Qi Huang1,2, Ye Tian2, Jason Mendes2, Ganesh Adluru1,2, and Edward DiBella1,2
1Biomedical Engineering, University of Utah, Salt Lake City, UT, United States, 2Utah Center for Advanced Imaging Research (UCAIR), University of Utah, salt lake city, UT, United States
Synopsis
Here we propose a unique perfusion acquisition
that applies a 2D/3D alternating acquisition method to obtain 2D simultaneous
multi-slice (SMS) and 3D stack-of-stars (SoS) data every other heartbeat.
Within each heartbeat, 2D SMS and 3D acquisitions are performed following a
saturation pulse. Potential advantages include different spatio-temporal
resolution and artifacts, accurate AIF acquisitions and the ability to compare
the sequences directly with a single injection. Preliminary quantitative
results of 7 dog and 3 human studies show the promise of this approach.
Introduction
Quantitative myocardial perfusion can be a
valuable tool for studying perfusion reserve in different disease states (1,2). Current Dynamic Contrast Enhanced (DCE) MRI
methods have trade-offs in resolution and coverage and image quality (3-5). Two relatively new methods have been proposed
for quantifying perfusion. The first is a hybrid radial 2D SMS sequence that
uses a single saturation pulse each heartbeat and then acquires continuously
(Fig. 1). This allows for an accurate AIF acquisition initially, and then
retrospective selection of rays to reconstruct at any phase of the cardiac
cycle, with a “hybrid” weighting of saturation recovery or steady-state gradient
echo. The second method is a 3D stack of stars sequence, with a radial 2D
acquisition for the AIF. The methods are quite different in terms of spatial
and temporal resolution, image contrast, cardiac cycle timing, and the effects
of cardiac and respiratory motion.
Here, we propose a unique perfusion acquisition that uses 2D
SMS and 3D alternating each heartbeat. This allows for direct comparison of the
methods, even at stress, and offers the possibility that the methods may
complement each other and offer more information than acquiring with only one
of the techniques. Since there has been relatively little study of quantitative
techniques that acquire every other beat, we first studied if every other beat
is a sufficient sampling.Methods
5 datasets of “full” every beat 2D SMS and 2 datasets of 3D
SoS were acquired. The 2D SMS typically obtained around 300 rays (golden ratio
ordering) each heartbeat, simultaneously at 3 slices, one. TR/TE=2.3/1.1msec,
~1.8x1.8x8mm pixel size. The 3D SoS (6)
acquired ~92 rays each heartbeat with 8 short axis slices (center 6 slices used
for analysis). TR/TE=2.1/1.1msec, ~1.8x1.8x5mm pixel size. A non-selective saturation pulse was played
once per heartbeat and actual data readouts started 20msec after the saturation
pulse for 2D SMS and for the 2D AIF portion of the 3D scan, and 100msec after
for 3D acquisition respectively. The AIF was generated by first 24 rays which
ensures the linearity between gadolinium concentration and signal intensities. The
3D SoS used an “efficient” 2D AIF (6)
and the tissue readout was acquired at systole. Both 2D SMS and 3D raw data were reconstructed
with an iterative spatial temporal constrained reconstruction (STCR) method (7).
The resulting images were then registered to compensate for respiratory motion
and converted to gadolinium concentration using proton density images. The
curves were fit to a compartment model to give perfusion values. Perfusion values from the full every
beat acquisition, and after every other beat was discarded were compared.
A sequence was created to alternate the 2D SMS and 3D SoS
each heartbeat and used in 3 human subjects and 7 dogs on a 3T Prisma (Siemens)
scanner. Scans were done at both
rest (n=12) and adenosine stress (n=4) with 0.05-0.075mmol/kg gadoteridol. Fig.
1 shows the 2D/3D alternating sequence. Results
Figure 2 shows an
example of good agreement of time curves when only every other beat is
retained. Figure 3(a) shows quantitative flow values from the alternating
2D/3D, 4 stress studies and 12 rest studies. Mean flow of six regions from each
slice of each subject indicates ~0.6-0.7ml/min/g for rest and ~1.35-1.55
ml/min/g for stress cases. Figure 3(b) shows the comparison of perfusion
results between 2D SMS and 3D SoS with efficient AIF methods. 2D generally showed
slightly higher perfusion results compared with 3D. Fig. 4 illustrates the perfusion
differences between 2D and 3D alternating sequences may be more related to
tissue curves in the 2D acquisition being slightly higher than that of 3D,
while the AIFs are similar.Discussion
We expected from
previous works by others that sampling every other beat provides reasonable
sampling of tissue curves, but may not be rapid enough to accurately realize
the AIF peak. The alternating 2D/3D can be combined to have a single AIF
sampled every beat, which did not seem essential in our initial investigations
but likely is important in some cases. Figure 5 showed the example of the
comparison between the 2D/3D alternating quantitative results and a method
with a composite AIF and interpolated tissue curves for both the stress and the
rest. By a composite AIF we mean the combination of 2D and 3D AIFs while tissue
curves of 2D and 3D acquisition are interpolated separately to match the
sampling of the composite AIF. Similar perfusion values as those of the 2D/3D
alternating method with separate AIFs were observed when using the composite AIF.Conclusion
We demonstrated an
alternating quantitative 2D/3D myocardial DCE perfusion imaging sequence with
reasonable agreement between 2D and 3D quantitative results. Further work is
needed to understand the trade-offs of the 2D and 3D sequences and the possible
complementary information that they may provide.Acknowledgements
No acknowledgement found.References
1. Adluru
G, McGann C, Speier P, Kholmovski EG, Shaaban A, Dibella EV. Acquisition and
reconstruction of undersampled radial data for myocardial perfusion magnetic
resonance imaging. J Magn Reson Imaging 2009;29(2):466-473.
2. Wang
H, Bangerter NK, Adluru G, Taylor MI, DiBella EV. Myocardial perfusion imaging
with an interleaved multi-slice acquisition for steady-state readout without
saturation preparation or gating. Proc Intern Soc Magn Reson Med(ISMRM)
2014;22:3934.
3. Chen
L, Adluru G, Schabel MC, McGann CJ, Dibella EV. Myocardial perfusion MRI with
an undersampled 3D stack-of-stars sequence. Med Phys 2012;39(8):5204-5211.
4. Motwani
M, Kidambi A, Sourbron S, Fairbairn TA, Uddin A, Kozerke S, Greenwood JP, Plein
S. Quantitative three-dimensional cardiovascular magnetic resonance myocardial
perfusion imaging in systole and diastole. Journal of Cardiovascular Magnetic
Resonance 2014;16(1):19.
5. Wissmann
L, Niemann M, Gotschy A, Manka R, Kozerke S. Quantitative three-dimensional
myocardial perfusion cardiovascular magnetic resonance with accurate
two-dimensional arterial input function assessment. Journal of Cardiovascular
Magnetic Resonance 2015;17(1):108.
6. Mendes
JK, Adluru G, Likhite D, Fair MJ, Gatehouse PD, Tian Y, Pedgaonkar A, Wilson B,
DiBella EVR. Quantitative 3D myocardial perfusion with an efficient arterial
input function. Magn Reson Med 2019.
7. Adluru G, Awate SP, Tasdizen T, Whitaker RT, Dibella EV.
Temporally constrained reconstruction of dynamic cardiac perfusion MRI. Magn
Reson Med 2007;57(6):1027-1036.