面向近红外脑功能成像的光学自导引漫射光断层成像方法

为降低近红外脑功能漫射光断层成像(DOT)固有的逆问题病态性,并避免多模态方法的图像配准等问题,提出了基于光学自导引提供先验功能信息的脑功能DOT方法(OT-DOT),并发展了图像重构方法.模拟验证表明:上皮厚度(TLT)已知时,OT-DOT获得的重构量化度(QR)约为传统DOT的4.2倍;当TLT的估计误差小于±10%时,OT-DOT重构的QR值可达92%以上,远远优于传统DOT;噪声鲁棒性测试表明,OT-DOT与传统DOT的噪声鲁棒性相近.利用连续光DOT测量系统的仿体实验重构结果表明,所发展的OT-DOT算法获得的重构结果优于传统DOT算法.     

无透镜显微成像的重构算法研究

针对单幅图像进行了无透镜显微成像的重构算法研究，介绍了无透镜显微成像系统实验装置和ASM(angle spectrum method)、改编后的L-R(Lucy-Richardson)两种重构算法。对比两种算法重构后的USAF分辨率板图像的分辨率，利用瑞利判据得出ASM获得的振幅图分辨率最高(即3.10 μm)，且计算用时最少(即0.9 s)，证明了ASM为最佳的单幅无透镜显微重构算法。其次，利用无透镜显微成像系统结合ASM重构的方法，进行细胞成像实验。该无透镜成像视场为5×显微镜的4.4倍，且分辨率介于5×及10×光学显微镜之间，统计学优势明显，在生物医学领域具有广阔的应用前景。     

Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction

In photoacoustic imaging (PAI), reconstruction from sparse-view sampling data is a remaining challenge in the cases of fast or real-time imaging. In this paper, we present our study on a total variation based gradient descent (TV-GD) algorithm for sparse-view PAI reconstruction. This algorithm involves the total variation (TV) method in compressed sensing (CS) theory. The objective function of the algorithm is modified by adding the TV value of the reconstructed image. With this modification, the reconstructed image could be closer to the real optical energy distribution map. Additionally in the proposed algorithm, the photoacoustic data is processed and the image is updated individually at each detection point. In this way, the calculation with large matrix can be avoided and a more frequent image update can be obtained. Through the numerical simulations, the proposed algorithm is verified and compared with other reconstruction algorithms which have been widely used in PAI. The peak signal-to-noise ratio (PSNR) of the image reconstructed by this algorithm is higher than those by the other algorithms. Additionally, the convergence of the algorithm, the robustness to noise and the tunable parameter are further discussed. The TV-based algorithm is also implemented in the in vitro experiment. The better performance of the proposed method is revealed in the experiments results. From the results, it is seen that the TV-GD algorithm may be a practical and efficient algorithm for sparse-view PAI reconstruction.     

An image reconstruction algorithm based on Bayesian theorem for electrical resistance tomography

Electrical resistance tomography (ERT) is considered to be one of the most promising process tomography techniques for two-phase/multiphase flow measurement due to the advantages such as high speed, low cost and non-intrusive sensing. The iterative image reconstruction algorithm based on Bayesian theorem has been derived to solve the ERT inverse problem. It is taken into account, which is different to the existing ERT image reconstruction algorithms, including the prior probability of the permittivity distribution and the noise information in the measurement data. Both simulation and experimental data were carried out for typical two-phase flow regimes. The flow regimes are identified according to the reconstructed images of the Bayesian iteration method and conventional methods. Results obtained indicate that the Bayesian iteration method improves the reconstructed image quality with the traditional linear back projection (LBP) and reweighted least squares (RLS). Therefore, the Bayesian iteration method is suitable for identification of online two-phase flow regimes.     

基于lp范数的压缩感知图像重建算法研究

图像重建是光学成像、光声成像、声纳成像、核磁共振成像、 天体成像等物理成像领域中的关键技术之一. 近年来提出的压缩感知理论指出: 对稀疏或者可压缩信号进行少量非自适应线性投影,投影信号含有足够的信息, 从而能对信号进行高概率重建. 压缩感知已被应用于多种物理成像系统. 将罚函数法和修正Hesse阵序列二次规划方法相结合, 并采用了分块压缩感知思想, 提出一种基于l_p范数的压缩感知图像重建算法. 以cameraman, barbara和mandrill图像为例, 采用该算法进行图像重建. 首先, 在不同采样率下对图像重建. 即便采样率低至0.3时, 也能获得高达32.23dB的信噪比, 重建图像清晰可辨. 验证了该算法的正确性. 其次, 将该算法与正交匹配追踪算法进行对比, 在采样率达到0.5以上时, 能够获得高信噪比的重建图像, 成像时间也大为减少, 特别是采样率为0.7时, 成像时间减少88%. 最后, 与现有基于l_p 范数的压缩感知图像重建算法进行对比, 计算结果表明在成像质量有所提高的基础上, 成像时间大为缩短. 关键词： 图像重建 压缩感知 罚函数 修正Hesse阵序列二次规划     

基于IR-SFS算法空间目标红外影像3D重建

在利用单幅影像的明暗恢复形状（Shape From Shading,SFS）三维重构算法的基础上,提出同时考虑外界辐射源和目标自身红外辐射的IR-SFS（infrared-SFS）算法。首先通过分析SFS算法原理和空间目标的红外成像特性,建立IR-SFS辐射方程并进行了仿真研究,然后利用温度场估计获得红外差值图,在人工合成的理想半球体、半圆柱体卫星红外影像上进行算法测试,并以美国STS107真实红外影像作为实验目标进行三维重建。实验结果表明,所提出IR-SFS算法经过参数优化后,与原SFS算法相比,重建模型的峰值信噪比更高,对STS107顶部舱门、尾翼、机舱、机舱内方形部件具有更佳显示度,整体效果得到明显改善。     

Tissue contrast enhancement: image reconstruction algorithm and selection of TI in inversion recovery MRI

It is clearly demonstrated that the proper application of the inversion recovery imaging pulse sequence is dependent on the method of image reconstruction and the selection of TI for optimum tissue contrast. There are two methods of 2DFT image reconstruction of IR sequence time-domain raw data. The first is a modulus-image reconstruction algorithm (contrast-obliterating option), and the second is a phase-correction routine for reconstructing "phase-sensitive" true IR-images. The second option generates proper "in-phase" images, retains proper scale of contrast, but can invert the algebraic sign of image-values under certain conditions. A series of "phase-sensitive" and "modulus" reconstructed brain images, obtained with conventional and optimized new IR pulse sequences, are shown to demonstrate these effects. They illustrate the considerable advantages gained, in practical clinical situations, if one generates "phase-sensitive" true IR-images from IR-sequence raw data at optimum TI for tissue contrast enhancement.     

光子计数集成成像的压缩与重构

集成成像技术作为一种重要的裸眼三维显示技术,在完整记录三维场景信息的同时,庞大的数据量给传输和存储带来了压力。为了实现图像的有效压缩和重构,根据光子计数集成成像的特点,基于分布式压缩感知理论,提出用于图像压缩与重构的方案。该方案将图像分为参考图像和非参考图像两类,对其设置不同的测量率并分别进行重构。为保证非参考图像的重构质量,提出一种联合重构算法。该算法首先对非参考图像进行分块测量,依据与参考图像之间的相关性进行图像块分类,然后结合参考图像测量值信息构建新的测量矢量,利用新的测量矢量完成初次图像重构。为了进一步提升图像重构质量,对初次重构结果进行二次残差补偿重构,获得最终重构结果。最后通过设置不同的测量率进行了大量实验,实验结果表明,所提算法在测量率为0.25时,图像重构质量可以达到30 dB,测量率为0.4时,图像质量可以达到35 dB,算法性能具有一定的优越性。     

探测器偏置重建算法在工业CT检测中的应用

工业CT检测对象的大小是不固定的,最大限度地利用已有探测器的成像面积非常重要。采用探测器偏置来获得更大的扫描视野,并推导相应的重建算法。该算法首先使用Parker类型函数对采集到的投影数据中的冗余部分进行加权,然后采用扇束滤波反投影重建算法重建得到断层图像。在实验中使用实际工业CT系统分别采集钢制线对块与铝合金变速器外壳的投影数据进行重建算法的验证,重建结果证明了使用的探测器偏置重建算法的正确性与有效性,且空间分辨率和标准扫描的重建结果保持一致,这个方法可以在工业CT成像上有效使用。     

高质量数字全息波面重建系统研究

基于阿贝成像理论对数字全息波面重建系统进行研究. 结果表明,利用成像系统对物光场进行变换,可以让物光场的高频角谱分量到达电荷耦合器件（CCD）探测器.为研究重建物光场质量,导出了CCD在光学系统后任意位置波面重建的计算公式,研究了物光通过傍轴光学系统到达CCD时波面重建系统的脉冲响应. 理论及实验研究表明,将CCD平面置于物光场像平面附近可以对物光场进行高质量重建. 关键词： 信息光学 数字全息 波面重建     

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

PurposeTo improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC).MethodsAPIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment.ResultsCompared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm³ isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results.ConclusionCompared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.     

基于自适应准则的闪光照相图像重建

 针对闪光照相图像低信噪比的特点,提出了一种自适应的图像重建算法。该算法以代数法(ART)迭代重建为基础,在迭代重建过程中引入了两个权重矩阵,其中一个矩阵用来控制重建过程中的高频分量以保持重建结果的边界;另一个矩阵控制重建结果的平坦区域以提高重建结果的信噪比。通过在迭代过程中更新这两个矩阵来实现图像的自适应重建。数值模拟结果表明,相对于传统的单准则ART重建算法,该方法具有更好的抗噪能力和边界保持能力。     

Compressed sensing MR image reconstruction using a motion-compensated reference

Compressed sensing (CS)-based methods have been proposed for image reconstruction from undersampled magnetic resonance data. Recently, CS-based schemes using reference images have also been proposed to further reduce the sampling requirement. In this study, we propose a new reference-constrained CS reconstruction method that accounts for the misalignment between the reference and the target image to be reconstructed. The proposed method uses a new image model that represents the target image as a linear combination of a motion-dependent reference image and a sparse difference image. We then use an efficient iterative algorithm to jointly estimate the motion parameters and the difference image from sparsely sampled data. Simulation results from a numerical phantom data set and an in vivo data set show that the proposed method can accurately compensate the motion effects between the reference and the target images and improve reconstruction quality. The proposed method should prove useful for several applications such as interventional imaging, longitudinal imaging studies and dynamic contrast-enhanced imaging.     

四光束剪切相干成像目标重构算法研究

针对传统剪切光束成像技术的准实时性问题,提出用口字形排布的四束光代替传统L形三束剪切光照射目标,研究了四光束剪切相干成像目标重构算法.只需单次测量就能同时重构出四幅目标图像,减少了用于降低散斑噪声、获取高质量图像所需的测量次数,同时大大减少了多组发射时的光束切换次数,提高了成像效率.在算法实现中,通过最小二乘法恢复出四组波前相位,利用散斑幅值的简单代数运算恢复波前幅值,从而重构出目标图像.仿真结果表明,与传统方法相比,在图像质量相同的前提下,本文方法所需的数据采集时间减少了至少1/2,不但提高了目标重构效率,还可为远程运动目标的成像识别提供更好的手段.     

Compressed sensing sparse reconstruction for coherent field imaging

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process,we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.     

Adaptive fixed-point iterative shrinkage/thresholding algorithm for MR imaging reconstruction using compressed sensing

Recently compressed sensing (CS) has been applied to under-sampling MR image reconstruction for significantly reducing signal acquisition time. To guarantee the accuracy and efficiency of the CS-based MR image reconstruction, it necessitates determining several regularization and algorithm-introduced parameters properly in practical implementations. The regularization parameter is used to control the trade-off between the sparsity of MR image and the fidelity measures of k-space data, and thus has an important effect on the reconstructed image quality. The algorithm-introduced parameters determine the global convergence rate of the algorithm itself. These parameters make CS-based MR image reconstruction a more difficult scheme than traditional Fourier-based method while implemented on a clinical MR scanner. In this paper, we propose a new approach that reveals that the regularization parameter can be taken as a threshold in a fixed-point iterative shrinkage/thresholding algorithm (FPIST) and chosen by employing minimax threshold selection method. No extra parameter is introduced by FPIST. The simulation results on synthetic and real complex-valued MRI data show that the proposed method can adaptively choose the regularization parameter and effectively achieve high reconstruction quality. The proposed method should prove very useful for practical CS-based MRI applications.     

Maximum entropy image reconstruction from projections

The maximum entropy method is applied to image reconstruction from projections, of which angular view is restricted. The relaxation parameters are introduced to the maximum entropy reconstruction and after iteration the median filtering is implemented. These procedures improve the quality of the reconstructed image from noisy projections.     

基于成像差分吸收光谱技术和压缩感知理论的烟囱烟羽断层重建研究

烟羽断层重建质量受两方面条件限制：其中一个限制条件是遥感设备的时间分辨率。以往的研究多使用多轴差分吸收光谱仪（MAX-DOAS）进行CT重建,受采集数据速度的限制,重建图像的时间分辨率较低。另一个限制条件是,采集到的数据量有限,是典型的不完全角度重建。过去多使用代数迭代重建算法或统计迭代重建算法,重建图像受测量误差的影响比较大,分辨率较低且伪影较多。构造了基于成像差分吸收光谱技术（IDOAS）的光谱数据采集系统,与多轴差分吸收光谱仪构造的系统相比,数据采集的时间分辨率提高了160多倍,基本解决了时间分辨率的问题。提出了一种基于压缩感知理论和低三阶导数模型的烟羽断层重建算法--投影凸函数集低三阶导数法,简称为POCS-LTD。在投影的过程中,使用代数重建算法使重建图像符合投影方程;在全变分迭代的过程中使用了优化算法,将低三阶导数模型的全变分归一化值作为优化算法的迭代方向,前次迭代运算结果与本次投影运算的差值的模作为迭代步长。对重建算法进行了数值模拟,并以重建图像的接近度和一致性相关因子为指标,对重建结果进行了分析。数值模拟表明,算法具有良好的抗误差能力,与传统的低三阶导数法相比,本文提出的算法将重建接近度减小了80%以上。使用烟羽数据采集系统进行了外场实验,用POCS-LTD算法对外场实验的数据进行了烟羽重建,重建图像显示烟羽图像清晰,伪影得到了较好的抑制。介绍的烟羽断层数据采集系统和烟羽断层重建算法,提高了烟羽断层重建图像的时间分辨率,减少了重建图像的伪影,扩大了光谱测量技术的应用范围。     

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.     

基于改进维纳逆滤波的衍射成像光谱仪数据误差分析与重构

衍射成像光谱仪探测到的高光谱数据需要进行计算与反演才可以得到成像光谱数据,本文对衍射成像光谱仪的成像过程及数据误差产生的原理从空间维和光谱维两方面进行了分析,并针对其光谱重构过程中系统点扩散函数标准差较大时重构结果清晰度较低、存在振铃等问题,提出了基于改进维纳逆滤波的光谱数据重构算法,该方法在分析衍射成像光谱仪数据特点与误差的基础上,将每一次维纳逆滤波的重构结果视为新的模糊图像,利用成像过程及维纳逆滤波的基本原理确定新的模糊图像对应的点扩散函数,反复进行维纳逆滤波达到提高图像清晰度的效果,再根据图像自身的空间和光谱特征分布,进行自适应性的噪声去除.利用模拟的衍射成像光谱数据进行验证,在系统点扩散函数的标准差为2.5的情况下,能得到无振铃的重构结果,且与传统维纳逆滤波法的重构结果进行比较,清晰度、细节能力等指标均有所提高,满足了衍射成像光谱数据应用需求.