4 research outputs found
Time-efficient, High Resolution 3T Whole Brain Quantitative Relaxometry using 3D-QALAS with Wave-CAIPI Readouts
Purpose: Volumetric, high resolution, quantitative mapping of brain tissues
relaxation properties is hindered by long acquisition times and SNR challenges.
This study, for the first time, combines the time efficient wave-CAIPI readouts
into the 3D-QALAS acquisition scheme, enabling full brain quantitative T1, T2
and PD maps at 1.15 isotropic voxels in only 3 minutes. Methods: Wave-CAIPI
readouts were embedded in the standard 3d-QALAS encoding scheme, enabling full
brain quantitative parameter maps (T1, T2 and PD) at acceleration factors of
R=3x2 with minimum SNR loss due to g-factor penalties. The quantitative maps
using the accelerated protocol were quantitatively compared against those
obtained from conventional 3D-QALAS sequence using GRAPPA acceleration of R=2
in the ISMRM NIST phantom, and ten healthy volunteers. To show the feasibility
of the proposed methods in clinical settings, the accelerated wave-CAIPI
3D-QALAS sequence was also employed in pediatric patients undergoing clinical
MRI examinations. Results: When tested in both the ISMRM/NIST phantom and 7
healthy volunteers, the quantitative maps using the accelerated protocol showed
excellent agreement against those obtained from conventional 3D-QALAS at R=2.
Conclusion: 3D-QALAS enhanced with wave-CAIPI readouts enables time-efficient,
full brain quantitative T1, T2 and PD mapping at 1.15 in 3 minutes at R=3x2
acceleration. When tested on the NIST phantom and 7 healthy volunteers, the
quantitative maps obtained from the accelerated wave-CAIPI 3D-QALAS protocol
showed very similar values to those obtained from the standard 3D-QALAS (R=2)
protocol, alluding to the robustness and reliability of the proposed methods.
This study also shows that the accelerated protocol can be effectively employed
in pediatric patient populations, making high-quality high-resolution full
brain quantitative imaging feasible in clinical settings
Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction
Purpose: We implemented the blip-up, blip-down circular echo planar imaging
(BUDA-cEPI) sequence with readout and phase partial Fourier to reduced
off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based
low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be
effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is
computationally intensive. Thus, we developed an ML-based reconstruction
technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction.
Methods: BUDA-cEPI RUN-UP - a model-based framework that incorporates
off-resonance and eddy current effects was unrolled through an artificial
neural network with only six gradient updates. The unrolled network alternates
between data consistency (i.e., forward BUDA-cEPI and its adjoint) and
regularization steps where U-Net plays a role as the regularizer. To handle the
partial Fourier effect, the virtual coil concept was also incorporated into the
reconstruction to effectively take advantage of the smooth phase prior, and
trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.
Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of
off-resonance, partial Fourier, and residual aliasing artifacts. However, the
reconstruction time is approximately 225 seconds per slice, which may not be
practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP
yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5%
normalized root mean square error detected, while the reconstruction time is
approximately 3 seconds.
Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by
~88x when compared to the state-of-the-art technique, while preserving imaging
details as demonstrated through DTI application.Comment: Number: Figures: 8 Tables: 3 References: 7