Structural filtering of functional data offered discriminative features for autism spectrum disorder.

This study attempted to answer the question, "Can filtering the functional data through the frequency bands of the structural graph provide data with valuable features which are not valuable in unfiltered data"?. The valuable features discriminate between autism spectrum disorder (ASD) and typically control (TC) groups. The resting-state fMRI data was passed through the structural graph's low, middle, and high-frequency band (LFB, MFB, and HFB) filters to answer the posed question. The structural graph was computed using the diffusion tensor imaging data. Then, the global metrics of functional graphs and metrics of functional triadic interactions were computed for filtered and unfiltered rfMRI data. Compared to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may indicate the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. There was no significant difference between ASDs and TCs when using the unfiltered data. All of these results demonstrated that significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. Also, these results demonstrated that frequency bands of the structural graph could offer significant findings which were not found in the unfiltered data. In conclusion, the results demonstrated the promising perspective of using structural graph frequency bands for attaining discriminative features and new knowledge, especially in the case of ASD.

Correlation-based modified delay-multiply-and-sum beamforming applied to medical ultrasound imaging.

BACKGROUND AND OBJECTIVE: Recently, the Filtered Delay-Multiply-and-Sum (F-DMAS) beamformer was successfully applied to Ultrasound Imaging (UI), improving the image quality compared to the conventional data-independent Delay-and-Sum (DAS) beamformer. However, its reconstructed images lead to restricted resolution, contrast, and dark regions in the speckle. Various beamformers based on F-DMAS were proposed to mitigate these issues; some improved resolution and contrast at the expense of more dark regions; others reduced the dark points with lower contrast than the F-DMAS beamformer. This study aims to propose a novel beamformer, improving resolution and contrast while reducing dark points in the speckle. METHODS: This study proposes a modified version of the F-DMAS beamformer, using two modifications to compensate for the aforesaid trade-off. Firstly, coupled signals' Correlation Coefficient (CC) was calculated and compared to a threshold value. The multiplications were applied only to the high-correlated (those whose CC is higher than the threshold value) signals. Secondly, a new Modified Coherence Factor (MCF) was applied to the high-correlated signals. Then, these two new beamformers were combined to reach a novel beamformer entitled "Modified DMAS (MDMAS)." RESULTS: The performance of MDMAS was evaluated using simulating Point-Spread-Function, Cyst phantom, the experimental geabr dataset, and an in vivo dataset. Moreover, we evaluated the performance of the MDMAS beamformer quantitatively. Full-width-half-maximum (FWHM), contrast-ratio (CR), contrast-to-noise-ratio (CNR), speckle signal-to-noise-ratio (sSNR), and generalized-CNR (gCNR) were assessed. CONCLUSIONS: This paper modified the conventional F-DMAS beamformer by adaptively multiplying signals. Then, CF was implemented on high correlated signals (MCF) and combined with the adaptive beamformer to compensate for the poor contrast. Results highlight that the MDMAS beamformer outperforms F-DMAS in terms of resolution and contrast without compromising the speckle from the dark region artifact.

Adaptive subarray coherence based post-filter using array gain in medical ultrasound imaging.

This paper presents an adaptive subarray coherence-based post-filter (ASCBP) applied to the eigenspace-based forward-backward minimum variance (ESB-FBMV) beamformer to simultaneously improve image quality and beamformer robustness. Additionally, the ASCBP can separate close targets. The ASCBP uses an adaptive noise power weight based on the concept of the beamformer's array gain (AG) to suppress the noise adaptively and achieve improved images. Moreover, a square neighborhood average was applied to the ASCBP in order to provide more smoothed square neighborhood ASCBP (SN-ASCBP) values and improve the speckle quality. Through simulations of point phantoms and cyst phantoms and experimental validation, the performance of the proposed methods was compared to that of delay-and-sum (DAS), MV-based beamformers, and subarray coherence-based post-filter (SCBP). The simulated results demonstrated that the ASCBP method improved the full width at half maximum (FWHM) by 57 % and the coherent interference suppression power (CISP) by 52 dB compared to the SCBP post-filter. Considering the experimental results, the SN-ASCBP method presented the best enhancement in terms of generalized contrast to noise ratio (gCNR) and contrast ratio (CR) while the ASCBP showed the best improvement in FWHM among other methods. Furthermore, the proposed methods presented a striking performance in low SNRs. The results of evaluating the different methods under aberration and sound speed error illustrated the better robustness of the proposed methods in comparison with others.

Automatic detection of code-modulated visual evoked potentials using novel covariance estimators and short-time EEG signals.

BACKGROUND AND OBJECTIVE: Over the last years, code-modulated visual evoked potentials (cVEP)-based brain-computer interfaces (BCIs) have been developed as robust and non-invasive tools to construct high-speed communication systems. Recently, beamforming-based algorithms have extensively been used in cVEP-based BCI systems because of the need for short-time stimulation and less training data. One of the main drawbacks of the beamforming-based approaches is that their performance highly depends on estimating data covariance matrix and calculating activation patterns. METHODS: In the present study, two novel covariance estimators (i.e., the modified convex combination (MCC) and the maximum likelihood (ML) techniques) are proposed to estimate a robust and more reliable covariance matrix. In the ML method, a new sparsity constraint is considered to express the specific eigendecomposition of the covariance matrix as a sparse matrix transform (SMT). Then, the SMT is calculated using the product of pairwise coordinate rotations. These rotations can be constructed by a cross-validation method. Two stimulation presentation rates of 60 and 120 Hz are used for the coding sequence. RESULTS: Both of the suggested approaches (i.e., the MCC and SMT-based techniques) can efficiently improve the performance of the conventional spatiotemporal beamforming-based methods by providing a robust estimate of the covariance matrix in short stimulation times. Based on the experimental results, it can be concluded that the proposed SMT and MCC methods achieve the best results for the 60 and 120 Hz stimulus presentation rates, respectively. However, for both stimulus presentation rates, the proposed SMT and MCC-based methods remarkably outperform other state-of-the-art methods in cVEP-based BCI, such as conventional spatiotemporal beamforming and optimized support vector machines (SVM). Also, the results showed that the 120 Hz stimulus presentation rate provided faster communication. This procedure is performed by obtaining a maximal Information Transfer Rate (ITR) of 187.38 bits/minute. CONCLUSION: Finally, the present study suggested that the proposed MCC and SMT-based techniques could automatically detect the gazed targets. Also, these methods could be used as non-invasive alternatives over conventional methods.

Classification of code-modulated visual evoked potentials using adaptive modified covariance beamformer and EEG signals.

OBJECTIVE: In general, brain computer interface (BCI) studies based on code-modulated Visual Evoked Potentials (c-VEP) use m-sequences to decode EEG responses to visual stimuli. BCI systems based on the c-VEP paradigm can simultaneously present a large number of commands, which results in a significantly high information transfer rate (ITR). Spatiotemporal beamforming (STB) is one of the commonly used approaches in c-VEP-based BCI systems. APPROACH: In the current work, a novel STB-based technique is proposed to detect the gazed targets. The proposed method improves the performance of conventional STB-based techniques by providing a robust estimation of the covariance matrix in short stimulation times. Different user parameter-free methods, including the convex combination (CC), the general linear combination (GLC), and the modified versions of these techniques, are used to estimate a reliable and robust covariance matrix when a small number of repetitions are available. MAIN RESULTS: The stimulus presentation rate of 120 Hz is used to assess the performance of the proposed structures. Our proposed methods improved the classification accuracy by an average of 20% compared to the conventional STB method at the shortest stimulation time. The proposed method achieves an average ITR of 157.07 bits/min by using only two repetitions of the m-sequences. SIGNIFICANCE: The results show that our proposed methods perform significantly better than the conventional STB technique in all stimulation times.

Rest-fMRI based comparison study between autism spectrum disorder and typically control using graph frequency bands.

Graph signal processing (GSP) is a subset of signal processing, allowing for the analysis of functional magnetic resonance imaging (fMRI) data in the topological domain of the brain. One of the most important and popular tools of GSP is graph Fourier transform (GFT), which can analyze the brain signals in different graph frequency bands. This paper has analyzed the resting-state fMRI (rfMRI) data of two sites using the GFT tool to discover new knowledge about autism spectrum disorder (ASD) and find features discriminating between ASD subjects and typical controls (TCs). The results were reported for both structural and functional atlases with different numbers of regions of interest (ROIs). For the ASD group, the signal energy concentrations in low and somewhat high-frequency bands declined by increasing age in most well-known brain networks. The changes of signal energy levels in different graph frequency bands were less for ASD subjects in comparison to TC ones. This result seems to reflect the difficulty in dynamic switching, which in turn leads to lower behavioral flexibility in the ASD group. In the low graph frequency band, the segregation of brain ROIs and brain networks increased with the age of ASD subjects. For TCs, growing up led to the integration of brain ROIs and segregation of brain networks in low and high-frequency bands, respectively. In the low-frequency band, the growing process was accompanied by lower activation and higher isolation of ASD brain networks. In addition, the segregation of salient-ventral attention network and dorsal attention network of ASD subjects grew with age. The structural atlas results indicated the reduced segregation of ASD subjects' default mode network in the high graph frequency band. The cross-frequency functional connectivity analysis showed that high-frequency signals of the right precentral gyrus and right precuneus posterior cingulate cortex had connections with almost all the low-frequency ROIs so that all connections were dramatically different between ASD and TC. The results of different scenarios at different graph frequency bands demonstrate that the combinatorial usage of functional and structural data through GSP can open a new avenue to investigate ASD.

Topological analysis of brain dynamics in autism based on graph and persistent homology.

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers.

Automatic Sleep Stage Classification Using Temporal Convolutional Neural Network and New Data Augmentation Technique from Raw Single-Channel EEG.

BACKGROUND AND OBJECTIVE: This paper presents a new framework for automatic classification of sleep stages using a deep learning algorithm from single-channel EEG signals. Each segmented EEG signal appended with its label of stages is fed into a deep learning model to create an automatic sleep stage classification. This is one of the most important problems that is critical to the realization of monitoring patients with sleep disorder. METHODS: In the present study, a neural network architecture is introduced utilizing Convolutional Neural Networks (CNNs) to extract features, followed by Temporal Convolutional Neural Network to extract the temporal features from the extracted features vector of CNN. Finally, the performance of our model is improved by a Conditional Random Field layer. We also employed a new data augmentation technique to enhance the CNNs training which has auxiliary effects. RESULTS: We evaluated our model by two different single-channel EEG signals (i.e., Fpz-Cz and Pz-Oz EEG channels) from two public sleep datasets, named Sleep-EDF-2013 and Sleep-EDF-2018. The evaluation results on both datasets showed that our model obtains the best total accuracy and kappa score (EDF-2013: 85.39%- 0.80, EDF-2018: 82.46%- 0.76) compared to the state-of-the-art methods. CONCLUSIONS: This study will possibly allow us to have a wearable sleep monitoring system with a single-channel EEG. Also, unlike hand-crafted features methods, our model finds its own patterns through training epochs, and therefore, it may minimize engineering bias.

Adaptive Spectral Doppler Estimation Based on the Modified Amplitude Spectrum Capon.

In conventional ultrasound systems, the compromise between frequency and temporal resolution limits the quality of the spectrograms and the ability to track fast blood flows. The main objective of this study was to identify a method that could reduce spectral broadening over time by reducing the observations and improving the spectral resolution and contrast. This problem is more pronounced in the process of imaging at higher blood velocities when using a short Doppler signal observation window (OW) in adaptive methods. The proposed adaptive technique, which is based on the covariance matrix Eigen space and the amplitude spectrum Capon (ASC) algorithm, managed to improve the spectral resolution and contrast compared with other adaptive algorithms within a shorter observation time, and it offered a narrower power spectrum and a more accurate spectrogram over time in combination with a coherence-based post filter. All methods were tested through various simulations. First, an analysis was carried out by simulating the femoral artery flow and the time-independent parabolic flow using the Field II simulator. Then, the performance of the proposed method was evaluated under more realistic conditions using a computational fluid dynamics simulation of complex flow fields in a carotid bifurcation model. Afterward, in vivo clinical data on the hepatic vein were used to validate the proposed method. Finally, the accuracy of the velocity estimated by different methods was evaluated through a mean-square-error assessment. Not only could the proposed method show significant improvements using extreme small OWs, N=[2, 4] , in the simulated data in terms of frequency resolution and contrast, but it also managed to offer an improvement of 74%, 73.3%, 22.2%, and 50% in frequency resolution, and an increase of 96.5, 90.2, 49, and 31.5 dB in contrast using in vivo clinical data compared with the Capon, amplitude and phase estimation (APES), projection-based Capon, and projection-based APES, respectively, for the OW of N=4 .

Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal.

BACKGROUND AND OBJECTIVE: This paper addresses the automated recognition of obstructive sleep apnea (OSA) from the analysis of single-lead ECG signals. This is one of the most important problems that is, critical to the realization of monitoring patients with sleep apnea. METHODS: In the present study, a novel solution based on autoregressive (AR) modeling of the single-lead ECG, and spectral autocorrelation function as an ECG feature extraction method is presented. The more effective features are opted by sequential forward feature selection (SFFS) technique and fed into the random forest for binary classification between the apnea and normal events. RESULTS: Experimental results on Apnea-ECG database proved that the introduced algorithm resulted in an accuracy of 93.90% (sensitivity of 92.26% and specificity of 94.92%) in per-segment classification, which outperforms the other cutting-edge automatic OSA recognition techniques. Moreover, the proposed algorithm provided an accuracy of 97.14% (sensitivity of 95.65% and specificity of 100%) in discrimination of apnea patients from the normal subjects, which is comparable to the traditional and existing approaches. CONCLUSIONS: This study suggests that automatic OSA recognition from single-lead ECG signals is possible, which can be used as an inexpensive and low complexity burden alternative to more conventional methods such as Polysomnography.

Eigenspace-based minimum variance beamformer combined with sign coherence factor: Application to linear-array photoacoustic imaging.

Photoacoustic (PA) imaging combining the advantages of high resolution of ultrasound imaging and high contrast of optical imaging provides images with good quality. PA imaging often suffers from disadvantages such as clutter noises and decreased signal-to-noise-ratio at higher depths. One studied method to reduce clutter noises is to use weighting factors such as coherence factor (CF) and its modified versions that improve resolution and contrast of images. In this study, we combined the Eigen-space based minimum variance (EIBMV) beamformer with the sign coherence factor (SCF) and show the ability of these methods for noise reduction when they are used in combination with each other. In addition, we compared the proposed method with delay-and-sum (DAS) and minimum variance (MV) beamformers in simulated and experimental studies. The simulation results show that the proposed EIBMV-SCF method improves the SNR about 94 dB, 87.65 dB, and 62.29 dB compared to the DAS, MV, and EIBMV, respectively, and the corresponding improvements were 79.37/34.43 dB, 77.25/26.96 dB, and 33.19/25.56 dB in the ex vivo/in vivo experiments.

Subspace-Based Blood Power Spectral Capon Combined with Wiener Postfilter to Provide a High-Quality Velocity Waveform with Low Mathematical Complexity.

In Doppler analysis, the power spectral density (PSD), which accounts for the axial velocity distribution of the blood scatterers, is estimated. The conventional spectral estimator is Welch's method, which suffers from frequency leakage at small observation window length. The performance of adaptive techniques such as blood power Capon (BPC) has been promising at the cost of higher computation complexity. Reducing the computational complexity while retaining the benefits of BPC would be necessary for real-time implementation. The purpose of the work described here was to investigate whether it is possible to decrease the computation load in BPC and still obtain acceptable results. The computation complexity in BPC is owing primarily to the matrix inversion required for computing the PSD estimate. We here propose the subspace blood power Capon technique, which employs a data covariance matrix with reduced number of rows in estimation of the weight vector. In maximum velocity estimation in the spectra, the signal noise slope intersection envelop estimator that makes use of the integrated power spectrum is employed. The evaluations are made based on both simulated and in vivo data. The results indicate that it is possible to reduce the order of complexity to almost 12.25% at the cost of 2.31% and 2.24% increases in the relative standard deviation and relative bias of the estimates. Moreover, the Wiener post-filter as a post-weighting factor, which will be multiplied by the final weight vector of the spectral estimator, estimates the power of the desired signal and the power of the interference plus noise to improve the contrast. The proposed estimator has exhibited a promising performance at beam-to-flow angles of 45°, 60° and 75°. Furthermore, the robust performance of the proposed estimator against variation in the flow rate is also documented.

Supervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images.

Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners, and color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of these problems, we propose a supervised saliency detection method tailored for dermoscopic images based on the discriminative regional feature integration (DRFI). A DRFI method incorporates multilevel segmentation, regional contrast, property, background descriptors, and a random forest regressor to create saliency scores for each region in the image. In our improved saliency detection method, mDRFI, we have added some new features to regional property descriptors. Also, in order to achieve more robust regional background descriptors, a thresholding algorithm is proposed to obtain a new pseudo-background region. Findings reveal that mDRFI is superior to DRFI in detecting the lesion as the salient object in dermoscopic images. The proposed overall lesion segmentation framework uses detected saliency map to construct an initial mask of the lesion through thresholding and postprocessing operations. The initial mask is then evolving in a level set framework to fit better on the lesion's boundaries. The results of evaluation tests on three public datasets show that our proposed segmentation method outperforms the other conventional state-of-the-art segmentation algorithms and its performance is comparable with most recent approaches that are based on deep convolutional neural networks.

An Adaptive Synthetic Aperture Method Applied to Ultrasound Tissue Harmonic Imaging.

In recent years, the minimum variance (MV) beamformer has been highly regarded since it provides high resolution and contrast in B-mode ultrasound imaging compared with nonadaptive delay-and-sum (DAS) beamformer. However, the performance of MV beamformer is degraded in the presence of the noise due to inaccurate estimation of the covariance matrix resulting in low-quality images. The conventional tissue harmonic imaging (THI) offers multiple advantages over conventional pulse-echo ultrasound imaging, including enhanced contrast resolution and improved axial and lateral resolutions, but low signal-to-noise ratio (SNR) is a major problem facing this imaging method, which uses a fixed transmit focus and dynamic receive focusing (DRF). In this paper, a synthetic aperture method based on the virtual source, namely, bidirectional pixel-based focusing (BiPBF), has been combined with the MV beamformer and then applied to second-harmonic ultrasound imaging. The main objective is suppressing the noise level to enhance the performance of the MV beamformer in the harmonic imaging, especially in lower and deeper depths where the SNR is low. In addition, combining the BiPBF and MV weighting results in simultaneous improvement in imaging resolution and contrast, in comparison with the conventional methods: DRF (DAS), BiPBF (DAS), and DRF (MV). The performance of the proposed method is evaluated on simulated and experimental RF data. The THI is achieved using the pulse-inversion technique. The results of the simulated wire phantom demonstrate that the proposed beamformer can achieve the best lateral resolution, along different depths, compared with DRF (DAS), BiPBF (DAS), and DRF (MV) methods. The results of the simulated and experimental cyst phantoms show that the new beamformer improves the contrast ratio (CR) and contrast-to-noise ratio (CNR) of the resulting images. In results of simulated cyst phantom, in average, the new beamformer improves the CR and CNR of the cyst about (7.4 dB, 49%), (3.2 dB, 16%), and (5 dB, 26%) compared with DRF (DAS), BiPBF (DAS), and DRF (MV), respectively. In results of experimental cyst phantom, these relative improvements are about (4.2 dB, 22%), (1.7 dB, 7%), and (2.6 dB, 15%). In addition, BiPBF (MV) method offers improved edge definition of cysts in comparison with the other methods.

A Fast and Robust Beamspace Adaptive Beamformer for Medical Ultrasound Imaging.

Minimum variance beamformer (MVB) increases the resolution and contrast of medical ultrasound imaging compared with nonadaptive beamformers. These advantages come at the expense of high computational complexity that prevents this adaptive beamformer to be applied in a real-time imaging system. A new beamspace (BS) based on discrete cosine transform is proposed in which the medical ultrasound signals can be represented with less dimensions compared with the standard BS. This is because of symmetric beampattern of the beams in the proposed BS compared with the asymmetric ones in the standard BS. This lets us decrease the dimensions of data to two, so a high complex algorithm, such as the MVB, can be applied faster in this BS. The results indicated that by keeping only two beams, the MVB in the proposed BS provides very similar resolution and also better contrast compared with the standard MVB (SMVB) with only 0.44% of needed flops. Also, this beamformer is more robust against sound speed estimation errors than the SMVB.

Synthetic Aperture Ultrasound Fourier Beamformation Using Virtual Sources.

An efficient Fourier beamformation algorithm is presented for multistatic synthetic aperture ultrasound imaging using virtual sources. The concept is based on the frequency domain wavenumber algorithm from radar and sonar and is extended to a multielement transmit/receive configuration using virtual sources. Window functions are used to extract the azimuth processing bandwidths and weight the data to reduce side lobes in the final image. Field II simulated data and SARUS (Synthetic Aperture Real-time Ultrasound System) measured data are used to evaluate the results in terms of point spread function, resolution, contrast, signal-to-noise ratio, and processing time. Lateral resolutions of 0.53 and 0.66 mm are obtained for Fourier Beamformation Using Virtual Sources (FBV) and delay and sum (DAS) on point target simulated data. Corresponding axial resolutions are 0.21 mm for FBV and 0.20 mm for DAS. The results are also consistent over different depths evaluated using a simulated phantom containing several point targets at different depths. FBV shows a better lateral resolution at all depths, and the axial and cystic resolutions of -6, -12, and -20 dB are almost the same for FBV and DAS. To evaluate the cyst phantom metrics, three different criteria of power ratio, contrast ratio, and contrast-to-noise ratio have been used. Results show that the algorithms have a different performance in the cyst center and near the boundary. FBV has a better performance near the boundary; however, DAS is better in the more central area of the cyst. Measured data from phantoms are also used for evaluation. The results confirm applicability of FBV in ultrasound, and 20 times less processing time is attained in comparison with DAS. Evaluating the results over a wide variety of parameters and having almost the same results for simulated and measured data demonstrates the ability of FBV in preserving the quality of image as DAS, while providing a more efficient algorithm with 20 times less computations.

Computational Complexity Reduction of Synthetic-aperture Focus in Ultrasound Imaging Using Frequency-domain Reconstruction.

A new frequency-domain implementation of a synthetic aperture focusing technique is presented in the paper. The concept is based on synthetic aperture radar (SAR) and sonar that is a developed version of the convolution model in the frequency domain. Compared with conventional line-by-line imaging, synthetic aperture imaging has a better resolution and contrast at the cost of more computational load. To overcome this problem, point-by-point reconstruction methods have been replaced by block-processing algorithms in radar and sonar; however, these techniques are relatively unknown in medical imaging. In this paper, we extended one of these methods called wavenumber to medical ultrasound imaging using a simple model of synthetic aperture focus. The model, derived here for monostatic mode, can be generalized to multistatic as well. The method consists of 4 steps: a 2D fast Fourier transform of the data, frequency shift of the data to baseband, interpolation to convert polar coordinates to rectangular ones, and returning the data to the spatial-domain using a 2D inverse Fourier transform. We have also used chirp pulse excitation followed by matched filtering and spotlighting algorithm to compensate the effect of differences in parameters between radar and medical imaging. Computational complexities of the two methods, wavenumber and delay-and-sum (DAS), have been calculated. Field II simulated point data have been used to evaluate the results in terms of resolution and contrast. Evaluations with simulated data show that for typical phantoms, reconstruction by the wavenumber algorithm is almost 20 times faster than classical DAS while retaining the resolution.

Combining the APES and Minimum-variance Beamformers for Adaptive Ultrasound Imaging.

In recent years, adaptive minimum-variance (MV) beamforming has been successfully applied to medical ultrasound imaging, resulting in simultaneous improvement in imaging resolution and contrast. MV has high resolution and hence can provide accurate estimates of the target locations. However, the MV amplitude estimates are significantly biased downward, especially when occurring the errors in model parameters. The amplitude and phase estimation (APES) beamformer gives much more accurate amplitude estimates at the target locations, but at the cost of lower resolution. To reap the benefits of both MV and APES, we have proposed a modified APES (MAPES) beamformer by adding a parameter which controls the trade-off between spatial and amplitude resolutions. We have also proposed an adaptive beamformer which combines the MV and APES. The proposed beamformer first estimates the peak locations using the MV estimator and then refines the amplitude estimates at these locations using the MAPES estimator. By using simulated and experimental data-point targets as well as cyst phantoms-we show the efficacy of the proposed beamformers.

Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging.

Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared with non-adaptive delay-and-sum (DAS) beamformers. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can significantly improve the imaging resolution, although their success in enhancing the contrast has not yet been satisfactory. It is desirable for the beamformer to improve the resolution and contrast at the same time. To this end, in this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and have shown a simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. Using EIBMV weights instead of the MV ones leads to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer. In addition, the proposed EIBMV beamformer presents a satisfactory robustness against data misalignment resulting from steering vector errors, outperforming the regularized MV beamformer.