Multichannel Receiver Coils Research Articles

A locally segmented reconstruction method for parallel imaging.

A locally segmented parallel imaging reconstruction method is proposed that efficiently utilizes sensitivity distribution of multichannel receiver coil. A method of locally segmenting a MR signal is introduced to maximize the differences in sensitivity between receiver channels. A 1D Fourier transformation of the undersampled k-space data is performed along the readout direction, which generates a hybrid 2D space. The hybrid space is partitioned into localized segments along the readout direction. In every localized segment, kernels representing relation between adjacent signals are estimated from autocalibration signals, and data at unsampled points are estimated using the kernels. Then, the images are reconstructed from full k-space data that consists of the sampled data and the estimated data at unsampled points. In a computer simulation and in vivo experiments, the locally segmented reconstruction method produced fewer residual artifacts compared to the conventional parallel imaging reconstruction methods with the same kernel geometry. The performance gain of the proposed method comes from maximizing encoding capability of receiver channels, thus resulting in the accurately estimated kernel weights that reflect the relation between adjacent signals. The proposed spatial segmentation method maximally utilizes differences in the sensitivity of receiver channels to reconstruct images with reduced artifacts.

Numerical Calculations of Multi-Channel Receiver Coil Array with High-Pass Spoke Coil for Parallel MRI

The proposed study reports the design and testing of spoke coil (SC), that compensates for the degradation in the g-factor that is caused by the insufficient sensitivity of the coil array along the main magnetic field direction. Electromagnetic (EM) simulations were performed to evaluate the performance of the proposed coil in a 7-T MRI system. 25-channel surface loop array and short dipole array containing SC or circular loop coil (LC) on the top of the phantom are 3D modeled. The SENSE g-factor and SNR of coil arrays are calculated and evaluated, and the quality factor (Q), scaling factor (α), and noise-corelated resistance (R) are considered. The directed effective magnetic field (B1−) generated by the SC improves the g-factor by compensating for the insufficient sensitivity information gathered by the array. However, this arrangement should be optimized to increase the available sensitivity information, depending on the type and structure of the coil elements in the array.

Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint

PurposeTo reduce acquisition time and to improve image quality in sodium magnetic resonance imaging (23Na MRI) using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS) with anatomical 1H prior knowledge. MethodsAn iterative reconstruction for 23Na MRI with multi-channel receiver coils is presented. Based on CS it utilizes a second order total variation (TV(2)), adopted by anatomical weighting factors (AnaWeTV(2)) obtained from a high-resolution 1H image. A support region is included as additional regularization. Simulated and measured 23Na multi-channel data sets (n = 3) of the female breast acquired at 7 T with different undersampling factors (USF = 1.8/3.6/7.2/14.4) were reconstructed and compared to a conventional gridding reconstruction. The structural similarity was used to assess image quality of the reconstructed simulated data sets and to optimize the weighting factors for the CS reconstruction. ResultsCompared with a conventional TV(2), the AnaWeTV(2) reconstruction leads to an improved image quality due to preserving of known structure and reduced partial volume effects. An additional incorporated support region shows further improvements for high USFs. Since the decrease in image quality with higher USFs is less pronounced compared to a conventional gridding reconstruction, proposed algorithm is beneficial especially for higher USFs. Acquisition time can be reduced by a factor of 4 (USF = 7.2), while image quality is still similar to a nearly fully sampled (USF = 1.8) gridding reconstructed data set. ConclusionEspecially for high USFs, the proposed algorithm allows improved image quality for multi-channel 23Na MRI data sets.

Real-Time Magnetic Resonance Imaging of Bubble Behavior and Particle Velocity in Fluidized Beds

Snapshots of particle concentration and velocity fields in bubbling gas–solid fluidized beds were acquired using magnetic resonance imaging. Using a recently developed multichannel radiofrequency receiver coil in combination with fast readout techniques, adapted from medical MRI protocols, the temporal resolution was 7 and 18 ms for two-dimensional images of particle concentration and velocity fields, respectively. A cylindrical bed with 190 mm diameter and 300 mm height was filled to heights of 100, 150, and 200 mm with spherical 1 and 3 mm diameter particles and fluidized at ratios of superficial gas velocity to minimum fluidization velocity (U/Umf) of 1.2, 1.5, 2.0, 3.0, and 4.0. The effects of these varying parameters on the number of bubbles, bubble diameter, bed height, and particle speed are investigated. It is hoped that these data sets will become important benchmarks against which computational, analytical, and empirical models can be validated.

GPU-Accelerated Self-Calibrating GRAPPA Operator Gridding for Rapid Reconstruction of Non-Cartesian MRI Data

Self-calibrating GRAPPA operator gridding (SC-GROG) is a method by which non-Cartesian (NC) data in magnetic resonance imaging (MRI) are shifted to the Cartesian k-space grid locations using the parallel imaging concept of GRAPPA operator. However, gridding with SC-GROG becomes computationally expensive and leads to longer reconstruction time when mapping a large number of NC samples in MRI data to the nearest Cartesian grid locations. This work aims to accelerate the SC-GROG for radial acquisitions in MRI, using massively parallel architecture of graphics processing units (GPUs). For this purpose, a novel implementation of GPU-accelerated SC-GROG is presented, which exploits the inherent parallelism in gridding operations. The proposed method employs the look-up-table (LUT)-based optimized kernels of compute unified device architecture (CUDA), to pre-calculate all the possible combinations of 2D-gridding weight sets and uses appropriate weight sets to shift the NC signals from multi-channel receiver coils at the nearest Cartesian grid locations. In the proposed method, LUTs are implemented to avoid the race condition among the CUDA kernel threads while shifting various NC points to the same Cartesian grid location. Several experiments using 24-channel simulated phantom and (12 and 30 channel) in vivo data sets are performed to evaluate the efficacy of the proposed method in terms of computation time and reconstruction accuracy. The results show that the GPU-based implementation of SC-GROG can significantly improve the image reconstruction efficiency, typically achieving 6× to 30× speed-up (including transfer time between CPU and GPU memory) without compromising the quality of image reconstruction.

Iterative Schemes to Solve Low-Dimensional Calibration Equations in Parallel MR Image Reconstruction with GRAPPA.

GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition) is a widely used parallel MRI reconstruction technique. The processing of data from multichannel receiver coils may increase the storage and computational requirements of GRAPPA reconstruction. Random projection on GRAPPA (RP-GRAPPA) uses random projection (RP) method to overcome the computational overheads of solving large linear equations in the calibration phase of GRAPPA, saving reconstruction time. However, RP-GRAPPA compromises the reconstruction accuracy in case of large reductions in the dimensions of calibration equations. In this paper, we present the implementation of GRAPPA reconstruction method using potential iterative solvers to estimate the reconstruction coefficients from the randomly projected calibration equations. Experimental results show that the proposed methods withstand the reconstruction accuracy (visually and quantitatively) against large reductions in the dimension of linear equations, when compared with RP-GRAPPA reconstruction. Particularly, the proposed method using conjugate gradient for least squares (CGLS) demonstrates more savings in the computational time of GRAPPA, without significant loss in the reconstruction accuracy, when compared with RP-GRAPPA. It is also demonstrated that the proposed method using CGLS complements the channel compression method for reducing the computational complexities associated with higher channel count, thereby resulting in additional memory savings and speedup.

Referenceless one-dimensional Nyquist ghost correction in multicoil single-shot spatiotemporally encoded MRI

Single-shot spatiotemporally encoded (SPEN) MRI is a novel fast imaging method capable of retaining the time efficiency of single-shot echo planar imaging (EPI) but with distortion artifacts significantly reduced. Akin to EPI, the phase inconsistencies between mismatched even and odd echoes also result in the so-called Nyquist ghosts. However, the characteristic of the SPEN signals provides the possibility of obtaining ghost-free images directly from even and odd echoes respectively, without acquiring additional reference scans. In this paper, a theoretical analysis of the Nyquist ghosts manifested in single-shot SPEN MRI is presented, a one-dimensional correction scheme is put forward capable of maintaining definition of image features without blurring when the phase inconsistency along SPEN encoding direction is negligible, and a technique is introduced for convenient and robust correction of data from multi-channel receiver coils. The effectiveness of the proposed processing pipeline is validated by a series of experiments conducted on simulation data, in vivo rats and healthy human brains. The robustness of the method is further verified by implementing distortion correction on ghost corrected data.

A robust method for suppressing motion-induced coil sensitivity variations during prospective correction of head motion in fMRI

Prospective motion correction is a promising candidate solution to suppress the effects of head motion during fMRI, ideally allowing the imaging plane to remain fixed with respect to the moving head. Residual signal artifacts may remain, however, because head motion in relation to a fixed multi-channel receiver coil (with non-uniform sensitivity maps) can potentially introduce unwanted signal variations comparable to the weak fMRI BOLD signal (~1%–4% at 1.5–3.0T). The present work aimed to investigate the magnitude of these residual artifacts, and characterize the regime over which prospective motion correction benefits from adjusting sensitivity maps to reflect relative positional change between the head and the coil. Numerical simulations were used to inform human fMRI experiments. The simulations indicated that for axial imaging within a commonly used 12-channel head coil, 5° of head rotation in-plane produced artifact signal changes of ~3%. Subsequently, six young adults were imaged with and without overt head motions of approximately this extent, with and without prospective motion correction using the Prospective Acquisition CorrEction (PACE) method, and with and without sensitivity map adjustments. Sensitivity map adjustments combined with PACE strongly protected against the artifacts of interest, as indicated by comparing three metrics of data quality (number of activated voxels, Dice coefficient of activation overlap, temporal standard deviation of baseline fMRI timeseries data) across the different experimental conditions. It is concluded that head motion in relation to a fixed multi-channel coil can adversely affect fMRI with prospective motion correction, and that sensitivity map adjustment can mitigate this effect at 3.0T.

Interactions between head motion and coil sensitivity in accelerated fMRI

BackgroundParallel imaging is widely adopted to accelerate functional MRI (fMRI) data acquisition, through various strategies that involve multi-channel receiver coils. However, the non-uniform spatial sensitivity of multi-channel receiver coils may introduce unwanted artifacts when head motion occurs during the few-minute long fMRI scans. Although prospective correction provides a promising solution for alleviating the head motion artifacts in fMRI, the relative position of the fixed multi-channel receiver coils moves in the moving reference frame, potentially resulting in artifactual signal. New methodWe used numerical simulations to investigate this effect on fMRI using two parallel imaging schemes: sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA) with acceleration factors 2 and 4, towards characterizing the regime over which parallel-imaging fMRI with prospective motion correction will benefit from updating coil sensitivities to reflect relative positional change between the head and the receiver coil. Moreover, six subjects were scanned with acceleration factors 2 and 4 while performing a simple finger-tapping task with and without overt head motion. ResultsUpdating coil sensitivities showed significant positive impact on standard deviation and activation maps in presence of overt head motion compared to that obtained with no overt head motion. Comparison with existing methodsThe parallel imaging fMRI with updated coil sensitivity maps were compared to that with the coil sensitivity maps acquired at the reference position. ConclusionsHead motion in relation to a fixed multi-channel coil can adversely affect the quality of parallel imaging fMRI data; and updating coil sensitivity map can mitigate this effect.

Simultaneous multislice (SMS) imaging techniques.

Simultaneous multislice imaging (SMS) using parallel image reconstruction has rapidly advanced to become a major imaging technique. The primary benefit is an acceleration in data acquisition that is equal to the number of simultaneously excited slices. Unlike in‐plane parallel imaging this can have only a marginal intrinsic signal‐to‐noise ratio penalty, and the full acceleration is attainable at fixed echo time, as is required for many echo planar imaging applications. Furthermore, for some implementations SMS techniques can reduce radiofrequency (RF) power deposition. In this review the current state of the art of SMS imaging is presented. In the Introduction, a historical overview is given of the history of SMS excitation in MRI. The following section on RF pulses gives both the theoretical background and practical application. The section on encoding and reconstruction shows how the collapsed multislice images can be disentangled by means of the transmitter pulse phase, gradient pulses, and most importantly using multichannel receiver coils. The relationship between classic parallel imaging techniques and SMS reconstruction methods is explored. The subsequent section describes the practical implementation, including the acquisition of reference data, and slice cross‐talk. Published applications of SMS imaging are then reviewed, and the article concludes with an outlook and perspective of SMS imaging. Magn Reson Med 75:63–81, 2016. © 2015 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance.

Constrained Source Space MR Spectroscopy: Multiple Voxels, No Gradient Readout.

Our goal was to develop a novel technique for measuring a small number of localized spectra simultaneously and in a time-efficient manner. Using appropriate radiofrequency pulses, the magnetization from multiple voxels is excited simultaneously and then separated (reconstructed) by using the individual coil-sensitivity profiles from a multichannel receiver coil. Because no gradients are used for k-space encoding, constrained source space MR spectroscopy provides a time advantage over conventional spectroscopic imaging and an improved signal-to-noise ratio per square root of unit time over single-voxel spectroscopy applied at each successive location. In the present work, we considered prototype application of constrained source space MR spectroscopy for 2 voxels. Experimental data from healthy volunteers and simulation results showed that constrained source space MR spectroscopy is effective at extracting 2 independent spectra even in the challenging scenario of the voxels being closely spaced. Also, from 6 patients with various types of brain cancer we obtained 2-voxel constrained source space MR spectroscopy data, which showed spectra of clinical quality in half the time required to perform successive single-voxel MR spectroscopy. Constrained source space MR spectroscopy provides clinical quality spectra and could be used to probe multiple voxels simultaneously in combination with Hadamard encoding for further scan-time reductions.

Signal intensity correction for multichannel MR images using radon transformation

ABSTRACTThe purpose of this study is to correct signal intensity at low‐field MRI system with multichannel receiver coils using Radon transformation and filtered backprojection (FBP) method. An open‐type 0.32 T MRI system and a body size phantom were used to acquire the MR images. We used various types of coils from 2‐channels to 4‐channels, which minimized the loss of signal. In the intensity correction process, Radon transform was used for the images of each channel and low‐pass filtering was applied to reduce noise. After that FBP was used for the space transform again from the Radon space to the image space. We also made changes to the projection ranges and their intervals, and then confirmed them to evaluate the optimal parameters. All the intensity corrected results were compared with its original sum‐of‐square (SOS) images, and the corrected images showed more uniform and homogeneous intensities than the images without correction. In addition, these results were also shown in the quantitative values through the signal intensity variations according to the cut view along the horizontal lines of the images. The feasibility of our approach and results for signal intensity correction may be useful and helpful for the researchers of low‐field system with multichannel coils.

Correction: Modelling Temporal Stability of EPI Time Series Using Magnitude Images Acquired with Multi-Channel Receiver Coils

Correction: Modelling Temporal Stability of EPI Time Series Using Magnitude Images Acquired with Multi-Channel Receiver Coils

Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE.

To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation. It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest. In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach. These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition.

Modelling Temporal Stability of EPI Time Series Using Magnitude Images Acquired with Multi-Channel Receiver Coils

In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR0). This model has been used to study physiological noise in fMRI, to optimize fMRI acquisition parameters, and to estimate maximum attainable tSNR for a given set of MR image acquisition and processing parameters. In its current form, this noise model requires the accurate estimation of image SNR. For multi-channel receiver coils, this is not straightforward because it requires export and reconstruction of large amounts of k-space raw data and detailed, custom-made image reconstruction methods. Here we present a simple extension to the model that allows characterization of the temporal noise properties of EPI time series acquired with multi-channel receiver coils, and reconstructed with standard root-sum-of-squares combination, without the need for raw data or custom-made image reconstruction. The proposed extended model includes an additional parameter κ which reflects the impact of noise correlations between receiver channels on the data and scales an apparent image SNR (SNR′0) measured directly from root-sum-of-squares reconstructed magnitude images so that κ = SNR′0/SNR0 (under the condition of SNR0>50 and number of channels ≤32). Using Monte Carlo simulations we show that the extended model parameters can be estimated with high accuracy. The estimation of the parameter κ was validated using an independent measure of the actual SNR0 for non-accelerated phantom data acquired at 3T with a 32-channel receiver coil. We also demonstrate that compared to the original model the extended model results in an improved fit to human task-free non-accelerated fMRI data acquired at 7T with a 24-channel receiver coil. In particular, the extended model improves the prediction of low to medium tSNR values and so can play an important role in the optimization of high-resolution fMRI experiments at lower SNR levels.

Constrained source space imaging: Application to fast, region‐based functional MRI

A new technique called constrained source space imaging is introduced that holds promise for ultrafast acquisition of functional magnetic resonance imaging data. A sparse set of arbitrarily positioned, coarse voxels is first localized using radiofrequency selective excitation, from which magnetization signals are separated using only the spatial sensitivities of multichannel receiver coils, without the need for k-space encoding using imaging gradients. This method permits very fast acquisitions of targeted magnetization without complex or time-consuming image reconstruction techniques. Furthermore, because the data acquisition is performed without imaging gradients, T2* decays can be densely sampled and processed for contrast enhancement to improve functional magnetic resonance imaging data quality. Here, the constrained source space imaging technique is validated in proof-of-concept form, for a simple functional magnetic resonance imaging motor task using a prototype dual-band stimulated echo acquisition mode excitation to image four voxels at TR = 250 ms. Results demonstrate good voxel signal separation and good characterization of hemodynamic responses in primary motor cortices (M1) and supplementary motor areas through T2* fitting of the measured signals. With further refinement, the constrained source space imaging method has potential utility in a priori ROI-based functional magnetic resonance imaging experiments with TR values under 100 ms. Rapid, multivoxel measurements of other sources of MR signal contrast are also possible.

Multichannel receiver coils for improved coverage in cardiac metabolic imaging using prepolarized 13 C substrates

MR imaging using hyperpolarized (13)C substrates has become a promising tool to study real-time cardiac-metabolism in vivo. For such fast imaging of nonrecoverable prepolarized magnetization it is important to optimize the RF-coils to obtain the best signal-to-noise ratio possible, given the required coverage. In this work, three different receiver-coil configurations were computed in pig and human models. The sensitivity maps were demonstrated in phantoms and in vivo experiments performed in pigs. Signal-to-noise ratio in the posterior heart was increased up to 80% with the best multichannel coil as expected. These new coil configurations will allow imaging of the different metabolite signals even in the posterior regions of the myocardium, which is not possible with a single-channel surface-coil.

Optimized density‐weighted imaging for dynamic contrast‐enhanced 3D‐MR mammography

To increase the spatial coverage and to reduce slice crosstalk combined with an optimal signal-to-noise ratio (SNR) in 3D dynamic contrast-enhanced (DCE) magnetic resonance (MR) mammography. Asymmetric sampling schemes and a new reconstruction strategy based on virtual coils are presented for density-weighted (DW) 3D imaging. Additionally, for MR mammography an alternating DW (ADW) sampling along the k(y) direction shifts the undersampling artifacts out of the signal reception region. Virtual coils for effective DW (VIDED) imaging suppresses the aliasing in undersampled DW imaging. VIDED and ADW were compared to the conventional Cartesian imaging in phantom and in vivo MR mammography studies. The slice crosstalk was significantly reduced by VIDED and compared to Cartesian imaging the SNR increased by 16%. Additionally, VIDED and ADW provided a substantially increased field of view (FOV) in the slice direction and allowed the spatial resolution to be improved (up to 60% for ADW and 30% for VIDED) without lengthening the scan time. VIDED and ADW improve the image quality in 3D DCE MR mammography by enhancing the spatial resolution, reducing the slice crosstalk at nearly optimal SNR, and increasing the FOV in the slice direction. For VIDED no lengthening of the scan time or usage of multichannel receiver coils is necessary.

Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3

Smoothly varying and multiplicative intensity variations within MR images that are artifactual, can reduce the accuracy of automated brain segmentation. Fortunately, these can be corrected. Among existing correction approaches, the nonparametric non-uniformity intensity normalization method N3 (Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. Nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imag. 17, 87–97.) is one of the most frequently used. However, at least one recent study (Boyes, R.G., Gunter, J.L., Frost, C., Janke, A.L., Yeatman, T., Hill, D.L.G., Bernstein, M.A., Thompson, P.M., Weiner, M.W., Schuff, N., Alexander, G.E., Killiany, R.J., DeCarli, C., Jack, C.R., Fox, N.C., 2008. Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. NeuroImage 39, 1752–1762.) suggests that its performance on 3 T scanners with multichannel phased-array receiver coils can be improved by optimizing a parameter that controls the smoothness of the estimated bias field. The present study not only confirms this finding, but additionally demonstrates the benefit of reducing the relevant parameter values to 30–50 mm (default value is 200 mm), on white matter surface estimation as well as the measurement of cortical and subcortical structures using FreeSurfer (Martinos Imaging Centre, Boston, MA). This finding can help enhance precision in studies where estimation of cerebral cortex thickness is critical for making inferences.

Mapping resting-state functional connectivity using perfusion MRI

Resting-state, low-frequency (<0.08 Hz) fluctuations of blood oxygenation level-dependent (BOLD) magnetic resonance signal have been shown to exhibit high correlation among functionally connected regions. However, correlations of cerebral blood flow (CBF) fluctuations during the resting state have not been extensively studied. The main challenges of using arterial spin labeling perfusion magnetic resonance imaging to detect CBF fluctuations are low sensitivity, low temporal resolution, and contamination from BOLD. This work demonstrates CBF-based quantitative functional connectivity mapping by combining continuous arterial spin labeling (CASL) with a neck labeling coil and a multi-channel receiver coil to achieve high perfusion sensitivity. In order to reduce BOLD contamination, the CBF signal was extracted from the CASL signal time course by high frequency filtering. This processing strategy is compatible with sinc interpolation for reducing the timing mismatch between control and label images and has the flexibility of choosing an optimal filter cutoff frequency to minimize BOLD fluctuations. Most subjects studied showed high CBF correlation in bilateral sensorimotor areas with good suppression of BOLD contamination. Root-mean-square CBF fluctuation contributing to bilateral correlation was estimated to be 29±19% (N=13) of the baseline perfusion, while BOLD fluctuation was 0.26±0.14% of the mean intensity (at 3 T and 12.5 ms echo time).