CN115101162A - A method and system for predicting thrombolysis efficiency based on biohydrodynamics - Google Patents
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Abstract
本发明提供一种基于生物流体力学的溶栓效率预测方法，包括获取血栓数据和溶栓数据；将血栓数据和溶栓数据导入由N‑S方程和对流扩散反应方程构建而成的预测模型中进行预测，得到溶栓率和溶栓时间。本发明还提供一种基于生物流体力学的溶栓效率预测系统。实施本发明，能提高预测准确度，且省时省力。
The invention provides a method for predicting thrombolysis efficiency based on biofluid mechanics, which includes acquiring thrombus data and thrombolysis data; importing the thrombus data and thrombolysis data into a prediction model constructed by N-S equation and convection-diffusion reaction equation Prediction is made to obtain thrombolysis rate and thrombolysis time. The invention also provides a thrombolysis efficiency prediction system based on biofluid mechanics. By implementing the present invention, the prediction accuracy can be improved, and time and effort can be saved.
Description
技术领域technical field
本发明涉及计算机仿真技术领域，尤其涉及一种基于生物流体力学的溶栓效率预测方法及系统。The invention relates to the technical field of computer simulation, in particular to a method and system for predicting thrombolysis efficiency based on biological fluid mechanics.
背景技术Background technique
生物医学仿真工程技术正逐步应用于各个科室的临床施治中。仿真预测不同于现行医学研究上的荟萃分析，是从基础生物学与基础物理学出发，从物质流动、物质传递、物质作用的角度出发，试图从具体微观的物质生效层面来对治疗效果进行预测。Biomedical simulation engineering technology is gradually being applied to the clinical treatment of various departments. Different from the meta-analysis of current medical research, simulation prediction is based on basic biology and basic physics, from the perspective of material flow, material transfer, and material action, trying to predict the treatment effect from the specific microscopic level of material effect. .
在临床诊治中，针对不同结果的静脉血栓栓塞患者只能依靠保守治疗的思维，大大延缓了患者的痊愈进程，导致不必要的痛苦与医治费用。因此，有必要提早预知不同的溶栓效率，有助于提高血栓栓塞症治疗效果的评估。In clinical diagnosis and treatment, venous thromboembolism patients with different outcomes can only rely on conservative treatment thinking, which greatly delays the patient's recovery process, resulting in unnecessary pain and medical expenses. Therefore, it is necessary to predict different thrombolytic efficiencies in advance, which will help to improve the evaluation of the treatment effect of thromboembolism.
目前，溶栓效率预测往往是通过已存患者数据库的关联对比、医生主观评估判断以及参照血栓治疗指南等方式，但这些方式存在以下问题：(1)预测方法的底层原理停留在利用宏观的患者数据库，未涉及微观下血栓增长消融的机理；(2)需要大量已有案例，且案例之间存在关联性，导致无法准确预测且耗时耗力。At present, the prediction of thrombolysis efficiency is often based on the correlation and comparison of existing patient databases, subjective evaluation and judgment of doctors, and reference to thrombosis treatment guidelines. However, these methods have the following problems: (1) The underlying principle of the prediction method is to use macroscopic patients. The database does not involve the mechanism of microscopic thrombus growth and ablation; (2) a large number of existing cases are required, and there are correlations between cases, resulting in inaccurate prediction and time-consuming and labor-intensive.
因此，为了解决上述问题，有必要结合生物流体力学仿真技术构建溶栓效率预测方法，帮助血管内外科医生制定血栓治疗方案及预测治疗效果。Therefore, in order to solve the above problems, it is necessary to build a thrombolysis efficiency prediction method combined with biofluid mechanics simulation technology to help vascular surgeons formulate thrombosis treatment plans and predict treatment effects.
发明内容SUMMARY OF THE INVENTION
本发明实施例所要解决的技术问题在于，提供一种基于生物流体力学的溶栓效率预测方法及系统，能提高预测准确度，且省时省力，有助于制定血栓治疗方案及预测治疗效果。The technical problem to be solved by the embodiments of the present invention is to provide a method and system for predicting thrombolysis efficiency based on biohydrodynamics, which can improve the prediction accuracy, save time and effort, and help formulate thrombosis treatment plans and predict treatment effects.
为了解决上述技术问题，本发明实施例提供了一种基于生物流体力学的溶栓效率预测方法，所述方法包括以下步骤：In order to solve the above-mentioned technical problems, the embodiment of the present invention provides a method for predicting thrombolysis efficiency based on biofluid mechanics, and the method includes the following steps:
获取血栓数据和溶栓数据；其中，所述血栓数据包括血液粘度、凝血物质、肌肉应力、血液流速和血管结构；所述溶栓数据包括药物种类、药物剂量、注射方式、注射部位、反应参数和反应时间；Acquire thrombus data and thrombolysis data; wherein, the thrombus data includes blood viscosity, coagulation substance, muscle stress, blood flow rate and vascular structure; the thrombolysis data includes drug type, drug dose, injection method, injection site, and reaction parameters and reaction time;
将所述血栓数据和所述溶栓数据导入由N-S方程和对流扩散反应方程构建而成的预测模型中进行预测，得到溶栓率和溶栓时间。The thrombus data and the thrombolysis data are imported into the prediction model constructed by the N-S equation and the convective diffusion reaction equation for prediction, and the thrombolysis rate and the thrombolysis time are obtained.
其中，所述血液粘度是由血液粘度分析仪测量得到的；所述凝血物质是由血液成分检测仪测量得到的；所述肌肉应力是由肌张力测试仪测量得到的；所述血液流速是由激光多普勒血流仪测量得到的；所述血管结构是由核磁共振成像仪测量得到的。The blood viscosity is measured by a blood viscosity analyzer; the coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow rate is measured by measured by laser Doppler flowmeter; the blood vessel structure is measured by nuclear magnetic resonance imager.
其中，所述对流扩散反应方程是通过以下公式实现的；Wherein, the convective diffusion reaction equation is realized by the following formula;
其中，Ci为相关物质的浓度，Di为物质的扩散系数，u为流场，Ri为不同物质的反应变化项。Among them, C i is the concentration of related substances, D i is the diffusion coefficient of the substance, u is the flow field, and R i is the reaction variation term of different substances.
其中，所述方法进一步包括：Wherein, the method further includes:
基于所述溶栓率和所述溶栓时间，评估出相应的溶栓效率等级。Based on the thrombolysis rate and the thrombolysis time, a corresponding thrombolysis efficiency grade is estimated.
本发明实施例还提供了一种基于生物流体力学的溶栓效率预测系统，包括：The embodiment of the present invention also provides a system for predicting thrombolysis efficiency based on biohydrodynamics, including:
数据获取单元，用于获取血栓数据和溶栓数据；其中，所述血栓数据包括血液粘度、凝血物质、肌肉应力、血液流速和血管结构；所述溶栓数据包括药物种类、药物剂量、注射方式、注射部位、反应参数和反应时间；a data acquisition unit for acquiring thrombus data and thrombolysis data; wherein the thrombus data includes blood viscosity, coagulation substance, muscle stress, blood flow rate and vascular structure; the thrombolysis data includes drug type, drug dosage, injection method , injection site, reaction parameters and reaction time;
预测单元，用于将所述血栓数据和所述溶栓数据导入由N-S方程和对流扩散反应方程构建而成的预测模型中进行预测，得到溶栓率和溶栓时间。The prediction unit is used for importing the thrombus data and the thrombolysis data into a prediction model constructed by the N-S equation and the convection-diffusion reaction equation for prediction, to obtain the thrombolysis rate and the thrombolysis time.
其中，所述血液粘度是由血液粘度分析仪测量得到的；所述凝血物质是由血液成分检测仪测量得到的；所述肌肉应力是由肌张力测试仪测量得到的；所述血液流速是由激光多普勒血流仪测量得到的；所述血管结构是由核磁共振成像仪测量得到的。The blood viscosity is measured by a blood viscosity analyzer; the coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow rate is measured by measured by laser Doppler flowmeter; the blood vessel structure is measured by nuclear magnetic resonance imager.
其中，所述对流扩散反应方程是通过以下公式实现的；Wherein, the convective diffusion reaction equation is realized by the following formula;
其中，Ci为相关物质的浓度，Di为物质的扩散系数，u为流场，Ri为不同物质的反应变化项.Among them, C i is the concentration of related substances, D i is the diffusion coefficient of the substance, u is the flow field, and R i is the reaction variation term of different substances.
其中，还包括：Among them, it also includes:
评估单元，用于基于所述溶栓率和所述溶栓时间，评估出相应的溶栓效率等级。An evaluation unit, configured to evaluate a corresponding thrombolysis efficiency grade based on the thrombolysis rate and the thrombolysis time.
实施本发明实施例，具有如下有益效果：Implementing the embodiment of the present invention has the following beneficial effects:
本发明将血栓数据和溶栓数据导入由N-S方程和对流扩散反应方程构建而成的预测模型进行预测，快速得到溶栓率和溶栓时间，从而提高了预测准确度，且省时省力，有助于制定血栓治疗方案及预测治疗效果。The invention imports the thrombus data and the thrombolysis data into the prediction model constructed by the N-S equation and the convective diffusion reaction equation for prediction, and quickly obtains the thrombolysis rate and the thrombolysis time, thereby improving the prediction accuracy, saving time and effort, and has Help to formulate thrombosis treatment plan and predict treatment effect.
附图说明Description of drawings
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，根据这些附图获得其他的附图仍属于本发明的范畴。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention, and for those of ordinary skill in the art, obtaining other drawings according to these drawings still belongs to the scope of the present invention without any creative effort.
图1为本发明实施例提供的一种基于生物流体力学的溶栓效率预测方法的流程图；1 is a flowchart of a method for predicting thrombolysis efficiency based on biohydrodynamics provided in an embodiment of the present invention;
图2为本发明实施例提供的一种基于生物流体力学的溶栓效率预测方法中血栓数据和溶栓数据的输入状态图；2 is an input state diagram of thrombus data and thrombolysis data in a method for predicting thrombolysis efficiency based on biohydrodynamics provided by an embodiment of the present invention;
图3为本发明实施例提供的一种基于生物流体力学的溶栓效率预测方法中预测模型的逻辑处理结构图；3 is a logical processing structure diagram of a prediction model in a method for predicting thrombolysis efficiency based on biohydrodynamics provided by an embodiment of the present invention;
图4为本发明实施例提供的一种基于生物流体力学的溶栓效率预测方法中预测模型的结果显示图；4 is a result display diagram of a prediction model in a method for predicting thrombolysis efficiency based on biohydrodynamics provided by an embodiment of the present invention;
图5为图4的具体计算结果示意图；Fig. 5 is the concrete calculation result schematic diagram of Fig. 4;
图6为本发明实施例提供的一种基于生物流体力学的溶栓效率预测系统的结构示意图。FIG. 6 is a schematic structural diagram of a system for predicting thrombolysis efficiency based on biohydrodynamics according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步地详细描述。In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings.
如图1所示，为本发明实施例中，提出的一种基于生物流体力学的溶栓效率预测方法，所述方法包括以下步骤：As shown in FIG. 1 , in the embodiment of the present invention, a proposed method for predicting thrombolysis efficiency based on biofluid mechanics, the method includes the following steps:
步骤S1、获取血栓数据和溶栓数据；其中，所述血栓数据包括血液粘度、凝血物质、肌肉应力、血液流速和血管结构；所述溶栓数据包括药物种类、药物剂量、注射方式、注射部位、反应参数和反应时间；Step S1, obtaining thrombus data and thrombolysis data; wherein, the thrombus data includes blood viscosity, coagulation substance, muscle stress, blood flow rate and vascular structure; the thrombolysis data includes drug type, drug dosage, injection method, injection site , reaction parameters and reaction time;
步骤S2、将所述血栓数据和所述溶栓数据导入由N-S方程和对流扩散反应方程构建而成的预测模型中进行预测，得到溶栓率和溶栓时间。Step S2, import the thrombus data and the thrombolysis data into a prediction model constructed by the N-S equation and the convection-diffusion reaction equation for prediction, and obtain the thrombolysis rate and the thrombolysis time.
具体过程为，在步骤S1中，如图2所示，分两块数据进行获取，包括血栓数据和溶栓数据。The specific process is that, in step S1, as shown in FIG. 2, two pieces of data are acquired, including thrombus data and thrombolysis data.
在血栓数据中，血液粘度是由血液粘度分析仪测量得到的；凝血物质是由血液成分检测仪测量得到的；所述肌肉应力是由肌张力测试仪测量得到的；血液流速是由激光多普勒血流仪测量得到的；血管结构是由核磁共振成像仪测量得到的。In the thrombus data, blood viscosity is measured by a blood viscosity analyzer; coagulation substances are measured by a blood component detector; the muscle stress is measured by a muscle tension tester; blood flow rate is measured by a laser doppler The vascular structure was measured by MRI.
在溶栓数据中，该溶栓数据来自于治疗方案设定。In the thrombolysis data, the thrombolysis data comes from the treatment protocol setting.
在步骤S2中，首先，基于N-S方程和对流扩散反应方程构建预测模型，即通过流体力学指标计算、生物反应设置等研究步骤，可以获得指定的指标。该预测模型为了节省计算时间，先计算流场与力场，再计算物质反应与传质情况，具体的处理逻辑如图3所示。In step S2, first, a prediction model is constructed based on the N-S equation and the convective-diffusion reaction equation, that is, the specified index can be obtained through research steps such as hydrodynamic index calculation and biological response setting. In order to save the calculation time, the prediction model first calculates the flow field and force field, and then calculates the material reaction and mass transfer. The specific processing logic is shown in Figure 3.
其中，对流扩散反应方程是通过以下公式实现的；Among them, the convective diffusion reaction equation is realized by the following formula;
其中，Ci为相关物质的浓度，Di为物质的扩散系数，u为流场，Ri为不同物质的反应变化项。Among them, C i is the concentration of related substances, D i is the diffusion coefficient of the substance, u is the flow field, and R i is the reaction variation term of different substances.
其次，将血栓数据和溶栓数据导入上述预测模型中进行预测，得到溶栓率和溶栓时间。Secondly, the thrombolysis data and thrombolysis data were imported into the above prediction model for prediction, and the thrombolysis rate and thrombolysis time were obtained.
最后，如图4所示，可以直接呈现稳定的溶栓最快时间、溶栓的最大程度等，甚至结合溶栓率与溶栓时间评估溶栓效率等级，即基于溶栓率和溶栓时间，评估出相应的溶栓效率等级。当然，具体呈现的指标可以在每一时刻予以体现，如图5模型计算结果所示。Finally, as shown in Figure 4, the fastest time of stable thrombolysis, the maximum degree of thrombolysis, etc. can be directly presented, and the thrombolysis efficiency grade can even be evaluated by combining the thrombolysis rate and thrombolysis time, that is, based on the thrombolysis rate and thrombolysis time , to evaluate the corresponding thrombolytic efficiency grade. Of course, the specific indicators can be reflected at every moment, as shown in the model calculation results in Figure 5.
如图6所示，为本发明实施例中，提供的一种基于生物流体力学的溶栓效率预测系统，包括：As shown in FIG. 6 , in the embodiment of the present invention, a system for predicting thrombolysis efficiency based on biofluid mechanics is provided, including:
数据获取单元110，用于获取血栓数据和溶栓数据；其中，所述血栓数据包括血液粘度、凝血物质、肌肉应力、血液流速和血管结构；所述溶栓数据包括药物种类、药物剂量、注射方式、注射部位、反应参数和反应时间；The
预测单元120，用于将所述血栓数据和所述溶栓数据导入由N-S方程和对流扩散反应方程构建而成的预测模型中进行预测，得到溶栓率和溶栓时间。The
其中，所述血液粘度是由血液粘度分析仪测量得到的；所述凝血物质是由血液成分检测仪测量得到的；所述肌肉应力是由肌张力测试仪测量得到的；所述血液流速是由激光多普勒血流仪测量得到的；所述血管结构是由核磁共振成像仪测量得到的。The blood viscosity is measured by a blood viscosity analyzer; the coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow rate is measured by measured by laser Doppler flowmeter; the blood vessel structure is measured by nuclear magnetic resonance imager.
其中，所述对流扩散反应方程是通过以下公式实现的；Wherein, the convective diffusion reaction equation is realized by the following formula;
其中，Ci为相关物质的浓度，Di为物质的扩散系数，u为流场，Ri为不同物质的反应变化项.Among them, C i is the concentration of related substances, D i is the diffusion coefficient of the substance, u is the flow field, and R i is the reaction variation term of different substances.
其中，还包括：Among them, it also includes:
评估单元，用于基于所述溶栓率和所述溶栓时间，评估出相应的溶栓效率等级。An evaluation unit, configured to evaluate a corresponding thrombolysis efficiency grade based on the thrombolysis rate and the thrombolysis time.
实施本发明实施例，具有如下有益效果：Implementing the embodiment of the present invention has the following beneficial effects:
本发明将血栓数据和溶栓数据导入由N-S方程和对流扩散反应方程构建而成的预测模型进行预测，快速得到溶栓率和溶栓时间，从而提高了预测准确度，且省时省力，有助于制定血栓治疗方案及预测治疗效果。The invention imports the thrombus data and the thrombolysis data into the prediction model constructed by the N-S equation and the convective diffusion reaction equation for prediction, and quickly obtains the thrombolysis rate and the thrombolysis time, thereby improving the prediction accuracy, saving time and effort, and has Help to formulate thrombosis treatment plan and predict treatment effect.
值得注意的是，上述系统实施例中，所包括的各个单元只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，各功能单元的具体名称也只是为了便于相互区分，并不用于限制本发明的保护范围。It is worth noting that, in the above system embodiment, the units included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units It is only for the convenience of distinguishing from each other, and is not used to limit the protection scope of the present invention.
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，所述的程序可以存储于一计算机可读取存储介质中，所述的存储介质，如ROM/RAM、磁盘、光盘等。Those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be implemented by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage Media such as ROM/RAM, magnetic disk, optical disk, etc.
以上所揭露的仅为本发明一种较佳实施例而已，当然不能以此来限定本发明之权利范围，因此依本发明权利要求所作的等同变化，仍属本发明所涵盖的范围。What is disclosed above is only a preferred embodiment of the present invention, and of course it cannot limit the scope of the rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.
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