医学影像技术在医学影像诊断中的合理运用

医学影像主要是以非侵入方式获得人体某部分内部组织影像的技术与处理的过程来开展医疗或医学研究,为临床疾病的诊断提供了极为重要的参考依据。本文对医学影像的分类进行了较为详细的整理分类,并较为深入的分析了医学影像技术与医学影像诊断之间的关系,从医学影像专业的互补性与专业独立性两个角度对医学影像技术在医学影像诊断中的重要作用进行了探讨与分析,并对医学影像未来的发展进行了展望。     

医学影像新技术在脑血管病诊断中的应用

本文综述了第四届全国脑血管病学术会议有关影像学检查,包括MRI、CT、SPECT、TCD等在脑血管病诊断方面的有关内容,MRI在发现早期脑梗塞方面较CT优越,CT依然是诊断脑梗塞及脑出血的主要检查手段,SPECT及TCD对缺血性脑血管病的诊断有很大帮助,合理选择以上检查方法对脑血管病的早期诊断、指导治疗、判断预后及随访有重要意义。     

医学影像技术在医学影像诊断中的临床应用研究

目的:探究医学影像技术在医学影像诊断中的临床应用价值。方法:对2018年1月-2019年1月在本院骨科收治的80例腰椎间盘突出症患者进行研究,其中对80例患者开展CT与MRI诊断,对比两组患者诊断准确率。结果:相较于CT诊断方式,MRI诊断准确性更高,误诊率、漏诊率发生率较低,差异性对比结果显示:P<0.05。而且MRI诊断准确率与病理学结果差异性无统计学意义:P>0.05。结论:MRI技术准确率较高,在腰椎间盘突出症诊断中能够有效提升诊断效果,为医生提供参考依据,有助于分析患者病情发展、治疗、预后情况,值得临床上广泛应用推广。     

医学影像新技术在脑血管疾病诊断中的应用

本综述了第四届全国脑血管病学术会议有关影像学检查，包括ＭＲＩ、ＣＴ、ＳＰＥＣＴ、ＴＣＤ等在脑血管病诊断方面的有关内容，ＭＲＩ在发现早期脑梗塞方面较ＣＴ优越，ＣＴ依然是诊断脑梗塞及脑出血的主要检查手段，ＳＰＥＣＴ及ＴＣＤ对缺血性脑血管病的诊断有很大帮助，合理选择以上检查方法了血管病的早期诊断、指导治疗、判预后及随访有重要意义。     

医学影像技术保障最佳疗效

随着医学科学的进步,对于治疗很多严重疾病现在已经发明了很有效的药物和手术,但是根据大量统计和研究,发现比药物和手术更重要的是时间,只有在早期诊断和治疗的条件下,药物和手术才能发挥最大的作用,一旦晚了,什么药物和手术都不能达到理想的治疗效果,而早期诊断病变的最有效的方法就是医学影像技术。因此可以说,对于一家医院来讲,缺少任何一个临床科室,医院可以照常运行,但是不能没有医学影像科,本文对此详加阐述。     

我国医学影像技术的最新发展

随着医学数码影像技术的迅速发展，我国已经大量引进各种现代化的先进医学数码影像装备和相关的技术，并且已广泛地应用于临床诊断和治疗，取得了良好的效果。最近召开的全国医学影像技术专业会议就着重研究这一方面的课题，尤其是计算机X线摄影CR和影像存档和通讯系统PACS等，本文对大会上发表的论文进行综述。     

从展览会看医学影像技术进步

由国家卫生部主办的第十一届中国国际医用设备仪器展览会暨技术交流会于９月９—１４日于北京召开。它既是国际性展览会，又是一次大型的技术交流会。国际著名的医疗设备和仪器制造厂家，如日本岛津、日立、柯尼卡、德国西门子、荷兰菲利浦、美国柯达等与会展示了最新的医疗产品。国内和国际上知名的医疗界学者、专家在学术研讨会上做专题报告。在该届展览会上，处于最佳展位和展示面积最大的是新型放射成像设备和技术。与会展览的有日本东芝、岛津、德国西门子、荷兰菲利浦的计算机X线CR成像系统、计算机断层CT成像系统、磁共振MRI成像…     

迅速发展的医学影像技术

本文总结了近些年来医学影像技术取得的新进展，如：计算机X线摄影CR、计算机断层X线摄影CT、磁共振成像MRI、数字减影血管造影DSA、图像存贮与通讯系统PACS等，并综述了中华医学会于2001年召开的医学影像学第三次全国学术会议上发表的一些学术论文。     

面向乳腺诊断的时域扩散光学层析图像重建方法

为了克服传统的X射线乳腺成像术在灵敏度、特异性、安全性和舒适性等方面存在的重大缺陷，提出了一种面向乳腺成像的时域扩散光学层析（diffuse optical tomography，DOT）图像重建技术．该技术以拉普拉斯变换时域扩散方程的有限元（finite element method，FEM）数值解作为正向模型，采用基于牛顿-拉夫逊迭代格式的逆模型框架．通过对锥形压缩乳房光学层析模式的模拟实验研究表明，该方法可有效重构出乳腺肿瘤组织的吸收系数μa和约化散射系数μs′的层析图像，且成像目标的空间位置准确，具有较高的图像质量．     

速冷医用睡袋的研制

本文介绍了一种医用装置－－速冷医用睡袋的研制机理：该装置以液氮做制冷剂,由单片微电脑控制,使专用睡袋内部形成不同的制冷区域,从而为发热病人提供了一个辅助降温的医疗设备。以此为基础,也提出对发热病人治疗室的设计。     

纳米金在肿瘤诊断中的应用

纳米金具有独特的理化性能,在肿瘤诊断和治疗中取得了许多重要进展。综述了纳米金的结构组成、粒径尺寸、形貌特征对其本身性能的影响,介绍了纳米金对肿瘤标志物的检测和作为造影剂对肿瘤成像的研究进展,着重评述了纳米金应用于体内外肿瘤光学成像方面的研究情况,展望了其未来的发展前景。     

X线诊断规范化探讨

本文对X线诊断规范化进行了探讨，认为可供评价因素必须包括人员基础因素、仪器操作因素、报告内容被临床认定程度、服务工作因素等，并拟定了评价标准，使诊断报告基本程序、格式、书写达规范化。围绕其就影像质量的提高、组织管理、人员培训等问题展开了讨论，突出实行系统工作程序化控制及严格的岗位责任制管理，强化全体人员的质量自控意识。     

高频医学断层成像系统的硬件设计

详细论述了高频医学电阻抗断层成像(EIT)系统设计的关键技术,其中包括激励信号源、电压控制电流源、模拟开关阵列以及信号解调等环节。设计了基于数字信号处理器(DSP)的主从式数据采集系统。PC机作为主控机,进行可视控制、图像重建、实时显示等操作;DSP实现电极选通、数据采集并与PC机进行数据通信。该系统具有数据采集速度快、使用灵活、可扩展性强的特点。     

A Functional CT Contrast Agent for In Vivo Imaging of Tumor Hypoxia

Hypoxia, which has been well established as a key feature of the tumor microenvironment, significantly influences tumor behavior and treatment response. Therefore, imaging for tumor hypoxia in vivo is warranted. Although some imaging modalities for detecting tumor hypoxia have been developed, such as magnetic resonance imaging, positron emission tomography, and optical imaging, these technologies still have their own specific limitations. As computed tomography (CT) is one of the most useful imaging tools in terms of availability, efficiency, and convenience, the feasibility of using a hypoxia‐sensitive nanoprobe (Au@BSA‐NHA) for CT imaging of tumor hypoxia is investigated, with emphasis on identifying different levels of hypoxia in two xenografts. The nanoprobe is composed of Au nanoparticles and nitroimidazole moiety which can be electively reduced by nitroreductase under hypoxic condition. In vitro, Au@BSA‐NHA attain the higher cellular uptake under hypoxic condition. Attractively, after in vivo administration, Au@BSA‐NHA can not only monitor the tumor hypoxic environment with CT enhancement but also detect the hypoxic status by the degree of enhancement in two xenograft tumors with different hypoxic levels. The results demonstrate that Au@BSA‐NHA may potentially be used as a sensitive CT imaging agent for detecting tumor hypoxia.     

A Comprehensive Review on Medical Diagnosis Using Machine Learning

The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine learning could assist the doctors in making decisions on time, and could also be used as a second opinion or supporting tool. This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases. We present the various machine learning algorithms used over the years to diagnose various diseases. The results of this study show the distribution of machine learning methods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research.     

Medical Diagnosis Using Machine Learning: A Statistical Review

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.