Optimizing T2-weighted magnetic resonance sequences for surface coil microimaging of the eye with regard to lid, eyeball and head moving artifacts

PURPOSE: To acquire high-resolution magnetic resonance (MR) images, we developed a new blinking artifact reduced pulse (BARP) sequence with a surface coil specialized for microscopic imaging (47 mm in diameter). MATERIALS AND METHODS: To reduce eye movement, we ascertained that the subjects' eyes were kept open and fixated to the target in the 1.5-T MR gantry. To reduce motion artifacts from blinking, we inserted rest periods for blinking (1.5 s within every 5 s) during MR scanning (T2-weighted fast spin echo; repetition time, 5 s; echo time, 100 ms; echo train, 11; matrix, 256 x 128; field of view, 5 cm; 1-mm thickness x 30 slices). Three scans (100 s x 3) were performed for each normal subject, and they were added together after automatic adjustment for location to reduce quality loss caused by head motion. RESULTS: T2-weighted MR images were acquired with a high resolution and a high signal-to-noise ratio. Motion artifacts were reduced with BARP, as compared with those with random blinking. Intraocular structures such as the iris and ciliary muscles were clearly visualized. Because the whole eye can be covered with a 1-mm thickness by this method, three-dimensional maps can easily be generated from the obtained images. CONCLUSION: The application of BARP with a surface coil of the human eye might become a useful and widely adopted procedure for MR microimaging.     

Rapid calculation of T1 using variable flip angle gradient refocused imaging

We present a method for rapid measurement of T1 relaxation times using gradient refocused images at limited flip angles and short repetition times. This "variable nutation" techniques was investigated using a T1 phantom. There was a high correlation between measurements obtained with the variable nutation and partial saturation techniques. The ability of this method to create calculated T1 images is also demonstrated. We conclude that the variable nutation method may allow measurement of T1 relaxation times with a significant reduction in acquisition time compared to partial saturation techniques.     

Vessel boundary extraction based on a global and local deformable physical model with variable stiffness

Reliable and efficient vessel cross-sectional boundary extraction is very important for many medical magnetic resonance (MR) image studies. General purpose edge detection algorithms often fail for medical MR images processing due to fuzzy boundaries, inconsistent image contrast, missing edge features, and the complicated background of MR images. In this regard, we present a vessel cross-sectional boundary extraction algorithm based on a global and local deformable model with variable stiffness. With the global model, the algorithm can handle relatively large vessel position shifts and size changes. The local deformation with variable stiffness parameters enable the model to stay right on edge points at the location where edge features are strong and at the same time, fit a smooth contour at the location where edge features are missing. Directional gradient information is used to help the model to pick correct edge segments. The algorithm was used to process MR cine phase-contrast images of the aorta from 20 volunteers (over 500 images) with excellent results.     

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.     

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.     

Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology

Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation.     

Semiautomated segmentation of the human spine based on echoplanar images

The number of functional magnetic resonance imaging (fMRI) studies performed on the human spinal cord (SC) has considerably increased in recent years. The lack of a validated processing pipeline is, however, a significant obstacle to the spread of SC fMRI. One component likely to be involved in any such pipeline is the process of SC masking, analogous to brain extraction in cerebral fMRI. In general, SC masking has been performed manually, with the incumbent costs of being very time consuming and operator dependent. To overcome these drawbacks, we have developed a tailored semiautomatic method for segmenting echoplanar images (EPI) of human spine that is able to identify the spinal canal and the SC. The method exploits both temporal and spatial features of the EPI series and was tested and optimized on EPI images of cervical spine acquired at 3 T. The dependence of algorithm performance on the degree of EPI image distortion was assessed by computing the displacement warping field that best matched the EPI to the corresponding high-resolution T(2) images. Segmentation accuracy was above 80%, a significant improvement over values obtained with similar approaches, but not exploiting temporal information. Geometric distortion was found to explain about 50% of the variance of algorithm classification efficiency.     

Spin-echo fluorine magnetic resonance imaging at 2 T: in vivo spatial distribution of halothane in the rabbit head

Spin-echo 19F magnetic resonance imaging was performed at 2.0 T to explore the in vivo spatial distribution of halothane in the rabbit head. Because the halothane concentration is low in vivo, and because the measured relaxation times of the 19F resonance peak for halothane were T1 approximately equal to 1.0 sec and T2 approximately equal to 3.5-65 msec, 1-3-h imaging times were required (TR = 1 sec, TE = 9 msec) in order to obtain adequate images with a 64 X 256 raw data matrix and a 20-mm slice thickness. With this technique, halothane was primarily detected in lipophilic regions of the rabbit head, but little or no halothane was observed in brain tissue. Because T2 was shorter in brain tissue than in surrounding fat, a shorter TE than we could obtain is needed for optimal spin-echo imaging of brain halothane.     

Magnetic resonance imaging of the penis: normal anatomy

This study was undertaken to investigate the magnetic resonance anatomy of the normal penis, by means of the use of a new designed surface coil specific for the study of male external genital organs. The combined evaluation of T1- and T2-weighted images obtained by choosing different repetition and echo time values together with an appropriate selection of scanning planes oriented according to various angles of incidence strongly contributed to perform a complete morphological analysis of the penis and constantly revealed fine anatomical details of diagnostic and clinical significance. Among these, the magnetic resonance examination (data referred to a 0.5 T apparatus) allowed a precise morphological differentiation of the penile covering tissues, such as outer skin, dartos tunica and hypodermal connective; the vascular characterization of cavernous tissue, belonging to corpora cavernosa and spongiosum urethrae, of deep arteries and superficial dorsal vein(s); the resolution of the albuginea and the identification of the urethral lumen. Such a capability of magnetic resonance imaging in providing accurate demonstration of penile structures without technical artifacts and use of ionizing radiations makes the actual technology able to cooperate efficaciously with computerized tomography and diagnostic sonography for an efficient clinical assessment of penis diseases.     

Prediction of total cerebral tissue volumes in normal appearing brain from sub-sampled segmentation volumes

The need for anatomical coverage and multi-spectral information must be balanced against examination and processing time to ensure high-quality, feasible imaging protocols for clinical research of cerebral development in normal-appearing brains. The focus of this study was to create and assess models to estimate total cerebral volumes of gray matter, white matter, and cerebrospinal fluid (CSF) from anatomically defined sub-samples of full clinical examinations. Pediatric patients (18F, 11M; aged 1.7 to 18.7, median 5.2 years) underwent a clinical imaging protocol consisting of 3 mm contiguous T1-, T2-, PD-, and FLAIR-weighted images after obtaining informed consent. Magnetic resonance imaging (MRI) sets were registered, RF-corrected, and then analyzed with a hybrid neural network segmentation and classification algorithm to identify normal brain parenchyma. The correlation between the image subsets and the total cerebral volumes of gray matter, white matter and CSF were examined through linear regression analyses. Five sub-sampled sets were defined and assessed in each patient to produce estimation models which were all significantly correlated (p < 0.001) with the total cerebral volumes of gray matter, white matter, and CSF. Volumes were estimated from as little as a single representative slice requiring minimal processing time, 27 min, but with an average estimation error of approximately 6%. Larger sub-samples of approximately three-quarters of the full cerebral volume required much more processing time, 2 h and 4 min, but produced estimates with an average error less than 2%. This study demonstrated that investigators can choose the amount of cerebrum sampled to optimize the acquisition and processing time against the degree of accuracy needed in the total cerebral volume estimates.     

MRI image plane nonuniformity in evaluation of ferrous sulphate dosimeter gel (FeGel) by means of T1-relaxation time

MR image nonuniformity can vary significantly with the spin-echo pulse sequence repetition time. When MR images with different nonuniformity shapes are used in a T1-calculation the resulting T1-image becomes nonuniform. As shown in this work the uniformity TR-dependence of the spin-echo pulse sequence is a critical property for T1 measurements in general and for ferrous sulfate dosimeter gel (FeGel) applications in particular. The purpose was to study the characteristics of the MR image plane nonuniformity in FeGel evaluation. This included studies of the possibility of decreasing nonuniformities by selecting uniformity optimized repetition times, studies of the transmitted and received RF-fields and studies of the effectiveness of the correction methods background subtraction and quotient correction. A pronounced MR image nonuniformity variation with repetition and T1 relaxation time was observed, and was found to originate from nonuniform RF-transmission in combination with the inherent differences in T1 relaxation for different repetition times. The T1 calculation itself, the uniformity optimized repetition times, nor none of the correction methods studied could sufficiently correct the nonuniformities observed in the T1 images. The nonuniformities were found to vary considerably less with inversion time for the inversion-recovery pulse sequence, than with repetition time for the spin-echo pulse sequence, resulting in considerably lower T1 image nonuniformity levels.     

Characterization of trabecular bone structure from high-resolution magnetic resonance images using fuzzy logic

The purpose of this work was to apply fuzzy logic image processing techniques to characterize the trabecular bone structure with high-resolution magnetic resonance images. Fifteen ex vivo high-resolution magnetic resonance images of specimens of human radii at 1.5 T and 12 in vivo high-resolution magnetic resonance images of the calcanei of peri- and postmenopausal women at 3 T were obtained. Soft segmentation using fuzzy clustering was applied to MR data to obtain fuzzy bone volume fraction maps, which were then analyzed with three-dimensional (3D) fuzzy geometrical parameters and measures of fuzziness. Geometrical parameters included fuzzy perimeter and fuzzy compactness, while measures of fuzziness included linear index of fuzziness, quadratic index of fuzziness, logarithmic fuzzy entropy, and exponential fuzzy entropy. Fuzzy parameters were validated at 1.5 T with 3D structural parameters computed from microcomputed tomography images, which allow the observation of true trabecular bone structure and with apparent MR structural indexes at 1.5 T and 3 T. The validation was statistically performed with the Pearson correlation coefficient as well as with the Bland-Altman method. Bone volume fraction correlation values (r) were up to .99 (P<.001) with good agreements based on Bland-Altman analysis showing that fuzzy clustering is a valid technique to quantify this parameter. Measures of fuzziness also showed consistent correlations to trabecular number parameters (r>.85; P<.001) and good agreements based on Bland-Altman analysis, suggesting that the level of fuzziness in high-resolution magnetic resonance images could be related to the trabecular bone structure.     

Variable disparity estimation based intermediate view reconstruction in dynamic flow allocation over EPON-based access networks

In this paper, a variable disparity estimation (VDE)-based intermediate view reconstruction (IVR) in dynamic flow allocation (DFA) over an Ethernet passive optical network (EPON)-based access network is proposed. In the proposed system, the stereoscopic images are estimated by a variable block-matching algorithm (VBMA), and they are transmitted to the receiver through DFA over EPON. This scheme improves a priority-based access network by converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR using VDE. Some experimental results indicate that the proposed system improves the peak-signal-to-noise ratio (PSNR) to as high as 4.86 dB and reduces the processing time to 3.52 s. Additionally, the network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results, including mathematical analyses, from this scheme are also provided.     

Centric scan SPRITE magnetic resonance imaging: optimization of SNR, resolution, and relaxation time mapping

Two strategies for the optimization of centric scan SPRITE (single point ramped imaging with T1 enhancement) magnetic resonance imaging techniques are presented. Point spread functions (PSF) for the centric scan SPRITE methodologies are numerically simulated, and the blurring manifested in a centric scan SPRITE image through PSF convolution is characterized. Optimal choices of imaging parameters and k-space sampling scheme are predicted to obtain maximum signal-to-noise ratio (SNR) while maintaining acceptable image resolution. The point spread function simulation predictions are verified experimentally. The acquisition of multiple FID points following each RF excitation is described and the use of the Chirp z-Transform algorithm for the scaling of field of view (FOV) of the reconstructed images is illustrated. Effective recombination of the rescaled images for SNR improvement and T*2 mapping is demonstrated.     

Precision in calculated rho, T1 and T2 images as a function of data analysis method

In NMR imaging rho, T1 and T2 images are usually calculated from a set of partial saturation, saturation recovery or inversion recovery experiments with multiple echoes and multiple repetition times. Several methods can be envisaged to extract parameter images from such a set of source images. These methods to a greater or lesser extent take advantage of the fact that a multiple echo/multiple repetition time experiment provides a set of largely independent T1 and T2 measurements. In this study several data analysis methods, including weighted and non-weighted averaging of results of independent T1 and T2 measurements, weighted and non-weighted averaging of source images prior to data reduction and simultaneous three-parameter fitting, were compared against another in terms of precision, computational efficiency and robustness. The predicted performance of the examined methods was verified by stochastic simulation experiments.     

基于PCAU-Net的快速多通道磁共振成像方法

多通道磁共振成像方法采用多个接收线圈同时欠采样k空间以加快成像速度,并基于后处理算法重建图像,但在较高加速因子时,其图像重建质量仍然较差．本文提出了一种基于PCAU-Net的快速多通道磁共振成像方法,将单通道实数U型卷积神经网络拓展到多通道复数卷积神经网络,设计了一种结构不对称的U型网络结构,通过在解码部分减小网络规模以降低模型的复杂度．PCAU-Net网络在跳跃连接前增加了1×1卷积,以实现跨通道信息交互．输入和输出之间利用残差连接为误差的反向传播提供捷径．实验结果表明,使用规则和随机采样模板,在不同加速因子时,相比常规的GRAPPA重建算法和SPIRiT重建方法,本文提出的PCAU-Net方法可高质量重建出磁共振复数图像,并且相比于PCU-Net方法,PCAU-Net减少了模型参数、缩短了训练时间．     

基于Gadgetron平台的多GPU分布式磁共振图像重建

为了满足磁共振成像（MRI）临床扫描的需求,磁共振图像重建算法的开发一直在不断进行.目前广泛使用的算法实现方式是利用中央处理器（CPU）对磁共振扫描数据进行数学变换得到图像,随着算法复杂度的提升,计算性能问题逐渐显露.利用CPU在大数据量下执行复杂算法时,计算并行性的缺失以及运算中产生的海量数据的存储负荷会导致计算变得极为缓慢,使得一些算法因为重建时间过长,在临床上面临难以推广的问题,也制约了基础研究中新算法的研发.本文设计并实现了一种新的重建算法执行方式,利用Gadgetron磁共振软件重建平台在多核CPU基础上搭载多块图形处理器（GPU）,将磁共振图像重建以分布式并行计算方式实现,并以重建耗时较长的3D径向数据采集Stack of Star（SOS）的图像重建为实例,展示这种重建的实现方法能以相对低廉的硬件成本极大提升重建的速度.     

One micrometer resolution NMR microscopy

We obtained a magnetic resonance image of 1 microm resolution and 75 microm(3) voxel volume for a phantom filled with hydrocarbon oil within an hour at 14.1 T. For this work, a specially designed probe with a high sensitivity RF coil and gradient coils generating over 1000 G/cm was built. The optimal pulse sequence was analyzed in consideration of the bandwidth, diffusion coefficients, and T(1) and T(2) relaxations of the medium. The system was applied to the in vivo imaging of a geranium leaf stem to get the images of 2 microm resolution and 200 microm(3) voxel volume.     

ViP-CSI: Virtual Phantom Chemical Shift Imaging

To apply the Virtual Phantom (ViP) method for generating reference signals, to the chemical shift imaging (CSI) technique. The ViP method, previously used for generating reference images in magnetic resonance imaging (MRI), was here extended to generate reference peaks in the MR spectra acquired with a 2D-CSI sequence. Theoretical analysis was carried out to design the ViP signal. ViP-2D-CSI experiments were performed on a 4.7 Tesla MR scanner. Data analysis was carried out with the jMRUI software to fit the ViP peaks to determine their amplitude and frequency. Based on the theoretical analysis, it was found that the ViP peak could be generated by transmitting the ViP signal only in one repetition time of the 2D-CSI sequence. The amplitude and frequency of the ViP peak could be precisely adjusted and fine-tuned. The ViP peak was uniform over all CSI voxels, both in amplitude and in frequency. Furthermore, a good stability of the ViP reference amplitude and frequency was observed. The ViP method provides a means to generate a reference MR peak in 2D-CSI experiments. This could be of interest for signal quantification in CSI experiments.     

IRIS-HSVD algorithm for automatic quantitation of in vivo 31P MRS

The rapid development of (31)P magnetic resonance spectroscopy (MRS) has enhanced non-invasive measurement of brain metabolites, which is important for biomedical research. The accuracy and efficiency of data post processing and quantification is paramount for MRS applications. One of the difficulties with in vivo(31)P MRS data quantification is the separation of broad line-width resonances from chemical compounds' resonances under a low signal-to-noise ratio condition. Furthermore, the chemical shift of some compounds caused by pH and Mg(2+) concentration can be troublesome. This work aims to develop an automatic algorithm using a state-space based quantification approach to solve the above mentioned problems. To achieve this aim, we utilized an HSVD based adaptive optimizing prior knowledge algorithm, which uses so called "interference" signals to optimize prior knowledge iteratively for parameter optimization. We termed this algorithm IRIS-HSVD, which stands for Iterative Reduction of Interference Signal HSVD. The Monte Carlo evaluations of the algorithm were conducted with simulated data using in vivo parameters commonly obtained from a 4T scanner. The performance of this algorithm using simulated data was compared to those of other automatic methods including HSVD and HTLS-PK. Examples of in vivo(31)P data obtained from brains of healthy subjects on a 4T MRI scanner were also presented, which demonstrated the superiority of the new method. The results were compared with those using AMARES.