51,407 research outputs found
Magnetic Resonance Fingerprinting with Total Nuclear Variation Regularisation
Magnetic Resonance Fingerprinting (MRF) accelerates quantitative magnetic resonance imaging. The reconstruction can be separated into two problems: reconstruction of a set of multi-contrast images from k-space signals, and estimation of parametric maps from the set of multi-contrast images. In this study we focus on the former problem, while leveraging dictionary matching for the estimation of parametric maps. Two different sparsity promoting regularisation strategies were investigated: contrast-wise Total Variation (TV) which encourages image sparsity separately; and Total Nuclear Variation (TNV) which promotes a measure of joint edge sparsity. We found improved results using joint sparsity
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
Conditional Score-Based Reconstructions for Multi-contrast MRI
Magnetic resonance imaging (MRI) exam protocols consist of multiple
contrast-weighted images of the same anatomy to emphasize different tissue
properties. Due to the long acquisition times required to collect fully sampled
k-space measurements, it is common to only collect a fraction of k-space for
some, or all, of the scans and subsequently solve an inverse problem for each
contrast to recover the desired image from sub-sampled measurements. Recently,
there has been a push to further accelerate MRI exams using data-driven priors,
and generative models in particular, to regularize the ill-posed inverse
problem of image reconstruction. These methods have shown promising
improvements over classical methods. However, many of the approaches neglect
the multi-contrast nature of clinical MRI exams and treat each scan as an
independent reconstruction. In this work we show that by learning a joint
Bayesian prior over multi-contrast data with a score-based generative model we
are able to leverage the underlying structure between multi-contrast images and
thus improve image reconstruction fidelity over generative models that only
reconstruct images of a single contrast
JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI
Multi-contrast Magnetic Resonance Imaging (MRI) generates multiple medical
images with rich and complementary information for routine clinical use;
however, it suffers from a long acquisition time. Recent works for accelerating
MRI, mainly designed for single contrast, may not be optimal for multi-contrast
scenario since the inherent correlations among the multi-contrast images are
not exploited. In addition, independent reconstruction of each contrast usually
does not translate to optimal performance of downstream tasks. Motivated by
these aspects, in this paper we design an end-to-end framework for accelerating
multi-contrast MRI which simultaneously optimizes the entire MR imaging
workflow including sampling, reconstruction and downstream tasks to achieve the
best overall outcomes. The proposed framework consists of a sampling mask
generator for each image contrast and a reconstructor exploiting the
inter-contrast correlations with a recurrent structure which enables the
information sharing in a holistic way. The sampling mask generator and the
reconstructor are trained jointly across the multiple image contrasts. The
acceleration ratio of each image contrast is also learnable and can be driven
by a downstream task performance. We validate our approach on a multi-contrast
brain dataset and a multi-contrast knee dataset. Experiments show that (1) our
framework consistently outperforms the baselines designed for single contrast
on both datasets; (2) our newly designed recurrent reconstruction network
effectively improves the reconstruction quality for multi-contrast images; (3)
the learnable acceleration ratio improves the downstream task performance
significantly. Overall, this work has potentials to open up new avenues for
optimizing the entire multi-contrast MR imaging workflow
Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction
Magnetic resonance imaging (MRI) tasks often involve multiple contrasts.
Recently, numerous deep learning-based multi-contrast MRI super-resolution (SR)
and reconstruction methods have been proposed to explore the complementary
information from the multi-contrast images. However, these methods either
construct parameter-sharing networks or manually design fusion rules, failing
to accurately model the correlations between multi-contrast images and lacking
certain interpretations. In this paper, we propose a multi-contrast
convolutional dictionary (MC-CDic) model under the guidance of the optimization
algorithm with a well-designed data fidelity term. Specifically, we bulid an
observation model for the multi-contrast MR images to explicitly model the
multi-contrast images as common features and unique features. In this way, only
the useful information in the reference image can be transferred to the target
image, while the inconsistent information will be ignored. We employ the
proximal gradient algorithm to optimize the model and unroll the iterative
steps into a deep CDic model. Especially, the proximal operators are replaced
by learnable ResNet. In addition, multi-scale dictionaries are introduced to
further improve the model performance. We test our MC-CDic model on
multi-contrast MRI SR and reconstruction tasks. Experimental results
demonstrate the superior performance of the proposed MC-CDic model against
existing SOTA methods. Code is available at
https://github.com/lpcccc-cv/MC-CDic.Comment: Accepted to IJCAI202
A synthesis-based approach to compressive multi-contrast magnetic resonance imaging
In this study, we deal with the problem of image reconstruction from compressive measurements of multi-contrast magnetic resonance imaging (MRI). We propose a synthesis based approach for image reconstruction to better exploit mutual information across contrasts, while retaining individual features of each contrast image. For fast recovery, we propose an augmented Lagrangian based algorithm, using Alternating Direction Method of Multipliers (ADMM). We then compare the proposed algorithm to the state-of-the-art Compressive Sensing-MRI algorithms, and show that the proposed method results in better quality images in shorter computation time. © 2017 IEEE
Deep Cardiac MRI Reconstruction with ADMM
Cardiac magnetic resonance imaging is a valuable non-invasive tool for
identifying cardiovascular diseases. For instance, Cine MRI is the benchmark
modality for assessing the cardiac function and anatomy. On the other hand,
multi-contrast (T1 and T2) mapping has the potential to assess pathologies and
abnormalities in the myocardium and interstitium. However, voluntary
breath-holding and often arrhythmia, in combination with MRI's slow imaging
speed, can lead to motion artifacts, hindering real-time acquisition image
quality. Although performing accelerated acquisitions can facilitate dynamic
imaging, it induces aliasing, causing low reconstructed image quality in Cine
MRI and inaccurate T1 and T2 mapping estimation. In this work, inspired by
related work in accelerated MRI reconstruction, we present a deep learning
(DL)-based method for accelerated cine and multi-contrast reconstruction in the
context of dynamic cardiac imaging. We formulate the reconstruction problem as
a least squares regularized optimization task, and employ vSHARP, a
state-of-the-art DL-based inverse problem solver, which incorporates
half-quadratic variable splitting and the alternating direction method of
multipliers with neural networks. We treat the problem in two setups; a 2D
reconstruction and a 2D dynamic reconstruction task, and employ 2D and 3D deep
learning networks, respectively. Our method optimizes in both the image and
k-space domains, allowing for high reconstruction fidelity. Although the target
data is undersampled with a Cartesian equispaced scheme, we train our model
using both Cartesian and simulated non-Cartesian undersampling schemes to
enhance generalization of the model to unseen data. Furthermore, our model
adopts a deep neural network to learn and refine the sensitivity maps of
multi-coil k-space data. Lastly, our method is jointly trained on both,
undersampled cine and multi-contrast data.Comment: 12 pages, 3 figures, 2 tables. CMRxRecon Challenge, MICCAI 202
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