613 research outputs found
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
Objective: Improve the reconstructed image with fast and multi-class
dictionaries learning when magnetic resonance imaging is accelerated by
undersampling the k-space data. Methods: A fast orthogonal dictionary learning
method is introduced into magnetic resonance image reconstruction to providing
adaptive sparse representation of images. To enhance the sparsity, image is
divided into classified patches according to the same geometrical direction and
dictionary is trained within each class. A new sparse reconstruction model with
the multi-class dictionaries is proposed and solved using a fast alternating
direction method of multipliers. Results: Experiments on phantom and brain
imaging data with acceleration factor up to 10 and various undersampling
patterns are conducted. The proposed method is compared with state-of-the-art
magnetic resonance image reconstruction methods. Conclusion: Artifacts are
better suppressed and image edges are better preserved than the compared
methods. Besides, the computation of the proposed approach is much faster than
the typical K-SVD dictionary learning method in magnetic resonance image
reconstruction. Significance: The proposed method can be exploited in
undersapmled magnetic resonance imaging to reduce data acquisition time and
reconstruct images with better image quality.Comment: 13 pages, 15 figures, 5 table
Memory reduced non-Cartesian MRI encoding using the mixed-radix tensor product on CPU and GPU
Multi-dimensional non-Cartesian MRI encoding using the precomputed
interpolator can encounter the curse of dimensionality, in which the
interpolator size exceeds the available memory on the parallel accelerators.
Here we reformulate the multi-dimensional non-uniform fast Fourier transform
(NUFFT) to a tensor form. The exponentially growing size of the fully
precomputed interpolator can be reduced by tensor analysis. We propose a
tree-like, mixed-radix tensor method which flexibly reduces the storage of the
NUFFT. A parallel tensor product algorithm is proposed and tested with in vivo
cardiac MRI data. Cross-architecture comparisons show that up to 88.1% and
62.4% memory savings are seen in 3D and 2D CINE MRI, respectively, subject only
to a negligible loss of accuracy compared to the double-precision CPU version.Comment: The submitted paper in its current form is inappropriat
Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction
Deep-neural-network-based image reconstruction has demonstrated promising
performance in medical imaging for under-sampled and low-dose scenarios.
However, it requires large amount of memory and extensive time for the
training. It is especially challenging to train the reconstruction networks for
three-dimensional computed tomography (CT) because of the high resolution of CT
images. The purpose of this work is to reduce the memory and time consumption
of the training of the reconstruction networks for CT to make it practical for
current hardware, while maintaining the quality of the reconstructed images.
We unrolled the proximal gradient descent algorithm for iterative image
reconstruction to finite iterations and replaced the terms related to the
penalty function with trainable convolutional neural networks (CNN). The
network was trained greedily iteration by iteration in the image-domain on
patches, which requires reasonable amount of memory and time on mainstream
graphics processing unit (GPU). To overcome the local-minimum problem caused by
greedy learning, we used deep UNet as the CNN and incorporated separable
quadratic surrogate with ordered subsets for data fidelity, so that the
solution could escape from easy local minimums and achieve better image
quality.
The proposed method achieved comparable image quality with state-of-the-art
neural network for CT image reconstruction on 2D sparse-view and limited-angle
problems on the low-dose CT challenge dataset.Comment: 33 pages, 14 figures, accepted by Medical Physic
Compressed Sensing Parallel MRI with Adaptive Shrinkage TV Regularization
Compressed sensing (CS) methods in magnetic resonance imaging (MRI) offer
rapid acquisition and improved image quality but require iterative
reconstruction schemes with regularization to enforce sparsity. Regardless of
the difficulty in obtaining a fast numerical solution, the total variation (TV)
regularization is a preferred choice due to its edge-preserving and structure
recovery capabilities. While many approaches have been proposed to overcome the
non-differentiability of the TV cost term, an iterative shrinkage based
formulation allows recovering an image through recursive application of linear
filtering and soft thresholding. However, providing an optimal setting for the
regularization parameter is critical due to its direct impact on the rate of
convergence as well as steady state error. In this paper, a regularizer
adaptively varying in the derivative space is proposed, that follows the
generalized discrepancy principle (GDP). The implementation proceeds by
adaptively reducing the discrepancy level expressed as the absolute difference
between TV norms of the consistency error and the sparse approximation error. A
criterion based on the absolute difference between TV norms of consistency and
sparse approximation errors is used to update the threshold. Application of the
adaptive shrinkage TV regularizer to CS recovery of parallel MRI (pMRI) and
temporal gradient adaptation in dynamic MRI are shown to result in improved
image quality with accelerated convergence. In addition, the adaptive TV-based
iterative shrinkage (ATVIS) provides a significant speed advantage over the
fast iterative shrinkage-thresholding algorithm (FISTA).Comment: 27 pages,9 figure
Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide
versatile information but it has a bottleneck problem "slow imaging speed".
Reducing the scanned measurements can accelerate MR imaging with the aid of
powerful reconstruction methods, which have evolved from linear analytic models
to nonlinear iterative ones. The emerging trend in this area is replacing
human-defined signal models with that learned from data. Specifically, from
2016, deep learning has been incorporated into the fast MR imaging task, which
draws valuable prior knowledge from big datasets to facilitate accurate MR
image reconstruction from limited measurements. This survey aims to review deep
learning based MR image reconstruction works from 2016- June 2020 and will
discuss merits, limitations and challenges associated with such methods. Last
but not least, this paper will provide a starting point for researchers
interested in contributing to this field by pointing out good tutorial
resources, state-of-the-art open-source codes and meaningful data sources.Comment: Invited review submitted to Biomedical signal processing and control
in Jan 202
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
In this work we reduce undersampling artefacts in two-dimensional ()
golden-angle radial cine cardiac MRI by applying a modified version of the
U-net. We train the network on spatio-temporal slices which are previously
extracted from the image sequences. We compare our approach to two and a
Deep Learning-based post processing methods and to three iterative
reconstruction methods for dynamic cardiac MRI. Our method outperforms the
spatially trained U-net and the spatio-temporal U-net. Compared to the
spatio-temporal U-net, our method delivers comparable results, but with
shorter training times and less training data. Compared to the Compressed
Sensing-based methods -FOCUSS and a total variation regularised
reconstruction approach, our method improves image quality with respect to all
reported metrics. Further, it achieves competitive results when compared to an
iterative reconstruction method based on adaptive regularization with
Dictionary Learning and total variation, while only requiring a small fraction
of the computational time. A persistent homology analysis demonstrates that the
data manifold of the spatio-temporal domain has a lower complexity than the
spatial domain and therefore, the learning of a projection-like mapping is
facilitated. Even when trained on only one single subject without
data-augmentation, our approach yields results which are similar to the ones
obtained on a large training dataset. This makes the method particularly
suitable for training a network on limited training data. Finally, in contrast
to the spatial U-net, our proposed method is shown to be naturally robust
with respect to image rotation in image space and almost achieves
rotation-equivariance where neither data-augmentation nor a particular network
design are required.Comment: To be published in IEEE Transactions on Medical Imagin
Self-Supervised Deep Active Accelerated MRI
We propose to simultaneously learn to sample and reconstruct magnetic
resonance images (MRI) to maximize the reconstruction quality given a limited
sample budget, in a self-supervised setup. Unlike existing deep methods that
focus only on reconstructing given data, thus being passive, we go beyond the
current state of the art by considering both the data acquisition and the
reconstruction process within a single deep-learning framework. As our network
learns to acquire data, the network is active in nature. In order to do so, we
simultaneously train two neural networks, one dedicated to reconstruction and
the other to progressive sampling, each with an automatically generated
supervision signal that links them together. The two supervision signals are
created through Monte Carlo tree search (MCTS). MCTS returns a better sampling
pattern than what the current sampling network can give and, thus, a better
final reconstruction. The sampling network is trained to mimic the MCTS results
using the previous sampling network, thus being enhanced. The reconstruction
network is trained to give the highest reconstruction quality, given the MCTS
sampling pattern. Through this framework, we are able to train the two networks
without providing any direct supervision on sampling
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
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
Optimization methods for MR image reconstruction (long version)
The development of compressed sensing methods for magnetic resonance (MR)
image reconstruction led to an explosion of research on models and optimization
algorithms for MR imaging (MRI). Roughly 10 years after such methods first
appeared in the MRI literature, the U.S. Food and Drug Administration (FDA)
approved certain compressed sensing methods for commercial use, making
compressed sensing a clinical success story for MRI. This review paper
summarizes several key models and optimization algorithms for MR image
reconstruction, including both the type of methods that have FDA approval for
clinical use, as well as more recent methods being considered in the research
community that use data-adaptive regularizers. Many algorithms have been
devised that exploit the structure of the system model and regularizers used in
MRI; this paper strives to collect such algorithms in a single survey. Many of
the ideas used in optimization methods for MRI are also useful for solving
other inverse problems.Comment: Extended (and revised) version of invited paper submitted to IEEE
SPMag special issue on "Computational MRI: Compressed Sensing and Beyond.
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