The ability to detect brain anatomy and pathophysiology with MRI is limited by the contrast-to-noise ratio (CNR), which depends on the contrast mechanism used and the spatial resolution. In this work, we show that in MRI of the human brain, large improvements in contrast to noise in high-resolution images are possible by exploiting the MRI signal phase at high magnetic field strength. Using gradient-echo MRI at 7.0 tesla and a multichannel detector, a nominal voxel size of 0.24 ؋ 0.24 ؋ 1.0 mm 3 (58 nl) was achieved. At this resolution, a strong phase contrast was observed both between as well as within gray matter (GM) and white matter (WM). In gradient-echo phase images obtained on normal volunteers at this high resolution, the CNR between GM and WM ranged from 3:1 to 20:1 over the cortex. This CNR is an almost 10-fold improvement over conventional MRI techniques that do not use image phase, and it is an Ϸ100-fold improvement when including the gains in resolution from high-field and multichannel detection. Within WM, phase contrast appeared to be associated with the major fiber bundles, whereas contrast within GM was suggestive of the underlying layer structure. The observed phase contrast is attributed to local variations in magnetic susceptibility, which, at least in part, appeared to originate from iron stores. The ability to detect cortical substructure from MRI phase contrast at high field is expected to greatly enhance the study of human brain anatomy in vivo.anatomy ͉ contrast mechanism ͉ cortex

Phase images in susceptibility-weighted MRI provide excellent contrast. However, the phase is affected by tissue geometry and orientation relative to the main magnetic field (B0) and phase changes extend beyond areas of altered susceptibility. Magnetic susceptibility, on the other hand, is an intrinsic tissue property, closely reflecting tissue composition. Therefore, recently developed inverse Fourier-based methods were applied to calculate susceptibility maps from high-resolution phase images acquired at a single orientation at 7 Tesla in the human brain (in vivo and fixed) and at 11.7 Tesla in fixed marmoset brain. In susceptibility images, the contrast of cortical layers was more consistent than in phase images and was independent of the structures’ orientation relative to B0. The contrast of iron-rich deep-brain structures (red nucleus and substantia nigra) in susceptibility images agreed more closely with iron-dominated R2* images than the phase image contrast which extended outside the structures. The mean susceptibility in these regions was significantly correlated with their estimated iron content. Susceptibility maps calculated using this method overcome the orientation-dependence and non-locality of phase image contrast and seem to reflect underlying tissue composition. Susceptibility images should be easier to interpret than phase images and could improve our understanding of the sources of susceptibility contrast.

Heart rate fluctuations occur in the low frequency region (< 0.1 Hz) probed in functional magnetic resonance imaging (fMRI) studies of resting-state functional connectivity and most fMRI block paradigms, and may be related to low frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations. To investigate this hypothesis, temporal correlations between cardiac rate and restingstate fMRI signal timecourses were assessed at 3 Tesla. Resting-state BOLD fMRI and accompanying physiological data were acquired and analyzed using cross-correlation and regression. Time-shifted cardiac rate timecourses were included as regressors in addition to established physiological regressors (RETROICOR (Glover et al., 2000) and respiration volume per unit time (Birn et al., 2006b)). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time-shifts of 6-12 seconds and positive correlations at time shifts of 30-42 seconds (TR = 6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 seconds explained an additional 1 % of the BOLD signal variance on average over the whole brain across 9 subjects, a similar additional variance to that explained by respiration volume per unit time and RETROICOR regressors, even when used in combination with these other physiological regressors. This suggests that including such time-shifted cardiac rate regressors will be beneficial for explaining physiological noise variance and will thereby improve the statistical power in future task-based and resting-state fMRI studies.