Coronary artery anomalies and variants: technical feasibility of assessment with coronary MR angiography at 3 T.

The purpose of this study was to prospectively use a whole-heart three-dimensional (3D) coronary magnetic resonance (MR) angiography technique specifically adapted for use at 3 T and a parallel imaging technique (sensitivity encoding) to evaluate coronary arterial anomalies and variants (CAAV). This HIPAA-compliant study was approved by the local institutional review board, and informed consent was obtained from all participants. Twenty-two participants (11 men, 11 women; age range, 18-62 years) were included. Ten participants were healthy volunteers, whereas 12 participants were patients suspected of having CAAV. Coronary MR angiography was performed with a 3-T MR imager. A 3D free-breathing navigator-gated and vector electrocardiographically-gated segmented k-space gradient-echo sequence with adiabatic T2 preparation pulse and parallel imaging (sensitivity encoding) was used. Whole-heart acquisitions (repetition time msec/echo time msec, 4/1.35; 20 degrees flip angle; 1 x 1 x 2-mm acquired voxel size) lasted 10-12 minutes. Mean examination time was 41 minutes +/- 14 (standard deviation). Findings included aneurysms, ectasia, arteriovenous fistulas, and anomalous origins. The 3D whole-heart acquisitions developed for use with 3 T are feasible for use in the assessment of CAAV.

Free-running 4D whole-heart self-navigated golden angle MRI: Initial results.

PURPOSE: To test the hypothesis that both coronary anatomy and ventricular function can be assessed simultaneously using a single four-dimensional (4D) acquisition. METHODS: A free-running 4D whole-heart self-navigated acquisition incorporating a golden angle radial trajectory was implemented and tested in vivo in nine healthy adult human subjects. Coronary magnetic resonance angiography (MRA) datasets with retrospective selection of acquisition window width and position were extracted and quantitatively compared with baseline self-navigated electrocardiography (ECG) -triggered coronary MRA. From the 4D datasets, the left-ventricular end-systolic, end-diastolic volumes (ESV & EDV) and ejection fraction (EF) were computed and compared with values obtained from conventional 2D cine images. RESULTS: The 4D datasets enabled dynamic assessment of the whole heart with isotropic spatial resolution of 1.15 mm(3) . Coronary artery image quality was very similar to that of the ECG-triggered baseline scan despite some SNR penalty. A good agreement between 4D and 2D cine imaging was found for EDV, ESV, and EF. CONCLUSION: The hypothesis that both coronary anatomy and ventricular function can be assessed simultaneously in vivo has been tested positive. Retrospective and flexible acquisition window selection allows to best visualize each coronary segment at its individual time point of quiescence. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.

Accuracy and Precision of Head Motion Information in Multi-Channel Free Induction Decay Navigators for Magnetic Resonance Imaging.

Free induction decay (FID) navigators were found to qualitatively detect rigid-body head movements, yet it is unknown to what extent they can provide quantitative motion estimates. Here, we acquired FID navigators at different sampling rates and simultaneously measured head movements using a highly accurate optical motion tracking system. This strategy allowed us to estimate the accuracy and precision of FID navigators for quantification of rigid-body head movements. Five subjects were scanned with a 32-channel head coil array on a clinical 3T MR scanner during several resting and guided head movement periods. For each subject we trained a linear regression model based on FID navigator and optical motion tracking signals. FID-based motion model accuracy and precision was evaluated using cross-validation. FID-based prediction of rigid-body head motion was found to be with a mean translational and rotational error of 0.14±0.21 mm and 0.08±0.13(°) , respectively. Robust model training with sub-millimeter and sub-degree accuracy could be achieved using 100 data points with motion magnitudes of ±2 mm and ±1(°) for translation and rotation. The obtained linear models appeared to be subject-specific as inter-subject application of a "universal" FID-based motion model resulted in poor prediction accuracy. The results show that substantial rigid-body motion information is encoded in FID navigator signal time courses. Although, the applied method currently requires the simultaneous acquisition of FID signals and optical tracking data, the findings suggest that multi-channel FID navigators have a potential to complement existing tracking technologies for accurate rigid-body motion detection and correction in MRI.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling.

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.