27,754 research outputs found
Deep Low-rank Prior in Dynamic MR Imaging
The deep learning methods have achieved attractive performance in dynamic MR
cine imaging. However, all of these methods are only driven by the sparse prior
of MR images, while the important low-rank (LR) prior of dynamic MR cine images
is not explored, which limits the further improvements on dynamic MR
reconstruction. In this paper, a learned singular value thresholding
(Learned-SVT) operation is proposed to explore deep low-rank prior in dynamic
MR imaging for obtaining improved reconstruction results. In particular, we
come up with two novel and distinct schemes to introduce the learnable low-rank
prior into deep network architectures in an unrolling manner and a
plug-and-play manner respectively. In the unrolling manner, we put forward a
model-based unrolling sparse and low-rank network for dynamic MR imaging,
dubbed SLR-Net. The SLR-Net is defined over a deep network flow graph, which is
unrolled from the iterative procedures in the Iterative Shrinkage-Thresholding
Algorithm (ISTA) for optimizing a sparse and low-rank based dynamic MRI model.
In the plug-and-play manner, we present a plug-and-play LR network module that
can be easily embedded into any other dynamic MR neural networks without
changing the network paradigm. Experimental results show that both schemes can
further improve the state-of-the-art CS methods, such as k-t SLR, and
sparsity-driven deep learning-based methods, such as DC-CNN and CRNN, both
qualitatively and quantitatively.Comment: 10 pages, 8 figure
CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
Dynamic magnetic resonance (MR) imaging has generated great research
interest, as it can provide both spatial and temporal information for clinical
diagnosis. However, slow imaging speed or long scanning time is still one of
the challenges for dynamic MR imaging. Most existing methods reconstruct
Dynamic MR images from incomplete k-space data under the guidance of compressed
sensing (CS) or low rank theory, which suffer from long iterative
reconstruction time. Recently, deep learning has shown great potential in
accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method
with both k-space and spatial prior knowledge integrated via multi-supervised
network training. Nevertheless, there was still a certain degree of smooth in
the reconstructed images at high acceleration factors. In this work, we propose
cascaded residual dense networks for dynamic MR imaging with edge-enhance loss
constraint, dubbed as CRDN. Specifically, the cascaded residual dense networks
fully exploit the hierarchical features from all the convolutional layers with
both local and global feature fusion. We further utilize the total variation
(TV) loss function, which has the edge enhancement properties, for training the
networks
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training
Dynamic MR image reconstruction from incomplete k-space data has generated
great research interest due to its capability in reducing scan time.
Nevertheless, the reconstruction problem is still challenging due to its
ill-posed nature. Most existing methods either suffer from long iterative
reconstruction time or explore limited prior knowledge. This paper proposes a
dynamic MR imaging method with both k-space and spatial prior knowledge
integrated via multi-supervised network training, dubbed as DIMENSION.
Specifically, the DIMENSION architecture consists of a frequential prior
network for updating the k-space with its network prediction and a spatial
prior network for capturing image structures and details. Furthermore, a
multisupervised network training technique is developed to constrain the
frequency domain information and reconstruction results at different levels.
The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the
state-of-the-art CNN-based method on in vivo datasets show our method can
achieve improved reconstruction results in shorter time.Comment: 11 pages, 12 figure
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI)
leads to a challenging ill-posed inverse problem, which has received great
interest from both the signal processing and machine learning community over
the last decades. The key ingredient to the problem is how to exploit the
temporal correlation of the MR sequence to resolve the aliasing artefact.
Traditionally, such observation led to a formulation of a non-convex
optimisation problem, which were solved using iterative algorithms. Recently,
however, deep learning based-approaches have gained significant popularity due
to its ability to solve general inversion problems. In this work, we propose a
unique, novel convolutional recurrent neural network (CRNN) architecture which
reconstructs high quality cardiac MR images from highly undersampled k-space
data by jointly exploiting the dependencies of the temporal sequences as well
as the iterative nature of the traditional optimisation algorithms. In
particular, the proposed architecture embeds the structure of the traditional
iterative algorithms, efficiently modelling the recurrence of the iterative
reconstruction stages by using recurrent hidden connections over such
iterations. In addition, spatiotemporal dependencies are simultaneously learnt
by exploiting bidirectional recurrent hidden connections across time sequences.
The proposed algorithm is able to learn both the temporal dependency and the
iterative reconstruction process effectively with only a very small number of
parameters, while outperforming current MR reconstruction methods in terms of
computational complexity, reconstruction accuracy and speed.Comment: Published in IEEE Transactions on Medical Imagin
RARE: Image Reconstruction using Deep Priors Learned without Ground Truth
Regularization by denoising (RED) is an image reconstruction framework that
uses an image denoiser as a prior. Recent work has shown the state-of-the-art
performance of RED with learned denoisers corresponding to pre-trained
convolutional neural nets (CNNs). In this work, we propose to broaden the
current denoiser-centric view of RED by considering priors corresponding to
networks trained for more general artifact-removal. The key benefit of the
proposed family of algorithms, called regularization by artifact-removal
(RARE), is that it can leverage priors learned on datasets containing only
undersampled measurements. This makes RARE applicable to problems where it is
practically impossible to have fully-sampled groundtruth data for training. We
validate RARE on both simulated and experimentally collected data by
reconstructing a free-breathing whole-body 3D MRIs into ten respiratory phases
from heavily undersampled k-space measurements. Our results corroborate the
potential of learning regularizers for iterative inversion directly on
undersampled and noisy measurements.Comment: In press for IEEE Journal of Special Topics in Signal Processin
MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR T2 mapping
Quantitative mapping of magnetic resonance (MR) parameters have been shown as
valuable methods for improved assessment of a range of diseases. Due to the
need to image an anatomic structure multiple times, parameter mapping usually
requires long scan times compared to conventional static imaging. Therefore,
accelerated parameter mapping is highly-desirable and remains a topic of great
interest in the MR research community. While many recent deep learning methods
have focused on highly efficient image reconstruction for conventional static
MR imaging, applications of deep learning for dynamic imaging and in particular
accelerated parameter mapping have been limited. The purpose of this work was
to develop and evaluate a novel deep learning-based reconstruction framework
called Model-Augmented Neural neTwork with Incoherent k-space Sampling (MANTIS)
for efficient MR parameter mapping. Our approach combines end-to-end CNN
mapping with k-space consistency using the concept of cyclic loss to further
enforce data and model fidelity. Incoherent k-space sampling is used to improve
reconstruction performance. A physical model is incorporated into the proposed
framework, so that the parameter maps can be efficiently estimated directly
from undersampled images. The performance of MANTIS was demonstrated for the
spin-spin relaxation time (T2) mapping of the knee joint. Compared to
conventional reconstruction approaches that exploited image sparsity, MANTIS
yielded lower errors and higher similarity with respect to the reference in the
T2 estimation. Our study demonstrated that the proposed MANTIS framework, with
a combination of end-to-end CNN mapping, signal model-augmented data
consistency, and incoherent k-space sampling, represents a promising approach
for efficient MR parameter mapping. MANTIS can potentially be extended to other
types of parameter mapping with appropriate models
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery
This paper puts forth a novel bi-linear modeling framework for data recovery
via manifold-learning and sparse-approximation arguments and considers its
application to dynamic magnetic-resonance imaging (dMRI). Each temporal-domain
MR image is viewed as a point that lies onto or close to a smooth manifold, and
landmark points are identified to describe the point cloud concisely. To
facilitate computations, a dimensionality reduction module generates
low-dimensional/compressed renditions of the landmark points. Recovery of the
high-fidelity MRI data is realized by solving a non-convex minimization task
for the linear decompression operator and those affine combinations of landmark
points which locally approximate the latent manifold geometry. An algorithm
with guaranteed convergence to stationary solutions of the non-convex
minimization task is also provided. The aforementioned framework exploits the
underlying spatio-temporal patterns and geometry of the acquired data without
any prior training on external data or information. Extensive numerical results
on simulated as well as real cardiac-cine and perfusion MRI data illustrate
noteworthy improvements of the advocated machine-learning framework over
state-of-the-art reconstruction techniques
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Following the success of deep learning in a wide range of applications,
neural network-based machine learning techniques have received interest as a
means of accelerating magnetic resonance imaging (MRI). A number of ideas
inspired by deep learning techniques from computer vision and image processing
have been successfully applied to non-linear image reconstruction in the spirit
of compressed sensing for both low dose computed tomography and accelerated
MRI. The additional integration of multi-coil information to recover missing
k-space lines in the MRI reconstruction process, is still studied less
frequently, even though it is the de-facto standard for currently used
accelerated MR acquisitions. This manuscript provides an overview of the recent
machine learning approaches that have been proposed specifically for improving
parallel imaging. A general background introduction to parallel MRI is given
that is structured around the classical view of image space and k-space based
methods. Both linear and non-linear methods are covered, followed by a
discussion of recent efforts to further improve parallel imaging using machine
learning, and specifically using artificial neural networks. Image-domain based
techniques that introduce improved regularizers are covered as well as k-space
based methods, where the focus is on better interpolation strategies using
neural networks. Issues and open problems are discussed as well as recent
efforts for producing open datasets and benchmarks for the community.Comment: 14 pages, 7 figure
MoDL: Model Based Deep Learning Architecture for Inverse Problems
We introduce a model-based image reconstruction framework with a convolution
neural network (CNN) based regularization prior. The proposed formulation
provides a systematic approach for deriving deep architectures for inverse
problems with the arbitrary structure. Since the forward model is explicitly
accounted for, a smaller network with fewer parameters is sufficient to capture
the image information compared to black-box deep learning approaches, thus
reducing the demand for training data and training time. Since we rely on
end-to-end training, the CNN weights are customized to the forward model, thus
offering improved performance over approaches that rely on pre-trained
denoisers. The main difference of the framework from existing end-to-end
training strategies is the sharing of the network weights across iterations and
channels. Our experiments show that the decoupling of the number of iterations
from the network complexity offered by this approach provides benefits
including lower demand for training data, reduced risk of overfitting, and
implementations with significantly reduced memory footprint. We propose to
enforce data-consistency by using numerical optimization blocks such as
conjugate gradients algorithm within the network; this approach offers faster
convergence per iteration, compared to methods that rely on proximal gradients
steps to enforce data consistency. Our experiments show that the faster
convergence translates to improved performance, especially when the available
GPU memory restricts the number of iterations.Comment: published in IEEE Transaction on Medical Imagin
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
Recently deep neural networks have been widely and successfully applied in
computer vision tasks and attracted growing interests in medical imaging. One
barrier for the application of deep neural networks to medical imaging is the
need of large amounts of prior training pairs, which is not always feasible in
clinical practice. In this work we propose a personalized representation
learning framework where no prior training pairs are needed, but only the
patient's own prior images. The representation is expressed using a deep neural
network with the patient's prior images as network input. We then applied this
novel image representation to inverse problems in medical imaging in which the
original inverse problem was formulated as a constraint optimization problem
and solved using the alternating direction method of multipliers (ADMM)
algorithm. Anatomically guided brain positron emission tomography (PET) image
reconstruction and image denoising were employed as examples to demonstrate the
effectiveness of the proposed framework. Quantification results based on
simulation and real datasets show that the proposed personalized representation
framework outperform other widely adopted methods.Comment: 11 pages, 7 figure
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