Denoising of pre-beamformed photoacoustic data using generative adversarial networks

We have trained generative adversarial networks (GANs) to mimic both the effect of temporal averaging and of singular value decomposition (SVD) denoising. This effectively removes noise and acquisition artifacts and improves signal-to-noise ratio (SNR) in both the radio-frequency (RF) data and in the corresponding photoacoustic reconstructions. The method allows a single frame acquisition instead of averaging multiple frames, reducing scan time and total laser dose significantly. We have tested this method on experimental data, and quantified the improvement over using either SVD denoising or frame averaging individually for both the RF data and the reconstructed images. We achieve a mean squared error (MSE) of 0.05%, structural similarity index measure (SSIM) of 0.78, and a feature similarity index measure (FSIM) of 0.85 compared to our ground-truth RF results. In the subsequent reconstructions using the denoised data we achieve a MSE of 0.05%, SSIM of 0.80, and a FSIM of 0.80 compared to our ground-truth reconstructions.     

Speckles Suppression Techniques for Ultrasound Images

Most medical images have a poorer signal to noise ratio than scenes taken with a digital camera, which often leads to incorrect diagnosis. Speckles suppression from ultrasound images is one of the most important concerns in computer-aided diagnosis. This article proposes two novel, robust and efficient ultrasound images denoising techniques. The first technique is the enhanced ultrasound images denoising (EUID) technique, which estimates automatically the speckle noise amount in the ultrasound images by estimating important input parameters of the filter and then denoising the image using the sigma filter. The second technique is the ultrasound image denoising using neural network (UIDNN) that is based on the second-order difference of pixels with adaptive threshold value in order to identify random valued speckles from images to achieve high efficient image restoration. The performances of the proposed techniques are analyzed and compared with those of other image denoising techniques. The experimental results show that the proposed techniques are valuable tools for speckles suppression, being accurate, less tedious, and preventing typical human errors associated with manual tasks in addition to preserving the edges from the image. The EUID algorithm has nearly the same peak signal to noise ratio (PSNR) as Frost and speckle-reducing anisotropic diffusion 1, whereas it achieves higher gains, on average—0.4 dB higher PSNR—than the Lee, Kuan, and anisotropic diffusion filters. The UIDNN technique outperforms all the other techniques since it can determine the noisy pixels and perform filtering for these pixels only. Generally, when relatively high levels of noise are added, the proposed algorithms show better performances than the other conventional filters.     

MRI noise estimation and denoising using non-local PCA

This paper proposes a novel method for MRI denoising that exploits both the sparseness and self-similarity properties of the MR images. The proposed method is a two-stage approach that first filters the noisy image using a non local PCA thresholding strategy by automatically estimating the local noise level present in the image and second uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed method internally estimates the amount of local noise presents in the images that enables applying it automatically to images with spatially varying noise levels and also corrects the Rician noise induced bias locally. The proposed approach has been compared with related state-of-the-art methods showing competitive results in all the studied cases.     

A comparative study and assessment of Doppler ultrasound spectral estimation techniques. Part I: Estimation methods

When compared to the classical Discrete Fourier Transform (DFT) or Fast Fourier Transform (FFT) approach, modern estimation methods offer the potential for achieving significant improvements in estimating the power density spectrum of Doppler ultrasound signals. Such improvements, for example, might enable minor flow disturbances to be detected, thereby improving the sensitivity in arterial disease assessment. Specifically, reduction in the variance and bias can be achieved, and this may enable disturbed flow to be detected in a more sensitive manner. The approach taken here, is to consider spectral estimation methods as a problem of fitting an assumed model to the Doppler signal. The models described assume that the signal is stationary. Since the Doppler signal is generally nonstationary, it is assumed that a short enough time window interval can be chosen over which the signal can be considered stationary. We shall review the various methods and when appropriate, relate them to the nature of the Doppler signal.     

Quantitative Doppler measures in coiled vessels: investigation on excised umbilical veins

Quantitative assessment of umbilical venous blood velocity with Doppler ultrasound (US) must cope with the coiled structure of the vein inside the cord. Both an experimental and a theoretical approach showed remarkable variations in the insonation angle when the probe was moved along the vein, provided the inclination between the Doppler probe and the cord was kept constant. Inaccurate signal processing, stochastic variability and flow disturbances could, however, mask the influence of the geometry. The above hypotheses were assessed by investigating five cords in vitro a few hours after delivery from normal pregnancies at term. The Doppler signal was sampled at different sites along each cord and the mean Doppler shift estimated by FFT spectral analysis, both directly and through the noise rejection D’Alessio’s algorithm, which proved effective in improving the Doppler shift estimate in condition of low signal-to–noise ratio (SNR).     

自适应非局域均值滤波的超声图像去噪

目的 提出一种去除超声图像噪声的新方法。方法 对超声图像进行非局域搜索,找到相似的图像块进行加权平均,降低噪声。通过定义一个特征强度,区分斑点噪声和图像边界;然后将特征强度引入非局域滤波方法中,对平坦区域和边界进行自适应滤波。结果 本方法可有效去除斑点噪声,提高噪声图像的峰值信噪比(PSNR)和结构相似度指数(SSIM),优于常规方法。结论 自适应非局域均值滤波可有效去噪,并保护超声图像特征。     

Comparison of four digital maximum frequency estimators for Doppler ultrasound

The performance of four methods for digitally estimating the maximum frequency waveform from the Doppler ultrasound spectrum, are described. The methods investigated are: a percentile method, D'Alessio's threshold crossing method [D'Alessio T. (1985) "Objective" algorithm for maximum frequency estimation in Doppler spectral analysers. Med. Biol. Engng and Comput. 23, 63-68.], a modified threshold crossing method, and a new hybrid algorithm. Evaluations of the variance and bias were performed using stationary simulated continuous wave (CW) Doppler signals of different bandwidths and signal/noise ratios (SNR) of 9 and 17 dB. Furthermore, a simulated nonstationary Doppler signal, similar to that from a normal internal carotid artery, was also used to compare the various methods. Overall, it was found that the modified threshold method and the new hybrid method have the best performance over a wide range of signal and noise hybrid method have the best performance over a wide range of signal and noise conditions; however, D'Alessio's method also performs well for low SNR's.     

Doppler angle estimation of pulsatile flows using AR modeling

In quantitative ultrasonic flow measurements, the beam-to-flow angle (i.e., Doppler angle) is an important parameter. An autoregressive (AR) spectral analysis technique in combination with the Doppler spectrum broadening effect was previously proposed to estimate the Doppler angle. Since only a limited number of flow samples are used, real-time two-dimensional Doppler angle estimation is possible. The method was validated for laminar flows with constant velocities. In clinical applications, the flow pulsation needs to be considered. For pulsatile flows, the flow velocity is time-varying and the accuracy of Doppler angle estimation may be affected. In this paper, the AR method using only a limited number of flow samples was applied to Doppler angle estimation of pulsatile flows. The flow samples were properly selected to derive the AR coefficients and then more samples were extrapolated based on the AR model. The proposed method was verified by both simulations and in vitro experiments. A wide range of Doppler angles (from 3o degrees to 78 degrees) and different flow rates were considered. The experimental data for the Doppler angle showed that the AR method using eight flow samples had an average estimation error of 3.50 degrees compared to an average error of 7.08 degrees for the Fast Fourier Transform (FFT) method using 64 flow samples. Results indicated that the AR method not only provided accurate Doppler angle estimates, but also outperformed the conventional FFT method in pulsatile flows. This is because the short data acquisition time is less affected by the temporal velocity changes. It is concluded that real-time two-dimensional estimation of the Doppler angle is possible using the AR method in the presence of pulsatile flows. In addition, Doppler angle estimation with turbulent flows is also discussed. Results show that both the AR and FFT methods are not adequate due to the spectral broadening effects from the turbulence.     

Denoising of arterial and venous Doppler signals using discrete wavelet transform: Effect on clinical parameters

In this paper, the effects of a wavelet transform based denoising strategy on clinical Doppler parameters are analyzed. The study scheme included: (a) Acquisition of arterial and venous Doppler signals by sampling the audio output of an ultrasound scanner from 20 healthy volunteers, (b) Noise reduction via decomposition of the signals through discrete wavelet transform, (c) Spectral analysis of noisy and noise-free signals with short time Fourier transform, (d) Curve fitting to spectrograms, (e) Calculation of clinical Doppler parameters, (f) Statistical comparison of parameters obtained from noisy and noise-free signals. The decomposition level was selected as the highest level at which the maximum power spectral density and its corresponding frequency were preserved. In all subjects, noise-free spectrograms had smoother trace with less ripples. In both arterial and venous spectrograms, denoising resulted in a significant decrease in the maximum (systolic) and mean frequency, with no statistical difference in the minimum (diastolic) frequency. In arterial signals, this leads to a significant decrease in the calculated parameters such as Systolic/Diastolic Velocity Ratio, Resistivity Index, Pulsatility Index and Acceleration Time. Acceleration Index did not change significantly. Despite a successful denoising, the effects of wavelet decomposition on high frequency components in the Doppler signal should be challenged by comparison with reference data, or, through clinical investigations.     

A comparison method for mean frequency estimators for Doppler ultrasound.

Mean frequency estimators as used in pulsed Doppler ultrasound equipment should provide an accurate (quality) and consistent (robustness) estimate over a wide range of signal conditions. In a simplified signal model, the main parameters to consider are the noise level, mean frequency, bandwidth and power of both the Doppler signal and the stationary component over a given time window. It may be expected that one estimator for a given parameter combination exhibits a good performance while another estimator for the same parameter combination behaves poorly. To allow direct comparison between different types of frequency estimators, a method is introduced to evaluate the quality and robustness of estimators for a common signal space covering a wide range of realistic parameter combinations. The method is illustrated using three different mean frequency estimators: (1) a first order autoregressive estimator in combination with a stationary echo filter; (2) a second order autoregressive estimator; and (3) a complex linear regression estimator in combination with a stationary echo filter. It is concluded that, for the parameter combination considered, the complex linear regression estimator exhibits the best quality (low variance and bias of the estimate) and robustness (consistent quality for all parameter combinations).     

The use of the wavelet transform to describe embolic signals.

A number of methods to detect cerebral emboli and differentiate them from artefacts using Doppler ultrasound have been described in the literature. In most, Fourier transform-based (FT) spectral analysis has been used. The FT is not ideally suited to analysis of short-duration embolic signals due to an inherent trade-off between temporal and frequency resolution. An alternative approach that might be expected to describe embolic signals well is the wavelet transform. Wavelets are ideally suited for the analysis of sudden short-duration signal changes. Therefore, we have implemented a wavelet-based analysis and compared the results of this with a conventional FFT-based analysis. The temporal resolution, as measured by the half-width maximum, was significantly better for the continuous wavelet transform (CWT), mean (SD) 8.40 (8.82) ms, compared with the 128-point FFT, 12.92 (9.70) ms, and 64-point FFT, 10.80 (5.69) ms. Time localization of the CWT for the embolic signal was also significantly better than the FFT. The wavelet transform appears well suited to the analysis of embolic signals offering superior time resolution and time localization to the FFT.     

Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal

Carbon dioxide is a potent cerebral vasodilator. We have identified a significant source of low-frequency variation in blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) signal at 3 T arising from spontaneous fluctuations in arterial carbon dioxide level in volunteers at rest. Fluctuations in the partial pressure of end-tidal carbon dioxide (Pet(CO(2))) of +/-1.1 mm Hg in the frequency range 0-0.05 Hz were observed in a cohort of nine volunteers. Correlating with these fluctuations were significant generalized grey and white matter BOLD signal fluctuations. We observed a mean (+/-standard error) regression coefficient across the group of 0.110 +/- 0.033% BOLD signal change per mm Hg CO(2) for grey matter and 0.049 +/- 0.022% per mm Hg in white matter. Pet(CO(2))-related BOLD signal fluctuations showed regional differences across the grey matter, suggesting variability of the responsiveness to carbon dioxide at rest. Functional magnetic resonance imaging (fMRI) results were corroborated by transcranial Doppler (TCD) ultrasound measurements of the middle cerebral artery (MCA) blood velocity in a cohort of four volunteers. Significant Pet(CO(2))-correlated fluctuations in MCA blood velocity were observed with a lag of 6.3 +/- 1.2 s (mean +/- standard error) with respect to Pet(CO(2)) changes. This haemodynamic lag was adopted in the analysis of the BOLD signal. Doppler ultrasound suggests that a component of low-frequency BOLD signal fluctuations is mediated by CO(2)-induced changes in cerebral blood flow (CBF). These fluctuations are a source of physiological noise and a potentially important confounding factor in fMRI paradigms that modify breathing. However, they can also be used for mapping regional vascular responsiveness to CO(2).     

Comparison of time-frequency estimators for peripheral embolus detection

Recently, a time-frequency processing of peripheral arterial Doppler signals, based on the spectrogram, was proposed to detect automatically high-intensity transient signal. Three time-frequency representations, the smooth-pseudo-Wigner-Ville, the Cho?-Williams and the cone-kernel distributions were compared with the spectrogram, following the detection scheme previously reported. The results showed that the spectrogram provided the best compromises between false-detection and no-detection compared with the other time-frequency representations.     

A UV-visible absorption spectrum denoising method based on EEMD and an improved universal threshold filter

When using ultraviolet-visible spectroscopy (UV-visible spectroscopy) to detect water quality parameters, the measured absorption spectrum signal often contains a lot of interference information. Therefore, denoising is extremely important in spectrum data processing and analysis, which directly affects the subsequent quantitative analysis and information mining. Choosing an appropriate denoising method is key to improve the spectral analysis accuracy and promote the spectral analysis ability. In this paper, a new UV-visible absorption spectrum denoising method is proposed: a denoising method based on ensemble empirical mode decomposition (EEMD) and improved universal threshold filtering (EEMD-based method). The noisy UV-visible absorption spectrum signal is firstly decomposed into a finite set of band limited signals called intrinsic mode functions (IMFs) via EEMD. Spearman''s rank correlation coefficient (Spearman''s rho) is then used as a criterion for the IMFs dominated by noise or useful signals, and the improved universal threshold filtering method is applied to the noise dominant IMFs to eliminate the noise. Finally, the denoised UV-visible absorption spectrum signal is reconstructed. In order to discuss the effectiveness of the EEMD-based denoising method proposed in this paper, we compare it with various wavelet-based threshold denoising methods. Both methods have been implemented on synthetic signals with diverse waveforms (‘Blocks’, ‘Bumps’ and ‘Heavy sine’). It is demonstrated that the proposed method outperforms the wavelet-based methods. Then, the measured UV-visible absorption spectra with different SNR were denoised by the wavelet and proposed methods. The method proposed also performs well in the spectrum denoising experiment.

When using ultraviolet-visible spectroscopy (UV-visible spectroscopy) to detect water quality parameters, the measured absorption spectrum signal often contains a lot of interference information.     

超声图像中基于经验模态分解自动提取内-中膜厚度

目的为改善传统人工标记测量血管内-中膜厚度(IMT)的准确性和稳定性,提出基于图像分割技术的经验模态分解(EMD)改进算法。方法采用EMD改进算法去噪,根据血管壁的特点,在其中的极值点插值步骤使用非均匀的二维B样条函数,在水平和垂直方向上控制网格的密度不同,分别满足不同的分辨精度和平滑程度要求,改进了原始的二维EMD算法;然后通过K均值方法从图像中分离出血管腔、血管壁和其他组织,使用数学形态学算法逐步得到最终的内-中膜组织分割结果。结果改进EMD算法取得了较好的重建和滤波效果,有效克服了超声图像的强噪声和低分辨力对图像分割的限制,整个算法分割比较准确,算法复杂度相对较小。结论改进EMD算法是在超声图像中自动提取内-中膜的较有潜力的方法,能有效去除超声噪声,同时保留条纹结构的细节和边缘信息,有望于其他强噪声环境下提取条纹结构。     

Structure-adaptive sparse denoising for diffusion-tensor MRI

Diffusion tensor magnetic resonance imaging (DT-MRI) is becoming a prospective imaging technique in clinical applications because of its potential for in vivo and non-invasive characterization of tissue organization. However, the acquisition of diffusion-weighted images (DWIs) is often corrupted by noise and artifacts, and the intensity of diffusion-weighted signals is weaker than that of classical magnetic resonance signals. In this paper, we propose a new denoising method for DT-MRI, called structure-adaptive sparse denoising (SASD), which exploits self-similarity in DWIs. We define a similarity measure based on the local mean and on a modified structure-similarity index to find sets of similar patches that are arranged into three-dimensional arrays, and we propose a simple and efficient structure-adaptive window pursuit method to achieve sparse representation of these arrays. The noise component of the resulting structure-adaptive arrays is attenuated by Wiener shrinkage in a transform domain defined by two-dimensional principal component decomposition and Haar transformation. Experiments on both synthetic and real cardiac DT-MRI data show that the proposed SASD algorithm outperforms state-of-the-art methods for denoising images with structural redundancy. Moreover, SASD achieves a good trade-off between image contrast and image smoothness, and our experiments on synthetic data demonstrate that it produces more accurate tensor fields from which biologically relevant metrics can then be computed.     

Power M-mode Doppler (PMD) for observing cerebral blood flow and tracking emboli

Difficulties in location of transcranial ultrasound (US) windows and blood flow in cerebral vessels, and unambiguous detection of microemboli, have limited expansion of transcranial Doppler US. We developed a new transcranial Doppler modality, power M-mode Doppler (PMD), for addressing these issues. A 2-MHz digital Doppler (Spencer Technologies TCD100M) having 33 sample gates placed with 2-mm spacing was configured to display Doppler signal power, colored red and blue for directionality, in an M-mode format. The spectrogram from a user-selected depth was displayed simultaneously. This system was then explored on healthy subjects and patients presenting with varying cerebrovascular pathology. PMD facilitated window location and alignment of the US beam to view blood flow from multiple vessels simultaneously, without sound or spectral clues. Microemboli appeared as characteristic sloping high-power tracks in the PMD image. Power M-mode Doppler is a new paradigm facilitating vessel location, diagnosis, monitoring and microembolus detection.     

Adaptive Wavelet Packet-Based De-speckling of Ultrasound Images with Bilateral Filter

A new adaptive wavelet packet-based approach to minimize speckle noise in ultrasound images is proposed. This method combines wavelet packet thresholding with a bilateral filter. Here, the best bases after wavelet packet decomposition are selected by comparing the first singular value of all sub-bands, and the noisy coefficients are thresholded using a modified NeighShrink technique. The algorithm is tested with various ultrasound images, and the results, in terms of peak signal-to-noise ratio and mean structural similarity values, are compared with those for some well-known de-speckling techniques. The simulation results indicate that the proposed method has better potential to minimize speckle noise and retain fine details of the ultrasound image.     

Arterial Doppler signal simulation by time domain processing

Objective: The study's aim was to develop and test a rapid and simply implemented computer simulation method for Doppler ultrasound signals incorporating mean frequency, bandwidth and signal power variation and vortex simulation. Methods: We describe a computer simulation method for arterial Doppler ultrasound signals based on the application of white noise to a filter with a time-varying impulse response. Results: This method is simple to implement and requires the input of only the mean frequency, spectrum shape and Doppler power variation during the cardiac cycle. Analysis with the short term Fourier transform shows good agreement with theoretical prediction. Conclusion: This simulation method can provide a useful tool for comparison of the performance of Doppler signal processing techniques.     

Automatic independent component labeling for artifact removal in fMRI

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.