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.     

A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering

Clustering analysis has been widely used to detect the functional connectivity from functional magnetic resonance imaging (fMRI) data. However, it has some limitations such as enormous computer memory requirement, and difficulty in estimating the number of clusters. In this study, in order to effectually resolve the deficiencies mentioned above, we have proposed a novel approach (SAAPC) for fMRI data analysis, which combines sparsity, an effective assumption for analyzing fMRI signal, with affinity propagation clustering (APC).     

SmartPhantom--an fMRI simulator

Many informatics tools have emerged to process the voluminous and complex data generated by functional magnetic resonance imaging (fMRI). The interpretation of fMRI exams is largely determined by these tools. However, their performance is hard to evaluate because there is no independent means of calibration. A novel fMRI calibration system called SmartPhantom has been developed to simulate functional blood oxygen level dependent (BOLD) imaging. SmartPhantom contains a quadrature radio frequency coil, comprising two perpendicular planar loops that can be externally activated or deactivated. The system is able to produce reasonably uniform signal enhancements in a calibration sample surrounded by the two loops during an MRI scan. The enhancement is controlled well in both magnitude and predefined timing and produces BOLD-like signals. Characteristics of SmartPhantom are discussed in detail, followed by a comparison of fMRI informatics tools. Two fMRI data sets are acquired with the SmartPhantom. One with high signal-to-noise ratio provides the calibration. Another with lower SNR is input into three software packages (BrainVoyager, FSL and Statistical Parametric Mapping 2) for data preprocessing and statistical analysis. Results from the three packages are compared in both sensitivity of detecting the activation and correlation between the predicted activation and calibration.     

A matched filter echo summation technique for MRI

A technique is presented to increase the signal-to-noise ratio (SNR) in two-dimensional (2D), phase-encoded imaging at low SNR. The essence of this technique is to combine multiple echoes in the time domain. As analyzed in the paper, phase discrepancies exist among different echoes and may deteriorate the combined echo. In particular, extraneous phase shifts can be created if unshielded gradient coils are used. To overcome these phase discrepancies, a matched filter was derived from the k = 0 component of image. This matched filter has the same phase discrepancies among its echoes as the imaging signal and its magnitude decays with an average T₂. In the echo summation with the matched filter, the phase of the matched filter was subtracted from the imaging signal and the magnitude of the matched filter was used as the weighting function. We have shown that this matched filter echo summation technique has better SNR than the case of 2D, phase-encoded imaging in both simulation and experiment. The SNR improvement is up to 60% in a phantom experiment. This technique is mostly useful in low SNR imaging that requires long imaging time, such as spectroscopic imaging and ¹⁹F imaging.     

The phase shift index for marking functional asynchrony in Alzheimer's disease patients using fMRI

Our previous study suggested that the functional magnetic resonance imaging MRI (fMRI) COSLOF Index (CI) could be used as a quantitative biomarker for Alzheimer's disease (AD). The fMRI CI was lowest in the AD group (0.13+/-0.10), followed by the mild cognitive impairment (MCI) group (0.20+/-0.05) and the control group (0.34+/-0.09). The current study continues an investigation into which of the following two factors has a dominant role in determining the CI: the signal-to-noise ratio (SNR) or the phase shift of spontaneous low-frequency (SLF) components. By using a theoretical model for SLF components, we demonstrated that the normalized CI does not depend on the SNR of the SLF components. Further analysis shows that by taking the ratio of the cross-correlation coefficient to the maximum-shifted cross-correlation coefficient, the SNR factor can be canceled. Therefore, the determination of the phase shift index (PSI) method is independent of the SNR, and the PSI provides an accurate measure of the phase shift between SLF components. By applying this PSI method to the control, MCI and AD groups of subjects, experimental results demonstrated that the PSI was highest in the AD group (72.6+/-11.3 degrees ), followed by the MCI group (58.6+/-5.7 degrees ) and, finally, the control group (40.6+/-8.4 degrees ). These results suggest that the larger is the PSI value, the more asynchrony exists between SLF components.     

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.     

Decreases in ADC observed in tissue areas during activation in the cat visual cortex at 9.4 T using high diffusion sensitization

Recent studies in the human visual cortex using diffusion-weighted functional magnetic resonance imaging (fMRI) have suggested that the apparent diffusion coefficient (ADC) decreases, in contrast to earlier studies that consistently reported ADC increases during neuronal activation. The changes, in either case, are hypothesized to provide the ability to improve the spatial specificity of fMRI over conventional blood-oxygenation-level-dependent (BOLD) methods. Most recently, the ADC decreases have been suggested as originating from transient cell swelling caused by either shrinkage of the extracellular space or some intracellular neuronal process that precedes the hemodynamic response. All of these studies have been conducted in humans and at lower magnetic fields, which can be limited by the signal-to-noise ratio (SNR). The low SNR can lead to significant partial-volume effects because of the lower spatial resolutions required to attain sufficient SNR in diffusion-weighted images. Human studies also have the potential confound of motion. At high magnetic fields and in animal model studies, these limitations are alleviated. At high fields, SNR increases, tissue signals are enhanced and signal changes inside the blood are significantly reduced compared to lower fields. In this work, we were able to measure a small but significant ADC decrease in tissue areas, in conjunction with brain activation in the cat visual cortex at 9.4 T when using highly diffusion-weighted images (b>1200 s/mm2) where intravascular effects are minimal. When using low b-values, delayed increases in the tissue ADC during activation were observed. No significant changes in ADC were observed in surface vessels for any diffusion weighting. Furthermore, we did not observe any temporal differences in the highly diffusion-weighted data compared to BOLD; however, although the changes may likely be vascular in nature, they are highly localized to the tissue areas.     

Adaptive anisotropic noise filtering for magnitude MR data

Conventional noise filtering schemes applied to magnitude magnetic resonance (MR) images tacitly assume Gauss distributed noise. Magnitude MR data, however, are Rice distributed. Not incorporating this knowledge leads inevitably to biased results, in particular when applying such filters in regions with low signal-to-noise ratio. In this work, we show how the Rice data probability distribution can be incorporated so as to construct a noise filter that is far less biased.     

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.     

小波包自适应阈值语音降噪新算法

为了克服低信噪比输入下,语音增强造成语音清音中的弱分量损失,造成重构信号包络失真的问题。论文提出了一种新的语音增强方法。该方法根据语音感知模型,采用不完全小波包分解拟合语音临界频带,并对语音按子带能量进行清浊音区分处理,在阈值计算上,提出了一种清浊音分离,基于子带信号能量的小波包自适应阈值算法。通过仿真实验,客观评测和听音测试表明,该算法在低信噪比输入时较传统算法,能够更加有效地减少重构信号包络失真,在不损伤语音清晰度和自然度的前提下,使输出信噪比明显提高。将该算法与能量谱减法结合,进行二次增强能进一步提高降噪输出的语音质量。     

Real-time functional magnetic resonance imaging: methods and applications

Functional magnetic resonance imaging (fMRI) has been limited by time-consuming data analysis and a low signal-to-noise ratio, impeding online analysis. Recent advances in acquisition techniques, computational power and algorithms increased the sensitivity and speed of fMRI significantly, making real-time analysis and display of fMRI data feasible. So far, most reports have focused on the technical aspects of real-time fMRI (rtfMRI). Here, we provide an overview of the different major areas of applications that became possible with rtfMRI: online analysis of single-subject data provides immediate quality assurance and functional localizers guiding the main fMRI experiment or surgical interventions. In teaching, rtfMRI naturally combines all essential parts of a neuroimaging experiment, such as experimental design, data acquisition and analysis, while adding a high level of interactivity. Thus, the learning of essential knowledge required to conduct functional imaging experiments is facilitated. rtfMRI allows for brain-computer interfaces (BCI) with a high spatial and temporal resolution and whole-brain coverage. Recent studies have shown that such BCI can be used to provide online feedback of the blood-oxygen-level-dependent signal and to learn the self-regulation of local brain activity. Preliminary evidence suggests that this local self-regulation can be used as a new paradigm in cognitive neuroscience to study brain plasticity and the functional relevance of brain areas, even being potentially applicable for psychophysiological treatment.     

可调谐二极管激光吸收光谱射流强化

可调谐二极管激光吸收光谱分析技术(TDLAS)是近年来兴起的一种痕量气体分析方法。因其高分辨率、高分析速度、非接触测量、可实时在线监测等优点,受到人们广泛青睐,已经广泛应于科研和工业自动化等领域的气体检测中。为了满足分析仪器测量灵敏度和精度的要求,对于很低浓度和信噪比较小的痕量气体浓度测量,往往需要较长的吸收光程和复杂的数据处理算法,这增加了分析仪器的软硬件成本。本文提出利用高压气体射流产生的减压作用,在不改变TDLAS分析仪器软硬件设置的条件下提高TDLAS的分析能力。对于安装于样气压力为0.3～0.5 MPa和排气压力为219.3 kPa的TDLAS分析系统,实验结果表明,通过射流强化的方法,可以使信噪比提高24倍,探测灵敏度提高一个数量级,测量精度提高6.3倍。     

Automated phase correction via maximization of the real signal

Due to improved quantification capabilities and enhanced signal-to-noise ratio (SNR), phase-corrected real reconstruction in magnetic resonance imaging is superior to the common magnitude reconstruction, especially at low SNR. This requires the development of an automated phase-correction algorithm. Existing methods are not well suited for multiple unconnected regions of very low SNR. For this situation, a method based on the real-signal maximization is implemented, in which the experimental image phase is approximated by a three-dimensional polynomial of up to third order. The presented implementation was successfully applied to data originating from different samples and pulse sequences.     

An empirical characterization of the quality of DTI data and the efficacy of dyadic sorting

Metrics calculated from images acquired using the diffusion tensor imaging (DTI) technique possess a systematic bias that depends on signal-to-noise ratio (SNR). Dyadic sorting provides a simple method for remediating some of this bias within a region(s) of interest (ROI). Although this bias and its removal using dyadic sorting have been studied previously within a theoretical framework, one can employ precise geometric knowledge of microstructures to perform an empirical comparison between expected DTI results and those measured with a scanner. In this project, the biasing effect of low SNR (approximately 1-10) on DTI eigenvalues was measured directly using water-filled capillary structures of two different sizes, and the magnitude of the corrective effect of dyadically sorting eigenvector-eigenvalue pairs was characterized. Multiple DTI series were acquired for determining DTI metrics at eight unique SNR values, using T(R) to vary signal intensity via T(1) contrast. Differences between the second and third eigenvalues, which should be equal for prolate geometry, ranged from approximately 23% to 45% and from 19% to 41% for large and small inner diameter capillaries after sorting eigenvalues by magnitude, and ranged from approximately 1% to 18% and from 1% to 4% after dyadic sorting. A high-resolution DTI series was used to observe the effect of ROI size on dyadic sorting. For restriction of diffusion on the scale of the small capillary at SNR approximately 18, an ROI with > or =50 pixels is adequate to determine fractional anisotropy to 99% accuracy, while larger ROI are required to resolve the two smaller eigenvalues to the same accuracy ( approximately 330-390 pixels). At low values of SNR, the iteration of dyadic sorting is suggested to achieve good accuracy. A method for the incorporation of empirical measurements into a bias-correction map, which would be useful for characterizing uncertainty and for reducing systematic bias in DTI data, is introduced.     

Unwrapping magnetic resonance phase maps with Chebyshev polynomials

A phase-unwrapping algorithm, based on the method of moments, is introduced in this work. The proposed algorithm expands the phase map in terms of a two-dimensional Chebyshev series. The expansion coefficients are calculated by exploiting the orthogonality of Chebyshev polynomials of the first kind. The performance of the proposed phase-unwrapping algorithm is tested on a synthetic phase map and experimental phase maps of a uniform phantom, a human brain and a mouse torso, all acquired from 3-T magnetic resonance (MR) scanners. To impose additional burdens on the algorithm, we introduced magnetic field inhomogeneities to the phantom and human brain data by an external gradient coil. The proposed phase-unwrapping algorithm is found to perform well on the phantom data set in a low signal-to-noise ratio (SNR) environment and on the synthetic data set. The proposed algorithm is also found to perform well in in vivo data sets of the human brain and mouse torso. Results obtained from the in vivo MR data sets show that the proposed algorithm produced unwrapped phase maps that are nearly identical to those produced by a widely used phase-unwrapping algorithm, PRELUDE 2D in the fMRI Software Library.     

基于多分辨率融合距离的低质量星系光谱红移测量方法

星系光谱红移测量是大规模天体光谱巡天项目中的一个重要研究内容,其目的是从在光谱中测量出对应星系由于多普勒效应引起的红移。随着银河系外巡天项目的开展,观测目标距离(红移)越来越远,其星等越来越暗,光谱的质量也随之越来越差,如何能够有效准确地从这些低质量的光谱测量出红移是河外巡天面临的一个重要问题。基于此问题,充分考虑到低质量星系光谱的特点及数据特征,新定义了一种针对低质量巡天光谱数据的多分辨率融合距离,以此为基础提出一种针对低质量星系光谱的红移测量方法。该方法充分结合不同分辨率下光谱的特征,计算距离时首先将模板光谱和待测光谱同时降到多个相同分辨率下,该分辨率下所有波长采样点都计算一个偏差进而得到一个距离,然后将多个分辨率下得到的距离通过加权得到一个融合距离。基于多分辨率融合距离提出的星系红移测量方法,能够有效的解决低质量星系光谱的红移无法准确测量的问题。研究了不同信噪比下红移测量的精度,在信噪比大于5之后,该方法测量准确率可以达到90%以上。大量实验表明,提出的方法在星系光谱质量较低的情况可以非常准确地从中测量出红移,测量误差和红移大小无关,可以很好地应用于大规模巡天数据的星系光谱红移测量中。     

Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images

In this work we investigate methods of statistical processing and background fitting of atomic resolution electron energy loss spectrum image (SI) data. Application of principal component analysis to SI data has been analyzed in terms of the spectral signal-to-noise ratio (SNR) and was found to improve both the spectral SNR and its standard deviation over the SI, though only the latter was found to improve significantly and consistently across all data sets analyzed. The influence of the number of principal components used in the reconstructed data set on the SNR and resultant elemental maps has been analyzed and the experimental results are compared to theoretical calculations.     

Acceleration target detection based on LFM radar

In radar systems, the echo signal caused by an accelerated target can be similarly considered as linear frequency modulation (LFM) signal. In high signal-to-noise ratio (SNR), discrete polynomial-phase transform (DPT) algorithm can be used to detect the echo signal, as it has low computation complexity and high real-time performance. However, in low SNR, the DPT algorithm has a large mean square error of the rate of frequency modulation and a low detection probability. In order to detect LFM signal in low SNR, this paper proposes a detection method, segment discrete polynomial-phase transform (SDPT), which means, at first, dividing the whole echo pulses into several segments with same duration in time domain, and then, using coherent accumulation method of DFT to segments, at last, processing this signal with DPT in intra-segment. In the case of a large number of segments, the SDPT can improve the output SNR. In addition, in a certain SNR, to the target signal with big sampling interval, large acceleration and less segments, this paper proposes an algorithm to detect the LFM signal generated from the combination of an improved DPT (IDPT) and fractional Fourier transform (FRFT). The output SNR of this algorithm is connected with the length of time delay. In the simulation, when the length of the time delay is 0.2 N, the output SNR is 2.5 dB more than that which results from directly using DPT. Finally, the detection performance and algorithm complexity of the proposed algorithm were analyzed, and the simulated and measured data verify the effectiveness of the algorithm.     

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis

Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying "activation." The contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques.     

Discussion on the choice of separated components in fMRI data analysis by spatial independent component analysis

By measuring the changes of magnetic resonance signals during a stimulation, the functional magnetic resonance imaging (fMRI) is able to localize the neural activation in the brain. In this report, we discuss the fMRI application of the spatial independent component analysis (spatial ICA), which maximizes statistical independence over spatial images. Included simulations show the possibility of the spatial ICA on discriminating asynchronous activations or different response patterns in an fMRI data set. An in vivo visual stimulation fMRI test was conducted, and the result shows a proper sum of the separated components as the final image is better than a single component, using fMRI data analysis by spatial ICA. Our result means that spatial ICA is a useful tool for the detection of different response activations and suggests that a proper sum of the separated independent components should be used for the imaging result of fMRI data processing.