Hesamoddin Jahanian

B0 field inhomogeneity considerations in pseudo-continuous arterial spin labeling (pCASL): effects on tagging efficiency and correction strategy

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 B0 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 B0 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. Copyright

Quantitative analysis of arterial spin labeling FMRI data using a general linear model

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.

Real-Time Functional MRI Using Pseudo-Continuous Arterial Spin Labeling

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. Magn Reson Med, 2011.

4D wavelet noise suppression of MR diffusion tensor data

Diffusion tensor imaging (DTI) is known to be promising for providing anatomical information about white-matter fiber bundles that cannot be obtained by other non-invasive in vivo imaging methods. However, its application is limited because of its low signal-to-noise ratio and significant imaging artifacts. To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging 4D wavelet denoising technique is used to improve the signal to noise ratio (SNR) of diffusion tensor images. To evaluate the proposed method, a high SNR data set is built by repeating and averaging the data acquisition several times and is compared to the denoised data. Our results revealed that wavelets would effectively reduce the noise in DTI data with less blurring of tissue types, especially in the white matter. It would suggest that by using the 4D wavelet noise suppression, one could decrease the acquisition time and still have an acceptable SNR.

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

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.

Novel approach to control false positive rate in fuzzy cluster analysis of fMRI

Fuzzy c-means (FCM) suffers from some limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results, when it is applied to the raw fMRI time series. Based on randomization, we developed a method to control the false positive detection rate in FCM and estimate the statistical significance of the results. Using this novel approach, we proposed an fMRI activation detection method which uses FCM with controlled false positive rate. The ability of the method in controlling the false positive rate is shown by an analysis of false positives in activation maps of resting-state fMRI data. Controlling the false positive rate allows comparison of different feature spaces and fuzzy clustering methods. A new feature space, in multi and scalar wavelet domain, is proposed for activation detection in fMRI to address the stability problem. Finally, using the proposed method for controlling the false positive rate, the proposed feature space is compared to the cross-correlation feature space.

Controlling the false positive detection rate in fuzzy clustering of fMRI data

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 paper, 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.

ROC-based determination of the number of clusters for fMRI activation detection

Fuzzy C-means (FCM), in spite of its potent advantages in exploratory analyze of functional magnetic resonance imaging (fMRI), suffers from limitations such as a priori determination of number of clusters, unknown statistical significance for the results, and instability of the results when it is applied on raw fMRI time series. Choosing different number of clusters, or thresholding the membership degree at different levels, lead to considerably different activation maps. However, research work for finding a standard index to determine the number of clusters has not yet succeeded. Using randomization, we developed a method to control false positive rate in FCM, which gives a meaningful statistical significance to the results. Making use of this novel method and an ROC-based cluster validity measure, we determined the optimal number of clusters. In this study, we applied the FCM on a feature space that takes the variability of hemodynamic response function into account (HRF-based feature space). The proposed method found the accurate number of clusters in simulated fMRI data. In addition, the proposed method generated excellent results for experimental fMRI data and showed a good reproducibility for determining the number of clusters.