5,289 research outputs found
Complex Fully Convolutional Neural Networks for MR Image Reconstruction
Undersampling the k-space data is widely adopted for acceleration of Magnetic
Resonance Imaging (MRI). Current deep learning based approaches for supervised
learning of MRI image reconstruction employ real-valued operations and
representations by treating complex valued k-space/spatial-space as real
values. In this paper, we propose complex dense fully convolutional neural
network (DFNet) for learning to de-alias the reconstruction
artifacts within undersampled MRI images. We fashioned a densely-connected
fully convolutional block tailored for complex-valued inputs by introducing
dedicated layers such as complex convolution, batch normalization,
non-linearities etc. DFNet leverages the inherently complex-valued
nature of input k-space and learns richer representations. We demonstrate
improved perceptual quality and recovery of anatomical structures through
DFNet in contrast to its real-valued counterparts.Comment: 9 pages, accepted in MICCAI-MLMIR 2018 Worsho
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
Many real-world signal sources are complex-valued, having real and imaginary
components. However, the vast majority of existing deep learning platforms and
network architectures do not support the use of complex-valued data. MRI data
is inherently complex-valued, so existing approaches discard the richer
algebraic structure of the complex data. In this work, we investigate
end-to-end complex-valued convolutional neural networks - specifically, for
image reconstruction in lieu of two-channel real-valued networks. We apply this
to magnetic resonance imaging reconstruction for the purpose of accelerating
scan times and determine the performance of various promising complex-valued
activation functions. We find that complex-valued CNNs with complex-valued
convolutions provide superior reconstructions compared to real-valued
convolutions with the same number of trainable parameters, over a variety of
network architectures and 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
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
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
Fidelity Imposed Network Edit (FINE) for Solving Ill-Posed Image Reconstruction
Deep learning (DL) is increasingly used to solve ill-posed inverse problems
in imaging, such as reconstruction from noisy or incomplete data, as DL offers
advantages over explicit image feature extractions in defining the needed
prior. However, DL typically does not incorporate the precise physics of data
generation or data fidelity. Instead, DL networks are trained to output some
average response to an input. Consequently, DL image reconstruction contains
errors, and may perform poorly when the test data deviates significantly from
the training data, such as having new pathological features. To address this
lack of data fidelity problem in DL image reconstruction, a novel approach,
which we call fidelity-imposed network edit (FINE), is proposed. In FINE, a
pre-trained prior network's weights are modified according to the physical
model, on a test case. Our experiments demonstrate that FINE can achieve
superior performance in two important inverse problems in neuroimaging:
quantitative susceptibility mapping (QSM) and under-sampled reconstruction in
MRI
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
Accelerating MR Imaging via Deep Chambolle-Pock Network
Compressed sensing (CS) has been introduced to accelerate data acquisition in
MR Imaging. However, CS-MRI methods suffer from detail loss with large
acceleration and complicated parameter selection. To address the limitations of
existing CS-MRI methods, a model-driven MR reconstruction is proposed that
trains a deep network, named CP-net, which is derived from the Chambolle-Pock
algorithm to reconstruct the in vivo MR images of human brains from highly
undersampled complex k-space data acquired on different types of MR scanners.
The proposed deep network can learn the proximal operator and parameters among
the Chambolle-Pock algorithm. All of the experiments show that the proposed
CP-net achieves more accurate MR reconstruction results, outperforming
state-of-the-art methods across various quantitative metrics.Comment: 4 pages, 5 figures, 1 table, Accepted at 2019 IEEE 41st Engineering
in Medicine and Biology Conference (EMBC 2019
Fast PET reconstruction using Multi-scale Fully Convolutional Neural Networks
Reconstruction of PET images is an ill-posed inverse problem and often
requires iterative algorithms to achieve good image quality for reliable
clinical use in practice, at huge computational costs. In this paper, we
consider the PET reconstruction a dense prediction problem where the large
scale contextual information is essential, and propose a novel architecture of
multi-scale fully convolutional neural networks (msfCNN) for fast PET image
reconstruction. The proposed msfCNN gains large receptive fields with both
memory and computational efficiency, by using a downscaling-upscaling structure
and dilated convolutions. Instead of pooling and deconvolution, we propose to
use the periodic shuffling operation from sub-pixel convolution and its inverse
to scale the size of feature maps without losing resolution. Residual
connections were added to improve training. We trained the proposed msfCNN
model with simulated data, and applied it to clinical PET data acquired on a
Siemens mMR scanner. The results from real oncological and neurodegenerative
cases show that the proposed msfCNN-based reconstruction outperforms the
iterative approaches in terms of computational time while achieving comparable
image quality for quantification. The proposed msfCNN model can be applied to
other dense prediction tasks, and fast msfCNN-based PET reconstruction could
facilitate the potential use of molecular imaging in interventional/surgical
procedures, where cancer surgery can particularly benefit
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
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