Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

Advances in spiral fMRI: A high-resolution study with single-shot acquisition.

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination.

Mono-planar T-Hex: Speed and flexibility for high-resolution 3D imaging.

PURPOSE: The aim of this work is the reconciliation of high spatial and temporal resolution for MRI. For this purpose, a novel sampling strategy for 3D encoding is proposed, which provides flexible k-space segmentation along with uniform sampling density and benign filtering effects related to signal decay. METHODS: For time-critical MRI applications such as functional MRI (fMRI), 3D k-space is usually sampled by stacking together 2D trajectories such as echo planar imaging (EPI) or spiral readouts, where each shot covers one k-space plane. For very high temporal and medium to low spatial resolution, tilted hexagonal sampling (T-Hex) was recently proposed, which allows the acquisition of a larger k-space volume per excitation than can be covered with a planar readout. Here, T-Hex is described in a modified version where it instead acquires a smaller k-space volume per shot for use with medium temporal and high spatial resolution. RESULTS: Mono-planar T-Hex sampling provides flexibility in the choice of speed, signal-to-noise ratio (SNR), and contrast for rapid MRI acquisitions. For use with a conventional gradient system, it offers the greatest benefit in a regime of high in-plane resolution <1 mm. The sampling scheme is combined with spirals for high sampling speed as well as with more conventional EPI trajectories. CONCLUSION: Mono-planar T-Hex sampling combines fast 3D encoding with SNR efficiency and favorable depiction characteristics regarding noise amplification and filtering effects from T2∗ decay, thereby providing flexibility in the choice of imaging parameters. It is attractive both for high-resolution time series such as fMRI and for applications that require rapid anatomical imaging.

A Hilbert-based method for processing respiratory timeseries.

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).

Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning.

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.

Whole-brain estimates of directed connectivity for human connectomics.

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

Feasibility of spiral fMRI based on an LTI gradient model.

Spiral imaging is very well suited for functional MRI, however its use has been limited by the fact that artifacts caused by gradient imperfections and B0 inhomogeneity are more difficult to correct compared to EPI. Effective correction requires accurate knowledge of the traversed k-space trajectory. With the goal of making spiral fMRI more accessible, we have evaluated image reconstruction using trajectories predicted by the gradient impulse response function (GIRF), which can be determined in a one-time calibration step. GIRF-predicted reconstruction was tested for high-resolution (0.8 mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the nominal trajectory and concurrent field monitoring. The reconstructions using nominal spiral trajectories contain substantial artifacts and the activation maps contain misplaced activation. Image artifacts are substantially reduced when using the GIRF-predicted reconstruction, and the activation maps for the GIRF-predicted and monitored reconstructions largely overlap. The GIRF reconstruction provides a large increase in the spatial specificity of the activation compared to the nominal reconstruction. The GIRF-reconstruction generates image quality and fMRI results similar to using a concurrently monitored trajectory. The presented approach does not prolong or complicate the fMRI acquisition. Using GIRF-predicted trajectories has the potential to enable high-quality spiral fMRI in situations where concurrent trajectory monitoring is not available.

Hemodynamic modeling of long-term aspirin effects on blood oxygenated level dependent responses at 7 Tesla in patients at cardiovascular risk.

Aspirin is considered a potential confound for functional magnetic resonance imaging (fMRI) studies. This is because aspirin affects the synthesis of prostaglandin, a vasoactive mediator centrally involved in neurovascular coupling, a process underlying blood oxygenated level dependent (BOLD) responses. Aspirin-induced changes in BOLD signal are a potential confound for fMRI studies of at-risk individuals or patients (e.g. with cardiovascular conditions or stroke) who receive low-dose aspirin prophylactically and are compared to healthy controls without aspirin. To examine the severity of this potential confound, we combined high field (7 Tesla) MRI during a simple hand movement task with a biophysically informed hemodynamic model. We compared elderly individuals receiving aspirin for primary or secondary prophylactic purposes versus age-matched volunteers without aspirin medication, testing for putative differences in BOLD responses. Specifically, we fitted hemodynamic models to BOLD responses from 14 regions activated by the task and examined whether model parameter estimates were significantly altered by aspirin. While our analyses indicate that hemodynamics differed across regions, consistent with the known regional variability of BOLD responses, we neither found a significant main effect of aspirin (i.e., an average effect across brain regions) nor an expected drug × region interaction. While our sample size is not sufficiently large to rule out small-to-medium global effects of aspirin, we had adequate statistical power for detecting the expected interaction. Altogether, our analysis suggests that patients with cardiovascular risk receiving low-dose aspirin for primary or secondary prophylactic purposes do not show strongly altered BOLD signals when compared to healthy controls without aspirin.

T-Hex: Tilted hexagonal grids for rapid 3D imaging.

PURPOSE: The purpose of this work is to devise and demonstrate an encoding strategy for 3D MRI that reconciles high speed with flexible segmentation, uniform k-space density, and benign T2∗ effects. METHODS: Fast sampling of a 3D k-space is typically accomplished by 2D readouts per shot using EPI trains or spiral readouts. Tilted hexagonal (T-Hex) sampling is a way of acquiring more k-space volume per excitation while maintaining uniform sampling density and a smooth T2∗ filter. The k-space volume covered per shot is controlled by the tilting angle. Image reconstruction is performed with a 3D extension of the iterative SENSE approach, incorporating actual field dynamics and static off-resonance. T-Hex imaging is compared with established 3D schemes in terms of speed and noise performance. RESULTS: Tilted hexagonal acquisition is found to achieve greater imaging speed than known alternatives, particularly in combination with spiral trajectories. The interplay of the proposed 3D trajectories, array detection, and off-resonance is successfully addressed by iterative inversion of the full signal model. Enhanced coverage per shot is of greatest utility for high speed in an intermediate resolution regime of 1 to 4 mm. T-Hex EPI combines the benefits of extended coverage per shot with increased robustness against off-resonance effects. CONCLUSION: Sampling of tilted hexagonal grids is a feasible means of gaining 3D imaging speed with near-optimal SNR efficiency and benign depiction properties. It is a particularly promising technique for time-resolved applications such as fMRI.

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge.

PURPOSE: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). RESULTS: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. DISCUSSION AND CONCLUSION: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.

A method for correcting breathing-induced field fluctuations in T2*-weighted spinal cord imaging using a respiratory trace.

PURPOSE: Spinal cord MRI at ultrahigh field is hampered by time-varying magnetic fields associated with the breathing cycle, giving rise to ghosting artifacts in multi-shot acquisitions. Here, we suggest a correction approach based on linking the signal from a respiratory bellows to field changes inside the spinal cord. The information is used to correct the data at the image reconstruction level. METHODS: The correction was demonstrated in the context of multi-shot T2*-weighted imaging of the cervical spinal cord at 7T. A respiratory trace was acquired during a high-resolution multi-echo gradient-echo sequence, used for structural imaging and quantitative T2* mapping, and a multi-shot EPI time series, as would be suitable for fMRI. The coupling between the trace and the breathing-induced fields was determined by a short calibration scan in each individual. Images were reconstructed with and without trace-based correction. RESULTS: In the multi-echo acquisition, breathing-induced fields caused severe ghosting in images with long TE, which led to a systematic underestimation of T2* in the spinal cord. The trace-based correction reduced the ghosting and increased the estimated T2* values. Breathing-related ghosting was also observed in the multi-shot EPI images. The correction largely removed the ghosting, thereby improving the temporal signal-to-noise ratio of the time series. CONCLUSIONS: Trace-based retrospective correction of breathing-induced field variations can reduce ghosting and improve quantitative metrics in multi-shot structural and functional T2*-weighted imaging of the spinal cord. The method is straightforward to implement and does not rely on sequence modifications or additional hardware beyond a respiratory bellows.

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.

A generative model of whole-brain effective connectivity.

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data - in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling - for example, for phenotyping individual patients in terms of whole-brain network structure.

Single-shot spiral imaging at 7 T.

PURPOSE: The purpose of this work is to explore the feasibility and performance of single-shot spiral MRI at 7 T, using an expanded signal model for reconstruction. METHODS: Gradient-echo brain imaging is performed on a 7 T system using high-resolution single-shot spiral readouts and half-shot spirals that perform dual-image acquisition after a single excitation. Image reconstruction is based on an expanded signal model including the encoding effects of coil sensitivity, static off-resonance, and magnetic field dynamics. The latter are recorded concurrently with image acquisition, using NMR field probes. The resulting image resolution is assessed by point spread function analysis. RESULTS: Single-shot spiral imaging is achieved at a nominal resolution of 0.8 mm, using spiral-out readouts of 53-ms duration. High depiction fidelity is achieved without conspicuous blurring or distortion. Effective resolutions are assessed as 0.8, 0.94, and 0.98 mm in CSF, gray matter and white matter, respectively. High image quality is also achieved with half-shot acquisition yielding image pairs at 1.5-mm resolution. CONCLUSION: Use of an expanded signal model enables single-shot spiral imaging at 7 T with unprecedented image quality. Single-shot and half-shot spiral readouts deploy the sensitivity benefit of high field for rapid high-resolution imaging, particularly for functional MRI and arterial spin labeling.

Physiology recording with magnetic field probes for fMRI denoising.

Physiological noise originating in cardiovascular and respiratory processes is a substantial confound in BOLD fMRI. When unaccounted for it reduces the temporal SNR and causes error in inferred brain activity and connectivity. Physiology correction typically relies on auxiliary measurements with peripheral devices such as ECG, pulse oximeters, and breathing belts. These require direct skin contact or at least a tight fit, impairing subject comfort and adding to the setup time. In this work, we explore a touch-free alternative for physiology recording, using magnetic detection with NMR field probes. Placed close to the chest such probes offer high sensitivity to cardiovascular and respiratory dynamics without mechanical contact. This is demonstrated by physiology regression in a typical fMRI scenario at 7T, including validation against standard devices. The study confirms essentially equivalent performance of noise models based on conventional recordings and on field probes. It is shown that the field probes may be positioned in the subject's back such that they could be readily integrated in the patient table.

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.

Single-shot spiral imaging enabled by an expanded encoding model: Demonstration in diffusion MRI.

PURPOSE: The purpose of this work was to improve the quality of single-shot spiral MRI and demonstrate its application for diffusion-weighted imaging. METHODS: Image formation is based on an expanded encoding model that accounts for dynamic magnetic fields up to third order in space, nonuniform static B0 , and coil sensitivity encoding. The encoding model is determined by B0 mapping, sensitivity mapping, and concurrent field monitoring. Reconstruction is performed by iterative inversion of the expanded signal equations. Diffusion-tensor imaging with single-shot spiral readouts is performed in a phantom and in vivo, using a clinical 3T instrument. Image quality is assessed in terms of artefact levels, image congruence, and the influence of the different encoding factors. RESULTS: Using the full encoding model, diffusion-weighted single-shot spiral imaging of high quality is accomplished both in vitro and in vivo. Accounting for actual field dynamics, including higher orders, is found to be critical to suppress blurring, aliasing, and distortion. Enhanced image congruence permitted data fusion and diffusion tensor analysis without coregistration. CONCLUSION: Use of an expanded signal model largely overcomes the traditional vulnerability of spiral imaging with long readouts. It renders single-shot spirals competitive with echo-planar readouts and thus deploys shorter echo times and superior readout efficiency for diffusion imaging and further prospective applications. Magn Reson Med 77:83-91, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Feedback field control improves the precision of T2 * quantification at 7 T.

T2 * mapping offers access to a number of important structural and physiological tissue parameters. It is robust against RF field variations and overall signal scaling. However, T2 * measurement is highly sensitive to magnetic field errors, including perturbations caused by breathing motion at high baseline field. The goal of this work is to assess this issue in T2 * mapping of the brain and to study the benefit of field stabilization by feedback field control. T2 * quantification in the brain was investigated by phantom and in vivo measurements at 7 T. Repeated measurements were made with and without feedback field control using NMR field sensing and dynamic third-order shim actuation. The precision and reliability of T2 * quantification was assessed by studying variation across repeated measurements as well as fitting errors. Breathing effects were found to introduce significant error in T2 * mapping results. Field control mitigates this problem substantially. In a phantom it virtually eliminates the effects of emulated breathing fluctuations in the head. In vivo it enhances the structural fidelity of T2 * maps and reduces fitting residuals along with standard deviation. In conclusion, feedback field control improves the fidelity of T2 * mapping in the presence of field perturbations. It is an effective means of countering bulk susceptibility effects of breathing and hence holds particular promise for efforts to leverage high field for T2 * studies in vivo.

Image reconstruction using a gradient impulse response model for trajectory prediction.

PURPOSE: Gradient imperfections remain a challenge in MRI, especially for sequences relying on long imaging readouts. This work aims to explore image reconstruction based on k-space trajectories predicted by an impulse response model of the gradient system. THEORY AND METHODS: Gradient characterization was performed twice with 3 years interval on a commercial 3 Tesla (T) system. The measured gradient impulse response functions were used to predict actual k-space trajectories for single-shot echo-planar imaging (EPI), spiral and variable-speed EPI sequences. Image reconstruction based on the predicted trajectories was performed for phantom and in vivo data. Resulting images were compared with reconstructions based on concurrent field monitoring, separate trajectory measurements, and nominal trajectories. RESULTS: Image reconstruction using model-based trajectories yielded high-quality images, comparable to using separate trajectory measurements. Compared with using nominal trajectories, it strongly reduced ghosting, blurring, and geometric distortion. Equivalent image quality was obtained with the recent characterization and that performed 3 years prior. CONCLUSION: Model-based trajectory prediction enables high-quality image reconstruction for technically challenging sequences such as single-shot EPI and spiral imaging. It thus holds great promise for fast structural imaging and advanced neuroimaging techniques, including functional MRI, diffusion tensor imaging, and arterial spin labeling. The method can be based on a one-time system characterization as demonstrated by successful use of 3-year-old calibration data. Magn Reson Med 76:45-58, 2016. © 2015 Wiley Periodicals, Inc.

A field camera for MR sequence monitoring and system analysis.

PURPOSE: MR image formation and interpretation relies on highly accurate dynamic magnetic fields of high fidelity. A range of mechanisms still limit magnetic field fidelity, including magnet drifts, eddy currents, and finite linearity and stability of power amplifiers used to drive gradient and shim coils. Addressing remaining errors by means of hardware, sequence, or signal processing optimizations, calls for immediate observation by magnetic field monitoring. The present work presents a stand-alone monitoring system delivering insight into such field imperfections for MR sequence and system analysis. METHODS: A flexible NMR field probe-based stand-alone monitoring system, built on a software-defined-radio approach, is introduced and used to sense field dynamics up to third-order in space in a selection of situations with different time scales. RESULTS: Highly sensitive trajectories are measured and successfully used for image reconstruction. Further field perturbations due to mechanical oscillations and thermal field drifts following demanding gradient use and external interferences are studied. CONCLUSION: A flexible and versatile monitoring system is presented, delivering camera-like access to otherwise hardly accessible field dynamics with nanotesla resolution. Its stand-alone nature enables field analysis even during unknown MR system states.