3,198 research outputs found
Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions
Purpose: A time-efficient strategy to acquire high-quality multi-contrast
images is to reconstruct undersampled data with joint regularization terms that
leverage common information across contrasts. However, these terms can cause
leakage of uncommon features among contrasts, compromising diagnostic utility.
The goal of this study is to develop a compressive sensing method for
multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally
utilizes shared information while preventing feature leakage.
Theory: Joint regularization terms group sparsity and colour total variation
are used to exploit common features across images while individual sparsity and
total variation are also used to prevent leakage of distinct features across
contrasts. The multi-channel multi-contrast reconstruction problem is solved
via a fast algorithm based on Alternating Direction Method of Multipliers.
Methods: The proposed method is compared against using only individual and
only joint regularization terms in reconstruction. Comparisons were performed
on single-channel simulated and multi-channel in-vivo datasets in terms of
reconstruction quality and neuroradiologist reader scores.
Results: The proposed method demonstrates rapid convergence and improved
image quality for both simulated and in-vivo datasets. Furthermore, while
reconstructions that solely use joint regularization terms are prone to
leakage-of-features, the proposed method reliably avoids leakage via
simultaneous use of joint and individual terms.
Conclusion: The proposed compressive sensing method performs fast
reconstruction of multi-channel multi-contrast MRI data with improved image
quality. It offers reliability against feature leakage in joint
reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio
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
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