Feasibility of using hyperpolarized [1-13C]lactate as a substrate for in vivo metabolic 13C MRSI studies

The development of dynamic nuclear polarization in solution has enabled in vivo 13C MR studies at high signal-to-noise ratio following injection of prepolarized 13C substrates. While prior studies have demonstrated the ability to observe metabolism following injection of hyperpolarized 13C pyruvate, the goal of this study was to develop and test a new hyperpolarized agent for investigating in vivo metabolism, [1-13C]lactate. A preparation for prepolarized 13C lactate and the requisite dissolution media were developed to investigate the feasibility for in vivo 13C MRS/MRSI studies following injection of this hyperpolarized agent. This study demonstrated, for the first time, not only the ability to detect hyperpolarized [1-13C]lactate in vivo but also the metabolic products 13C pyruvate, 13C alanine and 13C bicarbonate following injection in normal rats. The use of 13C lactate as a substrate provided the opportunity to study the conversion of lactate to pyruvate in vivo and to detect the secondary conversions to alanine and bicarbonate through pyruvate. This study also demonstrated the potential value of this hyperpolarized agent to investigate in vivo lactate uptake and metabolism in preclinical animal models.     

Learning-based compressed sensing for infrared image super resolution

This paper presents an infrared image super-resolution method based on compressed sensing (CS). First, the reconstruction model under the CS framework is established and a Toeplitz matrix is selected as the sensing matrix. Compared with traditional learning-based methods, the proposed method uses a set of sub-dictionaries instead of two coupled dictionaries to recover high resolution (HR) images. And Toeplitz sensing matrix allows the proposed method time-efficient. Second, all training samples are divided into several feature spaces by using the proposed adaptive k-means classification method, which is more accurate than the standard k-means method. On the basis of this approach, a complex nonlinear mapping from the HR space to low resolution (LR) space can be converted into several compact linear mappings. Finally, the relationships between HR and LR image patches can be obtained by multi-sub-dictionaries and HR infrared images are reconstructed by the input LR images and multi-sub-dictionaries. The experimental results show that the proposed method is quantitatively and qualitatively more effective than other state-of-the-art methods.     

Double spin-echo sequence for rapid spectroscopic imaging of hyperpolarized 13C

Dynamic nuclear polarization of metabolically active compounds labeled with (13)C has been introduced as a means for imaging metabolic processes in vivo. To differentiate between the injected compound and the various metabolic products, an imaging technique capable of separating the different chemical-shift species must be used. In this paper, the design and testing of a pulse sequence for rapid magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized (13)C is presented. The pulse sequence consists of a small-tip excitation followed by a double spin echo using adiabatic refocusing pulses and a "flyback" echo-planar readout gradient. Key elements of the sequence are insensitivity to calibration of the transmit gain, the formation of a spin echo giving high-quality spectral information, and a small effective tip angle that preserves the magnetization for a sufficient duration. Experiments in vivo showed three-dimensional coverage with excellent spectral quality and SNR.     

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.     

一种基于压缩传感的超分辨光学三维成像技术

为了改善光学成像中的成像质量和效率,提出一种基于压缩传感的超分辨光学三维成像技术.通过物镜、编码板、色散元件、准直镜、聚焦镜、探测器等组成前端成像系统,然后,利用稀疏重构算法在后端处理器上重构光谱数据,从而将成像运算量从前端转移到后端.同时,引入块重构、错位预处理、多帧重构技术,提高重构的准确度,减小后端处理内存,降低计算复杂度.通过仿真实验对原始数据和重构数据的光谱曲线、信噪比、光谱误差、分类识别效果等指标进行对比分析,结果表明,利用本文压缩传感技术可以实现超分辨光学三维成像,且成像质量较高,数据应用效果较好,可用于大幅宽、高分辨率、低功耗、动态目标的成像观测.     

Exploiting the wavelet structure in compressed sensing MRI

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms.     

Compressed sensing and the reconstruction of ultrafast 2D NMR data: Principles and biomolecular applications

A topic of active investigation in 2D NMR relates to the minimum number of scans required for acquiring this kind of spectra, particularly when these are dictated by sampling rather than by sensitivity considerations. Reductions in this minimum number of scans have been achieved by departing from the regular sampling used to monitor the indirect domain, and relying instead on non-uniform sampling and iterative reconstruction algorithms. Alternatively, so-called "ultrafast" methods can compress the minimum number of scans involved in 2D NMR all the way to a minimum number of one, by spatially encoding the indirect domain information and subsequently recovering it via oscillating field gradients. Given ultrafast NMR's simultaneous recording of the indirect- and direct-domain data, this experiment couples the spectral constraints of these orthogonal domains - often calling for the use of strong acquisition gradients and large filter widths to fulfill the desired bandwidth and resolution demands along all spectral dimensions. This study discusses a way to alleviate these demands, and thereby enhance the method's performance and applicability, by combining spatial encoding with iterative reconstruction approaches. Examples of these new principles are given based on the compressed-sensed reconstruction of biomolecular 2D HSQC ultrafast NMR data, an approach that we show enables a decrease of the gradient strengths demanded in this type of experiments by up to 80%.     

Development of high resolution 3D hyperpolarized carbon-13 MR molecular imaging techniques

The goal of this project was to develop and apply techniques for T₂ mapping and 3D high resolution (1.5 mm isotropic; 0.003 cm³) ¹³C imaging of hyperpolarized (HP) probes [1-¹³C]lactate, [1-¹³C]pyruvate, [2-¹³C]pyruvate, and [¹³C,¹⁵N₂]urea in vivo. A specialized 2D bSSFP sequence was implemented on a clinical 3T scanner and used to obtain the first high resolution T₂ maps of these different hyperpolarized compounds in both rats and tumor-bearing mice. These maps were first used to optimize timings for highest SNR for single time-point 3D bSSFP acquisitions with a 1.5 mm isotropic spatial resolution of normal rats. This 3D acquisition approach was extended to serial dynamic imaging with 2-fold compressed sensing acceleration without changing spatial resolution. The T₂ mapping experiments yielded measurements of T₂ values of > 1 s for all compounds within rat kidneys/vasculature and TRAMP tumors, except for [2-¹³C]pyruvate which was ~ 730 ms and ~ 320 ms, respectively. The high resolution 3D imaging enabled visualization the biodistribution of [1-¹³C]lactate, [1-¹³C]pyruvate, and [2-¹³C]pyruvate within different kidney compartments as well as in the vasculature. While the mouse anatomy is smaller, the resolution was also sufficient to image the distribution of all compounds within kidney, vasculature, and tumor. The development of the specialized 3D sequence with compressed sensing provided improved structural and functional assessments at a high (0.003 cm³) spatial and 2 s temporal resolution in vivo utilizing HP ¹³C substrates by exploiting their long T₂ values. This 1.5 mm isotropic resolution is comparable to ¹H imaging and application of this approach could be extended to future studies of uptake, metabolism, and perfusion in cancer and other disease models and may ultimately be of value for clinical imaging.     

Optimum pulse flip angles for multi-scan acquisition of hyperpolarized NMR and MRI

The optimum pulse flip angles were calculated for multi-scan acquisition of hyperpolarized NMR and MRI. The derived formulae could be correlated with the best angle for ordinary steady-state acquisition, the so-called Ernst angle. Although single-scan acquisition has been popular in hyperpolarized measurements, signal accumulation by increasing scans may become very effective for improving the total signal gain, especially when the sample's longitudinal spin relaxation time is long. The optimum angles were calculated from theoretical relations between the exponential of the pulse repetition time/relaxation time ratio and the total scan counts. Constant and variable flip angle cases are presented, both of which yield similar cumulative signal amplitudes. For the constant angle case, a numerically calculated semi-universal curve is presented for the rough estimation of the best angle, as the results were not significantly dependent upon the degree of hyperpolarization within the realistic range. Meanwhile, for the variable angle case, the best angles were approximated from a clean trigonometric series relation, in which the initial pulse became near the Ernst angle and the last pulse was always 90 degrees . A modification of the variable angle scheme enables the acquisition of uniform signal amplitude throughout all scans.     

Frequency-specific SSFP for hyperpolarized C metabolic imaging at 14.1 T

Metabolic imaging of hyperpolarized [1-¹³C] pyruvate co-polarized with [¹³C]urea by dynamic nuclear polarization with rapid dissolution is a promising new method for assessing tumor metabolism and perfusion simultaneously in vivo. Novel pulse sequences are required to enable dynamic imaging of multiple ¹³C spectral lines with high spatiotemporal resolution. The goal of this study was to investigate a new frequency-specific approach for rapid metabolic imaging of multiple ¹³C resonances using the spectral selectivity of steady-state free precession pulse (SSFP) trains. Methods developed in simulations were implemented in a dynamic frequency-cycled balanced SSFP pulse sequence on a 14.1-T animal magnetic resonance imaging scanner. This acquisition was tested in thermal and hyperpolarized phantom imaging studies and in a transgenic mouse with prostate cancer.     

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.     

氢气拉曼光谱压缩感知方法分析研究

气体监测与我们的生活息息相关,氢气作为一种理想的研究模型更是受到广泛关注.拉曼光谱作为一种气体分析手段,具有无损非接触等优点.气体拉曼光谱测量存在的一个主要问题是拉曼散射信号弱.在一些特定场景下,需要信号采集时间较短,因此获得的拉曼光谱信噪比低.压缩感知方法作为一种新发展起来的信号处理手段,不仅可以压缩采样,缩短采样时...     

In vivo measurement of normal rat intracellular pyruvate and lactate levels after injection of hyperpolarized [1-(13)C]alanine

Hyperpolarized technology utilizing dynamic nuclear polarization has enabled rapid and high-sensitivity measurements of ¹³C metabolism in vivo. The most commonly used in vivo agent for hyperpolarized ¹³C metabolic imaging thus far has been [1-¹³C]pyruvate. In preclinical studies, not only is its uptake detected, but also its intracellular enzymatic conversion to metabolic products including [1-¹³C]lactate and [1-¹³C]alanine. However, the ratio of ¹³C-lactate/¹³C-pyruvate measured in this data does not accurately reflect cellular values since much of the [1-¹³C]pyruvate is extracellular depending on timing, vascular properties, and extracellular space and monocarboxylate transporter activity. In order to measure the relative levels of intracellular pyruvate and lactate, in this project we hyperpolarized [1-¹³C]alanine and monitored the in vivo conversion to [1-¹³C]pyruvate and then the subsequent conversion to [1-¹³C]lactate. The intracellular lactate-to-pyruvate ratio of normal rat tissue measured with hyperpolarized [1-¹³C]alanine was 4.89±0.61 (mean±S.E.) as opposed to a ratio of 0.41±0.03 when hyperpolarized [1-¹³C]pyruvate was injected.     

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.     

Temporal/spatial resolution improvement of in vivo DCE-MRI with compressed sensing-optimized FLASH

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides critical information regarding tumor perfusion and permeability by injecting a T(1) contrast agent, such as Gd-DTPA, and making a time-resolved measurement of signal increase. Both temporal and spatial resolutions are required to be high to achieve an accurate and reproducible estimation of tumor perfusion. However, the dynamic nature of the DCE experiment limits simultaneous improvement of temporal and spatial resolution by conventional methods. Compressed sensing (CS) has become an important tool for the acceleration of imaging times in MRI, which is achieved by enabling the reconstruction of subsampled data. Similarly, CS algorithms can be utilized to improve the temporal/spatial resolution of DCE-MRI, and several works describing retrospective simulations have demonstrated the feasibility of such improvements. In this study, the fast low angle shot sequence was modified to implement a Cartesian, CS-optimized, sub-Nyquist phase encoding acquisition/reconstruction with multiple two-dimensional slice selections and was tested on water phantoms and animal tumor models. The mean voxel-level concordance correlation coefficient for Ak(ep) values obtained from ×4 and ×8 accelerated and the fully sampled data was 0.87±0.11 and 0.83±0.11, respectively (n=6), with optimized CS parameters. In this case, the reduction of phase encoding steps made possible by CS reconstruction improved effectively the temporal/spatial resolution of DCE-MRI data using an in vivo animal tumor model (n=6) and may be useful for the investigation of accelerated acquisitions in preclinical and clinical DCE-MRI trials.     

Fleet algorithm for X-ray pulsar profile construction and TOA solution based on compressed sensing

X-ray pulse profile and time of arrival (TOA) are the two important physical quantities for pulsar navigation. With the standard and integrated X-ray pulse profiles modeled, X-ray pulse profile construction is studied and TOA is solved using compressed sensing (CS) technology. The observation matrix and waveform complete dictionary are mainly examined. A column vector-based matching pursuit algorithm is presented. The feasibility of obtaining X-ray pulse profile construction by compressed sensing technology is verified by numerical simulation. Compared with the X-ray pulse profile construction method based on epoch folding, the proposed method exhibits improved real-time performance, and its detection time for integrated X-ray pulse profile could be reduced by one order of magnitude. This proposed method can also solve for TOA solution and construct the X-ray pulse profile simultaneously, which is essential to improve pulsar navigation efficiency.     

Stationary wavelet transform for under-sampled MRI reconstruction

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional l_p-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions.     

深海远程正交频分复用水声通信块间迭代稀疏信道估计方法

在深海远程正交频分复用(OFDM)水声通信中,信道时延长、频率选择性衰落严重,传统的块独立压缩感知稀疏估计需要较高导频插入密度才能保证一定的估计性能,通信频谱利用率较低。提出了一种基于信道稀疏时变建模的块间迭代信道估计方法,利用深海信道在两个相邻OFDM数据块之间的时间相关性建立块间信道稀疏多途结构的时变关系,在此基础上,对传统稀疏信道估计算法中的候选字典矩阵的字典原子进行删减并改进优化方程,实现了对前一数据块所估信道信息的有效利用,显著降低了信道估计所需的导频插入密度。在深海不同接收深度、不同距离条件下开展了海试验证,实验结果表明,与传统稀疏信道估计方法相比,本方法在导频插入密度减半的条件下可达到优于传统方法的估计性能。     

基于小波变换的压缩感知理论对水质检测紫外-可见光谱数据的去噪研究

消除噪声影响对提高直接光谱法水质检测系统的测量稳定性和精度都具有重要意义。直接光谱法在线水质检测系统通常采用长寿命、无需预热的脉冲氙灯和适用于复杂检测环境的工业级光谱探测装置。针对整个光谱探测系统受到光源、光路和光电转换器件的严重影响,测定的光谱数据含有大量噪声这一实际问题,提出了基于小波变换的压缩感知去噪算法,并与传统小波阈值去噪方法进行了比较实验。针对化学需氧量为200 mg·L-1的邻苯二甲酸氢钾标液的紫外-可见光谱数据进行去噪处理,采用压缩感知去噪算法,将信号在小波域内分解,得到含噪高频系数;采用随机高斯矩阵作为压缩感知算法的观测矩阵,压缩比设置为2,对高频系数进行观测;选择正交匹配追踪算法恢复高频小波系数的稀疏性,从而达到去噪目的。同时针对传统的小波阈值去噪,采用daubechies4作为小波基的软阈值滤波方法对光谱数据进行去噪处理。为验证去噪算法的可行性,采集某溪水和城市生活污水的光谱信号分别采用以上两种方法进行去噪处理,实验结果表明：基于小波变换的压缩感知去噪算法适用于紫外-可见光谱法在线水质检测系统,该方法能在保留水样原始光谱信号的吸收特征的前提下有效地去噪,且去噪效果优于小波阈值去噪算法。与小波阈值去噪算法相比,信噪比提高了12.201 5 dB,均方根误差减小了0.009 3,峰值信噪比增加了5.299 dB。不仅避免了小波阈值去噪过程中阈值的选取依靠主观判断问题,而且在重构过程中有效地抑制了噪声,为直接光谱法检测水质参数提供了一种新的解决方案。     

一种基于压缩感知的三维导体目标电磁散射问题的快速求解方法

提出了一种将压缩感知和特征基函数结合的方法来计算三维导体目标的雷达散射截面.利用压缩感知理论,将随机选择的矩量法阻抗矩阵作为测量矩阵,将激励电压视为测量值,然后再用恢复算法可实现二维或二维半目标感应电流的求解.对于三维导体目标,使用Rao-Wilton-Glisson基函数表示的感应电流在常用的离散余弦变换基、小波基等稀疏基上不稀疏.为此,本文将计算出的目标特征基函数作为稀疏基,用广义正交匹配追踪算法作为恢复算法来加速恢复过程,并应用到三维导体目标的雷达散射截面计算中.数值结果证明了本文方法的准确性与高效性.