672 research outputs found
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using large receptive field neural networks such as U-Net have
demonstrated impressive performance for sparse- view CT reconstruction.
However, theoretical justification is still lacking. Inspired by the recent
theory of deep convolutional framelets, the main goal of this paper is,
therefore, to reveal the limitation of U-Net and propose new multi-resolution
deep learning schemes. In particular, we show that the alternative U- Net
variants such as dual frame and the tight frame U-Nets satisfy the so-called
frame condition which make them better for effective recovery of high frequency
edges in sparse view- CT. Using extensive experiments with real patient data
set, we demonstrate that the new network architectures provide better
reconstruction performance.Comment: This will appear in IEEE Transaction on Medical Imaging, a special
issue of Machine Learning for Image Reconstructio
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally expensive. To address this problem, we recently proposed a
deep convolutional neural network (CNN) for low-dose X-ray CT and won the
second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the
texture were not fully recovered. To address this problem, here we propose a
novel framelet-based denoising algorithm using wavelet residual network which
synergistically combines the expressive power of deep learning and the
performance guarantee from the framelet-based denoising algorithms. The new
algorithms were inspired by the recent interpretation of the deep convolutional
neural network (CNN) as a cascaded convolution framelet signal representation.
Extensive experimental results confirm that the proposed networks have
significantly improved performance and preserves the detail texture of the
original images.Comment: This will appear in IEEE Transaction on Medical Imaging, a special
issue of Machine Learning for Image Reconstructio
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
Recently, deep learning approaches with various network architectures have
achieved significant performance improvement over existing iterative
reconstruction methods in various imaging problems. However, it is still
unclear why these deep learning architectures work for specific inverse
problems. To address these issues, here we show that the long-searched-for
missing link is the convolution framelets for representing a signal by
convolving local and non-local bases. The convolution framelets was originally
developed to generalize the theory of low-rank Hankel matrix approaches for
inverse problems, and this paper further extends the idea so that we can obtain
a deep neural network using multilayer convolution framelets with perfect
reconstruction (PR) under rectilinear linear unit nonlinearity (ReLU). Our
analysis also shows that the popular deep network components such as residual
block, redundant filter channels, and concatenated ReLU (CReLU) do indeed help
to achieve the PR, while the pooling and unpooling layers should be augmented
with high-pass branches to meet the PR condition. Moreover, by changing the
number of filter channels and bias, we can control the shrinkage behaviors of
the neural network. This discovery leads us to propose a novel theory for deep
convolutional framelets neural network. Using numerical experiments with
various inverse problems, we demonstrated that our deep convolution framelets
network shows consistent improvement over existing deep architectures.This
discovery suggests that the success of deep learning is not from a magical
power of a black-box, but rather comes from the power of a novel signal
representation using non-local basis combined with data-driven local basis,
which is indeed a natural extension of classical signal processing theory.Comment: This will appear in SIAM Journal on Imaging Science
200x Low-dose PET Reconstruction using Deep Learning
Positron emission tomography (PET) is widely used in various clinical
applications, including cancer diagnosis, heart disease and neuro disorders.
The use of radioactive tracer in PET imaging raises concerns due to the risk of
radiation exposure. To minimize this potential risk in PET imaging, efforts
have been made to reduce the amount of radio-tracer usage. However, lowing dose
results in low Signal-to-Noise-Ratio (SNR) and loss of information, both of
which will heavily affect clinical diagnosis. Besides, the ill-conditioning of
low-dose PET image reconstruction makes it a difficult problem for iterative
reconstruction algorithms. Previous methods proposed are typically complicated
and slow, yet still cannot yield satisfactory results at significantly low
dose. Here, we propose a deep learning method to resolve this issue with an
encoder-decoder residual deep network with concatenate skip connections.
Experiments shows the proposed method can reconstruct low-dose PET image to a
standard-dose quality with only two-hundredth dose. Different cost functions
for training model are explored. Multi-slice input strategy is introduced to
provide the network with more structural information and make it more robust to
noise. Evaluation on ultra-low-dose clinical data shows that the proposed
method can achieve better result than the state-of-the-art methods and
reconstruct images with comparable quality using only 0.5% of the original
regular dose
Low-Dose CT via Deep CNN with Skip Connection and Network in Network
A major challenge in computed tomography (CT) is how to minimize patient
radiation exposure without compromising image quality and diagnostic
performance. The use of deep convolutional (Conv) neural networks for noise
reduction in Low-Dose CT (LDCT) images has recently shown a great potential in
this important application. In this paper, we present a highly efficient and
effective neural network model for LDCT image noise reduction. Specifically, to
capture local anatomical features we integrate Deep Convolutional Neural
Networks (CNNs) and Skip connection layers for feature extraction. Also, we
introduce parallelized CNN, called Network in Network, to lower the
dimensionality of the output from the previous layer, achieving faster
computational speed at less feature loss. To optimize the performance of the
network, we adopt a Wasserstein generative adversarial network (WGAN)
framework. Quantitative and qualitative comparisons demonstrate that our
proposed network model can produce images with lower noise and more structural
details than state-of-the-art noise-reduction methods
Iterative PET Image Reconstruction Using Convolutional Neural Network Representation
PET image reconstruction is challenging due to the ill-poseness of the
inverse problem and limited number of detected photons. Recently deep neural
networks have been widely and successfully used in computer vision tasks and
attracted growing interests in medical imaging. In this work, we trained a deep
residual convolutional neural network to improve PET image quality by using the
existing inter-patient information. An innovative feature of the proposed
method is that we embed the neural network in the iterative reconstruction
framework for image representation, rather than using it as a post-processing
tool. We formulate the objective function as a constraint optimization problem
and solve it using the alternating direction method of multipliers (ADMM)
algorithm. Both simulation data and hybrid real data are used to evaluate the
proposed method. Quantification results show that our proposed iterative neural
network method can outperform the neural network denoising and conventional
penalized maximum likelihood methods
Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction
Limited-angle computed tomography (CT) is often used in clinical applications
such as C-arm CT for interventional imaging. However, CT images from limited
angles suffers from heavy artifacts due to incomplete projection data. Existing
iterative methods require extensive calculations but can not deliver
satisfactory results. Based on the observation that the artifacts from limited
angles have some directional property and are globally distributed, we propose
a novel multi-scale wavelet domain residual learning architecture, which
compensates for the artifacts. Experiments have shown that the proposed method
effectively eliminates artifacts, thereby preserving edge and global structures
of the image
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
Commercial iterative reconstruction techniques on modern CT scanners target
radiation dose reduction but there are lingering concerns over their impact on
image appearance and low contrast detectability. Recently, machine learning,
especially deep learning, has been actively investigated for CT. Here we design
a novel neural network architecture for low-dose CT (LDCT) and compare it with
commercial iterative reconstruction methods used for standard of care CT. While
popular neural networks are trained for end-to-end mapping, driven by big data,
our novel neural network is intended for end-to-process mapping so that
intermediate image targets are obtained with the associated search gradients
along which the final image targets are gradually reached. This learned dynamic
process allows to include radiologists in the training loop to optimize the
LDCT denoising workflow in a task-specific fashion with the denoising depth as
a key parameter. Our progressive denoising network was trained with the Mayo
LDCT Challenge Dataset, and tested on images of the chest and abdominal regions
scanned on the CT scanners made by three leading CT vendors. The best deep
learning based reconstructions are systematically compared to the best
iterative reconstructions in a double-blinded reader study. It is found that
our deep learning approach performs either comparably or favorably in terms of
noise suppression and structural fidelity, and runs orders of magnitude faster
than the commercial iterative CT reconstruction algorithms.Comment: 17 pages, 7 figure
Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising
Inspired by complexity and diversity of biological neurons, our group
proposed quadratic neurons by replacing the inner product in current artificial
neurons with a quadratic operation on input data, thereby enhancing the
capability of an individual neuron. Along this direction, we are motivated to
evaluate the power of quadratic neurons in popular network architectures,
simulating human-like learning in the form of quadratic-neuron-based deep
learning. Our prior theoretical studies have shown important merits of
quadratic neurons and networks in representation, efficiency, and
interpretability. In this paper, we use quadratic neurons to construct an
encoder-decoder structure, referred as the quadratic autoencoder, and apply it
to low-dose CT denoising. The experimental results on the Mayo low-dose CT
dataset demonstrate the utility of quadratic autoencoder in terms of image
denoising and model efficiency. To our best knowledge, this is the first time
that the deep learning approach is implemented with a new type of neurons and
demonstrates a significant potential in the medical imaging field
Improved low-count quantitative PET reconstruction with an iterative neural network
Image reconstruction in low-count PET is particularly challenging because
gammas from natural radioactivity in Lu-based crystals cause high random
fractions that lower the measurement signal-to-noise-ratio (SNR). In
model-based image reconstruction (MBIR), using more iterations of an
unregularized method may increase the noise, so incorporating regularization
into the image reconstruction is desirable to control the noise. New
regularization methods based on learned convolutional operators are emerging in
MBIR. We modify the architecture of an iterative neural network, BCD-Net, for
PET MBIR, and demonstrate the efficacy of the trained BCD-Net using XCAT
phantom data that simulates the low true coincidence count-rates with high
random fractions typical for Y-90 PET patient imaging after Y-90 microsphere
radioembolization. Numerical results show that the proposed BCD-Net
significantly improves CNR and RMSE of the reconstructed images compared to
MBIR methods using non-trained regularizers, total variation (TV) and non-local
means (NLM). Moreover, BCD-Net successfully generalizes to test data that
differs from the training data. Improvements were also demonstrated for the
clinically relevant phantom measurement data where we used training and testing
datasets having very different activity distributions and count-levels
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