Stationary wavelet transform for under-sampled MRI reconstruction

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional l_p-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions.     

OFDM symbol detection integrated with channel multipath gains estimation for doubly-selective fading channels

Orthogonal Frequency Division Multiplexing (OFDM) is a technique for wideband transmission that is commonly used in modern wireless communication systems because of its good performance over frequency selective channels. However OFDM systems are sensitive to channel time variations resulting in Inter-Carrier Interference (ICI), that without suitable detection methods can degrade performance significantly. Channel State Information (CSI) is essential to various OFDM detection schemes, and its acquisition is a critical factor over time varying channels. This work considers a Kalman filter channel multipath gains estimation technique for time varying environments, integrated with a novel detection scheme for OFDM based on a Sphere Decoding (SD) algorithm derived to exploit the banded structure of the channel matrix. This combined scheme employs decision–feedback from the SD requiring only a low pilot symbol density, and hence improves bandwidth efficiency. Three techniques for integrating the Kalman filter operating in decision–feedback mode, with SD data detection that produces these decisions, are considered in this paper. When compared with other competing schemes, this integrated symbol detection and channel multipath gains estimation approach for OFDM provides performance advantages over time varying channels. Furthermore, it is shown that for moderate Doppler shifts the degradation that carrier phase noise induces in this scheme is small.     

A segmentation based channel scheduling scheme for improving channel utilization in OBS networks

Channel scheduling is an important aspect in optical burst switching (OBS) networks. OBS is a promising solution for the future Internet backbone. In OBS network a key issue is data channel scheduling. In this paper we propose a channel scheduling scheme to optimize channel utilization for OBS networks with segmentation. In this paper, we propose an efficient way to organize the void intervals. The channel scheduling method is compared with existing scheduling schemes. Simulation results show that both channel utilization and burst loss ratio performance improved as compared to existing scheduling schemes.     

Lorentz force electrical impedance tomography using pulse compression technique

Lorentz force electrical impedance tomography(LFEIT) combines ultrasound stimulation and electromagnetic field detection with the goal of creating a high contrast and high resolution hybrid imaging modality. In this study, pulse compression working together with a linearly frequency modulated ultrasound pulse was investigated in LFEIT. Experiments were done on agar phantoms having the same level of electrical conductivity as soft biological tissues. The results showed that:(i) LFEIT using pulse compression could detect the location of the electrical conductivity variations precisely;(ii)LFEIT using pulse compression could get the same performance of detecting electrical conductivity variations as the traditional LFEIT using high voltage narrow pulse but reduce the peak stimulating power to the transducer by 25.5 dB;(iii)axial resolution of 1 mm could be obtained using modulation frequency bandwidth 2 MHz.     

光谱编码成像系统的数据压缩技术

利用时间拉伸显微成像系统观察并记录非重复动态随机现象,在其超高成像速度和高空间分辨率下必定会产生大量的数据。一种基于差分检测和游程编码的数据压缩方法,可以有效地解决时间拉伸成像系统的数据存储问题。差分检测可以消除连续相同的信号,只检测出相邻信号的差异,从而提高游程编码算法的有效性。实验中,采用扫描频率为77.76 MHz的时间拉伸显微成像对分辨率板、人红细胞和人乳腺癌细胞线性扫描成像。实验结果表明,数据压缩比可以达到8.47,对比分析发现经过差分检测方法可以获得更高的压缩比。另外,通过计算重建后的图像与原图的结构相似性(SSIM)值发现,经过数据压缩后高质量的图像可以被重建。     

Parameter optimization of pulse compression in ultrasound imaging systems with coded excitation

A linear array imaging system with coded excitation is considered, where the proposed excitation/compression scheme maximizes the signal-to-noise ratio (SNR) and minimizes sidelobes at the output of the compression filter. A pulse with linear frequency modulation (LFM) is used for coded excitation. The excitation/compression scheme is based on the fast digital mismatched filtering. The parameter optimization of the excitation/compression scheme includes (i) choice of an optimal filtering function for the mismatched filtering; (ii) choice of an optimal window function for tapering of the chirp amplitude; (iii) optimization of a chirp-to-transducer bandwidth ratio; (iv) choice of an appropriate n-bit quantizer. The simulation results show that the excitation/compression scheme can be implemented as a Dolph–Chebyshev filter including amplitude tapering of the chirp with a Lanczos window. An example of such an optimized system is given where the chirp bandwidth is chosen to be 2.5 times the transducer bandwidth and equals 6 MHz: The sidelobes are suppressed to −80 dB, for a central frequency of 4 MHz, and to −94 dB, for a central frequency of 8 MHz. The corresponding improvement of the SNR is 18 and 21 dB, respectively, when compared to a conventional short pulse imaging system. Simulation of B-mode images demonstrates the advantage of coded excitation systems of detecting regions with low contrast.     

稀疏信道条件下正交多载波扩频水声通信无导频信道估计算法

提出了一种无导频信道估计算法,该算法首先利用水声信道的稀疏特性对扩频信号进行识别,再利用扩频信号识别结果对水声信道响应进行判决反馈估计,从而实现对稀疏水声信道响应的可靠重建。仿真和湖上试验结果证实:无导频信道估计算法可以在不降低通信质量的前提下,实现对水声通信信号的稳健均衡,提升水声通信速率。     

Undersampled MRI reconstruction with patch-based directional wavelets

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a preconstructed basis or dictionary. In this paper, patch-based directional wavelets are proposed to reconstruct images from undersampled k-space data. A parameter of patch-based directional wavelets, indicating the geometric direction of each patch, is trained from the reconstructed image using conventional compressed sensing MRI methods and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. A reconstruction formulation is proposed and solved via an efficient alternating direction algorithm. Simulation results on phantom and in vivo data indicate that the proposed method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise. Besides, the proposed method is not sensitive to the initial image when training directions.     

A nonlinear regularization strategy for GRAPPA calibration

A regularization strategy is described for generalized autocalibrating partially parallel acquisition (GRAPPA) that allows successful calibration using a small number of autocalibration signals (ACS). The approach requires certain nonlinear relationships between the GRAPPA coefficients to be satisfied, which increases the redundancy so that fewer ACS need to be acquired.     

中子半影成像图像反演方法研究

给出了中子半影成像技术的原理;分析了系统分辨率的影响因子;利用MCNP软件对中子半影成像过程进行模拟;用改进的逆滤波和维纳滤波两种方法对成像主轴上一点源和偏轴两点源的编码图像进行重建,并对编码像在降低噪声和去除本底前后的反演结果进行了比较。模拟结果表明维纳滤波法能够更好地抑制统计噪声。     

中子半影成像图像反演方法研究

 给出了中子半影成像技术的原理;分析了系统分辨率的影响因子;利用MCNP软件对中子半影成像过程进行模拟;用改进的逆滤波和维纳滤波两种方法对成像主轴上一点源和偏轴两点源的编码图像进行重建,并对编码像在降低噪声和去除本底前后的反演结果进行了比较。模拟结果表明维纳滤波法能够更好地抑制统计噪声。     

Evaluation of a multiple spin- and gradient-echo (SAGE) EPI acquisition with SENSE acceleration: Applications for perfusion imaging in and outside the brain

Perfusion-based changes in MR signal intensity can occur in response to the introduction of exogenous contrast agents and endogenous tissue properties (e.g. blood oxygenation). MR measurements aimed at capturing these changes often implement single-shot echo planar imaging (ssEPI). In recent years ssEPI readouts have been combined with parallel imaging (PI) to allow fast dynamic multi-slice imaging as well as the incorporation of multiple echoes. A multiple spin- and gradient-echo (SAGE) EPI acquisition has recently been developed to allow measurement of transverse relaxation rate (R₂ and R₂^?) changes in dynamic susceptibility contrast (DSC)-MRI experiments in the brain. With SAGE EPI, the use of PI can influence image quality, temporal resolution, and achievable echo times. The effect of PI on dynamic SAGE measurements, however, has not been evaluated. In this work, a SAGE EPI acquisition utilizing SENSE PI and partial Fourier (PF) acceleration was developed and evaluated. Voxel-wise measures of R₂ and R₂^? in healthy brain were compared using SAGE EPI and conventional non-EPI multiple echo acquisitions with varying SENSE and PF acceleration. A conservative SENSE factor of 2 with PF factor of 0.73 was found to provide accurate measures of R₂ and R₂^? in white (WM) (r_R2 = [0.55–0.79], r_R2? = [0.47–0.71]) and gray (GM) matter (r_R2 = [0.26–0.59], r_R2? = [0.39–0.74]) across subjects. The combined use of SENSE and PF allowed the first dynamic SAGE EPI measurements in muscle, with a SENSE factor of 3 and PF factor of 0.6 providing reliable relaxation rate estimates when compared to multi-echo methods. Application of the optimized SAGE protocol in DSC-MRI of high-grade glioma patients provided T₁ leakage-corrected estimates of CBV and CBF as well as mean vessel diameter (mVD) and simultaneous measures of DCE-MRI parameters K^trans and v_e. Likewise, application of SAGE in a muscle reperfusion model allowed dynamic measures of R₂′, a parameter that has been shown to correlate with muscle oxy-hemoglobin saturation.     

DEIReconstructor:a software for diffraction enhanced imaging processing and tomography reconstruction

Diffraction enhanced imaging (DEI) has been widely applied in many fields, especially when imaging low-Z samples or when the difference in the attenuation coefficient between different regions in the sample is too small to be detected. Recent developments of this technique have presented a need for a new software package for data analysis. Here, the Diffraction Enhanced Image Reconstructor (DEIReconstructor), developed in Matlab, is presented. DEIReconstructor has a user-friendly graphical user interface and runs under any of the 32-bit or 64-bit Microsoft Windows operating systems including XP and Win7. Many of its features are integrated to support imaging preprocessing, extract absorption, refractive and scattering information of diffraction enhanced imaging and allow for parallel-beam tomography reconstruction for DEI-CT. Furthermore, many other useful functions are also implemented in order to simplify the data analysis and the presentation of results. The compiled software package is freely available.     

A single-channel single-winner auction model for homogeneous channel allocation in CRNs

Cognitive radio (CR) is a novel intelligent technology which enables opportunistic access to temporarily unused licensed frequency bands. A key functionality of CR is to distribute free channels efficiently amongst Secondary Users (SUs) boosting spectrum usage to assist the escalating wireless applications world wide. In this context, this paper introduces a channel allocation mechanism which enables SUs (CR enabled unlicensed users) to dynamically access unused spectrum bands to fulfill their spectrum needs. We model the channel allocation problem as a sealed-bid single-sided auction which primarily aims at maximizing the overall spectrum utilization. Market based spectrum auctions in CR networks motivate licensed users to participate and lease their under utilized radio resources to gain monetary benefits. Sequential bidding is applied to this model for auctioning homogeneous channels, which reduces communication overhead. Bid submission takes into account two major CR constraints, namely, dynamics in spectrum opportunities and differences in channel availability time, which on incorporation provide disruption free data transmission to the SUs. We reduce resource wastage in this model by performing multiple auction rounds. Application of second price auction determines winning bidders and their respective payments to auctioneer. The design of our auction mechanism is supported with the proofs of truthfulness and individually rational properties. Furthermore, experimental results indicate that our model outperforms an existing auction method. Spectrum utilization values show 22 to 75% improvement in our model with changing number of SUs, and 23 to 93% improvement in our model with changing number of channels.     

Efficient compression of motion-compensated sub-images with Karhunen-Loeve transform in three-dimensional integral imaging

An approach to highly enhance the compression efficiency of the integral images by applying the Karhunen-Loeve transform (KLT) algorithm to the motion-compensated sub-images is proposed. The sub-images transformed from the elemental images picked-up from the three-dimensional (3D) object might represent the different perspectives of the object. Thus, the similarity among the sub-images gets better than that among the elemental images, so that an improvement of compression efficiency of the sub-images could be obtained. However, motion vectors occurred among the sub-images might result in an additional increase of image data to be compressed. Accordingly, in this paper, motion vectors have been estimated and compensated in all sub-image in advance. Then the KLT algorithm was applied to these motion-compensated sub-images for compression. It is shown from some experimental results that compression efficiency of the proposed method has been improved up to 24.44%, 40.62%, respectively, on the average compared to that of the conventional KLT compression method and that of the JPEG.     

Composite MR image reconstruction and unaliasing for general trajectories using neural networks

In rapid parallel magnetic resonance imaging, the problem of image reconstruction is challenging. Here, a novel image reconstruction technique for data acquired along any general trajectory in neural network framework, called “Composite Reconstruction And Unaliasing using Neural Networks” (CRAUNN), is proposed. CRAUNN is based on the observation that the nature of aliasing remains unchanged whether the undersampled acquisition contains only low frequencies or includes high frequencies too. Here, the transformation needed to reconstruct the alias-free image from the aliased coil images is learnt, using acquisitions consisting of densely sampled low frequencies. Neural networks are made use of as machine learning tools to learn the transformation, in order to obtain the desired alias-free image for actual acquisitions containing sparsely sampled low as well as high frequencies. CRAUNN operates in the image domain and does not require explicit coil sensitivity estimation. It is also independent of the sampling trajectory used, and could be applied to arbitrary trajectories as well. As a pilot trial, the technique is first applied to Cartesian trajectory-sampled data. Experiments performed using radial and spiral trajectories on real and synthetic data, illustrate the performance of the method. The reconstruction errors depend on the acceleration factor as well as the sampling trajectory. It is found that higher acceleration factors can be obtained when radial trajectories are used. Comparisons against existing techniques are presented. CRAUNN has been found to perform on par with the state-of-the-art techniques. Acceleration factors of up to 4, 6 and 4 are achieved in Cartesian, radial and spiral cases, respectively.     

Parallel imaging with 3D TPI trajectory: SNR and acceleration benefits

Three-dimensional (3D) twisted projection imaging (TPI) trajectory has a unique advantage in sodium (²³Na) imaging on clinical MRI scanners at 1.5 or 3 T, generating a high signal-to-noise ratio (SNR) with a short acquisition time (∼10 min). Parallel imaging with an array of coil elements transits SNR benefits from small coil elements to acquisition efficiency by sampling partial k-space. This study investigates the feasibility of parallel sodium imaging with emphases on SNR and acceleration benefits provided by the 3D TPI trajectory. Computer simulations were used to find available acceleration factors and noise amplification. Human head studies were performed on clinical 1.5/3-T scanners with four-element coil arrays to verify simulation outcomes. In in vivo studies, proton (¹H) data, however, were acquired for concept–proof purpose. The sensitivity encoding (SENSE) method with the conjugate gradient algorithm was used to reconstruct images from accelerated TPI-SENSE data sets. Self-calibration was employed to estimate coil sensitivities. Noise amplification in TPI-SENSE was evaluated using multiple noise trials. It was found that the acceleration factor was as high as 5.53 (corresponding to acceleration number 2×3, ring-by-rotation), with a small image error of 6.9% when TPI projections were reduced in both polar (ring) and azimuthal (rotation) directions. The average noise amplification was as low as 98.7%, or 27% lower than Cartesian SENSE at that acceleration factor. The 3D nature of both TPI trajectory and coil sensitivities might be responsible for the high acceleration and low noise amplification. Consequently, TPI-SENSE may have potential advantages for parallel sodium imaging.