基于感兴趣区约束的并行磁共振成像重建算法

目的：并行磁共振成像利用敏感度编码降低成像所需的梯度编码步数，从而缩短数据扫描时间，本文旨在提出一种磁共振并行成像重建新算法，提高加速因子较大时的图像重建质量。材料与方法：获得精确的空间敏感度分布是提高其图像重建质量的关键之一。但是由于可用于估计的数据较少，敏感度分布中总是存在一定的噪声与伪影干扰，根据理想的敏感度分布应该在成像区域外取值为零这一特点，本文提出带感兴趣区约束的并行成像算法，首先基于区域生长和形态学方法提取出成像物体的外部轮廓构造感兴趣区，然后将该区域外的敏感度置零后引人并行重建算法，以避免成像区域外的伪影与噪声对重建的影响。结果：通过8通道线圈并行采集的体模数据重建实验表明，在加速因子取4时，本文算法可以更有效地抑制重建图像中的噪声及伪影，实现高质量的图像重建。结论：通过在并行成像算法中引人带感兴趣区的约束。可使加速因子较大时并行磁共振成像的图像重建质量获得一定的提升。     

定量磁化率重建方法探讨及其改进方法

为了评估定量磁化率成像(QSM)中常用背景场去除方法的优缺点,并分析磁化率反演过程中空间阈值截断法(TKD)产生严重伪影的原因,本文探讨了多种背景场去除方法,并提出了抑制磁化率反演伪影的改进方法。首先,本文利用梯度回波序列扫描磁共振相位图像,分别根据复杂调和伪影去除法(SHARP)、正则化复杂调和伪影去除法(RESHARP)以及拉普拉斯边界值法(LBV)的原理去除背景场,并对不同方法重建的图像质量和重建速度进行对比分析;其次,本文分析TKD方法造成数据的多次截断和不连续从而导致重建伪影的原因,通过增大阈值截断范围、提高数据连续性的方式,提出了改进的TKD方法;最后,根据改进方法完成磁化率反演并与原始TKD方法的反演结果进行对比和分析。结果表明,SHARP和RESHARP方法的重建速度快,但SHARP重建伪影严重且重建精度不高,而RESHARP的实现过程比较复杂;LBV方法重建速度缓慢,但重建图像的细节突出、重建精度很高。此外,在磁化率反演过程中,原始TKD方法重建图像的伪影严重,但改进的方法获得了良好的伪影抑制图像,并得到了伪影区域良好的磁化率反演结果。     

MR图像Ghost伪影的校正

原始K空间奇偶回波单独重建图像数据时，根据二维多项式拟合参考扫描各点相位漂移估计值对图像进行相位校证，可减轻伪影的影响。但在噪声较严重的情况下，校正后的图像仍含有较严重的残余伪影。按相位编码方向对图像数据采用基于最小二乘法多项式拟合的方法可减轻噪声对图像的干扰，再利用二维抑制Ghost伪影算法能更有效地消除EPI成像过程中由于涡流引起的伪影。该算法的缺点是会引起图像信息强度的变化及造成图像高频信息的衰减。但由于MRI图像主要集中为低频信息，且Ghost伪影信息强度不超过图像信息最大强度20％时，该方法对图像信息的损失不会影响对病灶的识别。     

基于频域约束和交叉融合特征网络的磁共振图像超分辨率重建算法

磁共振（MR）图像常用于临床医学诊断，获得高分辨率MR图像有利于进行医学分析。目前主流的基于参考的图像超分辨率重建算法重建的图像，其视觉效果取得了明显的提升，但仍存在明显的伪影问题。针对该问题，提出频域约束和交叉融合特征网络（FCCF）模型，即引入频域损失函数作为约束条件，并构建一种多分辨率特征融合机制，通过交叉融合不同分辨率的图像特征来提高生成图像的质量，使重建结果具有更清晰的细节，没有明显的伪影。在合成和真实的MR图像数据集上分别用PSNR和SSIM指标进行评估，实验结果明显优于现有的超分辨率重建方法。     

灵敏度编码磁共振谱成像(SENSE-SI)技术及图像重建方法

传统的相位编码磁共振谱成像(MRSI)采集数据需要很长的时间,使得MRSI在临床上的应用受阻.灵敏度编码磁共振谱成像(SENSE-SI)采用线圈矩阵来并行采集MRSI数据,是一种不仅可以大大减少数据采集时间,而且不影响空间和谱的分辨率的全新方法.在图像重建时利用各个线圈的空间灵敏度来对丢失的编码信息进行恢复,将像素折叠的图像进行展开,可以得到完全没有重叠伪影的代谢物图像.SENSE-SI采集数据的快速性和图像重建的高分辨性为MRSI真正应用于临床打下了坚实的基础.     

多次激发扩散磁共振成像中运动伪影及偏共振伪影的联合矫正

针对多次激发扩散磁共振成像中的运动伪影和偏共振伪影,本文提出了一种联合矫正方法,可以同时去除两种伪影,而且不需要额外采集运动导航数据和主磁场不均度图。该方法使用多次激发变密度螺旋线序列采集数据,通过自动偏共振矫正算法去除模糊,运动引入的相位误差在去除模糊的过程中采用直接法或迭代法矫正。在体磁共振成像实验表明,所提出的联合矫正方法可有效去除运动伪影和偏共振效应引起的图像模糊,获得结构清晰的成像结果,且不会增加扫描时间。     

口腔金属材料与影像学伪影的检测与比较

背景：磁共振成像是常用的临床影像检测方法,口腔金属材料在磁共振成像检查时会出现图像伪影,从而影响图像的质量以及诊断的正确性。 目的：分析不同口腔金属材料在磁共振成像中产生的伪影强度以及影响伪影的因素。 方法：分析9篇近10年关于口腔金属材料对磁共振成像影响的研究文献,从不同角度分析口腔常用金属材料钴铬合金、镍铬合金、钛合金、金铂合金、银铂合金以及纯钛等在磁共振影像中产生的伪影的大小,比较其影响伪影强度的因素。 结果与结论：钴铬合金在磁共振图像中产生的伪影最大,并且随着磁场强度的增强伪影增大,其中硬质钴铬合金产生的伪影强度大于软质钴铬合金产生的伪影强度,其次是镍铬合金产生的伪影,而金、银合金以及纯钛产生的伪影最小,并且磁场强度增强等外加干扰因素对伪影大小无明显影响。     

基于非广延熵先验的PET图像重建EI北大核心CSCD

最大化后验(MAP)方法已经被广泛应用于解决图像重建的病态问题。先验项的选择一直是研究的热点,但是传统先验形式往往会导致重建图像模糊或者产生阶梯状伪影。本文针对传统先验形式存在的不足,提出了一种基于非广延熵先验的正电子发射成像(PET)迭代重建方法。该方法主要利用最小化非广延熵先验来消除先验信息和估计图像之间的不确定性。我们将此算法在体模图像上进行了测试,并与基于传统先验的MAP方法比较。实验表明,本文算法能更好抑制噪声,获得较好的重建图像质量。     

基于非广延熵先验的PET图像重建

最大化后验(MAP)方法已经被广泛应用于解决图像重建的病态问题。先验项的选择一直是研究的热点,但是传统先验形式往往会导致重建图像模糊或者产生阶梯状伪影。本文针对传统先验形式存在的不足,提出了一种基于非广延熵先验的正电子发射成像(PET)迭代重建方法。该方法主要利用最小化非广延熵先验来消除先验信息和估计图像之间的不确定性。我们将此算法在体模图像上进行了测试,并与基于传统先验的MAP方法比较。实验表明,本文算法能更好抑制噪声,获得较好的重建图像质量。     

一种基于互信息量的并行磁共振图像重建新算法

平方和算法是多线圈采集技术与并行成像中常用的一种图像重建方法。但是,在数据采集过程中,某些类型的运动常常会使个别位置上的线圈数据发生异常,采用平方和算法会对最终的重建图像质量产生很大影响。本研究提出一种新的并行磁共振图像重建算法——加权平方和方法。算法以线圈图像间最大互信息量作为判据来统计破坏数据,在之后的图像结合过程中赋予不同的权值,最大限度地降低破坏数据对最终结合图像造成的影响,有效解决运动对原有算法造成的破坏。本算法分别对多线圈并行采集的体模数据与真实脑部数据进行了实验,结果显示:相比于现有方法,新算法可以有效地抑制破坏数据在重建图像中产生的伪影,重建图像在细节分辨率上也有更好的表现。     

Constrained reconstruction: a superresolution, optimal signal-to-noise alternative to the Fourier transform in magnetic resonance imaging

Many problems in physics involve imaging objects with high spatial frequency content in a limited amount of time. The limitation of available experimental data leads to the infamous problem of diffraction limited data which manifests itself by causing ringing in the image. This ringing is due to the interference phenomena in optics and is known as the Gibbs phenomenon in engineering. Present techniques to cope with this problem include filtering and regularization schemes based on minimum norm or maximum entropy constraints. In this paper, a new technique based on object modeling and estimation is developed to achieve superresolution reconstruction from partial Fourier transform data. The nonlinear parameters of the object model are obtained using the singular value decomposition (SVD)-based all-pole model framework, and the linear parameters are determined using a standard least squares estimation method. This technique is capable, in principle, of unlimited resolution and is robust with respect to Gaussian white noise perturbation to the measured data and with respect to systematic modeling errors. Reconstruction results from simulated data and real magnetic resonance data are presented to illustrate the performance of the proposed method.     

A new local multiscale Fourier analysis for medical imaging

The Stockwell transform (ST), recently developed for geophysics, combines features of the Fourier, Gabor and wavelet transforms; it reveals frequency variation over time or space. This valuable information is obtained by Fourier analysis of a small segment of a signal at a time. Localization of the Fourier spectrum is achieved by filtering the signal with frequency-dependent Gaussian scaling windows. This multi-scale time-frequency analysis provides information about which frequencies occur and more importantly when they occur. Furthermore, the Stockwell domain can be directly inferred from the Fourier domain and vice versa. These features make the ST a potentially effective tool to visualize, analyze, and process medical imaging data. The ST has proven useful in noise reduction and tissue texture analysis. Herein, we focus on the theory and effectiveness of the ST for medical imaging. Its effectiveness and comparison with other linear time-frequency transforms, such as the Gabor and wavelet transforms, are discussed and demonstrated using functional magnetic resonance imaging data.     

Noise and filtration in magnetic resonance imaging

Noise in two-dimensional Fourier transform magnetic resonance images has been investigated using noise power spectra and measurements of standard deviation. The measured effects of averaging, spatial filtering, temporal filtering, and sampling have been compared with theoretical calculations. The noise of unfiltered images is found to be white, as expected, and the choice of the temporal filter and sampling interval affects the noise in a manner predicted by sampling theory. The shapes of the imager's spatial frequency filters are extracted using noise power spectra.     

K-Bayes Reconstruction for Perfusion MRI II: Modeling and Technical Development

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.     

快速回波平面磁共振谱成像数据重建算法

传统的相位编码磁共振谱成像采集数据需要很长的时间,使得其在临床上的应用受阻。快速回波平面谱成像(Echoplanarspectroscopicimaging,EPSI)技术采用随时间变化的梯度对谱维和空间维同时进行编码,大大减少了数据采集时间。同时,改进EPSI的读出梯度形式还可以提高‘空间-谱’的分辨率。EPSI数据重建算法比较复杂在t方向先分别对奇偶回波数据进行快速傅立叶变换(FastFouriertranslation,FFT),再利用‘偏移’理论进行组合;在kx方向采用网格化算法将不等间隔采集的数据转换到等间隔的直线网格上,再利用FFT进行图像重建;ky方向是相位编码,不需要转换,直接进行FFT即可。     

K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT.     

Shape-Adaptive DCT for Denoising of 3D Scalar and Tensor Valued Images

During the last ten years or so, diffusion tensor imaging has been used in both research and clinical medical applications. To construct the diffusion tensor images, a large set of direction sensitive magnetic resonance image (MRI) acquisitions are required. These acquisitions in general have a lower signal-to-noise ratio than conventional MRI acquisitions. In this paper, we discuss computationally effective algorithms for noise removal for diffusion tensor magnetic resonance imaging (DTI) using the framework of 3-dimensional shape-adaptive discrete cosine transform. We use local polynomial approximations for the selection of homogeneous regions in the DTI data. These regions are transformed to the frequency domain by a modified discrete cosine transform. In the frequency domain, the noise is removed by thresholding. We perform numerical experiments on 3D synthetical MRI and DTI data and real 3D DTI brain data from a healthy volunteer. The experiments indicate good performance compared to current state-of-the-art methods. The proposed method is well suited for parallelization and could thus dramatically improve the computation speed of denoising schemes for large scale 3D MRI and DTI.     

The fast Padé transform in magnetic resonance spectroscopy for potential improvements in early cancer diagnostics

The convergence rates of the fast Padé transform (FPT) and the fast Fourier transform (FFT) are compared. These two estimators are used to process a time-signal encoded at 4 T by means of one-dimensional magnetic resonance spectroscopy (MRS) for healthy human brain. It is found systematically that at any level of truncation of the full signal length, the clinically relevant resonances that determine concentrations of metabolites in the investigated tissue are significantly better resolved in the FPT than in the FFT. In particular, the FPT has a better resolution than the FFT for the same signal length. Moreover, the FPT can achieve the same resolution as the FFT by using twice shorter signals. Implications of these findings for two-dimensional magnetic resonance spectroscopy as well as for two- and three-dimensional magnetic resonance spectroscopic imaging are highlighted. Self-contained cross-validation of all the results from the FPT is secured by using two conceptually different, equivalent algorithms (inside and outside the unit-circle), that are both valid in the entire complex frequency plane. The difference between the results from these two variants of the FPT is indistinguishable from the background noise. This constitutes robust error analysis of proven validity. The FPT shows promise in applications of MRS for early cancer detection.     

Sparsity regularization in dynamic elastography

We consider the inverse problem of continuum mechanics with the tissue deformation described by a mixed displacement-pressure finite element formulation. The mixed formulation is used to model nearly incompressible materials by simultaneously solving for both elasticity and pressure distributions. To improve numerical conditioning, a common solution to this problem is to use regularization to constrain the solutions of the inverse problem. We present a sparsity regularization technique that uses the discrete cosine transform to transform the elasticity and pressure fields to a sparse domain in which a smaller number of unknowns is required to represent the original field. We evaluate the approach by solving the dynamic elastography problem for synthetic data using such a mixed finite element technique, assuming time harmonic motion, and linear, isotropic and elastic behavior for the tissue. We compare our simulation results to those obtained using the more common Tikhonov regularization. We show that the sparsity regularization is less dependent on boundary conditions, less influenced by noise, requires no parameter tuning and is computationally faster. The algorithm has been tested on magnetic resonance elastography data captured from a CIRS elastography phantom with similar results as the simulation.     

Medical image compression using 3-D Hartley transform

In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modalities, namely, magnetic resonance images and X-ray angiograms and the performance results are compared with those of 3-D discrete cosine and Fourier transforms using the parameters such as PSNR and bit rate. It is shown that the 3-D discrete Hartley transform is better than the other two transforms for magnetic resonance brain images whereas for the X-ray angiograms, the 3-D discrete cosine transform is found to be superior.