244 research outputs found
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction
Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image
(MRI) processing and achieves accurate MRI reconstruction from under-sampled
k-space data. According to the current research, there are still several
problems with dynamic MRI k-space reconstruction based on CS. 1) There are
differences between the Fourier domain and the Image domain, and the
differences between MRI processing of different domains need to be considered.
2) As three-dimensional data, dynamic MRI has its spatial-temporal
characteristics, which need to calculate the difference and consistency of
surface textures while preserving structural integrity and uniqueness. 3)
Dynamic MRI reconstruction is time-consuming and computationally
resource-dependent. In this paper, we propose a novel robust low-rank dynamic
MRI reconstruction optimization model via highly under-sampled and Discrete
Fourier Transform (DFT) called the Robust Depth Linear Error Decomposition
Model (RDLEDM). Our method mainly includes linear decomposition, double Total
Variation (TV), and double Nuclear Norm (NN) regularizations. By adding linear
image domain error analysis, the noise is reduced after under-sampled and DFT
processing, and the anti-interference ability of the algorithm is enhanced.
Double TV and NN regularizations can utilize both spatial-temporal
characteristics and explore the complementary relationship between different
dimensions in dynamic MRI sequences. In addition, Due to the non-smoothness and
non-convexity of TV and NN terms, it is difficult to optimize the unified
objective model. To address this issue, we utilize a fast algorithm by solving
a primal-dual form of the original problem. Compared with five state-of-the-art
methods, extensive experiments on dynamic MRI data demonstrate the superior
performance of the proposed method in terms of both reconstruction accuracy and
time complexity
Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS
Purpose: To develop and evaluate methods for 1) reconstructing
3D-quantification using an interleaved Look-Locker acquisition sequence with T2
preparation pulse (3D-QALAS) time-series images using a low-rank subspace
method, which enables accurate and rapid T1 and T2 mapping, and 2) improving
the fidelity of subspace QALAS by combining scan-specific deep-learning-based
reconstruction and subspace modeling. Methods: A low-rank subspace method for
3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method
(i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2
mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system
phantom, the accuracy of the T1 and T2 maps estimated using the proposed
methods was evaluated by comparing them with reference techniques. The
reconstruction performance of the proposed subspace QALAS using Zero-DeepSub
was evaluated in vivo and compared with conventional QALAS at high reduction
factors of up to 9-fold. Results: Phantom experiments showed that subspace
QALAS had good linearity with respect to the reference methods while reducing
biases compared to conventional QALAS, especially for T2 maps. Moreover, in
vivo results demonstrated that subspace QALAS had better g-factor maps and
could reduce voxel blurring, noise, and artifacts compared to conventional
QALAS and showed robust performance at up to 9-fold acceleration with
Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm
isotropic resolution within 2 min of scan time. Conclusion: The proposed
subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid
whole-brain multiparametric quantification and time-resolved imaging.Comment: 17 figures, 3 table
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of -stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
Quantitative susceptibility mapping (QSM) is an MRI phase-based
post-processing method that quantifies tissue magnetic susceptibility
distributions. However, QSM acquisitions are relatively slow, even with
parallel imaging. Incoherent undersampling and compressed sensing
reconstruction techniques have been used to accelerate traditional
magnitude-based MRI acquisitions; however, most do not recover the full phase
signal due to its non-convex nature. In this study, a learning-based Deep
Complex Residual Network (DCRNet) is proposed to recover both the magnitude and
phase images from incoherently undersampled data, enabling high acceleration of
QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared
with two iterative and one deep learning methods on retrospectively
undersampled acquisitions from six healthy volunteers, one intracranial
hemorrhage and one multiple sclerosis patients, as well as one prospectively
undersampled healthy subject using a 7T scanner. Peak signal to noise ratio
(PSNR), structural similarity (SSIM) and region-of-interest susceptibility
measurements are reported for numerical comparisons. The proposed DCRNet method
substantially reduced artifacts and blurring compared to the other methods and
resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and
susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey
matter susceptibility than some existing methods, when the acquisition was
accelerated four times. The proposed DCRNet also dramatically shortened the
reconstruction time by nearly 10 thousand times for each scan, from around 80
hours using conventional approaches to only 30 seconds.Comment: 10 figure
Recommended from our members
Advanced H-1 Lung Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies
- …