A new similarity measure for non-local means filtering of MRI images

In this paper, the application of non-local means (NLM) filtering on MRI images is investigated. An essential component of any NLM-based algorithm is its similarity measure used to compare pixel intensities. Unfortunately, virtually all existing similarity measures used to denoise MRI images have been derived under the assumption of additive white Gaussian noise contamination. Since this assumption is known to fail at low values of signal-to-noise ratio (SNR), alternative formulations of these measures which take into account the correct (Rician) statistics of the noise are required. Accordingly, the main contribution of the present work is to introduce a new similarity measure for NLM filtering of MRI images, which is derived under bona fide statistical assumptions and proves to posses important theoretical advantages over alternative formulations. The utility and viability of the proposed method is demonstrated through a series of numerical experiments using both in silico and in vivo MRI data.     

基于动态全变差的动态磁共振图像重建

动态核磁共振(dMRI)成像技术要在极短的扫描时间内获取高时空精度的MR图像,目前仍然是一个难点。文中提出了一种新的基于动态全变差技术(DTV)的压缩感知(CS)动态磁共振图像重建方案,该方法充分利用dMRI数据在时空域的稀疏性。文中算法采用加权最小二乘算法解决重建速度过慢的问题。论文将该算法与目前比较先进的两种算法TV及k-t SLR做比较,实验数据来源于临床脑部电影数据集。实验结果显示该方法在重建精度上处于领先水平。     

基于代价函数的动态分布式解析映射机制

针对身份与位置的动态解析映射问题,依据终端的移动模式和通信特征,提出了一种基于代价函数的动态分布式移动解析映射机制。该机制将移动终端的通信状态划分为移动更新、均衡传输和解析查询3种通信模式,以最小化解析映射代价为目标,分别提出了基于分布式多播、一致性散列协同和主动式域内共享的解析映射方法,设计了对应的注册更新、解析查询和数据路由解析优化策略。仿真结果表明,该机制实现了移动过程中解析映射代价的最小化,具有较小的解析时延,对网络结构的动态变化具有良好的适应性。     

Regionally Optimized Reconstruction for Partially Parallel Imaging in MRI Applications

Based on the conventional SENSE and GRAPPA, a regionally optimized reconstruction method is developed for reduced noise and artifact level in partially parallel imaging. In this regionally optimized reconstruction, the field-of-view (FOV) is divided into a number of small regions. Over every small region, the noise amplification and data fitting error can be balanced and minimized locally by taking advantage of spatial correlation of neighboring pixels in reconstruction. The full FOV image can be obtained by ldquoregion-by-regionrdquo reconstruction. Compared with the conventional SENSE, this method gives better performance in the regions where there are pixels with high SENSE g-factors. Compared with GRAPPA, it is better in the regions where all the pixels have low SENSE g-factors. In this work, we applied the regionally optimized reconstruction in four important imaging experiments: brain, spine, breast, and cardiac. It was demonstrated in these experiments that the overall image quality using this regionally optimized reconstruction is better than that using the conventional SENSE or GRAPPA.     

曲线匹配法在雷达动态标校中的应用

雷达系统动态标校中,测量数据与真值之间一般通过记录时戳的匹配来计算测量值与真值的误差,从而进行雷达系统误差修正。时间匹配法在数据时戳精度较差时存在一定偏差,时戳精度越差,偏差越大。本文提出一种基于曲线匹配的方法,能忽略时戳误差的影响,并对比了两种方法的差异和优缺点,为雷达系统动态标校提供了一种精确、可行的数据匹配计算方法。     

Applications of active arrayed-waveguide gratings in dynamic WDMnetworking and routing

We describe how active arrayed-waveguide gratings (AWGs) may find a diverse range of applications in future dynamic wavelength division multiplexed (WDM) networking and routing. Our initial simulations indicate that these applications include dynamic signal power and erbium-doped fiber amplifier (EDFA) gain equalization with a dynamic range of 12 dB, and interchannel amplified spontaneous emission (ASE) suppression by more than 20 dB; optical add/drop multiplexing with passband-flattened channels and suppressions of 15 dB; and dynamic dispersion compensation of up to ±300 ps/nm     

Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series

We performed neural network clustering on dynamic contrast-enhanced perfusion magnetic resonance imaging time-series in patients with and without stroke. Minimal-free-energy vector quantization, self-organizing maps, and fuzzy c-means clustering enabled self-organized data-driven segmentation with respect to fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.     

Applications of XRD mapping to the control of compound semi-conductor processes

The demand for increasing volumes of devices at lower unit costs requires that successful producers of compound semiconductor devices move to full water processing and larger wafer sizes. Although processing of full wafers should, in theory, yield lower unit costs, this only true if the device yield remains independent of the area processed.     

A novel energy-aware multi-task dynamic mapping heuristic of NoC-based MPSoCs

Task mapping is an important issue in network-on-chip (NoC)-based multiprocessor systems-on-chips (MPSoCs) design. The dynamic characteristic of application execution enforces the use of dynamic task mapping. In this article, a hybrid energy-aware dynamic mapping strategy is proposed. The strategy consists of an off-line part and an on-line part. In the off-line part, optimisation tools are used to extract information that helps to reduce the energy consumption in the on-line mapping, while the on-line mapping heuristic makes use of the information. Experimental result shows that the energy consumption is reduced by 21%, on average, compared to the best neighbour heuristic.     

基于相似度的变精度容差关系扩充粗糙集模型

比较分析了已有扩充粗糙集模型,提出基于相似度的变精度容差关系扩充粗糙集模型。该模型根据两对象的相似度及属性值缺失情况划分客差类。通过与基于容差关系、相似关系、限制容差关系等扩充粗糙集模型比较,证实了该模型对不完备信息系统处理的现实可行性和有效性,最后给出了相对正域的概念及属性约简规则。     

A doubly adaptive approach to dynamic MRI sequence estimation

Dynamic magnetic resonance imaging (MRI) refers to the acquisition of a sequence of MRI images to monitor temporal changes in tissue structure. We present a method for the estimation of dynamic MRI sequences based on two complimentary strategies: an adaptive framework for the estimation of the MRI images themselves, and an adaptive method to tailor the MRI system excitations for each data acquisition. We refer to this method as the doubly adaptive temporal update method (DATUM) for dynamic MRI. Analysis of the adaptive image estimate framework shows that calculating the optimal system excitations for each new image requires complete knowledge of the next image in the sequence. Since this is not realizable, we introduce a linear predictor to aid in determining appropriate excitations. Simulated examples using real MRI data are included to illustrate that the doubly adaptive strategy can provide estimates with lower steady state error than previously proposed methods and also the ability to recover from dramatic changes in the image sequence.     

Three-dimensional mapping of acute ischemic regions usingartificial neural networks and tagged MRI

Many methods for mapping ischemic myocardial regions by functional analysis have been suggested. However, the complicated relationship between myocardial function and perfusion, and the inherent limitations of the imaging techniques used, have led to a generally low mapping accuracy. The authors show herein, that highly accurate mapping can be obtained by combining tagged magnetic resonance imaging (MRI), three-dimensional (3-D) analysis, and artificial neural networks. Nine canine hearts with acute ischemia were studied using multiplanar tagged MRI. Twenty-four myocardial cuboids were tagged in each heart and reconstructed in 3-D at end diastole (ED) and end systole (ES). The cuboids were arranged in three slices approximately 1 cm thick and covered most of the left ventricle (LV). Transmural thickening and endocardial area strain were calculated for each cuboid. Applying a post-mortem (PM) analysis, the percent ischemia in each cuboid was estimated using monastral blue dye; the PM analysis served as a “gold standard”. An artificial neural network (ANN), designed to estimate the percent ischemia in each cuboid from the functional indexes, was then created. The ANN “learned” the function-ischemia relationship in 192 cuboids taken from eight of the hearts and was asked to estimate the percent ischemia in the 24 cuboids of the ninth heart. The process was repeated nine times, each time using a different heart as test case. The average accuracy of mapping, i.e., the accuracy with which the ANN has mapped the normal and ischemic cuboids using the functional parameters, was 87.5%±7.8 (s.d.). This accuracy was superior to the accuracy obtained by optimal thresholding of the same thickening (80.1%) and endocardial strain (76.9%) data     

Tone mapping with contrast preservation and lightness correction in high dynamic range imaging

In real-world environments, the human visual system perceives a wide range of luminance in a scene. However, the full range of tones in a high dynamic range (HDR) scene cannot be displayed on standard display devices due to their low dynamic range (LDR). Therefore, tone mapping is necessary to faithfully display a HDR scene on an LDR display device. Existing tone mapping methods have problems because details and contrast in a scene are not preserved faithfully, and they also distort the colors in a scene. Thus, we propose a tone mapping method for preserving the detail in an HDR scene using a weighted least squares filter, which preserves the global contrast in a scene by using a competitive learning neural network, before applying a tone reproduction operator to preserve the color without shifting the lightness. According to the Helmholtz–Kohlrausch effect, the perception of brightness depends on the lightness, chroma, and hue of a color. For example, the luminance of pixels with specific colors such as red and blue is low in an HDR scene. The proposed method corrects the lightness of pixels according to the color (i.e., lightness, chroma, and hue) of a tone-mapped image. Experimental results with several test sets of images demonstrated that the proposed tone mapping method with contrast preservation and lightness correction is more suitable for dynamic range compression than existing tone mapping methods, while it also preserves the color of a scene in an effective way.     

Onboard dynamic RGB-D simultaneous localization and mapping for mobile robot navigation

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.     

Evaluation of Electrorheological Fluid Dampers for Applications at 3-T MRI Environment

This paper evaluates the use of electrorheological fluids (ERFs) within a magnetic resonance imaging (MRI) environment. ERF is a semiactive variable impedance material, which could be used as an alternative type of resistive force/torque generation or in combination with other actuators as a damper/clutch to modulate the output force/torque of the actuator. In this paper, an ERF damper/brake is introduced and its magnetic resonance (MR) compatibility is examined at a 3-T MR imaging environment by measuring the output performance of the damper and the SNR of the MRI images. The experimental results showed that damper's resistive force generation while positioned within the MRI is almost the same as that in normal operation. The signal-to-noise investigation was performed both with a phantom and human. The results indicated that the ERF damper did not affect the MRI images when it was operated over 30 cm away from the MRI's RF coil. We hope that the synthesis and tables presented in this paper can facilitate the choice of ERF brake actuation principle to various applications in an MR environment.     

Fast joint reconstruction of dynamic R2* and field maps in functional MRI

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing ${T}_{2}^{*}$-weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct ${R}_{2}^{*}$ maps which are quantifiable and have comparable BOLD contrast as ${T}_{2}^{*}$-weighted images. However, conventional ${R}_{2}^{*}$ mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs ${R}_{2}^{*}$ and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both ${R}_{2}^{*}$ and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for ${T}_{2}^{*}$-weighted imaging the resulting ${R}_{2}^{*}$ maps performed comparably to ${T}_{2}^{*}$-weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance.      

Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR

We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI) data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-dependent KL transform makes our approach ideally suited to a range of dynamic imaging problems, even when the motion is not periodic. In comparison to current KLT-based methods that rely on a two-step approach to first estimate the basis functions and then use it for reconstruction, we pose the problem as a spectrally regularized matrix recovery problem. By simultaneously determining the temporal basis functions and its spatial weights from the entire measured data, the proposed scheme is capable of providing high quality reconstructions at a range of accelerations. In addition to using the compact representation in the KLT domain, we also exploit the sparsity of the data to further improve the recovery rate. Validations using numerical phantoms and in vivo cardiac perfusion MRI data demonstrate the significant improvement in performance offered by the proposed scheme over existing methods.     

Accelerating dynamic spiral MRI by algebraic reconstruction from undersampled k--t space

The temporal resolution of dynamic magnetic resonance imaging (MRI) can be increased by sampling a fraction of k-space in an interleaved fashion, which introduces spatial and temporal aliasing. We describe algebraically and graphically the aliasing process caused by dynamic undersampled spiral imaging within 3-D xyf space (the Fourier transform of k(x)k(y)t space) and formulate the unaliasing problem as a set of independent linear inversions. Since each linear system is numerically underdetermined, the use of prior knowledge in the form of bounded support regions is proposed. To overcome the excessive memory requirements for handling large matrices, a fast implementation of the conjugate gradient (CG) method is used. Numerical simulation and in vivo experiments using spiral twofold undersampling demonstrate reduced motion artifacts and the improved depiction of fine cardiac structures. The achieved reduction of motion artifacts and motion blur is comparable to simple filtering, which is computationally more efficient, while the proposed algebraic framework offers greater flexibility to incorporate additional algebraic acceleration techniques and to handle arbitrary sampling schemes.     

Potential of MRI and Ultrasound Radiation Force in Elastography: Applications to Diagnosis and Therapy

Elastography has many exciting new areas of application in the domains of diagnosis and therapy. We present in this overview the current gold standard given by MR elastography, which uses a full three-dimensional approach to solve locally for the unknown complex shear modulus at one frequency. Clinical results for benign and malignant breast lesions are shown. Less rigorous in terms of data completeness, but significantly faster and easier to apply, we introduce the ultrasound-based supersonic shear imaging technique, which uses acoustic radiation force to generate inside the medium planar shear waves. Subsequent ultrafast imaging of the propagating shear wave allows one to recuperate detailed time-of-flight maps of in-vivo breast lesions. Lastly, we present initial results for using magnetic resonance imaging and acoustic radiation force together for high-intensity focused ultrasound interventions.