基于膨隆实验的血管非线性力学特性确定方法

目的基于实验测试和数值模拟结果,建立反方法确定血管的非线性力学特性。方法采用自行设计的实验装置,对血管进行压力加载,获取血管膨隆的实验数据;假设血管材料特性符合超弹性Ogden模型,建立血管膨隆的有限元模型,然后结合实测数据及数值模拟结果,利用优化算法建立反方法确定血管的力学特性。结果得到了描述兔腹主动脉非线性力学特性的Ogden模型一阶和二阶的材料参数,其中一阶Ogden模型参数α=10.86±1.98。经验证兔腹主动脉的力学特性可用超弹性力学模型描述。结论基于实验测试和数值模拟方法建立的反方法可用来识别血管的非线性力学特性。     

人耳鼓膜病变数值分析

目的研究鼓膜厚度和硬度对人耳传声的影响。方法利用CT获取志愿者耳部结构临床资料,使用Matlab软件提取相关结构的边界,将边界文件导入ANSYS建立人耳结构数值有限元模型。结果利用本文人耳数值模型,在外耳道口施加105dB声压,进行200～8000Hz频率范围的谐响应分析。以此研究在鼓膜病变情况下,鼓膜和镫骨底板位移幅值的变化规律。结论用数值方法解释了鼓膜病变对传声的影响,为鼓膜修补提供了力学参考。     

高压下中耳结构动力损伤研究

目的研究高压对中耳结构造成的损伤。方法基于CT扫描建立中耳结构有限元数值模型,对模型施加随时间变化的压力,分析鼓膜以及镫骨足板的应力、应变和位移变化。结果获得的计算结果与相关文献中的试验数据吻合,验证了所建中耳模型的准确性。高压会对中耳造成损伤,随着压力的增加,损伤加重;快速加压使得中耳损伤严重,对内耳的影响较小;慢速加压也能导致中耳损伤,但在中耳损伤前,内耳会损伤。结论高压容易导致人耳出现损伤,为避免听力受到影响,在加压过程中要控制好加压速率。     

下颌颏部骨折内固定的三维有限元模型及边界约束的建立

通过Pro／E和ANSYS建立下颌骨颏部骨折内固定的三维有限元模型，并模拟了各种功能状态下的边界约束。使用计算机图像处理软件对CT扫描图像进行预处理，然后采用自编程序获取下颌骨解剖结构的三维坐标数据，用ANSYS建立下颌骨的三维有限元模型。采用数值化建模软件Pro／E建立小型接骨板-螺钉固定体系统的实体模型。模拟下颌骨颏部正中骨折坚强内固定治疗，建立该内固定治疗的有限元模型。通过改变各组咀嚼肌力的大小，进行了三种咬合状态的边界约束。获得了形态逼真、相似性好的下颌颏部骨折内固定后的三维有限元模型，并模拟了各功能状态下的边界约束，为后期的生物力学分析奠定了基础。     

基于动物实验的应力与股骨近端生长关系的生物力学模型

目的　建立可数值量化的应力与骨生长目的建立可数值量化的应力与骨生长生物力学模型。方法　将动物实验、骨生长方程中未知参数的反演识别和计算机技术相结合，研究应力环境对快速生长期大鼠股骨生长与重建的影响，依据大鼠股骨所受外力刺激以及近端骨密度数值的改变，反演骨生长方程中的未知参数B和K。结果　文中所建的生物模型不仅能够数值模拟快速生长期大鼠股骨骨密度变化和外界刺激的关系，而且能够预测大鼠生命周期中某段时间内不同应力环境下股骨的生长趋势。结论：本文的建模思路和方法为人体骨骼重建适应模型的确立起到提示和借鉴作用。     

基于快速生长期大鼠在不同应力环境中活骨实验的骨适应生物模型数字量化研究

本研究提出了将动物实验、数学表达式的未知参数识别和计算机仿真技术相结合的方法建立数值量化的骨生长与重建自适应生物模型。通过设计一种新的动物实验，研究不同应力环境对快速生长期大鼠股骨生长与重建的影响，依据等时间间隔提取的大鼠股骨骨密度数值，反演了骨生长方程中的未知生物参数B和K，根据股骨的CT截面信息．重塑了三维几何模型。文中提出的这种模型及建模方法不仅能够量化表示骨密度变化量和外界刺激值之间的关系，而且能够预测大鼠整个生命周期内不同应力环境中骨的生长情况。本文的建模思路和研究方法对于人体骨骼生长重建适应模型的建立也会有一定的借鉴意义。     

结合细胞散点图和改进Snake模型的重叠细胞分割修复的方法

针对医学显微图像中常出现细胞重叠的现象,提出一种组合细胞散点图和改进Snake模型的分割方法:先使用可变腐蚀元的迭代腐蚀方法,获取细胞散点和分离细胞;再利用原始边缘信息,初始化活动轮廓;最后结合细胞散点信息,利用加入约束能量的改进Snake算法,获取细胞的完整边界。在获取边界重叠掩膜图像的基础上,针对因重叠导致重叠区域图像信息变化的情况,采用一种自适应迭代卷积的快速图像修复方法。实验表明,该方法能有效地分离重叠细胞,并能准确快速地获取细胞的完整边界,并在重叠掩膜图像的基础上快速修复重叠部分。     

应力强度因子对股骨干骨折接骨板裂纹扩展的分析

分析接骨板孔边裂纹前缘的应力强度因子分布,探究接骨板参数对裂纹扩展行为的影响。利用扩展有限元的方法计算裂纹前缘的应力强度因子,在ABAQUS中建立含预制裂纹的股骨干骨折内固定系统模型,观察裂纹位置和接骨板参数的变化对应力强度因子数值的影响。接骨板孔边的裂纹类型为复合型裂纹,张开型应力强度因子在裂纹扩展中起着主导作用;接骨板的工作长度对应力强度因子的数值影响较为显著,较大的接骨板厚度和宽度能明显降低裂纹的应力强度因子。     

基于中耳与耳蜗集成有限元模型的耳声传递模拟

建立外耳道、中耳和简化耳蜗集成的有限元模型,包含中耳和耳蜗结构、耳道和中耳腔内的空气以及耳蜗内的液体.采用声-结构耦合动力学分析,计算声音由外耳道向内耳的传递过程,获得了鼓膜、镫骨足板的位移、中耳的声压增益、前庭阶的压力分布,同时也模拟了基底膜自蜗底至顶端的频率选择特性.计算结果与相关文献的实验结果具有较好的一致性,说明本模型对中耳传声功能模拟的准确性.结果表明,改进的耳蜗模型可为耳蜗运动功能模拟的探索提供更充分和合理的信息.     

外耳道静压对声音传递和耳蜗输入影响数值分析

为分析静压变化对中耳听骨链运动和耳蜗输入的影响,运用有限元模型研究外耳道静压导致的中耳声音传递功能和耳蜗激励输入改变。首先,根据相关实验测量数据并结合有限元分析,拟合了耳道静压力与中耳各构件有效弹性模量关系的经验公式;然后,通过材料参数的改变模拟压力造成的中耳结构刚度增加,分析耳道静压对中耳机动性的影响和不同耳道压力下镫骨底板位移、速度和耳蜗两窗压力差的变化。计算结果与相关的文献实验数据有较好的符合,说明所建立的计算模型在模拟静压条件下中耳传递功能方面的合理性。结果表明在低频范围(0～0.6 kHz)外耳道静压对两窗压力差的影响稍大于对镫骨位移的影响。     

神经网络在骨密度超声波检测中的应用

定量超声骨密度检测方法是直接利用超声波信号的参数变化评价骨密度特性,它存在参数映射关系模糊、检测结果可信度低的问题.提出利用人工神经网络方法,建立不同年龄段正常女性跟骨超声波信号的参数变化与标准骨密度参考数据库之间的网络模型,采用Bayesian正则化算法训练网络.结果表明,建立的神经网络模型反映了不同年龄段超声波检测参数变化与标准骨密度参考值之间良好的映射能力,提高了定量超声法检测骨密度的可信性.     

Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. This paper illustrates the use of combined neural network models to guide model selection for diagnosis of ophthalmic and internal carotid arterial disorders. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models.     

Robust Nonlinear Autoregressive Moving Average Model Parameter Estimation Using Stochastic Recurrent Artificial Neural Networks

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate model predictions. Furthermore, we compare the performance of the new approach to that of the deterministic recurrent neural network approach. Using this simple two-step procedure, we obtain more robust model predictions than with the deterministic recurrent neural network approach despite the presence of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals. © 1999 Biomedical Engineering Society. PAC99: 8710+e, 8719Uv, 0705Mh     

Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression

The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the 7.5 MHz ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the time-frequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC) = 92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (0-39%, 40-59%, 60-79% and 80-99% diameter stenosis), it was found that the neural network (CC = 73.9%) became more efficient than the logistic regression analysis (CC = 67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.     

Adaptive prediction of internal target motion using external marker motion: a technical study

An adaptive prediction approach was developed to infer internal target position by external marker positions. First, a prediction model (or adaptive neural network) is developed to infer target position from its former positions. For both internal target and external marker motion, two networks with the same type are created. Next, a linear model is established to correlate the prediction errors of both neural networks. Based on this, the prediction error of an internal target position can be reconstructed by the linear combination of the prediction errors of the external markers. Finally, the next position of the internal target is estimated by the network and subsequently corrected by the reconstructed prediction error. In a similar way, future positions are inferred as their previous positions are predicted and corrected. This method was examined by clinical data. The results demonstrated that an improvement (10% on average) of correlation between predicted signal and real internal motion was achieved, in comparison with the correlation between external markers and internal target motion. Based on the clinical data (with correlation coefficient 0.75 on average) observed between external marker and internal target motions, a prediction error (23% on average) of internal target position was achieved. The preliminary results indicated that this method is helpful to improve the predictability of internal target motion with the additional information of external marker signals. A consistent correlation between external and internal signals is important for prediction accuracy.     

基于神经网络的慢性病危险因素人体模型的建模方法

本研究的目的是运用神经网络反向传播(BP)学习算法,建立一种个体化慢性病危险因素人体模型的方法,为状态控制研究提供人体模型基础。该模型以运动状况、饮食习惯(包括盐、谷类、蔬菜、水果、肉禽类、蛋类、鱼虾类、豆类、奶类、油脂、动物内脏等的摄入量)、饮酒和吸烟等为输入量,输出量包括与慢性病密切相关的收缩压、舒张压、血糖、心率、BMI等生理参数,并用残差分析检验所建模型的可靠性。通过13例志愿者的受试试验,有9例实测参数和估计参数的符合率超过80%。研究结果表明,所提出的基于神经网络的个体化慢性病危险因素预测模型的建模方法,在总体上是可行的,为个体化设计危险因素控制策略提供了依据慢     

基于Group-Depth U-Net的电子显微图像中神经元结构分割

针对电子显微（EM）成像存在边界有损、模糊不均匀以及神经元结构本身轮廓纹理复杂难以定位的问题，提出一种深层卷积神经网络模型Group-Depth U-Net，以实现EM图像中神经元结构的自动分割。该模型采用更加深层的U-Net架构作为骨架网络，以获取更加丰富的图像特征信息；同时采用分组卷积网络结构，使模型更加高效、防止过拟合，从而提高分割的准确性与效率。公开的数据集实验表明该模型相比U-Net达到了更好的分割准确率。     

基于神经网络法反求耳结构弹性模量

目的通过耳结构的位移反算耳结构弹性模量。方法基于Patran软件建立耳结构有限元模型,使用Mat-lab建立计算耳结构反问题的BP神经网络。对耳结构有限元模型进行频率响应分析,得到鼓膜凸和镫骨足板的位移响应;把位移作为BP神经网络的输入、相对应的结构弹性模量作为输出,对网络进行训练。结果利用训练成熟的BP网络反算出耳结构的弹性模量,相对误差非常小。结论反算结果表明,所使用的反问题方法求解耳结构弹性模量是可行的,可为临床提供确定生物结构力学参数简捷有效的方法。     

基于神经网络的脉搏波信号血压检测算法

脉搏波信号蕴含大量的人体生理与病理信息,与血压的变化息息相关,利用其特征参数可以无创连续检测血压。神经网络因其极强的学习能力、泛化能力以及可以充分逼近任意复杂的非线性关系而被应用于脉搏波血压提取算法中。本研究介绍了脉搏波特征参数,并简述了基于脉搏波特征参数进行血压测量的研究进展,详细叙述了基于神经网络的脉搏波特征参数血压检测算法,最后对不同神经网络模型的优缺点进行分析,并对基于神经网络的脉搏波特征参数血压监测算法的研究方向进行展望。     

Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects. The three ANFIS classifiers were used to detect internal carotid artery conditions (normal, stenosis and occlusion) when two features, resistivity and pulsatility indices, defining changes of internal carotid arterial Doppler waveforms were used as inputs. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of internal carotid artery stenosis and occlusion were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of classification accuracies and the results confirmed that the proposed ANFIS classifiers have some potential in detecting the internal carotid artery stenosis and occlusion. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.