Spatial encoding and the single-scan acquisition of high definition MR images in inhomogeneous fields

We have recently proposed a protocol for retrieving multidimensional magnetic resonance spectra and images within a single scan, based on a spatial encoding of the spin interactions. The spatial selectivity of this encoding process also opens up new possibilities for compensating magnetic field inhomogeneities; not by demanding extreme uniformities from the B(0) fields, but by compensating for their effects at an excitation and/or refocusing level. This potential is hereby discussed and demonstrated in connection with the single-scan acquisition of high-definition multidimensional images. It is shown that in combination with time-dependent gradient and radiofrequency manipulations, the new compensation approach can be used to counteract substantial field inhomogenities at either global or local levels over relatively long periods of time. The new compensation scheme could find uses in areas where heterogeneities in magnetic fields present serious obstacles, including rapid studies in regions near tissue/air interfaces. The principles of the B(0) compensation method are reviewed for one- and higher-dimensional cases, and experimentally demonstrated on a series of 1D and 2D single-scan MRI experiments on simple phantoms.     

Rapid acquisition technique for MR elastography of the liver

Magnetic resonance elastography (MRE) of the liver is a novel noninvasive clinical diagnostic tool to stage fibrosis based on measured stiffness. The purpose of this study is to design, evaluate and validate a rapid MRE acquisition technique for noninvasively quantitating liver stiffness which reduces by half the scan time, thereby decreasing image registration errors between four MRE phase offsets. In vivo liver MRE was performed on 16 healthy volunteers and 14 patients with biopsy-proven liver fibrosis using the standard clinical gradient recalled echo (GRE) MRE sequence (MREs) and a developed rapid GRE MRE sequence (MREr) to obtain the mean stiffness in an axial slice. The mean stiffness values obtained from the entire group using MREs and MREr were 2.72 ± 0.85 kPa and 2.7 ± 0.85 kPa, respectively, representing an insignificant difference. A linear correlation of R² = 0.99 was determined between stiffness values obtained using MREs and MREr. Therefore, we can conclude that MREr can replace MREs, which reduces the scan time to half of that of the current standard acquisition (MREs), which will facilitate MRE imaging in patients with inability to hold their breath for long periods.     

Improvement of the acquisition of a large amount of MR images on a conventional whole body system

Modern whole body MR systems are equipped with echo-planar-imaging capability, which allows the measurement of a single slice in a fraction of a second or of thousands of images in few minutes. A considerable restriction to the acquisition of series containing large amounts of images in patient examinations is the time-consuming data handling time of the images at conventional systems, which includes the time to insert the images into the systems database. We propose the arrangement of several images on a new image with a large matrix size like a mosaic. The handling time depends mostly on the number of images without consideration of their matrix size. Therefore, image handling is strongly reduced by the use of such mosaic images.     

An optimized multislice acquisition sequence for the inversion-recovery MR imaging

An optimized multislice data acquisition scheme for inversion-recovery MR imaging is proposed and experimental results are presented. In this new scheme, instead of forming a set of multislice inversion-recovery sequences in series for a given phase encoding step, 180° inversion pulses corresponding to different slices are interwoven with the spin echo data acquisition sequence in an optimal way depending on the desired inversion-recovery time. For example, between the 180° inversion RF pulse and the spin-echo imaging sequence, a number of imaging and inversion sequences are inserted with different slice combinations, i.e., long inversion-recovery time is effectively utilized for the other slice pre-inversion and data acquisition. With the optimized sequence, imaging time has been reduced by as much as a factor of four compared with the existing methods.     

Three-dimensional FLASH MR imaging of thorax and abdomen without triggering or gating

FLASH (fast low-angle shot) imaging is a new technique for rapid two- and three-dimensional (2D and 3D) MRI with high signal-to-noise, high spatial resolution and low sensitivity to motions. In particular, transverse magnetizations are strongly dephased by rapid motions with time constants of several milliseconds. They are not refocused by the gradient echo signal used for FLASH imaging and therefore do not contribute to the image. Artifacts due to motions with longer time constants are averaged out by multiple data acquisitions either because of a statistical nature of the dynamic process itself or, in the case of periodic motions, because of the incoherent data sampling with respect to the time period. In general, the large number of signal acquisitions necessary for a true 3D data set turns out to be sufficient for this purpose. Since 128³ 3D FLASH MR images require measuring times of only 4 min, even two or four accumulations of entire 3D data sets remain within acceptable clinical measuring times. The method for the first time depicts high-quality three-dimensional views of the thoracic and abdominal anatomy in enddiastole and endexspiratory rest without the need of gating or triggering. 3D FLASH images will be of great medical utility, especially in cases where topographic information is needed as, for example, for radiation therapy and angiography planning or preoperative diagnosis.     

Subtraction technique for contrast-enhanced MR images of musculoskeletal tumors

Vascularized malignant tissue, fat and hemorrhage may have similar intensities on Gd-DTPA-enhanced, T1-weighted MRI performed to evaluate musculoskeletal tumors. We describe a simple, rapid post-processing subtraction technique which resulted in improved definition of these tissues in 33 of 42 examinations. While the subtraction process is susceptible to complex patient motion, the improved contrast can be obtained without modifying standard pulse sequences.     

Investigations on the efficiency of cardiac-gated methods for the acquisition of diffusion-weighted images

Diffusion-weighted images are inherently very sensitive to motion. Pulsatile motion of the brain can give rise to artifactual signal attenuation leading to over-estimation of the apparent diffusion coefficients, even with snapshot echo planar imaging. Such miscalculations can result in erroneous estimates of the principal diffusion directions. Cardiac gating can be performed to confine acquisition to the quiet portion of the cycle. Although effective, this approach leads to significantly longer acquisition times. On the other hand, it has been demonstrated that pulsatile motion is not significant in regions above the corpus callosum. To reduce acquisition times and improve the efficiency of whole brain cardiac-gated acquisitions, the upper slices of the brain can be imaged during systole, reserving diastole for those slices most affected by pulsatile motion. The merits and disadvantages of this optimized approach are investigated here, in comparison to a more standard gating method and to the non-gated approach.     

A sparse Bayesian representation for super-resolution of cardiac MR images

High-quality cardiac magnetic resonance (CMR) images can be hardly obtained when intrinsic noise sources are present, namely heart and breathing movements. Yet heart images may be acquired in real time, the image quality is really limited and most sequences use ECG gating to capture images at each stage of the cardiac cycle during several heart beats. This paper presents a novel super-resolution algorithm that improves the cardiac image quality using a sparse Bayesian approach. The high-resolution version of the cardiac image is constructed by combining the information of the low-resolution series –observations from different non-orthogonal series composed of anisotropic voxels – with a prior distribution of the high-resolution local coefficients that enforces sparsity. In addition, a global prior, extracted from the observed data, regularizes the solution. Quantitative and qualitative validations were performed in synthetic and real images w.r.t to a baseline, showing an average increment between 2.8 and 3.2 dB in the Peak Signal-to-Noise Ratio (PSNR), between 1.8% and 2.6% in the Structural Similarity Index (SSIM) and 2.% to 4% in quality assessment (IL-NIQE). The obtained results demonstrated that the proposed method is able to accurately reconstruct a cardiac image, recovering the original shape with less artifacts and low noise.     

Implementation and evaluation of data compression of MR images

A full-frame bit-allocation technique of data compression has been implemented using the existing computer facilities of an MR imager. Images reconstructed from compressed data files have been compared with the original image and changes in noise and pixel value measured to evaluate any image degradation introduced by the compression process. A 256 x 256 x 8 bit brain image can be compressed in 20 seconds with a compression ratio of greater than 4:1 without significant loss of information.     

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.     

MR diagnosis of lymphangioleiomyomatosis: Visibility of pulmonary cysts on spin-echo images

The signs and symptoms of pulmonary lymphangioleiomyomatosis are nonspecific, but the radiographic findings, particularly those on computed tomographic (CT) scans, often suggest the diagnosis. Although evaluation of the lungs on magnetic resonance (MR) scans is limited, MR findings in some diffuse lung diseases have been described. This report illustrates a case of lymphangioleiomyomatosis in which the diagnosis was suggested by the appearance of the lungs on spin-echo MR images.     

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.     

Automatic phasing of MR images. Part I: linearly varying phase

In spin-echo and well shimmed gradient-echo images, the phase of the complex image often varies linearly in both the readout and phase-encode directions. Thus, in principle, it is possible to display an image in absorption mode. However, manually determining the two first-order and one zero-order phase parameters needed to display an absorption-mode image is a formidable task. In this paper, the Bayesian calculations needed to automatically determine these parameters are presented, and the calculations are illustrated using spin-echo images.     

Automatic phasing of MR images. Part II: voxel-wise phase estimation

Magnetic resonance images are typically displayed as the absolute value of the discrete Fourier transform of the k-space data. However, absorption-mode images, the real part of the discrete Fourier transform of the data after applying an appropriate phase correction, have significant advantages over absolute-value images. In a companion paper, the problem of estimating the phase parameters needed to produce an absorption-mode image when the phase of the complex image varies linearly as a function of position, a situation common in magnetic resonance images, was addressed. However, some magnetic resonance images have phases that can vary in a complicated, nonlinear, positionally dependent fashion. To produce an absorption-mode image from these data, one must first estimate the positionally dependent phase, and then use that phase estimate to produce an absorption-mode image. This paper addresses both of these problems by first using Bayesian probability theory to estimate the constant or zero-order phase as a function of image position, and then the calculations are illustrated by using them to generate absorption-mode images from data where the phase of the image is a nonlinear function of position.     

Denoising 3D MR images by the enhanced non-local means filter for Rician noise

The non-local means (NLM) filter removes noise by calculating the weighted average of the pixels in the global area and shows superiority over existing local filter methods that only consider local neighbor pixels. This filter has been successfully extended from 2D images to 3D images and has been applied to denoising 3D magnetic resonance (MR) images. In this article, a novel filter based on the NLM filter is proposed to improve the denoising effect. Considering the characteristics of Rician noise in the MR images, denoising by the NLM filter is first performed on the squared magnitude images. Then, unbiased correcting is carried out to eliminate the biased deviation. When performing the NLM filter, the weight is calculated based on the Gaussian-filtered image to reduce the disturbance of the noise. The performance of this filter is evaluated by carrying out a qualitative and quantitative comparison of this method with three other filters, namely, the original NLM filter, the unbiased NLM (UNLM) filter and the Rician NLM (RNLM) filter. Experimental results demonstrate that the proposed filter achieves better denoising performance over the other filters being compared.     

Viewing the effective k-space coverage of MR images: phantom experiments with fast Fourier transform

The purpose of this experimental study was to evaluate whether the effective k-space coverage of MR images can in principle be viewed after multidimensional Fourier transform back to k-space.     

A statistical method for characterizing the noise in nonlinearly reconstructed images from undersampled MR data: The POCS example

The projection-onto-convex-sets (POCS) algorithm is a powerful tool for reconstructing high-resolution images from undersampled k-space data. It is a nonlinear iterative method that attempts to estimate values for missing data. The convergence of the algorithm and its other deterministic properties are well established, but relatively little is known about how noise in the source data influences noise in the final reconstructed image. In this paper, we present an experimental treatment of the statistical properties in POCS and investigate 12 stochastic models for its noise distribution beside its nonlinear point spread functions. Statistical results show that as the ratio of the missing k-space data increases, the noise distribution in POCS images is no longer Rayleigh as with conventional linear Fourier reconstruction. Instead, the probability density function for the noise is well approximated by a lognormal distribution. For small missing data ratios, however, the noise remains Rayleigh distributed. Preliminary results show that in the presence of noise, POCS images are often dominated by POCS-enhanced noise rather than POCS-induced artifacts. Implicit in this work is the presentation of a general statistical method that can be used to assess the noise properties in other nonlinear reconstruction algorithms.     

Modeling brain tissue volumes over the lifespan: quantitative analysis of postmortem weights and in vivo MR images

Normative measurements of brain gray matter and white matter tissue volumes across the lifespan have not yet been established. The purpose of this article was to use mathematical modeling and analytical functions to demonstrate the growth trajectory of gray matter and white matter from age 0 to age 90. For each gender, brain weight functions were generated by utilizing existing autopsy data from 4400 subjects. Brain gray matter, white matter and lateral ventricular volumes were measured from 39 MR volumes of normal individuals. These were converted to weight by multiplying the tissue volumes by the specific gravity of that tissue. White matter volumes were described by a saturating exponential function, and the gray matter volume function was calculated by subtracting the white matter weight function from the brain weight function. For each gender, equations were generated for white matter and gray matter volumes as a function of age over the lifespan.