Optimization of EPI protocols for maximum BOLD sensitivity through numerical simulations
Steffen Volz1, Martina F. Callaghan1, Oliver Josephs1, and Nikolaus Weiskopf1,2

1Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, London, United Kingdom, 2Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Synopsis

fMRI studies can suffer substantially from BOLD sensitivity loss due to susceptibility-related magnetic field inhomogeneities. We developed an automated algorithm for optimising arbitrary EPI protocols with respect to BOLD sensitivity based on numerical simulations and a multi-subject field map database, saving time and expensive measurements. In contrast to previous experimental optimization approaches that were limited e.g. to z-shim, gradient polarity and slice tilt for a specific EPI protocol, this algorithm optimizes on a larger parameter space including resolution, echo time and slice orientation. Results were compared to earlier experimental approaches and verified by BOLD sensitivity measurements in healthy volunteers.

Purpose

fMRI studies require high BOLD sensitivity (BS), however susceptibility-related magnetic field inhomogeneities lead to signal dropouts and thus to BS loss in basal brain areas. There are a number of techniques for maximising BS in selected brain areas: e.g., z-shimming (1), inverting the phase encoding (PE) gradient polarity (2), optimizing the slice tilt (2, 3), increasing the spatial resolution (4) or optimizing the echo time TE (5). Previous optimization methods have been based on atlases derived from multiple EPI acquisitions (3) thus requiring resource and time and limiting the parameter range over which optimization can be performed. Here, we present an automated optimization method that can be employed for a large parameter space. It is based on numerical simulations informed by a large database of magnetic field (B0) maps. In contrast to previous experimental approaches, it saves time and expensive measurements and allows for optimizing arbitrary EPI protocols including variable resolution, TE and slice orientation. The parameter optimization was compared to earlier experimental approaches and verified by BS measurements in healthy volunteers.

Methods

All scans were acquired on a 3T whole body MR scanner (Magnetom TIM Trio, Siemens). B0 field maps (double echo GE) and anatomical data (3D FLASH) from 138 healthy volunteers were acquired as part of a whole brain quantitative MPM protocol (6). Field maps were calculated from the GE data and normalised to MNI space using the individual anatomical data. BS maps were calculated for each subject, by accounting for through-plane dephasing (1), local echo time/k-space shifts and signal loss due to susceptibility-induced in-plane gradients in the PE (1, 2) and readout (7) direction, and then averaged across volunteers. For the voxel-wise optimization results, shown in figures 2-4, the following EPI parameters were fixed: TE=30ms, resolution=3x3x3mm3, echo spacing 0.5ms and a 64x64 matrix. The following parameters were optimized for oblique transverse, sagittal and coronal slices with PE gradient directions pointing from anterior to posterior for transverse and sagittal slices and foot to head for coronal slices (see figure 1): slice tilt from -45° to 45° in steps of 5°, z-shim gradient pointing in slice direction with a moment from -5 to 5mT/m*ms in steps of 0.5mT/m*ms and a positive or negative polarity of the PE gradient. For comparison with earlier experimental approaches parameter optimization was performed using the same parameter space, orientation and resolution as used in (3). Additionally, for further validation EPI data was acquired for 36 different EPI protocols and four volunteers and the BS calculated from the complex raw data was then compared to the predictions of the numerical simulations.

Results

In the case of a transverse acquisition, the optimal choice of z-shim gradient moment (fig. 2) was found to be negative in the orbitofrontal cortex but positive in the temporal lobes. For the sagittal acquisition, a left-right asymmetric distribution of z-shim values was found in the orbitofrontal cortex and near zero values in the temporal lobe, while for the coronal acquisition positive and negative z-shim gradients were found close to each other in the orbitofrontal cortex and the temporal lobe. For transverse/sagittal acquisitions, optimal slice angulations/rotations (fig. 3) were found to be positive in the orbitofrontal cortex and negative in the temporal lobes. Areas with positive and negative slice angulations could be found close to each other in the orbitofrontal cortex and temporal lobes in case of coronal acquisition, as was the case for the optimized z-shim gradient moments. The respective maps of the achieved BS gain is shown in fig. 4. The table in fig. 5 compares the results of the simulation based BS optimization with literature results (3) in different ROIs. The resulting optimized parameters were in good agreement for most of the regions. The comparison between simulated and experimental BS gain showed a good agreement with Gaussian distributed deviations around 5% pooled over the brain mask and subjects for each protocol.

Discussion.

The advantage of the proposed method is that it allows for automated optimization of EPI protocols by simulation, thus avoiding time and resource consuming measurements, allowing a larger parameter space to be optimised as well as easy adaptation if the basic protocol is changed. This framework also promises to provide improved optimization for group studies, since the typical distribution of field inhomogeneities in the population is better captured. The results of the optimization by simulations are in good agreement with earlier experimental optimization outcomes (3) and the expected BS increases are in line with the experimental BS results.

Acknowledgements

This work is part of the BRAINTRAIN European research network (CollaborativeProject) supported by the European Commission under the Health Cooperation Work Programme of the 7th Framework Programme (Grant agreement n° 602186).

References

1. Deichmann et al. Neuroimage 19:430-441 (2003).

2. De Panfilis et al. Neuroimage 25:112-121 (2005).

3. Weiskopf et al. Neuroimage 33:493-504 (2006).

4. Robinson et al. Neuroimage 22:203–210 (2004).

5. Stöcker et al. NeuroImage 30:151-159 (2006).

6. Callaghan et al. Neurobiol Aging:1–11 (2014).

7. Weiskopf et al. MAGMA 20:39-49 (2007).

Figures

Definition of coordinate axis for phase encoding (PE), readout (RO) and slice direction (SL) for different orientations. The slice angulation/rotation directions are denoted with the red arrows respectively.

Maps of the optimal shim gradient moment for an in-plane resolution of 3x3mm2, a matrix size of 64x64 and a slice thickness of 3mm in the case of transverse slices (top row), sagittal slices (middle row) and coronal slices (bottom row).

Maps of the optimal slice angulation (for definition see fig. 1) for an in-plane resolution of 3x3mm2, a matrix size of 64x64 and a slice thickness of 3mm in the case of transverse slices (top row), sagittal slices (middle row) and coronal slices (bottom row).

Maps of the BS gain achieved with the optimal parameter set (compared to standard EPI with no shim gradient and slice angulation) in case of transverse slices (top row) and sagittal slices (middle row) and coronal slices (bottom row).

Comparison of simulation based BS optimization with literature: (a) Results of the simulation based optimization in this study and (b) previous optimization using data from multiple EPI acquisitions (3). Note that the experimental data were acquired on a different head only MRI scanner (Siemens Allegra), which may explain some of the observed differences.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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