Single-shot magnetic field mapping embedded in echo-planar time-course imaging

Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning

Identifying the circuits responsible for cognition and understanding their embedded computations is a challenge for neuroscience. We establish here a hierarchical cross-scale approach, from behavioral modeling and fMRI in task-performing mice to cellular recordings, in order to disentangle local network contributions to olfactory reinforcement learning. At mesoscale, fMRI identifies a functional olfactory-striatal network interacting dynamically with higher-order cortices. While primary olfactory cortices respectively contribute only some value components, the downstream olfactory tubercle of the ventral striatum expresses comprehensively reward prediction, its dynamic updating, and prediction error components. In the tubercle, recordings reveal two underlying neuronal populations with non-redundant reward prediction coding schemes. One population collectively produces stabilized predictions as distributed activity across neurons; in the other, neurons encode value individually and dynamically integrate the recent history of uncertain outcomes. These findings validate a cross-scale approach to mechanistic investigations of higher cognitive functions in rodents.

Where and how the brain learns from experience is not fully understood. Here the authors use a hierarchical approach from behavioural modelling to systems fMRI to cellular coding reveals brain mechanisms for history informed updating of future predictions.

Functional Activities Detected in the Olfactory Bulb and Associated Olfactory Regions in the Human Brain Using T2-Prepared BOLD Functional MRI at 7T

Olfaction is a fundamental sense that plays a vital role in daily life in humans, and can be altered in neuropsychiatric and neurodegenerative diseases. Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) using conventional echo-planar-imaging (EPI) based sequences can be challenging in brain regions important for olfactory processing, such as the olfactory bulb (OB) and orbitofrontal cortex, mainly due to the signal dropout and distortion artifacts caused by large susceptibility effects from the sinonasal cavity and temporal bone. To date, few studies have demonstrated successful fMRI in the OB in humans. T2-prepared (T2prep) BOLD fMRI is an alternative approach developed especially for performing fMRI in regions affected by large susceptibility artifacts. The purpose of this technical study is to evaluate T2prep BOLD fMRI for olfactory functional experiments in humans. Olfactory fMRI scans were performed on 7T in 14 healthy participants. T2prep BOLD showed greater sensitivity than GRE EPI BOLD in the OB, orbitofrontal cortex and the temporal pole. Functional activation was detected using T2prep BOLD in the OB and associated olfactory regions. Habituation effects and a bi-phasic pattern of fMRI signal changes during olfactory stimulation were observed in all regions. Both positively and negatively activated regions were observed during olfactory stimulation. These signal characteristics are generally consistent with literature and showed a good intra-subject reproducibility comparable to previous human BOLD fMRI studies. In conclusion, the methodology demonstrated in this study holds promise for future olfactory fMRI studies in the OB and other brain regions that suffer from large susceptibility artifacts.

Quantitative Assessment of the Impact of Geometric Distortions and Their Correction on fMRI Data Analyses

Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B₀ field inhomogeneity. The component derived from in-plane spin dephasing induces pixel intensity variations and, more critically, geometric distortions. Despite the physical mechanisms underlying the susceptibility artifact being well established, a systematic investigation on the impact of the associated geometric distortions, and the direct comparison of different approaches to tackle them, on fMRI data analyses is missing. Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE). We first characterized the geometric distortions and the correction approaches based on the estimated ΔB₀ field offset and voxel shift maps, and then conducted three types of analyses on the distorted and corrected fMRI data: (1) registration into structural data, (2) identification of resting-state networks (RSNs), and (3) mapping of task-related brain regions of interest. GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP. fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.

Resting State fMRI: Going Through the Motions

Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, “real-time” correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.

Influence of respiration-induced B0 variations in real-time phase-contrast echo planar imaging of the cervical cerebrospinal fluid

PURPOSE

Respiration induces temporal variations of the main magnetic field B₀ along the spinal cord. These variations are typically not compensated for in velocity quantifications using phase-contrast MRI. The goal of this study was to analyze errors caused by respiration-induced B₀ variations in real-time phase-contrast echo planar imaging (PCEPI) of cervical cerebrospinal fluid (CSF) velocity measurements and to evaluate this effect for various sequence parameters using numerical simulations.

METHODS

Real-time B₀ measurements with double gradient echo sequence and PCEPI measurements were acquired in the cervical CSF of 10 healthy subjects. Dynamic phase offsets attributed to respiration-induced B₀ variations were analyzed by quantifying amplitudes and comparing the temporal behavior with respiratory signals. In experiments and simulations, the influence of the echo time (TE) and the delay between PCEPI images (Δt) with respect to respiration on the dynamic phase offsets were investigated.

RESULTS

A good agreement was found between phase offsets extracted from both acquisition types. Furthermore, respiratory signals qualitatively matched the temporal behavior of the measured phase offsets showing a dependency on subject-dependent local B₀ distribution and respiration physiology. Simulations revealed residual background phases in PCEPI velocity quantification varying with TE and Δt.

CONCLUSION

Respiration-induced B₀ variations result in dynamic background phases in real-time PCEPI velocity quantifications of the CSF in the cervical spine. The current work underlines that these background phases need to be corrected to avoid confounding effects.

Accelerating multi-slice spatiotemporally encoded MRI with simultaneous echo refocusing

Single-shot spatiotemporally encoded (SPEN) ultrafast magnetic resonance imaging (MRI) is of great value to both scientific research and clinical application owing to its capability for delivering MR images with greater robustness to magnetic field inhomogeneity and chemical-shift displacement effects than conventional methods like EPI due to high effective phase-encoded bandwidth. Many SPEN MRI methods have been developed, among which multi-slice SPEN MRI arises as a promising supplement to ultrafast multi-slice sampling. In this work, we propose a new multi-slice SPEN MRI method, termed multi-echo segmented SPEN (ME-SeSPEN) method, which produces multiple images within a single train of echoes and successively samples widely separated slices. The resulting images were reconstructed using de-convolution super-resolved algorithm. The robustness and efficiency of the proposed method were demonstrated by phantom, lemon and in vivo experiments in comparison with spin-echo EPI, spin-echo simultaneous echo refocusing (SER), and segmented SPEN (SeSPEN) MRI. The results indicate that the new method effectively shortens the sampling time (20% reduction practically in comparison with SeSPEN when two slices are simultaneously sampled). ME-SeSPEN also reduces eddy current effects while maintaining the benefits of SPEN MRI, such as similar robustness to field inhomogeneity, spatial resolution and signal-to-noise ratio to SeSPEN MRI. The new method will promote the versatility of multi-slice MRI in practical applications.

Autobiography of James S. Hyde

The papers, book chapters, reviews, and patents by James S. Hyde in the bibliography of this document have been separated into EPR and MRI sections, and within each section by topics. Within each topic, publications are listed chronologically. A brief summary is provided for each patent listed. A few publications and patents that do not fit this schema have been omitted. This list of publications is preceded by a scientific autobiography that focuses on selected topics that are judged to have been of most scientific importance. References to many of the publications and patents in the bibliography are made in the autobiography.

Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI

In this paper we evaluate the three main methods for correcting the susceptibility-induced artefact in diffusion-weighted magnetic-resonance (DW-MR) data, and assess how correction is affected by the susceptibility field's interaction with motion. The susceptibility artefact adversely impacts analysis performed on the data and is typically corrected in post-processing. Correction strategies involve either registration to a structural image, the application of an acquired field-map or the use of additional images acquired with different phase-encoding. Unfortunately, the choice of which method to use is made difficult by the absence of any systematic comparisons of them. In this work we quantitatively evaluate these methods, by extending and employing a recently proposed framework that allows for the simulation of realistic DW-MR datasets with artefacts. Our analysis separately evaluates the ability for methods to correct for geometric distortions and to recover lost information in regions of signal compression. In terms of geometric distortions, we find that registration-based methods offer the poorest correction. Field-mapping techniques are better, but are influenced by noise and partial volume effects, whilst multiple phase-encode methods performed best. We use our simulations to validate a popular surrogate metric of correction quality, the comparison of corrected data acquired with AP and LR phase-encoding, and apply this surrogate to real datasets. Furthermore, we demonstrate that failing to account for the interaction of the susceptibility field with head movement leads to increased errors when analysing DW-MR data. None of the commonly used post-processing methods account for this interaction, and we suggest this may be a valuable area for future methods development.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Combining EPI and motion correction for fMRI human brain images with big motion

Motion correction is an important component in fMRI brain image analysis. Linear registration technique is mostly used in the process based on the assumption that there is not any shape changes of human brain during imaging process. Echo planar imaging (EPI) technique has been widely adapted in fMRI imaging to shorten encoding duration and increase temporal resolution. However, due to the magnetic field inhomogeneity caused by tissues, shape distortion and signal intensity lose are brought into fMRI images by the technique. On the other hand, subject's pose in scanner has a effect on magnetic field inhomogeneity, so the EPI distortions are subject to head movement, especially when the movement is big. As a result, most current motion correction techniques, which are based on rigid registration, cannot handle the problem. In this paper, a technique that combines EPI distortion correction and motion correction to handle the above-mentioned problem is proposed. Since it is almost impossible to obtain ground truth at present, a task-related fMRI BOLD time course image with big motion is selected as experimental material to test its performance. The image is pre-processed with the proposed EPI-motion correction scheme then analyzed by FSL feat tool. Compared with another process with only motion correction and FSL feat analysis, the experimental result using the proposed method has no false activation detection. It is suggested the proposed EPI-motion correction scheme has the ability to handle the fMRI human brain images with big motion.

Ultrafast multi-slice spatiotemporally encoded MRI with slice-selective dimension segmented

As a recently emerging method, spatiotemporally encoded (SPEN) magnetic resonance imaging (MRI) has a high robustness to field inhomogeneity and chemical shift effect. It has been broadened from single-slice scanning to multi-slice scanning. In this paper, a novel multi-slice SPEN MRI method was proposed. In this method, the slice-selective dimension was segmented to lower the specific absorption rate (SAR) and improve the image quality. This segmented method, dubbed SeSPEN method, was theoretically analyzed and demonstrated with phantom, lemon and in vivo rat brain experiments. The experimental results were compared with the results obtained from the spin-echo EPI, spin-echo SPEN method and multi-slice global SPEN method proposed by Frydman and coauthors (abbr. GlSPEN method). All the SPEN images were super-resolved reconstructed using deconvolution method. The results indicate that the SeSPEN method retains the advantage of SPEN MRI with respect to resistance to field inhomogeneity and can provide better signal-to-noise ratio than multi-slice GlSPEN MRI technique. The SeSPEN method has comparable SAR to the GlSPEN method while the T1 signal attenuation effect is alleviated. The proposed method will facilitate the multi-slice SPEN MRI to scan more slices within one scan with better image quality.

Spectral fitting using basis set modified by measured B0 field distribution

This study sought to demonstrate and evaluate a novel spectral fitting method to improve quantification accuracy in the presence of large magnetic field distortion, especially with high fields. MRS experiments were performed using a point-resolved spectroscopy (PRESS)-type sequence at 7 T. A double-echo gradient echo (GRE) sequence was used to acquire B0 maps following MRS experiments. The basis set was modified based on the measured B0 distribution within the MRS voxel. Quantification results were obtained after fitting the measured MRS data using the modified basis set. The proposed method was validated using numerical Monte Carlo simulations, phantom measurements, and comparison of occipital lobe MRS measurements under homogeneous and inhomogeneous magnetic field conditions. In vivo results acquired from voxels placed in thalamus and prefrontal cortex regions close to the frontal sinus agreed well with published values. Instead of noise-amplifying complex division, the proposed method treats field variations as part of the signal model, thereby avoiding inherent statistical bias associated with regularization. Simulations and experiments showed that the proposed approach reliably quantified results in the presence of relatively large magnetic field distortion. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

The influence of physiological noise correction on test-retest reliability of resting-state functional connectivity

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

Reduced field-of-view imaging for single-shot MRI with an amplitude-modulated chirp pulse excitation and Fourier transform reconstruction

PURPOSE

We employ an amplitude-modulated chirp pulse to selectively excite spins in one or more regions of interest (ROIs) to realize reduced field-of-view (rFOV) imaging based on single-shot spatiotemporally encoded (SPEN) sequence and Fourier transform reconstruction.

MATERIALS AND METHODS

The proposed rFOV imaging method was theoretically analyzed and illustrated with numerical simulation and tested with phantom experiments and in vivo rat experiments. In addition, point spread function was applied to demonstrate the feasibility of the proposed method. To evaluate the proposed method, the rFOV results were compared with those obtained using the EPI method with orthogonal RF excitation.

RESULTS

The simulation and experimental results show that the proposed method can image one or two separated ROIs along the SPEN dimension in a single shot with higher spatial resolution, less sensitive to field inhomogeneity, and practically no aliasing artifacts. In addition, the proposed method may produce rFOV images with comparable signal-to-noise ratio to the rFOV EPI images.

CONCLUSION

The proposed method is promising for the applications under severe susceptibility heterogeneities and for imaging separate ROIs simultaneously.

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.

Whole-brain three-dimensional T2-weighted BOLD functional magnetic resonance imaging at 7 Tesla

PURPOSE

A new acquisition scheme for T2-weighted spin-echo BOLD fMRI is introduced.

METHODS

It uses a T2-preparation module to induce blood-oxygenation-level-dependent (BOLD) contrast, followed by a single-shot three-dimensional (3D) fast gradient-echo readout with short echo time (TE). It differs from most spin-echo BOLD sequences in that BOLD contrast is generated before the readout, which eliminates the "dead time" due to long TE required for T2 contrast, and substantially improves acquisition efficiency. This approach, termed "3D T2prep-GRE," was implemented at 7 Tesla (T) with a typical spatial (2.5 × 2.5 × 2.5 mm(3) ) and temporal (TR = 2.3 s) resolution for functional MRI (fMRI) and whole-brain coverage (55 slices), and compared with the widely used 2D spin-echo EPI sequence.

RESULTS

In fMRI experiments of simultaneous visual/motor activities, 3D T2prep-GRE showed minimal distortion and little signal dropout across the whole brain. Its lower power deposition allowed greater spatial coverage (55 versus 17 slices with identical TR, resolution and power level), temporal SNR (60% higher) and CNR (35% higher) efficiency than 2D spin-echo EPI. It also showed smaller T2* contamination.

CONCLUSION

This approach is expected to be useful for ultra-high field fMRI, especially for regions near air cavities. The concept of using T2-preparation to generate BOLD contrast can be combined with many other sequences at any field strength.

Prospective slice-by-slice motion correction reduces false positive activations in fMRI with task-correlated motion

OBJECTIVE

We aimed to test the hypothesis that slice-by-slice prospective motion correction at 7T using an optical tracking system reduces the rate of false positive activations in an fMRI group study with a paradigm that involves task-correlated motion.

MATERIALS AND METHODS

Brain activation during right leg movement was measured using a block design on 15 volunteers, with and without prospective motion correction. Clearly erroneous activations were compared between both cases, at the individual level. Additionally, conventional group analysis was performed.

RESULTS

The number of falsely activated voxels with T-values higher than 5 was reduced by 48% using prospective motion correction alone, without additional retrospective realignment. In the group analysis, the statistical power was increased - the peak T-value was 26% greater, and the number of voxels in the cluster representing the right leg was increased by a factor of 9.3.

CONCLUSION

Slice-by-slice prospective motion correction in fMRI studies with task-correlated motion can substantially reduce false positive activations and increase statistical power.

On shimming approaches in 3T breast MRI

A comparative study is presented, analyzing quantitatively the impact of 15 shim strategies on the homogeneity of the main magnetic field over the three-dimensional breast region in 3T MRI. The results obtained in 12 female volunteers, spanning a wide range of body and breast types, indicate that the inclusion of the back and heart in the shim region of interest leads to considerable decrease in field homogeneity, and needs to be avoided. Comparison between shim strategies using volumetric B(0) maps, covering the entire breast region, and 1-6 plane B(0) maps indicate only minimally reduced performance for the latter. Interestingly, however, no single shim strategy relying on a limited number of B(0) maps as input was found to work best in all volunteers. This was attributed to the limited capability of a small number of B(0) maps to capture the B(0) variability existent within breast. On the average, a rectangular shim region of interest, encompassing the breast region alone, worked best for the cohort studied here.

Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series

As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression. Our findings suggest that including physiologic regressors provides some benefit in terms of reduction in phase noise power, but it is small compared to the benefit of dynamic field corrections and use of estimated motion parameters as nuisance regressors. Additionally, we show that the use of all three techniques reduces phase variance substantially, removes undesirable spatial phase correlations and improves detection of the functional response in magnitude and phase.

Correction of geometric distortion in fMRI data

The early functional MRI research programme at the National Institutes of Health, described by Robert Turner in an accompanying article in this volume, was the first to combine echo planar imaging (EPI) and high field in the pursuit of fMRI. As such, it soon became apparent that one of the obstacles to interpreting fMRI data using EPI was the presence of geometric distortions caused by static field inhomogeneities. This meant that EPI data did not properly align spatially with conventionally acquired MRI scans that showed structural information. This article describes some of the approaches that have been adopted to ensure that spatial warping caused by field inhomogeneities can be corrected so that functional and structural information can be co-aligned.

Direct magnetic field estimation based on echo planar raw data

Gradient recalled echo echo planar imaging is widely used in functional magnetic resonance imaging. The fast data acquisition is, however, very sensitive to field inhomogeneities which manifest themselves as artifacts in the images. Typically used correction methods have the common deficit that the data for the correction are acquired only once at the beginning of the experiment, assuming the field inhomogeneity distribution B(0) does not change over the course of the experiment. In this paper, methods to extract the magnetic field distribution from the acquired k-space data or from the reconstructed phase image of a gradient echo planar sequence are compared and extended. A common derivation for the presented approaches provides a solid theoretical basis, enables a fair comparison and demonstrates the equivalence of the k-space and the image phase based approaches. The image phase analysis is extended here to calculate the local gradient in the readout direction and improvements are introduced to the echo shift analysis, referred to here as "k-space filtering analysis." The described methods are compared to experimentally acquired B(0) maps in phantoms and in vivo. The k-space filtering analysis presented in this work demonstrated to be the most sensitive method to detect field inhomogeneities.

Joint estimation and correction of geometric distortions for EPI functional MRI using harmonic retrieval

Magnetic resonance imaging (MRI) uses applied spatial variations in the magnetic field to encode spatial position. Therefore, nonuniformities in the main magnetic field can cause image distortions. In order to correct the image distortions, it is desirable to simultaneously acquire data with a field map in registration. We propose a joint estimation (JE) framework with a fast, noniterative approach using harmonic retrieval (HR) methods, combined with a multi-echo echo-planar imaging (EPI) acquisition. The connection with HR establishes an elegant framework to solve the JE problem through a sequence of 1-D HR problems in which efficient solutions are available. We also derive the condition on the smoothness of the field map in order for HR techniques to recover the image with high signal-to-noise ratio. Compared to other dynamic field mapping methods, this method is not constrained by the absolute level of the field inhomogeneity over the slice, but relies on a generous pixel-to-pixel smoothness. Moreover, this method can recover image, field map, and T2* map simultaneously.

Motion robust magnetic susceptibility and field inhomogeneity estimation using regularized image restoration techniques for fMRI

In functional MRI, head motion may cause dynamic nonlinear field-inhomogeneity changes, especially with large out-of-plane rotations. This may lead to dynamic geometric distortion or blurring in the time series, which may reduce activation detection accuracy. The use of image registration to estimate dynamic field inhomogeneity maps from a static field map is not sufficient in the presence of such rotations. This paper introduces a retrospective approach to estimate magnetic susceptibility induced field maps of an object in motion, given a static susceptibility induced field map and the associated object motion parameters. It estimates a susceptibility map from a static field map using regularized image restoration techniques, and applies rigid body motion to the former. The dynamic field map is then computed using susceptibility voxel convolution. The method addresses field map changes due to out-of-plane rotations during time series acquisition and does not involve real time field map acquisitions.

Improving robustness and reliability of phase-sensitive fMRI analysis using temporal off-resonance alignment of single-echo timeseries (TOAST)

Echo Planar Imaging (EPI), often utilized in functional MRI (fMRI) experiments, is well known for its vulnerability to inconsistencies in the static magnetic field (B(0)). Correction for these field inhomogeneities usually involves measuring the magnetic field at a single time point, and using this static information to correct a series of images collected over the course of one or multiple experiments. However, common phenomena, such as respiration and motion, change the characteristics of the B(0) field homogeneity in a time-dependent and often unpredictable manner, rendering previous field measurements invalid. The effects of these changes are particularly large in the image phase, due to its direct and sensitive relationship to the magnetic field, and methods utilizing complex information can suffer enormously. This dependence can be exploited to estimate the temporal dynamics of the B(0) field. Use of this information to correct fMRI data can provide more effective motion correction, reduce temporal "noise," and can substantially restore statistically significant power to complex fMRI data analysis. All of the necessary information is embedded in complex EPI images, and results indicate this is a robust way to improve the quality of fMRI data, especially when used with complex analysis.

Fast joint reconstruction of dynamic R2* and field maps in functional MRI

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing T(2)(*)-weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct R(2)(*) maps which are quantifiable and have comparable BOLD contrast as T(2)(*)-weighted images. However, conventional R(2)(*) mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs R(2)(*) and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both R(2)(*) and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for T(2)(*)-weighted imaging the resulting R(2)(*) maps performed comparably to T(2)(*)-weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance.

Spin saturation artifact correction using slice-to-volume registration motion estimates for fMRI time series

Evaluation of functional magnetic resonance imaging (fMRI) as a reliable clinical imaging tool requires accurate assessment and correction of head motion artifacts. As the correction of bulk head motion is vital, the loss of signal strength from the confounding effect of head motion on spin magnetization may be an additional factor in activation analysis error. This study focuses on the evaluation and correction of the spin saturation artifact that occurs when parts of adjacent slices are selected due to changing head positions in single-shot multislice acquisitions. As a consequence of head movement, the acquired slices constituting a fMRI volume are no longer parallel to each other and the spin magnetization in fMRI voxels becomes dependent on head motion history. Motion corrections applying the same rigid motion estimates to all the slices in a volume may not be a reasonable approximation in cases where the magnitude of head motion exceeds a subvoxel range. For realistic ranges of motion in fMRI, an accurate estimate of rigid motion parameters for each echo planar imaging (EPI) slice is essential to correctly register voxel intensities. Previously we have implemented the map-slice-to-volume (MSV) motion correction method that maps each slice in a time series onto a reference anatomical volume, which proved to be effective in improving activation detection. To correctly evaluate the motion dependence of spin magnetization, each voxel is tracked with movement history that is available from MSV motion estimates. Relatively low in resolution, EPI voxels are composed of varying mixtures of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) and variations in the tissue composition give rise to voxel intensities that are functions of tissue T1 properties. We have developed a weighted-average spin saturation (WASS) correction method that can handle full rigid motion and account for the melange of different brain tissue isochromats at each EPI voxel location. We evaluated the effect of spin saturation artifacts and the performance of the WASS correction using simulated fMRI time series synthesized with known true activation, motion, and the associated spin saturation artifact. Two different ranges of head rotations, [-5,5] and [-2,2] deg, were introduced and the effect of the spin saturation artifact was quantified to show 18% and 13% reduction in activation detection rate, respectively. Following the MSV motion and WASS correction, results indicate that WASS correction can improve activation detection by 17% relative to MSV only correction.

Concurrent correction of geometric distortion and motion using the map-slice-to-volume method in echo-planar imaging

The accuracy of measuring voxel intensity changes between stimulus and rest images in fMRI echo-planar imaging (EPI) data is severely degraded in the presence of head motion. In addition, EPI is sensitive to susceptibility-induced geometric distortions. Head motion causes image shifts and associated field map changes that induce different geometric distortion at different time points. Conventionally, geometric distortion is "corrected" with a static field map independently of image registration. That approach ignores all field map changes induced by head motion. This work evaluates the improved motion correction capability of mapping slice to volume with concurrent iterative field corrected reconstruction using updated field maps derived from an initial static field map that has been spatially transformed and resampled. It accounts for motion-induced field map changes for translational and in-plane rotation motion. The results from simulated EPI time series data, in which motion, image intensity and activation ground truths are available, show improved accuracy in image registration, field corrected image reconstruction and activation detection.

High spatial resolution increases the specificity of block-design BOLD fMRI studies of overt vowel production

Functional MRI (fMRI) studies of tasks involving orofacial motion, such as speech, are prone to problems related to motion-induced magnetic field variations. Orofacial motion perturbs the static magnetic field, leading to signal changes that correlate with the task and corrupt activation maps with false positives or signal loss. These motion-induced signal changes represent a contraindication for the implementation of fMRI to study the neurophysiology of orofacial motion. An fMRI experiment of a structured, non-semantic vowel production task was performed using four different voxel volumes and three different slice orientations in an attempt to find a set of acquisition parameters leading to activation maps with maximum specificity. Results indicate that the use of small voxel volumes (2 x 2 x 3 mm(3)) yielded a significantly higher percentage of true positive activation compared to the use of larger voxel volumes. Slice orientation did not have as great an impact as spatial resolution, although coronal slices appeared superior at high spatial resolutions. Furthermore, it was found that combining the strategy of high spatial resolution with an optimum task duration and post-processing methods for separating true and false positives greatly improved the specificity of single-subject, block-design fMRI studies of structured, overt vowel production.

Reducing the unwanted draining vein BOLD contribution in fMRI with statistical post-processing methods

Recent BOLD fMRI data analysis methods show promise in reducing contributions from draining veins. The phase regressor method developed by [Menon, R.S., 2002. Post-acquisition suppression of large-vessel BOLD signals in high-resolution fMRI. Magn. Reson. Med., 47, 1-9] creates phase and magnitude images, regresses magnitude as a function of phase, and subtracts phase-estimated magnitudes from the observed magnitudes. The corrected magnitude images are used to compute cortical activations. The complex constant phase method, developed by [Rowe, D.B., Logan, B.R., 2004. A complex way to compute fMRI activation. NeuroImage, 23, 1078-1092], uses complex-valued reconstructed images and a nonlinear regressor model to compute magnitude cortical activations assuming temporally constant phase. In both methods, the usage of the phase information is claimed to bias against voxels with task-related phase changes caused by some draining veins. The behavior of the statistical methods in data with several task-related magnitude and phase changes is compared. The power of the statistical methods for determining voxels with specific task-related magnitude and phase change combinations are determined in ideal simulated data. The phase regressor and complex constant phase activation determination techniques are examined to characterize the responses of the models to select task-related phase and magnitude change combinations in representative simulated time series. Possible draining veins in human preliminary data are discussed and analyzed with the models and the current challenges which prevent these methods from being reliably implemented are discussed.

Zero and first-order phase shift correction for field map estimation with dual-echo GRE using bipolar gradients

A simple phase error correction technique used for field map estimation with a generally available dual-echo gradient-echo (GRE) sequence is presented. Magnetic field inhomogeneity maps estimated using two separate GRE volume acquisitions at different echo times are prone to dynamic motion errors between acquisitions. By using the dual-echo sequence, the data are collected during two back-to-back readout gradients in opposite polarity after a single radio frequency pulse, and interecho motion artifacts and alignment errors in field map estimation can be factored out. Residual phase error from the asymmetric readout pulses is modeled as an affine term in the readout direction. Results from phantom and human data suggest that the first-order phase correction term stays constant over time and, hence, can be applied to different data acquired with the same protocol over time. The zero-order phase correction term may change with time and is estimated empirically for different scans.

Monitoring and correcting spatio-temporal variations of the MR scanner's static magnetic field

The homogeneity and stability of the static magnetic field are of paramount importance to the accuracy of MR procedures that are sensitive to phase errors and magnetic field inhomogeneity. It is shown that intense gradient utilization in clinical horizontal-bore superconducting MR scanners of three different vendors results in main magnetic fields that vary on a long time scale both spatially and temporally by amounts of order 0.8-2.5 ppm. The observed spatial changes have linear and quadratic variations that are strongest along the z direction. It is shown that the effect of such variations is of sufficient magnitude to completely obfuscate thermal phase shifts measured by proton-resonance frequency-shift MR thermometry and certainly affect accuracy. In addition, field variations cause signal loss and line-broadening in MR spectroscopy, as exemplified by a fourfold line-broadening of metabolites over the course of a 45 min human brain study. The field variations are consistent with resistive heating of the magnet structures. It is concluded that correction strategies are required to compensate for these spatial and temporal field drifts for phase-sensitive MR protocols. It is demonstrated that serial field mapping and phased difference imaging correction protocols can substantially compensate for the drift effects observed in the MR thermometry and spectroscopy experiments.

MR susceptometry for measuring global brain oxygen extraction

Monitoring of oxygen saturation in jugular venous blood gives an estimate of the balance of global oxygen delivery and cerebral oxygen consumption. We present a noninvasive approach to measure oxygen saturation in vivo in the internal jugular vein using MR susceptometry by exploiting the characteristic susceptibility of deoxyhemoglobin, and demonstrate the feasibility of performing such measurements in a group of subjects. We assessed the sensitivity of the method for detecting small changes in oxygen saturation by monitoring the variations observed during breath-holding and hypoventilation experiments. Unlike alternative methods, the susceptometric technique does not require calibration.

EPI distortion correction from a simultaneously acquired distortion map using TRAIL

PURPOSE

To develop a method for shot-by-shot distortion correction of single-shot echo-planar imaging (EPI) that is capable of correcting each image individually using a distortion measurement performed during acquisition of the image itself.

MATERIALS AND METHODS

The recently-introduced method known as two reduced acquisitions interleaved (TRAIL) was extended to measure the distribution of the main magnetic field B0 with each shot. This corresponded to a map of distortion, and allowed distortion to be corrected in the acquired images.

RESULTS

Distortion-corrected images were demonstrated in the human brain. The distortion field could be directly visualized using the "stripe" distribution imposed by the TRAIL pulse sequence. This confirmed the success of the correction. Over a time-course measurement of 10 images, variance was reduced by using shot-by-shot distortion correction compared to correction with a constant field map.

CONCLUSION

Shot-by-shot distortion correction may be performed for EPI images acquired using an extension of the TRAIL technique, ensuring that the correction reflects the actual distortion pattern and not merely a previously measured, but possibly no longer valid, distortion field. This avoids errors due to changes in the distortion field or misregistration of a previously measured distortion map resulting from subject motion.

Application of k-space energy spectrum analysis to susceptibility field mapping and distortion correction in gradient-echo EPI

Echo-planar imaging (EPI) is widely used in functional MRI studies. It is well known that EPI quality is usually degraded by geometric distortions, when there exist susceptibility field inhomogeneities. EPI distortions may be corrected if the field maps are available. It is possible to estimate the susceptibility field gradients from the phase reconstruction of a single-TE EPI image, after a successful phase-unwrapping procedure. However, in regions affected by pronounced field gradients, the phase-unwrapping of a single-TE image may fail, and therefore the estimated field maps may be incorrect. It has been reported that the field inhomogeneity may be calculated more reliably from T2*-weighted images corresponding to multiple TEs. However, the multi-TE MRI field mapping increases the scan time. Furthermore, the measured field maps may be invalid if the subject's position changes during dynamic scans. To overcome the limitations in conventional field mapping approaches, a novel k-space energy spectrum analysis algorithm is developed, which quantifies the spatially dependent echo-shifting effect and the susceptibility field gradients directly from the k-space data of single-TE gradient-echo EPI. Using the k-space energy spectrum analysis, susceptibility field gradients can be reliably measured without phase-unwrapping, and EPI distortions can be corrected without extra field mapping scans or pulse sequence modification. The reported technique can be used to retrospectively improve the image quality of the previously acquired EPI and functional MRI data, provided that the complex-domain k-space data are still available.

Strategies for block-design fMRI experiments during task-related motion of structures of the oral cavity

Functional MRI (fMRI) studies of jaw motion, speech, and swallowing disorders have been hampered by motion artifacts. Tissue motion perturbs the static magnetic field, creating geometric distortions in echo-planar images that lead to many false positives in activation maps. These problems have restricted blood oxygenation level-dependent (BOLD) fMRI studies involving orofacial muscles to event-related designs, which offer weak contrast-to-noise ratios when compared to block designs. Two new approaches are described that greatly reduce false positives in the activation maps created by the distortions in block-design fMRI studies involving jaw and tongue motion during chewing. First, an appropriate task duration of 10-14 s was found to maximize functional contrast while minimizing motion artifacts. Second, three motion-sensitive postprocessing methods were applied successively to examine the temporal and spatial characteristics of responses and identify and remove false positives caused by motion artifacts. These techniques are shown to allow the use of block design in an fMRI study of a jaw motion task. Extension to speech and swallowing tasks is discussed.