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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85843/1/Fessler31.pd

Motion Correction in fMRI by Mapping Slice-to-Volume with Concurrent Field-Inhomogeneity Correction

Head motion is the major source of error in measuring intensity changes related to given stimuli in fMRI. The effects of head motion are image shifts and field inhomogeneity variations which cause local changes in geometric distortions. The previously developed motion correction method, mapping slice-to-volume (MSV), retrospectively remaps slices that are shifted by head motion to their spatially correct locations in an anatomical reference. Images exhibiting spatially varying geometric distortions require non-linear mapping solutions. An accurate field map can be used for the correction of such spatial distortions. However, field-map changes with head motion and, in practice, only one field-map is available typically. This work evaluates the improved motion correction capability of MSV with concurrent iterative field-corrected reconstruction using only an initial field-map. The results from simulated motion data show effective convergence and accuracy in image registration for the correction of image artifacts complicated by the motion induced field effects.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85942/1/Fessler206.pd

Regularized Field Map Estimation in MRI

In fast magnetic resonance (MR) imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard field map estimation methods yield noisy field maps, particularly in image regions with low spin density. This paper describes regularized methods for field map estimation from two or more MR scans having different echo times. These methods exploit the fact that field maps are generally smooth functions. The methods use algorithms that decrease monotonically a regularized least-squares cost function, even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of field map estimates relative to conventional unregularized methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85871/1/Fessler22.pd

Multishot versus Single-Shot Pulse Sequences in Very High Field fMRI: A Comparison Using Retinotopic Mapping

High-resolution functional MRI is a leading application for very high field (7 Tesla) human MR imaging. Though higher field strengths promise improvements in signal-to-noise ratios (SNR) and BOLD contrast relative to fMRI at 3 Tesla, these benefits may be partially offset by accompanying increases in geometric distortion and other off-resonance effects. Such effects may be especially pronounced with the single-shot EPI pulse sequences typically used for fMRI at standard field strengths. As an alternative, one might consider multishot pulse sequences, which may lead to somewhat lower temporal SNR than standard EPI, but which are also often substantially less susceptible to off-resonance effects. Here we consider retinotopic mapping of human visual cortex as a practical test case by which to compare examples of these sequence types for high-resolution fMRI at 7 Tesla. We performed polar angle retinotopic mapping at each of 3 isotropic resolutions (2.0, 1.7, and 1.1 mm) using both accelerated single-shot 2D EPI and accelerated multishot 3D gradient-echo pulse sequences. We found that single-shot EPI indeed led to greater temporal SNR and contrast-to-noise ratios (CNR) than the multishot sequences. However, additional distortion correction in postprocessing was required in order to fully realize these advantages, particularly at higher resolutions. The retinotopic maps produced by both sequence types were qualitatively comparable, and showed equivalent test/retest reliability. Thus, when surface-based analyses are planned, or in other circumstances where geometric distortion is of particular concern, multishot pulse sequences could provide a viable alternative to single-shot EPI

Resting state correlates of subdimensions of anxious affect

Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal-posterior cingulate cortex-precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity

Susceptibility Artifacts

In vivo magnetic resonance imaging of the spinal cord is challenging due to susceptibility variations between various tissue types, air in the lungs and trachea, and in some cases surgical implants that significantly distort the applied magnetic field. These field inhomogeneities create off-resonance induced artifacts in the images, such as signal dropouts and pileups, geometric distortions, and incomplete fat suppression. Bulk physiologic motion from cardiac and respiratory cycles, cerebrospinal fluid pulsation, as well as breathing and swallowing further cause temporal variations of these field inhomogeneities. Moreover, the anatomy of the spine requires a relatively large field of view (FOV), especially in the sagittal imaging plane, while the small cross-sectional size of the spinal cord mandates high-spatial-resolution images. The resulting long readout duration, especially that of echo planar imaging (EPI), further exacerbates the artifacts. This chapter reviews susceptibility artifacts, their impact on EPI of the spinal cord, and methods to limit these artifacts. Acquisition-based methods include multishot imaging, parallel acquisitions, reduced-FOV methods, and non-EPI techniques. Reconstruction-based methods involve distortion correction, phase correction, and other advanced techniques. © 2014 Elsevier Inc. All rights reserved

On the reproducibility of in vivo temporal signal-to-noise ratio and its utility as a predictor of subject-level t-values in a functional magnetic resonance imaging study

The aim of this study was to evaluate the reproducibility of voxel-wise temporal signal-to-noise ratio (tSNR) on repeated scans across runs, sessions, and days. A group of 21 participants was scanned 16 times (4 runs per session, 2 sessions per day, 2 separate days) in a functional magnetic resonance imaging (fMRI) study on a 3T Philips Achieva scanner. For each run, we calculated t-value and tSNR maps. To ascertain that the results were not specific to the scanner, one volunteer was scanned with four fMRI runs in a single session on the above 3T Philips scanner as well as a 3T Siemens Prisma scanner. The coefficient of variation of voxel-wise tSNR across the 16 repeats was up to 25%, while the range relative to the mean of all observations was up to 80%. The voxel-wise variability of tSNR on the two different scanners was similar, indicating a general issue. Despite its use in evaluating the quality of fMRI data, we found only a weak relationship between tSNR and t-values. There is very high variability in voxel-wise tSNR, which should be considered while planning future studies that aim to identify small and focal fMRI effects or the benefits of incremental improvement in methods

Mitigating susceptibility-induced distortions in high-resolution 3DEPI fMRI at 7T

Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI

NeoRS: A neonatal resting state fMRI data preprocessing pipeline

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can\u27t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets