Viktor Vegh1, Kieran O'Brien2, David C Reutens1, Steffen Bollmann1, and Markus Barth1
1Centre for Advanced Imaging, University of Queensland, Brisbane, Australia, 2Magnetic Resonance, Siemens Healthcare Pty. Ltd., Brisbane, Australia
Synopsis
Signal phase acquired via gradient recalled echo
MRI sequences provides and an important source of tissue contrast. The use of phased
array coils results in multiple-channel images that have to be combined to form
a single image. A robust method of computing phase images has been challenging
to develop, primarily due to the distribution of noise in phase images. We
propose a new approach of combining phase images by exploiting the inherent
noise in signal phase. Our selectively combined signal phase results show an
improvement in the quality of the combined phase image in comparison to
existing methods.Purpose
The
so-called “optimal” method of combining multiple channel data overcomes the
inability of computing combined phase images by working with the complex signal
and by using information about relative complex sensitivity profiles of
individual coils.
1 An adaptive technique to combine multiple channel
data has also been proposed,
2 the drawback of which can be phase
image singularities. More recently, a body coil scan was used to aid reconstruction
of combined phase images.
3 However, a pre-scan is required making
the scan sessions longer and the signal phase is highly sensitive to motion
which may affect the processing as two data sets are used. As an outcome of
existing work, coil profiles have been shown to be important in phase combine
reconstructions, as it has been indicated that information from individual
coils should be weighted by the signal-to-noise ratio. In fact, an averaging
operation does not make sense when signal-to-noise ratio in phase images is
less than three due to violation of the Gaussian distribution assumption.
4
The problem of obtaining high quality combined phase images remains a challenge,
and certain approaches as shown by Liu et al. improve phase images via multiple
echo time data and by applying a weighting factor to each channel based on the
temporal variance of the signals.
5 Existing approaches focus on the
notion that all channels should contribute to the final outcome and the amount
of contribution scales with the image signal-to-noise ratio. We propose a selective
channel combination method (i.e. different voxels have different channels contributing
to them) wherein noisy channels are not used in the reconstruction process.
Methods
32-channel MRI data was collected using a
Magnetom 7T scanner (Siemens Healthcare, Erlangen, Germany). A gradient
recalled echo sequence with the following parameter settings was used: TE =
20.4 ms, TR = 765 ms, resolution of 0.5 by 0.5 by 3 mm
3,
field-of-view = 224 by 154mm
2, slices 30, iPAT = 2, partial Fourier
= 75% and bandwidth = 257 Hz/pixel. Magnitude and phase images were saved for
each channel. We applied a homodyne filter with ¼ window size with respect to
the matrix size.
6 Relative phase noise maps were then reconstructed
by conjugating complex image data of all channels individually. That is, for
any two different channels
m and
n, we computed
ɛm,n = arg(
Imconj(
In)), where
Im and
In are complex valued (see Fig 1). The standard
deviation of the spatial variation in
ɛm,n
was computed by applying a moving window of size 3 by 3 voxels across the
entire range of
ɛm,n.
Following on, Gaussian smoothing with a window size of 11 by 11 voxels was
applied, and then thresholded resulting in a mask for coil combination m and n (examples are shown in Fig 2). Phase signals were averaged in
masked regions (Gudbjartsson and Patz provide
justification for averaging phase signals in high signal-to-noise regions
7).
Results
In
Fig 2, the combination of channels 6 and 2 (top row) lead to the classical
outcome wherein we expect regions of high signal-to-noise ratio to be used in
phase combine. For channels 12 and 1 (third row), however, the spatial
variation maps of
ɛm,n at location
of the arrow show that even though a high signal-to-noise ratio is expected in
this area, the noise in the phase is very high. Our mask shows that information
from this region was not used to combine individual phase from these two
channels. Furthermore, the combination of channels 1 and 4 (fourth row) shows
that not all regions with high signal-to-noise ratio provide information that
is useful in combining phase data. Fig 3 shows the results of optimal, adaptive
and selective combine of phase images. An appreciable level of improvement in
the phase image using selective phase combine is present in comparison to the adaptive
method. Our findings also suggest that the method used to combine phase images
can bias results.
Discussion
Additional
work is needed to examine other combinations of filter sizes for both the
standard deviation calculation and Gaussian smoothing for the evaluation of
image improvements. Nevertheless, we have been able to show that selective
combination of individual phase images results in combined phase images with
clear qualitative improvements. These findings are timely in view of the increased
use of frequency shift and quantitative susceptibility mapping relying on
accurate processing of ultra-high field signal phase.
Conclusion
Our
results imply that selective channel combination of phase images can improve
the quality of combined phase images.
Acknowledgements
Markus Barth acknowledges
funding from ARC Future Fellowship grant FT140100865. The authors acknowledge
the facilities of the National Imaging Facility at the Centre for Advanced
Imaging, University of Queensland.References
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