电阻抗成像中高速高精度数字相敏检波器设计

电阻抗成像对测量系统的精度和速度都有较高要求,为此研制了基于现场可编程门阵列(field programmable gate array,FPGA)的数字相敏检波器(digital phase-sensitive detector,DPSD)用于电阻抗成像的数据测量.在分析DPSD原理的基础上,推导出信噪比与采样点数和采样分辨率的关系.给出了测量系统的实现方案,提出了基于直接数字频率合成(direct digitalsynthesis,DDS)技术的模数转换器(analog-to-digital converter,ADC)时钟设计方法.采用高速多通道ADC芯片,辅以低抖动ADC时钟电路,最终由FPGA实现实时DPSD算法.实验测试结果显示,测量准确度可达0.03%,系统信噪比可达85dB.琼脂模型成像实验证明其性能可以较好地满足电阻抗成像的要求.     

CT扫描与磁共振成像技术用于急性胰腺炎诊断的效果比较

比较磁共振成像技术与电子计算机断层扫描（Computed Tomography,CT）用于急性胰腺炎诊断的效果。回顾性分析庆阳市人民医院收治的疑似急性胰腺炎患者80例,所有患者于入院后均进行磁共振成像技术及CT检查,金标准为病理结果,计算灵敏度、特异度、准确度及诊断效能。经检查,80例疑似胰腺炎患者中确诊58例,CT检查确诊54例患者,经磁共振成像诊断56例;CT诊断的准确度为65.00%,特异度为45.45%,灵敏度为72.41%,阴性预测值为38.46%,阳性预测值为77.78%;磁共振成像技术诊断急性胰腺炎的准确度为87.50%,特异度为81.82%,灵敏度为89.66%,阴性预测值为75.00%,阳性预测值为92.86%。2种检查方式比较差异均有统计学意义（P<0.05）。与CT检查相比,在诊断急性胰腺炎时,磁共振成像技术准确度、特异度、灵敏度、阳性预测价值、阴性预测价值较高,具有较高的诊断效果,能够为临床确诊提供更多帮助,利于对患者进行及时干预。     

乳腺电阻抗扫描成像数值分析

针对已有的电阻抗扫描成像分析模型与临床实际情况存在较大差异的问题,结合女性乳房结构、中国女性乳房实际大小、电参数及检测探头结构,构造了一个更加接近于实际情形的电阻抗扫描成像模型.采用有限元方法求解模型的静态电场方程并结合方差分析(Yates方法)对电阻抗扫描成像的相关参数进行统计分析.结果表明,当乳房中存在癌灶时,由于癌灶组织与周围正常组织在电参数上存在差异,乳房表面电流会存在相应的扰动.电流扰动程度受乳房大小、癌灶深度、大小以及周围组织电导参数及其交互因素的影响.如能通过测量夹具控制乳房大小,减少癌灶深度,可进一步提高电阻抗扫描成像的乳腺癌检测性能.     

基于粒子群优化算法的电阻抗图像重建

电阻抗成像的实际应用具有许多优越性,但电阻抗图像重建是一个严重病态的非线性逆问题。目前电阻抗成像的静态算法大多采用Newton-Raphson类算法,这类算法需要计算Jacobian矩阵、使用正则化技术等,算法复杂且稳定性较差。针对该问题,采用了一种新的求解逆问题的方法:粒子群优化算法(PSO)。PSO是一种基于种群搜索策略的自适应随机算法,具有算法简单、调节参数少、收敛速度快、易于实现等特点。给出了电阻抗成像的建模模型,并对粒子群优化算法做了适当的改进以适应电阻抗问题的求解。与牛顿类算法相比,它可以省去繁复的雅可比矩阵计算过程,而采用自适应搜索来求取最优解。仿真结果表明,应用PSO进行图像重构时,能够对突变区域进行准确的定位,图像分辨率较高。     

电阻抗成像正则化参数的优化方法

为改善电阻抗成像逆问题的不适定性,通常采用Tikhonov正则化算法来求得适当的解。正则化参数对重建图像的质量和计算速度影响较大。笔者提出了一种基于残差范数和解范数乘积的优化方法(PRS)求取电阻抗成像的正则化参数。为验证该方法的有效性,笔者针对不同的目标大小、目标位置、目标电导率、目标数目以及不同程度的噪声分别进行了重建图像的仿真实验和水槽实验。结果表明：这种优化方法可以快速找到相对最优的正则化参数,且具有良好的抗噪性能。与传统的L曲线方法相比,提高了图像重建质量。     

BA-ELISA方法检测乳腺癌患者血清p185蛋白

目的建立血清p185蛋白新的检测方法,评价血清p185蛋白在乳腺癌诊断中的意义.方法利用生物素化C-erbB-2单克隆抗体,建立血清p185蛋白BA-ELISA检测方法,并对乳腺癌、乳腺增生和正常对照血清进行p185蛋白检测,同时对组织进行p185免疫组化染色.结果乳腺癌血清p185蛋白与乳腺增生、正常对照相比差异显著(P<0.05),其中免疫组化阳性乳腺癌血清p185蛋白与乳腺增生、正常对照组相比有显著差异(P<0.01),而免疫组化阴性乳腺癌血清p185蛋白与其相比无显著差异(P>0.05);BA-ELISA方法检测血清p185蛋白的敏感度为43.3%,特异度为91.1%,准确度为74.4%.血清p185蛋白水平与乳腺组织p185蛋白表达高度相关(P<0.01).结论乳腺癌患者血清p185蛋白对乳腺癌具有诊断意义.血清p185蛋白与组织p185蛋白表达高度相关.     

基于磁致边界振动希尔伯特变换的电导率重建简化算法

磁声成像是一种结合电阻抗成像和超声成像优点的多物理场耦合新型成像方法,能实现组织的电阻抗对比成像,重建组织内部电阻抗分布的边界.本研究首先利用强指向性换能器来提高磁致振动检测的方向精确性,通过磁声声压导数实现边界振动声源法向声压的提取;然后从换能器的接收声压出发,利用三维空间的格林函数推导了一种基于磁致边界振动法向声压的电导率重建算法,明确了重建过程中声压及其导数的物理意义;最后建立了圆柱坐标下的柱状扫描系统,对双层偏心圆柱组织模型所产生的声压和换能器所接收到的磁声声波进行了模拟,利用Hilbert变换实现波簇包络定位和相位分析,恢复磁致边界振动声压,重建扫描面的二维电导率分布和模型截面具有较高的一致性,不但获得了组织的边界信息,还实现了组织内部电导率分布的精确重建.所提出的基于磁致边界振动希尔伯特变换的电阻抗重建简化算法为组织病变的电阻抗成像和磁声诊断提供了新方法.     

尿沉渣检查在尿路感染诊断的应用价值探究

探究尿沉渣检查在尿路感染诊断的应用价值.选取2019年6月-2020年8月来医院就诊的疑似尿路感染患者208例,取清洁中段尿液标本,均进行细菌培养及尿沉渣定量检测.以细菌培养结果为金标准,采用受试者工作特征(ROC)曲线分析尿沉渣白细胞(WBC)及细菌定量计数在尿路感染诊断中的应用价值.208份尿液标本中,阳性59例(28.37％);尿沉渣WBC及细菌诊断尿路感染的ROC曲线下面积分别为0.771、0.867,诊断界值分别为16.7个/μL、116.3个/μL;尿WBC诊断的灵敏度、特异度、准确度分别为71.19％、83.22％、79.81％,尿细菌诊断的灵敏度、特异度、准确度分别为79.66％、77.18％、77.88％,两者联合诊断的灵敏度、特异度、准确度分别为91.53％、85.91％、87.50％.尿沉渣WBC与细菌定量检测联合在尿路感染诊断中应用,具有较高的灵敏度、准确度,且具有检测快速、成本低等特点.     

兼顾技术经济性的永磁偏置型故障限流器优化配置算法

提出了一种兼顾技术性和经济性的大电网永磁偏置型故障限流器(Permanent-magnet-biased Saturation based Fault Current Limiter,PMFCL)优化配置算法.介绍了PMFCL限流机理,定义了短路电流裕量作为挑选超标节点的标准.将节点自阻抗作为节点短路电流水平的衡量指标,基于节点自阻抗增量,构建了兼顾全局限流效果与经济性的PMFCL优化配置评价函数.综合考虑了PMFCL启动条件和节点自阻抗对支路阻抗参数的灵敏度指标以缩小寻优空间,提出了PMFCL在大电网中配置优化算法.将该算法应用于IEEE 39节点标准算例,调用Matlab遗传算法函数完成仿真.结果表明,与不计及灵敏度相比,该算法寻优效率较高;所得最优配置方案能够使所有节点短路电流满足限流要求并保留一定裕量,对超标越严重的节点限流效果较好,验证了该算法的可行性及有效性.     

基于偏微分方程去躁的磁共振电阻抗成像

介绍了医学成像技术中磁共振电阻抗成像(MREIT)的数学模型和调和Bz算法。为克服调和Bz算法的不适定性,提出了一种偏微分方程去躁方法对Bz的躁声数据进行去噪。结合截断奇异值分解(TSVD)正则化方法,给出了基于Mat-lab偏微分方程工具箱的磁共振电阻抗成像的数值算法。用两个数值模拟算例验证了算法的有效性。     

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective： To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods： Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry （SELDI-TOF MS）. The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results： From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion： The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.     

循环肿瘤细胞富集和鉴定技术在乳腺癌患者中的临床验证

 临床验证了一种有效的循环肿瘤细胞富集和鉴定方法,并探讨了该方法应用于乳腺癌循环肿瘤细胞(CTC)的临床检测。分别取10名健康志愿者外周抗凝静脉血7.5mL,加入经标记的培养乳腺癌细胞SKBR-3,抗白细胞抗体偶联的纳米磁珠阴性富集后在荧光显微镜下读取细胞并计数,通过细胞的回收率对富集方法予以评价;分别取20名健康志愿者、15名临床诊断为浸润性乳腺癌患者外周抗凝静脉血7.5mL,对所取血液进行编盲处理,经富集、免疫荧光加免疫细胞化学染色后在显微镜下读取并判定循环肿瘤细胞,通过两组间检测到的循环肿瘤细胞的差异对该研究方法的特异性和敏感性进行评价。研究结果表明,10名健康志愿者血液中加入培养细胞的平均回收率为75%;加入的培养细胞抗角化蛋白(CK18)染色阳性、抗Her2染色阳性、抗CD45染色阴性,符合乳腺癌细胞的特征;揭盲后结果显示,20名健康志愿者血液均未报告有循环肿瘤细胞,而在乳腺癌患者中,CTC数目小于3个(0—2)者占所检测患者的33.3%,大于3个者占66.7%。由此得出结论：该循环肿瘤细胞富集和鉴定方法对外周血CTC具有较好的回收率,且对于CTC的临床诊断具有很好的特异性和灵敏度。     

Breast cancer-specific serum peptide profiles

Early detection of breast cancer is paramount to successful clinical therapy. Yet, early-stage breast cancer lacks specific symptoms or biomarkers. With the emerging of the mass spectrometric (MS)–based signatures as biomarkers, we investigated breast cancer-related serum profile pattern through class prediction and independent validation, and used Fourier transfer MS to identify breast cancer signature. We now show a distinctive serum peptide pattern that discriminates breast cancer from healthy controls with 93.2% sensitivity and 95.4% specificity. m/z 5901.70 and 4465.74 of ion fragment of FPA and alpha1-antichymotrypsin are found in the signatures that predominantly discriminate breast cancer from healthy individuals. These novel findings identify an MS-based serum peptide pattern of breast cancer that may have direct clinical utility in future. Contributed equally to this work Supported by National Natural Science Foundation of China (Grant No. 30321003) and National Key Basic Research and Development Program of China (Grant No. 2004CB518800)     

基于特征选择ELM的乳腺肿块检测算法

乳腺肿块检测是防治乳腺癌的有效途径,基于乳腺X射线图像特征模型的极限学习机(ELM)分类算法已被应用于计算机辅助检测乳腺肿块中.针对由于特征间的依赖性导致的ELM学习效率和检测准确度低的问题,提出了基于特征选择ELM的乳腺肿块检测算法.利用影响值选择、序列前向选择和遗传选择等方法进行特征选择,进而利用该结果提高ELM的性能.通过490例来自辽宁省肿瘤医院的乳腺X射线图像的实验表明,基于特征选择ELM的乳腺肿块检测算法能有效提升乳腺肿块检测的效果,其中以遗传选择对ELM性能提升最明显.     

复杂交通环境下二轮机动车乘员头盔检测算法

针对现有二轮机动车乘员头盔检测算法在目标密集分布、随机遮挡等情况下效果较差且难以在边缘设备上应用的问题,制作了具有针对性的数据集,对比现有模型后,以YOLOv7为参考提出一种复杂交通环境下二轮机动车乘员头盔检测算法.首先,采用EfficientNet-B3作为主干网络,可提高特征提取能力且更为轻量化;其次,将增大感受野模块(RFB)引入特征融合结构中,以增大模型感受野,提升小目标头盔检测能力;最后,在检测头嵌入SimAM机制,在不增加参数的前提下提高算法精度.结果表明：相较于YOLOv7,文中算法的准确率、召回率和平均准确率分别提高了2.84%,2.26%和3.26%,参数量和运算量分别为YOLOv7的33.1%,23.5%,可实现当前主流模型算法的最佳检测性能和效率;在NVIDIA Jetson Nano开发板上的处理速度达到47.58 F·s^-1,可满足边缘设备部署需求.     

基于极限学习快速分类方法的人体跌倒检测方法

为了准确快速检测人体跌倒状态,提出基于惯性测量单元(inertial measurement unit,IMU)测量和处理数据的极限学习机(extreme learning machine,ELM)快速分类判别方法。分析了人体运动行为特征,构建了腿部运动参数提取模型;通过IMU采集人体腿部运动特征数据,并进行姿态解算;采用ELM方法对人体运动特征的加速度、角速度和姿态进行分类,判断人体是否处于跌倒状态;根据机器学习评价指标对ELM参数进行优化,得到最佳参数。进行了人体运动状态测量实验,结果表明,ELM方法能够对IMU测量和处理数据进行准确快速地分类。当隐含层结点为1 000时,ELM检测方法跌倒检测的准确率为96. 45%,灵敏度为97. 32%,特异性为89. 32%。因此,采用ELM快速检测方法,可有效地对人体运动特征数据进行分类,实现对人体跌倒行为的准确检测。     

基于核酸适配体的荧光分析法定量检测MUC1粘蛋白

MUC1粘蛋白是一种高糖化、高分子量的糖蛋白,其在乳腺癌细胞中高度异常表达,其特异性高于组织多肽抗原,敏感性高于癌胚抗原,因此MUC1在乳腺癌诊断中具有很高的临床应用价值,而建立高灵敏的MUC1蛋白定量检测方法对临床诊断具有重要的意义.该研究建立了基于核酸适配体-滚环扩增（RCA）和氧化石墨烯-荧光共振能量转移（GO-FRET）技术的MUC1黏蛋白定量检测技术,实现了MUC1粘蛋白准确、灵敏的定量检测.结果表明,该方法定量检测线性范围为50~1 000 pg/mL,检测限为28.05 pg/mL,定量限为45.57 pg/mL,在人血样品中的回收率为96%~104%.     

Defect Detection Algorithm of Patterned Fabrics Based on Convolutional Neural Network

The background pattern of patterned fabrics is complex, which has a great interference in the extraction of defect features. Traditional machine vision algorithms rely on artificially designed features, which are greatly affected by background patterns and are difficult to effectively extract flaw features. Therefore, a convolutional neural network(CNN) with automatic feature extraction is proposed. On the basis of the two-stage detection model Faster R-CNN, Resnet-50 is used as the backbone network, and the problem of flaws with extreme aspect ratio is solved by improving the initialization algorithm of the prior frame aspect ratio, and the improved multi-scale model is designed to improve detection of small defects. The cascade R-CNN is introduced to improve the accuracy of defect detection, and the online hard example mining(OHEM) algorithm is used to strengthen the learning of hard samples to reduce the interference of complex backgrounds on the defect detection of patterned fabrics, and construct the focal loss as a loss function to reduce the impact of sample imbalance. In order to verify the effectiveness of the improved algorithm, a defect detection comparison experiment was set up. The experimental results show that the accuracy of the defect detection algorithm of patterned fabrics in this paper can reach 95.7%, and it can accurately locate the defect location and meet the actual needs of the factory.     

High Accuracy Gene Signature for Chemosensitivity Prediction in Breast Cancer

Neoadjuvant chemotherapy for breast cancer patients with large tumor size is a necessary treatment.After this treatment patients who achieve a pathologic Complete Response(p CR) usually have a favorable prognosis than those without. Therefore, p CR is now considered as the best prognosticator for patients with neoadjuvant chemotherapy. However, not all patients can benefit from this treatment. As a result, we need to find a way to predict what kind of patients can induce p CR. Various gene signatures of chemosensitivity in breast cancer have been identified, from which such predictors can be built. Nevertheless, many of them have their prediction accuracy around 80%. As such, identifying gene signatures that could be employed to build high accuracy predictors is a prerequisite for their clinical tests and applications. Furthermore, to elucidate the importance of each individual gene in a signature is another pressing need before such signature could be tested in clinical settings. In this study, Genetic Algorithm(GA) and Sparse Logistic Regression(SLR) along with t-test were employed to identify one signature. It had 28 probe sets selected by GA from the top 65 probe sets that were highly overexpressed between p CR and Residual Disease(RD) and was used to build an SLR predictor of p CR(SLR-28). This predictor tested on a training set(n = 81) and validation set(n = 52) had very precise predictions measured by accuracy,specificity, sensitivity, positive predictive value, and negative predictive value with their corresponding P value all zero. Furthermore, this predictor discovered 12 important genes in the 28 probe set signature. Our findings also demonstrated that the most discriminative genes measured by SLR as a group selected by GA were not necessarily those with the smallest P values by t-test as individual genes, highlighting the ability of GA to capture the interacting genes in p CR prediction as multivariate techniques. Our gene signature produced superior performance over a signature found in one previous study with prediction accuracy 92% vs 76%, demonstrating the potential of GA and SLR in identifying robust gene signatures in chemo response prediction in breast cancer.     

基于机器学习的跨患者癫痫自动检测算法

针对目前癫痫自动检测算法多集中于为单个患者建立检测模型,泛化能力较弱的问题,提出一种基于机器学习的跨患者癫痫自动检测算法.该算法使用多个癫痫患者的脑电数据,先对数据进行预处理后分析脑电数据间存在的特征,再对特征进行筛选,训练出一个跨患者的癫痫自动检测模型.该算法不需为每个患者建立单独的检测模型,实现了仅使用一个检测模型...