Gregory R. Lee

Estimation efficiency and statistical power in arterial spin labeling fMRI.

Arterial spin labeling (ASL) data are typically differenced, sometimes after interpolation, as part of preprocessing before statistical analysis in fMRI. While this process can reduce the number of time points by half, it simplifies the subsequent signal and noise models (i.e., smoothed box-car predictors and white noise). In this paper, we argue that ASL data are best viewed in the same data analytic framework as BOLD fMRI data, in that all scans are modeled and colored noise is accommodated. The data are not differenced, but the control/label effect is implicitly built into the model. While the models using differenced data may seem easier to implement, we show that differencing models fit with ordinary least squares either produce biased estimates of the standard errors or suffer from a loss in efficiency. The main disadvantage to our approach is that non-white noise must be modeled in order to yield accurate standard errors, however, this is a standard problem that has been solved for BOLD data, and the very same software can be used to account for such autocorrelated noise.

Vascular dynamics and BOLD fMRI: CBF level effects and analysis considerations.

Changes in the cerebral blood flow (CBF) baseline produce significant changes to the hemodynamic response. This work shows that increases in the baseline blood flow level produce blood oxygenation-level dependent (BOLD) and blood flow responses that are slower and lower in amplitude, while decreases in the baseline blood flow level produce faster and higher amplitude hemodynamic responses. This effect was characterized using a vascular model of the hemodynamic response that separated arterial blood flow response from the venous blood volume response and linked the input stimulus to the vascular response. The model predicted the baseline blood flow level effects to be dominated by changes in the arterial vasculature. Specifically, it predicted changes in the arterial blood flow time constant and venous blood volume time constant parameters of +294% and -24%, respectively, for a 27% increase in the baseline blood flow. The vascular model performance was compared to an empirical model of the hemodynamic response. The vascular and empirical hemodynamic models captured most of the baseline blood flow level effects observed and can be used to correct for these effects in fMRI data. While the empirical hemodynamic model is easy to implement, it did not incorporate any explicit physiological information.

Fast, pseudo‐continuous arterial spin labeling for functional imaging using a two‐coil system

A fast, two‐coil, pseudo‐continuous labeling scheme is presented. This new scheme permits the collection of a multislice subtraction pair in <3 s, depending on the subjects arterial transit times. The method consists of acquiring both control and tag images immediately after a labeling period that matches the arterial transit time. The theoretical basis of the technique, and simulations of the signal during changes in both transit time and perfusion are presented. Experimental data from functional imaging experiments were collected to demonstrate the technique and its characteristics. Magn Reson Med 51:577–585, 2004.

Quantification of Perfusion fMRI Using a Numerical Model of Arterial Spin Labeling That Accounts for Dynamic Transit Time Effects

A new approach to modeling the signal observed in arterial spin labeling (ASL) experiments during changing perfusion conditions is presented in this article. The new model uses numerical methods to extend first‐order kinetic principles to include the changes in arrival time of the arterial tag that occur during neuronal activation. Estimation of the perfusion function from the ASL signal using this model is also demonstrated. The estimation algorithm uses a roughness penalty as well as prior information. The approach is demonstrated in numerical simulations and human experiments. The approach presented here is particularly suitable for fast ASL acquisition schemes, such as turbo continuous ASL (Turbo‐CASL), which allows subtraction pairs to be acquired in less than 3 s but is sensitive to arrival time changes. This modeling approach can also be extended to other acquisition schemes. Magn Reson Med, 2005.

Functional Imaging With Turbo-CASL: Transit Time and Multislice Imaging Considerations

The optimal use of turbo continuous arterial spin labeling (Turbo‐CASL) for functional imaging in the presence of activation‐induced transit time (TT) changes was investigated. Functional imaging of a bilateral finger‐tapping task showed improved sensitivity for Turbo‐CASL as compared to traditional CASL techniques for four of six subjects when scanned at an appropriate repetition time (TR). Both experimental and simulation results suggest that for optimal functional sensitivity with Turbo‐CASL, the pulse TR should be set to a value that is 100–200 ms less than the resting‐state TT. Simulations were also run to demonstrate the differences in TT sensitivity of different slices within a multislice acquisition, and the signal loss that is expected as the number of slices is increased. Despite the lower baseline ASL signal provided by the Turbo‐CASL acquisition, one can achieve equal or improved functional sensitivity due in part to the signal enhancement that accompanies the decrease in TT upon activation. Turbo‐CASL is thus a promising technique for functional ASL at higher temporal resolution. Magn Reson Med 57:661–669, 2007.

Application of selective saturation to image the dynamics of arterial blood flow during brain activation using magnetic resonance imaging

A saturation‐based approach is proposed to image the arterial blood flow signal with temporal resolution of 1 to 2 s and in‐plane spatial resolution of a few millimeters. Using a saturation approach to suppress the undesired background stationary signal allows the blood water that enters the slice to be imaged at some specified later time. Since the blood protons that are being imaged are not restricted to the intravascular space, this technique is also sensitive to tissue perfusion signal contributions. The signal uptake characteristics of the saturation method proposed were used to study the different signal contributions as a function of the acquisition parameters. A typical perfusion acquisition (FAIR) was also used for comparison. The proposed method was demonstrated in a functional motor activation experiment and the observed signal changes were smaller than those obtained using the FAIR acquisition. The dynamics of the saturation method and FAIR temporal signal changes were investigated and time constants between 2 and 44 s were estimated. The tissue signal contribution to the saturation methods signal was small over the range of acquisition parameters that sensitized it to the arterial compartment. Magn Reson Med, 2006.

Three-dimensional through-time radial GRAPPA for renal MR angiography: 3D Through-Time Radial GRAPPA

Purpose To achieve high temporal and spatial resolution for contrast-enhanced time-resolved MR angiography exams (trMRAs), fast imaging techniques such as non-Cartesian parallel imaging must be employed. In this study, the 3D through-time radial GRAPPA method is used to reconstruct highly accelerated stack-of-stars data for time-resolved renal MRAs.To achieve high temporal and spatial resolution for contrast‐enhanced time‐resolved MR angiography exams (trMRAs), fast imaging techniques such as non‐Cartesian parallel imaging must be used. In this study, the three‐dimensional (3D) through‐time radial generalized autocalibrating partially parallel acquisition (GRAPPA) method is used to reconstruct highly accelerated stack‐of‐stars data for time‐resolved renal MRAs.

3D Through-Time Radial GRAPPA for Renal Magnetic Resonance Angiography

Purpose To achieve high temporal and spatial resolution for contrast-enhanced time-resolved MR angiography exams (trMRAs), fast imaging techniques such as non-Cartesian parallel imaging must be employed. In this study, the 3D through-time radial GRAPPA method is used to reconstruct highly accelerated stack-of-stars data for time-resolved renal MRAs.To achieve high temporal and spatial resolution for contrast‐enhanced time‐resolved MR angiography exams (trMRAs), fast imaging techniques such as non‐Cartesian parallel imaging must be used. In this study, the three‐dimensional (3D) through‐time radial generalized autocalibrating partially parallel acquisition (GRAPPA) method is used to reconstruct highly accelerated stack‐of‐stars data for time‐resolved renal MRAs.