摘要
为了解决现有影像增强检查过程中存在的造影剂注射方案不优化、注射器控制不智能,各操作环节不连通的问题,提出了一种基于Java开发的一体化注射方案智能推荐与控制系统。通过智能化注射方案推荐,建立智能化的注射方案推荐模型,实现自动推荐最合适注射方案,提高注射效果和安全性;通过结合注射设备和影像处理算法,实现自动化注射方案控制,提高注射的准确性和一致性;通过实时监测注射过程中的影像数据和生物参数,对现有的检查全流程进行集成,保存并显示过程数据,提高治疗效果和患者的体验。最终,通过规则、案例推理与深度神经网络协同应用,实现造影剂方案的智能生成,并通过长连接实现对高压注射器的反馈与控制,为提高影像增强的智能化程度、改善医疗过程和结果和促进医疗结果互认提供了良好的综合方案。该系统已于华西医院进行应用,能够在影像增强检查中简化操作,提高影像质量,减轻对患者的身体损害,减少医疗资源的浪费。
The existing imaging enhancement examination process faces issues such as suboptimal contrast agent injection schemes,lack of intelligent control over injectors,and disconnected operation steps,this paper proposed a Java-based integrated intelligent recommendation and control system for injection schemes to address these problems.Through the intelligent injection scheme recommendation,it established a model for recommending optimal injection schemes to realize the automatic recommendation of the most suitable injection scheme,thereby improving the effectiveness and safety of injections.Additionally,by integrating the injection equipment with image processing algorithms,the system achieved automated control over the injection scheme,thereby enhancing accuracy and consistency during injections.Furthermore,by monitoring the image data and physiological parameters during the injection process in real time,the system integrated the existing examination process,saved and displayed the process data to improve treatment effectiveness and enhance the patient experience.Ultimately,through the coordinated application of rules,case-based reasoning and deep neural networks,the system achieved intelligent generation of contrast agent schemes.And it also realized feedback and control over the high-pressure injector through a persistent connection,which provided a good comprehensive solution to improve the intelligence level of imaging enhancement,enhance the quality of medical processes and outcomes,and facilitate mutual recognition of medical results.The system has been successfully applied at West China Hospital,which can simplify operations in imaging enhancement examinations,improve image quality,reduce patient harm and medical resource waste.
作者
田宸宇
张涛
李真林
于泽源
Tian Chenyu;Zhang Tao;Li Zhenlin;Yu Zeyuan(West China Biomedical Big Data Center,West China hospital of Sichuan University,Chengdu 610041,China;School of Mechanical Engineering,Sichuan University,Chengdu 610065,China;School of Automation Engineering,University of Electronic Science&Technology of China,Chengdu 610097,China)
出处
《计算机应用研究》
CSCD
北大核心
2023年第8期2442-2446,共5页
Application Research of Computers
基金
四川省苗子工程项目(21MZGC0310)
四川省重点研发项目(21ZDYF3772)。
