No abstract

New ultrafast gradient systems and hybrid imaging sequences make it possible to acquire a complete image in real time, without the need for breathholding or electrocardiogram (ECG) triggering. In 21 patients, left ventricular function was assessed by the use of a turbo-gradient echo technique, an echo-planar imaging (EPI) technique, and a new real-time imaging technique. End-diastolic and end-systolic volumes, left ventricular muscle mass, and ejection fraction of the ultrafast techniques were compared with the turbo-gradient echo technique. Inter- and intraobserver variability was determined for each technique. Image quality was sufficient for automated contour detection in all but two patients in whom foldover occurred in the real-time images. Results of the ultrafast imaging techniques were comparable with conventional turbo-gradient echo techniques. There was a tendency to overestimate the end-diastolic volume by 3.9 and 1.3 ml with EPI real-time imaging, the end-systolic volume by 0.9 and 5.0 ml, and the left ventricular mass by 2.6 and 23.8 g. Ejection fraction showed a tendency to be overestimated by 1.1% with EPI and underestimated by 4.5% with real-time imaging. Correlation between EPI real-time imaging and turbo-gradient echo were 0.94 and O.95, respectively, for end-diastolic volumes, 0.98 and 0.96, respectively, for end-systolic volumes, and 0.96 and 0.89, respectively, for left ventricular mass. Inter- and intraobserver variability was low with all three techniques. Real-time imaging allows an accurate determination of left ventricular function without ECG triggering. Scan times can be reduced significantly with this new technique. Further studies will have to assess the value of real-time imaging for the detection of wall motion abnornmalities and the imaging of patients with atrial fibrillation.

The vascular organization of the human brain can determine neurological and neurophysiological functions, yet thus far it has not been comprehensively mapped. Aging and diseases such as dementia are known to be associated with changes to the vasculature and normative data could help detect these vascular changes in neuroimaging studies. Furthermore, given the well-known impact of venous vessels on the blood oxygen level dependent (BOLD) signal, information about the common location of veins could help detect biases in existing datasets. In this work, a quantitative atlas of the venous vasculature using quantitative susceptibility maps (QSM) acquired with a 0.6 mm isotropic resolution is presented. The Venous Neuroanatomy (VENAT) atlas was created from 5 repeated 7 Tesla MRI measurements in young and healthy volunteers (n = 20, 10 females, mean age = 25.1 ± 2.5 years) using a two-step registration method on 3D segmentations of the venous vasculature. This cerebral vein atlas includes the average vessel location, diameter (mean: 0.84 ± 0.33 mm) and curvature (0.11 ± 0.05 mm -1 ) from all participants and provides an in vivo measure of the angio-architectonic organization of the human brain and its variability. This atlas can be used as a basis to understand changes in the vasculature during aging and neurodegeneration, as well as vascular and physiological effects in neuroimaging.

Resting‐state (rs) functional magnetic resonance imaging (fMRI) is used to detect low‐frequency fluctuations in the blood oxygen‐level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD‐derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high‐resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI‐derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low‐frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel‐wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.