Concurrent correction of geometric distortion and motion using the map-slice-to-volume method in echo-planar imaging

The accuracy of measuring voxel intensity changes between stimulus and rest images in fMRI echo-planar imaging (EPI) data is severely degraded in the presence of head motion. In addition, EPI is sensitive to susceptibility-induced geometric distortions. Head motion causes image shifts and associated field map changes that induce different geometric distortion at different time points. Conventionally, geometric distortion is "corrected" with a static field map independently of image registration. That approach ignores all field map changes induced by head motion. This work evaluates the improved motion correction capability of mapping slice to volume with concurrent iterative field corrected reconstruction using updated field maps derived from an initial static field map that has been spatially transformed and resampled. It accounts for motion-induced field map changes for translational and in-plane rotation motion. The results from simulated EPI time series data, in which motion, image intensity and activation ground truths are available, show improved accuracy in image registration, field corrected image reconstruction and activation detection.     

显微数字全息数值再现中的自动聚焦

自动聚焦是显微数字全息数值再现中的一项关键技术。基于菲涅耳变换重建算法, 采用实验验证的方法, 对显微数字全息成像中六种典型聚焦评价函数的性能进行了对比研究。提出了利用再现像部分区域进行调焦判断的方法。结果表明, 这些函数都具有较好的单峰性、一致的调焦范围和准确性, 但与梯度运算相关的调焦函数运算时间太长; 傅里叶频谱函数调焦速度最快, 是首选的聚焦评价函数。选取再现像部分区域作为调焦计算区域, 可大大缩短调焦时间。对于低信噪比全息图, 利用维纳自适应滤波进行预处理及对再现像进行后处理, 有利于提高自动调焦的准确性及再现像的质量。     

Correction of out-of-FOV motion artifacts using convolutional neural network

PurposeSubject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-FOV motion happens. Currently available algorithms are not able to correct for image artifacts introduced by out-of-FOV motion. The purpose of this study is to demonstrate the feasibility of incorporating convolutional neural network (CNN) derived prior image into solving the out-of-FOV motion problem.Methods and materialsA modified U-net network was proposed to correct out-of-FOV motion artifacts by incorporating motion parameters into the loss function. A motion model based data fidelity term was applied in combination with the CNN prediction to further improve the motion correction performance. We trained the CNN on 1113 MPRAGE images with simulated oscillating and sudden motion trajectories, and compared our algorithm to a gradient-based autofocusing (AF) algorithm in both 2D and 3D images. Additional experiment was performed to demonstrate the feasibility of transferring the networks to different dataset. We also evaluated the robustness of this algorithm by adding Gaussian noise to the motion parameters. The motion correction performance was evaluated using mean square error (NMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).ResultsThe proposed algorithm outperformed AF-based algorithm for both 2D (NMSE: 0.0066 ± 0.0009 vs 0.0141 ± 0.008, P < .01; PSNR: 29.60 ± 0.74 vs 21.71 ± 0.27, P < .01; SSIM: 0.89 ± 0.014 vs 0.73 ± 0.004, P < .01) and 3D imaging (NMSE: 0.0067 ± 0.0008 vs 0.070 ± 0.021, P < .01; PSNR: 32.40 ± 1.63 vs 22.32 ± 2.378, P < .01; SSIM: 0.89 ± 0.01 vs 0.62 ± 0.03, P < .01). Robust reconstruction was achieved with 20% data missed due to the out-of-FOV motion.ConclusionIn conclusion, the proposed CNN-based motion correction algorithm can significantly reduce out-of-FOV motion artifacts and achieve better image quality compared to AF-based algorithm.     

Off-resonance correction using an estimated linear time map

Images acquired in the presence of magnetic field deviations and reconstructed without taking into account the off-resonance, are distorted and corrupted with artifacts. Several post-processing algorithms have been developed for correcting the distortion when it is not possible to fix the field inhomogeneities. These off-resonance correction methods are, in general, slow and computing intensive. To make them faster they are usually adapted to a particular situation or approximated. One of these approximations is to assume that the field map is linear. Although this assumption makes the algorithm fast and robust it is not well suited for arbitrary field maps. On the other hand, there are k-space trajectories with an almost linear time map (time at which each k-space value is acquired), such as 2DFT and EPI. This paper presents an algorithm for off-resonance correction based on a linear time map approximation. This approximation allows a fast algorithm that takes advantage of the almost linearity of the time map and uses the whole field map to correct the images. The proposed correction algorithm reduces the off-resonance induced artifacts while being fast. The linear approximation of the time map needs to be done only once for each trajectory because it does not depend on the acquired image or field map data. The method can also be extended to a multi-plane approximation for sequences with more complex time maps.     

An enhanced non-uniformity correction algorithm for IRFPA based on neural network

Influenced by detector materials’ non-uniformity, growth and etching techniques, etc., every detector’s responsivity of infrared focal plane arrays (IRFPA) is different, which results in non-uniformity of IRFPA. And non-uniformity of IRFPA generates fixed pattern noises (FPN) that are superposed on infrared image. And it may degrade the infrared image quality, which greatly limits the application of IRFPA. Non-uniformity correction (NUC) is an important technique for IRFPA. The traditional non-uniformity correction algorithm based on neural network and its modified algorithms are analyzed in this paper. And a new improved non-uniformity correction algorithm based on neural network is proposed in this paper. In this algorithm, the desired image is estimated by using three successive images in an infrared sequence. And blurring effect caused by motion is avoided by applying implicit motion detection and edge detection. So the estimation image is closer to real image than the estimation image estimated by other algorithms, which results in fast convergence speed of correction parameters. A comparison is made to these algorithms in this paper. And experimental results show that the algorithm proposed in this paper can correct the non-uniformity of IRFPA effectively and it prevails over other algorithms based on neural network.     

Motion correction properties of the shells k-space trajectory

The feasibility of a k-space trajectory that samples data on a set of 3D shells is demonstrated with phantom and volunteer experiments. Details of an interleaved multi-shot, helical spiral pulse sequence and a gridding reconstruction algorithm that uses Voronoi diagrams are provided. The motion-correction properties of the shells k-space trajectory are described. It is shown that when used in conjunction with three point markers, k-space data acquired with the shells trajectory provide a generalization of the RINGLET method, allowing for correction of arbitrary rigid-body motion with six degrees of freedom. Use of dedicated navigator echoes or redundant acquisitions of k-space data are not required. Retrospective motion correction is demonstrated with controlled phantom experiments and with seven healthy human volunteers. The motion correction is shown to improve the images, both qualitatively and quantitatively with a metric calculated from image entropy. Advantages and challenges of the shells trajectory are discussed, with particular attention to acquisition efficiency.     

基于优势特征图像融合的水下光学图像增强

针对水下光学图像颜色失真、非均匀光照、对比度低的问题,提出基于优势特征图像融合的水下光学图像增强算法.首先,提出改进的暗通道先验算法去除退化图像中的不均匀浑浊并均衡色彩;其次,对颜色校正图像分别使用基于加权分布的自适应伽玛校正算法和限制对比度自适应直方图均衡-同态滤波算法,增强颜色校正图像对比度并使其亮度均衡;最后,定义三幅融合图像即颜色校正图像、亮度均衡图像、对比度增强图像的关联权重图,通过多尺度融合算法获得融合图像.与单一预处理算法只能解决对应的退化现象相比,该算法对单幅退化图像进行多算法处理,得到三幅优势特征图像,通过不同权重的组合最大程度地将各优势特征相结合,得到的综合效果远超各单一算法优化效果,不再局限于解决颜色失真等单一问题.将本文算法与现有算法在主观评价和客观评价两方面进行实验对比,结果表明,该算法可以有效平衡水下图像的色度、饱和度及清晰度,视觉效果接近自然场景下的图像.     

Iterative autofocusing strategy for axial distance error correction in ptychography

The axial distance error can degrade the quality and resolution of the image reconstructed by ptychography. An iterative autofocusing method is proposed to correct the axial distance error. Based on the Fresnel integral theory, an axial distance error model is established to present the effect of the axial distance error on the recovered image. The information of the retrieved image is chosen to calculate the axial distance by autofocusing algorithm. The calculated distance is used to replace the axial distance and applied for the image reconstruction. When the calculated axial distance reaches the convergence, the distance can be accurately obtained that can be used to enhance the quality and resolution of the reconstructed image. Simulations and experiments have been conducted to verify the performance of the proposed method.     

Multi-frequency interpolation in spiral magnetic resonance fingerprinting for correction of off-resonance blurring

Magnetic resonance fingerprinting (MRF) pulse sequences often employ spiral trajectories for data readout. Spiral k-space acquisitions are vulnerable to blurring in the spatial domain in the presence of static field off-resonance. This work describes a blurring correction algorithm for use in spiral MRF and demonstrates its effectiveness in phantom and in vivo experiments. Results show that image quality of T1 and T2 parametric maps is improved by application of this correction. This MRF correction has negligible effect on the concordance correlation coefficient and improves coefficient of variation in regions of off-resonance relative to uncorrected measurements.     

Infrared and visible image fusion method based on saliency detection in sparse domain

Infrared and visible image fusion is a key problem in the field of multi-sensor image fusion. To better preserve the significant information of the infrared and visible images in the final fused image, the saliency maps of the source images is introduced into the fusion procedure. Firstly, under the framework of the joint sparse representation (JSR) model, the global and local saliency maps of the source images are obtained based on sparse coefficients. Then, a saliency detection model is proposed, which combines the global and local saliency maps to generate an integrated saliency map. Finally, a weighted fusion algorithm based on the integrated saliency map is developed to achieve the fusion progress. The experimental results show that our method is superior to the state-of-the-art methods in terms of several universal quality evaluation indexes, as well as in the visual quality.     

基于混合蛙跳优化算法的图像畸变校正研究

图像畸变校正是分布式孔径系统(DAS)需要解决的先期问题。介绍了描述图像径向与切向畸变的Brown模型及其畸变量(适应度)度量标准;讨论了智能优化算法之一的混合蛙跳算法(SFLA)及其初始种群拉丁超立方抽样(LHS)算法;分别对光线追迹畸变模型、Brown畸变模型进行了图像仿真和畸变校正处理。结果显示,原始点与校正点的最大误差距离在2个像素以内,验证了算法的有效性。最后应用该方法到实际广角CCD相机拍摄的靶纸校正中,得到了较满意的结果。该算法和仿真分析结果对DAS系统的研制具有一定理论和实验意义。     

Image Encryption Scheme Based on Mixed Chaotic Bernoulli Measurement Matrix Block Compressive Sensing

Many image encryption schemes based on compressive sensing have poor reconstructed image quality when the compression ratio is low, as well as difficulty in hardware implementation. To address these problems, we propose an image encryption algorithm based on the mixed chaotic Bernoulli measurement matrix block compressive sensing. A new chaotic measurement matrix was designed using the Chebyshev map and logistic map; the image was compressed in blocks to obtain the measurement values. Still, using the Chebyshev map and logistic map to generate encrypted sequences, the measurement values were encrypted by no repetitive scrambling as well as a two-way diffusion algorithm based on GF(257) for the measurement value matrix. The security of the encryption system was further improved by generating the Secure Hash Algorithm-256 of the original image to calculate the initial values of the chaotic mappings for the encryption process. The scheme uses two one-dimensional maps and is easier to implement in hardware. Simulation and performance analysis showed that the proposed image compression–encryption scheme can improve the peak signal-to-noise ratio of the reconstructed image with a low compression ratio and has good encryption against various attacks.     

基于场景的红外焦平面阵列非均匀性校正算法综述

红外焦平面阵列的非均匀性噪声是制约红外成像质量的主要因素。基于场景的非均匀校正算法通常利用图像序列并依赖帧间运动对焦平面阵列的非均匀性进行校正。介绍和分析了全局非均匀性校正,Kalman滤波器法,自适应滤波法,轨迹跟踪法,基于场景运动分析的校正算法,基于小波变换实现低通滤波的校正算法,高通滤波与神经网络相结合的算法,基于小波去噪、序列图像配准和正交最小二乘拟合的校正算法,改进的神经网络算法以及代数算法等,对算法进行了比较。     

一种卫星平台振动光谱成像数据分块校正方法

卫星平台的振动会降低光谱成像数据的质量.以星载色散型成像光谱仪为例,介绍了其运动成像的退化机理.针对传统二维反卷积算法存在的问题以及推扫机理的特殊性,提出了一种光谱成像数据分块校正方法.该方法将分块处理、升维运算以及灰度渐变拼接相结合,将基于自然景物统计规律的图像去模糊算法运用于降质成像光谱数据的校正中.分别进行了不同目标的光谱成像退化校正仿真实验,实验结果表明,光谱成像数据质量在空间维和光谱维都有了明显提高,校正效果优于传统二维反卷积算法.     

A Hybrid Domain Image Encryption Algorithm Based on Improved Henon Map

A hybrid domain image encryption algorithm is developed by integrating with improved Henon map, integer wavelet transform (IWT), bit-plane decomposition, and deoxyribonucleic acid (DNA) sequence operations. First, we improve the classical two-dimensional Henon map. The improved Henon map is called 2D-ICHM, and its chaotic performance is analyzed. Compared with some existing chaotic maps, 2D-ICHM has larger parameter space, continuous chaotic range, and more complex dynamic behavior. Second, an image encryption structure based on diffusion–scrambling–diffusion and spatial domain–frequency domain–spatial domain is proposed, which we call the double sandwich structure. In the encryption process, the diffusion and scrambling operations are performed in the spatial and frequency domains, respectively. In addition, initial values and system parameters of the 2D-ICHM are obtained by the secure hash algorithm-512 (SHA-512) hash value of the plain image and the given parameters. Consequently, the proposed algorithm is highly sensitive to plain images. Finally, simulation experiments and security analysis show that the proposed algorithm has a high level of security and strong robustness to various cryptanalytic attacks.     

基于伪维格纳分布的图像融合

提出了一种基于伪维格纳分布(PWVD)的融合方法.利用一维N像素的滑动窗口在各个方向上对各待融合图像进行伪维格纳变换,选择均方根最大的方向为各待融合网像的PWVD分解方向,分解形成待融合图像不同频段的能量谱图,然后,针对各待融合不同频段的能量谱图,融合原则足高频段取区域能量最大,低频段取能量方差最大,形成具有不同频段的融合能量谱网,最后,对能量谱网进行PWVD逆变换,形成融合图像.对红外与可见光图像、多聚焦图像、电子计算机X射线断层扫描(CT)图像与磁共振(MR)图像和红外与合成孔径雷达(SAR)图像进行了融合实验,并对融合图像和待融合图像进行了信息熵对比.实验结果表明,采用本文算法的融合图像保留了待融合图像的绝大部分信息.     

太赫兹逆合成孔径雷达相位误差分析和补偿方法

通过对太赫兹逆合成孔径雷达(THz-ISAR)成像原理进行分析,构造目标回波距离维相位误差的多项式表示形式,提出了基于启发式最小熵搜索的二次相位误差校正方法和基于距离维自聚焦的三次及三次以上相位误差校正方法,校正了THz-ISAR回波信号距离维的相位误差,进而消除THz-ISAR一维距离像和二维成像结果中的散焦和展宽现象。理论分析和仿真实验结果都表明该方法能够有效补偿目标回波距离维相位误差,提高一维距离像和二维成像的聚焦效果。     

Design and evaluation of an abbreviated pixelwise dynamic contrast enhancement analysis protocol for early extracellular volume fraction estimation

IntroductionT1-based method is considered as the gold standard for extracellular volume fraction (ECV) mapping. This technique requires at least a 10 min delay after injection to acquire the post injection T1 map. Quantitative analysis of Dynamic Contrast Enhancement (DCE) images could lead to an earlier estimation of an ECV like parameter (2 min). The purpose of this study was to design a quantitative pixel-wise DCE analysis workflow to assess the feasibility of an early estimation of ECV.MethodsFourteen patients with mitral valve prolapse were included in this study. The MR protocol, performed on a 3 T MR scanner, included MOLLI sequences for T1 maps acquisition and a standard SR-turboFlash sequence for dynamic acquisition. DCE data were acquired for at least 120 s. We implemented a full DCE analysis pipeline with a pre-processing step using an innovative motion correction algorithm (RC-REG algorithm) and a post-processing step using the extended Tofts Model (ECV_ETM). Estimated ECV_ETM maps were compared to standard T1-based ECV maps (ECV_T1) with both a Pearson correlation analysis and a group-wise analysis.ResultsImage and map quality assessment showed systematic improvements using the proposed workflow. Strong correlation was found between ECV_ETM, and ECV_T1 values (r-square = 0.87).ConclusionA DCE analysis workflow based on RC-REG algorithm and ETM analysis can provide good quality parametric maps. Therefore, it is possible to extract ECV values from a 2 min-long DCE acquisition that are strongly correlated with ECV values from the T1 based method.     

Correction of B0 susceptibility induced distortion in diffusion-weighted images using large-deformation diffeomorphic metric mapping

Geometric distortion caused by B0 inhomogeneity is one of the most important problems for diffusion-weighted images (DWI) using single-shot, echo planar imaging (SS-EPI). In this study, large-deformation, diffeomorphic metric mapping (LDDMM) algorithm has been tested for the correction of geometric distortion in diffusion tensor images (DTI). Based on data from nine normal subjects, the amount of distortion caused by B0 susceptibility in the 3-T magnet was characterized. The distortion quality was validated by manually placing landmarks in the target and DTI images before and after distortion correction. The distortion was found to be up to 15 mm in the population-averaged map and could be more than 20 mm in individual images. Both qualitative demonstration and quantitative statistical results suggest that the highly elastic geometric distortion caused by spatial inhomogeneity of the B0 field in DTI using SS-EPI can be effectively corrected by LDDMM. This postprocessing method is especially useful for correcting existent DTI data without phase maps.     

透镜检测中复合自动对焦技术的研究及应用

针对光学透镜参数测量系统中的对焦问题,设计了一种粗精结合的复合自动对焦方案.在粗对焦过程中,选用全局图像的方差函数作为清晰度评价函数,用均值比较的爬山法实现焦面搜索,并用三点法判断初始对焦方向.在精对焦过程中,利用基于数学形态学的条件膨胀算法和形状因子提取目标图像,并以日标图像区域内的Brenner函数作为清晰度评价函...