多通道相位图像的改进型自适应重建算法

自适应重建(Adaptive Reconstruction,AR)算法被广泛应用于磁共振图像的多通道合并问题上.AR算法不需要直接采集各个线圈的灵敏度信息,而是通过通道间信号及噪声相关矩阵,估算出各个通道的灵敏度,从而保证了合并的幅值图像具有较高的信噪比(Signal-to-Noise Ratio,SNR).然而,由于AR算法没有针对相位图像的合并问题进行优化,导致重建出的相位图像具有不确定性.另外,受各通道之间相位偏移及低信噪比相位图像的影响,重建结果可能包含伪影.该文提出了一种改进型AR算法,估算并移除了各通道之间的相位偏移,同时对多通道数据的相位进行质量评估及通道重排,用以进行后续自适应重建.仿体及在体实验表明,该方法可以有效提升AR算法稳定性、消除重建图像中存在的伪影,同时保持合并后幅值图像及相位图像的高信噪比.     

Parallel imaging with 3D TPI trajectory: SNR and acceleration benefits

Three-dimensional (3D) twisted projection imaging (TPI) trajectory has a unique advantage in sodium (²³Na) imaging on clinical MRI scanners at 1.5 or 3 T, generating a high signal-to-noise ratio (SNR) with a short acquisition time (∼10 min). Parallel imaging with an array of coil elements transits SNR benefits from small coil elements to acquisition efficiency by sampling partial k-space. This study investigates the feasibility of parallel sodium imaging with emphases on SNR and acceleration benefits provided by the 3D TPI trajectory. Computer simulations were used to find available acceleration factors and noise amplification. Human head studies were performed on clinical 1.5/3-T scanners with four-element coil arrays to verify simulation outcomes. In in vivo studies, proton (¹H) data, however, were acquired for concept–proof purpose. The sensitivity encoding (SENSE) method with the conjugate gradient algorithm was used to reconstruct images from accelerated TPI-SENSE data sets. Self-calibration was employed to estimate coil sensitivities. Noise amplification in TPI-SENSE was evaluated using multiple noise trials. It was found that the acceleration factor was as high as 5.53 (corresponding to acceleration number 2×3, ring-by-rotation), with a small image error of 6.9% when TPI projections were reduced in both polar (ring) and azimuthal (rotation) directions. The average noise amplification was as low as 98.7%, or 27% lower than Cartesian SENSE at that acceleration factor. The 3D nature of both TPI trajectory and coil sensitivities might be responsible for the high acceleration and low noise amplification. Consequently, TPI-SENSE may have potential advantages for parallel sodium imaging.     

Channel-combination method for phase-based |B1+| mapping techniques

PurposeThe aim of this study was to propose a channel combination method for |B₁⁺| mapping methods using phase difference to reconstruct |B₁⁺| map.Theory and methodsPhase-based |B₁⁺| mapping methods commonly consider the phase difference of two scans to measure |B₁⁺|. Multiple receiver coils acquire a number of images and the phase difference at each channel is theoretically the same in the absence of noise. Affected by noise, phase difference is approximately governed by Gaussian distribution. Considering data from all channels as samples, estimation can be achieved by maximum likelihood method. With this method, all phase differences at each channel are combined into one. In this study, the proposed method is applied with Bloch-Siegert shift |B₁⁺| mapping method. Simulations are performed to illustrate the phase difference distribution and demonstrate the feasibility and facility of the proposed method. Phantom and vivo experiments are carried out at 1.5 T scanner equipped with 8-channel receiver coil. In all experiments, the proposed method is compared with weighted averaging (WA) method.ResultsSimulations revealed appropriateness of approximating the distribution of phase difference to Gaussian distribution. Compared with WA method, the proposed method reduces errors of |B₁⁺| calculation. Phantom and vivo experiments provide further validation.ConclusionConsidering phase noise distribution, the proposed method achieves channel combination by finding the estimation from data acquired by multiple receivers coil. The proposed method reduces |B₁⁺| reconstruction errors caused by noise.     

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.     

Accelerated magnetic resonance imaging using the sparsity of multi-channel coil images

Joint estimation of coil sensitivities and output image (JSENSE) is a promising approach that improves the reconstruction of parallel magnetic resonance imaging (pMRI). However, when acceleration factor increases, the signal to noise ratio (SNR) of JSENSE reconstruction decreases as quickly as that of the conventional pMRI. Although sparse constraints have been used to improve the JSENSE reconstruction in recent years, these constraints only use the sparsity of the output image, which cannot fully exploit the prior information of pMRI. In this paper, we use the sparsity of coil images, instead of the output image, to exploit more prior information for JSENSE. Numerical simulation, phantom and in vivo experiments demonstrate that the proposed method has better performance than the SparseSENSE method and the constrained JSENSE method using the sparsity of the output image only.     

Analytically exact correction scheme for signal extraction from noisy magnitude MR signals

An analytically exact method is proposed to extract the signal intensity and the noise variance simultaneously from noisy magnitude MR signals. This method relies on a fixed point formula of signal-to-noise ratio (SNR) and a correction factor. The correction factor, which is a function of SNR, establishes a fundamental link between the variance of the magnitude MR signal and the variance of the underlying Gaussian noise in the two quadrature channels. A more general but very similar method is developed for parallel signal acquisitions with multiple receiver coils. In the context of MR imaging, the proposed method can be carried out on a pixel-by-pixel basis if the mean and the standard deviation of the magnitude signal are available.     

Optimal phase difference reconstruction: comparison of two methods

The present study compares the performance of the weighted mean (WM) and sensitivity encoding (SENSE) methods for reconstructing phase difference images over a large range of signal-to-noise ratio (SNR). It is found that the WM algorithm is suboptimal, compared to the SENSE method at low SNR. Numerical simulations, phantom and in vivo results are presented.     

In vivo measurements of multi-component T2 relaxation behaviour in guinea pig brain

Multi-echo Carr-Purcell-Meiboom-Gill (CPMG) imaging sequences were implemented on 1.5 T and 4.0 T imaging systems to test their ability to measure in vivo multi-component T2 relaxation behavior in normal guinea pig brain. The known dependence of accurate T2 measurements on the signal-to-noise ratio (SNR) was explored in vivo by comparing T2 decay data obtained using three methods to increase SNR (improved RF coil design, signal averaging and increased magnetic field strength). Good agreement between T2 values of nickel-doped agarose phantoms was found between imaging and spectroscopic methods. T2 values were determined for gray matter (GM) and white matter (WM) locations from images of guinea pig brain in vivo. T2 measurements of GM were found to be monoexponential at both field strengths. The mean T2 times for GM were 71 ms at 1.5 T, and 53 ms at 4.0T. The highest average SNR was achieved using an improved RF coil at 4.0T. In this case, two peaks were extracted in WM, a "short" T2 peak at approximately 6 ms, and a "medium" T2 peak at approximately 48 ms. T2 values in GM and the major component of WM were significantly decreased at 4.0T compared to 1.5 T. The improved SNR attained with this optimized imaging protocol at 4.0T has allowed for the first time extraction of the myelin-sensitive T2 component of WM in animal brain in vivo.     

Joint Reconstruction of Multi-contrast Images and Multi-channel Coil Sensitivities

Magnetic resonance imaging (MRI) has an important feature that it provides multiple images with different contrasts for complementary diagnostic information. However, a large amount of data is needed for multi-contrast images depiction, and thus, the scan is time-consuming. Many methods based on parallel magnetic resonance imaging (pMRI) and compressed sensing (CS) are applied to accelerate multi-contrast MR imaging. Nevertheless, the image reconstructed by sophisticated pMRI methods contains residual aliasing artifact that degrades the quality of the image when the acceleration factor is high. Other methods based on CS always suffer the regularization parameter-selecting problem. To address these issues, a new method is presented for joint multi-contrast image reconstruction and coil sensitivity estimation. The coil sensitivities can be shared during the reconstruction due to the identity of coil sensitivity profiles of different contrast images for imaging stationary tissues. The proposed method uses the coil sensitivities as sharable information during the reconstruction to improve the reconstruction quality. As a result, the residual aliasing artifact can be effectively removed in the reconstructed multi-contrast images even if the acceleration factor is high. Besides, as there is no regularization term in the proposed method, the troublesome regularization parameter selection in the CS can also be avoided. Results from multi-contrast in vivo experiments demonstrated that multi-contrast images can be jointly reconstructed by the proposed method with effective removal of the residual aliasing artifact at a high acceleration factor.     

彩色图像的非相干光相关识别

提出一种用非相干光系统识别彩色图像的方法,将脸色图像分解成三个单色像处理,设计一可实现的仅相位光学传递函数,对其进行带能优化,人而提高信噪比并使相关峰锐化,通过一对滤波器来合成该光学传递函数,获得了采色图像相关识别的实验结果。     

Weighted least-squares phase unwrapping algorithm based on derivative variance correlation map

Among different phase unwrapping approaches, the weighted least-squares minimization methods are gaining attention. In these algorithms, weighting coefficient is generated from a quality map. The intrinsic drawbacks of existing quality maps constrain the application of these algorithms. They often fail to handle wrapped phase data contains error sources, such as phase discontinuities, noise and undersampling. In order to deal with those intractable wrapped phase data, a new weighted least-squares phase unwrapping algorithm based on derivative variance correlation map is proposed. In the algorithm, derivative variance correlation map, a novel quality map, can truly reflect wrapped phase quality, ensuring a more reliable unwrapped result. The definition of the derivative variance correlation map and the principle of the proposed algorithm are present in detail. The performance of the new algorithm has been tested by use of a simulated spherical surface wrapped data and an experimental interferometric synthetic aperture radar (IFSAR) wrapped data. Computer simulation and experimental results have verified that the proposed algorithm can work effectively even when a wrapped phase map contains intractable error sources.     

目标图像结构和噪声对相关哈特曼-夏克波前传感精度的影响

以空间频谱描述图像结构,以图像灰度起伏的方均根值与噪声方均根值之比表示信噪比,系统分析了目标图像结构和噪声对相关哈特曼-夏克波前传感精度的影响。理论分析表明,两个子图像的相关函数峰值位置的亚像元插值误差等于其各离散频率成分的相关函数峰值位置插值误差的加权平均;相同功率下,低频成分的加权系数较小,高频成分的加权系数与亚像元偏移量有关。一维窄带图像的统计仿真表明,无噪声时,低频成分和接近奈奎斯特频率成分的误差较大,中频成分的误差较小;有噪声时,噪声对高频成分的影响低于低频成分。对典型频谱的32×32图像仿真表明,图像起伏信噪比为2∶1时,子图像平移量计算误差约0.03~0.11像元,与无噪声时相比增加不大。     

磁共振成像的正交线圈数字合成方法研究

当采用正交线圈来提高接收通道的性能时,合成方法对整体效果有很大的影响. 由于在一般情况下正交线圈的2个线圈的信噪比存在不平衡,故需要采用加权合成的方法来优化合成图像的信噪比. 在现有的模拟合成方法中,加权系数是固定的,不能根据每次扫描的实际情况来确定最优的参数,因此无法得到最佳的合成效果. 该文提出一种简单的磁共振正交线圈成像的数字合成方法,它采用2个完全独立的接收通道来采集磁共振信号,根据两通道信号的特性来动态地确定I/Q线圈的加权因子,最后进行加权合成.     

The rapid and automatic combination of proton MRSI data using multi-channel coils without water suppression

The use of multi-channel coils can efficiently increase the signal-to-noise ratio (SNR) of magnetic resonance spectroscopy data if the signals from multiple channels are optimally combined. Combining multi-channel signals requires proper alignment of the phases of the signals from each of the elements of the coil and then accurately weighting the summation of those signals. We present a procedure for acquiring proton magnetic resonance spectroscopic imaging (MRSI) data using an eight-channel coil without water suppression and a rapid and robust method that uses unsuppressed water signal as a reference both for aligning the phases and for weighting the summation of signals that originate in the multiple coil elements. We use both computer simulation and in vivo proton MRSI data to demonstrate the advantages of our method for optimizing the SNR of the combined signal compared with the SNRs of signals that were acquired either using a standard volume head coil or using an eight-channel coil with a metabolite signal as the reference for combination.     

Parallel magnetic resonance imaging using coils with localized sensitivities

The purpose of this study was to present clinical examples and illustrate the inefficiencies of a conventional reconstruction using a commercially available phased array coil with localized sensitivities. Five patients were imaged at 1.5 T using a cardiac-synchronized gadolinium-enhanced acquisition and a commercially available four-element phased array coil. Four unique sets of images were reconstructed from the acquired k-space data: (a) sum-of-squares image using four elements of the coil; localized sum-of-squares images from the (b) anterior coils and (c) posterior coils and a (c) local reconstruction. Images were analyzed for artifacts and usable field-of-view. Conventional image reconstruction produced images with fold-over artifacts in all cases spanning a portion of the image (mean 90 mm; range 36-126 mm). The local reconstruction removed fold-over artifacts and resulted in an effective increase in the field-of-view (mean 50%; range 20-70%). Commercially available phased array coils do not always have overlapping sensitivities. Fold-over artifacts can be removed using an alternate reconstruction method. When assessing the advantages of parallel imaging techniques, gains achieved using techniques such as SENSE and SMASH should be gauged against the acquisition time of the localized method rather than the conventional sum-of-squares method.     

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, DeepcomplexMRI takes advantage of the availability of a large number of existing multi-channel groudtruth images and uses them as target data to train the deep residual convolutional neural network offline. In particular, a complex convolutional network is proposed to take into account the correlation between the real and imaginary parts of MR images. In addition, the k-space data consistency is further enforced repeatedly in between layers of the network. The evaluations on in vivo datasets show that the proposed method has the capability to recover the desired multi-channel images. Its comparison with state-of-the-art methods also demonstrates that the proposed method can reconstruct the desired MR images more accurately.     

Modified LACIF filtering in background disjoint noise

This work deals with pattern recognition methods based on correlations for images in the presence of noise. We propose a modification of the nonlinear Locally Adaptive Contrast Invariant Filter (LACIF) that yields correlation peaks that are invariant to linear intensity changes of the target but that has some limitations in the presence low variance nonoverlapping background noise. The modification of the filter implies a normalization by a global variance of several distributions. The estimation of the variance distributions is done locally by means of correlations. Experimental results as well as comparisons with the classical matched filter and the common LACIF are given.     

An iterative method for coil sensitivity estimation in multi-coil MRI systems

This paper presents an iterative coil sensitivity estimation method for multi-coil MRI systems. The proposed method works with coil images in the magnitude image domain. It determines a region of support (RoS), a region being composed of the same type of tissues, by a region growing algorithm, which makes use of both intensities and intensity gradients of pixels. By repeating this procedure, it can determine multiple regions of support, which together cover most of the concerned image area. The union of these regions of support provides a rough estimate of the sensitivity of each coil through dividing the intensities of pixels by the average intensity inside every region of support. The obtained rough coil sensitivity estimate is further approached with the product of multiple low-order polynomials, rather than a single one. The product of these polynomials provides a smooth estimate of the sensitivity of each coil. With the obtained sensitivities of coils, it can produce a better reconstructed image, which determines more correct regions of support and yields preciser estimates of the sensitivities of coils. In other words, the method can be iteratively implemented to improve the estimation performance. The proposed method was verified through both simulated data and clinical data from different body parts. The experimental results confirm the superiority of our method to some conventional methods.     

高温超导磁共振成像射频接收线圈的研究

为了提高低场磁共振成像系统的信噪比,提出了具有失谐电路的Bi2223带高温超导射频接收线圈.该线圈采用了电耦合方式传输超导谐振回路的磁共振信号,这种方式有利于进一步制成正交结构或相阵结构的超导接收线圈.为了防止趋肤效应降低超导接收线圈的性能,采用化学腐蚀的方法先将超导带的包套去掉,然后再制成超导主谐振电感.采用一种双探测线圈法对高温超导接收线圈和相同结构的常规铜线圈的Q值进行了测量,结果表明超导接收线圈比常规铜线圈的Q值约高一倍.