Sriranga Kashyap1,2, Francisco J. Fritz1,2, Robbert L. Harms1, Laurentius Huber3, Dimo Ivanov1,2, Alard Roebroeck1,2, Benedikt A. Poser1,2, and Kâmil Uludağ1,2
1Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands, 2Maastricht Brain Imaging Centre (MBIC), Maastricht, Netherlands, 3Section on Functional Imaging Methods Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States
Synopsis
Despite
the availability of more sophisticated coil-combination methods like Roemer and
STARC, ultra-high field fMRI studies still use the conventional sum-of-squares
(SoS) method for combining the images of the individual coils from multi-channel
RF-coil arrays. Here
we use a memory-efficient, CPU/GPU accelerated coil-combine toolbox written in Python
to compare and characterise the effect of methods such as covariance-weighted
sum-of-squares (CovSoS), Roemer and STARC on sub-millimetre resolution GE-EPI laminar
fMRI data acquired at 9.4T, and demonstrate the benefit of using optimised
coil-combination for UHF fMRI studies.
Introduction
Ultra-high field (UHF) provides increase in functional
contrast-to-noise (fCNR) enabling functional MRI (fMRI) at sub-millimetre resolutions. Some
of the acquisition challenges at UHF, such as signal voids, spatially-varying
intensity due to Tx/Rx biases, can be partially mitigated using tailored pulse
sequences and acquisition strategies1 and improvements in RF technology2. In UHF fMRI acquisitions at high spatial resolutions,
the temporal signal instabilities are not dominated by physiological
fluctuations. Therefore, fCNR (defined as ratio of BOLD signal amplitude
relative to temporal noise) can be improved by optimally combining signals from
individual RF channels. Despite the availability of these methods, they have
thus far seen limited application in high-resolution fMRI studies.
Advanced coil-combination techniques can be computationally
expensive in terms of data size and processing time, although this can be
alleviated by modern multithreaded programming paradigms. In this work, we use
the open-source CPU/GPU accelerated MRI Coil-combine Toolbox (MCT, https://github.com/cbclab/MCT) for all efficient and parallelized coil
reconstructions. The aim of this work is to compare and characterize the effect
of the different coil-combinations on fMRI data, with a focus on sub-millimetre
laminar fMRI at 9.4T.Methods
Data was acquired on a Siemens 9.4T research scanner
using an 16Tx/31Rx phased-array coil3 with gradient-echo 3D-EPI4: 0.8mm isotropic resolution, iPAT=3, PE=R>>L,
slices=30, TRvol=1440 ms, TE=18 ms, FoV=150 mm, α=12° and PFphase=6/8.
The oblique-coronal acquisition slab covered the occipital lobe. A
flickering-checkerboard visual stimulation was presented for 20s and with a 40s
isoluminant rest period in a block design.
Coil-combination: GRAPPA reconstructed
magnitude and phase data for the individual channels, as well as raw k-space
data were obtained. All data recombination was done using MCT. Individual
channel data were converted to x-space complex data per channel (the inverse
Fourier transformed k-space) using . The following coil-combination methods were applied:
1. Sum-of-squares (SoS): The signal from the individual
channels is combined using the signal itself as a weighting factor.
2. Covariance-weighted
Sum-of-squares (CovSoS): The noise covariance matrix across channels was
calculated by using the pre-scan noise data5. The x-space complex signal per channel is weighted
with respect to its correlation to the other channels (obtained by the inverse
of the noise covariance matrix).
3. Roemer
(or SENSE R=1): Channel sensitivity profile was estimated as described in [6]. The x-space complex signal per channel is weighted
with the inverse of the noise covariance matrix and the channel sensitivity
profile.
4. STARC7:
A set of
weighting factors per channel are computed by optimising the tSNR7 of the channel time-series using the
GPU-accelerated Powell optimization routine as implemented in the Multithreaded
Optimization Toolbox (MOT)8. The individual channel
data are then combined using the optimised weights.
The reconstructed data were
motion-compensated using ANTs9, GLM-fitted using FSL FEAT and laminarly analysed using
CBS-Tools10.Results
The time-series mean illustrates the differences in
signal magnitude for the different reconstruction methods (Fig. 1a), with
Roemer having more uniformity albeit with lower signal intensity. The effect of
the optimised reconstruction methods can be observed in the standard deviation
maps of the time-series (Fig. 1b), with Roemer being the most temporally
stable, followed by STARC. It follows logically that the tSNR maps (Fig. 1c)
exhibit similar trends. The histograms in Fig 2. indicate that STARC and Roemer
show most tSNR gains compared to the SoS and that CovSoS shows little to no improvement
over SoS. It is important to note that activation was robust across all
reconstruction methods with z-scores>2.3 and nearly the same number of
activated voxels. Further laminar analyses (Fig. 3) elicit similar BOLD signal
profiles for the different reconstruction methods. Computational time with
multi-threading in MCT indicates that CovSoS (apart from SoS) is the fastest to
reconstruct, followed by Roemer and STARC. Discussion
CovSoS has been shown to be qualitatively better than SoS
in UHF diffusion11,
although we do not observe a significant difference compared to SoS for fMRI (Fig. 3). STARC is the only approach that computes new weights
from the data by explicitly optimising for high tSNR, yielding superior tSNR as
expected albeit slightly lower z-scores and %BOLD. Roemer outperforms all
optimised reconstruction approaches in terms of signal temporal stability. This
work bridges the gap between optimal acquisition and optimal analysis by
drawing attention to the importance of optimal coil-combination in UHF fMRI
research wherein the thermal noise effects dominate. In conclusion, we make
available a fast and efficient open-source toolbox for the community and
demonstrate that it can be beneficial for high-resolution fMRI studies to
consider suitable coil-combination strategies such as STARC or Roemer instead
of the conventional SoS.Acknowledgements
We
like to thank Valentin G. Kemper (Maastricht University, Netherlands) for his
valuable inputs and discussions regarding 9.4T fMRI. We like to thank David
Jangraw (NIH, USA) for sharing his MATLAB implementation of STARC, which was
the basis for our reimplementation in Python for the MRI Coil-combine Toolbox. The research was supported by the Netherlands Organization for
Scientific Research (NWO) VIDI grants 452-11-002 (KU), 016-178-052 (BAP) and
14637 (AR), and the European Research Council Starting Grant, MULTICONNECT
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