PROPELLER磁共振成像数据重建中的仿射运动校正新算法

 PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction)磁共振成像方法对刚性运动伪影的消除效果非常显著,已经在头部磁共振成像中获得了成功应用。但是刚性运动一般仅存在于头部成像中,人体其它部位成像往往伴随着不同程度的软组织拉伸变形。对于这种软组织变形必须基于非刚性运动模型才能准确地进行描述并加以校正。本文将PROPELLER采样中的每个k-空间条经过傅立叶逆变换重建得到临时图像,通过基于仿射运动模型的图像配准算法获得非刚性运动信息,然后根据仿射变换的频域性质,对PROPELLER采样中的每个k-空间条进行校正,最后经网格化重建得到最终图像。仿真实验与真实数据实验表明,相对于现有的PROPELLER重建算法,本文所提算法对于刚性运动与仿射运动造成的伪影均具有很好的校正效果。     

衍射层析成像的Voronoi图密度补偿算法的研究

网格算法是最常见的衍射层析成像的频域重建算法,然而这种算法却容易引入误差,且对采样点的分布形状较敏感,因此,本文提出了一种基于Voronoi图密度补偿的超声衍射层析成像重建算法.首先,用三角剖分快速生成算法生成投影数据的Voronoi图,并对在外凸壳上对应的Voronoi图面积是无穷大的点通过拟合、插值处理使之变为有限的补偿面积,从而得到整个点集的补偿面积.其次,提出了基于Voronoi图面积密度补偿的衍射层析成像的非均匀傅里叶变换网格重建算法,重建图像的质量较没有补偿的有很大提高.最后,提出了选取1/4圆弧的数据集重建方案,实验结果表明：在重建质量相当的情况下,1/4圆弧的重建时间比1/2圆弧少27.32%.     

一种可配置的CNN协加速器的FPGA实现方法

针对卷积神经网络中卷积运算复杂度高而导致计算时间过长的问题,本文提出了一种八级流水线结构的可配置CNN协加速器FPGA实现方法.通过在卷积运算控制器中嵌入池化采样控制器的复用手段使计算模块获得更多资源,利用mirror-tree结构来提高并行度,并采用Map算法来提高计算密度,同时加快了计算速度.实验结果表明,当精度为32位定点数/浮点数时,该实现方法的计算性能达到22.74GOPS.对比MAPLE加速器,计算密度提高283.3%,计算速度提高了224.9%,对比MCA（Memory-Centric Accelerator）加速器,计算密度提高了14.47%,计算速度提高了33.76%,当精度为8-16位定点数时,计算性能达到58.3GOPS,对比LBA（Layer-Based Accelerator）计算密度提高了8.5%.     

基于ZYNQ的可重构卷积神经网络加速器

针对卷积神经网络中卷积运算复杂度高、计算量大及算法在CPU和GPU上计算时存在延时及功耗限制问题,从提高现有硬件平台计算速率、降低功耗角度出发,设计了一种基于ZYNQ的具有高吞吐率和低功耗的可重构神经网络加速系统.为充分利用运算资源,探索了一种卷积运算循环优化电路;为降低带宽访问量,设计了一种数据在内存中的特殊排列方式...     

点云隐式曲面快速重建算法研究

提出一种点云数据隐式曲面高效重建算法。该算法首先基于传统径向基函数隐式曲面重建算法对点云数据进行低解析度、低精度快速插值,然后采用三线性插值对点云数据进行高解析度、低精度插值,最后根据欧氏距离确定点云零水平集附近需要处理的区域,处理过程中只对区域内点云数据进行滤波降噪。与传统方法相比,本文算法既可以保证曲面重建精度,又可以缩短计算时间。在头部点云数据的曲面重建过程中,本文算法能够实现与传统算法相近的精度,同时使插值运算时间减少63.21%。     

复杂密度场测量中的莫尔层析重建技术

将一种新的莫尔层析重建算法应用于复杂流场测量.该算法由莫尔偏折原理和网格重建技术推导得出,直接使用光线偏折角进行修正迭代,适用于有限角采样和包含遮挡物等非完全数据条件下的层析重建.应用该算法,对火箭燃气射流场和高超音速流场的莫尔偏折图进行了处理和计算,重建出流场截面的密度分布,并对重建结果进行了分析.     

FIR数字滤波器的一种快速算法

FIR数字滤波器本质上是一种线性卷积的运算,当数字滤波器的阶次N很大时,计算量很大,计算速度很慢,达不到系统对实时性的要求。文章介绍了一种数字信号处理算法,该算法将线性卷积运算转换成加法运算,利用加法运算进行求解,避免了数据堆积,加快了运算速度,从而使数字滤波器处理过程实时、快速。     

改进的bregman加速算法

在使用CT进行图像重建的过程中,需要在不同角度下对目标对象进行采样,然后利用图像重建算法生成重建结果,由于采样的数据越多,重建速率越慢,往往需要在不完全的采样角度下对图像进行重建,即稀疏重建.为了对传统稀疏重建算法的迭代速度进行改进,在传统bregman图像重建算法的基础上提出了一种新的加速迭代算法.该算法以bre-g...     

基于光谱成像技术的地理遥感信息三维显示研究

考虑地理遥感图像尺度空间极点这样一种二维观测数据,通过光谱成像技术实现地理遥感信息的三维显示,对于提高地理遥感图像重建质量具有重要应用价值。以光谱成像原理为基础,通过高斯差分函数利用卷积创建光谱成像技术获取的地理遥感图像尺度空间,利用高斯金字塔采用降采样计算获得尺度空间各层金字塔,通过对金字塔内的每张图片实行差分运算,获得地理遥感图像尺度空间极值点;将该极值点作为二维观测数据中的一种,依据二维观测数据基于压缩感知理论以及采用编码孔径快照光谱成像系统完成地理遥感图像信息的光谱压缩采样后,采用两步迭代收缩阈值重建法重建三维光谱图像。实验验证该方法重建的地理遥感图像信噪比高、重建效率和精度高。     

聚束SAR快速卷积反投影成像算法的研究

对应用于聚束合成孔径雷达成像的卷积反投影(CBP)算法进行了研究,提出了一种新的快速反投影算法,在分割图像及角度降采样基础上,采用递归调用方法,降低了CBP算法的计算量,相对于直接反投影的计算量O(N3),快速反投影算法的计算复杂度可降低为O(N21bN)。同时讨论了递归参数和角过采样参数的选择对算法的影响,仿真了四种插值方法对运算性能的影响,从而分析了计算复杂度和精确度的关系,并且通过与直接反投影算法仿真结果的比较,验证了快速卷积反投影算法在较小均方根误差水平下可以显著减小计算量。     

Conjugate phase MRI reconstruction with spatially variant sample density correction

A new image reconstruction method to correct for the effects of magnetic field inhomogeneity in non-Cartesian sampled magnetic resonance imaging (MRI) is proposed. The conjugate phase reconstruction method, which corrects for phase accumulation due to applied gradients and magnetic field inhomogeneity, has been commonly used for this case. This can lead to incomplete correction, in part, due to the presence of gradients in the field inhomogeneity function. Based on local distortions to the k-space trajectory from these gradients, a spatially variant sample density compensation function is introduced as part of the conjugate phase reconstruction. This method was applied to both simulated and experimental spiral imaging data and shown to produce more accurate image reconstructions. Two approaches for fast implementation that allow the use of fast Fourier transforms are also described. The proposed method is shown to produce fast and accurate image reconstructions for spiral sampled MRI.     

Real-Time Reconstruction of Sensitivity Encoded Radial Magnetic Resonance Imaging Using a Graphics Processing Unit

A barrier to the adoption of non-Cartesian parallel magnetic resonance imaging for real-time applications has been the times required for the image reconstructions. These times have exceeded the underlying acquisition time thus preventing real-time display of the acquired images. We present a reconstruction algorithm for commodity graphics hardware (GPUs) to enable real time reconstruction of sensitivity encoded radial imaging (radial SENSE). We demonstrate that a radial profile order based on the golden ratio facilitates reconstruction from an arbitrary number of profiles. This allows the temporal resolution to be adjusted on the fly. A user adaptable regularization term is also included and, particularly for highly undersampled data, used to interactively improve the reconstruction quality. Each reconstruction is fully self-contained from the profile stream, i.e., the required coil sensitivity profiles, sampling density compensation weights, regularization terms, and noise estimates are computed in real-time from the acquisition data itself. The reconstruction implementation is verified using a steady state free precession (SSFP) pulse sequence and quantitatively evaluated. Three applications are demonstrated; real-time imaging with real-time SENSE 1) or $k$-$t$ SENSE 2) reconstructions, and 3) offline reconstruction with interactive adjustment of reconstruction settings.      

Field inhomogeneity correction based on gridding reconstruction for magnetic resonance imaging

Spatial variations of the main field give rise to artifacts in magnetic resonance images if disregarded in reconstruction. With non-Cartesian k-space sampling, they often lead to unacceptable blurring. Data from such acquisitions are usually reconstructed with gridding methods and optionally restored with various correction methods. Both types of methods essentially face the same basic problem of adequately approximating an exponential function to enable an efficient processing with fast Fourier transforms. Nevertheless, they have commonly addressed it differently so far. In the present work, a unified approach is pursued. The principle behind gridding methods is first generalized to nonequispaced sampling in both domains and then applied to field inhomogeneity correction. Three new algorithms, which are compatible with a direct conjugate phase and an iterative algebraic reconstruction, are derived in this way from a straightforward embedding of the data into a higher dimensional space. Their evaluation in simulations and phantom experiments with spiral k-space sampling shows that one of them promises to provide a favorable compromise between fidelity and complexity compared with existing algorithms. Moreover, it allows a simple choice of key parameters involved in approximating an exponential function and a balance between the accuracy of reconstruction and correction.     

TOF相机实时高精度深度误差补偿方法

TOF（Time-Of-Flight）相机获取的深度值存在着边角畸变和精度偏移，目前主要是通过误差查找表或曲线拟合等技术进行误差补偿，计算量大且补偿速度慢。通过对TOF相机在不同距离的深度误差分布规律的分析，提出了一种实时、高精度的误差补偿方法。该方法利用TOF深度图像的旋转对称性以及误差分布的特性，简化了误差补偿模型、降低参数数量级，有效提升了补偿的精度和速度。将算法应用于基于TOF原理的Kinect v2深度传感器进行深度补偿，使得有效距离内平面度误差下降到0.63 mm内，平均误差下降到0.704 0 mm内，单帧数据补偿时间在90 ms内。由于该算法仅基于光径差进行补偿，因此适用于所有TOF原理的相机。实验结果表明，该算法能够快速有效减少TOF相机的深度误差，适用于实时、高精度的大视场三维重建。     

On the optimality of the gridding reconstruction algorithm

Gridding reconstruction is a method to reconstruct data onto a Cartesian grid from a set of nonuniformly sampled measurements. This method is appreciated for being robust and computationally fast. However, it lacks solid analysis and design tools to quantify or minimize the reconstruction error. Least squares reconstruction (LSR), on the other hand, is another method which is optimal in the sense that it minimizes the reconstruction error. This method is computationally intensive and, in many cases, sensitive to measurement noise. Hence, it is rarely used in practice. Despite their seemingly different approaches, the gridding and LSR methods are shown to be closely related. The similarity between these two methods is accentuated when they are properly expressed in a common matrix form. It is shown that the gridding algorithm can be considered an approximation to the least squares method. The optimal gridding parameters are defined as the ones which yield the minimum approximation error. These parameters are calculated by minimizing the norm of an approximation error matrix. This problem is studied and solved in the general form of approximation using linearly structured matrices. This method not only supports more general forms of the gridding algorithm, it can also be used to accelerate the reconstruction techniques from incomplete data. The application of this method to a case of two-dimensional (2-D) spiral magnetic resonance imaging shows a reduction of more than 4 dB in the average reconstruction error.     

Least-Square NUFFT Methods Applied to 2-D and 3-D Radially Encoded MR Image Reconstruction

Radially encoded MRI has gained increasing attention due to its motion insensitivity and reduced artifacts. However, because its samples are collected nonuniformly in the $k$-space, multidimensional (especially 3-D) radially sampled MRI image reconstruction is challenging. The objective of this paper is to develop a reconstruction technique in high dimensions with on-the-fly kernel calculation. It implements general multidimensional nonuniform fast Fourier transform (NUFFT) algorithms and incorporates them into a $k$-space image reconstruction framework. The method is then applied to reconstruct from the radially encoded $k$-space data, although the method is applicable to any non-Cartesian patterns. Performance comparisons are made against the conventional Kaiser–Bessel (KB) gridding method for 2-D and 3-D radially encoded computer-simulated phantoms and physically scanned phantoms. The results show that the NUFFT reconstruction method has better accuracy–efficiency tradeoff than the KB gridding method when the kernel weights are calculated on the fly. It is found that for a particular conventional kernel function, using its corresponding deapodization function as a scaling factor in the NUFFT framework has the potential to improve accuracy. In particular, when a cosine scaling factor is used, the NUFFT method is faster than KB gridding method since a closed-form solution is available and is less computationally expensive than the KB kernel (KB griding requires computation of Bessel functions). The NUFFT method has been successfully applied to 2-D and 3-D in vivo studies on small animals.      

基于NUFFT 算法的综合孔径辐射计图像反演方法

圆形阵列和旋转阵列排布方式的提出是为进一步降低综合孔径辐射计成像系统的复杂度和成本,然 而其缺陷在于过于复杂的亮温图像反演过程,因为该阵列排布方式下的采样样本不是均匀分布于整个采样平面,常 规的标准傅里叶变换不能直接运用于此反演过程。提出了一种非标准快速傅里叶变换(NUFFT)算法,用于非均匀 采样样本的综合孔径辐射计图像反演计算,该算法结合了高斯栅格算法和对过采样样本的快速傅里叶变换算法,仿 真结果表明该算法能够准确地得到反演图像。     

改进贪婪投影三角化算法的激光点云快速三维重建

通过三维激光扫描仪获取的点云数据具有密度大、精度高等特点。本文针对贪婪投影三角化算法在对采集的大量点云数据进行三维重建时耗时长,重构的模型表面不够光滑,存在细小孔洞的问题,提出一种改进的点云三维重建算法。该方法首先用体像素网格滤波算法对点云进行下采样;然后使用移动最小二乘算法对输入的点云进行平滑及重采样,并且使用八叉树来代替KD树进行近邻域搜索;最后使用基于移动最小二乘算法的点云法线估计的贪婪投影三角化算法对点云进行重建。经过实验验证,该方法可以缩短重建时间,减少孔洞,并构建出平滑、点云拓扑结构更为准确的模型。     

Accelerating the nonequispaced fast Fourier transform on commodity graphics hardware

We present a fast parallel algorithm to compute the nonequispaced fast Fourier transform on commodity graphics hardware (the GPU). We focus particularly on a novel implementation of the convolution step in the transform as it was previously its most time consuming part. We describe the performance for two common sample distributions in medical imaging (radial and spiral trajectories), and for different convolution kernels as these parameters all influence the speed of the algorithm. The GPU-accelerated convolution is up to 85 times faster as our reference, the open source NFFT library on a state-of-the-art 64 bit CPU. The accuracy of the proposed GPU implementation was quantitatively evaluated at the various settings. To illustrate the applicability of the transform in medical imaging, in which it is also known as gridding, we look specifically at non-Cartesian magnetic resonance imaging and reconstruct both a numerical phantom and an in vivo cardiac image.