MRI at very high spatial resolution is challenging due to several limitations, including lower SNR, increased sensitivity to motion and longer scan-times. For T2*-weighted imaging of the brain there is the further complication of artifacts arising due to changes in the $$$B_0$$$-field with the respiratory cycle. Fat navigators (FatNavs) have already demonstrated excellent motion tracking and retrospective correction at 7T [1] and we have also shown their sensitivity to $$$B_0$$$-field tracking [2]. Extremely rapid FID navigators (FIDNavs) have previously demonstrated to be sensitive to $$$B_0$$$ changes [3] and to motion [4,5], but the mapping between the FID signals and the $$$B_0$$$ and motion parameters requires per-session calibration.

Here we propose a GRE sequence with hybrid integration of segmented dual-echo FatNavs and FIDNavs which allows the low temporal resolution dual-echo FatNavs to calibrate the FIDNav mapping for each session. This allows the estimation of high temporal-resolution motion (6 parameters) and zeroth and first-order $$$B_0$$$ variations (4 parameters) which can be used for retrospective correction of the T2*-weighted GRE k-space.

Using a 7T head-only MR scanner (Siemens Healthcare, Germany) and a 32-channel RF coil (Nova Medical Inc.), we scanned a healthy volunteer who was asked to breathe deeply to amplify the $$$B_0$$$ variations. His cardiac and respiratory traces were recorded by the external monitoring unit during the high-resolution slab-selective 3D-GRE. An FID readout (FIDNav) was inserted between the excitation pulse and the phase encoding gradients. After each host sequence TR, three lines of a highly accelerated double-echo FatNav were acquired, resulting in an entire FatNav volume every 36 TRs (~2s). Figure 1 summarizes the sequence design and protocol parameters used. The effective TR (time between two host excitation pulses) was 56.2 ms. GRAPPA reference lines for FatNavs were acquired in a 2.3s pre-scan. Total acquisition time: 32min25s, whereas navigator-free would have been 23min2s (although SNR efficiency is similar due to increased effective TR for water).

FatNavs were retrospectively co-registered using SPM to obtain motion-estimates, and a $$$B_0 = \beta_0 + \vec \beta \cdot \vec x$$$ fit (constrained to the host sequence FOV) was computed. Each FIDNav was assigned to the nearest FatNav, a linear mapping $$M = \Lambda F $$ was determined, where $$$M$$$ are the motion and field parameters and $$$F$$$ the mean FIDNav signals for the given FatNav volume for each receive channel. This linear mapping is expected to hold for sufficiently small variations [4]. Once $$$\Lambda$$$ was found it was reapplied to each individual FIDNav to obtain higher temporal resolution motion estimates. To observe cardiac related variations in these estimates, each FIDNav and its time-to-peak was mapped to the closest cardiac peak (from external monitoring), and a binning (150 bins) of the global time-to-peak range allowed a bin-wise mean of the estimated motion and $$$B_0$$$ field estimates.

For retrospective correction, the motion parameters and field coefficients were temporally smoothed via a sliding-window of 7 FIDNavs. Hence the effective temporal resolution of these estimates was approximately 400 ms. The correction itself was performed coil-wise using the NUFFT adjoint operator [6].

Temporally smoothed FIDNav-estimated motion parameters are shown in Figure 2.A. One can readily observe the clear correlation between the cardiac peaks (dashed vertical line) and the motion estimates. Figure 2.B shows the binning result for translations and linear $$$B_0$$$ coefficients. As expected, the maximal cardiac effect appears slightly before the peak in the physiological data, as the pulse was measured on the right index finger where its arrival time is later than for the brain. Comparison to a similar measurement of the ballistocardiogram obtained from a camera/marker system [7] is encouraging, although we observe the strongest effect on x and y axes in this example – rather than the z direction. It is likely that the direction and amplitude of the effect is strongly subject-dependent, and also affected by the position of the subject in the scanner. Further studies will allow quantification of the associated variability.

The quality of the corrected image is greatly improved, as exemplified in Figures 3 and 4 (zoomed section), where high-resolution improvements in features are clearly identifiable, implying that the motion estimates are of reasonable accuracy. Global improvement is especially visible in a minimal intensity projection contrast over a 9mm slab (Figure 5) where the diffuse blotches associated with respiratory artifacts are greatly reduced.