Daming Shen1,2, Kyungpyo Hong3, Bradley D Allen2, Daniel C Lee4, and Daniel Kim1,2
1Biomedical Engineering, Northwestern University, Evanston, IL, United States, 2Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, 3Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 4Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
Synopsis
Late gadolinium enhanced (LGE) CMR is the gold
standard test for assessment of myocardial scarring. There is unmet need for
high resolution, free-breathing LGE CMR for patients with arrhythmia and/or dyspnea.
The purpose of this study was to develop and clinically evaluate a high
resolution, free-breathing LGE CMR sequence combined with radial k-space
sampling and compressed sensing (CS), which enables image contrast optimization
without a TI scout.
Introduction
Late gadolinium enhanced (LGE)(1-3) cardiovascular
magnetic resonance (CMR) is the gold standard test for assessment of myocardial
scarring. While single-shot LGE CMR is the preferred method for patients with
arrhythmia and/or dyspnea, its relatively low spatial resolution (~2 mm x 3 mm)
may reduce accuracy for visualizing small, subendocardial infarcts, quantifying
myocardial scar volume, and identifying peri-infarct zones. One approach to
achieve high spatial resolution with single-shot LGE is to accelerate it using
compressed sensing (CS), but only at a moderate acceleration factor (2 or 3). In
this study, we sought to exploit temporal redundancy in multi-frame (i.e. cine)
information by achieving 16-fold acceleration using Golden-angle RAdial Sparse
Parallel (GRASP) reconstruction with high spatial resolution (~ 1.3 mm x 1.3
mm) and evaluate its performance against clinical standard single-shot LGE in
patients undergoing routine clinical CMR.Methods
Human Subject:
We enrolled 14 consecutive patients (mean
age = 63.9 ± 18.6 years; 10 males; 4 females) undergoing clinical CMR at a 1.5
Tesla scanner (Avanto, Siemens) with 0.15-0.2 mmol/kg of gadobutrol. For this
study, clinical standard single-shot LGE was performed approximately 10-15 min
after administration of contrast agent, and our single-shot LGE was performed
immediately after it.
Pulse Sequence: As shown in Figure 1, the sequence continuously
acquires data starting at mid diastole of the first heartbeat, immediately
after the inversion pulse and 20 ramp-up pulses, through the early systole of
the second heartbeat, and this unit is repeated for all slices thereafter. For
our new LGE, the relevant imaging parameters included: FOV of 300 x 300 mm,
matrix size of 224 x 224, spatial resolution = 1.3 mm x 1.3 mm, slice thickness
of 8 mm, TR = 3.1 ms, TE = 1.6 ms, 224 rays per slice (rebinned into 14 rays
per frame, 16 time frames) with 32.038º angular increments, single-shot readout
duration = 694 ms, flip angle 45º, inversion time (TI) = 460 ms (minimum TI),
effective triggering every 2nd heartbeat. For clinical single-shot
LGE, the relevant imaging parameters include: FOV of 380 mm x 345 mm, matrix
size of 176 x 128, spatial resolution = 2.2 mm x 2.7 mm, slice thickness of 6
mm, TI = 275-330 ms, flip angle 40º.
Image Reconstruction: Dynamic LGE data with 224 k-space rays were
rebinned to 16 frames with 14 rays per frame and reconstructed using the GRASP
[Feng] (4) framework, resulting in effective
acceleration factor of 16 and temporal resolution of 43.4 ms. To accelerate the GRASP reconstruction
on a GPU workstation (Tesla V100, NVIDIA, 32 GB memory), we applied coil
compressing using principal component analysis (PCA)(5) to produce 8 virtual coils and
used GPU-based Non-Uniform
Fast Fourier Transform (NUFFT)(6).
Compressed sensing (CS) part of GRASP was performed by enforcing sparsity in
time with temporal total variation (TTV) and temporal PCA (TPCA) as two
orthogonal sparsifying transforms. We used nonlinear conjugate gradient with back-tracking
line search as the optimization algorithm with 30 iterations with normalized
regularization weight 0.004 for TTV and 0.002 for TPCA. We empirically
determined these weights as highest values that do not produce significant
blurring artifacts on training datasets. Any residual noise-like artifacts were
suppressed during post processing using block-wise, low-rank filtering with 2
iterations.
Reader scores: A total of 28 multi-slice LGE data sets
(14 each for clinical and GRASP) were randomized and de-identified for visual
analysis by two cardiovascular imaging attendings. Each multi-slice set was
graded on a 5-point Likert scale (1: worst – 5: best) to evaluate: conspicuity
of myocardial (or scar, if exists) border, artifact, and image noise, with 3
defined as clinically acceptable. We used the Wilcoxon signed-rank test to detect
differences between two groups with average reader scores.Results
Figure 3 shows representative
multi-TI LGE images for a single slice with different TIs, corresponding to
different tissue contrast. Figure 4 shows representative clinical single-shot
LGE images and multi-TI LGE images with the best TI for 4 different patients.
The median conspicuity score was significantly different between the two
groups, but both scores were greater than clinical acceptable (3.0) cut point.
The median artifact and noise scores were not significantly different between the
two groups.Discussions
This study demonstrates a novel single-shot LGE
sequence with high spatial resolution and multi-TI information. Our approach
allows for direct free-breathing scans without a TI scout and provides
clinically acceptable image quality. The technique also has the potential to avoid
poor image contrast due to incorrect TI, although this was not directly
assessed in our current study. A future study includes automated identification
of optimal TI. Factors that may have influenced the difference in conspicuity
scores include: a) pulse sequence order, where multi-TI LGE (slightly less
gadolinium) was acquired after the clinical LGE and b) slice thickness (6 mm
for clinical vs. 8 mm for multi-TI LGE). Additional studies are warranted to
further explore the potential of our multi-TI sequence to improve image
quality, diagnostic confidence, and better optimize clinical workflow. Acknowledgements
This work was supported in part by the following grants: National Institutes of Health
(R01HL116895, R01HL138578, R21EB024315, R21AG055954) and American Heart
Association (19IPLOI34760317). References
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