Multiframe blind image deconvolution with split Bregman method

In the paper, a multiframe blind image deconvolution method based on total variation and framelet regularizer is proposed. An adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problems. In each iteration, four sub-problems need to be solved, one of which can be very efficiently and easily solved via fast Fourier transform implementation or closed form solution. Both simulated noisy and blurred frames and real degraded frames are used to verify the effectiveness of the proposed method. Comparative experimental results show that the proposed method can efficiently remove the blur and noises and restore high quality sharp image.     

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.     

Three-dimensional image correlator using fast computational integral imaging reconstruction method based on pixel-to-pixel mapping

In this paper, a three-dimensional (3D) image correlator using a fast computational integral imaging reconstruction (CIIR) method based on a pixel-to-pixel mapping is proposed. In order to implement the fast CIIR method, we replace the magnification process in the conventional CIIR by a pixel-to-pixel mapping. The proposed fast CIIR method reconstructs two sorts of plane images; a plane image whose quality is sufficient, and a dot pattern plane image insufficient to view. This property is very useful to enhance the performance of a CIIR-based image correlator. Thus, we apply the fast CIIR method to a CIIR-based image correlator. To show the feasibility of the proposed method, some preliminary experiments on both pattern correlation and computational cost are carried out, and the results are presented. Our experimental results indicate that the proposed image correlator is superior to the previous method in terms of both correlation performance and complexity.     

Visualizing the lateral habenula using susceptibility weighted imaging and quantitative susceptibility mapping

The habenulae consist of a pair of small nuclei which bridge the limbic forebrain and midbrain monoaminergic centers. They are implicated in major depressive disorders due to abnormal phasic response when provoked by a conditioned stimulus. The lateral habenula (Lhb) is believed to be involved in dopamine metabolism and is now a target for deep brain stimulation, a treatment which has shown promising anti-depression effects. We imaged the habenulae with susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) in order to localize the lateral habenula. Fifty-six healthy controls were recruited for this study. For the quantitative assessment, we traced the structure to compute volume from magnitude images and mean susceptibility bilaterally for the habenula on QSM. Thresholding methods were used to delineate the Lhb habenula on QSM. SWI, true SWI (tSWI), and QSM data were subjectively reviewed for increased Lhb contrast. SWI, QSM, and tSWI showed bilateral signal changes in the posterior location of the habenulae relative to the anterior location, which may indicate increased putative iron content within the Lhb. This signal behavior was shown in 41/44 (93%) subjects. In summary, it is possible to localize the lateral component of the habenula using SWI and QSM at 3 T.     

Parallel variable-density spiral imaging using nonlocal total variation reconstruction

The relatively long scan time is still a bottleneck for both clinical applications and research of magnetic resonance imaging. To reduce the data acquisition time, we propose a novel fast magnetic resonance imaging method based on parallel variable-density spiral acquisition, which combines undersampling optimization and nonlocal total variation reconstruction.The undersampling optimization promotes the incoherence of resultant aliasing artifact via the "worst-case" residual error metric, and thus accelerates the data acquisition. Moreover, nonlocal total variation reconstruction is utilized to remove such an incoherent aliasing artifact and so improve image quality. The feasibility of the proposed method is demonstrated by both numerical phantom simulation and in vivo experiment. The experimental results show that the proposed method can achieve high acceleration factor and effectively remove an aliasing artifact from data undersampling with well-preserved image details. The image quality is better than that achieved with the total variation method.     

基于非均匀螺旋线数据和布雷格曼迭代的快速磁共振成像方法

数据采集时间长是制约磁共振成像技术发展的重要瓶颈.为了解决这一问题, 本文基于压缩感知成像理论, 提出了一种结合非均匀螺旋线磁共振数据采集序列和布雷格曼迭代重建的快速磁共振成像方法, 通过欠采样缩短数据采集时间.欠采样引起混迭伪影则通过非均匀螺旋线欠采样特性和布雷格曼迭代重建去除.水模磁共振成像实验和在体磁共振成像实验结果表明: 欠采样情况下, 所提出的方法能有效去除欠采样导致的混迭伪影, 获得的图像结构信息完整的成像结果, 在缩短采样时间的同时, 具有较高的准确度. 关键词： 磁共振成像 非均匀螺旋线 全变分 布雷格曼迭代     

Estimating cerebral venous oxygenation in human fetuses with ventriculomegaly using quantitative susceptibility mapping

Rationale and objectivesThe goal of this study was to estimate venous blood oxygen saturation (SvO2) in the superior sagittal sinus (SSS) in fetal brains with ventriculomegaly (VM) using quantitative susceptibility mapping (QSM).Materials and methodsA radiofrequency spoiled gradient echo sequence was used to evaluate data on 19 fetuses with VM (gestational age(GA): median = 29.9 weeks (range 23 to 37.3 weeks)) and 20 healthy fetuses (GA: median = 30.9 (range 22.7 to 38.7 weeks)) at 1.5 T. Susceptibility weighted images encompassing the entire fetal brain were acquired within 1 min. An iterative, geometry constraint-based thresholded k-space division algorithm was used for generating QSM data of the fetal brain. The venous oxygen saturation was calculated using the magnetic susceptibility of the SSS obtained from the QSM data. Mixed-model analysis of variance and interobserver variability assessment were used to analyze the results.ResultsThe median SvO2 values in the entire VM cohort as well as for second and third trimester fetuses (with interquartile range) were: 67.8% (63.2%, 73.6%), 73.1% (69.1%, 77.3%) and 63.8% (59.4%, 68.1%), respectively. The corresponding median SvO2 value in the healthy control group was: 65.3% (58.3%, 68.2%), 67.5% (61.7%, 69.2%) and 60.8% (53.6%, 68.2%), respectively. However, the difference of SvO2 between VM and control groups was not significant at the p = 0.05 level (p = 0.076). The SvO2 was found decreasing significantly with GA in the healthy control group (p < 0.05).ConclusionsWe report for the first time the estimation of cerebral SvO2 in human fetuses with VM using QSM. This measure of oxygen saturation might be beneficial in assessing and monitoring the metabolic status of the fetus in various clinical conditions.     

Decreasing iron susceptibility with temperature in quantitative susceptibility mapping: A phantom study

To clarify the temperature dependence of susceptibility estimated by quantitative susceptibility mapping (QSM) analysis, we investigated the relationship between temperature and susceptibility using a cylinder phantom with varying temperatures. Six solutions with various concentrations of superparamagnetic iron oxide (SPIO) nanoparticles were employed. These tubes were placed in a cylinder phantom and surrounded with water. The temperature of the circulated water was adjusted to change the temperature in the cylinder phantom from 25.8 °C to 42.5 °C. The cylinder phantom was scanned via a three-dimensional multiple spoiled gradient-echo sequence for R2* and QSM analyses with varying temperatures. The relationships between temperature, susceptibility, and R2* values were determined. Moreover, the temperature coefficients of susceptibility (χ-Tc) and (R2*-Tc) were calculated at each concentration and the linearities in these indices against each SPIO concentration were validated. Significant inverse correlations were found between temperature, susceptibility, and R2* values at each SPIO concentration due to the decrease in paramagnetic iron susceptibility that occurred with increasing temperature based on Curie's law. Moreover, although there were significant correlations between the susceptibility and R2* values at any temperature, the slopes of the regression lines grew in height with greater temperatures. The percentage of difference per Celsius degree in susceptibility in any SPIO concentration was lower than the corresponding finding among the R2* results. There were strong linearities between the SPIO concentration, χ-Tc (r = −0.994; p < 0.001), and R2*-Tc (r = −0.998; p < 0.001). The χ-Tc and R2*-Tc outcomes in a particular voxel varied considerably with the iron contents. Although there was an inverse correlation noted between temperature and susceptibility, the susceptibility analysis showed smaller temperature dependence relative to the R2* analysis. QSM analysis might be a more suitable option for magnetic resonance-based iron quantification in comparison with R2* relaxometry.     

一种用于中子散射成像的快速反投影重建方法

针对中子散射成像建立了一种快速反投影重建方法,这种方法首先根据每一个散射事件确定粒子可能所在的空间范围,然后该空间范围中进行重建,大大减少了重建的运行时间。应用所建立的方法对模拟的成像数据进行处理,结果表明:重建源与实际源的位向一致,对1000个散射数据进行处理,用时约3 s;对5探测单元成像得到的模拟数据处理,得到重建后点源的位向分辨为8.0°;重建源的位向分辨不随设定重建平面的位置而改变,但重建平面距探测平面越近,重建速度越快;当重建平面达到一定尺寸时,继续增加大重建平面尺寸,重建的运行时间增加不明显,该方法在大视场空间探测成像应用中,具有较高的重建效率。     

一种用于中子散射成像的快速反投影重建方法

针对中子散射成像建立了一种快速反投影重建方法,这种方法首先根据每一个散射事件确定粒子可能所在的空间范围,然后该空间范围中进行重建,大大减少了重建的运行时间。应用所建立的方法对模拟的成像数据进行处理,结果表明：重建源与实际源的位向一致,对1000个散射数据进行处理,用时约3 s;对5探测单元成像得到的模拟数据处理,得到重建后点源的位向分辨为8.0;重建源的位向分辨不随设定重建平面的位置而改变,但重建平面距探测平面越近,重建速度越快;当重建平面达到一定尺寸时,继续增加大重建平面尺寸,重建的运行时间增加不明显,该方法在大视场空间探测成像应用中,具有较高的重建效率。     

Novel Fourier-based iterative reconstruction for sparse fan projection using alternating direction total variation minimization

Sparse-view x-ray computed tomography(CT) imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform(NUFFT) is presented in this work along with advanced total variation(TV) regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively.     

基于全变分最小化和交替方向法的康普顿散射成像重建算法

康普顿散射成像技术利用射线与物质作用后的散射光子信息对物质的电子密度进行成像.与传统的透射成像方式相比,康普顿散射成像具有系统结构灵活、成像对比度高、辐射剂量低等优势,在无损检测、医疗诊断、安全检查等领域有着广阔的应用前景.但其重建问题是一个非线性的逆问题,通常是不适定的,其解对噪声和测量误差非常敏感.为解决此问题,本文结合全变分最小化正则化方法和交替方向法提出了一种新的康普顿散射成像重建算法.该算法首先将问题对应的TV模型转化为与之等价的带约束的优化问题,然后利用增广拉格朗日乘子法将优化问题分解为两个具有解析解的子问题,并通过交替求解子问题使增广拉格朗日函数达到最小,进而得到重建的图像.在仿真实验中,通过与主流的ASD-POCS方法进行对比,证明了该算法在重建精度和重建效率方面的优势.     

Weighted variation after projection method for low-lying nonyrast states

We propose a simple algorithm to further improve the previous variation after projection(VAP) wave functions for low-lying nonyrast states. We attach a weight factor to each calculated energy; then, the sum of these weighted energies is minimized. It turns out that a low-lying nonyrast VAP wave function can be further optimized when the weight factor for the corresponding energy is far larger than the other ones. Based on the improved WVAP wave functions, the energy-variance extrapolation method...     

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.     

Computational integral imaging reconstruction method of 3D images using pixel-to-pixel mapping and image interpolation

In this paper, we propose a novel computational integral imaging reconstruction (CIIR) method to improve the visual quality of the reconstructed images using a pixel-to-pixel mapping and an interpolation technique. Since an elemental image is magnified inversely through the corresponding pinhole and mapped on the reconstruction output plane based on pinhole-array model in the conventional CIIR method, the visual quality of reconstructed output image (ROI) degrades due to the interference problem between adjacent pixels during the superposition of the magnified elemental images. To avoid this problem, the proposed CIIR method generates dot-pattern ROIs using a pixel-to-pixel mapping and substitutes interpolated values for the empty pixels within the dot-pattern ROIs using an interpolation technique. The interpolated ROIs provides a much improved visual quality compared with the conventional method because of the exact regeneration of pixel positions sampled in the pickup process without interference between pixels. Moreover, it can enable us to reduce a computational cost by eliminating the magnification process used in the conventional CIIR. To confirm the feasibility of the proposed system, some experiments are carried out and the results are presented.     

High total variation-based method for sparse-view photoacoustic reconstruction

We propose a novel method by combining the total variation（TV） with the high-degree TV（HDTV） to improve the reconstruction quality of sparse-view sampling photoacoustic imaging（PAI）. A weighing function is adaptively updated in an iterative way to combine the solutions of the TV and HDTV minimizations. The fast iterative shrinkage/thresholding algorithm is implemented to solve both the TV and the HDTV minimizations with better convergence rate. Numerical results demonstrate the superiority and efficiency of the proposed method on sparse-view PAI. In vitro experiments also illustrate that the method can be used in practical sparse-view PAI.     

A Compton scattering image reconstruction algorithm based on total variation minimization

Compton scattering imaging is a novel radiation imaging method using scattered photons.Its main characteristics are detectors that do not have to be on the opposite side of the source,so avoiding the rotation process.The reconstruction problem of Compton scattering imaging is the inverse problem to solve electron densities from nonlinear equations,which is ill-posed.This means the solution exhibits instability and sensitivity to noise or erroneous measurements.Using the theory for reconstruction of sparse images,a reconstruction algorithm based on total variation minimization is proposed.The reconstruction problem is described as an optimization problem with nonlinear data-consistency constraint.The simulated results show that the proposed algorithm could reduce reconstruction error and improve image quality,especially when there are not enough measurements.     

An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM)

Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T₁-weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of > 2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method.     

A fast 3D look-locker method for volumetric T1 mapping.

We introduce a fast technique, based on the principles of the 2D Look-Locker T1 measurement scheme, to rapidly acquire the data for accurate maps of T1 in three dimensions. The acquisition time has been shortened considerably by segmenting the acquisition of the k(y) phase encode lines. Using this technique, the data for a 256 x 128 x 32 volumetric T1 measurement can be acquired in 7.6 min. T1 measurements made in phantoms with T1s between 200 and 1200 ms had an accuracy of 4% and a reproducibility of 3.5%. Measurements of T1 made in normal brain using the fast 3D sequence corresponded well with inversion-recovery fast spin-echo measurements.     

A fast mesh deformation method using explicit interpolation

A novel mesh deformation algorithm for unstructured polyhedral meshes is developed utilizing a tree-code optimization of a simple direct interpolation method. The algorithm is shown to provide mesh quality that is competitive with radial basis function based methods, with markedly better performance in preserving boundary layer orthogonality in viscous meshes. The parallelization of the algorithm is described, and the algorithm cost is demonstrated to be O(n log n). The parallel implementation was used to deform meshes of 100 million nodes on nearly 200 processors demonstrating that the method scales to large mesh sizes. Results are provided for a simulation of a high Reynolds number fluid–structure interaction case using this technique.