Diffusion MRI with concurrent magnetic field monitoring

Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring

PURPOSE

To compare MR axon radius estimation in human white matter using a multiband spiral sequence combined with field monitoring to the current state-of-the-art echo-planar imaging (EPI)-based approach.

METHODS

A custom multiband spiral sequence was used for diffusion-weighted imaging at ultra-highb $$ b $$ -values. Field monitoring and higher order image reconstruction were employed to greatly reduce artifacts in spiral images. Diffusion weighting parameters were chosen to match a state-of-the art EPI-based axon radius mapping protocol. The spiral approach was compared to the EPI approach by comparing the image signal-to-noise ratio (SNR) and performing a test-retest study to assess the respective variability and repeatability of axon radius mapping. Effective axon radius estimates were compared over white matter voxels and along the left corticospinal tract.

RESULTS

Increased SNR and reduced artifacts in spiral images led to reduced variability in resulting axon radius maps, especially in low-SNR regions. Test-retest variability was reduced by a factor of approximately 1.5 using the spiral approach. Reduced repeatability due to significant bias was found for some subjects in both spiral and EPI approaches, and attributed to scanner instability, pointing to a previously unknown limitation of the state-of-the-art approach.

CONCLUSION

Combining spiral readouts with field monitoring improved mapping of the effective axon radius compared to the conventional EPI approach.

Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.

Maximizing SNR per unit time in diffusion MRI with multiband T-Hex spirals

PURPOSE

The characterization of tissue microstructure using diffusion MRI (dMRI) signals is rapidly evolving, with increasing sophistication of signal representations and microstructure models. However, this progress often requires signals to be acquired with very high b-values (e.g., b > 30 ms/μm2 ), along many directions, and using multiple b-values, leading to long scan times and extremely low SNR in dMRI images. The purpose of this work is to boost the SNR efficiency of dMRI by combining three particularly efficient spatial encoding techniques and utilizing a high-performance gradient system (Gmax  ≤ 300 mT/m) for efficient diffusion encoding.

METHODS

Spiral readouts, multiband imaging, and sampling on tilted hexagonal grids (T-Hex) are combined and implemented on a 3T MRI system with ultra-strong gradients. Image reconstruction is performed through an iterative cg-SENSE algorithm incorporating static off-resonance distributions and field dynamics as measured with an NMR field camera. Additionally, T-Hex multiband is combined with a more conventional EPI-readout and compared with state-of-the-art blipped-CAIPIRINHA sampling. The advantage of the proposed approach is furthermore investigated for clinically available gradient performance and diffusion kurtosis imaging.

RESULTS

High fidelity in vivo images with b-values up to 40 ms/μm2 are obtained. The approach provides superior SNR efficiency over other state-of-the-art multiband diffusion readout schemes.

CONCLUSION

The demonstrated gains hold promise for the widespread dissemination of advanced microstructural scans, especially in clinical populations.

Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring

PURPOSE

To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI.

METHODS

A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera.

RESULTS

Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy."

CONCLUSION

Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction.

Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients

Introduction

Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations.

Methods

In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values.

Results

Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature.

Discussion

In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.

A correction algorithm for improved magnetic field monitoring with distal field probes

PURPOSE

A significant source of artifacts in MRI are field fluctuations. Field monitoring is a new technology that allows measurement of field dynamics during a scan via "field probes," which can be used to improve image reconstruction. Ideally, probes are located within the volume where gradients produce nominally linear field patterns. However, in some situations probes must be located far from isocenter where rapid field variation can arise, leading to erroneous field-monitoring characterizations and images. This work aimed to develop an algorithm that improves the robustness of field dynamics in these situations.

METHODS

The algorithm is split into three components. Component 1 calculates field dynamics one spatial order at a time, whereas the second implements a weighted least squares solution based on probe distance. Component 3 then calculates phase residuals and removes the residual phase for distant probes before recalculation. Two volunteers and a phantom were scanned on a 7T MRI using diffusion-weighted sequences, and field monitoring was performed. Image reconstructions were informed with field dynamics calculated conventionally, and with the correction algorithm, after which in vivo images were compared qualitatively and phantom image error was quantitatively assessed.

RESULTS

The algorithm was able to correct corrupted field dynamics, resulting in image-quality improvements. Significant artifact reduction was observed when correcting higher-order fits. Stepwise fitting provided the most correction benefit, which was marginally improved when adding the other correction strategies.

CONCLUSION

The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved characterization of field dynamics.

Arterial spin labeling MRI using spiral acquisitions and concurrent field monitoring

Perfusion MRI based on arterial spin labeling (ASL) has intrinsically very low signal-to-noise ratio (SNR). Signal acquisition at shorter echo times (TE) is necessary to boost the SNR of the ASL images. Spiral trajectories provide substantially shorter TE yielding increased SNR and are among the fastest k-space sampling schemes to encode a given field of view and resolution. Moreover, they provide approximately isotropic point-spread functions and inherent refocusing of motion- and flow-induced phase errors. However, the efficiency of the spiral acquisitions in ASL-MRI has been limited because these advantages are counterbalanced by practical technical challenges. This is because spiral acquisitions are highly sensitive to encoding deficiencies such as static off-resonance in the main magnetic field manifested as blurring artifacts in the image. Moreover, deviation of the gradient fields from the nominal waveforms due to the imperfection of the employed hardware critically limits the practical utilization of spiral trajectories. In this work, I provide single- and multiple-shot spiral ASL images that are robust against typical spiral encoding drawbacks enabled by deploying a comprehensive signal model involving static off-resonance and coil sensitivity maps and actual B0 and gradient field dynamics up to third order in space. The spiral ASL signal acquisition was concurrently monitored using a 3rd order dynamic field camera based on NMR field probes. The reconstructed ASL images at 3 mm and 2 mm in-plane resolution associating with the monitored field dynamics and the static off-resonances exhibited strongly reduced blurring- and aliasing artifacts and distortion. Concurrent field monitoring also enables to account for quasi-static B0 drifts by encompassing the parametric input data with consistent encoding geometry and physiological field fluctuations. In conclusion, concurrent field monitoring in spiral ASL acquisition largely overcomes traditional vulnerability of spiral trajectories in practice providing high quality ASL images with increased SNR, speed and motion robustness.

Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL)

The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.

High-resolution diffusion-weighted imaging at 7 Tesla: single-shot readout trajectories and their impact on signal-to-noise ratio, spatial resolution and accuracy

Diffusion MRI (dMRI) is a valuable imaging technique to study the connectivity and microstructure of the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various multi-shot acquisition strategies have been developed to achieve sub-millimeter resolution, but they require long scan times which can be restricting for patient scans. Alternatively, the SNR of single-shot acquisitions can be increased by using a spiral readout trajectory to minimize the sequence echo time. Imaging at ultra-high fields (UHF) could further increase the SNR of single-shot dMRI; however, the shorter T2* of brain tissue and the greater field non-uniformities at UHFs will degrade image quality, causing image blurring, distortions, and signal loss. In this study, we investigated the trade-off between the SNR and resolution of different k-space trajectories, including echo planar imaging (EPI), partial Fourier EPI, and spiral trajectories, over a range of dMRI resolutions at 7T. The effective resolution, spatial specificity and sharpening effect were measured from the point spread function (PSF) of the simulated diffusion sequences for a nominal resolution range of 0.6-1.8 mm. In-vivo partial brain scans at a nominal resolution of 1.5 mm isotropic were acquired using the three readout trajectories to validate the simulation results. Field probes were used to measure dynamic magnetic fields offline up to the 3^rd order of spherical harmonics. Image reconstruction was performed using static ΔB₀ field maps and the measured trajectories to correct image distortions and artifacts, leaving T2* effects as the primary source of blurring. The effective resolution was examined in fractional anisotropy (FA) maps calculated from a multi-shell dataset with b-values of 300, 1000, and 2000 s/mm² in 5, 16, and 48 directions, respectively. In-vivo scans at nominal resolutions of 1, 1.2, and 1.5 mm were acquired and the SNR of the different trajectories calculated using the multiple replica method to investigate the SNR. Finally, in-vivo whole brain scans with an effective resolution of 1.5 mm isotropic were acquired to explore the SNR and efficiency of different trajectories at a matching effective resolution. FA and intra-cellular volume fraction (ICVF) maps calculated using neurite orientation dispersion and density imaging (NODDI) were used for the comparison. The simulations and in vivo imaging results showed that for matching nominal resolutions, EPI trajectories had the highest specificity and effective resolution with maximum image sharpening effect. However, spirals have a significantly higher SNR, in particular at higher resolutions and even when the effective image resolutions are matched. Overall, this work shows that the higher SNR of single-shot spiral trajectories at 7T allows us to achieve higher effective resolutions compared to EPI and PF-EPI to map the microstructure and connectivity of small brain structures.

Comparison of uniform-density, variable-density, and dual-density spiral samplings for multi-shot DWI

PURPOSE

To compare the performances of uniform-density spiral (UDS), variable-density spiral (VDS), and dual-density spiral (DDS) samplings in multi-shot diffusion imaging, and determine a sampling strategy that balances reliability of shot navigator and overall DWI image quality.

THEORY AND METHODS

UDS, VDS, and DDS trajectories were implemented to achieve four-shot diffusion-weighted spiral imaging. First, the static B0 off-resonance effects in UDS, VDS, and DDS acquisitions were analyzed based on a signal model. Then, in vivo experiments were performed to verify the theoretical analyses, and fractional anisotropy (FA) fitting residuals were used to quantitatively assess the quality of spiral diffusion data for tensor estimation. Finally, the SNR performances and g-factor behavior of the three spiral samplings were evaluated using a Monte Carlo-based pseudo multiple replica method.

RESULTS

Among the three spiral trajectories with the same readout duration, UDS sampling exhibited the least off-resonance artifacts. This was most evident when the static B0 off-resonance effect was severe. The UDS diffusion images had higher anatomical fidelity and lower FA fitting residuals than the other two counterparts. Furthermore, the four-shot UDS acquisition achieved the best SNR performance in diffusion imaging with 12.11% and 40.85% improvements over the VDS and DDS acquisitions with the same readout duration, respectively.

CONCLUSION

UDS sampling is an efficient spiral acquisition scheme for high-resolution diffusion imaging with reliable navigator information. It provides superior off-resonance performance and SNR efficiency over the VDS and DDS samplings for the tested scenarios.

Eddy current-induced artifacts correction in high gradient strength diffusion MRI with dynamic field monitoring: demonstration in ex vivo human brain imaging

Purpose

To demonstrate the advantages of spatiotemporal magnetic field monitoring to correct eddy current-induced artifacts (ghosting and geometric distortions) in high gradient strength diffusion MRI (dMRI).

Methods

A dynamic field camera with 16 NMR field probes was used to characterize eddy current fields induced from diffusion gradients for different gradients strengths (up to 300 mT/m), diffusion directions, and shots in a 3D multi-shot EPI sequence on a 3T Connectom scanner. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-resolution whole brain ex vivo dMRI. A 3D multi-shot image reconstruction framework was informed with the actual nonlinear phase evolution measured with the dynamic field camera, thereby accounting for high-order eddy currents fields on top of the image encoding gradients in the image formation model.

Results

Eddy current fields from diffusion gradients at high gradient strength in a 3T Connectom scanner are highly nonlinear in space and time, inducing high-order spatial phase modulations between odd/even echoes and shots that are not static during the readout. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting approaches such as navigator- and structured low-rank-based methods or MUSE, followed by image-based distortion correction with eddy. Improved dMRI analysis is demonstrated with diffusion tensor imaging and high-angular resolution diffusion imaging.

Conclusion

Strong eddy current artifacts characteristic of high gradient strength dMRI can be well corrected with dynamic field monitoring-based image reconstruction, unlike the two-step approach consisting of ghosting correction followed by geometric distortion reduction with eddy.

CSR-PERT: Joint framework for MRI and HARDI data reconstruction using perturbed radial trajectory estimated from compressively sensed measurements

Radial sampling pattern is an important signal acquisition strategy in magnetic resonance imaging (MRI) owing to better immunity to motion-induced artifacts and less pronounced aliasing due to undersampling compared to the Cartesian sampling. These advantages of radial sampling can be exploited in acquisition of multidimensional signals such as High Angular Resolution Diffusion Imaging (HARDI), with tremendous scope of acceleration. Despite such benefits, gradient delays lead to samples being acquired from unknown miscentered radial trajectories, severely degrading the image reconstruction quality. In the present work, we propose Csr-Pert that is a joint framework, wherein these perturbed radial trajectories are estimated and utilized for image reconstruction from the compressively sensed measurements of (i) MRI data and (ii) HARDI data. The proposed Csr-Pert method is tested on one real MRI dataset with trajectory deviations and is observed to perform better than the existing state-of-the-art method at high acceleration factors up to 8. To the best of our knowledge, this is the first work to address the problem of estimating perturbed trajectories using the compressively sensed MRI and HARDI data. The method is also tested for varying combinations of trajectory deviations and sampling proportions. It is observed to yield very good quality HARDI reconstruction for a wide variety of scenarios. We have also demonstrated the robustness of the proposed method on real datasets in clinical settings assuming perturbed as well as noisy trajectories.

Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.

What's new and what's next in diffusion MRI preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.

Integration of an RF coil and commercial field camera for ultrahigh-field MRI

PURPOSE

To develop an RF coil with an integrated commercial field camera for ultrahigh field (7T) neuroimaging. The RF coil would operate within a head-only gradient coil and be subject to the corresponding design constraints. The RF coil can thereafter be used for subject-specific correction of k-space trajectories-notably in gradient-sensitive sequences such as single-shot spiral imaging.

METHODS

The transmit and receive performance was evaluated before and after the integration of field probes, whereas field probes were evaluated when in an optimal configuration external to the coil and after their integration. Diffusion-weighted EPI and single-shot spiral acquisitions were employed to evaluate the efficacy of correcting higher order field perturbations and the consequent effect on image quality.

RESULTS

Field probes had a negligible effect on RF-coil performance, including the transmit efficiency, transmit uniformity, and mean SNR over the brain. Modest reductions in field-probe signal lifetimes were observed, caused primarily by nonidealities in the gradient and shim fields of the head-only gradient coil at the probe positions. The field-monitoring system could correct up to second-order field perturbations in single-shot spiral imaging.

CONCLUSION

The integrated RF coil and field camera was capable of concurrent-field monitoring within a 7T head-only scanner and facilitated the subsequent correction of k-space trajectories during spiral imaging.

Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome

The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain - from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

On the signal-to-noise ratio benefit of spiral acquisition in diffusion MRI

PURPOSE

Spiral readouts combine several favorable properties that promise superior net sensitivity for diffusion imaging. The purpose of this study is to verify the signal-to-noise ratio (SNR) benefit of spiral acquisition in comparison with current echo-planar imaging (EPI) schemes.

METHODS

Diffusion-weighted in vivo brain data from three subjects were acquired with a single-shot spiral sequence and several variants of single-shot EPI, including full-Fourier and partial-Fourier readouts as well as different diffusion-encoding schemes. Image reconstruction was based on an expanded signal model including field dynamics obtained by concurrent field monitoring. The effective resolution of each sequence was matched to that of full-Fourier EPI with 1 mm nominal resolution. SNR maps were generated by determining the noise statistics of the raw data and analyzing the propagation of equivalent synthetic noise through image reconstruction. Using the same approach, maps of noise amplification due to parallel imaging (g-factor) were calculated for different acceleration factors.

RESULTS

Relative to full-Fourier EPI at b = 0 s/mm² , spiral acquisition yielded SNR gains of 42-88% and 40-89% in white and gray matter, respectively, depending on the diffusion-encoding scheme. Relative to partial-Fourier EPI, the gains were 36-44% and 34-42%. Spiral g-factor maps exhibited less spatial variation and lower maxima than their EPI counterparts.

CONCLUSION

Spiral readouts achieve significant SNR gains in the order of 40-80% over EPI in diffusion imaging at 3T. Combining systematic effects of shorter echo time, readout efficiency, and favorable g-factor behavior, similar benefits are expected across clinical and neurosciences uses of diffusion imaging.

Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain

The quantification of brain white matter properties is a key area of application of Magnetic Resonance Imaging (MRI), with much effort focused on using MR techniques to quantify tissue microstructure. While diffusion MRI probes white matter (WM) microstructure by characterising the sensitivity of Brownian motion of water molecules to anisotropic structures, susceptibility-based techniques probe the tissue microstructure by observing the effect of interaction between the tissue and the magnetic field. Here, we unify these two complementary approaches by combining ultra-strong (300 mT/m) gradients with a novel Diffusion-Filtered Asymmetric Spin Echo (D-FASE) technique. Using D-FASE we can separately assess the evolution of the intra- and extra-axonal signals under the action of susceptibility effects, revealing differences in the behaviour in different fibre tracts. We observed that the effective relaxation rate of the ASE signal in the corpus callosum decreases with increasing b-value in all subjects (from 17.1 ± 0.7 s⁻¹ at b = 0 s/mm² to 14.6 ± 0.7 s⁻¹ at b = 4800 s/mm²), while this dependence on b in the corticospinal tract is less pronounced (from 12.0± 1.1 s⁻¹ at b = 0s/mm² to 10.7 ± 0.5 s⁻¹ at b = 4800 s/mm²). Voxelwise analysis of the signal evolution with respect to b-factor and acquisition delay using a microscopic model demonstrated differences in gradient echo signal evolution between the intra- and extra-axonal pools.

A field-monitoring-based approach for correcting eddy-current-induced artifacts of up to the 2nd spatial order in human-connectome-project-style multiband diffusion MRI experiment at 7T: A pilot study

Over the recent years, significant advances in Spin-Echo (SE) Echo-Planar (EP) Diffusion MRI (dMRI) have enabled improved fiber tracking conspicuity in the human brain. At the same time, pushing the spatial resolution and using higher b-values inherently expose the acquired images to further eddy-current-induced distortion and blurring. Recently developed data-driven correction techniques, capable of significantly mitigating these defects, are included in the reconstruction pipelines developed for the Human Connectome Project (HCP) driven by the NIH BRAIN initiative. In this case, however, corrections are derived from the original diffusion-weighted (DW) magnitude images affected by distortion and blurring. Considering the complexity of k-space deviations in the presence of time varying high spatial order eddy currents, distortion and blurring may not be fully reversed when relying on magnitude DW images only. An alternative approach, consisting of iteratively reconstructing DW images based on the actual magnetic field spatiotemporal evolution measured with a magnetic field monitoring camera, has been successfully implemented at 3T in single band dMRI (Wilm et al., 2017, 2015). In this study, we aim to demonstrate the efficacy of this eddy current correction method in the challenging context of HCP-style multiband (MB ​= ​2) dMRI protocol. The magnetic field evolution was measured during the EP-dMRI readout echo train with a field monitoring camera equipped with 16 ¹⁹F NMR probes. The time variation of 0^th, 1^st and 2^nd order spherical field harmonics were used to reconstruct DW images. Individual DW images reconstructed with and without field correction were compared. The impact of eddy current correction was evaluated by comparing the corresponding direction-averaged DW images and fractional anisotropy (FA) maps. ¹⁹F field monitoring data confirmed the existence of significant field deviations induced by the diffusion-encoding gradients, with variations depending on diffusion gradient amplitude and direction. In DW images reconstructed with the field correction, residual aliasing artifacts were reduced or eliminated, and when high b-values were applied, better gray/white matter delineation and sharper gyri contours were observed, indicating reduced signal blurring. The improvement in image quality further contributed to sharper contours and better gray/white matter delineation in mean DW images and FA maps. In conclusion, we demonstrate that up-to-2^nd-order-eddy-current-induced field perturbation in multiband, in-plane accelerated HCP-style dMRI acquisition at 7T can be corrected by integrating the measured field evolution in image reconstruction.

Gradient Response Harvesting for Continuous System Characterization During MR Sequences

MRI gradient systems are required to generate magnetic field gradient waveforms with very high fidelity. This is commonly implemented by gradient system calibration and pre-emphasis. However, a number of mechanisms, particularly thermal changes, cause variation in the gradient response over time, which cannot be addressed by calibration approaches. To overcome this limitation, we present a novel method termed gradient response harvesting, where the gradient response is continuously characterized during the course of a normal MR sequence. Snippets of field measurements are repeatedly acquired during an MR sequence, and from these multiple field measurements and the known nominal MR sequence gradients, the gradient response and gradient/field offsets are calculated. The calculation is implemented in a model-based and a model-free variant. The method is demonstrated for EPI with high gradient duty-cycle, where the continuous gradient characterization is used to obtain k-space trajectory estimates that are employed in the subsequent image reconstruction. During the course of the MR sequence, changes in both the envelope and the phase of the gradient response functions were observed, including shifts of mechanical resonances. The gradient response changes were also reflected in the calculated uninterrupted gradient waveforms and thus in the k-space trajectories. Using the updated encoding information in the image reconstruction removed ghosting artifacts, that otherwise impaired the image quality. We introduced the concept of gradient response harvesting and demonstrated its feasibility. The obtained gradient response functions may be used for quality assurance/preventive maintenance, real-time adaptation of gradient pre-emphasis or to calculate uninterrupted gradient field evolutions.

A comprehensive approach for correcting voxel-wise b-value errors in diffusion MRI

PURPOSE

In diffusion MRI, the actual b-value played out on the scanner may deviate from the nominal value due to magnetic field imperfections. A simple image-based correction method for this problem is presented.

METHODS

The apparent diffusion constant (ADC) of a water phantom was measured voxel-wise along 64 diffusion directions at b = 1000 s/mm² . The true diffusion constant of water was estimated, considering the phantom temperature. A voxel-wise correction factor, providing an effective b-value including any magnetic field deviations, was determined for each diffusion direction by relating the measured ADC to the true diffusion constant. To test the method, the measured b-value map was used to calculate the corrected voxel-wise ADC for additionally acquired diffusion data sets on the same water phantom and data sets acquired on a small water phantom at three different positions. Diffusion tensor was estimated by applying the measured b-value map to phantom and in vivo data sets.

RESULTS

The b-value-corrected ADC maps of the phantom showed the expected spatial uniformity as well as a marked improvement in consistency across diffusion directions. The b-value correction for the brain data resulted in a 5.8% and 5.5% decrease in mean diffusivity and angular differences of the primary diffusion direction of 2.71° and 0.73° inside gray and white matter, respectively.

CONCLUSION

The actual b-value deviates significantly from its nominal setting, leading to a spatially variable error in the common diffusion outcome measures. The suggested method measures and corrects these artifacts.

Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI

The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'.

An industrial design solution for integrating NMR magnetic field sensors into an MRI scanner

PURPOSE

Neuroimaging research relies on the skills of increasingly multidisciplinary individuals and often requires the installation and use of additional home-built or third-party equipment. The purpose of the present work was the safe, ergonomic, durable, and aesthetically pleasing installation of magnetic field monitoring equipment into a scanner, while keeping the setup compatible with standard operating procedures.

METHODS

An extensive set of steps was required to design a 3D printed solution to install a magnetic field camera into the eight-channel head coil of a 3T MRI scanner. First, the outer surface of the plastic coil housing was recreated into a 3D model, and the installation of the magnetic field sensors around this 3D model was performed in a virtual environment. The 3D printed solution was then assembled and tested for safety, reproducible performance, and image quality.

RESULTS

The 3D printed solution holds the probes in stable positions and guides the necessary cables in an organized fashion and away from the volunteer. Assembly is easy and the solution is ergonomic, durable, and safe. We did not find excessive heating in the 3D printed parts, nor in the electronics, that they help to incorporate. The material used interferes minimally with transmit B1+ field.

CONCLUSION

The design met all of the boundary conditions for a durable, safe, cost-effective, attractive, and functional installation. This work will provide the basis for installing the magnetic field sensors into other available head coils, and for designing the experimental setup for projects with varying experimental requirements. Magn Reson Med 80:833-839, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Decoupled dynamic magnetic field measurements improves diffusion-weighted magnetic resonance images

Field probes are miniature receiver coils with localized NMR-active samples inside. They are useful in monitoring magnetic field. This information can be used to improve magnetic resonance image quality. While field probes are coupled to each other marginally in most applications, this coupling can cause incorrect resonance frequency estimates and image reconstruction errors. Here, we propose a method to reduce the coupling between field probes in order to improve the accuracy of magnetic field estimation. An asymmetric sensitivity matrix describing the coupling between channels of field probes and NMR active droplets within field probes was empirically measured. Localized signal originating from each probe was derived from the product of the inverse of the sensitivity matrix and the coupled probe measurements. This method was used to estimate maps of dynamic magnetic fields in diffusion weighted MRI. The estimated fields using decoupled probe measurement led to images more robust to eddy currents caused by diffusion sensitivity gradients along different directions.

Rapid anatomical brain imaging using spiral acquisition and an expanded signal model

We report the deployment of spiral acquisition for high-resolution structural imaging at 7T. Long spiral readouts are rendered manageable by an expanded signal model including static off-resonance and B₀ dynamics along with k-space trajectories and coil sensitivity maps. Image reconstruction is accomplished by inversion of the signal model using an extension of the iterative non-Cartesian SENSE algorithm. Spiral readouts up to 25 ms are shown to permit whole-brain 2D imaging at 0.5 mm in-plane resolution in less than a minute. A range of options is explored, including proton-density and T₂* contrast, acceleration by parallel imaging, different readout orientations, and the extraction of phase images. Results are shown to exhibit competitive image quality along with high geometric consistency.

Analysis and correction of field fluctuations in fMRI data using field monitoring

This work investigates the role of magnetic field fluctuations as a confound in fMRI. In standard fMRI experiments with single-shot EPI acquisition at 3 Tesla the uniform and gradient components of the magnetic field were recorded with NMR field sensors. By principal component analysis it is found that differences of field evolution between the EPI readouts are explainable by few components relating to slow and within-shot field dynamics of hardware and physiological origin. The impact of fluctuating field components is studied by selective data correction and assessment of its influence on image fluctuation and SFNR. Physiological field fluctuations, attributed to breathing, were found to be small relative to those of hardware origin. The dominant confounds were hardware-related and attributable to magnet drift and thermal changes. In raw image time series, field fluctuation caused significant SFNR loss, reflected by a 67% gain upon correction. Large part of this correction can be accomplished by traditional image realignment, which addresses slow and spatially uniform field changes. With realignment, explicit field correction increased the SFNR on the order of 6%. In conclusion, field fluctuations are a relevant confound in fMRI and can be addressed effectively by retrospective data correction. Based on the physics involved it is anticipated that the advantage of full field correction increases with field strength, with non-Cartesian readouts, and upon phase-sensitive BOLD analysis.

Simple noise reduction for diffusion weighted images

Our purpose in this study was to reduce the noise in order to improve the SNR of Dw images with high b-value by using two correction schemes. This study was performed with use of phantoms made from water and sucrose at different concentrations, which were 10, 30, and 50 weight percent (wt%). In noise reduction for Dw imaging of the phantoms, we compared two correction schemes that are based on the Rician distribution and the Gaussian distribution. The highest error values for each concentration with use of the Rician distribution scheme were 7.3 % for 10 wt%, 2.4 % for 30 wt%, and 0.1 % for 50 wt%. The highest error values for each concentration with use of the Gaussian distribution scheme were 20.3 % for 10 wt%, 11.6 % for 30 wt%, and 3.4 % for 50 wt%. In Dw imaging, the noise reduction makes it possible to apply the correction scheme of Rician distribution.