基于深度递归级联卷积神经网络的并行磁共振成像方法

快速磁共振成像是磁共振研究领域重要的课题之一.随着大数据和深度学习的兴起,神经网络成为快速磁共振技术的重要方法.然而网络性能表现和网络参数量之间较难取得平衡,且对于多通道数据重建的并行成像问题,相关研究较少.本文构建了一种深度递归级联卷积神经网络结构,用于处理并行成像问题.这种网络结构在减少网络参数量的同时,能够尽可能地提高网络的表达能力,提高网络重建的精确度.实验结果表明,相较于传统并行成像方法,通过训练好的神经网络对欠采样磁共振数据进行重建,可以得到更准确的重建结果,且重建时间大大缩短.     

In vivo whole-body imaging of optical agent dynamics using full angle fluorescence diffuse optical tomography

Dynamic fluorescence diffuse optical tomography （FDOT） is important in drug deliver research. In this letter, we first image the metabolic processes of micelles indocyanine green throughout the whole body of a nude mouse using the full-angle FDOT system with line illumination （L-FDOT）. The resolution of L-FDOT is evaluated using phantom experiment. Next, in vivo dynamic tomographic images （100 frames; approximately 170 min） of mouse liver and abdomen are shown and cross-validated by planar fluorescence reflectance imaging in vitro. Results provide evidence on applicability of the tomographic image wholebody biological activities in vivo on minute timescale （approximately 1.7 min） using L-FDOT.     

Optimal illumination and wave form design for imaging in random media

The problem of optimal illumination for selective array imaging of small and not well separated scatterers in clutter is considered. The imaging algorithms introduced are based on the coherent interferometric (CINT) imaging functional, which can be viewed as a smoothed version of travel-time migration. The smoothing gives statistical stability to the image but it also causes blurring. The trade-off between statistical stability and blurring is optimized with an adaptive version of CINT. The algorithm for optimal illumination and for selective array imaging uses CINT. It is a constrained optimization problem that is based on the quality of the image obtained with adaptive CINT. The resulting optimal illuminations and selectivity improve the resolution of the images significantly, as can be seen in the numerical simulations presented in the paper.     

Compressed MRI reconstruction exploiting a rotation-invariant total variation discretization

Inspired by the first-order method of Malitsky and Pock, we propose a new variational framework for compressed MR image reconstruction which introduces the application of a rotation-invariant discretization of total variation functional into MR imaging while exploiting BM3D frame as a sparsifying transform. In the first step, we provide theoretical and numerical analysis establishing the exceptional rotation-invariance property of this total variation functional and observe its superiority over other well-known variational regularization terms in both upright and rotated imaging setups. Thereupon, the proposed MRI reconstruction model is presented as a constrained optimization problem, however, we do not use conventional ADMM-type algorithms designed for constrained problems to obtain a solution, but rather we tailor the linesearch-equipped method of Malitsky and Pock to our model, which was originally proposed for unconstrained problems. As attested by numerical experiments, this framework significantly outperforms various state-of-the-art algorithms from variational methods to adaptive and learning approaches and in particular, it eliminates the stagnating behavior of a previous work on BM3D-MRI which compromised the solution beyond a certain iteration.     

Edge-enhanced spatiotemporal constrained reconstruction of undersampled dynamic contrast-enhanced radial MRI

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a technique used to study and track contrast kinetics in an area of interest in the body over time. Reconstruction of images with high contrast and sharp edges from undersampled data is a challenge. While good results have been reported using a radial acquisition and a spatiotemporal constrained reconstruction (STCR) method, we propose improvements from using spatially adaptive weighting and an additional edge-based constraint. The new method uses intensity gradients from a sliding window reference image to improve the sharpness of edges in the reconstructed image. The method was tested on eight radial cardiac perfusion data sets with 24 rays and compared to the STCR method. The reconstructions showed that the new method, termed edge-enhanced spatiotemporal constrained reconstruction, was able to reconstruct images with sharper edges, and there were a 36%±13.7% increase in contrast-to-noise ratio and a 24%±11% increase in contrast near the edges when compared to STCR. The novelty of this paper is the combination of spatially adaptive weighting for spatial total variation (TV) constraint along with a gradient matching term to improve the sharpness of edges. The edge map from a reference image allows the reconstruction to trade-off between TV and edge enhancement, depending on the spatially varying weighting provided by the edge map.     

基于稀疏优化的计算机断层成像图像不完全角度重建综述

计算机断层成像(computed tomography,CT)技术在医学和工业无损检测中都具有非常广泛的应用,CT重建算法是其中的核心,而不完全角度重建问题则是实际应用中重建算法研究领域的一个热点和难点问题.近年来,随着稀疏优化理论与算法的飞速发展,基于稀疏优化的重建算法已经在不完全角度重建问题中得到了较广泛的应用,且表现出了良好的精度与速度性能.本文首先对稀疏优化的基本理论结论与常用算法进行了介绍;而后对稀疏优化理论在CT图像不完全角度重建中的应用进行归纳,分类介绍了其主要研究成果及稀疏优化所发挥的作用;最后对基于稀疏优化的不完全角度重建研究进行了展望.     

像面数字全息的重建相位误差分析和改善

像面数字全息是数字全息技术中常用的测量和成像方式,它通常采用离散傅里叶变换和频率滤波的方法进行物光波的重建.本文讨论了这些算法对重建相位的影响.首先分析了频谱泄露对于相位误差的影响,结果表明当采样周期为整数时,重建相位误差很小,因此具有极高的相位重建精度;而当不满足整周期采样时,相位重建误差有了明显的增加.为了改善频谱泄露所引起的相位误差,采用Hanning函数对数字全息图进行了预处理,结果表明Hanning窗的加入能够有效地提高重建相位的准确程度.     

基于自适应阈值方法实现迭代降噪鬼成像

为了有效降低传统鬼成像中相关噪声对成像质量的影响,本文提出一种基于最佳阈值的迭代降噪鬼成像.首先在迭代降噪鬼成像的基础上,采用自适应阈值迭代法,在不需要目标先验信息的前提下,找到一个逼近传统鬼成像中相关噪声的阈值,根据得到的阈值构造噪声干扰项.为了每次迭代初值更接近原始目标的透射系数,对其进行二值化,降低重构图像背景噪声对迭代性能的影响.仿真以及实验结果表明,本文提出的方法与传统方法相比,视觉效果以及峰值信噪比值有明显提高.     

Targeted-ROI imaging in electron paramagnetic resonance imaging

Electron paramagnetic resonance imaging (EPRI) is a technique that has been used for in vivo oxygen imaging of small animals. In continuous wave (CW) EPRI, the measurement can be interpreted as a sampled 4D Radon transform of the image function. The conventional filtered-backprojection (FBP) algorithm has been used widely for reconstructing images from full knowledge of the Radon transform acquired in CW EPRI. In practical applications of CW EPRI, one often is interested in information only in a region of interest (ROI) within the imaged subject. It is desirable to accurately reconstruct an ROI image only from partial knowledge of the Radon transform because acquisition of the partial data set can lead to considerable reduction of imaging time. The conventional FBP algorithm cannot, however, reconstruct accurate ROI images from partial knowledge of the Radon transform of even dimension. In this work, we describe two new algorithms, which are referred to as the backprojection filtration (BPF) and minimum-data filtered-backprojection (MDFBP) algorithms, for accurate ROI-image reconstruction from a partial Radon transform (or, truncated Radon transform) in CW EPRI. We have also performed numerical studies in the context of ROI-image reconstruction of a synthetic 2D image with density similar to that found in a small animal EPRI. This demonstrates both the inadequacy of the conventional FBP algorithm and the success of BPF and MDFBP algorithms in ROI reconstruction. The proposed ROI imaging approach promises a means to substantially reduce image acquisition time in CW EPRI.     

Double-threshold technique for achieving denoising in compressive imaging applications

Single-pixel cameras, which employ either structured illumination or image modulation and compressive sensing algorithms, provide an alternative approach to imaging in scenarios where the use of a detector array is restricted or difficult because of cost or technological constraints. In this work, we present a robust imaging method based on compressive imaging that sets two thresholds to select the measurement data for image reconstruction.The experimental and numerical simulation results show that the proposed double-threshold compressive imaging protocol provides better image quality than previous compressive imaging schemes. Faster imaging speeds can be attained using this scheme because it requires less data storage space and computing time. Thus,this denoising method offers a very effective approach to promote the implementation of compressive imaging in real-time practical applications.     

Deep learning for image reconstruction in thermoacoustic tomography

Microwave-induced thermoacoustic tomography(TAT)is a rapidly-developing noninvasive imaging technique that integrates the advantages of microwave imaging and ultrasound imaging.While an image reconstruction algorithm is critical for the TAT,current reconstruction methods often creates significant artifacts and are computationally costly.In this work,we propose a deep learning-based end-to-end image reconstruction method to achieve the direct reconstruction from the sinogram data to the initial pressure density image.We design a new network architecture TAT-Net to transfer the sinogram domain to the image domain with high accuracy.For the scenarios where realistic training data are scarce or unavailable,we use the finite element method(FEM)to generate synthetic data where the domain gap between the synthetic and realistic data is resolved through the signal processing method.The TAT-Net trained with synthetic data is evaluated through both simulations and phantom experiments and achieves competitive performance in artifact removal and robustness.Compared with other state-of-the-art reconstruction methods,the TAT-Net method can reduce the root mean square error to 0.0143,and increase the structure similarity and peak signal-to-noise ratio to 0.988 and 38.64,respectively.The results obtained indicate that the TAT-Net has great potential applications in improving image reconstruction quality and fast quantitative reconstruction.     

基于压缩感知与扩展小波树的自适应压缩成像

为了在现有的采样条件下,通过新的压缩采样方式获得计算量小且质量更好的图像,提出了基于压缩感知与扩展小波树的自适应压缩成像方法。首先将图像投影到分区控制的DMD上,获得图像在低分辨率下的测量值,并通过压缩感知重构算法重构出低分辨图像,接着利用扩展小波树预测重要小波位置,通过DMD在小波域采样获取图像的细节信息,最后由小波逆变换恢复高分辨率图像。将该方法与最小化全变分算法（TVAL3）和近来提出的基于扩展小波树的自适应成像算法（EWT-ACS）效果进行对比,实验结果表明,以boat图像为例,在压缩感知采样率为0.75,整体采样率为10%的无噪声条件下,该方法相较于TVAL3、EWT-ACS算法信噪比提高了4.63 dB和2.87 dB,在附加噪声条件下成像效果也较好。该方法能极大地降低压缩感知重建算法的运行时间,同时减少采样次数,具有较好的抗噪性。     

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 efficient lattice Boltzmann method for fluorescent diffuse optical tomography on GPUs

Optical Review - Fluorescent diffuse optical tomography (FDOT) is an emerging imaging modality, with great prospects in areas such as biology and medicine. However, current FDOT encounters...     

Progressive EPR imaging with adaptive projection acquisition

Continuous wave electron paramagnetic resonance imaging (EPRI) of living biological systems requires rapid acquisition and visualization of free radical images. In the commonly used multiple-stage back-projection image reconstruction algorithm, the EPR image cannot be reconstructed until a complete set of projections is collected. If the data acquisition is incomplete, the previously acquired incomplete data set is no longer useful. In this work, a 3-dimensional progressive EPRI technique was implemented based on inverse Radon transform in which a 3-dimensional EPR image is acquired and reconstructed gradually from low resolution to high resolution. An adaptive data acquisition strategy is proposed to determine the significance of projections and acquire them in an order from the most significant to the least significant. The image acquisition can be terminated at any time if further collection of projections does not improve the image resolution distinctly, providing flexibility to trade image quality with imaging time. The progressive imaging technique was validated using computer simulations as well as imaging experiments. The adaptive acquisition uses 50-70% less projections as compared to the regular acquisition. In conclusion, adaptive data acquisition with progressive image reconstruction should be very useful for the accelerated acquisition and visualization of free radical distribution.     

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.     

基于位相变更的非相干数字全息自适应成像

菲涅耳非相干数字全息作为一种非扫描的三维成像技术具有其独特的优势,但其成像过程中会受到各种像差的影响,导致成像分辨率、再现像的质量降低.为了解决这一问题,可以结合适当的自适应光学技术对波前像差进行探测和校正.位相变更是一种基于两幅具有已知位相差的强度图像实现波前探测和像差校正的技术.本文发展了基于位相变更的非相干数字全息自适应成像技术,不需要引入引导星,利用全息记录过程中的两幅相移全息图,实现波前像差的探测.本文给出了所发展技术的数值仿真和实验结果,结合位相变更算法求解出系统像差的位相分布,将像差的共轭位相加载到光瞳面上,在全息图记录的同时校正像差,从而提高重建像的质量.     

荧光寿命显微成像技术及应用的最新研究进展

由于荧光寿命不受探针浓度、激发光强度和光漂白效应等因素影响,荧光寿命显微成像技术(fluorescence lifetime imaging microscopy, FLIM)在监测微环境变化、反映分子间相互作用方面具有高特异性、高灵敏度、可定量测量等优点,近年来已被广泛应用于生物医学等领域.然而,尽管FLIM的发明和发展已历经数十年时间,其在实际应用中仍然面临着许多挑战.例如,其成像分辨率受衍射极限限制,而其成像速度与成像质量和寿命测量精度则存在相互制约的关系.近几年来,相关硬件和软件的快速发展及其与其他光学技术的结合,极大地推动了FLIM技术及其应用的新发展.本文简要介绍了基于时域和频域的不同寿命探测方法的FLIM技术的基本原理及特点,在此基础上概述了该技术的最新研究进展,包括其成像性能的提升和在生物医学应用中的研究现状,详细阐述了近几年来研究者们通过硬件和软件算法的改进以及与自适应光学、超分辨成像技术等新型光学技术的结合来提升FLIM的成像速度、寿命测量精度、成像质量和空间分辨率等方面所做的努力,以及FLIM在生物医学基础研究、疾病诊断与治疗、纳米材料的生物医学研究等方面的应用,最后对其未来发展趋势进行了展望.     

基于联合正则化及压缩传感的MRI图像重构

基于压缩传感的MRI图像重构利用图像稀疏的先验知识能从很少的投影值重构原图像。目前MRI重构算法只利用MRI图像稀疏性表示或只利用基于其局部光滑性的先验知识,重构效果不理想。针对此问题,结合两种先验知识,提出一种基于联合正则化及压缩传感的MRI图像重构方法。利用块坐标下降法将求解联合正则化问题转化为交替求解二次凸优化、稀疏正则化和全变差正则化三个简单的优化问题。并提出分别采用共轭梯度法、二元自适应收缩法以及梯度下降法对以上优化问题求解。实验结果表明,该算法重构效果比现有算法有明显地提高。