fMRI bold signal analysis using a novel nonparametric statistical method

We present in this article a novel analytical method that enables the application of nonparametric rank-order statistics to fMRI data analysis, since it takes the omnipresent serial correlations (temporal autocorrelations) properly into account. Comparative simulations, using the common General Linear Model and the permutation test, confirm the validity and usefulness of our approach. Our simulations, which are performed with both synthetic and real fMRI data, show that our method requires significantly less computation time than permutation-based methods, while offering the same order of robustness and returning more information about the evoked response when combined with/compared to the results obtained with the common General Lineal Model approach.     

Phase shifting interferometry using a robust parameter estimation method

The paper proposes a maximum-likelihood (ML) method based on spectral estimation theory for the estimation of phase distribution in interferometry in the presence of nonsinusoidal waveforms, noise, and the miscalibration of the piezoelectric device. The proposed method also allows the use of arbitrary phase steps. ML estimators are asymptotically efficient for large number of data samples. The method is complemented well by the incorporation of global search algorithm known as Probabilistic Global Search Lausanne for minimizing the ML function. The performance of the proposed method is studied in the presence of noise.     

鲁棒性有源头枕系统的设计方法*

为了降低舱室内休息区域乘客双耳处的低频宽带噪声,采用最优维纳滤波器的设计准则,设计出一种基于局域空间有源降噪的多通道前馈有源头枕系统。实验对比了人工头在不同位置处和不同转动角度时两通道和四通道有源头枕系统在人耳处的降噪效果;进一步地,针对人头转动时控制效果变差的情况,提出了一种低复杂度的鲁棒性算法。仿真和实验表明,通道数量增加可使该系统在人耳附近获得更大的降噪量,使用鲁棒性算法的头枕系统在人头转动时有更好的控制性能。     

A kernel machine-based fMRI physiological noise removal method

Functional magnetic resonance imaging (fMRI) technique with blood oxygenation level dependent (BOLD) contrast is a powerful tool for noninvasive mapping of brain function under task and resting states. The removal of cardiac- and respiration-induced physiological noise in fMRI data has been a significant challenge as fMRI studies seek to achieve higher spatial resolutions and characterize more subtle neuronal changes. The low temporal sampling rate of most multi-slice fMRI experiments often causes aliasing of physiological noise into the frequency range of BOLD activation signal. In addition, changes of heartbeat and respiration patterns also generate physiological fluctuations that have similar frequencies with BOLD activation. Most existing physiological noise-removal methods either place restrictive limitations on image acquisition or utilize filtering or regression based post-processing algorithms, which cannot distinguish the frequency-overlapping BOLD activation and the physiological noise. In this work, we address the challenge of physiological noise removal via the kernel machine technique, where a nonlinear kernel machine technique, kernel principal component analysis, is used with a specifically identified kernel function to differentiate BOLD signal from the physiological noise of the frequency. The proposed method was evaluated in human fMRI data acquired from multiple task-related and resting state fMRI experiments. A comparison study was also performed with an existing adaptive filtering method. The results indicate that the proposed method can effectively identify and reduce the physiological noise in fMRI data. The comparison study shows that the proposed method can provide comparable or better noise removal performance than the adaptive filtering approach.     

Performance of blind source separation algorithms for fMRI analysis using a group ICA method

Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist; however, the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely, information maximization, maximization of non-Gaussianity, joint diagonalization of cross-cumulant matrices and second-order correlation-based methods, when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study variability among different ICA algorithms, and we propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA and joint approximate diagonalization of eigenmatrices (JADE) all yield reliable results, with each having its strengths in specific areas. Eigenvalue decomposition (EVD), an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for iterative ICA algorithms, it is important to investigate the variability of estimates from different runs. We test the consistency of the iterative algorithms Infomax and FastICA by running the algorithm a number of times with different initializations, and we note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis.     

一种宽容的宽带加权聚焦定向算法

提出一种宽带加权聚焦高分辨定向算法,用于解决宽带阵列信号各频率分量上信噪比不同时的高分辨定向问题.WangH提出的CSM算法假设备频率分量上信噪比相同,而实际情况往往与此不符,因此在应用中性能不理想、本文在详细分析协方差矩阵结构的基础上,导出单源情况下利用协方差矩阵进行各个频率信噪比估计的表达式、然后进一步向多源情况推广.通过对各频率分量进行加权聚焦,最终改善方位估计性能.文中利用计算机仿真、水池实验及实测海洋舰船数据对该方法进行了详细验证,结论认为,加权聚焦算法能有效改善原方法的宽容性,估计精度和分辨率都有明显提高.     

一类Hopf分岔系统的通用鲁棒稳定控制器设计方法

针对一类多项式形式的Hopf分岔系统, 提出了一种鲁棒稳定的控制器设计方法. 使用该方法设计控制器时不需要求解出系统在分岔点处的分岔参数值, 只需要估算出分岔参数的上下界, 然后设计一个参数化的控制器, 并通过Hurwitz判据和柱形代数剖分技术求解出满足上下界条件的控制器参数区域, 最后在得到的这个区域内确定出满足鲁棒稳定的控制器参数值. 该方法设计的控制器是由包含系统状态的多项式构成, 形式简单, 具有通用性, 且添加控制器后不会改变原系统平衡点的位置. 本文首先以Lorenz系统为例说明了控制器的推导和设计过程, 然后以van der Pol振荡系统为例, 进行了工程应用. 通过对这两个系统的控制器设计和仿真, 说明了文中提出的控制器设计方法能够有效地应用于这类Hopf分岔系统的鲁棒稳定控制, 并且具有通用性.     

A robust edge detection method with sub-pixel accuracy

In this paper, a new edge detection approach combining gray-moments operator with smoothing spline algorithm is proposed, which is invariant to additive and multiplicative noises in the image. This approach consists of two steps: firstly, a continuous blurred edge model is obtained using the smoothing spline algorithm in the edge region detected by Sobel operator; then a gray-moment solution is derived for both the one- and two-dimensional situations using the blurred edge model. Testing of this new detection approach demonstrates more robustness against the white Gaussian noise and speckle noise, and run time very close to the gray-moment and space-moment operators. The above advantages indicate this approach is very suitable for on-line accurate detection.     

Input permutation method to detect active voxels in fMRI study

Correctly identifying voxels or regions of interest (ROI) that actively respond to a given stimulus is often an important objective/step in many functional magnetic resonance imaging (fMRI) studies. In this article, we study a nonparametric method to detect active voxels, which makes minimal assumption about the distribution of blood oxygen level-dependent (BOLD) signals. Our proposal has several interesting features. It uses time lagged correlation to take into account the delay in response to the stimulus, due to hemodynamic variations. We introduce an input permutation method (IPM), a type of block permutation method, to approximate the null distribution of the test statistic. Also, we propose to pool the permutation-derived statistics of preselected voxels for a better approximation to the null distribution. Finally, we control multiple testing error rate using the local false discovery rate (FDR) by Efron [Correlation and large-scale simultaneous hypothesis testing. J Am Stat Assoc 102 (2007) 93–103] and Park et al. [Estimation of empirical null using a mixture of normals and its use in local false discovery rate. Comput Stat Data Anal 55 (2011) 2421–2432] to select the active voxels.     

A SVM-based quantitative fMRI method for resting-state functional network detection

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.     

High-resolution imaging without iteration: a fast and robust method for breast ultrasound tomography

Breast ultrasound tomography has the potential to improve the cost, safety, and reliability of breast cancer screening and diagnosis over the gold-standard of mammography. Vital to achieving this potential is the development of imaging algorithms to unravel the complex anatomy of the breast and its mechanical properties. The solution most commonly relied upon is time-of-flight tomography, but this exhibits low resolution due to the presence of diffraction effects. Iterative full-wave inversion methods present one solution to achieve higher resolution, but these are slow and are not guaranteed to converge to the correct solution. Presented here is HARBUT, the hybrid algorithm for robust breast ultrasound tomography, which utilizes the complementary strengths of time-of-flight and diffraction tomography resulting in a direct, fast, robust and accurate high resolution method of reconstructing the sound speed through the breast. The algorithm is shown to produce accurate reconstructions with realistic data from a complex three-dimensional simulation, with masses as small as 4 mm being clearly visible.     

A robust method for the synthesis of colloidal PbS nanosheets

In the synthesis of colloidal PbS nanosheets, acetic acid – either injected externally or produced during the reaction – has a significant effect on the growth of the nanosheets. When the acetic acid to lead molar ratio is above 1:8, no nanosheets are observed in the product. By replacing lead acetate with lead oxide to prepare the lead precursor for the reaction, the effect of acetic acid is avoided, resulting in a robust synthesis with nearly 100% success rate. In the new synthesis, the purity of trioctylphosphine (the co‐solvent for sulfur precursor) has no significant effect on the formation of nanosheets. Thickness tunability is achieved in the acetate‐free synthesis by tuning the reaction temperature. (© 2016 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)     

稳健宽带波束形成器设计的低阶统计量法*

由于传声器阵列通常对阵元失配误差较为敏感,因此稳健波束形成器的设计已成为传声器阵列处理领域的研究热点之一。概率密度法是目前传声器阵列稳健波束形成器设计中的一类重要方法,但该方法所需的阵元失配误差的概率密度信息在实际中较难获取。针对这一问题,本文研究了基于阵元失配误差低阶统计量的稳健波束形成器设计方法,该方法仅利用在实际中较易获取的阵元失配误差的一阶和二阶统计量信息。本文分别研究了基于阵元失配误差低阶统计量的固定权和变加权最小二乘波束形成器设计,给出了两种波束形成器的相关设计理论。理论和仿真分析表明,在小误差条件下,低阶统计量法所设计的波束形成器仍保持与概率密度法相当的性能。     

A robust method for automated background subtraction of tissue fluorescence

This paper introduces a new robust method for the removal of background tissue fluorescence from Raman spectra. Raman spectra consist of noise, fluorescence and Raman scattering. In order to extract the Raman scattering, both noise and background fluorescence must be removed, ideally without human intervention and preserving the original data. We describe the rationale behind our robust background subtraction method, determine the parameters of the method and validate it using a Raman phantom against other methods currently used. We also statistically compare the methods using the residual mean square (RMS) with a fluorescence‐to‐signal (F/S) ratio ranging from 0.1 to 1000. The method, ‘adaptive minmax’, chooses the subtraction method based on the F/S ratio. It uses multiple fits of different orders to maximize each polynomial fit. The results show that the adaptive minmax method was significantly better than any single polynomial fit across all F/S ratios. This method can be implemented as part of a modular automated real‐time diagnostic in vivo Raman system. Copyright © 2007 John Wiley & Sons, Ltd.     

An efficient block variant of robust structured multifrontal factorization method

Based on the two-dimensional three-temperature （2D3T） radiation diffusion equations and its discrete system, using the block diagonal structure of the three-temperature matrix, the reordering and symbolic decomposition parts of the RSMF method are replaced with corresponding block operation in order to improve the solution efficiency. We call this block form method block RSMF （in brief, BRSMF） method. The new BRSMF method not only makes the reordering and symbolic decomposition become more effective, but also keeps the cost of numerical factorization from increasing and ensures the precision of solution very well. The theoretical analysis of the computation complexity about the new BRSMF method shows that the solution efficiency about the BRSMF method is higher than the original RSMF method. The numerical experiments also show that the new BRSMF method is more effective than the original RSMF method.     

An accurate and robust numerical method for micromagnetics simulations

We propose a new robust, accurate, and fast numerical method for solving the Landau–Lifshitz equation which describes the relaxation process of the magnetization distribution in ferromagnetic material. The proposed numerical method is second-order accurate in both space and time. The approach uses the nonlinear multigrid method for handling the nonlinearities at each time step. We perform numerical experiments to show the efficiency and accuracy of the new algorithm on two- and three-dimensional space. The numerical results show excellent agreements with exact analytical solutions, the second-order accuracy in both space and time, and the energy conservation or dissipation property.     

一种稳健的恒定束宽波束形成方法

考虑到信号的方向信息存在误差时,宽带聚焦变换波束形成方法的稳健性会急剧下降,为了克服这种缺点,将稳健的二阶锥规划波束形成用于聚焦变换方法中,提出一种稳健宽带恒定束宽波束形成方法并将其应用于声呐探测。二阶锥规划波束形成是通过对阵列流形进行约束求解得到准确的权值,从而减少聚焦数据误差,避免估计畸变,得到恒定束宽,提高了聚焦变换恒定束宽波束形成器的性能。借助计算机对圆环形传感器阵列在宽频段内进行恒定束宽波束优化设计,并进行了实验研究,结果表明该方法对信号信息误差有很好的宽容性,能实现我们期望得到的恒定束宽波束图,能应用于实际的声呐探测中。并且比传统的二阶锥恒定束宽方法有更高的稳健性。     

Measurement and characterization of the human spinal cord SEEP response using event-related spinal fMRI

Although event-related fMRI is able to reliably detect brief changes in brain activity and is now widely used throughout systems and cognitive neuroscience, there have been no previous reports of event-related spinal cord fMRI. This is likely attributable to the various technical challenges associated with spinal fMRI (e.g., imaging a suitable length of the cord, reducing image artifacts from the vertebrae and intervertebral discs, and dealing with physiological noise from spinal cord motion). However, with many of these issues now resolved, the largest remaining impediment for event-related spinal fMRI is a deprived understanding of the spinal cord fMRI signal time course. Therefore, in this study, we used a proton density-weighted HASTE sequence, with functional contrast based on signal enhancement by extravascular water protons (SEEP), and a motion-compensating GLM analysis to (i) characterize the SEEP response function in the human cervical spinal cord and (ii) demonstrate the feasibility of event-related spinal fMRI. This was achieved by applying very brief (1 s) epochs of 22°C thermal stimulation to the palm of the hand and measuring the impulse response function. Our results suggest that the spinal cord SEEP response (time to peak ≈8 s; FWHM ≈4 s; and probably lacking pre- and poststimulus undershoots) is slower than previous estimates of SEEP or BOLD responses in the brain, but faster than previously reported spinal cord BOLD responses. Finally, by detecting and mapping consistent signal-intensity changes within and across subjects, and validating these regions with a block-designed experiment, this study represents the first successful demonstration of event-related spinal fMRI.     

Development and characterization of rodent cardiac phantoms: comparison with in vivo cardiac imaging

The increasing availability of rodent models of human cardiovascular disease has led to a need to translate noninvasive imaging techniques such as magnetic resonance imaging (MRI) from the clinic to the animal laboratory. The aim of this study was to develop phantoms simulating the short-axis view of left ventricular motion of rats and mice, thus reducing the need for live animals in the development of MRI. Cylindrical phantoms were moulded from polyvinyl alcohol (PVA) Cryogel and attached via stiff water-filled tubing to a gear pump. Pulsatile distension of the phantoms was effected by suitable programming of the pump. Cine MRI scanning was carried out at 7 T and compared with in vivo rodent cardiac imaging. Suitable pulsatile performance was achieved with phantoms for which the PVA material had been subjected to two freeze-thaw cycles, resulting in T1 and T2 relaxation time constants of 1656±124 ms and 55±10 ms, respectively. For the rat phantom operating at 240 beats per min (bpm), the dynamic range of the outer diameter was from 10.3 to 12.4 mm with the wall thickness varying between 1.9 and 1.2 mm. Corresponding figures for the mouse phantom at 480 bpm were outer diameter range from 5.4 to 6.4 mm and wall thickness from 1.5 to 1.2 mm. Dynamic cardiac phantoms simulating rodent left ventricular motion in the short-axis view were successfully developed and compared with in vivo imaging. The phantoms can be used for future development work with reduced need of live animals.