超极化气体肺部磁共振成像

磁共振成像(MRI)技术具有非侵入、无放射性的特点,在临床疾病诊断中具有独特的优势,但是肺部空腔的特殊结构使传统质子MRI无法对其直接成像.自旋交换光抽运(SEOP)方法可以使惰性气体原子的极化度增强4个量级以上,从而使肺部的气体MRI成为可能.该文介绍了超极化惰性气体肺部MRI的最新研究进展,包括超极化气体磁共振相关参数的测量方法、肺部通气结构成像、肺部气体交换功能成像,同时比较了常用于肺部MRI气体的优点和缺点.     

Optimal experiments for maximizing coherence transfer between coupled spins

Hyperpolarized noble gases (HNGs) provide exciting possibilities for MR imaging at ultra-low magnetic field strengths (<0.15 T) due to the extremely high polarizations available from optical pumping. The fringe field of many superconductive magnets used in clinical MR imaging can provide a stable magnetic field for this purpose. In addition to offering the benefit of HNG MR imaging alongside conventional high field proton MRI, this approach offers the other useful advantage of providing different field strengths at different distances from the magnet. However, the extremely strong field gradients associated with the fringe field present a major challenge for imaging since impractically high active shim currents would be required to achieve the necessary homogeneity. In this work, a simple passive shimming method based on the placement of a small number of ferromagnetic pieces is proposed to reduce the fringe field inhomogeneities to a level that can be corrected using standard active shims. The method explicitly takes into account the strong variations of the field over the volume of the ferromagnetic pieces used to shim. The method is used to obtain spectra in the fringe field of a high-field (1.89 T) superconducting magnet from hyperpolarized 129Xe gas samples at two different ultra-low field strengths (8.5 and 17 mT). The linewidths of spectra measured from imaging phantoms (30 Hz) indicate a homogeneity sufficient for MRI of the rat lung.     

Exploiting the wavelet structure in compressed sensing MRI

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms.     

Parallel MRI at microtesla fields

Parallel imaging techniques have been widely used in high-field magnetic resonance imaging (MRI). Multiple receiver coils have been shown to improve image quality and allow accelerated image acquisition. Magnetic resonance imaging at ultra-low fields (ULF MRI) is a new imaging approach that uses SQUID (superconducting quantum interference device) sensors to measure the spatially encoded precession of pre-polarized nuclear spin populations at microtesla-range measurement fields. In this work, parallel imaging at microtesla fields is systematically studied for the first time. A seven-channel SQUID system, designed for both ULF MRI and magnetoencephalography (MEG), is used to acquire 3D images of a human hand, as well as 2D images of a large water phantom. The imaging is performed at 46 mu T measurement field with pre-polarization at 40 mT. It is shown how the use of seven channels increases imaging field of view and improves signal-to-noise ratio for the hand images. A simple procedure for approximate correction of concomitant gradient artifacts is described. Noise propagation is analyzed experimentally, and the main source of correlated noise is identified. Accelerated imaging based on one-dimensional undersampling and 1D SENSE (sensitivity encoding) image reconstruction is studied in the case of the 2D phantom. Actual threefold imaging acceleration in comparison to single-average fully encoded Fourier imaging is demonstrated. These results show that parallel imaging methods are efficient in ULF MRI, and that imaging performance of SQUID-based instruments improves substantially as the number of channels is increased.     

Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters

Magnetic resonance imaging (MRI) is an outstanding medical imaging modality but the quality often suffers from noise pollution during image acquisition and transmission. The purpose of this study is to enhance image quality using feature-preserving denoising method. In current literature, most existing MRI denoising methods did not simultaneously take the global image prior and local image features into account. The denoising method proposed in this paper is implemented based on an assumption of spatially varying Rician noise map. A two-step wavelet-domain estimation method is developed to extract the noise map. Following a Bayesian modeling approach, a generalized total variation-based MRI denoising model is proposed based on global hyper-Laplacian prior and Rician noise assumption. The proposed model has the properties of backward diffusion in local normal directions and forward diffusion in local tangent directions. To further improve the denoising performance, a local variance estimator-based method is introduced to calculate the spatially adaptive regularization parameters related to local image features and spatially varying noise map. The main benefit of the proposed method is that it takes full advantage of the global MR image prior and local image features. Numerous experiments have been conducted on both synthetic and real MR data sets to compare our proposed model with some state-of-the-art denoising methods. The experimental results have demonstrated the superior performance of our proposed model in terms of quantitative and qualitative image quality evaluations.     

Application of perceptual difference model on regularization techniques of parallel MR imaging

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or to improve image SNR. The quality of reconstructed images is limited by the inaccurate estimation of the sensitivity map, noise in the acquired k-space data and the ill-conditioned nature of the coefficient matrix. Tikhonov regularization is a popular method to reduce or eliminate the ill-conditioned nature of the problem. In this approach, selection of the regularization map and the regularization parameter is very important. Perceptual difference model (PDM) is a quantitative image quality evaluation tool that has been successfully applied to varieties of MR applications. High correlation between the human rating and PDM score shows that PDM should be suitable to evaluate image quality in parallel MR imaging. By applying PDM, we compared four methods of selecting the regularization map and four methods of selecting the regularization parameter. We found that a regularization map obtained using generalized series (GS) together with a spatially adaptive regularization parameter gave the best reconstructions. PDM was also used as an objective function for optimizing two important parameters in the spatially adaptive method. We conclude that PDM enables one to do comprehensive experiments and that it is an effective tool for designing and optimizing reconstruction methods in parallel MR imaging.     

Enhancing the acquisition efficiency of fast magnetic resonance imaging via broadband encoding of signal content

Current efficient magnetic resonance imaging (MRI) methods such as parallel-imaging and k-t methods encode MR signals using a set of effective encoding functions other than the Fourier basis. This work revisits the proposition of directly manipulating the set of effective encoding functions at the radiofrequency excitation step in order to increase MRI efficiency. This approach, often termed "broadband encoding," enables the application of algebraic matrix factorization technologies to extract efficiency by representing and encoding MR signal content in a compacted form. Broadband imaging equivalents of fast multiecho, parallel and k-t MRI are developed and analyzed. The potential of these techniques to increase the time efficiency of data acquisition is experimentally verified on a commercial MRI scanner using simple spin-echo imaging. A three-dimensional gradient-echo dynamic imaging application that demonstrates the potential benefits of this approach compared to the present state of the art for certain applications is also presented.     

Measurement of hyperpolarized gas diffusion at very short time scales

We present a new pulse sequence for measuring very-short-time-scale restricted diffusion of hyperpolarized noble gases. The pulse sequence is based on concatenating a large number of bipolar diffusion-sensitizing gradients to increase the diffusion attenuation of the MR signal while maintaining a fundamentally short diffusion time. However, it differs in several respects from existing methods that use oscillating diffusion gradients for this purpose. First, a wait time is inserted between neighboring pairs of gradient pulses; second, consecutive pulse pairs may be applied along orthogonal axes; and finally, the diffusion-attenuated signal is not simply read out at the end of the gradient train but is periodically sampled during the wait times between neighboring pulse pairs. The first two features minimize systematic differences between the measured (apparent) diffusion coefficient and the actual time-dependent diffusivity, while the third feature optimizes the use of the available MR signal to improve the precision of the diffusivity measurement in the face of noise. The benefits of this technique are demonstrated using theoretical calculations, Monte-Carlo simulations of gas diffusion in simple geometries, and experimental phantom measurements in a glass sphere containing hyperpolarized (3)He gas. The advantages over the conventional single-bipolar approach were found to increase with decreasing diffusion time, and thus represent a significant step toward making accurate surface-to-volume measurements in the lung airspaces.     

Fast magnetic resonance spectroscopic imaging (MRSI) using wavelet encoding and parallel imaging: in vitro results

In previous work we have shown that wavelet encoding spectroscopic imaging (WE-SI) reduces acquisition time and voxel contamination compared to the standard Chemical Shift Imaging (CSI) also known as phase encoding (PE). In this paper, we combine the wavelet encoding method with parallel imaging (WE-PI) technique to further reduce the acquisition time by the acceleration factor R, and preserve the spatial metabolite distribution. Wavelet encoding provides results with a lower signal-to-noise ratio (SNR) than the phase encoding method. Their combination with parallel imaging, introduces an intrinsic SNR reduction. The rate of SNR reduction is slower in wavelet encoding with PI than PE with parallel imaging (PE-PI). This is due to the fact that in WE-PI, the SNR reduction is a function of the acceleration factor R and the voxel number N, whereas in PE-PI it is a function of the acceleration factor R only.     

Visually weighted reconstruction of compressive sensing MRI

Compressive sensing (CS) enables the reconstruction of a magnetic resonance (MR) image from undersampled data in k-space with relatively low-quality distortion when compared to the original image. In addition, CS allows the scan time to be significantly reduced. Along with a reduction in the computational overhead, we investigate an effective way to improve visual quality through the use of a weighted optimization algorithm for reconstruction after variable density random undersampling in the phase encoding direction over k-space. In contrast to conventional magnetic resonance imaging (MRI) reconstruction methods, the visual weight, in particular, the region of interest (ROI), is investigated here for quality improvement. In addition, we employ a wavelet transform to analyze the reconstructed image in the space domain and fully utilize data sparsity over the spatial and frequency domains. The visual weight is constructed by reflecting the perceptual characteristics of the human visual system (HVS), and then applied to ?₁ norm minimization, which gives priority to each coefficient during the reconstruction process. Using objective quality assessment metrics, it was found that an image reconstructed using the visual weight has higher local and global quality than those processed by conventional methods.     

MR-compatible ventilator for small animals: computer-controlled ventilation for proton and noble gas imaging

We describe an MR-compatible ventilator that is computer controlled to generate a variety of breathing patterns, to minimize image degrading effects of breathing motion, and to support delivery of gas anesthesia and experimental inhalational gases. A key feature of this ventilator is the breathing valve that attaches directly to the endotracheal tube to reduce dead volume and allows independent control of inspiratory and expiratory phases of ventilation. This ventilator has been used in a wide variety of MR and x-ray microscopy studies of small animals, especially for MR imaging the lungs with hyperpolarized gases ((3)He & (129)Xe).     

Magnetic resonance imaging of dissolved hyperpolarized Xe using a membrane-based continuous flow system

A technique for continuous production of solutions containing hyperpolarized ¹²⁹Xe is explored for MRI applications. The method is based on hollow fiber membranes which inhibit the formation of foams and bubbles. A systematic analysis of various carrier agents for hyperpolarized ¹²⁹Xe has been carried out, which are applicable as contrast agents for in vivo MRI. The image quality of different hyperpolarized Xe solutions is compared and MRI results obtained in a clinical as well as in a nonclinical MRI setting are provided. Moreover, we demonstrate the application of ¹²⁹Xe contrast agents produced with our dissolution method for lung MRI by imaging hyperpolarized ¹²⁹Xe that has been both dissolved in and outgassed from a carrier liquid in a lung phantom, illustrating its potential for the measurement of lung perfusion and ventilation.     

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.     

Adaptive data acquisition in MRI

We propose an adaptive data acquisition technique that depends on the object to be imaged in magnetic resonance (MR) imaging. In this paper, we employed a matching pursuit (MP) algorithm to achieve the adaptive data acquisition. Since the matching pursuit is a greedy algorithm to find RF and gradient waveforms which are the best match for an object-signal, the signal can be decomposed with a few iterations and thereby lead reduction of imaging time in MR. To adopt the matching pursuit algorithm to the adaptive data acquisition in MRI, we have designed a dictionary which contains a windowed Fourier basis set. Because the basis set is localized spatially, the image signal could be divided into segmented signals so that matching pursuit with the segmented signals could lead to effective and object-dependent data acquisition. To verify the proposed technique, computer simulations and experiments are performed with a 1.0 T whole body MRI system.     

基于Hartley变换的磁共振成象

许多磁共振成象的应用场合需要利用正交双通道来采集一个具有时间和空间分辨力的对象序列。传统的基于Ｆｏｕｒｉｅｒ变换的成象方法，一方面，图象序列的重建时各帧图象是独立地进行重建的，因而图象序列的时间分辨力受到编码的限制；另一方面，来自两个通道之间的Ｆａｒｔｌｅｙ变换的磁共振成象技术。     

Solving the problem of concomitant gradients in ultra-low-field MRI

In ultra-low-field magnetic resonance imaging (ULF MRI), spin precession is detected typically in magnetic fields of the order of 10-100 μT. As in conventional high-field MRI, the spatial origin of the signals can be encoded by superposing gradient fields on a homogeneous main field. However, because the main field is weak, gradient field amplitudes become comparable to it. In this case, the concomitant gradients forced by Maxwell's equations cause the assumption of linearly varying field gradients to fail. Thus, image reconstruction with Fourier transformation would produce severe image artifacts. We propose a direct linear inversion (DLI) method to reconstruct images without limiting assumptions about the gradient fields. We compare the quality of the images obtained using the proposed reconstruction method and the Fourier reconstruction. With simulations, we show how the reconstruction errors of the methods depend on the strengths of the concomitant gradients. The proposed approach produces nearly distortion-free images even when the main field reaches zero.     

MRI of the lung gas-space at very low-field using hyperpolarized noble gases

In hyperpolarized (HP) noble-gas magnetic resonance imaging, large nuclear spin polarizations, about 100,000 times that ordinarily obtainable at thermal equilibrium, are created in 3He and 129Xe. The enhanced signal that results can be employed in high-resolution MRI studies of void spaces such as in the lungs. In HP gas MRI the signal-to-noise ratio (SNR) depends only weakly on the static magnetic field (B(0)), making very low-field (VLF) MRI possible; indeed, it is possible to contemplate portable MRI using light-weight solenoids or permanent magnets. This article reports the first in vivo VLF MR images of the lungs in humans and in rats, obtained at a field of only 15 millitesla (150 Gauss).     

Wavelet-based characterization of vertebral trabecular bone structure from magnetic resonance images at 3 T compared with micro-computed tomographic measurements

Trabecular bone structure and bone density contribute to the strength of bone and are important in the study of osteoporosis. Wavelets are a powerful tool in characterizing and quantifying texture in an image. The purpose of this study was to validate wavelets as a tool in computing trabecular bone thickness directly from gray-level images. To this end, eight cylindrical cores of vertebral trabecular bone were imaged using 3-T magnetic resonance imaging (MRI) and micro-computed tomography (microCT). Thickness measurements of the trabecular bone from the wavelet-based analysis were compared with standard 2D structural parameters analogous to bone histomorphometry (MR images) and direct 3D distance transformation methods (microCT images). Additionally, bone volume fraction was determined using each method. The average difference in trabecular thickness between the wavelet and standard methods was less than the size of 1 pixel size for both MRI and microCT analysis. A correlation (R) of .94 for microCT measurements and that of .52 for MRI were found for the bone volume fraction. Based on these results, we conclude that wavelet-based methods deliver results comparable with those from established MR histomorphometric measurements. Because the wavelet transform is more robust with respect to image noise and operates directly on gray-level images, it could be a powerful tool for computing structural bone parameters from MR images acquired using high resolution and thus limited signal scenarios.     

A 3D trajectory for undersampling k-space in MRSI applications

Magnetic resonance spectroscopic imaging (MRSI) is a noninvasive technique for producing spatially localized spectra. MRSI presents the important challenge of reducing the scan time while maintaining the spatial resolution. The preferred approach for this is to use time-varying readout gradients to collect the spatial and chemical-shift information. Fast, three-dimensional (3D) spatial encoded methods also reduce the scan time. Despite the existence of several new and faster 3D encoded methods, or k-space trajectories, for magnetic resonance imaging (MRI), only stack of spirals and echo planar have been studied in 3D MRSI. A novel formulation for designing fast, 3D k-space trajectory applicable to 3D MRSI is presented. This approach is simple and consists of rays expanding from the origin of k-space into a revolving sphere, collecting spectral data of all 3D spatial k-space at different times in the same scan. This article describes this new method and presents some results of its application to 3D MRSI. This technique allows some degree of undersampling; hence, it is possible to reconstruct high-quality undersampled spectroscopic imaging in order to recognize different compounds in short scan times. Additionally, the method is tested in regular 3D MRI. This proposed method can also be used for dynamic undersampled imaging.