when the blood vessel diameter is the smallest;

[0020] The third module is used to use the third average value as the average value of the minimum diameter of the blood vessel wall at the initial phase of the vascular contraction period and the peak phase of the vascular contraction period;

[0021] a seventh acquisition module, used for acquiring a fourth average value between the maximum diameter of the blood vessel wall and the minimum diameter of the blood vessel wall in a phase when the blood vessel diameter is the largest;

[0022] The fourth module is used to use the fourth average value as the average value of the maximum diameter of the blood vessel wall at the initial phase of the systolic period and the peak phase of the systolic period.

[0023] Exemplarily, the third determining module specifically includes:

[0024] an eighth acquisition module, configured to acquire a first difference between an average value of a maximum diameter of the flow cavity and an average value of a minimum diameter of the flow cavity;

[0025] a ninth acquisition module, configured to acquire a first ratio between the first difference and an average value of the minimum diameter of the lumen;

[0026] A fifth module is used to use the first ratio as the expansion rate of the flow cavity;

[0027] a tenth acquisition module, configured to acquire a second difference between an average value of the maximum diameter of the blood vessel wall and an average value of the minimum diameter of the blood vessel wall;

[0028] an eleventh obtaining module, configured to obtain a second ratio between the second difference and an average value of the minimum diameter of the blood vessel wall;

[0029] A sixth module is used to use the second ratio as the expansion rate of the blood vessel wall;

[0030] a twelfth acquisition module, configured to acquire a third ratio between the expansion rate of the blood vessel wall and the expansion rate of the lumen;

[0031] The seventh module is used to use the third ratio as the relative expansion rate between the flow cavity and the blood vessel wall.

[0032] Exemplarily, it also includes: a judgment module;

[0033] The judgment module is used to judge whether the relative expansion rate between the flow cavity and the blood vessel wall is within a preset range; wherein the lower limit value of the preset range is 0, and the upper limit value of the preset range is 1; if so, the second determination module is triggered, and if not, the first acquisition module is returned to be triggered.

[0034] Exemplarily, the second determining module specifically includes:

[0035] A setting module, used to pre-set the value of each morphological parameter used to characterize the state of the blood vessel and the weight corresponding to each morphological parameter used to characterize the state of the blood vessel; wherein the value of each morphological parameter at least includes the minimum diameter average value of the lumen, the maximum diameter average value of the lumen, the minimum diameter average value of the blood vessel wall, the maximum diameter average value of the blood vessel wall, the expansion rate of the lumen, the expansion rate of the blood vessel wall and the relative expansion rate between the lumen and the blood vessel wall;

[0036] A fourth determination module, used to determine the current rupture risk value of the hemangioma according to the sum of the products of the values ​​of the morphological parameters used to characterize the state of the blood vessels and the corresponding weights;

[0037] A comparison module, used for comparing the current rupture risk value with a quantitative interval of rupture risk established according to the current clinical standard for diagnosis and treatment of the maximum diameter of an aneurysm and obtaining a comparison result;

[0038] The fifth determination module is used to determine the current state of the hemangioma according to the comparison result.

[0039] Exemplarily, the setting module specifically includes:

[0040] A thirteenth acquisition module is used to acquire the sample value of each morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma under each sample value;

[0041] The sixth determination module is used to determine the weight corresponding to each morphological parameter used to characterize the state of the blood vessel according to the sample value of each morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma under each sample value.

[0042] Exemplarily, the second determining module specifically includes:

[0043] An input module, used for inputting the values ​​of the morphological parameters used to characterize the state of blood vessels into a pre-established deep learning model for prediction;

[0044] An output module, used for outputting the state of the hemangioma through the deep learning model;

[0045] The data processing device also includes a training module; the training module is used to train the deep learning model;

[0046] The training module specifically includes:

[0047] A fourteenth acquisition module, used to acquire sample values ​​of the morphological parameters used to characterize the state of the blood vessels and the state of the hemangioma at each of the sample values;

[0048] The training submodule is used to train the pre-established deep learning model by combining the sample values, the state of the hemangioma under each of the sample values ​​and the current clinical standards for diagnosis and treatment of the maximum diameter of the aneurysm.

[0049] Exemplarily, it also includes:

[0050] A sending module, used for sending the value of the morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma to a memory;

[0051] A storage module is used to store the value of the morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma in the memory.

[0052] Exemplarily, it also includes: a display module;

[0053] The display module is used to display the value of the morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma.

[0054] The data processing device provided by the present invention acquires images of a hemangioma at a target site in multiple phases through a first acquisition module; acquires a target section corresponding to the maximum diameter of the aneurysm from all images through a second acquisition module and acquires the maximum diameter and minimum diameter of the blood vessel part (including the flow cavity and the blood vessel wall) in the target section; in a first determination module, determines the value of a morphological parameter used to characterize the state of the blood vessel according to the maximum diameter and the minimum diameter of the blood vessel part; in a second determination module, determines the state of the hemangioma according to the value of the morphological parameter used to characterize the state of the blood vessel and the current clinical standard for diagnosis and treatment of the maximum diameter of the aneurysm. In the data processing device, in addition to using the maximum diameter as a treatment indication, morphological parameters for characterizing the state of the blood vessels are also used based on at least the expansion rate of the flow cavity, the expansion rate of the blood vessel wall, and the relative expansion rate between the flow cavity and the blood vessel wall. That is, through rich morphological parameters and combined with current clinical treatment indicators, personalized and accurate detection of the state of hemangioma is achieved; in addition, compared with the complex pre-processing process and time-consuming calculation process of biomechanical analysis, the new morphological parameters proposed in the present invention can efficiently complete the measurement and calculation, and at the same time these parameters contain some information on the mechanical characteristics of the blood vessel wall, further improving the efficiency and accuracy of hemangioma state detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1A schematic diagram of a data processing device provided by an embodiment of the present invention;

[0057] Figure 2 A schematic diagram of a maximum diameter section structure of an aneurysm provided in an embodiment of the present invention;

[0058] Figure 3 A schematic diagram of measuring the maximum diameter and minimum diameter of the lumen in the maximum diameter section of an aneurysm at time phase t provided by an embodiment of the present invention;

[0059] Figure 4 A schematic diagram of measuring the maximum diameter and minimum diameter of a blood vessel wall in a maximum diameter section of an aneurysm at time phase t provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] The core of the present invention is to provide a data processing device to solve the technical problem that the morphological parameters simply measured based on single-phase CTA imaging technology cannot meet the needs of patients for personalized and accurate assessment of aneurysm risk.

[0062] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 A schematic diagram of a data processing device provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the device comprises:

[0063] A first acquisition module 10 is used to acquire images of the hemangioma at the target site in multiple phases;

[0064] The second acquisition module 11 is used to acquire the target section corresponding to the maximum diameter of the aneurysm from all images and acquire the maximum diameter and the minimum diameter of the blood vessel part in the target section;

[0065] A first determination module 12 is used to determine the value of the morphological parameter used to characterize the state of the blood vessel according to the maximum diameter and the minimum diameter of the blood vessel portion; wherein the morphological parameter used to characterize the state of the blood vessel at least includes the lumen diameter, the vascular wall diameter, the expansion rate of the lumen, the expansion rate of the vascular wall, and the relative expansion rate between the lumen and the vascular wall;

[0066] The second determination module 13 is used to determine the state of the aneurysm according to the value of the morphological parameter used to characterize the state of the blood vessel and the current clinical standard for diagnosing and treating the maximum diameter of the aneurysm.

[0067] The processor can obtain image data of the hemangioma at the target site under multiple phases (i.e., multi-dimensional dynamic images), measure and calculate the image data, measure the image under the t phase, and determine the section with the maximum diameter of the tumor.

[0068] Figure 2 A schematic diagram of a maximum diameter cross-sectional structure of an aneurysm provided in an embodiment of the present invention, such as Figure 2 As shown, the interior is the lumen, the lumen has a corresponding lumen boundary, there is a mural thrombus outside the lumen boundary, and the outermost is the vessel wall boundary. The vessel portion includes the lumen and the vessel wall. Afterwards, the maximum diameter and minimum diameter of the lumen in the maximum diameter section of the aneurysm at phase t are measured using a processor. Figure 3 A schematic diagram of measuring the maximum diameter and minimum diameter of the lumen in the maximum diameter section of an aneurysm at time phase t provided by an embodiment of the present invention, such as Figure 3 As shown, It represents the maximum diameter of the lumen in the maximum diameter section of the aneurysm at phase t. It represents the minimum diameter of the lumen in the maximum diameter section of the aneurysm at time t. Similarly, the processor measures the maximum diameter and minimum diameter of the vessel wall in the maximum diameter section of the aneurysm at time t. Figure 4 A schematic diagram of measuring the maximum diameter and minimum diameter of the blood vessel wall in the maximum diameter section of the aneurysm at time phase t provided by an embodiment of the present invention, such as Figure 4 As shown, represents the maximum diameter of the vessel wall in the maximum diameter section of the aneurysm at phase t, represents the minimum diameter of the vessel wall in the maximum diameter section of the aneurysm at phase t, where Represents the blood vessel wall.

[0069] When evaluating the risk of aneurysm rupture based on the morphological parameters of three-dimensional CTA images, these parameters cannot meet the patient's needs for personalized and accurate assessment of aneurysm risk when used clinically. The main reason is that the conclusions of these parameters used to indicate the risk of aneurysm rupture come from statistical studies. For example, in clinical practice, the maximum diameter of an aortic aneurysm of 5.5 cm in men and 5 cm in women is currently used as a treatment indication. However, in clinical practice, cases of aneurysms smaller than treatment indications rupture are not uncommon, and cases of giant aneurysms that are much larger than treatment indications (such as abdominal aortic aneurysms larger than 8 cm) that do not rupture are also often found. Therefore, in the embodiments of the present invention, new morphological parameters for characterizing vascular status are combined to evaluate the risk of aneurysm rupture.

[0070] After determining the maximum diameter and the minimum diameter of the blood vessel part, the value of the morphological parameter used to characterize the blood vessel state can be determined according to the maximum diameter and the minimum diameter of the blood vessel part. In the embodiment of the present invention, in order to accurately assess the personalized risk of aneurysm of a patient, the morphological parameters used to characterize the blood vessel state include at least the lumen diameter, the vascular wall diameter, the expansion rate of the lumen, the expansion rate of the vascular wall, and the relative expansion rate between the lumen and the vascular wall. When determining the expansion rate of the lumen, the difference between the maximum diameter and the minimum diameter of the lumen can be obtained first, and the ratio of the difference to the minimum diameter of the lumen is the expansion rate of the lumen; when determining the expansion rate of the vascular wall, the difference between the maximum diameter and the minimum diameter of the vascular wall can be obtained first, and the ratio of the difference to the minimum diameter of the vascular wall is the expansion rate of the vascular wall. After obtaining the expansion rate of the lumen and the expansion rate of the vascular wall, the relative expansion rate between the lumen and the vascular wall can be determined according to the ratio of the two.

[0071] After obtaining the values ​​of the morphological parameters used to characterize the state of the blood vessels, the state of the hemangioma can be determined according to the values ​​of the morphological parameters used to characterize the state of the blood vessels and the current clinical standards for the diagnosis and treatment of the maximum diameter of the aneurysm. The state of the hemangioma can be divided into three risk levels: low-risk state, medium-risk state, and high-risk state. There is no limitation on the method of determining the state of the hemangioma. A weight function can be established for evaluation, or a model can be used for prediction.

[0072] Related aneurysm rupture risk assessment based on biomechanical analysis, including hemodynamic analysis, solid mechanics analysis of vascular wall, and fluid-solid coupling analysis, are currently mainly in the theoretical research stage. The main reasons are as follows: First, biomechanical analysis is highly dependent on and sensitive to vascular wall material models and parameters, blood pressure and flow rate, and other parameters, and it is often difficult to obtain the true values ​​of these parameters; second, the accuracy of biomechanical analysis is greatly affected by factors such as model reconstruction accuracy and grid division; third, biomechanical analysis is more time-consuming than direct measurement of morphological parameters. The new morphological parameters proposed in the present invention can efficiently complete the measurement and calculation, and these parameters also contain some information about the mechanical characteristics of the vascular wall. And on the basis of the existing maximum aneurysm diameter and its growth rate parameters, new morphological parameters that can better reflect the individual differences of patients are provided to more accurately assess the individualized risk of aneurysms in patients.

[0073] The data processing device provided by the present invention acquires images of a hemangioma at a target site in multiple phases through a first acquisition module; acquires a target section corresponding to the maximum diameter of the aneurysm from all images through a second acquisition module and acquires the maximum diameter and minimum diameter of the blood vessel part (including the flow cavity and the blood vessel wall) in the target section; in a first determination module, determines the value of a morphological parameter used to characterize the state of the blood vessel according to the maximum diameter and the minimum diameter of the blood vessel part; in a second determination module, determines the state of the hemangioma according to the value of the morphological parameter used to characterize the state of the blood vessel and the current clinical standard for diagnosis and treatment of the maximum diameter of the aneurysm. In the data processing device, in addition to using the maximum diameter as a treatment indication, morphological parameters for characterizing the state of the blood vessels are also used based on at least the expansion rate of the flow cavity, the expansion rate of the blood vessel wall, and the relative expansion rate between the flow cavity and the blood vessel wall. That is, through rich morphological parameters and combined with current clinical treatment indicators, personalized and accurate detection of the state of hemangioma is achieved; in addition, compared with the complex pre-processing process and time-consuming calculation process of biomechanical analysis, the new morphological parameters proposed in the present invention can efficiently complete the measurement and calculation, and at the same time these parameters contain some information on the mechanical characteristics of the blood vessel wall, further improving the efficiency and accuracy of hemangioma state detection.

[0074] In order to improve the accuracy of detection by the data processing device, the first determination module specifically includes:

[0075] The third acquisition module is used to obtain the target section corresponding to the maximum diameter of the aneurysm from all images, and obtain the minimum diameter average value and the maximum diameter average value of the blood vessel part at the initial phase of the systolic period and the peak phase of the systolic period;

[0076] The third determination module is used to determine the value of the morphological parameter used to characterize the blood vessel state according to the minimum diameter average value and the maximum diameter average value of the blood vessel portion.

[0077] Specifically, the third acquisition module specifically includes:

[0078] A fourth acquisition module is used to acquire the target section corresponding to the maximum diameter of the aneurysm from all images, and to acquire a first average value between the maximum diameter of the lumen and the minimum diameter of the lumen at the phase when the blood vessel diameter is minimum;

[0079] The first module is used to use the first average value as the average value of the minimum diameter of the lower lumen at the beginning phase of the systolic period and the peak phase of the systolic period of the blood vessel;

[0080] A fifth acquisition module, used to acquire a second average value between the maximum diameter of the lumen and the minimum diameter of the lumen at the phase when the blood vessel diameter is the largest;

[0081] The second is used as a module, for taking the second average value as the average value of the maximum diameter of the lower lumen at the beginning phase of the systolic period and the peak phase of the systolic period of the blood vessel;

[0082] a sixth acquisition module, configured to acquire a third average value between the maximum diameter of the blood vessel wall and the minimum diameter of the blood vessel wall at a time phase when the blood vessel diameter is the smallest;

[0083] The third is used as a module for taking the third average value as the average value of the minimum diameter of the blood vessel wall at the initial phase of the systolic period and the peak phase of the systolic period;

[0084] a seventh acquisition module, used for acquiring a fourth average value between the maximum diameter of the blood vessel wall and the minimum diameter of the blood vessel wall in a phase when the blood vessel diameter is the largest;

[0085] The fourth module is used to use the fourth average value as the average value of the maximum diameter of the blood vessel wall at the initial phase of the systolic period and the peak phase of the systolic period.

[0086] In some embodiments, the third determination module specifically includes:

[0087] An eighth acquisition module, configured to acquire a first difference between an average value of a maximum diameter of the flow cavity and an average value of a minimum diameter of the flow cavity;

[0088] A ninth acquisition module, configured to acquire a first ratio between the first difference and an average value of the minimum diameter of the flow cavity;

[0089] The fifth module is used to use the first ratio as the expansion rate of the flow cavity;

[0090] a tenth acquisition module, configured to acquire a second difference between an average value of the maximum diameter of the blood vessel wall and an average value of the minimum diameter of the blood vessel wall;

[0091] an eleventh obtaining module, configured to obtain a second ratio between the second difference and an average value of the minimum diameter of the blood vessel wall;

[0092] a sixth module for using the second ratio as the expansion rate of the blood vessel wall;

[0093] a twelfth acquisition module, configured to acquire a third ratio between the expansion rate of the blood vessel wall and the expansion rate of the lumen;

[0094] The seventh is used as a module for using the third ratio as a relative expansion rate between the flow cavity and the blood vessel wall.

[0095] In order to enable those skilled in the art to better understand the process of obtaining various morphological parameters for characterizing the state of blood vessels in the present invention, various morphological parameters are described below in conjunction with specific formulas.

[0096] based on Figure 3 In and , the equivalent (i.e., average) lumen diameter at time t The calculation formula is shown in formula (1):

[0097] ; (1)

[0098] in, Indicates equivalence. The max and min in the subscripts indicate the maximum and minimum values ​​measured in the current measurement plane. It is a dynamic parameter that changes with time. Define and use the processor to calculate the minimum equivalent lumen diameter at the beginning of systole and the peak of systole (as shown in formula (2)) and the maximum equivalent flow cavity diameter (As shown in formula (3)) are:

[0099] ; (2)

[0100] ; (3)

[0101] In formula (2) Indicates the maximum diameter of the lumen in the aneurysm maximum diameter section at the phase when the vessel diameter is the smallest. It represents the minimum diameter of the lumen in the aneurysm maximum diameter section at the phase when the vessel diameter is the smallest. In formula (3) Indicates the maximum diameter of the lumen in the aneurysm maximum diameter section at the phase when the blood vessel diameter is the largest. Indicates the minimum diameter of the lumen in the maximum diameter section of the aneurysm at the phase with the largest blood vessel diameter. Based on the above two parameters, the lumen expansion rate is defined and calculated using the processor according to formula (4). (Lumen Expansion rate):

[0102] ; (4)

[0103] Similarly, based on Figure 4 In and , the equivalent vessel wall diameter at time t can be calculated using formula (5) in the processor (Cavity + thrombus):

[0104] ; (5)

[0105] It is a dynamic parameter that changes with time, defining and calculating the minimum equivalent vessel wall diameter at the beginning of systole and the peak of systole using a processor (Formula (6)) and the maximum equivalent vessel wall diameter (Formula (7)) is:

[0106] ; (6)

[0107] ; (7)

[0108] In formula (6) Indicates the maximum diameter of the blood vessel wall in the maximum diameter section of the aneurysm at the phase when the blood vessel diameter is the smallest. It represents the minimum diameter of the vessel wall in the maximum diameter section of the aneurysm at the phase when the vessel diameter is the smallest. In formula (7) Indicates the maximum diameter of the blood vessel wall in the maximum diameter section of the aneurysm at the phase when the blood vessel diameter is the largest. Indicates the minimum diameter of the vessel wall in the maximum diameter section of the aneurysm at the phase with the largest vessel diameter. Based on the above two parameters, the vessel wall expansion rate is defined and calculated using the processor according to formula (8). (Wall Expansion rate) is:

[0109] ; (8)

[0110] Combining the two parameters of lumen expansion rate and vascular wall expansion rate, the relative expansion rate of lumen-vascular wall is defined and calculated by the processor according to formula (9): (lumen-wall expansion ratio) is:

[0111] ; (9)

[0112] The relative expansion rate mainly reflects the relative expandability of the flow cavity and the blood vessel wall, and reflects the difference in the response of the two important boundaries to pulsating blood pressure during the cardiac cycle. The relative expansion rate refers to its definition and combines the characteristics of the flow cavity and the blood vessel wall. Its main value range is . The larger it is, the closer the distensibility of the lumen boundary and the inner boundary of the vascular wall is. The smaller it is, the greater the difference in expandability between the two.

[0113] In implementation, it may happen that the calculated relative expansion rate value is not within the value range, that is, the parameter value is wrong, which leads to evaluation error. Therefore, in some embodiments, the data processing device further includes: a judgment module;

[0114] The judgment module is used to judge whether the relative expansion rate between the flow cavity and the blood vessel wall is within a preset range; wherein the lower limit value of the preset range is 0, and the upper limit value of the preset range is 1; if so, the second determination module is triggered, if not, the first acquisition module is returned to be triggered.

[0115] In some embodiments, the second determining module in the data processing device specifically includes:

[0116] A setting module, used to pre-set the values ​​of the morphological parameters used to characterize the state of the blood vessels and the weights corresponding to the morphological parameters used to characterize the state of the blood vessels; wherein the values ​​of the morphological parameters at least include the minimum diameter average value of the lumen, the maximum diameter average value of the lumen, the minimum diameter average value of the blood vessel wall, the maximum diameter average value of the blood vessel wall, the expansion rate of the lumen, the expansion rate of the blood vessel wall and the relative expansion rate between the lumen and the blood vessel wall;

[0117] a fourth determination module, configured to determine a current rupture risk value of the hemangioma according to the sum of the products of the values ​​of the morphological parameters used to characterize the state of the blood vessels and the corresponding weights;

[0118] A comparison module, used to compare the current rupture risk value with the quantitative interval of rupture risk established according to the current clinical standard for diagnosis and treatment of the maximum diameter of aneurysms and obtain the comparison result;

[0119] The fifth determination module is used to determine the current state of the hemangioma according to the comparison result.

[0120] In order to determine the weight of each morphological parameter, the setting module specifically includes:

[0121] A thirteenth acquisition module is used to acquire sample values ​​of morphological parameters used to characterize the state of blood vessels and the state of the hemangioma under each sample value;

[0122] The sixth determination module is used to determine the weight corresponding to each morphological parameter used to characterize the state of the blood vessel according to the sample value of each morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma under each sample value.

[0123] Combining the above parameters with the current clinical standards for aneurysm diagnosis and treatment, the processor is used to assess the risk of aortic aneurysm rupture, establish a quantitative interval for rupture risk, and divide the rupture risk into three risk levels: low risk, medium risk, and high risk. The quantitative evaluation of can be achieved by establishing a weight function for the above parameters (as shown in Formula 10):

[0124] ; (10)

[0125] In formula (10), each Respectively represent weight values. After collecting a large amount of follow-up data, a scientific quantitative interval can be established based on the patient's treatment and rupture conditions. At the same time, as the patient data increases, the corresponding interval threshold can be optimized and updated.

[0126] In addition to the risk assessment method based on the weight function described above, in some embodiments, the second determination module in the data processing device specifically includes:

[0127] An input module, used for inputting the values ​​of the morphological parameters used to characterize the state of blood vessels into a pre-established deep learning model for prediction;

[0128] An output module, used to output the state of the hemangioma through a deep learning model;

[0129] The data processing device also includes a training module; the training module is used to train the deep learning model;

[0130] The training modules include:

[0131] A fourteenth acquisition module is used to acquire sample values ​​of morphological parameters used to characterize the state of blood vessels and the state of the hemangioma under each sample value;

[0132] The training submodule is used to train the pre-established deep learning model by combining the sample values, the state of the hemangioma under each sample value and the current clinical standards for the diagnosis and treatment of the maximum diameter of the aneurysm.

[0133] In addition, the risk of hemangioma can be assessed by logistic regression and other methods in the data processing device.

[0134] In order to facilitate the user to view the data later, in some embodiments, the data processing device further includes:

[0135] A sending module, used for sending the value of the morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma to a memory;

[0136] The storage module is used to store the values ​​of the morphological parameters used to characterize the state of the blood vessels and the state of the hemangioma in the memory.

[0137] The data processing device further includes: a display module;

[0138] The display module is used to display the values ​​of morphological parameters used to characterize the state of blood vessels and the state of hemangioma.

[0139] The above parameters and rupture risk assessment results are sent to a memory, which is used to store the above parameters and rupture risk assessment results, and the above parameters and rupture risk assessment results are output and displayed by an output device for users to view.

[0140] In addition to the data processing device described above, this embodiment also provides an aortic aneurysm rupture risk assessment system based on multi-dimensional dynamic images, the system includes a processor and an output device, the processor is used to measure and analyze the multi-phase image data of the target part, determine the section where the maximum diameter of the aneurysm is located, and based on the measurement results, calculate the maximum equivalent diameter of the lumen, the minimum equivalent diameter of the lumen, the maximum equivalent diameter of the vascular wall, the minimum equivalent diameter of the vascular wall, the lumen expansion rate, the vascular wall expansion rate and the vascular wall-lumen relative expansion rate and other morphological parameters, combine the morphological parameters with the clinical treatment standards, and determine the rupture risk level of the aortic aneurysm; the processor is used to send the determined aortic aneurysm rupture risk level to the output device. The present invention can provide rich morphological parameters through the measurement, calculation and analysis of multi-dimensional dynamic images, and determine the patient's rupture risk level in combination with current clinical treatment indicators, so as to provide a reference for the clinical management of the risk of aortic aneurysm rupture.

[0141] The above is a detailed introduction to a data processing device provided by the present invention. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, the present invention can also be improved and modified, and these improvements and modifications also fall within the scope of protection of the present invention.

[0142] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

Claims

1. A data processing device, characterized in that: include: A first acquisition module is used to acquire images of the hemangioma at the target site in multiple phases; A second acquisition module is used to acquire the target section corresponding to the maximum diameter of the aneurysm from all images and acquire the maximum diameter and minimum diameter of the blood vessel part in the target section; wherein the blood vessel part includes the flow cavity and the blood vessel wall; A first determination module is used to determine the value of the morphological parameter used to characterize the state of the blood vessel according to the maximum diameter and the minimum diameter of the blood vessel portion; wherein the morphological parameter used to characterize the state of the blood vessel at least includes the lumen diameter, the vascular wall diameter, the expansion rate of the lumen, the expansion rate of the vascular wall, and the relative expansion rate between the lumen and the vascular wall; A second determination module is used to determine the state of the hemangioma according to the value of the morphological parameter used to characterize the state of the blood vessel and the current clinical standard for diagnosis and treatment of the maximum diameter of the aneurysm; The first determination module specifically includes: The third acquisition module is used to obtain the target section corresponding to the maximum diameter of the aneurysm from all images, and obtain the minimum diameter average value and the maximum diameter average value of the blood vessel portion at the initial phase of the systolic period and the peak phase of the systolic period; a third determination module, configured to determine the value of the morphological parameter for characterizing the state of the blood vessel according to the minimum diameter average value and the maximum diameter average value of the blood vessel portion; The third acquisition module specifically includes: A fourth acquisition module is used to acquire the target section corresponding to the maximum diameter of the aneurysm from all images, and to acquire a first average value between the maximum diameter of the lumen and the minimum diameter of the lumen at the phase when the blood vessel diameter is minimum; The first module is used to use the first average value as the average value of the minimum diameter of the flow cavity at the initial phase of the systolic period and the peak phase of the systolic period of the blood vessel; A fifth acquisition module, used to acquire a second average value between the maximum diameter of the lumen and the minimum diameter of the lumen at the phase when the blood vessel diameter is the largest; The second module is used to use the second average value as the average value of the maximum diameter of the lumen at the initial phase of the systolic period and the peak phase of the systolic period of the blood vessel; a sixth acquisition module, configured to acquire a third average value between the maximum diameter of the blood vessel wall and the minimum diameter of the blood vessel wall at a time phase when the blood vessel diameter is the smallest; The third module is used to use the third average value as the average value of the minimum diameter of the blood vessel wall at the initial phase of the vascular contraction period and the peak phase of the vascular contraction period; a seventh acquisition module, used for acquiring a fourth average value between the maximum diameter of the blood vessel wall and the minimum diameter of the blood vessel wall in a phase when the blood vessel diameter is the largest; The fourth module is used to use the fourth average value as the average value of the maximum diameter of the blood vessel wall at the initial phase of the vascular contraction period and the peak phase of the vascular contraction period; The third determination module specifically includes: an eighth acquisition module, configured to acquire a first difference between an average value of a maximum diameter of the flow cavity and an average value of a minimum diameter of the flow cavity; a ninth acquisition module, configured to acquire a first ratio between the first difference and an average value of the minimum diameter of the lumen; A fifth module is used to use the first ratio as the expansion rate of the flow cavity; a tenth acquisition module, configured to acquire a second difference between an average value of the maximum diameter of the blood vessel wall and an average value of the minimum diameter of the blood vessel wall; an eleventh obtaining module, configured to obtain a second ratio between the second difference and an average value of the minimum diameter of the blood vessel wall; A sixth module is used to use the second ratio as the expansion rate of the blood vessel wall; a twelfth acquisition module, configured to acquire a third ratio between the expansion rate of the blood vessel wall and the expansion rate of the lumen; The seventh module is used to use the third ratio as the relative expansion rate between the flow cavity and the blood vessel wall.

2. The data processing device according to claim 1, characterized in that: Also includes: Judgment module; The judgment module is used to judge whether the relative expansion rate between the flow cavity and the blood vessel wall is within a preset range; wherein the lower limit value of the preset range is 0, and the upper limit value of the preset range is 1; If yes, the second determining module is triggered; if no, the first obtaining module is triggered again.

3. The data processing device according to claim 1 or 2, characterized in that: The second determination module specifically includes: A setting module, used to pre-set the value of each morphological parameter used to characterize the state of the blood vessel and the weight corresponding to each morphological parameter used to characterize the state of the blood vessel; wherein the value of each morphological parameter at least includes the minimum diameter average value of the lumen, the maximum diameter average value of the lumen, the minimum diameter average value of the blood vessel wall, the maximum diameter average value of the blood vessel wall, the expansion rate of the lumen, the expansion rate of the blood vessel wall and the relative expansion rate between the lumen and the blood vessel wall; A fourth determination module, used to determine the current rupture risk value of the hemangioma according to the sum of the products of the values ​​of the morphological parameters used to characterize the state of the blood vessels and the corresponding weights; A comparison module, used for comparing the current rupture risk value with a quantitative interval of rupture risk established according to the current clinical standard for diagnosis and treatment of the maximum diameter of an aneurysm and obtaining a comparison result; The fifth determination module is used to determine the current state of the hemangioma according to the comparison result.

4. The data processing device according to claim 3, characterized in that: The setting module specifically includes: A thirteenth acquisition module is used to acquire the sample value of each morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma under each sample value; The sixth determination module is used to determine the weight corresponding to each morphological parameter used to characterize the state of the blood vessel according to the sample value of each morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma under each sample value.

5. The data processing device according to claim 1 or 2, characterized in that: The second determination module specifically includes: An input module, used for inputting the values ​​of the morphological parameters used to characterize the state of blood vessels into a pre-established deep learning model for prediction; An output module, used for outputting the state of the hemangioma through the deep learning model; The data processing device also includes a training module; the training module is used to train the deep learning model; The training module specifically includes: A fourteenth acquisition module, used to acquire sample values ​​of the morphological parameters used to characterize the state of the blood vessels and the state of the hemangioma at each of the sample values; The training submodule is used to train the pre-established deep learning model by combining the sample values, the state of the hemangioma under each of the sample values ​​and the current clinical standards for diagnosis and treatment of the maximum diameter of the aneurysm.

6. The data processing device according to claim 1, characterized in that: Also includes: A sending module, used for sending the value of the morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma to a memory; A storage module is used to store the value of the morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma in the memory.

7. The data processing device according to claim 1, characterized in that: Also includes: Display module; The display module is used to display the value of the morphological parameter used to characterize the state of the blood vessel and the state of the hemangioma.

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