Principal component analysis‐assisted energy dispersive X‐ray fluorescence spectroscopy for non‐invasive quality assurance characterization of complex matrix materials

The analytical challenges in direct quality assurance analysis of complex matrices (extreme matrix effects, spectral overlap, poor signal‐to‐noise ratio (SNR) for trace analytes, ‘dark matrix’, imprecise geometry, need for sample integrity) by energy dispersive X‐ray fluorescence (EDXRF) spectrometry necessitate development of novel techniques for material characterization. We demonstrate the utility of principal component analysis (PCA) in isotope‐excited EDXRF spectrometry of a complex matrix (in this case lubricating oil) in the context of a newly developed EDXRF and scattering (EDXRFS) technique. Lubricating oil quality may be interpreted in terms of its viscosity, anti‐wear, anti‐oxidation, and anti‐rust properties, which are detectable via B, Ca, Mg, Zn, Fe, Na additives (quality markers). Our method involves simultaneous non‐invasive acquisition of both fluorescence and scatter spectra from samples held in a propylene dish, and their modeling in a reduced multidimensional space for an interpretable overview that is analytically more useful than, and complementary to, fluorescence peak‐based quantitation of the additives; by this method, only Fe and Zn are directly detectable, but with SNR of the fluorescence peak 15–20 times poorer compared with analysis after sample digestion. Although Fe and Zn cannot distinguish the various lubricating oil brands, it can differentiate authentic from adulterated. The method was however found to be analytically useful when combined with PCA: various brands of lubricating oil were discriminated in addition to the detection of adulteration. PCA processing of the spectra showed that the most important quality assurance spectral signature information responsible for the success is contained in the scatter region (low‐Z elements). Evaluation of the performance of the method with respect to SNR (i.e. analysis time and therefore speed) showed that there was no significant difference in method performance of analysis live time in the range 100–1000 s, showing proof of concept for rapid characterization of complex matrix materials by PCA‐assisted EDXRFS. Copyright © 2012 John Wiley & Sons, Ltd.     

Effect of signal intensity normalization on the multivariate analysis of spectral data in complex ‘real‐world’ datasets

Spectral signal intensities, especially in ‘real‐world’ applications with nonstandardized sample presentation due to uncontrolled variables/factors, commonly require additional spectral processing to normalize signal intensity in an effective way. In this study, we have demonstrated the complexity of choosing a normalization routine in the presence of multiple spectrally distinct constituents by probing a dataset of Raman spectra. Variation in absolute signal intensity (90.1% of total variance) of the Raman spectra of these complex biological samples swamps the variation in useful signals (9.4% of total variance), degrading its diagnostic and evaluative potential. Using traditional spectral band choices, it is shown that normalization results are more complex than generally encountered in traditionally designed sample sets investigating limited chemical species. We demonstrate that no choice of a single band proves to be appropriate for predicting all the reference parameters, instead requiring a tailored normalization routine for each parameter. Of the reference parameters studied in the chosen system, signals from pathogenic adducts in ocular tissues called advanced glycation endproducts were most prominent when normalizing about the 1550–1690 cm⁻¹ region of the spectrum (17.5% of total variance, compared with 0.3% for unnormalized), while prediction of pentosidine and gender were optimized by normalization about the 1570 (R² = 0.97 vs 0.57 for unnormalized) and 1003 cm⁻¹ (p < 0.0000001 vs p < 0.01 for unnormalized) bands, respectively. The data obtained point to the extreme sensitivity of multivariate analysis to signal intensity normalization. Some general guidelines for making appropriate band choices are given, including the use of peak‐finding routines. Copyright © 2008 John Wiley & Sons, Ltd.     

Estimation of signal backgrounds on multivariate loadings improves model generation in face of complex variation in backgrounds and constituents

The present study is designed to understand further implications of using multivariate loadings for the correction of background signal, which has previously been shown to be highly reproducible even for very low quality signals. Singular value decomposition (SVD)‐based background correction was compared with the traditional per‐signal paradigm for a biomedical dataset to generate qualitative and quantitative models. The qualitative effect on a principal component analysis model and the quantitative effect on a partial least square regression model were assessed for these background correction methods. The chosen quantitative parameter was the concentration of a pathologically relevant protein modification, pentosidine. Of the approaches tested, the SVD‐based paradigm provided the regression model with the highest correlations, highest accuracy (lowest standard error of prediction) and repeatability (lowest sampling error). Contrasted against the traditional approaches, it was determined that the improved accuracy and repeatability of the SVD‐based approach arises from its ability to simultaneously handle very complex background shapes alongside the complex variation in biochemical species that resulted in Raman signals with incompatible baseline regions. A better understanding of the interaction of SVD‐based baseline correction, and data will give the reader more insight into the potential applicability of the procedure for other datasets. Copyright © 2012 John Wiley & Sons, Ltd.     

Truncation artifact reduction in spectroscopic imaging using a dual-density spiral k-space trajectory

Truncation artifacts arise in magnetic resonance spectroscopic imaging (MRSI) of the human brain due to limited coverage of k-space necessitated by low SNR of metabolite signal and limited scanning time. In proton MRSI of the head, intense extra-cranial lipid signals “bleed” into brain regions, thereby contaminating signals of metabolites therein. This work presents a data acquisition strategy for reducing truncation artifact based on extended k-space coverage achieved with a dual-SNR strategy. Using the fact that the SNR in k-space increases monotonically with sampling density, dual-SNR is achieved in an efficient manner with a dual-density spiral k-space trajectory that permits a smooth transition from high density to low density. The technique is demonstrated to be effective in reducing “bleeding” of extra-cranial lipid signals while preserving the SNR of metabolites in the brain.     

Radiation dose and image quality in K‐edge subtraction computed tomography of lung in vivo

K‐edge subtraction computed tomography (KES‐CT) allows simultaneous imaging of both structural features and regional distribution of contrast elements inside an organ. Using this technique, regional lung ventilation and blood volume distributions can be measured experimentally in vivo. In order for this imaging technology to be applicable in humans, it is crucial to minimize exposure to ionizing radiation with little compromise in image quality. The goal of this study was to assess the changes in signal‐to‐noise ratio (SNR) of KES‐CT lung images as a function of radiation dose. The experiments were performed in anesthetized and ventilated rabbits using inhaled xenon gas in O₂ at two concentrations: 20% and 70%. Radiation dose, defined as air kerma (K_a), was measured free‐in‐air and in a 16 cm polymethyl methacrylate phantom with a cylindrical ionization chamber. The dose free‐in‐air was varied from 2.7 mGy to 8.0 Gy. SNR in the images of xenon in air spaces was above the Rose criterion (SNR > 5) when K_a was over 400 mGy with 20% xenon, and over 40 mGy with 70% xenon. Although in human thorax attenuation is higher, based on these findings it is estimated that, by optimizing the imaging sequence and reconstruction algorithms, the radiation dose could be further reduced to clinically acceptable levels.     

Repetition time and flip angle variation in SPRITE imaging for acquisition time and SAR reduction

Single point imaging methods such as SPRITE are often the technique of choice for imaging fast-relaxing nuclei in solids. Single point imaging sequences based on SPRITE in their conventional form are ill-suited for in vivo applications since the acquisition time is long and the SAR is high. A new sequence design is presented employing variable repetition times and variable flip angles in order to improve the characteristics of SPRITE for in vivo applications. The achievable acquisition time savings as well as SAR reductions and/or SNR increases afforded by this approach were investigated using a resolution phantom as well as PSF simulations. Imaging results in phantoms indicate that acquisition times may be reduced by up to 70% and the SAR may be reduced by 40% without an appreciable loss of image quality.     

基于VSLMS-Rake模型的高阶多径BOC信号捕获方法*

针对高阶多径BOC信号捕获存在模糊性的问题,提出了一种基于VSLMS-Rake模型的捕获方法。该方法首先将接收的多径BOC信号经过VSLMS-Rake模型,然后再对处理后的信号进行功率谱估计。通过二者结合,不仅可以抑制多径干扰增强信号能量,还可以减小估计误差提高捕获概率,最终实现对高阶多径BOC信号的精确捕获。理论和仿真表明,在较低信噪比下,该方法优于现有其它捕获算法。     

Improving the time-resolution and signal-to-noise ratio of ultrasonic NDE signals

Ultrasonic testing signals are sometimes hard to interpret because of low signal-to-noise ratio (SNR) or overlapping echoes. The combination of Wiener filtering and autoregressive spectral extrapolation has proven to be capable of improving the SNR and time resolution. However, these signal processing techniques are not sufficiently robust to be used in industrial non-destructive testing applications. This is mostly due to arbitrary manner of selection of the signal processing parameters associated with these techniques. In this paper, a new approach, which eliminates the need for arbitrary assignment of some of the parameters is described. This new approach is more robust and suitable for practical applications, and is demonstrated by considering both simulated and experimental signals.     

Towards the in vivo prediction of fragility fractures with Raman spectroscopy

Fragility fractures, those fractures which result from low level trauma, have a large and growing socio‐economic cost in countries with aging populations. Bone‐density‐based assessment techniques are vital for identifying populations that are at higher risk of fracture, but do not have high sensitivity when it comes to identifying individuals who will go on to have their first fragility fracture. We are developing Spatially Offset Raman Spectroscopy (SORS) as a tool for retrieving chemical information from bone non‐invasively in vivo. Unlike X‐ray‐based techniques SORS can retrieve chemical information from both the mineral and protein phases of the bone. This may enable better discrimination between those who will or will not go on to have a fragility fracture because both phases contribute to bone's mechanical properties. In this study we analyse excised bone with Raman spectroscopy and multivariate analysis, and then attempt to look for similar Raman signals in vivo using SORS. We show in the excised work that on average, bone fragments from the necks of fractured femora are more mineralised (by 5–10%) than (cadaveric) non‐fractured controls, but the mineralisation distributions of the two cohorts are largely overlapped. In our in vivo measurements, we observe similar, but as yet statistically underpowered, differences. After the SORS data (the first SORS measurements reported of healthy and diseased human cohorts), we identify methodological developments which will be used to improve the statistical significance of future experiments and may eventually lead to more sensitive prediction of fragility fractures. © 2015 The Authors. Journal of Raman Spectroscopy Published by John Wiley & Sons, Ltd.     

Analytically exact correction scheme for signal extraction from noisy magnitude MR signals

An analytically exact method is proposed to extract the signal intensity and the noise variance simultaneously from noisy magnitude MR signals. This method relies on a fixed point formula of signal-to-noise ratio (SNR) and a correction factor. The correction factor, which is a function of SNR, establishes a fundamental link between the variance of the magnitude MR signal and the variance of the underlying Gaussian noise in the two quadrature channels. A more general but very similar method is developed for parallel signal acquisitions with multiple receiver coils. In the context of MR imaging, the proposed method can be carried out on a pixel-by-pixel basis if the mean and the standard deviation of the magnitude signal are available.     

SNR and functional sensitivity of BOLD and perfusion-based fMRI using arterial spin labeling with spiral SENSE at 3 T

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies using parallel imaging to reduce the readout window have reported a loss in temporal signal-to-noise ratio (SNR) that is less than would be expected given a purely thermal noise model. In this study, the impact of parallel imaging on the noise components and functional sensitivity of both BOLD and perfusion-based fMRI data was investigated. Dual-echo arterial spin labeling data were acquired on five subjects using sensitivity encoding (SENSE), at reduction factors (R) of 1, 2 and 3. Direct recording of cardiac and respiratory activity during data acquisition enabled the retrospective removal of physiological noise. The temporal SNR of the perfusion time series closely followed the thermal noise prediction of a √R loss in SNR as the readout window was shortened, with temporal SNR values (relative to the R=1 data) of 0.72 and 0.56 for the R=2 and R=3 data, respectively, after accounting for physiological noise. However, the BOLD temporal SNR decreased more slowly than predicted even after accounting for physiological noise, with relative temporal SNR values of 0.80 and 0.63 for the R=2 and R=3 data, respectively. Spectral analysis revealed that the BOLD trends were dominated by low-frequency fluctuations, which were not dominant in the perfusion data due to signal processing differences. The functional sensitivity, assessed using mean F values over activated regions of interest (ROIs), followed the temporal SNR trends for the BOLD data. However, results for the perfusion data were more dependent on the threshold used for ROI selection, most likely due to the inherently low SNR of functional perfusion data.     

面阵CCD信号采集系统的噪声抑制

分析了CCD输出噪声及其一般抑制方法,提出了一种基于面阵CCD信号采集系统的噪声抑制方法。设计了CCD信号采集系统的噪声抑制电路和处理电路,应用于CCD442A型面阵CCD;并使用积分球对采集系统进行辐射定标,计算得到系统的信噪比。仿真和辐射定标实验表明,该面阵CCD信号采集系统具有相关双采样和暗电平校正功能,抑制了CCD输出信号的复位噪声和暗电流噪声;在中等照度条件下,系统信噪比达到40dB。     

Lidar signal de-noising based on wavelet trimmed thresholding technique

Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.     

1.5 T磁共振化学交换饱和转移成像的影响因素分析

本文探讨1.5 T磁共振化学交换饱和转移（Chemical Exchange Saturation Transfer,CEST）成像的影响因素.通过试管模型和临床病例,采用GE Signa HDe 1.5 T磁共振成像（Magnetic Resonance Imaging,MRI）扫描仪分别进行不同矩阵、激励次数、翻转角、磁化传递翻转角的CEST成像对比分析,以及不同激励次数、磁化传递翻转角的Z谱分析,并从成像组织、成像设备、成像技术等方面对原始图信号、酰胺质子转移（Amide Proton Transfer,APT）信号及Z谱进行分析研究.实验结果表明1.5 T MRI扫描仪的CEST图像信噪比相对较低,且磁场稳定性及均匀度影响了CEST成像的效果.在其他参数不变的情况下,降低采集矩阵和增加激励次数与翻转角可以增加原始图像信噪比.磁化传递翻转角为105°时,CEST成像效果最好.激励次数为2、磁化传递翻转角为105°时,所得数据符合组织Z谱情况.模型Z谱在磁化传递频率为-294~-194 Hz范围可显示30%谷氨酸（Glu）、碘剂（I₃₂₀）、纯水（H₂O）、肌酸（Cr）的信号差异,与H₂O差异最大处在-244~-214 Hz.原始图像信号30% I₃₂₀明显高于Glu、H₂O、Cr,Cr略低于Glu,APT图Cr略低于Glu.25例脑肿瘤的APT图呈高信号、12例脑梗塞的APT图呈低信号,CEST原始图像均可区分病变区域.有12例因采集时间、患者配合情况、环境及室温等影响导致CEST成像的失败.由此得出1.5 T场强下,CEST技术受到成像组织、设备、技术等因素的影响,需要进行多方面优化.在保证磁场稳定性及均匀度的情况下,优化参数的CEST成像和Z谱成像可以区分代谢物及其浓度.     

Real‐time image‐content‐based beamline control for smart 4D X‐ray imaging

Real‐time processing of X‐ray image data acquired at synchrotron radiation facilities allows for smart high‐speed experiments. This includes workflows covering parameterized and image‐based feedback‐driven control up to the final storage of raw and processed data. Nevertheless, there is presently no system that supports an efficient construction of such experiment workflows in a scalable way. Thus, here an architecture based on a high‐level control system that manages low‐level data acquisition, data processing and device changes is described. This system is suitable for routine as well as prototypical experiments, and provides specialized building blocks to conduct four‐dimensional in situ, in vivo and operando tomography and laminography.     

Effects of laser excitation wavelength and optical mode on Raman spectra of human fresh colon,pancreas, and prostate tissues

Early cancer detection is the central and most important factor for allowing successful treatment and resultant positive long‐term patient outcomes. Recently, optical techniques have been applied to this purpose, although each has inherent limitations. In particular, Raman spectroscopy applied in the pathological diagnosis of cancerous tissues has received increasing attention, with the merit of being highly sensitive to the biochemical alterations in tissue compositions and applicable in vivo. Nevertheless, its application has been impeded by the high background intensity, which masks the Raman signal of biological molecules. In this work, the influence of laser excitation wavelength (785 vs. 830 nm) and optical mode (single mode vs. multimode) on the background intensity of fresh human tissues was studied. Based on the results, laser with 830 nm excitation demonstrated better background reduction than that with 785 nm excitation for the same optical mode, but the Raman signal intensity was conversely reduced, and the signal‐to‐noise ratio (SNR) not improved. In contrast, by comparing single‐mode and multimode 785 nm excitations, it was shown that the single‐mode laser with its smaller beam waist and beam propagation factor had better background reduction ability and an improvement of the SNRs. It is speculated that this decrease in background intensity comes from the effect of the optical mode on the Mie scattering from the biological tissue. High‐quality spectra based on a careful selection of both laser excitation wavelength and optical mode will benefit Raman measurements in further research focusing on spectral interpretation and histopathological correlation ultimately aimed toward intraoperative applications. Copyright © 2014 John Wiley & Sons, Ltd.     

混沌信号的压缩感知去噪

对非线性时间序列进行噪声抑制是从中提取有效信息的前提. 混沌信号的去噪算法不仅要使滤波后的信号具有较高的信噪比, 也要具有较好的不确定性. 从压缩感知的角度出发,提出了一种新的噪声抑制方法. 该方法包括估计噪声方差, 以及依据动态的稀疏度将观测值往确定的过完备字典上投影. 仿真实验表明, 该方法比常用的小波阈值法和局部曲线拟合法具有更高的输出信噪比, 而原始信号的混沌特性也能得到较大程度的恢复.     

Parametric spectrum analysis of 2D NMR signals. Application to in Vivo J spectroscopy

Parametric modeling techniques for spectrum analysis, based on the linear prediction principle, have previously been proposed to process NMR data. In this paper, they are tested on different practical NMR signals, and especially on in vivo 2D NMR spectroscopy data. The linear prediction version of the maximum entropy method, using AR modeling, and the Prony method are outlined with some considerations about the choice of the AR algorithm. Then simulation and experimental results obtained with the Prony method are presented and compared with those obtained with classical 2D Fourier transform processing. The data processed here result from homonuclear 2D J-resolved spectroscopy experiments performed to measure the spin-spin coupling constants between the three phosphorus nuclei of ATP in the rat brain. The parametric techniques (especially the Prony method) applied in both dimensions yield increased resolution and sensitivity and their ability to process limited data allows the total acquisition time to be reduced without loss of resolution. Although the noise may damage the performances, the results obtained here, on in vivo 2D data, are quite encouraging.     

High-contrast imaging based on wavefront shaping to improve low signal-to-noise ratio photoacoustic signals using superpixel method

Photoacoustic(PA) imaging has drawn tremendous research interest for various applications in biomedicine and experienced exponential growth over the past decade. Since the scattering effect of biological tissue on ultrasound is two-to three-orders magnitude weaker than that of light, photoacoustic imaging can effectively improve the imaging depth.However, as the depth of imaging further increases, the incident light is seriously affected by scattering that the generated photoacoustic signal is very weak and the signal-to-noise ratio(SNR) is quite low. Low SNR signals can reduce imaging quality and even cause imaging failure. In this paper, we proposed a new wavefront shaping and imaging method of low SNR photoacoustic signal using digital micromirror device(DMD) based superpixel method. We combined the superpixel method with DMD to modulate the phase and amplitude of the incident light, and the genetic algorithm(GA) was used as the wavefront shaping algorithm. The enhancement of the photoacoustic signal reached 10.46. Then we performed scanning imaging by moving the absorber with the translation stage. A clear image with contrast of 8.57 was obtained while imaging with original photoacoustic signals could not be achieved. The proposed method opens new perspectives for imaging with weak photoacoustic signals.     

Signal-to-noise ratio of Raman signal measured by multichannel detectors

Raman spectroscopy has been widely used to characterize the physical properties of two-dimensional materials(2 DMs). The signal-to-noise ratio(SNR or S/N ratio) of Raman signal usually serves as an important indicator to evaluate the instrumental performance rather than Raman intensity itself. Multichannel detectors with outstanding sensitivity, rapid acquisition speed and low noise level have been widely equipped in Raman instruments for the measurement of Raman signal. In this mini-review, we first introduce the recent advances of Raman spectroscopy of 2 DMs. Then we take the most commonly used CCD detector and IGA array detector as examples to overview the various noise sources in Raman measurements and analyze their potential influences on SNR of Raman signal in experiments. This overview can contribute to a better understanding on the SNR of Raman signal and the performance of multichannel detector for numerous researchers and instrumental design for industry, as well as offer practical strategies for improving spectral quality in routine measurement.