Autocalibration Signal Research Articles

Improved Parallel Magnetic Resonance Imaging Reconstruction with Sampling based on Normal Distribution

Generalized autocalibrating partially parallel acquisitions (GRAPPA) is a k-space-based magnetic resonance imaging (MRI) reconstruction algorithm that obtains coefficients by acquiring k-space center data with the Nyquist frequency requirement, fitting these data as auto-calibration signal (ACS). The under-sampled k-space data are then filled using the linear correlation of the neighboring points of the k-space. At high reduction factors, images reconstructed by using GRAPPA have high levels of noise even when the number of ACS lines is significantly increased. NL-GRAPPA (Non-linear GRAPPA) can improve GRAPPA for a comparatively impressive image quality. However, it is extremely difficult to reduce the sampling time significantly because of the high number of ACS lines that must be sampled. In this paper, we proposed an undersampling parallel MRI (pMRI) reconstruction technique based on the ideology of normal distribution to improve the traditional GRAPPA and NL-GRAPPA methods. The proposed method mainly takes advantage of the fact that the data in the center of the k-space had a greater impact on the imaging quality than the data at the edges, and then samples the data based on the ideology of normal distribution, i.e., discarding part of the data at the edges of the k-space. The experimental results show that our proposed method can significantly reduce the number of sampled lines and have higher imaging quality with a lower number of ACS lines.

A miniature U-net for k-space-based parallel magnetic resonance imaging reconstruction with a mixed loss function.

BackgroundDeep learning-based magnetic resonance imaging (MRI) methods require in most cases a separate dataset with thousands of images for each anatomical site to train the network model. This paper proposes a miniature U-net method for k-space-based parallel MRI where the network model is trained individually for each scan using scan-specific autocalibrating signal data.MethodsThe original U-net was tailored with fewer layers and channels, and the network was trained using the autocalibrating signal data with a mixing loss function involving magnitude loss and phase loss. The performance of the proposed method was measured using both phantom and in vivo datasets compared to scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) and generalized autocalibrating partially parallel acquisitions (GRAPPA).ResultsThe proposed method alleviates aliasing artifacts and reduces noise with an acceleration factor of four for phantom and in vivo data. Compared with RAKI and GRAPPA, the proposed method represents an improvement with a structural similarity index measure of between 0.02 and 0.05 and a peak signal-to-noise ratio (PSNR) of between 0.1 and 3.ConclusionsThe proposed method introduces a miniature U-net to reconstruct the missing k-space data, which can provide an optimal trade-off between network performance and requirement of training samples. Experimental results indicate that the proposed method can improve image quality compared with the deep learning-based k-space parallel magnitude resonance imaging method.

Impact of autocalibration method on accelerated EPI of the cervical spinal cord at 7 T.

The spinal cord contains sensorimotor neural circuits of scientific and clinical interest. However, spinal cord functional MRI (fMRI) is significantly more technically demanding than brain fMRI, due primarily to its proximity to the lungs. Accelerated echo-planar imaging (EPI) at 7 T is particularly vulnerable to k-space phase inconsistencies induced by motion or B0 fluctuation, during either autocalibration signal (ACS) or time-series acquisition. For 7 T brain fMRI, sensitivity to motion and B0 fluctuation can be reduced using a re-ordered segmented EPI ACS based on the fast low-angle excitation echo-planar technique (FLEET). However, respiration-induced B0 fluctuations (exceeding 100 Hz at C7) are greater, and fewer k-space lines per slice are required for cervical spinal cord fMRI at 7 T, necessitating a separate evaluation of ACS methods. We compared 24-line single-shot EPI with 48-line two-shot segmented EPI, two-shot FLEET, and gradient echo (GRE)-based ACS acquisition methods, performed under various physiological conditions, in terms of temporal signal-to-noise ratio and prevalence of artifacts in generalized autocalibrating partially parallel acquisition (GRAPPA)-accelerated EPI of the cervical spinal cord at 7 T. Segmented EPI and FLEET ACS produce images with nearly identical patterns of severe image artifacts. GRE and single-shot EPI ACS consistently produce images free from significant artifacts, and temporal signal-to-noise ratio is significantly greater for GRE ACS, particularly in lower slices where through-slice dephasing is most severe. GRE and single-shot EPI-ACS acquisition methods, which are robust to respiration-induced phase errors between k-space segments, produce images with fewer and less severe artifacts than either FLEET or conventionally segmented EPI for accelerated EPI of the cervical spinal cord at7T.

A dual-interpolator method for improving parallel MRI reconstruction

Autocalibration signal is acquired in the k-space-based parallel MRI reconstruction for estimating interpolation coefficients and reconstructing missing unacquired data. Many ACS lines can suppress aliasing artifacts and noise by covering the low-frequency signal region. However, more ACS lines will delay the data acquisition process and therefore elongate the scan time. Furthermore, a single interpolator is often used for recovering missing k-space data, and model error may exist if the single interpolator size is not selected appropriately. In this work, based on the idea of the disagreement-based semi-supervised learning, a dual-interpolator strategy is proposed to collaboratively reconstruct missing k-space data. Two interpolators with different sizes are alternatively applied to estimate and re-estimate missing data in k-space. The disagreement between two interpolators is converged and real missing values are co-estimated from two views. The experimental results show that the proposed method outperforms GRAPPA, SPIRiT, and Nonlinear GRAPPA methods using relatively low number of ACS data, and reduces aliasing artifacts and noise in reconstructed images.

Group feature selection for enhancing information gain in MRI reconstruction

Magnetic resonance imaging (MRI) has revolutionized radiology. As a leading medical imaging modality, MRI not only visualizes the structures inside the body but also produces functional imaging. However, due to the slow imaging speed constrained by magnetic resonance physics, the MRI cost is expensive, and patients may feel not comfortable in a scanner for a long time. Parallel MRI (pMRI) has accelerated the imaging speed through a sub-Nyquist sampling strategy and the missing data are interpolated by the multiple coil data acquired. Kernel learning has been used in pMRI reconstruction to learn the interpolation weights and reconstruct the undersampled data. However, noise and aliasing artifacts still exist in the reconstructed image and a large number of auto-calibration signal lines are needed. To further improve kernel-learning-based MRI reconstruction and accelerate the speed, this paper proposes a group feature selection strategy to improve the learning performance and enhance the reconstruction quality. An explicit kernel mapping is used for selecting a subset of features which contribute most to estimating the missing k-space data. The experimental results show that the learning behaviors can be better predicted and therefore the reconstructed image quality can be improved.

Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI.

To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data. Scan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan-specific models, such as robust artificial-neural-networks for k-space interpolation (RAKI) and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded imaging. SPARK yields SSIM improvement and 1.5 - 2× root mean squared error (RMSE) reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves RMSE performance by ~2×, SSIM performance, and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-Cartesian, 2D and 3D wave-encoding imaging by reducing RMSE between 20% and 25% and providing qualitative improvements. SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.

Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.

Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling

PurposeTo accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks.MethodsSelf-consistent robust artificial-neural-networks for k-space interpolation (sRAKI) performs iterative parallel imaging reconstruction by enforcing self-consistency among coils. The approach bears similarity to SPIRiT, but extends the linear convolutions in SPIRiT to nonlinear interpolation using convolutional neural networks (CNNs). These CNNs are trained individually for each scan using the scan-specific autocalibrating signal (ACS) data. Reconstruction is performed by imposing the learned self-consistency and data-consistency, which enables sRAKI to support random undersampling patterns. Fully-sampled targeted right coronary artery MRI was acquired in six healthy subjects. The data were retrospectively undersampled, and reconstructed using SPIRiT, l1-SPIRiT and sRAKI for acceleration rates of 2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was acquired to further evaluate reconstruction performance.ResultssRAKI reduces noise amplification and blurring artifacts compared with SPIRiT and l1-SPIRiT, especially at high acceleration rates in targeted coronary MRI. Quantitative analysis shows that sRAKI outperforms these techniques in terms of normalized mean-squared-error (~44% and ~21% over SPIRiT and -SPIRiT at rate 5) and vessel sharpness (~10% and ~20% over SPIRiT and l1-SPIRiT at rate 5). Whole-heart data shows the sharpest coronary arteries when resolved using sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and l1-SPIRiT, respectively.ConclusionsRAKI is a database-free neural network-based reconstruction technique that may further accelerate coronary MRI with arbitrary undersampling patterns, while improving noise resilience over linear parallel imaging and image sharpness over l1 regularization techniques.

Optimized fast GPU implementation of robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction.

BackgroundRobust Artificial-neural-networks for k-space Interpolation (RAKI) is a recently proposed deep-learning-based reconstruction algorithm for parallel imaging. Its main premise is to perform k-space interpolation using convolutional neural networks (CNNs) trained on subject-specific autocalibration signal (ACS) data. Since training is performed individually for each subject, the reconstruction time is longer than approaches that pre-train on databases. In this study, we sought to reduce the computational time of RAKI.MethodsRAKI was implemented using CPU multi-processing and process pooling to maximize the utility of GPU resources. We also proposed an alternative CNN architecture that interpolates all output channels jointly for specific skipped k-space lines. This new architecture was compared to the original CNN architecture in RAKI, as well as to GRAPPA in phantom, brain and knee MRI datasets, both qualitatively and quantitatively.ResultsThe optimized GPU implementations were approximately 2-to-5-fold faster than a simple GPU implementation. The new CNN architecture further improved the computational time by 4-to-5-fold compared to the optimized GPU implementation using the original RAKI CNN architecture. It also provided significant improvement over GRAPPA both visually and quantitatively, although it performed slightly worse than the original RAKI CNN architecture.ConclusionsThe proposed implementations of RAKI bring the computational time towards clinically acceptable ranges. The new CNN architecture yields faster training, albeit at a slight performance loss, which may be acceptable for faster visualization in some settings.

A GRAPPA algorithm for arbitrary 2D/3D non-Cartesian sampling trajectories with rapid calibration.

GRAPPA is a popular reconstruction method for Cartesian parallel imaging, but is not easily extended to non-Cartesian sampling. We introduce a general and practical GRAPPA algorithm for arbitrary non-Cartesian imaging. We formulate a general GRAPPA reconstruction by associating a unique kernel with each unsampled k-space location with a distinct constellation, that is, local sampling pattern. We calibrate these generalized kernels using the Fourier transform phase shift property applied to fully gridded or separately acquired Cartesian Autocalibration signal (ACS) data. To handle the resulting large number of different kernels, we introduce a fast calibration algorithm based on nonuniform FFT (NUFFT) and adoption of circulant ACS boundary conditions. We applied our method to retrospectively under-sampled rotated stack-of-stars/spirals in vivo datasets, and to a prospectively under-sampled rotated stack-of-spirals functional MRI acquisition with a finger-tapping task. We reconstructed all datasets without performing any trajectory-specific manual adaptation of the method. For the retrospectively under-sampled experiments, our method achieved image quality (i.e., error and g-factor maps) comparable to conjugate gradient SENSE (cg-SENSE) and SPIRiT. Functional activation maps obtained from our method were in good agreement with those obtained using cg-SENSE, but required a shorter total reconstruction time (for the whole time-series): 3minutes (proposed) vs 15minutes (cg-SENSE). This paper introduces a general 3D non-Cartesian GRAPPA that is fast enough for practical use on today's computers. It is a direct generalization of original GRAPPA to non-Cartesian scenarios. The method should be particularly useful in dynamic imaging where a large number of frames are reconstructed from a single set of ACS data.

Improved Parallel Magnertic Resonance Imaging reconstruction with Complex Proximal Support Vector Regression

Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) has been widely used to reduce imaging time in Magnetic Resonance Imaging. GRAPPA synthesizes missing data by using a linear interpolation of neighboring measured data over all coils, thus accuracy of the interpolation weights fitting to the auto-calibrating signal data is crucial for the GRAPPA reconstruction. Conventional GRAPPA algorithms fitting the interpolation weights with a least squares solution are sensitive to interpolation window size. MKGRAPPA that estimates the interpolation weights with support vector machine reduces the sensitivity of the k-space reconstruction to interpolation window size, whereas it is computationally expensive. In this study, a robust GRAPPA reconstruction method is proposed that applies an extended proximal support vector regression (PSVR) to fit the interpolation weights with wavelet kernel mapping. Experimental results on in vivo MRI data show that the proposed PSVR-GRAPPA method visually improves overall quality compared to conventional GRAPPA methods, while it has faster reconstruction speed compared to MKGRAPPA.

Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data. Robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction trains convolutional neural networks on ACS data. This enables nonlinear estimation of missing k-space lines from acquired k-space data with improved noise resilience, as opposed to conventional linear k-space interpolation-based methods, such as GRAPPA, which are based on linear convolutional kernels. The training algorithm is implemented using a mean square error loss function over the target points in the ACS region, using a gradient descent algorithm. The neural network contains 3 layers of convolutional operators, with 2 of these including nonlinear activation functions. The noise performance and reconstruction quality of the RAKI method was compared with GRAPPA in phantom, as well as in neurological and cardiac in vivo data sets. Phantom imaging shows that the proposed RAKI method outperforms GRAPPA at high (≥4) acceleration rates, both visually and quantitatively. Quantitative cardiac imaging shows improved noise resilience at high acceleration rates (rate 4:23% and rate 5:48%) over GRAPPA. The same trend of improved noise resilience is also observed in high-resolution brain imaging at high acceleration rates. The RAKI method offers a training database-free deep learning approach for MRI reconstruction, with the potential to improve many existing reconstruction approaches, and is compatible with conventional data acquisition protocols.

Subject-Specific Convolutional Neural Networks for Accelerated Magnetic Resonance Imaging.

Magnetic Resonance Imaging (MRI) is one of the leading modalities for medical imaging, providing excellent soft-tissue contrast without exposure to ionizing radiation. Despite continuing advances in MRI, long scan times remain a major limitation in clinical applications. Parallel imaging is a technique for scan time acceleration in MRI, which utilizes the spatial variations in the reception profiles of receiver coil arrays to reconstruct images from undersampled Fourier space, i.e. k-space. One of the most commonly used parallel imaging techniques employs interpolation of missing k-space information by using linear shift-invariant convolutional kernels. These kernels are trained on a limited amount of autocalibration signal (ACS) for each scan. We propose a novel method for parallel imaging, Robust Artificial-neural-networks for k-space Interpolation (RAKI), which uses scan-specific convolutional neural networks (CNNs) to perform improved k-space interpolation. Three-layer CNNs are trained using only scan-specific ACS data, alleviating the need for large training databases. The proposed method was tested in ultra-high resolution brain MRI and quantitative cardiac MRI, acquired with various acceleration rates. Improved noise resilience as compared to existing parallel imaging methods was observed for high acceleration rates or in the presence of low signal-to-noise ratio (SNR). Furthermore, RAKI successfully reconstructed images for quantitative cardiac MRI, even when using the same CNN across images with varying contrasts. These results indicate that RAKI achieves improved noise performance without overfitting to specific image contents, and offers great promise for improved acceleration in a wide range of MRI applications.

Improving parallel imaging by jointly reconstructing multi-contrast data.

To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition. We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquired with different contrast preparations, and show its application in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions. We extend the joint parallel imaging concept to exploit limited support and smooth phase constraints through Joint (J-) LORAKS formulation. J-LORAKS allows joint parallel imaging from limited autocalibration signal region, as well as permitting partial Fourier sampling and calibrationless reconstruction. We demonstrate highly accelerated 2D balanced steady-state free precession with phase cycling, SMS multi-echo spin echo, 3D multi-echo magnetization-prepared rapid gradient echo, and multi-echo gradient recalled echo acquisitions in vivo. Compared to conventional GRAPPA, proposed joint acquisition/reconstruction techniques provide more than 2-fold reduction in reconstruction error. JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Simultaneous multi-slice combined with PROPELLER.

Simultaneous multi-slice (SMS) imaging is an advantageous method for accelerating MRI scans, allowing reduced scan time, increased slice coverage, or high temporal resolution with limited image quality penalties. In this work we combine the advantages of SMS acceleration with the motion correction and artifact reduction capabilities of the PROPELLER technique. A PROPELLER sequence was developed with support for CAIPIRINHA and phase optimized multiband radio frequency pulses. To minimize the time spent on acquiring calibration data, both in-plane-generalized autocalibrating partial parallel acquisition (GRAPPA) and slice-GRAPPA weights for all PROPELLER blade angles were calibrated on a single fully sampled PROPELLER blade volume. Therefore, the proposed acquisition included a single fully sampled blade volume, with the remaining blades accelerated in both the phase and slice encoding directions without additional auto calibrating signal lines. Comparison to 3D RARE was performed as well as demonstration of 3D motion correction performance on the SMS PROPELLER data. We show that PROPELLER acquisitions can be efficiently accelerated with SMS using a short embedded calibration. The potential in combining these two techniques was demonstrated with a high quality 1.0 × 1.0 × 1.0 mm3 resolution T2 -weighted volume, free from banding artifacts, and capable of 3D retrospective motion correction, with higher effective resolution compared to 3D RARE. With the combination of SMS acceleration and PROPELLER imaging, thin-sliced reformattable T2 -weighted image volumes with 3D retrospective motion correction capabilities can be rapidly acquired with low sensitivity to flow and head motion. Magn Reson Med 80:496-506, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A Frequency-Dependent Regularization for Autocalibrating Parallel MRI Using the Generalized Discrepancy Principle

This paper quantitatively evaluates regularized GRAPPA SPIRiT reconstruction in autocalibrating parallel MRI and develops a frequency-dependent regularization (FDR) aimed at achieving higher SNR with reduced penalty on image resolution. The latter is achieved by imposing an upper limit on the extent of regularization for each k -space location. The procedure starts with selection of a suitable truncation parameter, followed by perturbation of the AutoCalibrating Signal (ACS) lines using addition of white Gaussian noise samples of a predetermined variance. The noise variance chosen is such that first-order perturbation rules apply. In each perturbation step, the regularized solutions are estimated using a first-order update of singular values, and conditions stipulated by the generalized discrepancy principle (GDP) with regards to the norms of calibration and perturbation errors. The procedure is stopped at the crossover limit, once the GDP conditions are violated. The crossover information yields an upper limit for perturbation, enabling computation of an error bound as the difference between k -spaces estimated with filter weights from calibrations performed on original and the perturbed ACS lines at cross-over. Imposition of an upper limit on the extent of regularization is achieved by matching this error bound to the difference in k -spaces reconstructed with varying offsets of regularization from that of a reference. Furthermore, the crossover distance defined in terms of difference between minimum singular values of the initial and crossover calibration matrices serves as a proxy indicator of the achievable reduction in reconstruction error using regularization.

High-resolution diffusion MRI at 7T using a three-dimensional multi-slab acquisition

High-resolution diffusion MRI can provide the ability to resolve small brain structures, enabling investigations of detailed white matter architecture. A major challenge for in vivo high-resolution diffusion MRI is the low signal-to-noise ratio. In this work, we combine two highly compatible methods, ultra-high field and three-dimensional multi-slab acquisition to improve the SNR of high-resolution diffusion MRI. As each kz plane is encoded using a single-shot echo planar readout, scan speeds of the proposed technique are similar to the commonly used two-dimensional diffusion MRI. In-plane parallel acceleration is applied to reduce image distortions. To reduce the sensitivity of auto-calibration signal data to subject motion and respiration, several new adaptions of the fast low angle excitation echo-planar technique (FLEET) that are suitable for 3D multi-slab echo planar imaging are proposed and evaluated. A modified reconstruction scheme is proposed for auto-calibration with the most robust method, Slice-FLEET acquisition, to make it compatible with navigator correction of motion induced phase errors. Slab boundary artefacts are corrected using the nonlinear slab profile encoding method recently proposed by our group. In vivo results demonstrate that using 7T and three-dimensional multi-slab acquisition with improved auto-calibration signal acquisition and nonlinear slab boundary artefacts correction, high-quality diffusion MRI data with ~1mm isotropic resolution can be achieved.

A quantitative survey of GRAPPA reconstruction in parallel MRI: impact on noise reduction and aliasing

AbstractIn recent years, several attempts have been made to improve the quality of GRAPPA reconstruction by concomitant reduction of noise amplification and aliasing. While it is true that accuracy of the underlying GRAPPA model depends on the goodness of fit, variations in input noise and channel encoding can serve to introduce differences in reconstruction. In this article, we quantitatively compare the performance of different GRAPPA types, in relation to input signal‐to‐noise ratio, channel number, and acceleration factor. The GRAPPA types considered in this article are obtained using: (1) calibration matrix modification, (2) regularization of calibration equation, (3) updating of coil coefficients, (4) intermediate domain GRAPPA, and (5) iterative method. All the above GRAPPA types are tested on retrospectively sampled in vivo datasets acquired using head, spine and knee‐array coils. For each GRAPPA type and dataset analyzed, reconstruction is performed using number of Autocalibration Signal lines set at 10% of the number of phase‐encodes used in full acquisition and kernel size optimized for minimum root‐mean square‐error (RMSE). Each GRAPPA type is rated based on their individual performance, in relation to a mean performance measure obtained by averaging across different methods. Noise amplification in each GRAPPA type is visually examined, and compared with the respective g‐factor map. RMSEs and g‐factors of in vivo data are compared against those of simulated coil images with added complex Gaussian noise having a pre‐determined covariance structure. A summary of performance ratings and execution times are provided for comparison with standard GRAPPA.

Reducing sensitivity losses due to respiration and motion in accelerated echo planar imaging by reordering the autocalibration data acquisition.

To reduce the sensitivity of echo-planar imaging (EPI) auto-calibration signal (ACS) data to patient respiration and motion to improve the image quality and temporal signal-to-noise ratio (tSNR) of accelerated EPI time-series data. ACS data for accelerated EPI are generally acquired using segmented, multishot EPI to distortion-match the ACS and time-series data. The ACS data are, therefore, typically collected over multiple TR periods, leading to increased vulnerability to motion and dynamic B0 changes. The fast low-angle excitation echo-planar technique (FLEET) is adopted to reorder the ACS segments so that segments within any given slice are acquired consecutively in time, thereby acquiring ACS data for each slice as rapidly as possible. Subject breathhold and motion phantom experiments demonstrate that artifacts in the ACS data reduce tSNR and produce tSNR discontinuities across slices in the accelerated EPI time-series data. Accelerated EPI data reconstructed using FLEET-ACS exhibit improved tSNR and increased tSNR continuity across slices. Additionally, image quality is improved dramatically when bulk motion occurs during the ACS acquisition. FLEET-ACS provides reduced respiration and motion sensitivity in accelerated EPI, which yields higher tSNR and image quality. Benefits are demonstrated in both conventional-resolution 3T and high-resolution 7T EPI time-series data.

Paradoxical effect of the signal-to-noise ratio of GRAPPA calibration lines: A quantitative study.

Intuitively, GRAPPA auto-calibration signal (ACS) lines with higher signal-to-noise ratio (SNR) may be expected to boost the accuracy of kernel estimation and increase the SNR of GRAPPA reconstructed images. Paradoxically, Sodickson and his colleagues pointed out that using ACS lines with high SNR may actually lead to lower SNR in the GRAPPA reconstructed images. A quantitative study of how the noise in the ACS lines affects the SNR of the GRAPPA reconstructed images is presented. In a phantom, the singular values of the GRAPPA encoding matrix and the root-mean-square error of GRAPPA reconstruction were evaluated using multiple sets of ACS lines with variant SNR. In volunteers, ACS lines with high and low SNR were estimated, and the SNR of corresponding TGRAPPA reconstructed images was evaluated. We show that the condition number of the GRAPPA kernel estimation equations is proportional to the SNR of the ACS lines. In dynamic image series reconstructed with TGRAPPA, high SNR ACS lines result in reduced SNR if appropriate regularization is not applied. Noise has a similar effect to Tikhonov regularization. Without appropriate regularization, a GRAPPA kernel estimated from ACS lines with higher SNR amplifies random noise in the GRAPPA reconstruction. Magn Reson Med 74:231-239, 2015. © 2014 Wiley Periodicals, Inc.