Parallel MRI Research Articles

Rapid dynamic contrast-enhanced MRI for small animals at 7T using 3D ultra-short echo time and golden-angle radial sparse parallel MRI.

To develop a rapid dynamic contrast-enhanced MRI method with high spatial and temporal resolution for small-animal imaging at 7 Tesla. An ultra-short echo time (UTE) pulse sequence using a 3D golden-angle radial sampling was implemented to achieve isotropic spatial resolution with flexible temporal resolution. Continuously acquired radial spokes were grouped into subsets for image reconstruction using a multicoil compressed sensing approach (Golden-angle RAdial Sparse Parallel; GRASP). The proposed 3D-UTE-GRASP method with high temporal and spatial resolutions was tested using 7 mice with GL261 intracranial glioma models. Iterative reconstruction with different temporal resolutions and regularization factors λ showed that, in all cases, the cost function decreased to less than 2.5% of its starting value within 20 iterations. The difference between the time-intensity curves of 3D-UTE-GRASP and nonuniform fast Fourier transform (NUFFT) images was minimal when λ was 1% of the maximum signal intensity of the initial NUFFT images. The 3D isotropic images were used to generate pharmacokinetic parameter maps to show the detailed images of the tumor characteristics in 3D and also to show longitudinal changes during tumor growth. This feasibility study demonstrated that the proposed 3D-UTE-GRASP method can be used for effective measurement of the 3D spatial heterogeneity of tumor pharmacokinetic parameters.

DRF-GRAPPA: A Parallel MRI Method with a Direct Reconstruction Filter

A novel multi-channel image reconstruction algorithm for parallel MRI is proposed to reconstruct a complex-valued image with both magnitude and phase information in a reduced computation time, so called the DRF-GRAPPA. The DRF-GRAPPA combines multichannel images by separately obtaining magnitude information and phase information. The method is applied to parallel MRI to reconstruct an image from under-sampled k-space data by using a direct reconstruction filter that can simultaneously estimate full k-space signals and combine multi-channel images. To obtain both magnitude and phase information, the DRF-GRAPPA provides two different direct reconstruction filters. The accuracy of the magnitude and phase information is validated through simulations. In addition, the experiment results of spherical water phantom imaging and in-vivo imaging demonstrate that the DRF-GRAPPA can successfully reconstruct images with both magnitude and phase information within a reduced reconstruction time.

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction.

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

Optimized respiratory‐resolved motion‐compensated 3D Cartesian coronary MR angiography

PurposeTo develop a robust and efficient reconstruction framework that provides high‐quality motion‐compensated respiratory‐resolved images from free‐breathing 3D whole‐heart Cartesian coronary magnetic resonance angiography (CMRA) acquisitions.MethodsRecently, XD‐GRASP (eXtra‐Dimensional Golden‐angle RAdial Sparse Parallel MRI) was proposed to achieve 100% scan efficiency and provide respiratory‐resolved 3D radial CMRA images by exploiting sparsity in the respiratory dimension. Here, a reconstruction framework for Cartesian CMRA imaging is proposed, which provides respiratory‐resolved motion‐compensated images by incorporating 2D beat‐to‐beat translational motion information to increase sparsity in the respiratory dimension. The motion information is extracted from interleaved image navigators and is also used to compensate for 2D translational motion within each respiratory phase. The proposed Optimized Respiratory‐resolved Cartesian Coronary MR Angiography (XD‐ORCCA) method was tested on 10 healthy subjects and 2 patients with cardiovascular disease, and compared against XD‐GRASP.ResultsThe proposed XD‐ORCCA provides high‐quality respiratory‐resolved images, allowing clear visualization of the right and left coronary arteries, even for irregular breathing patterns. Compared with XD‐GRASP, the proposed method improves the visibility and sharpness of both coronaries. Significant differences (p < .05) in visible vessel length and proximal vessel sharpness were found between the 2 methods. The XD‐GRASP method provides good‐quality images in the absence of intraphase motion. However, motion blurring is observed in XD‐GRASP images for respiratory phases with larger motion amplitudes and subjects with irregular breathing patterns.ConclusionA robust respiratory‐resolved motion‐compensated framework for Cartesian CMRA has been proposed and tested in healthy subjects and patients. The proposed XD‐ORCCA provides high‐quality images for all respiratory phases, independently of the regularity of the breathing pattern.

Simultaneous auto-calibration and gradient delays estimation (SAGE) in non-Cartesian parallel MRI using low-rank constraints.

To correct gradient timing delays in non-Cartesian MRI while simultaneously recovering corruption-free auto-calibration data for parallel imaging, without additional calibration scans. The calibration matrix constructed from multi-channel k-space data should be inherently low-rank. This property is used to construct reconstruction kernels or sensitivity maps. Delays between the gradient hardware across different axes and RF receive chain, which are relatively benign in Cartesian MRI (excluding EPI), lead to trajectory deviations and hence data inconsistencies for non-Cartesian trajectories. These in turn lead to higher rank and corrupted calibration information which hampers the reconstruction. Here, a method named Simultaneous Auto-calibration and Gradient delays Estimation (SAGE) is proposed that estimates the actual k-space trajectory while simultaneously recovering the uncorrupted auto-calibration data. This is done by estimating the gradient delays that result in the lowest rank of the calibration matrix. The Gauss-Newton method is used to solve the non-linear problem. The method is validated in simulations using center-out radial, projection reconstruction and spiral trajectories. Feasibility is demonstrated on phantom and in vivo scans with center-out radial and projection reconstruction trajectories. SAGE is able to estimate gradient timing delays with high accuracy at a signal to noise ratio level as low as 5. The method is able to effectively remove artifacts resulting from gradient timing delays and restore image quality in center-out radial, projection reconstruction, and spiral trajectories. The low-rank based method introduced simultaneously estimates gradient timing delays and provides accurate auto-calibration data for improved image quality, without any additional calibration scans.

Sparsity Promoting Adaptive Regularization for Compressed Sensing Parallel MRI

Low sampling density and reduced signal-to-noise ratio resulting from accelerated magnetic resonance data acquisition calls for sparsity promoting regularization for improved reconstruction. The simplest optimization strategy relies on thresholded Landweber algorithm that results in reconstructions with slow rate of convergence. A varying threshold can accelerate the rate of convergence, but does not guarantee low steady-state errors. In this paper, we propose a statistical approach to optimize the tradeoff between steady-state reconstruction error and speed of convergence, by iteratively adapting the regularization parameter. Analogous to the generalized discrepancy principle that implicates the nonnegativity of discrepancy level (absolute difference between $l_{2}$ -norms of consistency and perturbation errors) as provided by regularized reconstruction, the proposed method of adaptation entails reduction of the discrepancy level in $l_{1}$ -norm of the consistency and sparse approximation errors. In discrete form, this is deduced from the convergence properties of Landweber updates soft thresholded using monotonically decreasing thresholds. With very large number of iterations, the thresholds approach a lower bound determined by the noise level and adaptation function. Further improvements in image quality can be achieved by separately adapting the thresholds in each wavelet subband. Application of adaptive regularization combined with over-relaxation to parallel MRI shows significant improvements in both speed and image quality.

Accelerated T2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI.

Quantitative T2 measurements are sensitive to intra- and extracellular water accumulation and myelin loss. Therefore, quantitative T2 promises to be a good biomarker of disease. However, T2 measurements require long acquisition times. To accelerate T2 quantification and subsequent generation of synthetic T2 -weighted (T2 -w) image contrast for clinical research and routine. To that end, a recently developed model-based approach for rapid T2 and M0 quantification (MARTINI) based on undersampling k-space, was extended by parallel imaging (GRAPPA) to enable high-resolution T2 mapping with access to T2 -w images in less than 2 minutes acquisition time for the entire brain. Prospective cross-sectional study. Fourteen healthy subjects and a multipurpose phantom. Carr-Purcell-Meiboom-Gill sequence at a 3T scanner. The accuracy and reproducibility of the accelerated T2 quantification was assessed. Validations comprised MRI studies on a phantom as well as the brain, knee, prostate, and liver from healthy volunteers. Synthetic T2 -w images were generated from computed T2 and M0 maps and compared to conventional fast spin-echo (SE) images. Root mean square distance (RMSD) to the reference method and region of interest analysis. The combination of MARTINI and GRAPPA (GRAPPATINI) lead to a 10-fold accelerated T2 mapping protocol with 1:44 minutes acquisition time and full brain coverage. The RMSD of GRAPPATINI increases less (4.3%) than a 10-fold MARTINI reconstruction (37.6%) in comparison to the reference. Reproducibility tests showed low standard deviation (SD) of T2 values in regions of interest between scan and rescan (<0.4 msec) and across subjects (<4 msec). GRAPPATINI provides highly reproducible and fast whole-brain T2 maps and arbitrary synthetic T2 -w images in clinically compatible acquisition times of less than 2 minutes. These abilities are expected to support more widespread clinical applications of quantitative T2 mapping. 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:359-368.

On-the-Fly Adaptive ${k}$ -Space Sampling for Linear MRI Reconstruction Using Moment-Based Spectral Analysis.

In high-dimensional magnetic resonance imaging applications, time-consuming, sequential acquisition of data samples in the spatial frequency domain ( -space) can often be accelerated by accounting for dependencies in linear reconstruction, at the cost of noise amplification that depends on the sampling pattern. Common examples are support-constrained, parallel, and dynamic MRI, and -space sampling strategies are primarily driven by image-domain metrics that are expensive to compute for arbitrary sampling patterns. It remains challenging to provide systematic and computationally efficient automatic designs of arbitrary multidimensional Cartesian sampling patterns that mitigate noise amplification, given the subspace to which the object is confined. To address this problem, this paper introduces a theoretical framework that describes local geometric properties of the sampling pattern and relates them to the spread in the eigenvalues of the information matrix described by its first two spectral moments. This new criterion is then used for very efficient optimization of complex multidimensional sampling patterns that does not require reconstructing images or explicitly mapping noise amplification. Experiments with in vivo data show strong agreement between this criterion and traditional, comprehensive image-domain- and -space-based metrics, indicating the potential of the approach for computationally efficient (on-the-fly), automatic, and adaptive design of sampling patterns.

Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.

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.

Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding.

Golden-angle radial sparse parallel (GRASP) MRI reconstruction requires gridding and regridding to transform data between radial and Cartesian k-space. These operations are repeatedly performed in each iteration, which makes the reconstruction computationally demanding. This work aimed to accelerate GRASP reconstruction using self-calibrating GRAPPA operator gridding (GROG) and to validate its performance in clinical imaging. GROG is an alternative gridding approach based on parallel imaging, in which k-space data acquired on a non-Cartesian grid are shifted onto a Cartesian k-space grid using information from multicoil arrays. For iterative non-Cartesian image reconstruction, GROG is performed only once as a preprocessing step. Therefore, the subsequent iterative reconstruction can be performed directly in Cartesian space, which significantly reduces computational burden. Here, a framework combining GROG with GRASP (GROG-GRASP) is first optimized and then compared with standard GRASP reconstruction in 22 prostate patients. GROG-GRASP achieved approximately 4.2-fold reduction in reconstruction time compared with GRASP (∼333 min versus ∼78 min) while maintaining image quality (structural similarity index ≈ 0.97 and root mean square error ≈ 0.007). Visual image quality assessment by two experienced radiologists did not show significant differences between the two reconstruction schemes. With a graphics processing unit implementation, image reconstruction time can be further reduced to approximately 14 min. The GRASP reconstruction can be substantially accelerated using GROG. This framework is promising toward broader clinical application of GRASP and other iterative non-Cartesian reconstruction methods. Magn Reson Med 80:286-293, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Controlling the object phase for g-factor reduction in phase-Constrained parallel MRI using spatially selective RF pulses.

Parallel imaging generally entails a reduction in the signal-to-noise ratio of the final image. Phase-constrained methods aim to improve reconstruction quality by using symmetry properties of k-space. Noise amplification in phase-constrained reconstruction depends heavily on the object background phase. The purpose of this work is to present a new approach of using tailored radiofrequency pulses to optimize the object phase distribution in order to maximize the benefit of phase-constrained reconstruction, and to minimize the noise amplification. Intrinsic object phase and coil sensitivity profiles are measured in a prescan. Optimal phase distribution is computed to maximize signal-to-noise ratio in the given setup. Tailored radiofrequency pulses are designed to introduce the optimal phase map in the following accelerated acquisitions, subsequently reconstructed by phase-constrained methods. The potential of the method is demonstrated in vivo with in-plane accelerated (8x) and simultaneous multislice (3x) acquisitions. Mean g-factors are reduced by up to a factor of 2 compared with conventional techniques when an appropriate phase-constrained reconstruction is applied to phase-optimized acquisitions, enhancing the signal-to-noise ratio of the final images and the visibility of small details. Combining phase-constrained reconstruction and phase optimization by tailored radiofrequency pulses can provide notable improvement in the signal-to-noise ratio and reconstruction quality of accelerated MRI. Magn Reson Med 79:2113-2125, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Whole body MRI of the non-human primate using a clinical 3T scanner: initial experiences.

With the advent of parallel imaging MRI techniques, whole-body MRI is being increasingly used in clinical diagnosis. However, its application in preclinical research using large animals remains very limited. In the present study, the whole-body MRI techniques for adult macaque monkeys were explored using a conventional clinic 3T scanner. The T1, T2 anatomical images, and MR angiography of adult macaque whole bodies were illustrated. The preliminary results suggest whole-body MRI can be a robust tool to examine multiple organs of non-human primate (NHP) models from head to toe non-invasively and simultaneously using a conventional clinical setting. As NHPs are intensely used in biomedical research such as HIV/AIDS and vaccine discovery, whole body MRI techniques can have a wide range of applications in translational research using NHPs.

K-t accelerated aortic 4D flow MRI in under two minutes: Feasibility and impact of resolution, k-space sampling patterns, and respiratory navigator gating on hemodynamic measurements.

To assess the performance of highly accelerated free-breathing aortic four-dimensional (4D) flow MRI acquired in under 2 minutes compared to conventional respiratory gated 4D flow. Eight k-t accelerated nongated 4D flow MRI (parallel MRI with extended and averaged generalized autocalibrating partially parallel acquisition kernels [PEAK GRAPPA], R = 5, TRes = 67.2 ms) using four ky -kz Cartesian sampling patterns (linear, center-out, out-center-out, random) and two spatial resolutions (SRes1 = 3.5 × 2.3 × 2.6 mm3 , SRes2 = 4.5 × 2.3 × 2.6 mm3 ) were compared in vitro (aortic coarctation flow phantom) and in 10 healthy volunteers, to conventional 4D flow (16 mm-navigator acceptance window; R = 2; TRes = 39.2 ms; SRes = 3.2 × 2.3 × 2.4 mm3 ). The best k-t accelerated approach was further assessed in 10 patients with aortic disease. The k-t accelerated in vitro aortic peak flow (Qmax), net flow (Qnet), and peak velocity (Vmax) were lower than conventional 4D flow indices by ≤4.7%, ≤ 11%, and ≤22%, respectively. In vivo k-t accelerated acquisitions were significantly shorter but showed a trend to lower image quality compared to conventional 4D flow. Hemodynamic indices for linear and out-center-out k-space samplings were in agreement with conventional 4D flow (Qmax ≤ 13%, Qnet ≤ 13%, Vmax ≤ 17%, P > 0.05). Aortic 4D flow MRI in under 2 minutes is feasible with moderate underestimation of flow indices. Differences in k-space sampling patterns suggest an opportunity to mitigate image artifacts by an optimal trade-off between scan time, acceleration, and k-space sampling. Magn Reson Med 79:195-207, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

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.

Instrument Variables for Reducing Noise in Parallel MRI Reconstruction.

Generalized autocalibrating partially parallel acquisition (GRAPPA) has been a widely used parallel MRI technique. However, noise deteriorates the reconstructed image when reduction factor increases or even at low reduction factor for some noisy datasets. Noise, initially generated from scanner, propagates noise-related errors during fitting and interpolation procedures of GRAPPA to distort the final reconstructed image quality. The basic idea we proposed to improve GRAPPA is to remove noise from a system identification perspective. In this paper, we first analyze the GRAPPA noise problem from a noisy input-output system perspective; then, a new framework based on errors-in-variables (EIV) model is developed for analyzing noise generation mechanism in GRAPPA and designing a concrete method—instrument variables (IV) GRAPPA to remove noise. The proposed EIV framework provides possibilities that noiseless GRAPPA reconstruction could be achieved by existing methods that solve EIV problem other than IV method. Experimental results show that the proposed reconstruction algorithm can better remove the noise compared to the conventional GRAPPA, as validated with both of phantom and in vivo brain data.

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter-based low-rank Hankel matrix approach is proposed as a general framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. Specifically, our framework is based on a novel observation that the transform domain sparsity in the primary space implies the low-rankness of weighted Hankel matrix in the reciprocal space. This converts pMRI and CS-MRI to a k-space interpolation problem using a structured matrix completion. Experimental results using in vivo data for single/multicoil imaging as well as dynamic imaging confirmed that the proposed method outperforms the state-of-the-art pMRI and CS-MRI.

Efficient, Convergent SENSE MRI Reconstruction for Nonperiodic Boundary Conditions via Tridiagonal Solvers.

Undersampling is an effective method for reducing scan acquisition time for MRI. Strategies for accelerated MRI such as parallel MRI and Compressed Sensing MRI present challenging image reconstruction problems with non-differentiable cost functions and computationally demanding operations. Variable splitting (VS) can simplify implementation of difficult image reconstruction problems, such as the combination of parallel MRI and Compressed Sensing, CS-SENSE-MRI. Combined with augmented Lagrangian (AL) and alternating minimization strategies, variable splitting can yield iterative minimization algorithms with simpler auxiliary variable updates. However, arbitrary variable splitting schemes are not guaranteed to converge. Many variable splitting strategies are combined with periodic boundary conditions. The resultant circulant Hessians enable 𝒪(n log n) computation but may compromise image accuracy at the spatial boundaries. We propose two methods for CS-SENSE-MRI that use regularization with non-periodic boundary conditions to prevent wrap-around artifacts. Each algorithm computes one of the resulting variable updates efficiently in 𝒪(n) time using a parallelizable tridiagonal solver. AL-tridiag is a VS method designed to enable efficient computation for non-periodic boundary conditions. Another proposed algorithm, ADMM-tridiag, uses a similar VS scheme but also ensures convergence to a minimizer of the proposed cost function using the Alternating Direction Method of Multipliers (ADMM). AL-tridiag and ADMM-tridiag show speeds competitive with previous VS CS-SENSE-MRI reconstruction algorithm AL-P2. We also apply the tridiagonal VS approach to a simple image inpainting problem.

CUSTOM: A Calibration Region Recovery Approach for Highly Subsampled Dynamic Parallel Magnetic Resonance Imaging

We propose a recovery approach for highly subsampled dynamic parallel MRI image without auto-calibration signals (ACSs) or prior knowledge of coil sensitivity maps. By exploiting the between-frame redundancy of dynamic parallel MRI data, we first introduce a new low-rank matrix recovery-based model, termed as calibration using spatial---temporal matrix (CUSTOM), for ACSs recovery. The recovered ACSs from data are used for estimating coil sensitivity maps and further dynamic image reconstruction. The proposed non-convex and non-smooth minimization for the CUSTOM step is solved by a proximal alternating linearized minimization method, and we provide its convergence result for this specific minimization problem. Numerical experiments on several highly subsampled test data demonstrate that the proposed overall approach outperforms other state-of-the-art methods for calibrationless dynamic parallel MRI reconstruction.

In Vivo Non-Invasive Tracking of Macrophage Recruitment to Experimental Stroke.

Brain-infiltrating monocyte-derived macrophages are one of the key players in the local immune response after stroke. It is now widely accepted that the inflammatory response is not an exclusively destructive process. However, the underlying molecular mechanisms needed for proper regulation still remain to be elucidated. Here, we propose an in vitro labelling strategy for multimodal in vivo observation of macrophage dynamics distinguished from brain-residing microglia response. Prior to intracerebral transplantation into the striatum of recipient mice or systemic administration, monocytes and macrophages, isolated from luciferase-expressing mice, were labelled with superparamagnetic iron oxide particles. Temporo-spatial localization was monitored by magnetic resonance imaging, whereas survival of grafted cells was investigated using bioluminescence imaging. The labelling procedure of the isolated cells did not significantly influence cell characteristics and resulted in detection of as few as 500 labelled cells in vivo. Two weeks after stereotactic transplantation, the luciferase signal was sustained traceable, with approximately 18% of the original luciferase signal detectable for monocytes and about 30% for macrophages. Hypointensity in MRI of the graft appeared unaltered in spatial location. In a therapeutically relevant approach, systemic cell administration after stroke resulted in accumulation mostly in thoracic regions, as could be visualized with BLI. For detection of homing to ischemic brain tissue more cells need to be administered. Nevertheless, during parallel MRI sessions recruitment of i.v. injected cells to the lesion site could be detected by day 2 post stroke as scattered hypointense signal voids. With further increase in sensitivity, our multi-facetted labelling strategy will provide the basis for in vivo tracking and fate specification of tissue-infiltrating macrophages and their distinct role in stroke-related neuro-inflammation.