Sensitivity profiles from an array of coils for encoding and reconstruction in parallel (SPACE RIP)

In vivo diffusion tensor imaging of thoracic and cervical rat spinal cord at 7 T

In vivo diffusion tensor imaging (DTI) of rat cervical and thoracic spinal cord was performed using a three-element phased array coil at 7 T. The magnetic field was shimmed over the spinal cord in real time using an in-house developed automatic algorithm. Echo planar imaging (EPI)-based diffusion-weighted images (DWIs) were acquired with 21 gradient encoding directions. The DWIs were tensor encoded, and diffusion tensor metrics, fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (lambda(0)) and transverse diffusivity (lambda( perpendicular)) were determined for both white matter (WM) and gray matter (GM). The results on six normal rats indicated no significant differences in the diffusion tensor metrics between thoracic and cervical regions. However, the DTI-derived metrics in cervical spinal cord from our study are somewhat different from the published results in rats. The possible reasons for these differences are suggested.

The anatomical location of the arcuate fasciculus in the human brain: a diffusion tensor tractography study

OBJECTIVES

Little is known about the detailed anatomical location of the arcuate fasciculus (AF) in the human brain, which could easily be applied for use in the management of patients with aphasia. In the current study, we attempted to describe the anatomy of the AF location in the normal human brain using diffusion tensor tractography (DTT).

METHOD

We recruited 32 healthy volunteers for this study. Diffusion tensor images were scanned using 1.5-T, and DTT was obtained using PRIDE software. The AFs consisted of the horizontal part (anterior, superior) and the vertical part (posterior, inferior). We measured the location of the three points (the most posterior point of the vertical part of the AF at the level of the lower lateral ventricle, the most posterior point of the horizontal part, and the most anterior point of the horizontal part).

RESULT

The most posterior point of the vertical part at the level of the lower lateral ventricle was located at the deep white matter around the posterior horn. At the level of the upper lateral ventricle, the most posterior point of the horizontal part was located at the deep white matter around the posterior horn, and the most anterior point of that was located at the deep white matter around the mid-portion between the anterior horn and posterior horn of the lateral ventricle.

CONCLUSION

We have reported the detailed anatomy of the AF location in the human brain. We believe that these results will be helpful to clinical neuroscientists for application in patients with aphasia.

Regionally optimized reconstruction for partially parallel imaging in MRI applications

Based on the conventional SENSE and GRAPPA, a regionally optimized reconstruction method is developed for reduced noise and artifact level in partially parallel imaging. In this regionally optimized reconstruction, the field-of-view (FOV) is divided into a number of small regions. Over every small region, the noise amplification and data fitting error can be balanced and minimized locally by taking advantage of spatial correlation of neighboring pixels in reconstruction. The full FOV image can be obtained by "region-by-region" reconstruction. Compared with the conventional SENSE, this method gives better performance in the regions where there are pixels with high SENSE g-factors. Compared with GRAPPA, it is better in the regions where all the pixels have low SENSE g-factors. In this work, we applied the regionally optimized reconstruction in four important imaging experiments: brain, spine, breast, and cardiac. It was demonstrated in these experiments that the overall image quality using this regionally optimized reconstruction is better than that using the conventional SENSE or GRAPPA.

Improved matrix inversion in image plane parallel MRI

A new 3D parallel magnetic resonance imaging (MRI) method named Generalized Unaliasing Incorporating Support constraint and sensitivity Encoding (GUISE) is presented. GUISE allows direct image recovery from arbitrary Cartesian k-space trajectories. However, periodic k-space sampling patterns are considered for reconstruction efficiency. Image recovery methods such as 2D SENSE (SENSitivity Encoding) and 2D CAIPIRINHA (Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration) are special instances of GUISE where specific restrictions are placed on the k-space sampling patterns used. It is shown that the sampling pattern has large impacts on the image reconstruction error due to noise. An efficient sampling pattern design method that incorporates prior knowledge of object support and coil sensitivity profile is proposed. It requires no experimental trials and could be used in clinical imaging. Comparison of the proposed sampling pattern design method with 2D SENSE and 2D CAIPIRINHA are made based on both simulation and experiment results. It is seen that this new adaptive sampling pattern design method results in a lower noise level in reconstructions due to better exploitation of the coil sensitivity variation and object support constraint. In addition, elimination of the non-object region from reconstruction potentially allows an acceleration factor higher than the number of receiver coils used.

Three-element phased-array coil for imaging of rat spinal cord at 7T

To overcome some of the limitations of an implantable coil, including its invasive nature and limited spatial coverage, a three-element phased-array coil is described for high-resolution magnetic resonance imaging (MRI) of rat spinal cord. This coil allows imaging both thoracic and cervical segments of rat spinal cord. In the current design, coupling between the nearest neighbors was minimized by overlapping the coil elements. A simple capacitive network was used for decoupling the next neighbor elements. The dimensions of individual coils in the array were determined based on the signal-to-noise ratio (SNR) measurements performed on a phantom with three different surface coils. SNR measurements on a phantom demonstrated higher SNR for the phased array coil relative to two different volume coils. In vivo images acquired on rat spinal cord with our coil demonstrated excellent gray and white matter contrast. To evaluate the performance of the phased array coil under parallel imaging, g-factor maps were obtained for acceleration factors of 2 and 3. These simulations indicate that parallel imaging with an acceleration factor of 2 would be possible without significant image reconstruction-related noise amplifications.

Comparison of parallel MRI reconstruction methods for accelerated 3D fast spin-echo imaging

Parallel MRI (pMRI) achieves imaging acceleration by partially substituting gradient-encoding steps with spatial information contained in the component coils of the acquisition array. Variable-density subsampling in pMRI was previously shown to yield improved two-dimensional (2D) imaging in comparison to uniform subsampling, but has yet to be used routinely in clinical practice. In an effort to reduce acquisition time for 3D fast spin-echo (3D-FSE) sequences, this work explores a specific nonuniform sampling scheme for 3D imaging, subsampling along two phase-encoding (PE) directions on a rectilinear grid. We use two reconstruction methods-2D-GRAPPA-Operator and 2D-SPACE RIP-and present a comparison between them. We show that high-quality images can be reconstructed using both techniques. To evaluate the proposed sampling method and reconstruction schemes, results via simulation, phantom study, and in vivo 3D human data are shown. We find that fewer artifacts can be seen in the 2D-SPACE RIP reconstructions than in 2D-GRAPPA-Operator reconstructions, with comparable reconstruction times.

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.

128-channel body MRI with a flexible high-density receiver-coil array

PURPOSE

To determine whether the promise of high-density many-coil MRI receiver arrays for enabling highly accelerated parallel imaging can be realized in practice.

MATERIALS AND METHODS

A 128-channel body receiver-coil array and custom MRI system were developed. The array comprises two clamshells containing 64 coils each, with the posterior array built to maximize signal-to-noise ratio (SNR) and the anterior array design incorporating considerations of weight and flexibility as well. Phantom imaging and human body imaging were performed using a variety of reduction factors and 2D and 3D pulse sequences.

RESULTS

The ratio of SNR relative to a 32-element array of similar footprint was 1.03 in the center of an elliptical loading phantom and 1.7 on average in the outer regions. Maximum g-factors dropped from 5.5 (for 32 channels) to 2.0 (for 128 channels) for 4x4 acceleration and from 25 to 3.3 for 5x5 acceleration. Residual aliasing artifacts for a right/left (R/L) reduction factor of 8 in human body imaging were significantly reduced relative to the 32-channel array.

CONCLUSION

MRI with a large number of receiver channels enables significantly higher acceleration factors for parallel imaging and improved SNR, provided losses from the coils and electronics are kept negligible.

A statistical approach to SENSE regularization with arbitrary k-space trajectories

SENSE reconstruction suffers from an ill-conditioning problem, which increasingly lowers the signal-to-noise ratio (SNR) as the reduction factor increases. Ill-conditioning also degrades the convergence behavior of iterative conjugate gradient reconstructions for arbitrary trajectories. Regularization techniques are often used to alleviate the ill-conditioning problem. Based on maximum a posteriori statistical estimation with a Huber Markov random field prior, this study presents a new method for adaptive regularization using the image and noise statistics. The adaptive Huber regularization addresses the blurry edges in Tikhonov regularization and the blocky effects in total variation (TV) regularization. Phantom and in vivo experiments demonstrate improved image quality and convergence speed over both the unregularized conjugate gradient method and Tikhonov regularization method, at no increase in total computation time.

Using GRAPPA to improve autocalibrated coil sensitivity estimation for the SENSE family of parallel imaging reconstruction algorithms

Two strategies are widely used in parallel MRI to reconstruct subsampled multicoil image data. SENSE and related methods employ explicit receiver coil spatial response estimates to reconstruct an image. In contrast, coil-by-coil methods such as GRAPPA leverage correlations among the acquired multicoil data to reconstruct missing k-space lines. In self-referenced scenarios, both methods employ Nyquist-rate low-frequency k-space data to identify the reconstruction parameters. Because GRAPPA does not require explicit coil sensitivities estimates, it needs considerably fewer autocalibration signals than SENSE. However, SENSE methods allow greater opportunity to control reconstruction quality though regularization and thus may outperform GRAPPA in some imaging scenarios. Here, we employ GRAPPA to improve self-referenced coil sensitivity estimation in SENSE and related methods using very few auto-calibration signals. This enables one to leverage each methods' inherent strength and produce high quality self-referenced SENSE reconstructions.

Preservation of the integrity of the corticospinal tract in a patient with medulla infarct

Diffusion tensor tractography allows the visualization of the corticospinal tract (CST). In the current study, we attempted to demonstrate the preservation of the integrity of the CST within an infarct in a hemiparetic patient with a medulla infarct. A 76-yr-old male patient showed severe paralysis of the left extremities at stroke onset. Over the 4 mos following onset, motor functions of the affected extremities rapidly recovered to a normal state. On the diffusion tensor tractography taken at 1 wk after onset, the tract of the both hemispheres descended through the known CST pathway. Notably, the tract of the affected (right) hemisphere descended through the anterior portion of the infarct in the medulla. The motor-evoked potential obtained from the affected (left) hand muscle had the characteristics of the CST. In conclusion, we demonstrated that the integrity of CST had been spared in the anterior portion of the medulla infarct by using diffusion tensor tractography and transcranial magnetic stimulation.

Reconstruction of undersampled non-Cartesian data sets using pseudo-Cartesian GRAPPA in conjunction with GROG

Most k-space-based parallel imaging reconstruction techniques, such as Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), necessitate the acquisition of regularly sampled Cartesian k-space data to reconstruct a nonaliased image efficiently. However, non-Cartesian sampling schemes offer some inherent advantages to the user due to their better coverage of the center of k-space and faster acquisition times. On the other hand, these sampling schemes have the disadvantage that the points acquired generally do not lie on a grid and have complex k-space sampling patterns. Thus, the extension of Cartesian GRAPPA to non-Cartesian sequences is nontrivial. This study introduces a simple, novel method for performing Cartesian GRAPPA reconstructions on undersampled non-Cartesian k-space data gridded using GROG (GRAPPA Operator Gridding) to arrive at a nonaliased image. Because the undersampled non-Cartesian data cannot be reconstructed using a single GRAPPA kernel, several Cartesian patterns are selected for the reconstruction. This flexibility in terms of both the appearance and number of patterns allows this pseudo-Cartesian GRAPPA to be used with undersampled data sets acquired with any non-Cartesian trajectory. The successful implementation of the reconstruction algorithm using several different trajectories, including radial, rosette, spiral, one-dimensional non-Cartesian, and zig-zag trajectories, is demonstrated.

Alteration of fractional anisotropy and apparent diffusion coefficient in obsessive-compulsive disorder: a diffusion tensor imaging study

BACKGROUND

Abnormalities of fractional anisotropy (FA) have been reported in previous diffusion tensor imaging (DTI) studies in patients with obsessive-compulsive disorder (OCD). However, there are some inconsistencies in the results and the apparent diffusion coefficient (ADC) has not been investigated. The goal of this study was to investigate white matter abnormalities and water diffusivity, as reflected by FA and ADC, using DTI in patients with OCD.

METHODS

Fifteen patients with OCD and 15 healthy volunteers underwent DTI. Voxelwise analysis was used to compare FA in white matter and ADC in gray matter/white matter of the two groups.

RESULTS

Compared with healthy volunteers, the patients had higher FA in the bilateral semioval center extending to the subinsular white matter; and a higher ADC in the left medial frontal cortex. There were no areas with a significantly lower FA or ADC in patients compared with healthy volunteers.

CONCLUSIONS

A significantly higher FA was found in regions associated with the emotion of disgust and a trend for a higher ADC was found in a region associated with the regulation of emotions. These findings suggest that neurocircuits involved in disgust processing may play an important role in the pathophysiology of OCD.

Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods

The class of autocalibrating "data-driven" parallel imaging (PI) methods has gained attention in recent years due to its ability to achieve high quality reconstructions even under challenging imaging conditions. The aim of this work was to perform a formal comparative study of various data-driven reconstruction techniques to evaluate their relative merits for certain imaging applications. A total of five different reconstruction methods are presented within a consistent theoretical framework and experimentally compared in terms of the specific measures of reconstruction accuracy and efficiency using one-dimensional (1D)-accelerated Cartesian datasets. It is shown that by treating the reconstruction process as two discrete phases, a calibration phase and a synthesis phase, the reconstruction pathway can be tailored to exploit the computational advantages available in certain data domains. A new "split-domain" reconstruction method is presented that performs the calibration phase in k-space (k(x), k(y)) and the synthesis phase in a hybrid (x, k(y)) space, enabling highly accurate 2D neighborhood reconstructions to be performed more efficiently than previously possible with conventional techniques. This analysis may help guide the selection of PI methods for a given imaging task to achieve high reconstruction accuracy at minimal computational expense.

On the complimentarity of SENSE and GRAPPA in parallel MR imaging

Two image reconstruction methods currently dominate parallel MR imaging: SENSE and GRAPPA. While both seek to reconstruct images from subsampled multi-channel MRI data, there exist fundamental differences between the two. In particular, SENSE reconstructs an image of the excited spin-density directly whereas GRAPPA reconstructs estimates of the fully sampled raw coil data and then combines them to obtain an image. In this work we show that these differences can be exploited such that each method can compliment the other. In the case of SENSE, which requires an estimate of the coil sensitivity map before reconstruction, one can use GRAPPA to improve the coil sensitivity estimates. Alternatively, using coil sensitivity estimates and the SENSE reconstruction equations, one can improve the GRAPPA reconstruction parameter estimation. Together, these approaches can provide higher image quality than either method alone.

Parallel imaging reconstruction for arbitrary trajectories using k-space sparse matrices (kSPA)

Although the concept of receiving MR signal using multiple coils simultaneously has been known for over two decades, the technique has only recently become clinically available as a result of the development of several effective parallel imaging reconstruction algorithms. Despite the success of these algorithms, it remains a challenge in many applications to rapidly and reliably reconstruct an image from partially-acquired general non-Cartesian k-space data. Such applications include, for example, three-dimensional (3D) imaging, functional MRI (fMRI), perfusion-weighted imaging, and diffusion tensor imaging (DTI), in which a large number of images have to be reconstructed. In this work, a systematic k-space-based reconstruction algorithm based on k-space sparse matrices (kSPA) is introduced. This algorithm formulates the image reconstruction problem as a system of sparse linear equations in k-space. The inversion of this system of equations is achieved by computing a sparse approximate inverse matrix. The algorithm is demonstrated using both simulated and in vivo data, and the resulting image quality is comparable to that of the iterative sensitivity encoding (SENSE) algorithm. The kSPA algorithm is noniterative and the computed sparse approximate inverse can be applied repetitively to reconstruct all subsequent images. This algorithm, therefore, is particularly suitable for the aforementioned applications.

Compressed sensing parallel magnetic resonance imaging

Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) can significantly reduce imaging time in MRI, the former by utilizing multiple channel receivers and the latter by utilizing the sparsity of MR images in a transformed domain. In this work, pMRI and CS are integrated to take advantages of the sensitivity information from multiple coils and sparsity characteristics of MR images. Specifically, CS is used as a regularization method for the inverse problem raised by pMRI based on the L1 norm and a Total Variation (TV) term. We test the new method with a set of 8-channel, in-vivo brain MRI data at reduction factors from 2 to 8. Reconstruction results show that the proposed method outperforms several other regularized parallel MRI reconstruction such as the truncated Singular Value Decomposition (SVD) and Tikhonov regularization methods, in terms of residual artifacts and SNR, especially at reduction factors larger than 4.

Highly accelerated cardiovascular MR imaging using many channel technology: concepts and clinical applications

Cardiovascular magnetic resonance imaging (CVMRI) is of proven clinical value in the non-invasive imaging of cardiovascular diseases. CVMRI requires rapid image acquisition, but acquisition speed is fundamentally limited in conventional MRI. Parallel imaging provides a means for increasing acquisition speed and efficiency. However, signal-to-noise (SNR) limitations and the limited number of receiver channels available on most MR systems have in the past imposed practical constraints, which dictated the use of moderate accelerations in CVMRI. High levels of acceleration, which were unattainable previously, have become possible with many-receiver MR systems and many-element, cardiac-optimized RF-coil arrays. The resulting imaging speed improvements can be exploited in a number of ways, ranging from enhancement of spatial and temporal resolution to efficient whole heart coverage to streamlining of CVMRI work flow. In this review, examples of these strategies are provided, following an outline of the fundamentals of the highly accelerated imaging approaches employed in CVMRI. Topics discussed include basic principles of parallel imaging; key requirements for MR systems and RF-coil design; practical considerations of SNR management, supported by multi-dimensional accelerations, 3D noise averaging and high field imaging; highly accelerated clinical state-of-the art cardiovascular imaging applications spanning the range from SNR-rich to SNR-limited; and current trends and future directions.

Reconstruction in image space using basis functions for partially parallel imaging

General theory of a new reconstruction technique for partially parallel imaging (PPI) is presented in this study. Reconstruction in Image space using Basis functions (RIB) is based on the general principle that the PPI reconstruction in image space can be represented by a pixel-wise weighted summation of the aliased images directly from undersampled data. By assuming that these weighting coefficients for unaliasing can be approximated from the linear combination of a few predefined basis functions, RIB is capable of reconstructing the image within an arbitrary region. This paper discusses the general theory of RIB and its relationship to the classical reconstruction method, GRAPPA. The presented experiments demonstrate RIB with several MRI applications. It is shown that the performance of RIB is comparable to that of GRAPPA. In some cases, RIB shows advantages of increasing reconstruction efficiency, suppressing artifacts and alleviating the nonuniformity of noise distribution. It is anticipated that RIB would be especially useful for cardiac and prostate imaging, where the field of view during data acquisition is required to be much larger than the region of interest.

Weighted H(infinity) optimization approach to parallel MR image reconstruction

Parallel Magnetic Resonance Imaging (pMRI) is one of the most important technical advances in current MRI technology. Although many reconstruction methods have been proposed for pMRI, the reconstructed images still suffer visible artifacts especially when high acceleration is used. To improve the reconstruction quality of pMRI, this paper proposes a new reconstruction method. Instead of achieving perfect reconstruction under ideal assumptions, the proposed method uses weighted H(infinity) optimization to minimize the total reconstruction error, including that raised by the noise and uncertainty in sensitivity map. The experimental studies on in vivo data sets demonstrate that the proposed method outperforms significantly the existing methods at high acceleration.

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.

Fast method for 1D non-cartesian parallel imaging using GRAPPA

MRI with non-Cartesian sampling schemes can offer inherent advantages. Radial acquisitions are known to be very robust, even in the case of vast undersampling. This is also true for 1D non-Cartesian MRI, in which the center of k-space is oversampled or at least sampled at the Nyquist rate. There are two main reasons for the more relaxed foldover artifact behavior: First, due to the oversampling of the center, high-energy foldover artifacts originating from the center of k-space are avoided. Second, due to the non-equidistant sampling of k-space, the corresponding field of view (FOV) is no longer well defined. As a result, foldover artifacts are blurred over a broad range and appear less severe. The more relaxed foldover artifact behavior and the densely sampled central k-space make trajectories of this type an ideal complement to autocalibrated parallel MRI (pMRI) techniques, such as generalized autocalibrating partially parallel acquisitions (GRAPPA). Although pMRI can benefit from non-Cartesian trajectories, this combination has not yet entered routine clinical use. One of the main reasons for this is the need for long reconstruction times due to the complex calculations necessary for non-Cartesian pMRI. In this work it is shown that one can significantly reduce the complexity of the calculations by exploiting a few specific properties of k-space-based pMRI.

Investigation of coil phase compensation in 3D imaging at very high acceleration factors

PURPOSE

To investigate the confounding effect of the coil phase in highly accelerated parallel imaging with small coils, contextualize the effect in terms of single-echo acquisition (SEA) imaging, and show that it can be managed in the case of 3D imaging.

MATERIALS AND METHODS

The effects of the coil phase variations in a 64-channel array of surface microcoils were modeled. Fully encoded 64 x 128 x 64 (N(phase enc) x N(readout) x N(slice enc)) 3D data sets were obtained, from which factor of 64 accelerated 3D image sets (1 x 128 x 64 each) were extracted from single phase-encoding lines, each representing a different phase compensation value.

RESULTS

A comparison of the SEA images indicates that the choice of a compromise value for phase compensation successfully enabled a straightforward extension of SEA imaging to three dimensions. The use of the single compromise compensation value in the 3D acquisition resulted in a signal-to-noise ratio (SNR) penalty ranging from 6% to 41% through the slab when compared to the highest SNR possible using any phase compensation value.

CONCLUSION

The coil-related phase shift issues inherent to highly accelerated imaging will require further study, but this work indicates the general nature of the problem and, more auspiciously, shows that it can be mitigated for at least this application.

Parallel magnetic resonance imaging

Parallel MRI started with the introduction of coil arrays in improving radiofrequency (RF) acquisition (what is called parallel imaging) and continued with an analogous development for RF transmission (parallel transmission). Based on differences in the spatial sensitivity distributions of the involved array elements, both techniques try to shorten the respective k-space trajectory. Parallel imaging refers to the acquisition of k-space data, whereas parallel transmission is dealing with the deposition of RF energy packages in the excitation k-space. However, parallel transmission is not simply the reciprocal of parallel imaging. The main goal of parallel imaging is the shortening of the acquisition time. The main goal of parallel transmission is the shortening of the pulse duration of spatially selective RF pulses. The present article describes the basic concept, the state of the art, and the similarities and differences of both technologies.

Clinical multishot DW-EPI through parallel imaging with considerations of susceptibility, motion, and noise

Geometric distortions and poor image resolution are well known shortcomings of single-shot echo-planar imaging (ss-EPI). Yet, due to the motion immunity of ss-EPI, it remains the most common sequence for diffusion-weighted imaging (DWI). Moreover, both navigated DW interleaved EPI (iEPI) and parallel imaging (PI) methods, such as sensitivity encoding (SENSE) and generalized autocalibrating parallel acquisitions (GRAPPA), can improve the image quality in EPI. In this work, DW-EPI accelerated by PI is proposed as a self-calibrated and unnavigated form of interleaved acquisition. The PI calibration is performed on the b = 0 s/mm2 data and applied to each shot in the rest of the DW data set, followed by magnitude averaging. Central in this study is the comparison of GRAPPA and SENSE in the presence of off-resonances and motion. The results show that GRAPPA is more robust than SENSE against both off-resonance and motion-related artifacts. The SNR efficiency was also investigated, and it is shown that the SNR/scan time ratio is equally high for one- to three-shot high-resolution diffusion scans due to the shortened EPI readout train length. The image quality improvements without SNR efficiency loss, together with motion tolerance, make the GRAPPA-driven DW-EPI sequence clinically attractive.

On Tikhonov regularization for image reconstruction in parallel MRI

Parallel imaging using multiple receiver coils has emerged as an effective tool to reduce imaging time in various MRI applications. When a large number of receiver channels are used to achieve large acceleration factors, the image reconstruction problem can become very ill conditioned. This problem can be alleviated by optimizing the geometry of the coils or by mathematical regularization. Among the regularization methods, the Tikhonov scheme is most popular because of rough Gaussianity of the data noise, the easiness to incorporate prior information, as well as the existence of a closed-form solution. A central issue in implementing the Tikhonov scheme is the choice of the regularization parameter and the regularization image, which is addressed systematically in this paper. A new algorithm is also proposed for generating the regularization image and selecting the regularization parameter. Experimental results will be shown to demonstrate the performance of the algorithm.

Adaptive SENSE reconstruction for parallel imaging with massive array coils

This work presents an adaptive SENSE reconstruction method for parallel magnetic resonance imaging with a large number of localized coils. This method uses a Gaussian model to obtain improved coil sensitivity estimate. For image reconstruction, it dynamically selects a subset of receiver channels, in a pixel-by-pixel fashion, to improve computational efficiency and the signal-to-noise ratio (SNR). Computer simulations and real experiments show that the proposed method reconstructs images with reduced artifacts and higher SNR than the SENSE method.

Evaluation of motion effects on parallel MR imaging with precalibration

Several parallel imaging techniques such as SMASH, SENSE, k-space inherited parallel acquisition (KIPA) and others use reference (calibration) scans to find the parameters required for image reconstruction. Reference data is used to estimate coil sensitivity profiles for image domain techniques such as SENSE or reconstruction coefficients for k-space domain methods such as SMASH and KIPA. Any motion between the reference and accelerated imaging scans can make the reconstruction coefficients determined from the reference scan data suboptimal, resulting in an artifactual reconstruction. This work aims at comparing the effects of motion on the performance of three parallel imaging methods: SENSE, variable-density SENSE and KIPA, which all require one or more reference scans for calibration.

Parallel magnetic resonance imaging

Parallel imaging has been the single biggest innovation in magnetic resonance imaging in the last decade. The use of multiple receiver coils to augment the time consuming Fourier encoding has reduced acquisition times significantly. This increase in speed comes at a time when other approaches to acquisition time reduction were reaching engineering and human limits. A brief summary of spatial encoding in MRI is followed by an introduction to the problem parallel imaging is designed to solve. There are a large number of parallel reconstruction algorithms; this article reviews a cross-section, SENSE, SMASH, g-SMASH and GRAPPA, selected to demonstrate the different approaches. Theoretical (the g-factor) and practical (coil design) limits to acquisition speed are reviewed. The practical implementation of parallel imaging is also discussed, in particular coil calibration. How to recognize potential failure modes and their associated artefacts are shown. Well-established applications including angiography, cardiac imaging and applications using echo planar imaging are reviewed and we discuss what makes a good application for parallel imaging. Finally, active research areas where parallel imaging is being used to improve data quality by repairing artefacted images are also reviewed.

Parallel MR Imaging with Accelerations Beyond the Number of Receiver Channels Using Real Image Reconstruction

Parallel imaging using multiple phased-array coils and receiver channels has become an effective approach to high-speed magnetic resonance imaging (MRI). To obtain high spatiotemporal resolution, the k-space is subsampled and later interpolated using multiple channel data. Higher subsampling factors result in faster image acquisition. However, the subsampling factors are upper-bounded by the number of parallel channels. Phase constraints have been previously proposed to overcome this limitation with some success. In this paper, we demonstrate that in certain applications it is possible to obtain acceleration factors potentially up to twice the channel numbers by using a real image constraint. Data acquisition and processing methods to manipulate and estimate of the image phase information are presented for improving image reconstruction. In-vivo brain MRI experimental results show that accelerations up to 6 are feasible with 4-channel data.

Transmitting focused B/sub 1/ field and SENSE reconstruction using an 8-element transceive torso phased array coil

In this work, a new design concept in chest imaging for MRI application is presented. A focused, 8-element transceive torso phased array coil is designed to investigate transmitting focused B/sup 1/ field deep within the torso to enhance signal intensity and use in conjunction with SENSE reconstruction technique. Hybrid FDTD/MOM method is used to accurately predict the RF behavior inside the human torso. The simulation results reported herein demonstrate the feasibility of the design concept which shows that B/sub 1/ field focusing with SENSE reconstruction is achievable, and the 8-element transceive torso phased array coil has the advantage to be used in transmit and receive mode for optimum and fast chest imaging.

Overcoming phase effects of voxel-sized coils in planar and cylindrical arrays

Parallel imaging in MRI (using multiple coils to partially encode k-space) is currently the primary route to decreasing scan time. Single echo acquisition (SEA) imaging is a completely parallel imaging method recently developed by our group that collects a full image in a single echo. Phase encoding is eliminated and replaced by the spatial localization of long and very narrow coils. The fact that the coils are on the order of the voxel size for the first time in an MR application has led to an examination of the effect of the phase of the coil on the signal received from the voxel and how to most effectively manipulate it. Obtaining full signal from a voxel in planar arrays can be accomplished with a single gradient compensation pulse, but the phase effects in cylindrical arrays are more complex due to the changing coil angle with regard to the Cartesian axes of the gradients. This paper discusses the signal-phase interactions of planar and cylindrical arrays of voxel-sized coils and suggests methods for phase manipulation for optimization.

2D-GRAPPA-operator for faster 3D parallel MRI

When using parallel MRI (pMRI) methods in combination with three-dimensional (3D) imaging, it is beneficial to subsample the k-space along both phase-encoding directions because one can then take advantage of coil sensitivity variations along two spatial dimensions. This results in an improved reconstruction quality and therefore allows greater scan time reductions as compared to subsampling along one dimension. In this work we present a new approach based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique that allows Fourier-domain reconstructions of data sets that are subsampled along two dimensions. The method works by splitting the 2D reconstruction process into two separate 1D reconstructions. This approach is compared with an extension of the conventional GRAPPA method that directly regenerates missing data points of a 2D subsampled k-space by performing a linear combination of acquired data points. In this paper we describe the theoretical background and present computer simulations and in vivo experiments.

Parallel acquisition for effective density weighted imaging: PLANED imaging

OBJECTIVE

Density weighted phase-encoding has proven to be a highly efficient method for k-space sampling as it improves the localization properties and increases the signal-to-noise ratio for extended samples at the same time. But either density weighted imaging lengthens the minimum scan time or, if the Nyquist criterion is violated in parts of the sampled k-space, undersampling artefacts occur. Purpose of this work was to combine density weighted imaging and parallel imaging techniques to improve the spatial response function and consequently the signal-to-noise ratio without spoiling image quality by undersampling artefacts.

MATERIALS AND METHODS

Images were acquired with parallel acquisition for effective density weighted imaging (PLANED imaging) and compared to results sampled with conventional Cartesian phase-encoding with the same spatial resolution and the same number of excitations.

RESULTS

Both in vivo and phantom measurements recorded with the PLANED method revealed a considerable enhancement of the signal-to-noise ratio and a remarkable reduction of Gibbs artefacts compared to standard Cartesian imaging.

CONCLUSION

It has been demonstrated that PLANED improves image quality by suppressing truncation artefacts and increasing the SNR without lengthening the measurement time.

Direct parallel image reconstructions for spiral trajectories using GRAPPA

The use of spiral trajectories is an efficient way to cover a desired k-space partition in magnetic resonance imaging (MRI). Compared to conventional Cartesian k-space sampling, it allows faster acquisitions and results in a slight reduction of the high gradient demand in fast dynamic scans, such as in functional MRI (fMRI). However, spiral images are more susceptible to off-resonance effects that cause blurring artifacts and distortions of the point-spread function (PSF), and thereby degrade the image quality. Since off-resonance effects scale with the readout duration, the respective artifacts can be reduced by shortening the readout trajectory. Multishot experiments represent one approach to reduce these artifacts in spiral imaging, but result in longer scan times and potentially increased flow and motion artifacts. Parallel imaging methods are another promising approach to improve image quality through an increase in the acquisition speed. However, non-Cartesian parallel image reconstructions are known to be computationally time-consuming, which is prohibitive for clinical applications. In this study a new and fast approach for parallel image reconstructions for spiral imaging based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) methodology is presented. With this approach the computational burden is reduced such that it becomes comparable to that needed in accelerated Cartesian procedures. The respective spiral images with two- to eightfold acceleration clearly benefit from the advantages of parallel imaging, such as enabling parallel MRI single-shot spiral imaging with the off-resonance behavior of multishot acquisitions.

Highly accelerated MRI by skipped phase encoding and edge deghosting with array coil enhancement (SPEED-ACE)

The fast MRI method of skipped phase encoding and edge deghosting (SPEED) is further developed with array coil enhancement, and thus is termed SPEED-ACE. In SPEED-ACE, k space is sparsely sampled with skipped phase encoding at every Nth step using a set of receiver coils simultaneously, similar to SENSE, leading to sensitivity-weighted images with up to N layers of overlapping aliasing ghosts. The ghosted images are edge enhanced by a differential filter to yield ghosted edge maps, in which the ghost overlapping layers are greatly reduced since the sparseness of edges reduces the chance of ghost overlapping. Typical ghosted edge maps can be adequately modeled with a double-layer structure. By using data from at least three coils through least-square-error minimization, a deghosted edge map is obtained and inverse-filtered into a final deghosted image. In this way, SPEED-ACE partially samples k space with a skip size of N by using multiple receiver coils in parallel, and obtains a fairly good deghosted image with an undersampling factor of N. SPEED-ACE is not limited to the double-layer ghost model, but can be generalized to include more layers of ghosts for more flexible and improved performance. As a new parallel imaging method, SPEED-ACE was tested using in vivo data to demonstrate the possibility of achieving undersampling factors even greater than the number of receiver coils, which is so far not achievable by other parallel imaging methods.

MR imaging of the cervical spine: assessment of image quality with parallel imaging compared to non-accelerated MR measurements

To compare the quality of cervical spine MR images obtained by parallel imaging [generalized autocalibrating partially parallel acquisition (GRAPPA)] with those of non-accelerated imaging, we conducted both phantom studies and examinations of ten volunteers at 1.5Tesla with a dedicated 12-element coil system and a head-spine-neck coil combination. Acquisitions included axial T2-weighted (T2w) images with both methods, and sagittal T2w and T1w images in vivo with the latter coil combination. Non-accelerated MRI with two averages and GRAPPA (acceleration factor 2) with two averages (GRAPPA/2AV, time reduction of approximately 50%) and four averages (GRAPPA/4AV) were compared. In the phantom, the signal-to-noise ratio of the GRAPPA/2AV was lower than that of the other two settings. In vivo, the image inhomogeneity (non-uniformity, NU) was significantly higher in T2w GRAPPA/2AV than in both other settings, and in T1w GRAPPA/2AV compared to GRAPPA/4AV. Subjectively, the delineation of anatomical structures was sufficient in all sequences. Only the spinal cord was considered to be better delineable on the non-accelerated T1w sequence compared to GRAPPA/2AV. In part, GRAPPA/4AV performed better than the other settings. The subjective image noise was lowest with GRAPPA/4AV. In cervical spine MRI, the examination time can be reduced by nearly 42% with GRAPPA, while preserving sufficient image quality.

Truncation effects in SENSE reconstruction

Finite sampling is an important practical issue in Fourier imaging systems. Although data truncation effects are well understood in conventional Fourier imaging where a single uniform receiver channel is used for data acquisition, this issue is not yet fully addressed in parallel imaging where an array of nonuniform receiver channels is used for sensitivity encoding to enable sub-Nyquist sampling of k-space. This article presents a systematic analysis of the problem by comparing the truncation effects in parallel imaging with those in conventional Fourier imaging. Specifically, it derives a convolution kernel function to characterize the truncation effects, which is shown to be approximately equal to that associated with the conventional Fourier imaging scheme. This article also describes a set of conditions under which significant differences between the truncation effects in parallel imaging and conventional Fourier imaging occur. The results should provide useful insight into interpreting and reducing data truncation effects in parallel imaging.

32-element receiver-coil array for cardiac imaging

A lightweight 32-element MRI receiver-coil array was designed and built for cardiac imaging. It comprises an anterior array of 21 copper rings (75 mm diameter) and a posterior array of 11 rings (107 mm diameter) that are arranged in hexagonal lattices so as to decouple nearest neighbors, and curved around the left side of the torso. Imaging experiments on phantoms and human volunteers show that it yields superior performance relative to an eight-element cardiac array as well as a 32-element whole-torso array for both traditional nonaccelerated cardiac imaging and 3D parallel imaging with acceleration factors as high as 16.

Accelerated volumetric MRI with a SENSE/GRAPPA combination

PURPOSE

To combine the specific advantages of the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique and sensitivity encoding (SENSE) with two-dimensional (2D) undersampling.

MATERIALS AND METHODS

By splitting the 2D reconstruction process into multiple one-dimensional (1D) reconstructions, the normal 1D GRAPPA method can be used for image reconstruction. Due to this data-handling process, a GRAPPA reconstruction is performed along the phase-encoding (PE) direction and effectively a SENSE reconstruction is performed along the partition-encoding (PAE) direction.

RESULTS

In vivo experiments demonstrate the successful implementation of the SENSE/GRAPPA combination. Experimental results with up to 9.6-fold acceleration using a prototype 32-channel receiver head coil array are presented.

CONCLUSION

The proposed SENSE/GRAPPA combination for 3D imaging allows the GRAPPA method to be applied in combination with 2D undersampling. Because the SENSE/GRAPPA combination is not based on knowledge of spatial coil sensitivities, it should be the method of choice whenever it is difficult to extract the sensitivity information.

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.

Miniature array postdetection-encoded MRI

A method for performing nuclear magnetic resonance (NMR) measurements simultaneously from more than a single radiofrequency (RF) coil is presented. The method employs the detection of magnetic resonance signals in an array of detectors, where each detector is responsible for detecting a unique frequency bandwidth or a magnetic resonance signal from a unique location in a region in a primary, substantially homogeneous, static magnetic field. The detectors may be separated logically into groups, whereby all the detectors in a given group are essentially RF-decoupled from each other to substantially eliminate cross-talk by switching circuits or by being placed from each other sufficiently remotely. Sampling of detected signals from detectors in this array is done simultaneously over groups of noninteracting detectors. The detected signals from all detectors in a given group are simultaneously transmitted to a single preamplifier, thus increasing significantly the signal-to-noise ratio (SNR) in that preamplifier. Prior to transmitting each detected NMR signal of each detector to the preamplifier, each detected signal is separately and uniquely encoded electronically. This provides a method whereby the signal of each detector is uniquely encoded. Accumulating all these encoded signals, which were simultaneously received in a number of RF detectors into a single amplifier, results in the total signal having a high SNR ratio. This total amplified signal is later decoded into each detector's original signal by a decoding circuitry. Conventional magnetic resonance imaging (MRI) techniques may be thereafter applied to obtain an image. Or else, conventional NMR techniques may be thereafter applied to obtain an improved SNR from a sample, using a single preamplifier with a multitude of detectors. Applying this method to a large number of miniature and closely packed RF detectors placed in an array-like configuration results in an MRI technique with a very fast acquisition time, an increased SNR and a high spatial resolution equivalent to the number of detectors per unit of length. Deblurring and decoupling algorithms allow for images from layers as deep as 6 mm to be acquired.

Auto-calibrated parallel spiral imaging

This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is defined along radial directions so that missing spiral interleaves can be estimated directly from neighboring interleaves. This kernel is invariant along the radial direction but varies azimuthally. Therefore, the k-space is divided into angular sectors and sector-specific kernels are used. It is demonstrated experimentally that relatively few sectors are sufficient for accurate reconstruction, allowing for efficient implementation. The interpolation kernels can be derived either from a separate calibration scan or self-calibration data available with a dual-density spiral acquisition. The reconstruction method is implemented with two sampling strategies and experimentally demonstrated to be robust.