Diagnostic value of breast MRI for predicting metastatic axillary lymph nodes in breast cancer patients: diffusion-weighted MRI and conventional MRI

Purposes

To evaluate the diagnostic value of diffusion-weighted MRI (DWI) and combination of conventional MRI and DWI to predict metastatic axillary lymph nodes in breast cancer.

Materials and methods

Two hundred fifty-two breast cancer patients with 253 axillae were included. The morphological parameters on axial T2-weighted images without fat saturation and apparent diffusion coefficient (ADC) values were retrospectively analyzed. An independent t-test/chi-square test and receiver operating characteristics (ROC) curve analysis were used.

Results

On conventional MRI, short and long axis length, maximal cortical thickness, relative T2 value, loss of fatty hilum (p < 0.001 for each), and eccentric cortical thickening (p < 0.003) were statistically significantly different between the metastatic and nonmetastatic groups. The short axis to long axis ratio was not a statistically significant parameter. The ADC value was significantly different between the 2 groups, with an AUC that was higher than that of conventional MR parameters (AUC, 0.815; threshold, ≤ 0.986 × 10–3 mm²/sec; sensitivity, 75.8%; specificity, 83.9%). Using the adopted thresholds for each parameter, a total number of findings suggesting malignancy of 4 or higher was determined as the threshold, with high specificity (90.1%).

Conclusion

Using conventional MRI and DWI, we can evaluate the axilla in breast cancer with high specificity.     

A potential integration method for Birkhoffian system

This paper is intended to apply the potential integration method to the differential equations of the Birkhoffian system. The method is that, for a given Birkhoffian system, its differential equations are first rewritten as 2n first-order differential equations. Secondly, the corresponding partial differential equations are obtained by potential integration method and the solution is expressed as a complete integral. Finally, the integral of the system is obtained.     

A progressive processing method for breast cancer detection via UWB based on an MRI-derived model

Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages： primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image （MRI）. Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.     

A method of integration for the inhomogeneous layer

The inhomogeneous layer of an arbitrary index profile is replaced by the conventional multilayer of homogeneous elements. When the number of layers converges to infinity, the solution is performed exactly using the method of rational functions previously described by the author. As a result the reflection coefficient is expressed as the ratio of two infinite power series in (2/)j, the coefficients being defined as multiple integrals of increasing order. The Drude formulae follow as a first approximation. The paper is dedicated to the memory of Dr. A. Vaíek, Professor of the J. E. Purkyn University in Brno, who died on November 16, 1966.     

高分辨率快速数字化光声CT乳腺肿瘤成像

提出了一种基于聚焦线性阵列探测器的快速光声计算机断层成像技术（光声CT）.在光声二维图像重建中,根据阵列探测器机械扫描和电子扫描相结合的组合扫描模式,提出了改进的有限场滤波反投影重建算法.一方面该算法适合多元探测器旋转扫描模式,另一方面探测器的指向性函数作为反投影的权重因子提高了系统的横向分辨率.同时,该成像系统还利用柱面声透镜实现Z轴方向上的聚焦扫描以实现三维层析成像.实验中,这套成像系统空间分辨率达到0.2mm,Z轴方向分辨率为1.5mm,扫描一幅二维图像仅需150s,得到 关键词： 光声CT 有限场滤波反投影算法 声透镜聚焦 乳腺肿瘤检测     

A subfringe integration method for multiple-beam Fizeau fringe analysis

Some aspects concerning the subfringe integration method in interferogram analysis have been investigated and modified. The modified algorithm, introduced in this paper, is capable of reconstructing the phase in the presence of noise or errors in carrier frequency. The subfringe integration method was applied to analyze two computer simulated patterns of equispaced Fizeau fringes using N bucket integration. Also, it is used to analyze the multiple-beam Fizeau fringe. The refractive index profile of polyethylene fiber is obtained by using two methods, subfringe integration method, and Fourier transform method. A comparison between the obtained results using the maintained methods is presented.     

A double constrained robust capon beamforming based imaging method for early breast cancer detection

Ultra-wideband （UWB） microwave images are proposed for detecting small malignant breast tumors based on the large contrast of electric parameters between a malignant tumor and normal breast tissue. In this study, an antenna array composed of 9 antennas is applied to the detection. The double constrained robust capon beamforming （DCRCB） algorithm is used for reconstructing the breast image due to its better stability and high signal-to-interference-plus-noise ratio （SINR）. The successful detection of a tumor of 2 mm in diameter shown in the reconstruction demonstrates the robustness of the DCRCB beamforming algorithm. This study verifies the feasibility of detecting small breast tumors by using the DCRCB imaging algorithm.     

基于信息融合的模拟电路故障的特征提取与融合方法

在模拟电路故障诊断中,故障特征的提取是一个非常重要的环节,其提取结果的好坏将直接影响最终的诊断正确率。对现有文献研究发现,每种特征提取方法单独使用时都有一定的局限性,为了能够更加充分的提取模拟电路故障特征,提出了小波包分析与主元分析并行应用的方法,并将两种方法提取的特征向量依据不同规则进行了三种类型的融合,方便对比实验。为获取最优小波特征,提出了特征偏离度,并以此为标准选择最优小波基。最后,通过设计一种改进的神经网络分类器模型,将融合后的三种特征向量送入其中进行仿真验证,得出最终诊断结果。结果表明,该方法能够有效克服单一特征提取方法提取不充分的缺点,提高故障诊断的正确率,并且融合因子 适中时诊断正确率最高。     

Water-selective spectral-spatial contrast-enhanced breast MRI for cancer detection in patients with extracapsular and injected free silicone

OBJECTIVE: This study investigates the use of contrast-enhanced, T1-weighted, water-selective spectral-spatial 3D gradient echo magnetic resonance imaging (MRI) with magnetization transfer (3DSSMT) for detecting breast cancer in patients with intraparenchymal silicone. CONCLUSION: Water-selective 3DSSMT provides superior fat and silicone suppression in patients with free silicone as compared with conventional fat saturation. It enables direct, high-quality, high-spatial-resolution, T1-weighted breast MRI of contrast enhancement without the need for subtraction processing and aids diagnosis of cancer in the breast with free silicone.     

近红外影像诊断的物理基础和乳腺癌的早期诊断

乳腺癌的早期诊断是提高妇女生活品质、降低死亡率的重要环节.作为乳腺癌主流诊断技术的钼靶软X射线诊断(mammography)对人体具有射线损伤,而近红外影像诊断(CDI)是无损伤、可反复检查的新诊断技术,但假阳性率偏高,目前受到医学界的质疑.文章从影像诊断的物理基础入手,讨论了癌症病变组织的生长、转移和血管分布异常对于近红外光透过率的影响,分析了CDI的优缺点,并从理论上得出了该技术满足影像诊断三要素的结论.文章作者还评述了市场竞争因素对于这一技术发展的影响,最后简单提及物理学在与生命科学交叉领域的研究课题和发展机会.     

近红外影像诊断的物理基础和乳腺癌的早期诊断

乳腺癌的早期诊断是提高妇女生活品质、降低死亡率的重要环节.作为乳腺癌主流诊断技术的钼靶软X射线诊断（mammography）对人体具有射线损伤,而近红外影像诊断（CDI）是无损伤、可反复检查的新诊断技术,但假阳性率偏高,目前受到医学界的质疑.文章从影像诊断的物理基础入手,讨论了癌症病变组织的生长、转移和血管分布异常对于近红外光透过率的影响,分析了CDI的优缺点,并从理论上得出了该技术满足影像诊断三要素的结论.文章作者还评述了市场竞争因素对于这一技术发展的影响,最后简单提及物理学在与生命科学交叉领域的研究课题和发展机会.     

基于流形学习的声目标特征提取方法研究

为解决目前声目标识别面临的鲁棒性不足问题,提出将流形学习应用到声目标的特征提取中,在经典流形学习算法的基础上,研究讨论了目标声信号频域中存在的低维流形,通过两种实际的地面和低空飞行声目标数据集进行对比识别实验,分析了基于流形学习的声目标特征提取方法的性能,结果表明基于流形学习的特征提取方法可以发现声信号的本质特征,提高了声目标识别系统的准确性和鲁棒性. 关键词： 声目标识别 特征提取 流形学习     

A new multi-spectral feature level image fusion method for human interpretation

Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level. However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing paradigm. To test our method, we compared human observer performance in a three-task experiment using MSSF against two established methods: averaging and principle components analysis (PCA), and against its two source bands, visible and infrared. The three tasks that we studied were: (1) simple target detection, (2) spatial orientation, and (3) camouflaged target detection. MSSF proved superior to the other fusion methods in all three tests; MSSF also outperformed the source images in the spatial orientation and camouflaged target detection tasks. Based on these findings, current speculation about the circumstances in which multi-spectral image fusion in general and specific fusion methods in particular would be superior to using the original image sources can be further addressed.     

A new method for weak fault feature extraction based on piecewise mixed stochastic resonance

In a continuous bistable system, when the input signal is continuously increased, the output signal tends to be stable and no longer increases. At this time, the weak signal under strong background noise is difficult to be extracted, which means saturation occurs. Aiming at the saturation characteristics of stochastic resonance (SR), the proposed piecewise nonlinear bistable system (PNBSR) model has achieved certain results. However, the potential barrier in the middle of the PNBSR method still completely uses the potential function of classical bistable stochastic resonance (CBSR). There is no fundamental solution to the fourth-order limitation. This paper explores an improved piecewise mixed stochastic resonance (PMSR) potential model. The fourth-order potential function that restricts particle motion in CBSR is improved to a piecewise second-order potential function. This potential function subverts the shape of the traditional potential function and presents a symmetrical double-hook shape. Based on PMSR model, this paper uses particle swarm optimization (PSO) to select system parameters and elaborates the characteristics of the potential function curve in detail. Under the same conditions, the output signal-to-noise ratio (SNR) curve of the improved system is generally higher than that of the CBSR and PNBSR systems. Experiments on bearings and gears show that the proposed method can accurately extract weak fault features, and the effect is better than the PNBSR method.     

A hierarchical feature graph matching method for recognition of complex human activities

Complex human activities in natural videos are often composed of several atomic-level actions organized hierarchically. We should not only consider the appearance variability of these action units, but also model the spatiotemporal relationships between them when recognizing such high-level complex activities. In this paper, we focus on the problem of recognition of complex human activities in an example-based video retrieval framework and propose a new method based on hierarchical feature-graph matching. A video depicting an activity is represented as a high-level feature graph (HLFG), and each node of the HLFG is a mid-level feature graph (MLFG) constructed on a local collection of spatiotemporal interest points. MLFG, the first level of our two-level graph structure, describes the local feature contents and spatiotemporal arrangements of interest points. HLFG, the second level, describes the appearance variability and spatiotemporal arrangements of atomic-level actions in a way. Final recognition is accomplished by matching the HLFGs of the query and test videos, and matching two HLFGs involves matching the MLFGs between them. We use an efficient spectral method to solve these two graph-matching problems. Our method does not require any preprocessing and gives reasonable results with even a small number of query examples. We evaluate our approach with one publicly available complex human activity dataset and achieve results comparable to other systems that have studied this problem.     

联合特征选择与改进集成学习模型的海底底质分类

针对侧扫声呐获取类型复杂的海底底质数据分类问题,提出联合特征选择与改进Stacking模型的数据自驱动分类方法。该方法首先在海底散射数据多域态特征的基础上采用ReliefF算法提取有效的低维度特征,然后将人工鱼群算法与Stacking模型结合形成改进集成学习分类器,完成海底底质分类。海上数据处理结果表明该方法可对多种海底底质类型进行分类,分类准确率、Kappa系数和F1-score分别达到85.55%,0.857和0.887,证明了该方法的有效性。     

Monte Carlo simulations of an innovative molecular breast imaging system for the small breast cancer diagnosis using GATE

ABSTRACT

This paper draws attention to the study of performance of a new Molecular Breast Imaging (MBI) device, whose purpose is the early diagnosis of breast cancer, using Monte Carlo simulations. MBI provides functional and specific information that are more appropriated to dense breasts. Two asymmetric heads with different types of collimators, facing each other in anti-parallel viewing direction, characterize the system. Detectors and phantoms, together with the data taking procedure, are shortly reported. Monte Carlo simulations using the GATE (GEANT4 Application for Tomographic Emission) simulation toolkit have been implemented to evaluate the optimal detector configuration, in terms of sensitivity and spatial resolution, and also to reproduce the real experimental data. The device can be used both in spot compression and in Limited Angle Tomography (LAT); in the latter configuration one detector head with pinhole collimator is able to rotate around the breast in order to diagnose and localized the small tumors.     

A deep learning method for solving third-order nonlinear evolution equations

It has still been difficult to solve nonlinear evolution equations analytically. In this paper, we present a deep learning method for recovering the intrinsic nonlinear dynamics from spatiotemporal data directly. Specifically, the model uses a deep neural network constrained with given governing equations to try to learn all optimal parameters. In particular, numerical experiments on several third-order nonlinear evolution equations, including the Korteweg–de Vries (KdV) equation, modified KdV equation, KdV–Burgers equation and Sharma–Tasso–Olver equation, demonstrate that the presented method is able to uncover the solitons and their interaction behaviors fairly well.     

A deep learning method for solving high-order nonlinear soliton equations

We propose an effective scheme of the deep learning method for high-order nonlinear soliton equations and explore the influence of activation functions on the calculation results for higher-order nonlinear soliton equations. The physics-informed neural networks approximate the solution of the equation under the conditions of differential operator, initial condition and boundary condition. We apply this method to high-order nonlinear soliton equations, and verify its efficiency by solving the fourth-order Boussinesq equation and the fifth-order Korteweg–de Vries equation. The results show that the deep learning method can be used to solve high-order nonlinear soliton equations and reveal the interaction between solitons.