Influence of GRAPPA pre-scan methods on temporal SNR of rapid GE-EPI measurements at 9.4 Tesla

Poster No:


Submission Type:

Abstract Submission 


Edyta Leks1,2,3, Jonas Bause2, Rahel Heule2, Philipp Ehses4, Wolfgang Grodd2, Klaus Scheffler1,2


1Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany, 2Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 3International Max Planck Research School for Cognitive and Systems Neuroscience, Tuebingen, Germany, 4German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

First Author:

Edyta Leks  
Department of Biomedical Magnetic Resonance, University of Tuebingen|Max Planck Institute for Biological Cybernetics|International Max Planck Research School for Cognitive and Systems Neuroscience
Tuebingen, Germany|Tuebingen, Germany|Tuebingen, Germany


Jonas Bause  
Max Planck Institute for Biological Cybernetics
Tuebingen, Germany
Rahel Heule  
Max Planck Institute for Biological Cybernetics
Tuebingen, Germany
Philipp Ehses  
German Center for Neurodegenerative Diseases (DZNE)
Bonn, Germany
Wolfgang Grodd  
Max Planck Institute for Biological Cybernetics
Tuebingen, Germany
Klaus Scheffler  
Department of Biomedical Magnetic Resonance, University of Tuebingen|Max Planck Institute for Biological Cybernetics
Tuebingen, Germany|Tuebingen, Germany


In functional MRI (fMRI) echo planar imaging (EPI) is often combined with parallel imaging, e.g. GRAPPA (1), to increase temporal resolution. The auto-calibration scans (ACS) required for the calculation of the coil sensitivities in the parallel imaging reconstruction are conventionally acquired in a segmented fashion (number of segments = parallel imaging factor), with the individual segments of each slice separated by the repetition time (TR). However, in case of TRs in the range of several seconds, ACS segments may be acquired at different B0-field offsets e.g. due to respiration or motion. These fluctuations can result in variations in temporal SNR (tSNR) across different slices particularly at high-field (3). The sensitivity of tSNR on physiological effects can be reduced by acquiring all segments of a slice successively with minimum delay in the so called FLEET technique (3). Alternatively, a FLASH readout, which is more robust against B0-field changes, can be used to obtain the ACS data (2). Although physiological influences are usually considered to be the main cause of tSNR variations at long TRs, as far as we know, the performance of various GRAPPA pre-scan methods (conventional, FLEET and FLASH) has not previously been investigated for a TR in the sub-second range.


Four healthy subjects were measured at 9.4 Tesla (Siemens Healthineers, Germany) using an in-house-built 16Tx-31Rx head-coil (4). Gradient-echo EPIs were acquired for two regions covering a major part of the thalamus (ROI 1) and the motor cortex (ROI 2). Imaging parameters: TE/TR = 23/600ms, FA = 40°, 12 slices, 150 volumes. Two different spatial resolutions were used:
• 1 x 1 x 2 mm³: mtx = 192x192, 6/8 partial Fourier, GRAPPA = 4 (60 ACS lines), echo spacing = 1.01 ms.
• 2 x 2 x 2 mm³: mtx = 96x96, GRAPPA = 3 (45 ACS lines), echo spacing = 0.8 ms.
The two protocols were repeated for both ROIs for all three ACS sampling methods: conventional, FLEET, and FLASH. The excitation flip angle for the FLEET and FLASH ACS scans was 10° and 15°, respectively.
Temporal SNR maps were calculated as the mean signal value across time divided by its temporal standard deviation. To quantify the tSNR for the different GRAPPA pre-scan methods, mean tSNR values were assessed for each ROI after performing manual brain masking.


Figure 1 shows the calculated tSNR for the different GRAPPA pre-scan methods and brain regions in an example volunteer. The lowest tSNR is visible for the data measured with conventional ACS and low spatial resolution in particular. This observation is consistent for both ROIs. Averaged over all slices, the tSNR values in images acquired with FLEET or FLASH ACS sampling are higher than with conventional ordering, too (Figure 2). This is especially the case at low image resolution. At high spatial resolution, the tSNR of data reconstructed using FLEET and FLASH sampled data is almost identical and the improvement compared to the conventional method is rather small (∼12% in ROI 1 and ∼25% in ROI 2).
Supporting Image: Figure1.png
Supporting Image: Figure2.png


Although physiological influences and respiration effects in particular are expected to be reduced for sub-second TR, the FLEET and FLASH pre-scan methods yielded clearly higher tSNR compared to the conventional approach. One explanation is, that despite the short TR, the acquisition of all ACS lines still took about 1.8 s (2x2x2 mm³) and 2.4 s (1x1x2 mm³), respectively, due to the slice-segment acquisition scheme, whereas the FLEET method only required about 200 ms (1x1x2 mm³). This study also confirmed that the impact of physiological fluctuations on tSNR heavily scales with the spatial resolution, as it is the case for un-accelerated imaging (5). Thus, even though less tSNR improvement can be expected for alternative ACS acquisition techniques at high spatial resolutions, it still has to be considered as a potential source for effect size differences even in sub-second TR fMRI studies.

Modeling and Analysis Methods:

Other Methods

Novel Imaging Acquisition Methods:

Imaging Methods Other 2


Other - GRAPPA; high-resolution fMRI; image reconstruction; parallel imaging

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My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Other, Please specify  -   Functional MRI - image reconstruction, comparison of parallel imaging reconstructions

For human MRI, what field strength scanner do you use?

If Other, please list  -   9.4T

Which processing packages did you use for your study?

Other, Please list  -   custom-built script in Matlab

Provide references using author date format

1. Griswold, MA. (2002), ‘Generalized autocalibrating partially parallel acquisitions (GRAPPA)’, Magnetic Resonance in Medicine, vol. 47: 1202–1210
2. Griswold, MA. (2006), ‘Autocalibrated coil sensitivity estimation for parallel imaging’, NMR in Biomedicine, vol. 19: 316– 324
3. Polimeni, JR. (2016), ‘Reducing sensitivity losses due to respiration and motion in accelerated echo planar imaging by reordering the autocalibration data acquisition’, Magnetic Resonance in Medicine, vol. 75: 665-79
4. Shajan, G. (2014), ‘A 16-channel dual-row transmit array in combination with a 31-element receive array for human brain imaging at 9.4 T’, Magnetic Resonance in Medicine, vol. 71(2): 870-9
5. Triantafyllou, C. (2005), ‘Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters’, Neuroimage, vol. 26:243–50