Rajiv G Menon1, Marcelo V.W. Zibetti1, Azadeh Sharafi1, Li Feng1, Leon Axel1, and Ravinder R Regatte1
1New York University School of Medicine, New York, NY, United States
Synopsis
In
this study, we present a technique for T1rho mapping of myocardium using a
combination of T1rho preparation, radial sampling, and low rank-based compressed
sensing (CS) reconstruction. We acquire free-breathing, ungated data using a 3D
T1rho prepared radial acquisition
scheme with different spin lock times (TSL). After retrospectively
synchronizing the data to a window in diastole, a sparse 3D k-dataset is given
as input to compressed sensing reconstruction that uses a low rank constraint.
Mono-exponential modeling of the reconstructed data yields the T1rho maps. The CS reconstruction results in improved images
and the mean T1rho values estimated in the myocardium are consistent with literature.
Introduction
Ischemic
heart disease is one of the leading causes of mortality in humans1.
Late gadolinium enhancement (LGE) is the top choice for characterizing
myocardial tissue. but its use of contrast agent precludes many patients with
renal clearance dysfunctions. Hence, development of non-invasive techniques to
detect myocardial infarcts, edema and myocardial fibrosis is highly desirable.
T1rho cardiovascular magnetic
resonance (CMR) is emerging as an endogenous contrast method to characterize
the heart non-invasively2-3. In this study, we present a technique
that uses a combination of T1rho preparation, golden-angle radial sampling, and a low-rank based compressed
sensing reconstruction scheme to perform T1rho mapping for characterizing the myocardium
non-invasively.Methods
Imaging Sequence and Data Acquisition
The
imaging sequence consisted of a RAVE (RAdial Volumetric Encoding) sequence4
with a customized T1rho preparation module5. A 3D golden-angle, stack-of-stars radial sampling
trajectory was used to acquire all spokes along the kz-dimension in each TR. One
second delay was inserted between consecutive TRs to allow for T1 decay.
Five
healthy volunteers (3 male, 2 female, age=28 ± 6 years) were recruited
following informed consent. The imaging study consisted of a 3D free-breathing acquisition
with the in-plane direction aligned along the short axis orientation of the
heart. The resolution was 1.6x1.6x2.0 mm3. FOV=300mm2, TR=5ms,
TE=2ms, in-plane radial spokes = 200, acquisition matrix=384x200x44, T1rho spin lock frequency=350 Hz, 10 spin lock duration
(TSL) datasets were obtained with TSL= [2,4,6,8,10,15,25,35,45,55]. Acquisition
time for each TSL was 4 minutes, resulting in a total acquisition time of 40
minutes. Each radial stack was acquired in 200ms. Electrocardiogram (ECG) leads
were connected, and the ECG logs were saved for the duration of the
acquisition.
ECG Synchronization
Using
the start-times of the data acquisition from raw data and ECG logs, the ECG
triggers were synchronized with the start times of each radial spoke stack. A
window of 200 ms prior to each trigger was used in order to choose the spokes
in mid to late diastole. For each accepted spoke, all the spokes along the kz
dimension were included too. This resulting sparse 3D k-data was used as input
to compressed sensing reconstruction. In addition to compressed sensing the
sparse k-data was reconstructed using a gridding algorithm.
Compressed Sensing using Low Rank Prior
The compressed sensing reconstruction problem is formulated as:
$$x=\arg\min_x ||m-FCx||_2^2+λ||X||_*.$$
where $$$x$$$
represents the image series to be reconstructed (different TSLs), $$$C$$$ is
the coil sensitivities, $$$F$$$ represents the NUFFT operator, and $$$λ$$$ is
the regularization parameter. The low rank (LR) regularizer utilize the nuclear-norm6 $$$||X||_*$$$,
where $$$X$$$ is $$$x$$$ reshaped as space-time matrix, and each row contains
the complex magnetization signal of a voxel over TSLs.
T1rho Mapping
After reconstruction, curve
fitting is performed to generate the T1rho map using a mono-exponential model:
$$|x(t,n)|=a(n)exp\left(\frac{-t}{τ(n)}
\right)+b,$$
where $$$a(n)$$$ is magnitude, while $$$τ(n)$$$ is the T1rho relaxation time. Non-linear least squares is utilized as cost function, where
the minimization is done utilizing conjugate gradient Steihaug’s trust-region7.
A 3x3 voxel averaging is utilized prior to the fitting process.
Results
Figure
2(a-d) shows gridding reconstruction of the ECG synchronized sparse k-data for
different TSL durations. The resulting T1rho map is shown in figure 2(e). Figure 2(f-i) shows the
results from the compressed sensing reconstruction. The resulting T1rho map is shown in figure 2(k). Compressed sensing
results in significant reduction of streaking artifacts, and improved resulting
T1rho maps. Figure 3 rows (a) through (d) show the reconstructed images from base to apex at different TSL
durations. The last row, figure 3 (e) shows the T1rho maps calculated from the CS reconstruction. The mean
T1rho in myocardium was 50.48
± 5.58 ms.Discussion and Conclusion
In
this study, we demonstrated the use of golden-angle radial sampling and
compressed sensing reconstruction for 3D T1rho mapping of the heart. Retrospective sorting of
radial spokes in synchronization with ECG logs and compressed sensing significantly reduce the
streaking artifacts and image blurring compared to gridding reconstruction,and
the computed T1rho maps are consistent with literature.Acknowledgements
This
study was supported by NIH grants R01-AR060238, R01 AR067156, and R01 AR068966,
and was performed under the rubric of the Center of Advanced Imaging Innovation
and Research (CAI2R), a NIBIB Biomedical Technology Resource Center (NIH P41
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