Improved venous suppression and spatial resolution with SENSE in elliptical centric 3D contrast-enhanced MR angiography.

The elliptical centric (EC) view order samples a 3DFT acquisition from the center of k-space outward, and when applied to contrast-enhanced MR angiography (CE-MRA) provides intrinsic venous suppression. This is because the veins enhance several seconds after the scan is initiated, and are thus encoded solely by noncentral k-space frequencies. A separate method, sensitivity encoding (SENSE), accelerates the k-space sampling rate by reducing the phase FOV or, equivalently, by increasing the k-space sampling interval, and has been used to increase spatiotemporal resolution. We hypothesized that by combining SENSE with EC, sampling of central k-space would be accelerated and the k-space radius at which the veins first showed contrast enhancement would be increased over a reference scan, thus providing improved venous suppression and spatial resolution without additional scan time. This hypothesis was studied with the use of phantom and carotid CE-MRA experiments, and the results demonstrated an approximate 25% reduction in venous signal when SENSE was used.     

Coil-by-coil image reconstruction with SMASH.

The SiMultaneous Acquisition of Spatial Harmonics (SMASH) technique uses linear combinations of undersampled datasets from the component coils of an RF coil array to reconstruct fully sampled composite datasets in reduced imaging times. In previously reported implementations, SMASH reconstructions were designed to reproduce the images that would otherwise be obtained by simple sums of fully gradient encoded component coil images. This strategy has left SMASH images vulnerable to phase cancellation artifacts when the sensitivities of RF coil array elements are not suitably phase-aligned. In fully gradient encoded imaging schemes these artifacts can be eliminated using a variety of methods for combining the individual coil images, including matched filter combinations as well as sum of squares combinations. Until now, these reconstruction schemes have been unavailable to SMASH reconstructions as SMASH produced a final composite image directly from the raw component coil k-space datasets. This article demonstrates a modification to SMASH that allows reconstruction of a full set of accelerated individual component coil images by fitting component coil sensitivity functions to a complete set of spatial harmonics tailored for each coil in the array. Standard component coil combinations applied to the individual reconstructed images produce final composite images free of phase cancellation artifacts.     

Decomposed direct matrix inversion for fast non-cartesian SENSE reconstructions.

A new k-space direct matrix inversion (DMI) method is proposed here to accelerate non-Cartesian SENSE reconstructions. In this method a global k-space matrix equation is established on basic MRI principles, and the inverse of the global encoding matrix is found from a set of local matrix equations by taking advantage of the small extension of k-space coil maps. The DMI algorithm's efficiency is achieved by reloading the precalculated global inverse when the coil maps and trajectories remain unchanged, such as in dynamic studies. Phantom and human subject experiments were performed on a 1.5T scanner with a standard four-channel phased-array cardiac coil. Interleaved spiral trajectories were used to collect fully sampled and undersampled 3D raw data. The equivalence of the global k-space matrix equation to its image-space version, was verified via conjugate gradient (CG) iterative algorithms on a 2x undersampled phantom and numerical-model data sets. When applied to the 2x undersampled phantom and human-subject raw data, the decomposed DMI method produced images with small errors (< or = 3.9%) relative to the reference images obtained from the fully-sampled data, at a rate of 2 s per slice (excluding 4 min for precalculating the global inverse at an image size of 256 x 256). The DMI method may be useful for noise evaluations in parallel coil designs, dynamic MRI, and 3D sodium MRI with fixed coils and trajectories.     

Artifact reduction in moving-table acquisitions using parallel imaging and multiple averages.

Two-dimensional (2D) axial continuously-moving-table imaging has to deal with artifacts due to gradient nonlinearity and breathing motion, and has to provide the highest scan efficiency. Parallel imaging techniques (e.g., generalized autocalibrating partially parallel acquisition GRAPPA)) are used to reduce such artifacts and avoid ghosting artifacts. The latter occur in T(2)-weighted multi-spin-echo (SE) acquisitions that omit an additional excitation prior to imaging scans for presaturation purposes. Multiple images are reconstructed from subdivisions of a fully sampled k-space data set, each of which is acquired in a single SE train. These images are then averaged. GRAPPA coil weights are estimated without additional measurements. Compared to conventional image reconstruction, inconsistencies between different subsets of k-space induce less artifacts when each k-space part is reconstructed separately and the multiple images are averaged afterwards. These inconsistencies may lead to inaccurate GRAPPA coil weights using the proposed intrinsic GRAPPA calibration. It is shown that aliasing artifacts in single images are canceled out after averaging. Phantom and in vivo studies demonstrate the benefit of the proposed reconstruction scheme for free-breathing axial continuously-moving-table imaging using fast multi-SE sequences.     

Parallel magnetic resonance imaging with adaptive radius in k-space (PARS): constrained image reconstruction using k-space locality in radiofrequency coil encoded data.

A parallel image reconstruction algorithm is presented that exploits the k-space locality in radiofrequency (RF) coil encoded data. In RF coil encoding, information relevant to reconstructing an omitted datum rapidly diminishes as a function of k-space separation between the omitted datum and the acquired signal data. The proposed method, parallel magnetic resonance imaging with adaptive radius in k-space (PARS), harnesses this physical property of RF coil encoding via a sliding-kernel approach. Unlike generalized parallel imaging approaches that might typically involve inverting a prohibitively large matrix for arbitrary sampling trajectories, the PARS sliding-kernel approach creates manageable and distributable independent matrices to be inverted, achieving both computational efficiency and numerical stability. An empirical method designed to measure total error power is described, and the total error power of PARS reconstructions is studied over a range of k-space radii and accelerations, revealing "minimal-error" conditions at comparatively modest k-space radii. PARS reconstructions of undersampled in vivo Cartesian and non-Cartesian data sets are shown and are compared selectively with traditional SENSE reconstructions. Various characteristics of the PARS k-space locality constraint (such as the tradeoff between signal-to-noise ratio and artifact power and the relationship with iterative parallel conjugate gradient approaches or nonparallel gridding approaches) are discussed.     

Image reconstruction by regularized nonlinear inversion—Joint estimation of coil sensitivities and image content

The use of parallel imaging for scan time reduction in MRI faces problems with image degradation when using GRAPPA or SENSE for high acceleration factors. Although an inherent loss of SNR in parallel MRI is inevitable due to the reduced measurement time, the sensitivity to image artifacts that result from severe undersampling can be ameliorated by alternative reconstruction methods. While the introduction of GRAPPA and SENSE extended MRI reconstructions from a simple unitary transformation (Fourier transform) to the inversion of an ill‐conditioned linear system, the next logical step is the use of a nonlinear inversion. Here, a respective algorithm based on a Newton‐type method with appropriate regularization terms is demonstrated to improve the performance of autocalibrating parallel MRI—mainly due to a better estimation of the coil sensitivity profiles. The approach yields images with considerably reduced artifacts for high acceleration factors and/or a low number of reference lines. Magn Reson Med, 2008. © 2008 Wiley‐Liss, Inc.     

k-space interpretation of the Rose Model: noise limitation on the detectable resolution in MRI.

Noise limitation on the detected spatial resolution, described by the Rose Model, is well known in X-ray imaging and routinely used in designing X-ray imaging protocols. The purpose of this article is to revisit the Rose Model in the context of MRI where image data are acquired in the spatial frequency domain. A k-space signal-to-noise ratio (kSNR) is introduced to measure the relative signal and noise powers in a circular annulus in k-space. It is found that the kSNR diminishes rapidly with k-space radius. The Rose criterion that the voxel SNR approximately 4 is translated to kSNR cutoff values was tested using theoretical derivation and experimental histogram analysis. Experiments demonstrate that data acquisition beyond this cutoff k-space radius adds little or no information to the image. In order to reduce the noise limit on spatial resolution, the signal strength must be improved through means such as increasing the coil sensitivity, contrast enhancement, and signal averaging. This finding implies that the optimal k-space volume to be sampled or the optimal scan time in MRI should be matched to the relative SNR level.     

SENSE with improved tolerance to inaccuracies in coil sensitivity maps

In this work, an extension of the Cartesian sensitivity encoding (SENSE) parallel imaging framework is proposed. In the well‐known SENSE solution, the overdetermined reconstruction inversion problem is optimized to get the highest signal‐to‐noise ratio in the image. In this extension, the probability of artifacts due to incorrect knowledge of the receiver coil sensitivities is also taken into account. This is realized by assuming an uncertainty in measured receiver coil sensitivities to enable weighting of residual artifact level and signal‐to‐noise ratio in the inversion problem. This inversion problem can still be solved by a least‐squares optimization without the need of any complex iterative scheme. Results in abdominal imaging show that artifact levels can be substantially reduced, at the cost of a signal‐to‐noise ratio penalty. The size of the signal‐to‐noise ratio penalty depends on the assumed inaccuracy of the coil sensitivities, sensitivity encoding acceleration factor, and coil configuration. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.     

Ghost artifact removal using a parallel imaging approach.

Parallel imaging techniques, which use several receive coils simultaneously, have been shown to enable a significant scan time reduction by subsampling k-space. Nevertheless, the data acquired with multiple coils in parallel exhibit some redundancy if the number of receive coils exceeds the subsampling factor. This redundancy leads to an overdetermination of the reconstruction problem, which is generally used to optimize the signal-to-noise ratio (SNR). However, it can yield further information about the quality of the reconstructed image, and can thus be used to identify and correct image artifacts. While some known approaches try to solve this problem in k-space, this study addresses it in the spatial domain and uses a modified SENSE reconstruction to reduce or completely remove ghost-type artifacts arising from processes such as motion or flow during data acquisition. Phantom and in vivo studies show significant improvements in image quality after correction, and serve as a basis for the discussion of the performance and limitations of this new approach.     

Sensitivity-encoded spectroscopic imaging.

Sensitivity encoding (SENSE) offers a new, highly effective approach to reducing the acquisition time in spectroscopic imaging (SI). In contrast to conventional fast SI techniques, which accelerate k-space sampling, this method permits reducing the number of phase encoding steps in each phase encoding dimension of conventional SI. Using a coil array for data acquisition, the missing encoding information is recovered exploiting knowledge of the distinct spatial sensitivities of the individual coil elements. In this work, SENSE is applied to 2D spectroscopic imaging. Fourfold reduction of scan time is achieved at preserved spectral and spatial resolution, maintaining a reasonable SNR. The basic properties of the proposed method are demonstrated by phantom experiments. The in vivo feasibility of SENSE-SI is verified by metabolic imaging of N-acetylaspartate, creatine, and choline in the human brain. These results are compared to conventional SI, with special attention to the spatial response and the SNR.     

Sensitivity encoding for fast MR imaging of the brain in patients with stroke

PURPOSE: To evaluate sensitivity encoding (SENSE) technique in a clinical setting for magnetic resonance (MR) imaging in patients who are suspected of having infarction. MATERIALS AND METHODS: This intraindividual comparative study included 62 patients suspected of having cerebral ischemia. Patients underwent T2-weighted fluid-attenuated inversion-recovery (FLAIR) (n = 62), T2-weighted turbo spin-echo (TSE) (n = 48), and single-shot echo-planar diffusion-weighted imaging (n = 27) with standard sequential and SENSE MR acquisitions with a 1.5-T magnet and phased-array coil. With SENSE, acquisition time was reduced from 1 minute 12 seconds to 35 seconds for FLAIR and from 1 minute 18 seconds to 39 seconds for T2-weighted TSE imaging. For diffusion-weighted imaging, echo train length was shortened (78 vs 71 msec) to reduce susceptibility effects while acquisition time was maintained. Two radiologists scored quality of standard and SENSE images with a five-point scale and assessed presence of artifacts (motion, susceptibility) and lesion conspicuity. To assess statistical significance, Wilcoxon signed rank and chi2 tests were used. RESULTS: Statistical analysis revealed no significant difference in terms of image quality and presence of artifacts between standard and SENSE T2-weighted TSE (image quality, P =.724; presence of artifacts, P =.378) and FLAIR (image quality, P =.127; presence of artifacts, P =.275) images. Image quality at SENSE diffusion-weighted imaging was scored significantly higher compared with that at standard diffusion-weighted imaging (P =.002). Susceptibility artifacts were significantly reduced at SENSE diffusion-weighted imaging when compared with those at standard diffusion-weighted imaging (P <.001). Conspicuity of 84 lesions was rated equivalent with both standard and SENSE protocols. CONCLUSION: SENSE allowed acquisition of T2-weighted TSE and FLAIR images with image quality and lesion conspicuity that did not differ from those of standard acquisition techniques but in only half the acquisition time. Use of SENSE with diffusion-weighted imaging significantly reduces susceptibility artifacts while lesion conspicuity is maintained.     

Feasibility of dynamic susceptibility contrast perfusion MR imaging at 3T using a standard quadrature head coil and eight-channel phased-array coil with and without SENSE reconstruction

PURPOSE: To investigate changes in image and dynamic signal-to-noise ratios (SNRs) of the DeltaR2* curve, as well as magnetic susceptibility-induced artifacts between a standard quadrature head coil and an eight-channel phased-array coil with and without sensitivity-encoding (SENSE) at 3T, compared to the current clinical standard head coil acquisition at 1.5T. MATERIALS AND METHODS: Dynamic susceptibility contrast (DSC) perfusion MRI was performed on 80 brain tumor patients using a gradient-echo, echo-planar imaging (EPI) sequence. Image and dynamic SNR were compared between 1.5T and 3T field strengths, a quadrature and eight-channel phased-array coil, and a conventional vs. partially parallel EPI acquisition with SENSE reconstruction. The amount of geometric distortion and signal dropout was quantified and compared between conventional and SENSE EPI acquisitions within the same exam at 3T. RESULTS: An initial 2.6-fold elevation in dynamic SNR was observed in normal-appearing white matter when doubling the field strength (P < 0.001), with an additional 1.7-fold increase found when employing an eight-channel phased-array coil (P < 0.002). Compared to the standard 3T eight-channel coil acquisition, the implementation of SENSE reduced the number of voxels experiencing large anterior shifts in the phase-encode direction, lowered the volume of signal dropout by 2.0-11.5%, and allowed a 1.4-fold increase in slice coverage, while only decreasing the dynamic SNR by 22%. CONCLUSION: SENSE EPI at 3T yielded a significant improvement in dynamic SNR over the 1.5T acquisitions. A significant reduction in magnetic susceptibility-induced artifacts was achieved with SENSE EPI compared to the standard EPI eight-channel coil acquisition at 3T.     

Joint image reconstruction and sensitivity estimation in SENSE (JSENSE).

Parallel magnetic resonance imaging (pMRI) using multichannel receiver coils has emerged as an effective tool to reduce imaging time in various applications. However, the issue of accurate estimation of coil sensitivities has not been fully addressed, which limits the level of speed enhancement achievable with the technology. The self-calibrating (SC) technique for sensitivity extraction has been well accepted, especially for dynamic imaging, and complements the common calibration technique that uses a separate scan. However, the existing method to extract the sensitivity information from the SC data is not accurate enough when the number of data is small, and thus erroneous sensitivities affect the reconstruction quality when they are directly applied to the reconstruction equation. This paper considers this problem of error propagation in the sequential procedure of sensitivity estimation followed by image reconstruction in existing methods, such as sensitivity encoding (SENSE) and simultaneous acquisition of spatial harmonics (SMASH), and reformulates the image reconstruction problem as a joint estimation of the coil sensitivities and the desired image, which is solved by an iterative optimization algorithm. The proposed method was tested on various data sets. The results from a set of in vivo data are shown to demonstrate the effectiveness of the proposed method, especially when a rather large net acceleration factor is used.     

Reduced aliasing artifacts using variable-density k-space sampling trajectories.

A variable-density k-space sampling method is proposed to reduce aliasing artifacts in MR images. Because most of the energy of an image is concentrated around the k-space center, aliasing artifacts will contain mostly low-frequency components if the k-space is uniformly undersampled. On the other hand, because the outer k-space region contains little energy, undersampling that region will not contribute severe aliasing artifacts. Therefore, a variable-density trajectory may sufficiently sample the central k-space region to reduce low-frequency aliasing artifacts and may undersample the outer k-space region to reduce scan time and to increase resolution. In this paper, the variable-density sampling method was implemented for both spiral imaging and two-dimensional Fourier transform (2DFT) imaging. Simulations, phantom images and in vivo cardiac images show that this method can significantly reduce the total energy of aliasing artifacts. In general, this method can be applied to all types of k-space sampling trajectories.     

VD-AUTO-SMASH imaging.

Recently a self-calibrating SMASH technique, AUTO-SMASH, was described. This technique is based on PPA with RF coil arrays using auto-calibration signals. In AUTO-SMASH, important coil sensitivity information required for successful SMASH reconstruction is obtained during the actual scan using the correlation between undersampled SMASH signal data and additionally sampled calibration signals with appropriate offsets in k-space. However, AUTO-SMASH is susceptible to noise in the acquired data and to imperfect spatial harmonic generation in the underlying coil array. In this work, a new modified type of internal sensitivity calibration, VD-AUTO-SMASH, is proposed. This method uses a VD k-space sampling approach and shows the ability to improve the image quality without significantly increasing the total scan time. This new k-space adapted calibration approach is based on a k-space-dependent density function. In this scheme, fully sampled low-spatial frequency data are acquired up to a given cutoff-spatial frequency. Above this frequency, only sparse SMASH-type sampling is performed. On top of the VD approach, advanced fitting routines, which allow an improved extraction of coil-weighting factors in the presence of noise, are proposed. It is shown in simulations and in vivo cardiac images that the VD approach significantly increases the potential and flexibility of rapid imaging with AUTO-SMASH.     

Diffusion tensor imaging using single-shot SENSE-EPI.

SENSitivity Encoding (SENSE) greatly enhances the quality of diffusion-weighted echo-planar imaging (EPI) by reducing blurring and off-resonance artifacts. Such improvement would also be desirable for diffusion tensor imaging (DTI), but measures derived from the diffusion tensor can be extremely sensitive to any kind of image distortion. Whether DTI is feasible in combination with SENSE has not yet been explored, and is the focus of this study. Using a SENSE-reduction factor of 2, DTI scans in eight healthy volunteers were carried out with regular- and high-resolution acquisition matrices. To further improve the stability of the SENSE reconstruction, a new coil-sensitivity estimation technique based on variational calculus and the principles of matrix regularization was applied. With SENSE, maps of the trace of the diffusion tensor and of fractional anisotropy (FA) had improved spatial resolution and less geometric distortion. Overall, the geometric distortions were substantially removed and a significant resolution enhancement was achieved with almost the same scan time as regular EPI. DTI was even possible without the use of quadrature body coil (QBC) reference scans. Geometry-factor-related noise enhancement was only discernible in maps generated with higher-resolution matrices. Error boundaries for residual fluctuations in SENSE reconstructions are discussed. Our results suggest that SENSE can be combined with DTI and may present an important adjunct for future neuroimaging applications of this technique.     

Simulation study for artifacts on Gd-EOB-DTPA-enhanced liver dynamic MR imaging in arterial dominant phase

We performed a simulation for artifacts on liver dynamic MR imaging with the contrast agent gadolinium-ethoxybenzyl (Gd-EOB)-DTPA. The signal enhancement of the image by the contrast agent in the arterial dominant phase was assumed, and the time-enhancement curve was numerically generated. The data in k-space was obtained by the Fourier transform of a liver image. By assuming the scan timing and duration in the time-enhancement curve, the data set of each phase-encoding step in k-space was increased in proportion to the corresponding intensity in the time-enhancement curve. We obtained the simulated image by the Fourier transform of the k-space data, and investigated artifacts in the image. Assuming the use of the centric k-space filling scheme, blurring in the image is found when the scan timing is delayed. When the scan is started in an early timing, we observe the effect of edge enhancement in the image. These artifacts of blurring and edge enhancement are decreased by shortening the scan duration. Assuming the use of the sequential k-space filling scheme, those artifacts are not prominent. The use of the sequential scheme would be effective for the purpose of avoiding the artifacts. It is known that the contrast enhancement would not be sufficient without optimal scan timing; in addition, artifacts should be noted. For basic study of the contrast enhancement and artifacts, our simulation technique based on the time-enhancement curve would be useful.     

Augmented generalized SENSE reconstruction to correct for rigid body motion.

The correction of motion artifacts continues to be a significant problem in MRI. In the case of uncooperative patients, such as children, or patients who are unable to remain stationary, the accurate determination and correction of motion artifacts becomes a very important prerequisite for achieving good image quality. The application of conventional motion-correction strategies often produces inconsistencies in k-space data. As a result, significant residual artifacts can persist. In this work a formalism is introduced for parallel imaging in the presence of motion. The proposed method can improve overall image quality because it diminishes k-space inconsistencies by exploiting the complementary image encoding capacity of individual receiver coils. Specifically, an augmented version of an iterative SENSE reconstruction is used as a means of synthesizing the missing data in k-space. Motion is determined from low-resolution navigator images that are coregistered by an automatic registration routine. Navigator data can be derived from self-navigating k-space trajectories or in combination with other navigation schemes that estimate patient motion. This correction method is demonstrated by interleaved spiral images collected from volunteers. Conventional spiral scans and scans corrected with proposed techniques are shown, and the results illustrate the capacity of this new correction approach.     

k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations.

Dynamic images of natural objects exhibit significant correlations in k-space and time. Thus, it is feasible to acquire only a reduced amount of data and recover the missing portion afterwards. This leads to an improved temporal resolution, or an improved spatial resolution for a given amount of acquisition. Based on this approach, two methods were developed to significantly improve the performance of dynamic imaging, named k-t BLAST (Broad-use Linear Acquisition Speed-up Technique) and k-t SENSE (SENSitivity Encoding) for use with a single or multiple receiver coils, respectively. Signal correlations were learned from a small set of training data and the missing data were recovered using all available information in a consistent and integral manner. The general theory of k-t BLAST and k-t SENSE is applicable to arbitrary k-space trajectories, time-varying coil sensitivities, and under- and overdetermined reconstruction problems. Examples from ungated cardiac imaging demonstrate a 4-fold acceleration (voxel size 2.42 x 2.52 mm(2), 38.4 fps) with either one or six receiver coils. k-t BLAST and k-t SENSE are applicable to many areas, especially those exhibiting quasiperiodic motion, such as imaging of the heart, the lungs, the abdomen, and the brain under periodic stimulation.     

Brain magnetic resonance imaging at 3 Tesla using BLADE compared with standard rectilinear data sampling

OBJECTIVES: We sought to evaluate Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER; BLADE) data acquisition in comparison with standard k-space sampling techniques for axial and sagittal brain imaging at 3 T regarding imaging artifacts. MATERIAL AND METHODS: Forty patients who gave consent were included in a prospective comparison of standard and PROPELLER (BLADE) k-space sampling techniques. All examinations were performed at 3 T with comparison of standard T2-weighted fluid-attenuated inversion recovery (FLAIR) to PROPELLER T2-weighted FLAIR in the axial image orientation and standard T1-weighted gradient echo to PROPELLER T1-weighted FLAIR in the sagittal image orientation. Imaging protocols were matched for spatial resolution, with data evaluation performed by 2 experienced neuroradiologists. Image data were compared regarding various image artifacts and overall image quality. Reader agreement was assessed by Cohen's kappa statistics. RESULTS: PROPELLER T2-weighted axial data acquisition showed significantly less pulsation and Gibb's artifacts than the standard T2-weighted scan. Even without motion correction, the frequency of ghosting (motion) artifacts was substantially lower in the PROPELLER T2-weighted data and readers concordantly (kappa = 1) rated PROPELLER as better than or equal to the standard T2-weighted scan in the majority of cases (95%; P < 0.0001). In the comparison of sagittal T1-weighted data sets, readers showed only fair agreement (kappa = 0.24) and noted consistent wrap artifacts in PROPELLER T1-weighted FLAIR. CONCLUSION: PROPELLER (BLADE) brain magnetic resonance imaging is also applicable at 3 T. In addition to minimizing motion artifacts, the PROPELLER acquisition scheme reduces other magnetic resonance artifacts that would otherwise degrade scan quality.