2,657 research outputs found
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network
Magnetic resonance imaging (MRI) reconstruction is an active inverse problem
which can be addressed by conventional compressed sensing (CS) MRI algorithms
that exploit the sparse nature of MRI in an iterative optimization-based
manner. However, two main drawbacks of iterative optimization-based CSMRI
methods are time-consuming and are limited in model capacity. Meanwhile, one
main challenge for recent deep learning-based CSMRI is the trade-off between
model performance and network size. To address the above issues, we develop a
new multi-scale dilated network for MRI reconstruction with high speed and
outstanding performance. Comparing to convolutional kernels with same receptive
fields, dilated convolutions reduce network parameters with smaller kernels and
expand receptive fields of kernels to obtain almost same information. To
maintain the abundance of features, we present global and local residual
learnings to extract more image edges and details. Then we utilize
concatenation layers to fuse multi-scale features and residual learnings for
better reconstruction. Compared with several non-deep and deep learning CSMRI
algorithms, the proposed method yields better reconstruction accuracy and
noticeable visual improvements. In addition, we perform the noisy setting to
verify the model stability, and then extend the proposed model on a MRI
super-resolution task.Comment: 27 pages and 13 figure
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
Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver
Purpose: To improve the quality of images obtained via dynamic
contrast-enhanced MRI (DCE-MRI) that include motion artifacts and blurring
using a deep learning approach. Methods: A multi-channel convolutional neural
network (MARC) based method is proposed for reducing the motion artifacts and
blurring caused by respiratory motion in images obtained via DCE-MRI of the
liver. The training datasets for the neural network included images with and
without respiration-induced motion artifacts or blurring, and the distortions
were generated by simulating the phase error in k-space. Patient studies were
conducted using a multi-phase T1-weighted spoiled gradient echo sequence for
the liver containing breath-hold failures during data acquisition. The trained
network was applied to the acquired images to analyze the filtering
performance, and the intensities and contrast ratios before and after denoising
were compared via Bland-Altman plots. Results: The proposed network was found
to significantly reduce the magnitude of the artifacts and blurring induced by
respiratory motion, and the contrast ratios of the images after processing via
the network were consistent with those of the unprocessed images. Conclusion: A
deep learning based method for removing motion artifacts in images obtained via
DCE-MRI in the liver was demonstrated and validated.Comment: 11 pages, 6 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
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction
In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory
can accelerate imaging by sampling fewer measurements within each contrast. The
conventional optimization-based models suffer several limitations: strict
assumption of shared sparse support, time-consuming optimization and "shallow"
models with difficulties in encoding the rich patterns hiding in massive MRI
data. In this paper, we propose the first deep learning model for
multi-contrast MRI reconstruction. We achieve information sharing through
feature sharing units, which significantly reduces the number of parameters.
The feature sharing unit is combined with a data fidelity unit to comprise an
inference block. These inference blocks are cascaded with dense connections,
which allows for information transmission across different depths of the
network efficiently. Our extensive experiments on various multi-contrast MRI
datasets show that proposed model outperforms both state-of-the-art
single-contrast and multi-contrast MRI methods in accuracy and efficiency. We
show the improved reconstruction quality can bring great benefits for the later
medical image analysis stage. Furthermore, the robustness of the proposed model
to the non-registration environment shows its potential in real MRI
applications.Comment: 13 pages, 16 figures, 3 table
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
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks
Purpose: Neural networks have received recent interest for reconstruction of
undersampled MR acquisitions. Ideally network performance should be optimized
by drawing the training and testing data from the same domain. In practice,
however, large datasets comprising hundreds of subjects scanned under a common
protocol are rare. The goal of this study is to introduce a transfer-learning
approach to address the problem of data scarcity in training deep networks for
accelerated MRI.
Methods: Neural networks were trained on thousands of samples from public
datasets of either natural images or brain MR images. The networks were then
fine-tuned using only few tens of brain MR images in a distinct testing domain.
Domain-transferred networks were compared to networks trained directly in the
testing domain. Network performance was evaluated for varying acceleration
factors (2-10), number of training samples (0.5-4k) and number of fine-tuning
samples (0-100).
Results: The proposed approach achieves successful domain transfer between MR
images acquired with different contrasts (T1- and T2-weighted images), and
between natural and MR images (ImageNet and T1- or T2-weighted images).
Networks obtained via transfer-learning using only tens of images in the
testing domain achieve nearly identical performance to networks trained
directly in the testing domain using thousands of images.
Conclusion: The proposed approach might facilitate the use of neural networks
for MRI reconstruction without the need for collection of extensive imaging
datasets
Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR
Purpose: The radial k-space trajectory is a well-established sampling
trajectory used in conjunction with magnetic resonance imaging. However, the
radial k-space trajectory requires a large number of radial lines for
high-resolution reconstruction. Increasing the number of radial lines causes
longer acquisition time, making it more difficult for routine clinical use. On
the other hand, if we reduce the number of radial lines, streaking artifact
patterns are unavoidable. To solve this problem, we propose a novel deep
learning approach with domain adaptation to restore high-resolution MR images
from under-sampled k-space data.
Methods: The proposed deep network removes the streaking artifacts from the
artifact corrupted images. To address the situation given the limited available
data, we propose a domain adaptation scheme that employs a pre-trained network
using a large number of x-ray computed tomography (CT) or synthesized radial MR
datasets, which is then fine-tuned with only a few radial MR datasets.
Results: The proposed method outperforms existing compressed sensing
algorithms, such as the total variation and PR-FOCUSS methods. In addition, the
calculation time is several orders of magnitude faster than the total variation
and PR-FOCUSS methods.Moreover, we found that pre-training using CT or MR data
from similar organ data is more important than pre-training using data from the
same modality for different organ.
Conclusion: We demonstrate the possibility of a domain-adaptation when only a
limited amount of MR data is available. The proposed method surpasses the
existing compressed sensing algorithms in terms of the image quality and
computation time.Comment: This paper has been accepted and will soon appear in Magnetic
Resonance in Medicin
Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
The goal of MRI reconstruction is to restore a high fidelity image from
partially observed measurements. This partial view naturally induces
reconstruction uncertainty that can only be reduced by acquiring additional
measurements. In this paper, we present a novel method for MRI reconstruction
that, at inference time, dynamically selects the measurements to take and
iteratively refines the prediction in order to best reduce the reconstruction
error and, thus, its uncertainty. We validate our method on a large scale knee
MRI dataset, as well as on ImageNet. Results show that (1) our system
successfully outperforms active acquisition baselines; (2) our uncertainty
estimates correlate with error maps; and (3) our ResNet-based architecture
surpasses standard pixel-to-pixel models in the task of MRI reconstruction. The
proposed method not only shows high-quality reconstructions but also paves the
road towards more applicable solutions for accelerating MRI
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
Compressive sensing (CS) is an effective approach for fast Magnetic Resonance
Imaging (MRI). It aims at reconstructing MR images from a small number of
under-sampled data in k-space, and accelerating the data acquisition in MRI. To
improve the current MRI system in reconstruction accuracy and speed, in this
paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and
generalized versions. ADMM-Nets are defined over data flow graphs, which are
derived from the iterative procedures in Alternating Direction Method of
Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They
take the sampled k-space data as inputs and output reconstructed MR images.
Moreover, we extend our network to cope with complex-valued MR images. In the
training phase, all parameters of the nets, e.g., transforms, shrinkage
functions, etc., are discriminatively trained end-to-end. In the testing phase,
they have computational overhead similar to ADMM algorithm but use optimized
parameters learned from the data for CS-based reconstruction task. We
investigate different configurations in network structures and conduct
extensive experiments on MR image reconstruction under different sampling
rates. Due to the combination of the advantages in model-based approach and
deep learning approach, the ADMM-Nets achieve state-of-the-art reconstruction
accuracies with fast computational speed
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