Xinwei Shi1,2, Joseph Y Cheng1,2, Michael Lustig3, John M Pauly2, and Shreyas S Vasanawala1
1Radiology, Stanford University, Stanford, CA, United States, 2Electrical Engineering, Stanford University, Stanford, CA, United States, 3Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA, United States
Synopsis
In cardiac MRI, self-gating using the DC signal
provides a promising alternative to EKG gating. However, the DC signal is often
affected by other moving structures in the FOV, such as the liver, which
degrades the accuracy of the extracted cardiac triggers. In this work, we
demonstrate the use of virtual coils to focus the DC signal on cardiac motion
and to provide a robust and generalized self-gating approach for 3D cardiac MRI.
In free-breathing 4D-Flow scans of pediatric subjects, the proposed method
improved the accuracy of self-gating triggers, and the self-gated images showed
comparable quality with EKG gated reference. PURPOSE
In cardiac MRI, the EKG signal is commonly used for
synchronizing data acquisition to the cardiac cycle. However, the EKG suffers
from multiple sources of interferences in MRI[1], increases the
complexity of scan setup, and the leads can be intimidating for children. Self-gating
using the DC signal (center of k-space)[1-3] provides a promising
alternative. This DC signal can be repeatedly acquired with minimal scan time
penalty for most sequences.
Especially in 3D
imaging, the cardiac motion extracted from DC signal is mixed with motion of
other structures (such as the liver) in the FOV, which may degrade the accuracy
of self-gating triggers. Band-pass filtering[4] can reduce the
effect of respiratory motion, but may be unreliable during arrhythmias or
irregular breathing. Here, we demonstrate the use of virtual coils (VC’s)[5]
to focus the DC signal on cardiac motion and to provide a robust, simple and
generalized approach for self-gated 3D cardiac MRI.
METHODS
Fitting
for Virtual Coils
The virtual coil is a
linear combination of array coil elements, which has high sensitivity only inside
the chosen ROI.
The weights of the combination $$$w(c)$$$ are fitted by $$$\mathrm{minimizing}_{w}\|Sw-\mathrm{diag}(m)b\|_2$$$, where $$$S(\textbf{r},c)$$$ and $$$b(\textbf{r})$$$ are
the multi-coil images and the root-sum-of-squares (RSOS) image, 3D spatial
locations and coils are indexed with $$$\textbf{r}$$$ and $$$c$$$. $$$m(\textbf{r})$$$ is a mask selecting voxels inside
the ROI, which has smooth transitions at the edge of the ROI. Since changes in the DC
signal of the VC correspond to changes of the total signal in its sensitivity
region, the ROI of the VC is placed around the edge of the left ventricle (Fig.1),
so that the DC signal over time reflects cardiac motion.
Extraction
of Self-Gating Signals
For each TR, N
samples of the DC signal are collected for each coil element. The multi-coil DC
data are first linearly combined using the computed weights $$$w(c)$$$ in the coil dimension. The magnitude
sum of the N virtual DC samples of
each TR is taken as the DC signal. A bandpass
filter with a 0.5-2.5Hz passband is applied to remove the baseline drift and
high-frequency noise. A template matching algorithm[3] is used to
extract the cardiac triggers.
Experiments
Free-breathing 4D-Flow scans were performed on 4
pediatric subjects in a 3T GE MR750 scanner using a 32-channel cardiac coil. Ferumoxytol
contrast-enhancement and a 3D SPGR sequence with fat saturation were used with
the parameters in Table 1. Both the DC signal and Butterfly navigators were
acquired without modifying the gradient waveforms[6,7] (Fig.2). The
original array coil element with the highest DC energy in the frequency range
of cardiac motion was used for comparison with the VC. The same filtering
procedure was applied to the DC waveforms of the VC and the selected original
coil element. The cardiac triggers extracted from the VC and this original coil
element were compared with the EKG triggers by evaluating the trigger
variability $$$\mathrm{TV}=\sqrt{\Sigma_{n=1}^{N}{\frac{(S_n-R_n)^2}{N-1}}}$$$, where N is the
number of cardiac cycles, $$$S_n$$$ and $$$R_n$$$ represent the self-gating (mean
trigger delay corrected) and EKG trigger points. A soft-gated ESPIRiT reconstruction[8]
with local translational motion correction[6] was used to reconstruct
the end-diastolic images, based on retrospective self-gating using the VC and
EKG gating.
RESULT & DISCUSSION
The virtual coil had superior spatial
selectivity compared to the original coil (Fig.1) and suppressed most of the
unwanted influence from liver in the DC waveform (Fig.3). The self-gating triggers
from the VC had lower TVs of 10.3±2.2ms (mean ± standard deviation), compared
with the results of the original coil (14.2±4.7ms). The self-gating TVs of VC were
similar to the results in breath-hold 2D cine scans reported in a previous
study[3]. The EKG missed 1-3 trigger points in 3 studies, but there
were no misses or false positives in self-gating triggers. Self-gated images had comparable quality to
the EKG-gated reference (Fig.4).
The self-gating method is applicable to general
cardiac imaging. It is especially useful for 4D-Flow, since the EKG gating is more
likely to fail in the middle of a long scan, particularly in small children. The
DC signal has a high temporal resolution of one TR for extracting accurate
trigger times; the operator has more flexibility and control for tuning how the
self-gating signal is extracted.
CONCLUSION
With virtual coils, the influence of respiratory
motion on the DC signal is suppressed and the accuracy of self-gating triggers
is improved. It permits robust self-gating in free-breathing 4D-Flow scans and general
3D cardiac scans, with minimal modification to most pulse sequences.
Acknowledgements
NIH R01-EB009690, NIH P41-EB015891, research support from GE Healthcare. References
[1] AC Larson et al, MRM 2004, 51:93-102;
[2] AC Brau et al, MRM 2006, 55:263-270;
[3] GM Nijm et al, JMRI 2008,
28:767-772;
[4] J Liu et al, MRM 2010, 63:
1230-1237;
[5] X Shi et al, ISMRM 2015,
p811;
[6] JY Cheng et al, MRM 2012,
68: 1785-1797;
[7] JY Cheng et al, ISMRM 2015,
p451;
[8] JY Cheng et al, JMRI 2015,
42: 407-420.