Choukri Mekkaoui1, Timothy G Reese1, Marcel P Jackowski2, Stephen F Cauley1, Kawin Setsompop1, William J Kostis3, Himanshu Bhat4, and David E Sosnovik1
1Harvard Medical School - Massachusetts General Hospital, Boston, MA, United States, 2University of São Paulo, São Paulo, Brazil, 3Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, United States, 4Siemens Healthcare, Boston, MA, United States
Synopsis
We introduce
an approach to perform free-breathing DTI and tractography of the whole heart
based on simultaneous multislice excitation, sequential reordering of the diffusion-encoding
gradients, combined with a retrospective entropy-based image alignment and
selection method. The approach was tested in 7 healthy volunteers, in whom
breath-hold and free-breathing scans were performed. Coherent tracts of the
entire heart could be derived in all cases, and no significant differences were
seen in mean diffusivity, fractional anisotropy, or myofiber helix angle. Accelerated
free-breathing DTI of the entire heart could be performed in less than 15
minutes without significant loss of image quality.Purpose
Diffusion Tensor Imaging (DTI)
of the heart
in vivo has previously been performed
using multiple breath-holds or respiratory navigation, providing a subset of
short-axis slices of the left ventricle (LV), comprising ~25% of the LV in
about 20 minutes (1,2). Here we demonstrate a clinically feasible whole heart
free-breathing DTI approach, with scan time under 15 minutes, based on a simultaneous multi-slice (SMS)
acquisition technique combined with a retrospective image alignment and
selection method. This approach avoids navigator echoes and synchronized
breathing and enables anatomic coverage of the entire LV.
Methods
DTI was performed in healthy volunteers (n=7) on a clinical
3T scanner (Skyra, Siemens, Erlangen, Germany) equipped with a 45 mT/m gradient
system and a 34-element cardiac receive coil. Imaging was performed with an
ECG-gated diffusion-encoded STE sequence, volume selected in the phase-encode
axis using a slab selective radiofrequency (RF) pulse. Acquisition parameters
were: FOV = 360 x 200 mm2, resolution 2.5 x 2.5 mm2,
slice thickness = 8 mm, in-plane GRAPPA rate 2, TE = 34 ms, b-values = 0 and
500 s/mm2, 10 diffusion-encoding directions, and 8 magnitude
averages (repetitions). Twelve short-axis and six 4-chamber slices were
acquired in the systolic sweet spot of the cardiac cycle to mitigate strain
effects (3). SMS excitation was
followed by a blipped-CAIPI (Controlled Aliasing in Parallel Imaging) readout
(4). Free-breathing scans were performed with rate 2 SMS and compared with
breath-hold scans acquired with rate 3 SMS.
Breath-hold DTI was performed with the default interleaved
gradient ordering scheme. For free-breathing DTI a sequential gradient ordering
scheme was implemented, which ensured that enough uncorrupted samples of each
diffusion-encoding direction could be selected to determine the diffusion
tensor accurately. Spatiotemporal registration (STR) was performed by matching
radial intensity profiles over all repetitions, and reduced the misregistration
resulting from respiratory motion. With free breathing, following
STR, an entropy-based retrospective image selection (ERIS) was applied to
accept or reject both the diffusion-free and diffusion-weighted images. An
entropy measure (EM), based on Shannon’s definition of entropy, was
calculated from the distribution of signal intensity within the myocardium,
across repetitions (5). Images with EM within a prescribed range ER
were accepted, while those adversely affected by artifact were rejected.
For each voxel, we computed a dyadic tensor
comprising the primary, secondary, and tertiary eigenvectors and their
associated eigenvalues. Mean diffusivity (MD), fractional anisotropy (FA) and
myofiber helix angle (HA) were calculated, as previously described (6).
Tractography was performed by numerically integrating the primary eigenvector
field into streamlines using an adaptive 5th order Runge-Kutta approach
(6). Comparisons between the breath-hold and free-breathing data were performed
using paired t-tests.
Results
Combining
STR and ERIS yielded the best quality of MD, FA, and HA maps during free
breathing (Fig 1). For a mid-ventricular slice, the entropy measure E
M
varied between 6.39 and 7.17, rejecting images with E
M less than
6.54, corresponding to the bottom 20% of E
M values. DTI and tractography of the
entire heart in both the short-axis and 4-chamber views could be successfully
performed with the blipped-CAIPI SMS approach during both breath-hold and
free-breathing (Fig 2). Each approach produced similar tractograms. Using STR
and ERIS, no significant differences were found in MD (0.9 ± 0.09, 0.89 ± 0.08 ×10-3
mm
2/s), FA (0.42 ± 0.06, 0.42 ± 0.05) or HA profiles between the free-breathing
and breath-hold scans (Fig 3).
Discussion
Clinical translation of
cardiac diffusion imaging has proven technically challenging. Previous studies
have required multiple breath-holds or synchronized breathing (3,7), with
partial anatomic coverage of the LV and comparatively long scan times (1,2),
even with accelerated techniques such as SMS (8). In this study, we establish a
free-breathing DTI approach with complete coverage of the human heart in less
than 15 minutes. Respiratory motion during diffusion encoding can cause
cancellation of signal in the myocardium. Lengthy breath-hold acquisitions will
be burdensome for many patients. Free-breathing acquisition overcomes this
hurdle using respiratory-gating with navigator echoes, or by retrospectively
eliminating respiration-affected images. In this work, we used STR and ERIS to
align and select the usable scans.
Conclusion
This is the first
demonstration of whole heart DTI producing
3D tractograms of the entire LV using free breathing, of either the short-axis
or 4-chamber views, in under 15 minutes. This clinically feasible protocol
could facilitate the translation of whole heart DTI to patients with heart
failure and other disease states. The characterization of myocardial
microstructure using DTI in the clinical setting could provide important
insights into disease pathogenesis and treatment.
Acknowledgements
This work was supported by
the following grants from the National Institutes of Health: R56HL125590
(C.M.), R01HL093038 (D.E.S.), R01HL112831 (D.E.S.), P41RR14075 (Athinoula A.
Martinos Center for Biomedical Imaging).References
1. Nielles-Vallespin S, Mekkaoui C, Gatehouse P, et al. In
vivo diffusion tensor MRI of the human heart: reproducibility of breath-hold
and navigator-based approaches. Magn Reson Med. 2013;70(2):454-65.
2. Wu MT, Su MY, Huang YL, et al. Sequential Changes of
Myocardial Microstructure in Patients Postmyocardial Infarction by
Diffusion-Tensor Cardiac MR: Correlation With Left Ventricular Structure and
Function. Circ Cardiovasc Imaging. 2009;2:32-40.
3. Tseng WY, Reese TG, Weisskoff RM, Wedeen VJ. Cardiac
diffusion tensor MRI in vivo without strain correction. Magn Reson Med.
1999;42(2):393-403.
4. Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen
VJ, Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous
multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med.
2012;67(5):1210-24.
5. Tsai D-Y, Lee Y, Matsuyama E. Information Entropy Measure
for Evaluation of Image Quality. Journal of Digital Imaging. 2008;21(3):338-47.
6. Mekkaoui C, Huang S, Chen HH, et al. Fiber architecture
in remodeled myocardium revealed with a quantitative diffusion CMR tractography
framework and histological validation. J Cardiovasc Magn Reson. 2012;14:70.
7. Reese TG, Weisskoff RM, Smith RN, Rosen BR, Dinsmore RE,
Wedeen VJ. Imaging myocardial fiber architecture in vivo with magnetic
resonance. Magn Reson Med. 1995;34(6):786-91.
8. Lau AZ, Tunnicliffe EM, Frost R, Koopmans PJ,
Tyler DJ, Robson MD. Accelerated human cardiac diffusion tensor imaging using
simultaneous multislice imaging. Magn Reson Med. 2015;73(3):995-1004.