Three‐dimensional first‐pass myocardial perfusion MRI using a stack‐of‐spirals acquisition

Three‐dimensional cardiac magnetic resonance perfusion imaging is promising for the precise sizing of defects and for providing high perfusion contrast, but remains an experimental approach primarily due to the need for large‐dimensional encoding, which, for traditional 3DFT imaging, requires either impractical acceleration factors or sacrifices in spatial resolution. We demonstrated the feasibility of rapid three‐dimensional cardiac magnetic resonance perfusion imaging using a stack‐of‐spirals acquisition accelerated by non‐Cartesian k‐t SENSE, which enables entire myocardial coverage with an in‐plane resolution of 2.4 mm. The optimal undersampling pattern was used to achieve the largest separation between true and aliased signals, which is a prerequisite for k‐t SENSE reconstruction. Flip angle and saturation recovery time were chosen to ensure negligible magnetization variation during the transient data acquisition. We compared the proposed three‐dimensional perfusion method with the standard 2DFT approach by consecutively acquiring both data during each R–R interval in cardiac patients. The mean and standard deviation of the correlation coefficients between time intensity curves of three‐dimensional versus 2DFT were 0.94 and 0.06 across seven subjects. The linear correlation between the two sets of upslope values was significant (r = 0.78, P < 0.05). Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.     

Influence of SENSE on image properties in high-resolution single-shot echo-planar DTI.

Limited spatial resolution is a key obstacle to the study of brain white matter structure with diffusion tensor imaging (DTI). In its frequent implementation with single-excitation spin-echo echo-planar sequences, DTI's ability to resolve small structures is strongly restricted by T2 and T2* decay, B0 inhomogeneity, and limited signal-to-noise ratio (SNR). In this work the influence of sensitivity encoding (SENSE) on diffusion-weighted (DW) image properties is investigated. Computer simulations showed that the PSF becomes narrower with increasing SENSE reduction factors, R, enhancing the intrinsic resolution. After a brief theoretical discussion, we describe the estimation of SNR on a pixel-by-pixel basis as a function of R. The mean image SNR behavior is manifold: SENSE is capable of increasing SNR efficiency by reducing the echo time (TE). Each SNR(R) curve reveals a maximum that depends on the amount of partial Fourier encoding used. The overall best SNR efficiency for an eight-element head coil array and a b-factor of 1000 s/mm2 is achieved at R = 2.1 and partial Fourier encoding of 60%. In vivo tensor maps of volunteers and a patient, with an in-plane resolution of 0.78 x 0.78 mm2, are also presented to demonstrate the practical implementation of the parallel approach.     

Ghost artifact cancellation using phased array processing.

In this article, a method for phased array combining is formulated which may be used to cancel ghosts caused by a variety of distortion mechanisms, including space variant distortions such as local flow or off-resonance. This method is based on a constrained optimization, which optimizes SNR subject to the constraint of nulling ghost artifacts at known locations. The resultant technique is similar to the method known as sensitivity encoding (SENSE) used for accelerated imaging; however, in this formulation it is applied to full field-of-view (FOV) images. The method is applied to multishot EPI with noninterleaved phase encode acquisition. A number of benefits, as compared to the conventional interleaved approach, are reduced distortion due to off-resonance, in-plane flow, and EPI delay misalignment, as well as eliminating the need for echo-shifting. Experimental results demonstrate the cancellation for both phantom as well as cardiac imaging examples.     

Potential and feasibility of parallel MRI at high field

This survey focuses on the fusion of two major lines of recent progress in MRI methodology: parallel imaging with receiver coil arrays and the transition to high and ultra-high field strength for human applications. As discussed in this paper, combining the two developments has vast potential due to multiple specific synergies. First, parallel acquisition and high field are highly complementary in terms of their individual advantages and downsides. As a consequence, the joint approach generally offers enhanced flexibility in the design of scanning strategies. Second, increasing resonance frequency changes the electrodynamics of the MR signal in such a way that parallel imaging becomes more effective in large objects. The underlying conceptual and theoretical considerations are reviewed in detail. In further sections, technical challenges and practical aspects are discussed. The feasibility of parallel MRI at ultra-high field is illustrated by current results of parallel human MRI at 7 T.     

Unaliasing lipid contamination for MR spectroscopic imaging of gliomas at 3T using sensitivity encoding (SENSE).

3D magnetic resonance spectroscopic imaging (MRSI) has been successfully employed to extract information about brain tumor metabolism, such as cell membrane breakdown, cellular energetics, and neuronal integrity, through its ability to differentiate signals coming from choline (Cho), creatine (Cr), and N-acetyl aspartate (NAA) molecules. The additional presence of lipids within subregions of the tumor may indicate cellular membrane breakdown due to cell death. Another potential source of lipids is subcutaneous fat, which may be excited with point-resolved spectroscopy (PRESS) volume selection and aliased into the spectral field of view (FOV) due to the chemical shift artifact and the low bandwidth of the selection pulses. The purpose of our study was to employ a postprocessing method for unaliasing lipid resonances originating from in-slice subcutaneous lipids from the 3D MRSI of gliomas at 3T, using an eight-channel phased-array coil and sensitivity encoding (SENSE).     

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.     

Parallel magnetic resonance imaging using the GRAPPA operator formalism.

In this article it is shown that GRAPPA reconstruction can be reformulated as a matrix operator, similar to ladder or propagator operators used in quantum mechanics, that shifts data in k-space. Using this formalism, it is shown that there exists an infinitesimal GRAPPA operator that shifts data in k-space by arbitrarily small amounts. Other desired k-space shifts can then be accomplished through repeated applications of this infinitesimal GRAPPA operator. Implications of these ideas are described.     

Self-calibrating parallel imaging with automatic coil sensitivity extraction.

Calibration of the spatial sensitivity functions of coil arrays is a crucial element in parallel magnetic resonance imaging (PMRI). The most common approach has been to measure coil sensitivities directly using one or more low-resolution images acquired before or after accelerated data acquisition. However, since it is difficult to ensure that the patient and coil array will be in exactly the same positions during both calibration scans and accelerated imaging, this approach can introduce sensitivity miscalibration errors into PMRI reconstructions. This work shows that it is possible to extract sensitivity calibration images directly from a fully sampled central region of a variable-density k-space acquisition. These images have all the features of traditional PMRI sensitivity calibrations and therefore may be used for any PMRI reconstruction technique without modification. Because these calibration data are acquired simultaneously with the data to be reconstructed, errors due to sensitivity miscalibration are eliminated. In vivo implementations of self-calibrating parallel imaging using a flexible coil array are demonstrated in abdominal imaging and in real-time cardiac imaging studies.     

Functional MRI using regularized parallel imaging acquisition.

Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions.     

Self-calibrated spiral SENSE.

Current standard sensitivity-encoded parallel imaging (SENSE) utilizes a fully sampled low-resolution reference scan to estimate the coil sensitivities. This reference scan adds scan time and may introduce misregistration artifacts. The purpose of this study was to investigate the feasibility of estimating the coil sensitivities for spiral SENSE directly from an undersampled k-space center. The limited spatial frequencies of the coil sensitivities, and the undersampling beyond the Nyquist radius cause image artifacts. A point spread function (PSF) analysis and experiments on both phantoms and humans identified an optimal radius for the k-space center by minimizing these image artifacts. The preliminary data indicate that self-calibrated SENSE is as accurate as standard SENSE, which uses a fully sampled reference scan.