16 research outputs found
Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints
Objective: The purpose of this manuscript is to accelerate cardiac diffusion
tensor imaging (CDTI) by integrating low-rankness and compressed sensing.
Methods: Diffusion-weighted images exhibit both transform sparsity and
low-rankness. These properties can jointly be exploited to accelerate CDTI,
especially when a phase map is applied to correct for the phase inconsistency
across diffusion directions, thereby enhancing low-rankness. The proposed
method is evaluated both ex vivo and in vivo, and is compared to methods using
either a low-rank or sparsity constraint alone. Results: Compared to using a
low-rank or sparsity constraint alone, the proposed method preserves more
accurate helix angle features, the transmural continuum across the myocardium
wall, and mean diffusivity at higher acceleration, while yielding significantly
lower bias and higher intraclass correlation coefficient. Conclusion:
Low-rankness and compressed sensing together facilitate acceleration for both
ex vivo and in vivo CDTI, improving reconstruction accuracy compared to
employing either constraint alone. Significance: Compared to previous methods
for accelerating CDTI, the proposed method has the potential to reach higher
acceleration while preserving myofiber architecture features which may allow
more spatial coverage, higher spatial resolution and shorter temporal footprint
in the future.Comment: 11 pages, 16 figures, published on IEEE Transactions on Biomedical
Engineerin
Edge-weighted pFISTA-Net for MRI Reconstruction
Deep learning based on unrolled algorithm has served as an effective method
for accelerated magnetic resonance imaging (MRI). However, many methods ignore
the direct use of edge information to assist MRI reconstruction. In this work,
we present the edge-weighted pFISTA-Net that directly applies the detected edge
map to the soft-thresholding part of pFISTA-Net. The soft-thresholding value of
different regions will be adjusted according to the edge map. Experimental
results of a public brain dataset show that the proposed yields reconstructions
with lower error and better artifact suppression compared with the
state-of-the-art deep learning-based methods. The edge-weighted pFISTA-Net also
shows robustness for different undersampling masks and edge detection
operators. In addition, we extend the edge weighted structure to joint
reconstruction and segmentation network and obtain improved reconstruction
performance and more accurate segmentation results
Echo Planar Time-Resolved Imaging (EPTI) with Subspace Reconstruction and Optimized Spatiotemporal Encoding
Purpose: To develop new encoding and reconstruction techniques for fast
multi-contrast quantitative imaging. Methods: The recently proposed Echo Planar
Time-resolved Imaging (EPTI) technique can achieve fast distortion- and
blurring-free multi-contrast quantitative imaging. In this work, a subspace
reconstruction framework is developed to improve the reconstruction accuracy of
EPTI at high encoding accelerations. The number of unknowns in the
reconstruction is significantly reduced by modeling the temporal signal
evolutions using low-rank subspace. As part of the proposed reconstruction
approach, a B0-update algorithm and a shot-to-shot B0 variation correction
method are developed to enable the reconstruction of high-resolution tissue
phase images and to mitigate artifacts from shot-to-shot phase variations.
Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a
new temporal-variant of CAIPI encoding is proposed to further improve
performance. Results: The effectiveness of the proposed subspace reconstruction
was demonstrated first in 2D GESE EPTI, where the reconstruction achieved
higher accuracy when compared to conventional B0-informed GRAPPA. For 3D
GE-EPTI, a retrospective undersampling experiment demonstrates that the new
temporal-variant CAIPI encoding can achieve up to 72x acceleration with close
to 2x reduction in reconstruction error when compared to conventional
spatiotemporal-CAIPI encoding. In a prospective undersampling experiment,
high-quality whole-brain T2* and QSM maps at 1 mm isotropic resolution was
acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI
encoding. Conclusion: The proposed subspace reconstruction and optimized
temporal-variant CAIPI encoding can further improve the performance of EPTI for
fast quantitative mapping
High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting
Purpose: This study aims to develop a high-resolution whole-brain
multi-parametric quantitative MRI approach for simultaneous mapping of
myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a
clinically feasible scan time.
Methods: We developed 3D ViSTa-MRF, which combined Visualization of Short
Transverse relaxation time component (ViSTa) technique with MR Fingerprinting
(MRF), to achieve high-fidelity whole-brain MWF and T1/T2/PD mapping on a
clinical 3T scanner. To achieve fast acquisition and memory-efficient
reconstruction, the ViSTa-MRF sequence leverages an optimized 3D
tiny-golden-angle-shuffling spiral-projection acquisition and joint
spatial-temporal subspace reconstruction with optimized preconditioning
algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF
mapping was achieved without a need for multi-compartment fitting that could
introduce bias and/or noise from additional assumptions or priors.
Results: The in-vivo results demonstrate the effectiveness of the proposed
acquisition and reconstruction framework to provide fast multi-parametric
mapping with high SNR and good quality. The in-vivo results of 1mm- and
0.66mm-iso datasets indicate that the MWF values measured by the proposed
method are consistent with standard ViSTa results that are 30x slower with
lower SNR. Furthermore, we applied the proposed method to enable 5-minute
whole-brain 1mm-iso assessment of MWF and T1/T2/PD mappings for infant brain
development and for post-mortem brain samples.
Conclusions: In this work, we have developed a 3D ViSTa-MRF technique that
enables the acquisition of whole-brain MWF, quantitative T1, T2, and PD maps at
1mm and 0.66mm isotropic resolution in 5 and 15 minutes, respectively. This
advancement allows for quantitative investigations of myelination changes in
the brain.Comment: 38 pages, 12 figures and 1 tabl