Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine
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Functional MRI (fMRI) generally employs gradient-echo echo-planar imaging (GE-EPI) to measure blood oxygen level-dependent (BOLD) signal changes that result from changes in tissue relaxation time T(*) (2) between activation and rest. Since T(*) (2) strongly varies across the brain and BOLD contrast is maximal only where the echo time (TE) equals the local T(*) (2), imaging at a single TE is a compromise in terms of overall sensitivity. Furthermore, the long echo train makes EPI very sensitive to main field inhomogeneities, causing strong image distortion. ⋯ The method was evaluated using an approach that allows differential BOLD CNR to be calculated without stimulation, as well as with a Stroop experiment. Results obtained at 3 T showed that BOLD sensitivity improved by 11% or more in all brain regions, with larger gains in areas typically affected by strong susceptibility artifacts. The use of parallel imaging markedly reduces image distortion, and hence the method should find widespread application in functional brain imaging.
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Dynamic susceptibility contrast (DSC)-MRI is commonly used to measure cerebral perfusion in acute ischemic stroke. Quantification of perfusion parameters involves deconvolution of the tissue concentration-time curves with an arterial input function (AIF), typically with the use of singular value decomposition (SVD). To mitigate the effects of noise on the estimated cerebral blood flow (CBF), a regularization parameter or threshold is used. ⋯ We present a method that partially corrects for the systematic error in the presence of an exponential residue function by applying a linear fit, which removes underestimates of long mean transit time (MTT) and overestimates of short MTT. For example, the correction reduced the error at a temporal resolution of 2.5 s and an SNR of 30 from 29.1% to 11.7%. However, the error is largest in the presence of noise and at MTTs that are likely to be encountered in areas of hypoperfusion; furthermore, even though it is reduced, it cannot be corrected for exactly.
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In pathologies in which slow or collateral flow conditions may exist, conventional arterial spin labeling (ASL) methods that apply magnetic tags based on the location of arterial spins may not provide robust measures of cerebral blood flow (CBF), as the transit delay for the delivery of blood to target tissues may far exceed the relaxation time of the tag. Here we describe current methods for ASL with velocity-selective (VS) tags (termed VSASL) that do not require spatial selectivity and can thus provide quantitative measures of CBF under slow and collateral flow conditions. The implementation of a robust multislice VSASL technique is described in detail, and data obtained with this technique are compared with those obtained with conventional pulsed ASL (PASL). The technical considerations described here include the design of VS pulses, background suppression, anisotropy with respect to velocity-encoding directions, and CBF quantitation issues.
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Task-related head movement during acquisition of fMRI data represents a serious confound for both motion correction and estimates of task-related activation. Cost functions implemented in most conventional motion-correction algorithms compare two volumes for similarity but fail to account for signal variability that is not due to motion (e.g., brain activation). We therefore recently proposed the theoretical basis for a novel method for fMRI motion correction, termed motion-corrected independent component analysis (MCICA), that allows for brain activation present in an fMRI time-series to be implicitly modeled and mitigates motion-induced signal changes without having to directly estimate the motion parameters (Liao et al., IEEE Transactions on Medical Imaging 2005;25:29-44). ⋯ Specifically, in simulations MCICA was more robust to the addition of simulated activation, and did not lead to the detection of false activations after correction for simulated task-correlated motion. With actual data from a motor fMRI experiment, the time course of the derived continually task-related ICA component became more correlated with the underlying behavioral task after preprocessing with MCICA compared to other methods, and the associated activation map was more clustered in the primary motor and supplementary motor cortices without spurious activation at the brain edge. We conclude that assessing the statistical properties of a motion-corrupted volume in relation to other volumes in the series, as is done with MCICA, is an accurate means of differentiating between motion-induced signal changes and other sources of variability in fMRI data.
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A new propeller EPI pulse sequence with reduced sensitivity to field inhomogeneities is proposed. Image artifacts such as blurring due to Nyquist ghosting and susceptibility gradients are investigated and compared with those obtained in previous propeller EPI studies. The proposed propeller EPI sequence uses a readout that is played out along the short axis of the propeller blade, orthogonal to the readout used in previous propeller methods. ⋯ Diffusion-weighted imaging (DWI) was also performed on the volunteer. Short-axis propeller EPI produced considerably fewer image artifacts compared to the other two sequences. Further, the oblique blades for the long-axis propeller EPI were also prone to one order of magnitude higher residual ghosting than the proposed short-axis propeller EPI.