Parallel magnetic resonance imaging using the GRAPPA operator formalism

Rational approximation of golden angles: Accelerated reconstructions for radial MRI

PURPOSE

To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle between consecutive spokes in golden ratio-based sampling schemes enables a flexible retrospective choice of temporal resolution, while preserving good coverage of k-space for each individual bin. Nevertheless, irrational increments prohibit precomputation of the point-spread function (PSF), can lead to numerical problems, and require more complex processing steps. To avoid these problems, a new sampling scheme based on a rational approximation of golden angles (RAGA) is developed.

METHODS

The theoretical properties of RAGA sampling are mathematically derived. Sidelobe-to-peak ratios (SPR) are numerically computed and compared to the corresponding golden ratio sampling schemes. The sampling scheme is implemented in the BART toolbox and in a radial gradient-echo sequence. Feasibility is shown for quantitative imaging in a phantom and a cardiac scan of a healthy volunteer.

RESULTS

RAGA sampling can accurately approximate golden ratio sampling and has almost identical PSF and SPR. In contrast to golden ratio sampling, each frame can be reconstructed with the same equidistant trajectory using different sampling masks, and the angle of each acquired spoke can be encoded as a small index, which simplifies processing of the acquired data.

CONCLUSION

RAGA sampling provides the advantages of golden ratio sampling while simplifying data processing, rendering it a valuable tool for dynamic and quantitative MRI.

Designing Clinical MRI for Enhanced Workflow and Value

MRI is an expensive and traditionally time-intensive modality in imaging. With the paradigm shift toward value-based healthcare, radiology departments must examine the entire MRI process cycle to identify opportunities to optimize efficiency and enhance value for patients. Digital tools such as "frictionless scheduling" prioritize patient preference and convenience, thereby delivering patient-centered care. Recent advances in conventional and deep learning-based accelerated image reconstruction methods have reduced image acquisition time to such a degree that so-called nongradient time now constitutes a major percentage of total room time. For this reason, architectural design strategies that reconfigure patient preparation processes and decrease the turnaround time between scans can substantially impact overall throughput while also improving patient comfort and privacy. Real-time informatics tools that provide an enterprise-wide overview of MRI workflow and Picture Archiving and Communication System (PACS)-integrated instant messaging can complement these efforts by offering transparent, situational data and facilitating communication between radiology team members. Finally, long-term investment in training, recruiting, and retaining a highly skilled technologist workforce is essential for building a pipeline and team of technologists committed to excellence. Here, we highlight various opportunities for optimizing MRI workflow and enhancing value by offering many of our own on-the-ground experiences and conclude by anticipating some of the future directions for process improvement and innovation in clinical MR imaging. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 1.

A Four-Channel Broadband MRI Receive Array Coil

Low-impedance preamplifier decoupling is commonly used in RF coil array construction to minimize coupling between elements through mutual impedance. The trap circuit is an essential component in preamp decoupling techniques, but becomes a limiting factor in constructing multi-tuned, multi-nuclear coil arrays. In principle, it is possible to double-tune or multi-tune the trap circuits, but will add complexity and loss. We present a broadband decoupling approach using high impedance preamplifiers. A dual-tuned prototype four-channel array using this approach which targets 2H and 23 Na at 4.7T, has been previously constructed, evaluated and reported. Without any retuning of the array, the same setup is tested at the 23Na and 31P frequencies for 3T. Initial bench measurements and Chemical Shift Imaging (CSI) results are acquired and presented in this study.Clinical Relevance- This study could reduce the complexity of multi-nuclear array coil design.

K-space and image domain collaborative energy-based model for parallel MRI reconstruction

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned from or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Equipped with parallel and sequential orders, experimental comparisons with the state-of-the-arts demonstrated that they involve less error in reconstruction accuracy and are more stable under different acceleration factors.

Artificial Intelligence-Driven Ultra-Fast Superresolution MRI: 10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach

ABSTRACT

Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence acceleration methods enable 4-fold turbo spin echo pulse sequence acceleration in clinical practice; however, at this speed, conventional image reconstruction approaches the signal-to-noise limits of temporal, spatial, and contrast resolution. Novel deep learning image reconstruction methods can minimize signal-to-noise interdependencies to better advantage than conventional image reconstruction, leading to unparalleled gains in image speed and quality when combined with parallel imaging and simultaneous multislice acquisition. The enormous potential of deep learning-based image reconstruction promises to facilitate the 10-fold acceleration of the turbo spin echo pulse sequence, equating to a total acquisition time of 2-3 minutes for entire MRI examinations of joints without sacrificing spatial resolution or image quality. Current investigations aim for a better understanding of stability and failure modes of image reconstruction networks, validation of network reconstruction performance with external data sets, determination of diagnostic performances with independent reference standards, establishing generalizability to other centers, scanners, field strengths, coils, and anatomy, and building publicly available benchmark data sets to compare methods and foster innovation and collaboration between the clinical and image processing community. In this article, we review basic concepts of deep learning-based acquisition and image reconstruction techniques for accelerating and improving the quality of musculoskeletal MRI, commercially available and developing deep learning-based MRI solutions, superresolution, denoising, generative adversarial networks, and combined strategies for deep learning-driven ultra-fast superresolution musculoskeletal MRI. This article aims to equip radiologists and imaging scientists with the necessary practical knowledge and enthusiasm to meet this exciting new era of musculoskeletal MRI.

Model-based dynamic off-resonance correction for improved accelerated fMRI in awake behaving nonhuman primates

Purpose

To estimate dynamic off‐resonance due to vigorous body motion in accelerated fMRI of awake behaving nonhuman primates (NHPs) using the echo‐planar imaging reference navigator, in order to attenuate the effects of time‐varying off‐resonance on the reconstruction.

Methods

In NHP fMRI, the animal’s head is usually head‐posted, and the dynamic off‐resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off‐resonance at each frame can be approximated as a spatially linear perturbation of the off‐resonance at a reference frame, and is manifested as a relative linear shift in k‐space. Using GRAPPA operators, we estimated these shifts by comparing the navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data.

Results

The proposed method is shown to successfully estimate spatially low‐order dynamic off‐resonance perturbations, including induced linear off‐resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multislice in vivo acquisitions in awake‐behaving NHPs.

Conclusion

A method is proposed that does not need sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state‐of‐the‐art human imaging.

Aliasing-free reduced field-of-view parallel imaging

PURPOSE

To reconstruct aliasing-free full field-of-view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods.

THEORY AND METHODS

The sensitivity encoding method (SENSE) was extended to the Soft-SENSE models supporting multiple-set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple-set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft-SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction.

RESULTS

Soft-SENSE using multiple-set full FOV CSM and PSF successfully reconstruct aliasing-free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high-resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem.

CONCLUSION

The proposed Soft-SENSE using full FOV CSM and PSF could reconstruct aliasing-free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction.

A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse k -space data. Conventional methods typically optimize an energy function, producing restoration of high quality, but their iterative numerical solvers unavoidably bring extremely large time consumption. Recent deep techniques provide fast restoration by either learning direct prediction to final reconstruction or plugging learned modules into the energy optimizer. Nevertheless, these data-driven predictors cannot guarantee the reconstruction following principled constraints underlying the domain knowledge so that the reliability of their reconstruction process is questionable. In this paper, we propose a deep framework assembling principled modules for CS-MRI that fuses learning strategy with the iterative solver of a conventional reconstruction energy. This framework embeds an optimal condition checking mechanism, fostering efficient and reliable reconstruction. We also apply the framework to three practical tasks, i.e., complex-valued data reconstruction, parallel imaging and reconstruction with Rician noise. Extensive experiments on both benchmark and manufacturer-testing images demonstrate that the proposed method reliably converges to the optimal solution more efficiently and accurately than the state-of-the-art in various scenarios.

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

PURPOSE

To advance research in the field of machine learning for MR image reconstruction with an open challenge.

METHODS

We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi-coil and single-coil data. We performed a two-stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019.

RESULTS

We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches.

CONCLUSIONS

The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.

Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety. Recently, Deep Learning Neuronal Network (DL) has been suggested in order to reconstruct MRI scans with conserving the structural details and improving parallel imaging-based fast MRI. In this paper, we propose Deep Convolutional Encoder-Decoder architecture for CS-MRI reconstruction. Such architecture bridges the gap between the non-learning techniques, using data from only one image, and approaches using large training data. The proposed approach is based on autoencoder architecture divided into two parts: an encoder and a decoder. The encoder as well as the decoder has essentially three convolutional blocks. The proposed architecture has been evaluated through two databases: Hammersmith dataset (for the normal scans) and MICCAI 2018 (for pathological MRI). Moreover, we extend our model to cope with noisy pathological MRI scans. The normalized mean square error (NMSE), the peak-to-noise ratio (PSNR), and the structural similarity index (SSIM) have been adopted as evaluation metrics in order to evaluate the proposed architecture performance and to make a comparative study with the state-of-the-art reconstruction algorithms. The higher PSNR and SSIM values as well as the lowest NMSE values could attest that the proposed architecture offers better reconstruction and preserves textural image details. Furthermore, the running time is about 0.8 s, which is suitable for real-time processing. Such results could encourage the neurologist to adopt it in their clinical routines. Graphical abstract.

From 2D to 4D Phase-Contrast MRI in the Neurovascular System: Will It Be a Quantum Jump or a Fancy Decoration?

Considering the crosstalk between the flow and vessel wall, hemodynamic assessment of the neurovascular system may offer a well-integrated solution for both diagnosis and management by adding prognostic significance to the standard CT/MR angiography. 4D flow MRI or time-resolved 3D velocity-encoded phase-contrast MRI has long been promising for the hemodynamic evaluation of the great vessels, but challenged in clinical studies for assessing intracranial vessels with small diameter due to long scan times and low spatiotemporal resolution. Current accelerated MRI techniques, including parallel imaging with compressed sensing and radial k-space undersampling acquisitions, have decreased scan times dramatically while preserving spatial resolution. 4D flow MRI visualized and measured 3D complex flow of neurovascular diseases such as aneurysm, arteriovenous shunts, and atherosclerotic stenosis using parameters including flow volume, velocity vector, pressure gradients, and wall shear stress. In addition to the noninvasiveness of the phase contrast technique and retrospective flow measurement through the wanted windows of the analysis plane, 4D flow MRI has shown several advantages over Doppler ultrasound or computational fluid dynamics. The evaluation of the flow status and vessel wall can be performed simultaneously in the same imaging modality. This article is an overview of the recent advances in neurovascular 4D flow MRI techniques and their potential clinical applications in neurovascular disease. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

Estimation error bound for GRAPPA diffusion-weighted MRI

In recent years, diffusion weight magnetic resonance imaging (DW-MRI) has become one of the most important MRI imaging modalities. The importance of the DW-MRI grew thanks to the combination of parallel magnetic resonance imaging (pMRI) techniques with the echo-planar imaging (EPI), which minimize scan time and lead to reduced distortion, allowing the DW-MRI to become a routine clinical exam. Additionally, this has brought various new parameters that influence image quality and biomarkers used in DW-MRI. This work aims to investigate the effects of these parameters on the estimation quality, by using the Cramér-Rao bound tool, which gives analytical expressions of the lower limit on the estimation error variance of different DW-MRI variables when using the pMRI technique. In particular, these bounds will be used to study and optimize the impact of different factors of generalized autocalibrating partially parallel acquisition (GRAPPA) technique and system parameters on the estimation quality of the desired clinical metrics. Moreover, the obtained results of this study can be exploited and adapted in all human body DW-MRI clinical routines, further improving disease diagnosis, and tractography studies.

Investigation of Low-Cost Op-Amps as Decoupling Preamplifiers for MRI Array Coils

The benefits of array coils in MRI and MRS are well known. A key component of essentially all array coils used today is the decoupling preamplifier. Unlike conventional 50 ohm low-noise preamps, decoupling preamps present a reactive impedance to the coil, which can be used to 'block' currents from being induced in the receive coil, reducing the impact of any electromagnetic coupling between array elements. While available from a number of vendors, a lower-cost solution would be advantageous. We investigate the use of conventional operational amplifiers as low-noise decoupling preamplifiers. In this paper the performance of the op-amp preamplifier is compared to conventional 50 Ω. The op-amp preamp design shows promise for use as a decoupling preamplifier with array coils.Clinical Relevance- This work could facilitate the development of array coils for spectroscopy and imaging.

fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning

A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.

Dual-polarity slice-GRAPPA for concurrent ghost correction and slice separation in simultaneous multi-slice EPI

PURPOSE

A ghost correction strategy for Simultaneous Multi-Slice (SMS) EPI methods that provides improved ghosting artifact reduction compared to conventional methods is presented. Conventional Nyquist ghost correction methods for SMS-EPI rely on navigator data that contain phase errors from all slices in the simultaneously acquired slice-group. These navigator data may contain spatially nonlinear phase differences near regions of B0 inhomogeneity, which violates the linear model employed by most EPI ghost correction algorithms, resulting in poor reconstructions.

METHODS

Dual-Polarity GRAPPA (DPG) was previously shown to accurately model and correct both spatially nonlinear and 2D phase errors in conventional single-slice EPI data. Here, an extension we call Dual-Polarity slice-GRAPPA (DPsG) is adapted to the slice-GRAPPA method and applied to SMS-EPI data for slice separation and ghost correction concurrently-eliminating the need for a separate ghost correction step while also providing improved slice-specific EPI phase error correction.

RESULTS

Images from in vivo SMS-EPI data reconstructed using DPsG in place of conventional Nyquist ghost correction and slice-GRAPPA are presented. DPsG is shown to reduce ghosting artifacts and provide improved temporal SNR compared to the conventional reconstruction.

CONCLUSION

The proposed use of DPsG for SMS-EPI reconstruction can provide images with lower artifact levels, higher image fidelity, and improved time-series stability compared to conventional reconstruction methods.

4D flow MRI for the analysis of celiac trunk and mesenteric artery stenoses

PURPOSE

This study aims to assess the feasibility of 4D flow MRI measurements in complex vascular territories; namely, the celiac artery (CA) and superior mesenteric artery (SMA).

MATERIALS AND METHODS

In this prospective study, 22 healthy volunteers and 10 patients were scanned at 3 T. Blood flow parameters were compared between healthy volunteers and patients with stenosis of the CA and/or SMA as a function of stenosis grade characterized by prior contrast-enhanced computed tomography (CE-CT). The 4D flow MRI acquisition covered the CA, SMA and adjusting parts of the abdominal aorta (AO). Measurements of velocity- (peak velocity [PV], average velocity [AV]) and volume-related parameters (peak flow [PF], stroke volume [SV]) were conducted. Further, stenosis grade and wall shear stress in the CA, SMA and AO were evaluated.

RESULTS

In patients, prior evaluation by CE-CT revealed 11 low- and 5 mid-grade stenoses of the CA and/or SMA. PV and AV were significantly higher in patients than in healthy volunteers [PV: p < 0.0001; AV: p = 0.03, p < 0.001]. PF and SV did not differ significantly between healthy volunteers and patients; however, a trend towards lower PF and SV could be detected in patients with mid-grade stenoses. Comparison of 4D flow MRI with CE-CT revealed a strong positive correlation in estimated degree of stenosis (CA: r = 0.86, SMA: r = 0.98). Patients with mid-grade stenoses had a significantly higher average WSS magnitude (AWM) than healthy volunteers (p = 0.02).

CONCLUSION

This feasibility study suggests that 4D flow MRI is a viable technique for the evaluation of complex flow characteristics in small vessels such as the CA and SMA. 4D flow MRI approves comparable to the morphologic assessment of complex vascular territories using CE-CT but, in addition, offers the functional evaluation of flow parameters that goes beyond the morphology.

Magnetic resonance angiography for the primary diagnosis of pulmonary embolism: A review from the international workshop for pulmonary functional imaging

Pulmonary contrast enhanced magnetic resonance angiography (CE-MRA) is useful for the primary diagnosis of pulmonary embolism (PE). Many sites have chosen not to use CE-MRA as a first line of diagnostic tool for PE because of the speed and higher efficacy of computerized tomographic angiography (CTA). In this review, we discuss the strengths and weaknesses of CE-MRA and the appropriate imaging scenarios for the primary diagnosis of PE derived from our unique multi-institutional experience in this area. The optimal patient for this test has a low to intermediate suspicion for PE based on clinical decision rules. Patients in extremis are not candidates for this test. Younger women (< 35 years of age) and patients with iodinated contrast allergies are best served by using this modality We discuss the history of the use of this test, recent technical innovations, artifacts, direct and indirect findings for PE, ancillary findings, and the effectiveness (patient outcomes) of CE-MRA for the exclusion of PE. Current outcomes data shows that CE-MRA and NM V/Q scans are effective alternative tests to CTA for the primary diagnosis of PE.

Sequential combination of k-t principle component analysis (PCA) and partial parallel imaging: k-t PCA GROWL

PURPOSE

k-t principle component analysis (k-t PCA) is a distinguished method for high spatiotemporal resolution dynamic MRI. To further improve the accuracy of k-t PCA, a combination with partial parallel imaging (PPI), k-t PCA/SENSE, has been tested. However, k-t PCA/SENSE suffers from long reconstruction time and limited improvement. This study aims to improve the combination of k-t PCA and PPI on both reconstruction speed and accuracy.

METHODS

A sequential combination scheme called k-t PCA GROWL (GRAPPA operator for wider readout line) was proposed. The GRAPPA operator was performed before k-t PCA to extend each readout line into a wider band, which improved the condition of the encoding matrix in the following k-t PCA reconstruction. k-t PCA GROWL was tested and compared with k-t PCA and k-t PCA/SENSE on cardiac imaging.

RESULTS

k-t PCA GROWL consistently resulted in better image quality compared with k-t PCA/SENSE at high acceleration factors for both retrospectively and prospectively undersampled cardiac imaging, with a much lower computation cost. The improvement in image quality became greater with the increase of acceleration factor.

CONCLUSION

By sequentially combining the GRAPPA operator and k-t PCA, the proposed k-t PCA GROWL method outperformed k-t PCA/SENSE in both reconstruction speed and accuracy, suggesting that k-t PCA GROWL is a better combination scheme than k-t PCA/SENSE. Magn Reson Med 77:1058-1067, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Auto-calibration approach for k-t SENSE

PURPOSE

The goal of this work is to increase the spatial resolution of training data, used by reconstruction methods such as k-t SENSE in order to calculate the missing data in a series of dynamic images, without compromising their temporal resolution or acquisition time.

THEORY

The k-t SENSE method allows dynamic imaging at high acceleration factors with high reconstruction quality. However, the low resolution training data required by k-t SENSE may cause undesired temporal filtering effects in the reconstructed images.

METHODS

In this work, a feedback regularization approach is applied to realize auto-calibration of the k-t SENSE algorithm. To that end, a full resolution training data set is calculated from the accelerated data itself using a TSENSE reconstruction. The reconstructed training data are then fed back for the actual k-t SENSE reconstruction. For evaluation of our approach, temporal filtering effects are quantified by calculating the modulation transfer function and noise measurements are done by Monte-Carlo simulations.

RESULTS

Computer simulations and cardiac imaging experiments demonstrate an improved temporal fidelity of auto-calibrated k-t SENSE compared to standard k-t SENSE.

CONCLUSION

Auto-calibrated k-t SENSE provides high quality reconstructions for dynamic imaging applications.

Accelerating magnetic resonance fingerprinting (MRF) using t-blipped simultaneous multislice (SMS) acquisition

PURPOSE

We incorporate simultaneous multislice (SMS) acquisition into MR fingerprinting (MRF) to accelerate the MRF acquisition.

METHODS

The t-Blipped SMS-MRF method is achieved by adding a Gz blip before each data acquisition window and balancing it with a Gz blip of opposing polarity at the end of each TR. Thus the signal from different simultaneously excited slices are encoded with different phases without disturbing the signal evolution. Furthermore, by varying the Gz blip area and/or polarity as a function of repetition time, the slices' differential phase can also be made to vary as a function of time. For reconstruction of t-Blipped SMS-MRF data, we demonstrate a combined slice-direction SENSE and modified dictionary matching method.

RESULTS

In Monte Carlo simulation, the parameter mapping from multiband factor (MB) = 2 t-Blipped SMS-MRF shows good accuracy and precision when compared with results from reference conventional MRF data with concordance correlation coefficients (CCC) of 0.96 for T1 estimates and 0.90 for T2 estimates. For in vivo experiments, T1 and T2 maps from MB=2 t-Blipped SMS-MRF have a high agreement with ones from conventional MRF.

CONCLUSION

The MB=2 t-Blipped SMS-MRF acquisition/reconstruction method has been demonstrated and validated to provide more rapid parameter mapping in the MRF framework.

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.

Increased cerebral water content in hemodialysis patients

Little information is available on the impact of hemodialysis on cerebral water homeostasis and its distribution in chronic kidney disease. We used a neuropsychological test battery, structural magnetic resonance imaging (MRI) and a novel technique for quantitative measurement of localized water content using 3T MRI to investigate ten hemodialysis patients (HD) on a dialysis-free day and after hemodialysis (2.4±2.2 hours), and a matched healthy control group with the same time interval. Neuropsychological testing revealed mainly attentional and executive cognitive dysfunction in HD. Voxel-based-morphometry showed only marginal alterations in the right inferior medial temporal lobe white matter in HD compared to controls. Marked increases in global brain water content were found in the white matter, specifically in parietal areas, in HD patients compared to controls. Although the global water content in the gray matter did not differ between the two groups, regional increases of brain water content in particular in parieto-temporal gray matter areas were observed in HD patients. No relevant brain hydration changes were revealed before and after hemodialysis. Whereas longer duration of dialysis vintage was associated with increased water content in parieto-temporal-occipital regions, lower intradialytic weight changes were negatively correlated with brain water content in these areas in HD patients. Worse cognitive performance on an attention task correlated with increased hydration in frontal white matter. In conclusion, long-term HD is associated with altered brain tissue water homeostasis mainly in parietal white matter regions, whereas the attentional domain in the cognitive dysfunction profile in HD could be linked to increased frontal white matter water content.

Quantitative water content mapping at clinically relevant field strengths: a comparative study at 1.5 T and 3 T

PURPOSE

Quantitative water content mapping in vivo using MRI is a very valuable technique to detect, monitor and understand diseases of the brain. At 1.5 T, this technology has already been successfully used, but it has only recently been applied at 3T because of significantly increased RF field inhomogeneity at the higher field strength. To validate the technology at 3T, we estimate and compare in vivo quantitative water content maps at 1.5 T and 3T obtained with a protocol proposed recently for 3T MRI.

METHODS

The proposed MRI protocol was applied on twenty healthy subjects at 1.5 T and 3T; the same post-processing algorithms were used to estimate the water content maps. The 1.5 T and 3T maps were subsequently aligned and compared on a voxel-by-voxel basis. Statistical analysis was performed to detect possible differences between the estimated 1.5 T and 3T water maps.

RESULTS

Our analysis indicates that the water content values obtained at 1.5 T and 3T did not show significant systematic differences. On average the difference did not exceed the standard deviation of the water content at 1.5 T. Furthermore, the contrast-to-noise ratio (CNR) of the estimated water content map was increased at 3T by a factor of at least 1.5.

CONCLUSIONS

Vulnerability to RF inhomogeneity increases dramatically with the increasing static magnetic field strength. However, using advanced corrections for the sensitivity profile of the MR coils, it is possible to preserve quantitative accuracy while benefiting from the increased CNR at the higher field strength. Indeed, there was no significant difference in the water content values obtained in the brain at 1.5 T and 3T.

Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data

Nontask functional magnetic resonance imaging (fMRI) has become one of the most popular noninvasive areas of brain mapping research for neuroscientists. In nontask fMRI, various sources of "noise" corrupt the measured blood oxygenation level-dependent signal. Many studies have aimed to attenuate the noise in reconstructed voxel measurements through spatial and temporal processing operations. While these solutions make the data more "appealing," many commonly used processing operations induce artificial correlations in the acquired data. As such, it becomes increasingly more difficult to derive the true underlying covariance structure once the data have been processed. As the goal of nontask fMRI studies is to determine, utilize, and analyze the true covariance structure of acquired data, such processing can lead to inaccurate and misleading conclusions drawn from the data if they are unaccounted for in the final connectivity analysis. In this article, we develop a framework that represents the spatiotemporal processing and reconstruction operations as linear operators, providing a means of precisely quantifying the correlations induced or modified by such processing rather than by performing lengthy Monte Carlo simulations. A framework of this kind allows one to appropriately model the statistical properties of the processed data, optimize the data processing pipeline, characterize excessive processing, and draw more accurate functional connectivity conclusions.

Using self-consistency for an iterative trajectory adjustment (SCITA)

PURPOSE

To iteratively correct for deviations in radial trajectories with no need of additionally performed calibration scans.

THEORY AND METHODS

Radially acquired data sets-even when undersampled to a certain extend-inherently feature an oversampled area in the center of k-space. Thus, for a perfectly measured trajectory and neglecting noise, information is consistent between multiple measurements gridded to the same Cartesian position within this region. In the case of erroneous coordinates, this accordance-and therefore a correction of the trajectory-can be enforced by an algorithm iteratively shifting the projections with respect to each other by applying the GRAPPA operator. The method was validated in numerical simulations, as well as in radial acquisitions of a phantom and in vivo images at 3T. The results of the correction were compared to a previously proposed correction method.

RESULTS

The newly introduced technique allowed for a reliable trajectory correction in each of the presented examples. The method was able to remove artifacts as effectively as methods that are based on data from additional calibration scans.

CONCLUSION

The iterative technique introduced in this paper allows for a correction of trajectory errors in radial imaging with no need for additional calibration data.

Combined dynamic contrast-enhanced liver MRI and MRA using interleaved variable density sampling

PURPOSE

To develop and evaluate a method for volumetric contrast-enhanced MRI of the liver, with high spatial and temporal resolutions, for combined dynamic imaging and MR angiography (MRA) using a single injection of contrast agent.

METHODS

An interleaved variable density (IVD) undersampling pattern was implemented in combination with a real-time-triggered, time-resolved, dual-echo 3D spoiled gradient echo sequence. Parallel imaging autocalibration lines were acquired only once during the first time frame. Imaging was performed in 10 subjects with focal nodular hyperplasia (FNH) and compared with their clinical MRI. The angiographic phase of the proposed method was compared with a dedicated MR angiogram acquired during a second injection of contrast.

RESULTS

A total of 21 FNH, three cavernous hemangiomas, and 109 arterial segments were visualized in 10 subjects. The temporally resolved images depicted the characteristic arterial enhancement pattern of the lesions with a 4-s update rate. Images were graded as having significantly higher quality compared with the clinical MRI. Angiograms produced from the IVD method provided noninferior diagnostic assessment compared with the dedicated MR angiogram.

CONCLUSION

Using an undersampled IVD imaging method, we have demonstrated the feasibility of obtaining high spatial and temporal resolution dynamic contrast-enhanced imaging and simultaneous MRA of the liver.

Quantifying the statistical impact of GRAPPA in fcMRI data with a real-valued isomorphism

The interpolation of missing spatial frequencies through the generalized auto-calibrating partially parallel acquisitions (GRAPPA) parallel magnetic resonance imaging (MRI) model implies a correlation is induced between the acquired and reconstructed frequency measurements. As the parallel image reconstruction algorithms in many medical MRI scanners are based on the GRAPPA model, this study aims to quantify the statistical implications that the GRAPPA model has in functional connectivity studies. The linear mathematical framework derived in the work of Rowe , 2007, is adapted to represent the complex-valued GRAPPA image reconstruction operation in terms of a real-valued isomorphism, and a statistical analysis is performed on the effects that the GRAPPA operation has on reconstructed voxel means and correlations. The interpolation of missing spatial frequencies with the GRAPPA model is shown to result in an artificial correlation induced between voxels in the reconstructed images, and these artificial correlations are shown to reside in the low temporal frequency spectrum commonly associated with functional connectivity. Through a real-valued isomorphism, such as the one outlined in this manuscript, the exact artificial correlations induced by the GRAPPA model are not simply estimated, as they would be with simulations, but are precisely quantified. If these correlations are unaccounted for, they can incur an increase in false positives in functional connectivity studies.

Calibrationless parallel magnetic resonance imaging: a joint sparsity model

State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two datasets-eight channel brain and eight channel Shepp-Logan phantom. Two sampling methods were used-Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the other an acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. We compared our work with four state-of-the-art parallel imaging techniques; two calibrated methods-CS SENSE and l1SPIRiT and two calibration free techniques-Distributed CS and SAKE. Our method yields better reconstruction results than all of them.

Sparsity-promoting calibration for GRAPPA accelerated parallel MRI reconstruction

The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.

MRI of the lung (1/3): methods

Proton magnetic resonance imaging (MRI) has recently emerged as a clinical tool to image the lungs. This paper outlines the current technical aspects of MRI pulse sequences, radiofrequency (RF) coils and MRI system requirements needed for imaging the pulmonary parenchyma and vasculature. Lung MRI techniques are presented as a “technical toolkit”, from which MR protocols will be composed in the subsequent papers for comprehensive imaging of lung disease and function (parts 2 and 3). This paper is pitched at MR scientists, technicians and radiologists who are interested in understanding and establishing lung MRI methods. Images from a 1.5 T scanner are used for illustration of the sequences and methods that are highlighted.

Main Messages

• Outline of the hardware and pulse sequence requirements for proton lung MRI

• Overview of pulse sequences for lung parenchyma, vascular and functional imaging with protons

• Demonstration of the pulse-sequence building blocks for clinical lung MRI protocols

Myocardial perfusion magnetic resonance imaging using sliding-window conjugate-gradient highly constrained back-projection reconstruction for detection of coronary artery disease

Myocardial perfusion magnetic resonance imaging (MRI) with sliding-window conjugate-gradient highly constrained back-projection reconstruction (SW-CG-HYPR) allows whole left ventricular coverage, improved temporal and spatial resolution and signal/noise ratio, and reduced cardiac motion-related image artifacts. The accuracy of this technique for detecting coronary artery disease (CAD) has not been determined in a large number of patients. We prospectively evaluated the diagnostic performance of myocardial perfusion MRI with SW-CG-HYPR in patients with suspected CAD. A total of 50 consecutive patients who were scheduled for coronary angiography with suspected CAD underwent myocardial perfusion MRI with SW-CG-HYPR at 3.0 T. The perfusion defects were interpreted qualitatively by 2 blinded observers and were correlated with x-ray angiographic stenoses ≥50%. The prevalence of CAD was 56%. In the per-patient analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of SW-CG-HYPR was 96% (95% confidence interval 82% to 100%), 82% (95% confidence interval 60% to 95%), 87% (95% confidence interval 70% to 96%), 95% (95% confidence interval 74% to100%), and 90% (95% confidence interval 82% to 98%), respectively. In the per-vessel analysis, the corresponding values were 98% (95% confidence interval 91% to 100%), 89% (95% confidence interval 80% to 94%), 86% (95% confidence interval 76% to 93%), 99% (95% confidence interval 93% to 100%), and 93% (95% confidence interval 89% to 97%), respectively. In conclusion, myocardial perfusion MRI using SW-CG-HYPR allows whole left ventricular coverage and high resolution and has high diagnostic accuracy in patients with suspected CAD.

Nonlinear GRAPPA: a kernel approach to parallel MRI reconstruction

GRAPPA linearly combines the undersampled k-space signals to estimate the missing k-space signals where the coefficients are obtained by fitting to some auto-calibration signals (ACS) sampled with Nyquist rate based on the shift-invariant property. At high acceleration factors, GRAPPA reconstruction can suffer from a high level of noise even with a large number of auto-calibration signals. In this work, we propose a nonlinear method to improve GRAPPA. The method is based on the so-called kernel method which is widely used in machine learning. Specifically, the undersampled k-space signals are mapped through a nonlinear transform to a high-dimensional feature space, and then linearly combined to reconstruct the missing k-space data. The linear combination coefficients are also obtained through fitting to the ACS data but in the new feature space. The procedure is equivalent to adding many virtual channels in reconstruction. A polynomial kernel with explicit mapping functions is investigated in this work. Experimental results using phantom and in vivo data demonstrate that the proposed nonlinear GRAPPA method can significantly improve the reconstruction quality over GRAPPA and its state-of-the-art derivatives.

K-t sparse GROWL: sequential combination of partially parallel imaging and compressed sensing in k-t space using flexible virtual coil

Because dynamic MR images are often sparse in x-f domain, k-t space compressed sensing (k-t CS) has been proposed for highly accelerated dynamic MRI. When a multichannel coil is used for acquisition, the combination of partially parallel imaging and k-t CS can improve the accuracy of reconstruction. In this work, an efficient combination method is presented, which is called k-t sparse Generalized GRAPPA fOr Wider readout Line. One fundamental aspect of this work is to apply partially parallel imaging and k-t CS sequentially. A partially parallel imaging technique using a Generalized GRAPPA fOr Wider readout Line operator is adopted before k-t CS reconstruction to decrease the reduction factor in a computationally efficient way while preserving temporal resolution. Channel combination and relative sensitivity maps are used in the flexible virtual coil scheme to alleviate the k-t CS computational load with increasing number of channels. Using k-t FOCUSS as a specific example of k-t CS, the experiments with Cartesian and radial data sets demonstrate that k-t sparse Generalized GRAPPA fOr Wider readout Line can produce results with two times lower root-mean-square error than conventional channel-by-channel k-t CS while consuming up to seven times less computational cost.

Parallel reconstruction using null operations

A novel iterative k-space data-driven technique, namely parallel reconstruction using null operations (PRUNO), is presented for parallel imaging reconstruction. In PRUNO, both data calibration and image reconstruction are formulated into linear algebra problems based on a generalized system model. An optimal data calibration strategy is demonstrated by using singular value decomposition, and an iterative conjugate-gradient approach is proposed to efficiently solve missing k-space samples during reconstruction. With its generalized formulation and precise mathematical model, PRUNO reconstruction yields good accuracy, flexibility, and stability. Both computer simulation and in vivo studies have shown that PRUNO produces much better reconstruction quality than generalized autocalibrating partially parallel acquisition (GRAPPA), especially under high accelerating rates. With the aid of PRUNO reconstruction, ultra-high accelerating parallel imaging can be performed with decent image quality. For example, we have done successful PRUNO reconstruction at a reduction factor of 6 (effective factor of 4.44) with eight coils and only a few autocalibration signal lines.

Peripheral MRA with k-space segmentation and blood-pool contrast agent

RATIONALE AND OBJECTIVES

The purpose of this study was to perform high-resolution contrast-enhanced peripheral multistation magnetic resonance angiography using a new blood-pool contrast agent (gadofosveset trisodium; Vasovist) while suppressing venous signal by using an acquisition scheme with k-space segmentation.

MATERIALS AND METHODS

Multistation peripheral magnetic resonance angiography with Vasovist was performed in 20 patients with known peripheral arterial occlusive disease. The k-space of the three-dimensional data sets was segmented such that the central parts were acquired during the first pass of the blood-pool agent, while the peripheral parts were acquired in the steady state. A third magnetic resonance angiographic data set acquired using the conventional technique served as comparison for venous overlay. Two radiologists blindly compared vessel sharpness, conspicuity, and venous contamination. Furthermore, a semiautomatic program to compare edge sharpness was used. Results were compared by means of Wilcoxon's signed rank sum test.

RESULTS

Comparison of vessel sharpness revealed statistically significant differences in favor of the fused data sets in all three stations. Arteries were depicted more sharply in the fused images and over longer parts, while veins were almost completely suppressed.

CONCLUSIONS

Peripheral contrast-enhanced magnetic resonance angiography with first-pass and steady-state k-space segmentation using a blood-pool contrast agent proved feasible and provided high-resolution data with sharp delineation of the arteries while reducing venous contamination.

RT-GROG: parallelized self-calibrating GROG for real-time MRI

A real-time implementation of self-calibrating Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) operator gridding for radial acquisitions is presented. Self-calibrating GRAPPA operator gridding is a parallel-imaging-based, parameter-free gridding algorithm, where coil sensitivity profiles are used to calculate gridding weights. Self-calibrating GRAPPA operator gridding's weight-set calculation and image reconstruction steps are decoupled into two distinct processes, implemented in C++ and parallelized. This decoupling allows the weights to be updated adaptively in the background while image reconstruction threads use the most recent gridding weights to grid and reconstruct images. All possible combinations of two-dimensional gridding weights G(x)(m)G(y)(n) are evaluated for m,n = {-0.5, -0.4, ..., 0, 0.1, ..., 0.5} and stored in a look-up table. Consequently, the per-sample two-dimensional weights calculation during gridding is eliminated from the reconstruction process and replaced by a simple look-up table access. In practice, up to 34x faster reconstruction than conventional (parallelized) self-calibrating GRAPPA operator gridding is achieved. On a 32-coil dataset of size 128 x 64, reconstruction performance is 14.5 frames per second (fps), while the data acquisition is 6.6 fps.

Motion correction using an enhanced floating navigator and GRAPPA operations

A method for motion correction in multicoil imaging applications, involving both data collection and reconstruction, is presented. The floating navigator method, which acquires a readout line off center in the phase-encoding direction, is expanded to detect translation/rotation and inconsistent motion. This is done by comparing floating navigator data with a reference k-space region surrounding the floating navigator line, using a correlation measure. The technique of generalized autocalibrating partially parallel acquisition is further developed to correct for a fully sampled, motion-corrupted dataset. The flexibility of generalized autocalibrating partially parallel acquisition kernels is exploited by extrapolating readout lines to fill in missing "pie slices" of k-space caused by rotational motion and regenerating full k-space data from multiple interleaved datasets, facilitating subsequent rigid-body motion correction or proper weighting of inconsistent data (e.g., with through-plane and nonrigid motion). Phantom and in vivo imaging experiments with turbo spin-echo sequence demonstrate the correction of severe motion artifacts.

Elliptical magnetic resonance spectroscopic imaging with GRAPPA for imaging brain tumors at 3 T

Magnetic Resonance Spectroscopic Imaging (MRSI) is a technique for imaging spatial variation of metabolites and has been very useful in characterizing biochemical changes associated with disease as well as response to therapy in malignant pathologies. This work presents a self-calibrated undersampling to accelerate 3D elliptical MRSI and an extrapolation-reconstruction algorithm based on the GRAPPA method. The accelerated MRSI technique was tested in three volunteers and five brain tumor patients. Acceleration allowed larger spatial coverage and consequently, less lipid contamination in spectra, compared to fully sampled acquisition within the same scantime. Metabolite concentrations measured from the accelerated acquisitions were in good agreement with measurements obtained from fully sampled MRSI scans.

Acute psychological stress reduces working memory-related activity in the dorsolateral prefrontal cortex

BACKGROUND

Acute psychological stress impairs higher-order cognitive function such as working memory (WM). Similar impairments are seen in various psychiatric disorders that are associated with higher susceptibility to stress and with prefrontal cortical dysfunctions, suggesting that acute stress may play a potential role in such dysfunctions. However, it remains unknown whether acute stress has immediate effects on WM-related prefrontal activity.

METHODS

Using functional magnetic resonance imaging (fMRI), we investigated neural activity of 27 healthy female participants during a blocked WM task (numerical N-back) while moderate psychological stress was induced by viewing strongly aversive (vs. neutral) movie material together with a self-referencing instruction. To assess stress manipulation, autonomic and endocrine, as well as subjective, measurements were acquired throughout the experiment.

RESULTS

Successfully induced acute stress resulted in significantly reduced WM-related activity in the dorsolateral prefrontal cortex (DLPFC), and was accompanied by less deactivation in brain regions that are jointly referred to as the default mode network.

CONCLUSIONS

This study demonstrates that experimentally induced acute stress in healthy volunteers results in a reduction of WM-related DLPFC activity and reallocation of neural resources away from executive function networks. These effects may be explained by supraoptimal levels of catecholamines potentially in conjunction with elevated levels of cortisol. A similar mechanism involving acute stress as a mediating factor may play an important role in higher-order cognitive deficits and hypofrontality observed in various psychiatric disorders.

Data consistency criterion for selecting parameters for k-space-based reconstruction in parallel imaging

k-space-based reconstruction in parallel imaging depends on the reconstruction kernel setting, including its support. An optimal choice of the kernel depends on the calibration data, coil geometry and signal-to-noise ratio, as well as the criterion used. In this work, data consistency, imposed by the shift invariance requirement of the kernel, is introduced as a goodness measure of k-space-based reconstruction in parallel imaging and demonstrated. Data consistency error (DCE) is calculated as the sum of squared difference between the acquired signals and their estimates obtained based on the interpolation of the estimated missing data. A resemblance between DCE and the mean square error in the reconstructed image was found, demonstrating DCE's potential as a metric for comparing or choosing reconstructions. When used for selecting the kernel support for generalized autocalibrating partially parallel acquisition (GRAPPA) reconstruction and the set of frames for calibration as well as the kernel support in temporal GRAPPA reconstruction, DCE led to improved images over existing methods. Data consistency error is efficient to evaluate, robust for selecting reconstruction parameters and suitable for characterizing and optimizing k-space-based reconstruction in parallel imaging.