B0 field inhomogeneity considerations in pseudo-continuous arterial spin labeling (pCASL): effects on tagging efficiency and correction strategy
NMR IN BIOMEDICINE
2011; 24 (10): 1202-1209
Real-Time Functional MRI Using Pseudo-Continuous Arterial Spin Labeling
MAGNETIC RESONANCE IN MEDICINE
2011; 65 (6): 1570-1577
Pseudo-continuous arterial spin labeling (pCASL) is a very powerful technique to measure cerebral perfusion, which circumvents the problems affecting other continuous arterial spin labeling schemes, such as magnetization transfer and duty cycle. However, some variability in the tagging efficiency of the pCASL technique has been reported. This article investigates the effect of B(0) field inhomogeneity on the tagging efficiency of the pCASL pulse sequence as a possible cause of this variability. Both theory and simulated data predict that the efficiency of pseudo-continuous labeling pulses can be degraded in the presence of off-resonance effects. These findings are corroborated by human in vivo measurements of tagging efficiency. On the basis of this theoretical framework, a method utilizing B(0) field map information is proposed to correct for the possible loss in tagging efficiency of the pCASL pulse sequence. The efficiency of the proposed correction method is evaluated using numerical simulations and in vivo implementation. The data show that the proposed method can effectively recover the lost tagging efficiency and signal-to-noise ratio of pCASL caused by off-resonance effects.
View details for DOI 10.1002/nbm.1675
View details for Web of Science ID 000298745600003
View details for PubMedID 21387447
Quantitative analysis of arterial spin labeling FMRI data using a general linear model
MAGNETIC RESONANCE IMAGING
2010; 28 (7): 919-927
The first implementation of real-time acquisition and analysis of arterial spin labeling-based functional magnetic resonance imaging time series is presented in this article. The implementation uses a pseudo-continuous labeling scheme followed by a spiral k-space acquisition trajectory. Real-time reconstruction of the images, preprocessing, and regression analysis of the functional magnetic resonance imaging data were implemented on a laptop computer interfaced with the MRI scanner. The method allows the user to track the current raw data, subtraction images, and the cumulative t-statistic map overlaid on a cumulative subtraction image. The user is also able to track the time course of individual time courses and interactively selects a region of interest as a nuisance covariate. The pulse sequence allows the user to adjust acquisition and labeling parameters while observing their effect on the image within two successive pulse repetition times. This method is demonstrated by two functional imaging experiments: a simultaneous finger-tapping and visual stimulation paradigm, and a bimanual finger-tapping task.
View details for DOI 10.1002/mrm.22922
View details for Web of Science ID 000291115500007
View details for PubMedID 21446035
Functional magnetic resonance imaging activation detection: Fuzzy cluster analysis in wavelet and multiwavelet domains
JOURNAL OF MAGNETIC RESONANCE IMAGING
2005; 22 (3): 381-389
Arterial spin labeling techniques can yield quantitative measures of perfusion by fitting a kinetic model to difference images (tagged-control). Because of the noisy nature of the difference images investigators typically average a large number of tagged versus control difference measurements over long periods of time. This averaging requires that the perfusion signal be at a steady state and not at the transitions between active and baseline states in order to quantitatively estimate activation induced perfusion. This can be an impediment for functional magnetic resonance imaging task experiments. In this work, we introduce a general linear model (GLM) that specifies Blood Oxygenation Level Dependent (BOLD) effects and arterial spin labeling modulation effects and translate them into meaningful, quantitative measures of perfusion by using standard tracer kinetic models. We show that there is a strong association between the perfusion values using our GLM method and the traditional subtraction method, but that our GLM method is more robust to noise.
View details for DOI 10.1016/j.mri.2010.03.035
View details for Web of Science ID 000281046100001
View details for PubMedID 20456889
Controlling the false positive rate in fuzzy clustering using randomization: application to fMRI activation detection
MAGNETIC RESONANCE IMAGING
2004; 22 (5): 631-638
To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents.Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared.The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis.More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.
View details for DOI 10.1002/jmri.20392
View details for Web of Science ID 000231747100008
View details for PubMedID 16104010
Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false-positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false-positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this article, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space. In both cases, the HRF-based feature space provides a greater sensitivity compared to the cross-correlation feature space and conventional cross-correlation analysis. Application of the proposed method to finger-tapping fMRI data, using HRF-based feature space, detected activation in sub-cortical regions, whereas both of the FCM with cross-correlation feature space and the conventional cross-correlation method failed to detect them.
View details for DOI 10.1016/j.mri.2004.01.035
View details for Web of Science ID 000221688400005
View details for PubMedID 15172056