Quantitative susceptibility mapping using principles of echo shifting with a train of observations sequence on 1.5 T MRI

PurposeTo evaluate the accuracy of susceptibility estimated from the principles of echo shifting with a train of observations (PRESTO) sequence using a 1.5 T MRI system, we conducted experiments on the human brain using the PRESTO sequence and compared our results with the susceptibility obtained from spoiled gradient-recalled echo (GRE) sequence with flow compensation using quantitative susceptibility mapping (QSM) reconstruction.Materials and methodsExperiments on the human brain were conducted on 12 healthy volunteers (27 ± 4 years) using PRESTO and spoiled GRE sequences on a 1.5 T scanner. The PRESTO sequence is an echo-shifted gradient echo sequence that allows high susceptibility sensitivity and rapid acquisition because of TE > TR compared with the spoiled GRE sequence. QSM analysis was performed on the obtained phase images using the iLSQR method. Estimated susceptibility maps were used for region of interest analyses and estimation of line profiles through iron-rich tissue and major vessels.ResultsOur results demonstrated that susceptibility maps were accurately estimated, without error, by QSM analysis of PRESTO and spoiled GRE sequences. Acquisition time in the PRESTO sequence was reduced by 43% compared with that in the spoiled GRE sequence. Differences did exist between susceptibility maps in PRESTO and spoiled GRE sequences for visualization and quantitative values of major blood vessels and the areas around themConclusionThe PRESTO sequence enables correct estimation of tissue susceptibility with rapid acquisition and may be useful for QSM analysis of clinical use of 1.5 T scanners.     

Impact of gradient timing error on the tissue sodium concentration bioscale measured using flexible twisted projection imaging

The rapid biexponential transverse relaxation of the sodium MR signal from brain tissue requires efficient k-space sampling for quantitative imaging in a time that is acceptable for human subjects. The flexible twisted projection imaging (flexTPI) sequence has been shown to be suitable for quantitative sodium imaging with an ultra-short echo time to minimize signal loss. The fidelity of the k-space center location is affected by the readout gradient timing errors on the three physical axes, which is known to cause image distortion for projection-based acquisitions. This study investigated the impact of these timing errors on the voxel-wise accuracy of the tissue sodium concentration (TSC) bioscale measured with the flexTPI sequence. Our simulations show greater than 20% spatially varying quantification errors when the gradient timing errors are larger than 10 μs on all three axes. The quantification is more tolerant of gradient timing errors on the Z-axis. An existing method was used to measure the gradient timing errors with <1 μs error. The gradient timing error measurement is shown to be RF coil dependent, and timing error differences of up to ～16 μs have been observed between different RF coils used on the same scanner. The measured timing errors can be corrected prospectively or retrospectively to obtain accurate TSC values.     

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.     

一种适用于大测绘带合成孔径声呐的运动补偿方法

当合成孔径声呐测绘带较大时,运动误差的空变效应严重,经典的相位中心重叠算法难以适用。为此,提出了一种适用于大测绘带合成孔径声呐的运动补偿方法。首先使用混合调制的拉格朗日时延估计算法对前后两帧回波的时延进行估计,之后使用线性回归方法拟合出运动误差,最后利用运动误差的估计值对回波进行逐点精确补偿。仿真数据的结果表明,该算法能够获得比相位中心重叠算法更好的运动估计结果,运动补偿后成像分辨率接近理论分辨率。使用该算法分别对高、低频合成孔径声呐的湖试数据进行了处理,水下地貌和小目标的成像质量均有明显提高。      

大口径干涉成像光谱仪扫描速度偏差的评估与校正

大口径干涉成像光谱仪通过视场扫描和数据重组获取目标的干涉序列,理想情况下帧间视场移动量为1个像素。若扫描速度偏快或偏慢,按照正常重组算法得到的干涉序列不是源自同一目标,导致图像和光谱出现误差。根据像点在数据立方体中的移动规律,提出了像点轨迹追踪法进行校正,按照轨迹坐标对数据进行重采样获得同一目标的干涉序列信号并校正横向尺寸变形。并利用干涉立方体的投影图像对运动误差校正效果进行评估。     

Automatic correction of in-plane bulk motion artifacts in self-navigated radial MRI

Radial MRI is typically used for scans that are sensitive to unavoidable motion. While the translational motion artifact can be easily removed from the radial trajectory data by phase correction, correction of rotational motion still remains a challenge in radial MRI. We present a novel method to refocus the image corrupted by view-to-view motion in the view-interleaved radial MRI data. In this method, the error in rotational view angles was modeled as a polynomial function of the view order and the model parameters were estimated by minimizing the self-navigator image metrics such as image entropy, gradient entropy, normalized gradient squared and mean square difference. Translational motion correction was conducted by aligning the projection profiles. Simulation studies were conducted to demonstrate the robustness of both translational and rotational motion correction methods in different noise levels. The proposed method was successfully applied to correct for motion of healthy subjects. Substantial motion correction with relative error of less than 5% was achieved by using either first- or second-order model with the image metrics. This study demonstrates the potential of the method for motion-sensitive applications.     

Improving motion estimation by accounting for local image distortion

Cardiac elastography is a useful diagnostic technique for detection of heart function abnormalities, based on analysis of echocardiograms. The analysis of the regional heart motion allows assessing the extent of myocardial ischemia and infarction. In this paper, a new two-stage algorithm for cardiac motion estimation is proposed, where the data is taken from a sequence of 2D echocardiograms. The method combines the advantages of block-matching and optical flow techniques. The first stage employs a standard block-matching algorithm (sum of absolute differences) to provide a displacement estimate with accuracy of up to one pixel. At the second stage, this estimate is corrected by estimating the parameters of a local image transform within a test window. The parameters of the image transform are estimated in the least-square sense. In order to account for typical heart motions, like contraction/expansion, translation and rotation, a local affine model is assumed within the test window. The accuracy of the new algorithm is evaluated using a sequence of 500 grayscale B-mode images, which are generated as distorted, but known copies of an original ROI, taken from a real echocardiogram. The accuracy of the motion estimation is expressed in terms of errors: maximum absolute error, root-mean-square error, average error and standard deviation. The errors of the proposed algorithm are compared with these of the known block-matching technique with cross-correlation and interpolation in the sub-pixel space. Statistical analysis of the errors shows that the proposed algorithm provides more accurate estimates of the heart motion than the cross-correlation technique with interpolation in the sub-pixel space.     

三维多回波流动补偿定量磁化率成像(QSM)方法研究

定量磁化率成像(QSM)利用一般成像技术舍弃的相位信息得到局部磁场变化特性,通过复杂的场到源反演计算,可直接得到定量的磁化率图,它广泛应用于测量血氧饱和度、脑部微出血、铁沉积、组织钙化等方面．然而,梯度磁场中流动会引起相位错误,并且产生显著的流动伪影,最终得到错误的QSM图像．为了矫正流动的影响,该文在3 T磁共振系统上实现了三维多回波流动补偿梯度回波序列,并用该序列采集流动水模和志愿者颅脑数据．流动水模和颅脑数据均显示,流动补偿能够明显矫正相位错误,消除流动伪影．颅脑横断位QSM结果证明,流动补偿序列可以消除血液流动引起的QSM的错误,提高QSM的准确性．     

Improving motion estimation by accounting for local image distortion

Cardiac elastography is a useful diagnostic technique for detection of heart function abnormalities, based on analysis of echocardiograms. The analysis of the regional heart motion allows assessing the extent of myocardial ischemia and infarction. In this paper, a new two-stage algorithm for cardiac motion estimation is proposed, where the data is taken from a sequence of 2D echocardiograms. The method combines the advantages of block-matching and optical flow techniques. The first stage employs a standard block-matching algorithm (sum of absolute differences) to provide a displacement estimate with accuracy of up to one pixel. At the second stage, this estimate is corrected by estimating the parameters of a local image transform within a test window. The parameters of the image transform are estimated in the least-square sense. In order to account for typical heart motions, like contraction/expansion, translation and rotation, a local affine model is assumed within the test window. The accuracy of the new algorithm is evaluated using a sequence of 500 grayscale B-mode images, which are generated as distorted, but known copies of an original ROI, taken from a real echocardiogram. The accuracy of the motion estimation is expressed in terms of errors: maximum absolute error, root-mean-square error, average error and standard deviation. The errors of the proposed algorithm are compared with these of the known block-matching technique with cross-correlation and interpolation in the sub-pixel space. Statistical analysis of the errors shows that the proposed algorithm provides more accurate estimates of the heart motion than the cross-correlation technique with interpolation in the sub-pixel space.     

Scene based nonuniformity correction based on block ergodicity for infrared focal plane arrays

This paper puts forward a new scene based nonuniformity correction algorithm for IRFPA. This method adopts phase-correlation method for motion estimation and takes the sum of mean-square errors of the pixel brightness between several adjacent frames as the cost function when the brightness constancy assumption between two adjacent frames is satisfied. Nonuniformity correction parameters could be estimated by minimizing such cost function. In order to reduce calculation quantity, we can divide these images into several subblocks, and solve for the optimum solution of the cost function respectively in each subblock. From the analysis, it is shown that the optimum solution is of global uniqueness when all the elements in subblocks could satisfy the ergodicity condition. Then the estimated value of nonuniformity correction parameters could be deduced by minimizing the cost functions. The nonuniformity correction experiments for both infrared image sequence with simulated nonuniformity and infrared imagery with real nonuniformity show that the proposed algorithm could achieve a great correction effect by only analyzing a small number of frames.     

MRI phase offset correction method impacts quantitative susceptibility mapping

Individual channel ultra-high field (7T) phase images have to be phase offset corrected prior to the mapping of magnetic susceptibility of tissue. Whilst numerous methods have been proposed for gradient recalled echo MRI phase offset correction, it remains unclear how they affect quantitative magnetic susceptibility values derived from phase images. Methods already proposed either employ a single or multiple echo time MRI data. In terms of the latter, offsets can be derived using an ultra-short echo time acquisition, or by estimating the offset based on two echo points with the assumption of linear phase evolution with echo time. Our evaluation involved 32 channel multi-echo time 7T GRE (Gradient Recalled Echo) and ultra-short echo time PETRA (Pointwise Encoding Time Reduction with Radial Acquisition) MRI data collected for a susceptibility phantom and three human brains. The combined phase images generated using four established offset correction methods (two single and two multiple echo time) were analysed, followed by an assessment of quantitative susceptibility values obtained for a phantom and human brains. The effectiveness of each method in removing the offsets was shown to reduce with increased echo time, decreased signal intensity and reduced overlap in coil sensitivity profiles. Quantitative susceptibility values and how they change with echo time were found to be method specific. Phase offset correction methods based on single echo time data have a tendency to produce more accurate and less noisy quantitative susceptibility maps in comparison with methods employing multiple echo time data.     

双侧回波联合的合成孔径声呐运动补偿算法

利用单侧回波可估计合成孔径声呐基阵的斜距误差,但无法区分横荡误差和升沉误差。针对此问题,提出了一种双侧回波联合的运动补偿方法。该方法首先根据双侧基阵运动误差的几何关系,建立了双侧基阵的运动误差模型,再结合偏移相位中心算法估计基阵的横荡误差和升沉误差,最后利用所估计的运动误差对不同掠射角上的回波进行运动补偿。仿真结果表明:该方法能精确估计双侧基阵的运动误差,其估计值与实际值的偏差为10-4 m左右,估计结果的标准差接近克拉美罗下界;对回波进行运动补偿后,能获得比基于单侧回波运动补偿方法更好的成像效果。水下球串目标的湖试数据的成像结果显示,与基于单侧回波的运动补偿方法相比,所提方法能更好地抑制图像的散焦现象。      

圆合成孔径声呐多点定位运动补偿

圆合成孔径声呐(CSAS)的成像性能受平台运动误差影响而下降,利用单侧回波可估计CSAS基阵的斜距误差,但单侧回波在小测绘带时无法估计升沉误差,针对此问题,提出了一种利用单侧回波信号的声呐平台三维运动估计和补偿方法。首先,对CSAS在不同观测角度的目标回波取极大值获得目标回波的到达时间;其次,基于多个点目标的到达时间建立CSAS三维定位模型;然后利用列文伯格-马夸尔特方法对声呐三维坐标进行估计;最后将位置估计结果与时域反投影成像方法结合实现对目标的成像.仿真结果表明:该方法能精确估计声呐平台运动误差,其空间坐标的估计误差小于仿真信号波长的1/8,从而精确补偿了CSAS在不同空间采样点上的阵元回波时间差,显著提高了目标成像质量。湖上试验结果表明,该算法能够实现对CSAS的运动误差补偿。仿真和试验结果均验证了方法的可行性和有效性。      

Model-based PRFS thermometry using fat as the internal reference and the extended Prony algorithm for model fitting

A model-based proton resonance frequency shift (PRFS) thermometry method was developed to significantly reduce the temperature quantification errors encountered in the conventional phase mapping method and the spatiotemporal limitations of the spectroscopic thermometry method. Spectral data acquired using multi-echo gradient echo (GRE) is fit into a two-component signal model containing temperature information and fat is used as the internal reference. The noniterative extended Prony algorithm is used for the signal fitting and frequency estimate. Monte Carlo simulations demonstrate the advantages of the method for optimal water-fat separation and temperature estimation accuracy. Phantom experiments demonstrate that the model-based method effectively reduces the interscan motion effects and frequency disturbances due to the main field drift. The thermometry result of ex vivo goose liver experiment with high intensity focused ultrasound (HIFU) heating was also presented in the paper to indicate the feasibility of the model-based method in real tissue.     

Estimation of chaotic coupled map lattices using symbolic vector dynamics

In [K. Wang, W.J. Pei, Z.Y. He, Y.M. Cheung, Phys. Lett. A 367 (2007) 316], an original symbolic vector dynamics based method has been proposed for initial condition estimation in additive white Gaussian noisy environment. The estimation precision of this estimation method is determined by symbolic errors of the symbolic vector sequence gotten by symbolizing the received signal. This Letter further develops the symbolic vector dynamical estimation method. We correct symbolic errors with backward vector and the estimated values by using different symbols, and thus the estimation precision can be improved. Both theoretical and experimental results show that this algorithm enables us to recover initial condition of coupled map lattice exactly in both noisy and noise free cases. Therefore, we provide novel analytical techniques for understanding turbulences in coupled map lattice.     

Quantitative assessment of motion correction for high angular resolution diffusion imaging

Several methods have been proposed for motion correction of high angular resolution diffusion imaging (HARDI) data. There have been few comparisons of these methods, partly due to a lack of quantitative metrics of performance. We compare two motion correction strategies using two figures of merit: displacement introduced by the motion correction and the 95% confidence interval of the cone of uncertainty of voxels with prolate tensors. What follows is a general approach for assessing motion correction of HARDI data that may have broad application for quality assurance and optimization of postprocessing protocols. Our analysis demonstrates two important issues related to motion correction of HARDI data: (1) although neither method we tested was dramatically superior in performance, both were dramatically better than performing no motion correction, and (2) iteration of motion correction can improve the final results. Based on the results demonstrated here, iterative motion correction is strongly recommended for HARDI acquisitions.     

结合Sage-Husa滤波的合成孔径声呐多传感器组合运动补偿算法

在运动测量设备噪声统计特性不确定的情况下,提出结合Sage-Husa滤波的合成孔径声呐多传感器组合运动补偿方法。使用Sage-Husa卡尔曼滤波处理多种异类运动传感器的数据,自适应估计声呐速度的最优值,计算实际航迹与理想航迹之间的横荡误差和升沉误差,最后通过时延校正原始回波数据。仿真结果表明,Sage-Husa滤波对运动误差估计精度至少提高37%,运动补偿后,目标峰值旁瓣比和积分旁瓣比有所降低,峰值旁瓣比接近理论值.湖试数据处理结果表明,目标能量分散的情况有所改善,能量集中在主瓣,散焦得到抑制。Sage-Husa滤波在多传感器系统噪声先验知识缺失的条件下,能减小运动数据估计误差,提高运动补偿的准确性。      

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.     

结合稳健中国余数定理的合成孔径声呐多子带运动补偿算法

针对运动误差过大导致基于相位的时延估计发生模糊的问题,提出了一种多子带处理和稳健中国余数定理结合的合成孔径声呐运动补偿方法。该方法利用前后两帧回波在多个子频带上的相位差获得模糊时延估计,再根据模糊时延之间的关联性,应用稳健中国余数定理估计真实的时延,最后通过所估计的时延对原始回波进行校正。湖试数据处理结果表明该方法能精确补偿运动误差对成像的影响,进行运动补偿后,包裹目标成像结果的方位向虚假目标被有效消除,且目标的轮廓也变得更加清晰;对线缆目标的成像结果进行斜距向剖面分析,剖面图的-3 dB宽度由运动补偿前的9.05个像素点缩小为运动补偿后的2.06个像素点,线缆目标的聚焦程度明显增强。