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.     

The ellipsoidal area ratio: an alternative anisotropy index for diffusion tensor imaging

In the processing and analysis of diffusion tensor imaging (DTI) data, certain predefined morphological features of diffusion tensors are often represented as simplified scalar indices, termed diffusion anisotropy indices (DAIs). When comparing tensor morphologies across differing voxels of an image, or across corresponding voxels in different images, DAIs are mathematically and statistically more tractable than are the full tensors, which are probabilistic ellipsoids consisting of three orthogonal vectors that each has a direction and an associated scalar magnitude. We have developed a new DAI, the "ellipsoidal area ratio" (EAR), to represent the degree of anisotropy in the morphological features of a diffusion tensor. The EAR is a normalized geometrical measure of surface curvature in the 3D diffusion ellipsoid. Monte Carlo simulations and applications to the study of in vivo human data demonstrate that, at low noise levels, EAR provides a similar contrast-to-noise ratio (CNR) but a higher signal-to-noise ratio (SNR) than does fractional anisotropy (FA), which is currently the most popular anisotropy index in active use. Moreover, at the high noise levels encountered most commonly in real-world DTI datasets, EAR compared with FA is consistently much more robust to perturbations from noise and it provides a higher CNR, features useful for the analysis of DTI data that are inherently noise sensitive.     

Fast color contrast enhancement method for color night vision

The increasing availability and deployment of imaging sensors operating in multiple spectral bands has led to a large research effort in color image fusion, resulting in a plethora of pixel-level image fusion algorithms. In this study a simple and fast fusion approach for color night vision is presented. The contrast of infrared and visible images is adjusted by local histogram equalization. Then the two enhanced images are fused into the three components of a Lab image in terms of a simple linear fusion strategy. To obtain false color images possessing a natural day-time color appearance, this paper adopts an approach which transfers color from the reference to the fused images in a simplified Lab space. To enhance the contrast between the target and the background, a stretch factor is introduced into the transferring equation in the b channel. Experimental results based on three different data sets show that the hot targets are popped out with intense colors while the background details present natural color appearance. Target detection experiments also show that the presented method has a better performance than the former methods, owing to the target recognition area, detection rate, color distance and running time.     

A complex target terrain SAR raw data generation and evaluation based on inversed equalized hybrid-domain algorithm processing

In this paper, synthetic aperture radar raw data generation of complex target terrain based on inverse equalized hybrid-domain processing technique is proposed. Firstly, the basic synthetic aperture radar (SAR) echo model is derived via spectral analysis of extended chirp scaling. Then, the inverse equalized extended chirp scaling algorithm (ECSA) procedure is applied directly step by step to a real input SAR image in order to generate the raw data of the reference SAR image. The whole raw data generation (RDG) procedure only consists of Inverse Equalized ECSA (IEECSA) without integral equation and computation complexity, which means easier implementation and higher efficiency. By applying the resulted RDG into different image formation algorithms (IFAs), not only the final images are reconstructed but also the resulted RDG is evaluated in practice. Finally, valid image quality assessment techniques are implemented on the reconstructed images. The simulations not only confirm the validity of the proposed RDG method based on inverse equalized hybrid-domain technique but also evaluate the quality metrics of reconstructed images as a method of reliability assurance.     

Localized high-resolution DTI of the human midbrain using single-shot EPI,parallel imaging,and outer-volume suppression at 7 T

Localized high-resolution diffusion tensor images (DTI) from the midbrain were obtained using reduced field-of-view (rFOV) methods combined with SENSE parallel imaging and single-shot echo planar (EPI) acquisitions at 7 T. This combination aimed to diminish sensitivities of DTI to motion, susceptibility variations, and EPI artifacts at ultra-high field. Outer-volume suppression (OVS) was applied in DTI acquisitions at 2- and 1-mm² resolutions, b = 1000 s/mm², and six diffusion directions, resulting in scans of 7- and 14-min durations. Mean apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values were measured in various fiber tract locations at the two resolutions and compared. Geometric distortion and signal-to-noise ratio (SNR) were additionally measured and compared for reduced-FOV and full-FOV DTI scans. Up to an eight-fold data reduction was achieved using DTI-OVS with SENSE at 1 mm², and geometric distortion was halved. The localization of fiber tracts was improved, enabling targeted FA and ADC measurements. Significant differences in diffusion properties were observed between resolutions for a number of regions suggesting that FA values are impacted by partial volume effects even at a 2-mm² resolution. The combined SENSE DTI-OVS approach allows large reductions in DTI data acquisition and provides improved quality for high-resolution diffusion studies of the human brain.     

Reduced field-of-view DTI segmentation of cervical spine tissue

The number of diffusion tensor imaging (DTI) studies regarding the human spine has considerably increased and it is challenging because of the spine’s small size and artifacts associated with the most commonly used clinical imaging method. A novel segmentation method based on the reduced field-of-view (rFOV) DTI dataset is presented in cervical spinal canal cerebrospinal fluid, spinal cord grey matter and white matter classification in both healthy volunteers and patients with neuromyelitis optica (NMO) and multiple sclerosis (MS). Due to each channel based on high resolution rFOV DTI images providing complementary information on spinal tissue segmentation, we want to choose a different contribution map from multiple channel images. Via principal component analysis (PCA) and a hybrid diffusion filter with a continuous switch applied on fourteen channel features, eigen maps can be obtained and used for tissue segmentation based on the Bayesian discrimination method. Relative to segmentation by a pair of expert readers, all of the automated segmentation results in the experiment fall in the good segmentation area and performed well, giving an average segmentation accuracy of about 0.852 for cervical spinal cord grey matter in terms of volume overlap. Furthermore, this has important applications in defining more accurate human spinal cord tissue maps when fusing structural data with diffusion data. rFOV DTI and the proposed automatic segmentation outperform traditional manual segmentation methods in classifying MR cervical spinal images and might be potentially helpful for detecting cervical spine diseases in NMO and MS.     

An investigation of motion correction algorithms for pediatric spinal cord DTI in healthy subjects and patients with spinal cord injury

Patient and physiological motion can cause artifacts in DTI of the spinal cord which can impact image quality and diffusion indices. The purpose of this investigation was to determine a reliable motion correction method for pediatric spinal cord DTI and show effects of motion correction on DTI parameters in healthy subjects and patients with spinal cord injury. Ten healthy subjects and ten subjects with spinal cord injury were scanned using a 3 T scanner. Images were acquired with an inner field-of-view DTI sequence covering cervical spine levels C1 to C7. Images were corrected for motion using two types of transformation (rigid and affine) and three cost functions. Corrected images and transformations were examined qualitatively and quantitatively using in-house developed code. Fractional anisotropy (FA) and mean diffusivity (MD) indices were calculated and tested for statistical significance pre- and post- motion correction. Images corrected using rigid methods showed improvements in image quality, while affine methods frequently showed residual distortions in corrected images. Blinded evaluation of pre and post correction images showed significant improvement in cord homogeneity and edge conspicuity in corrected images (p < 0.0001). The average FA changes were statistically significant (p < 0.0001) in the spinal cord injury group, while healthy subjects showed less FA change and were not significant. In both healthy subjects and subjects with spinal cord injury, quantitative and qualitative analysis showed the rigid scaled-least-squares registration technique to be the most reliable and effective in improving image quality.     

Comparison of mouse brain DTI maps using K-space average,image-space average,or no average approach

Diffusion tensor imaging (DTI) is achieved by collecting a series of diffusion-weighted images (DWIs). Signal averaging of multiple repetitions can be performed in the k-space (k-avg) or in the image space (m-avg) to improve the image quality. Alternatively, one can treat each acquisition as an independent image and use all of the data to reconstruct the DTI without doing any signal averaging (no-avg). To compare these three approaches, in this study, in vivo DTI data were collected from five normal mice. Noisy data with signal-to-noise ratios (SNR) that varied between five and 30 (before averaging) were then simulated. The DTI indices, including relative anisotropy (RA), trace of diffusion tensor (TR), axial diffusivity (λ║), and radial diffusivity (λ ⊥), derived from the k-avg, m-avg, and no-avg, were then compared in the corpus callosum white matter, cortex gray matter, and the ventricles. We found that k-avg and m-avg enhanced the SNR of DWI with no significant differences. However, k-avg produced lower RA in the white matter and higher RA in the gray matter, compared to the m-avg and no-avg, regardless of SNR. The latter two produced similar DTI quantifications. We concluded that k-avg is less preferred for DTI brain imaging.     

Geometric analysis of the b-dependent effects of Rician signal noise on diffusion tensor imaging estimates and determining an optimal b value

The optimal diffusion weighting (DW) factor, b, for use in diffusion tensor imaging (DTI) studies remains uncertain. In this study, the geometric relations of DW quantities are examined, in particular, the effects of Rician noise in the measured magnetic resonance signal. This geometric analysis is used to make theoretical predictions for selecting a b value to reduce the influence of noise. It is shown that the optimal b value for DTI studies in healthy human parenchyma is approximately b=1200 s mm⁻², with a simple relation given as well for a given expected apparent diffusion coefficient. Monte-Carlo simulations on sets of realistic DTI measures are then performed, verifying the optimal DW for minimizing estimate errors. The effects of noise on various DTI parameters such as anisotropy indices (fractional anisotropy and scaled relative anisotropy), mean diffusivity, radial diffusivity, eigenvalues and the direction of the first eigenvector are investigated as well.     

Brain tissue classification based on DTI using an improved Fuzzy C-means algorithm with spatial constraints

We present an effective method for brain tissue classification based on diffusion tensor imaging (DTI) data. The method accounts for two main DTI segmentation obstacles: random noise and magnetic field inhomogeneities. In the proposed method, DTI parametric maps were used to resolve intensity inhomogeneities of brain tissue segmentation because they could provide complementary information for tissues and define accurate tissue maps. An improved fuzzy c-means with spatial constraints proposal was used to enhance the noise and artifact robustness of DTI segmentation. Fuzzy c-means clustering with spatial constraints (FCM_S) could effectively segment images corrupted by noise, outliers, and other imaging artifacts. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to the exploitation of spatial contextual information. We proposed an improved FCM_S applied on DTI parametric maps, which explores the mean and covariance of the feature spatial information for automated segmentation of DTI. The experiments on synthetic images and real-world datasets showed that our proposed algorithms, especially with new spatial constraints, were more effective.     

Affine and polynomial mutual information coregistration for artifact elimination in diffusion tensor imaging of newborns

We have investigated the use of two different image coregistration algorithms for identifying local regions of erroneously high fractional anisotropy (FA) as derived from diffusion tensor imaging (DTI) data sets in newborns. The first algorithm uses conventional affine registration of each of the diffusion-weighted images to the unweighted (b = 0) image for each slice, while the second algorithm uses second-order polynomial warping. Similarity between images was determined using the mutual information (MI) criterion, which is the preferred 'cost' criterion for coregistration of images with significantly different image intensity distributions. We have found that subtle differences exist in the FA values resulting from affine and second-order polynomial coregistration and demonstrate that nonlinear distortions introduce artifacts of spatial extent similar to real white matter structures in the newborn subcortex. We show that polynomial coregistration systematically reduces the presence of erroneous regions of high FA and that such artifacts can be identified by visual inspection of FA maps resulting from affine and polynomial coregistrations. Furthermore, we show that nonlinear distortions may be particularly pronounced when acquiring image slices of axial orientation at the height of the nasal cavity. Finally, we show that third-order polynomial MI coregistration (using the images resulting from second-order coregistration as input) has no observable effect on the resulting FA maps.     

一种结合并行成像和压缩感知的快速磁共振成像新方法

压缩感知（CS）技术和并行成像技术（主要是SENSE技术、GRAPPA技术等）都能通过减少k空间数据的采集量来加快磁共振成像速度,目前已有一些将两种方法相结合进一步加速磁共振成像速度的方法（例如CS-GRAPPA）.本文针对数据采集和重建这两方面对现有CS-GRAPPA方法进行了改进,采集方式上采用了局部等间隔采集模板以满足GRAPPA重建的要求,并对采集模板进行随机放置以满足CS重建的要求;数据重建时,根据自动校正数据估算GRAPPA算法中欠采行的重建误差,并利用误差的大小确定在CS算法中保真的程度.不同磁共振图像重建实验的结果表明：与现有方法相比,本文方法能够更好地保留原有图像细节并有效减少伪影.     

High-resolution in vivo diffusion tensor imaging of the injured cat spinal cord using self-navigated,interleaved, variable-density spiral acquisition (SNAILS-DTI)

Diffusion tensor magnetic resonance imaging (DTI) is useful for studying the microstructural changes in the spinal cord following traumatic injury; however, image quality is generally poor due to the small size of the spinal cord, physiological motion and susceptibility artifacts. Self-navigated, interleaved, variable-density spiral diffusion tensor imaging (SNAILS-DTI) is a distinctive pulse sequence that bypasses many of the challenges associated with DTI of the spinal cord, particularly if imaging gradient hardware is of conventional quality. In the current study, we have demonstrated the feasibility of implementing SNAILS-DTI on a clinical 3.0-T MR scanner and examined the effect of navigator filter parameters on image quality and reconstruction time. Results demonstrate high-quality, high-resolution (546 μm×546 μm) in vivo DTI images of the cat spinal cord after traumatic spinal cord injury.     

A simple application of compressed sensing to further accelerate partially parallel imaging

Compressed sensing (CS) and partially parallel imaging (PPI) enable fast magnetic resonance (MR) imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, “CS+GRAPPA,” to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS and averaged the results to get a final CS k-space reconstruction. We used both a standard CS and an edge- and joint-sparsity-guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data and performed a human observer experiment to determine the effectiveness of decomposition and to optimize the number of subsets. We then used these CS reconstructions to calibrate the generalized autocalibrating partially parallel acquisitions (GRAPPA) complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge- and joint-sparsity-guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.     

Effective contrast enhancement method for color night vision

Image fusion refers to the techniques that integrate complementary information from multiple image sensors’ data in a way that makes the new images more suitable for human visual perception. The paper focuses on the low color contrast problem of linear fusion algorithms with color transfer method. Firstly, the contrast of infrared and visible images is enhanced using local histogram equalization and median filter. Then the two enhanced images are fused into the three components of a Lab image in terms of a simple linear fusion strategy. To enhance the color contrast between the target and the background, the scaling factor is introduced into the transferring equation in the b channel. Experimental results based on three different data sets show that the hot and cold targets are all popped out with intense colors while the background details present natural color appearance. Target detection experiments through target recognition area, detection rate, target-background discrimination also show that the presented method has a better performance than the former methods.     

MRI梯度预加重模块的分时复用设计

为获得更优的成像质量和更快的成像速度,磁共振成像（MRI）系统的梯度预加重模块需要具有更多的补偿通道和调节参数,常规预加重模块的设计方案使现场可编程门阵列（FPGA）面临巨大的资源消耗.为解决高性能梯度预加重模块的资源消耗大的问题,本文提出了一种基于分时复用技术的梯度预加重实现方案,以常规方案1/44的资源实现了11通道×4组参数的梯度预加重模块.将该模块用于0.35 T MRI系统,测试了补偿前后的涡流曲线和磁共振图像,结果表明该模块有效降低了系统的涡流,减小了磁共振图像中的涡流伪影.     

DTI-based segmentation and quantification of human brain lateral ventricular CSF volumetry and mean diffusivity: Validation,age, gender effects and biophysical implications

The human brain lateral ventricular (LV) cerebrospinal fluid (CSF) volume has been used as a neuroimaging marker of brain changes in health and disease. The LV CSF diffusivity may offer a useful quality assurance measure and become a potential noninvasive marker of deep brain temperature. In this work we sought to validate a method for human brain lateral ventricular (LV) cerebrospinal fluid (CSF) using diffusion tensor imaging (DTI) contrast to provide LV volume and corresponding DTI metrics. We compared LV volume obtained using DTI with that obtained using validated segmentations of the LV on T1-weighted data. DTI and T1-weighted data were acquired at 3 T on 49 healthy males and 56 age-matched females aged 18–59 years. We showed histogram distributions of LV DTI metrics to establish quality assurance measures. We also analyzed the age and gender effects of LV volume and diffusivity. LV volumes estimated using both T1-weighted and DTI correlated strongly in males and females (ICC = 0.99; median Dice index ~ 80%). The LV-to-intracranial volume percentage increased significantly with age only in males, using the DTI-based approach (r = 0.39; p = 0.005). LV CSF Mean diffusivity was greater in males than females ((~ 1.2%; p = 0.03). Mean diffusivity of lateral ventricular CSF decreased significantly with age in healthy adults (r = − 0.30; p = 0.02). Our results highlight the importance of age and gender-based analyses and the potential of LV diffusivity measures as a quantitative marker.     

Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstrapping estimation) that use univariate or multivariate regression methods to fit MRI data to a pre-specified model.     

Keyhole and zero-padding approaches for reduced-encoding diffusion tensor imaging of the mouse brains

Keyhole diffusion tensor imaging (keyhole DTI) was previously proposed in cardiac imaging to reconstruct DTI maps from the reduced phase-encoding images. To evaluate the feasibility of keyhole DTI in brain imaging, keyhole and zero-padding DTI algorithms were employed on in vivo mouse brain. The reduced phase-encoding portion, also termed as the sharing rate, was varied from 50% to 90% of the full k-space. Our data showed that zero-padding DTI resulted in decreased fractional anisotropy (FA) and decreased mean apparent diffusion coefficient (mean ADC) in white matter (WM) regions. Keyhole DTI showed a better edge preservation on mean ADC maps but not on FA maps as compared to the zero-padding DTI. When increasing the sharing rate in keyhole approach, an underestimation of FA and an over- or underestimation of mean ADC were measured in WM depending on the selected reference image. The inconsistency of keyhole DTI may add a challenge for the wide use of this modality. However, with a carefully selected directive diffusion-weighted image to serve as the reference image in the keyhole approach, this study demonstrated that one may obtain DTI indices of reduced-encoding images with high consistency to those derived with full k-space DTI.     

Real‐space analysis of radiation‐induced specific changes with independent component analysis

A method of analysis is presented that allows for the separation of specific radiation‐induced changes into distinct components in real space. The method relies on independent component analysis (ICA) and can be effectively applied to electron density maps and other types of maps, provided that they can be represented as sets of numbers on a grid. Here, for glucose isomerase crystals, ICA was used in a proof‐of‐concept analysis to separate temperature‐dependent and temperature‐independent components of specific radiation‐induced changes for data sets acquired from multiple crystals across multiple temperatures. ICA identified two components, with the temperature‐independent component being responsible for the majority of specific radiation‐induced changes at temperatures below 130 K. The patterns of specific temperature‐independent radiation‐induced changes suggest a contribution from the tunnelling of electron holes as a possible explanation. In the second case, where a group of 22 data sets was collected on a single thaumatin crystal, ICA was used in another type of analysis to separate specific radiation‐induced effects happening on different exposure‐level scales. Here, ICA identified two components of specific radiation‐induced changes that likely result from radiation‐induced chemical reactions progressing with different rates at different locations in the structure. In addition, ICA unexpectedly identified the radiation‐damage state corresponding to reduced disulfide bridges rather than the zero‐dose extrapolated state as the highest contrast structure. The application of ICA to the analysis of specific radiation‐induced changes in real space and the data pre‐processing for ICA that relies on singular value decomposition, which was used previously in data space to validate a two‐component physical model of X‐ray radiation‐induced changes, are discussed in detail. This work lays a foundation for a better understanding of protein‐specific radiation chemistries and provides a framework for analysing effects of specific radiation damage in crystallographic and cryo‐EM experiments.