5,149 research outputs found
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
Sparse-View X-Ray CT Reconstruction Using Prior with Learned Transform
A major challenge in X-ray computed tomography (CT) is reducing radiation
dose while maintaining high quality of reconstructed images. To reduce the
radiation dose, one can reduce the number of projection views (sparse-view CT);
however, it becomes difficult to achieve high-quality image reconstruction as
the number of projection views decreases. Researchers have applied the concept
of learning sparse representations from (high-quality) CT image dataset to the
sparse-view CT reconstruction. We propose a new statistical CT reconstruction
model that combines penalized weighted-least squares (PWLS) and prior
with learned sparsifying transform (PWLS-ST-), and a corresponding
efficient algorithm based on Alternating Direction Method of Multipliers
(ADMM). To moderate the difficulty of tuning ADMM parameters, we propose a new
ADMM parameter selection scheme based on approximated condition numbers. We
interpret the proposed model by analyzing the minimum mean square error of its
(-norm relaxed) image update estimator. Our results with the extended
cardiac-torso (XCAT) phantom data and clinical chest data show that, for
sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST- improves
the quality of reconstructed images compared to the CT reconstruction methods
using edge-preserving regularizer and prior with learned ST. These
results also show that, for sparse-view 2D fan-beam CT, PWLS-ST-
achieves comparable or better image quality and requires much shorter runtime
than PWLS-DL using a learned overcomplete dictionary. Our results with clinical
chest data show that, methods using the unsupervised learned prior generalize
better than a state-of-the-art deep "denoising" neural network that does not
use a physical imaging model.Comment: The first two authors contributed equally to this wor
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Over the past few years, dictionary learning (DL)-based methods have been
successfully used in various image reconstruction problems. However,
traditional DL-based computed tomography (CT) reconstruction methods are
patch-based and ignore the consistency of pixels in overlapped patches. In
addition, the features learned by these methods always contain shifted versions
of the same features. In recent years, convolutional sparse coding (CSC) has
been developed to address these problems. In this paper, inspired by several
successful applications of CSC in the field of signal processing, we explore
the potential of CSC in sparse-view CT reconstruction. By directly working on
the whole image, without the necessity of dividing the image into overlapped
patches in DL-based methods, the proposed methods can maintain more details and
avoid artifacts caused by patch aggregation. With predetermined filters, an
alternating scheme is developed to optimize the objective function. Extensive
experiments with simulated and real CT data were performed to validate the
effectiveness of the proposed methods. Qualitative and quantitative results
demonstrate that the proposed methods achieve better performance than several
existing state-of-the-art methods.Comment: Accepted by IEEE TM
AIR: fused Analytical and Iterative Reconstruction method for computed tomography
Purpose: CT image reconstruction techniques have two major categories:
analytical reconstruction (AR) method and iterative reconstruction (IR) method.
AR reconstructs images through analytical formulas, such as filtered
backprojection (FBP) in 2D and Feldkamp-Davis-Kress (FDK) method in 3D, which
can be either mathematically exact or approximate. On the other hand, IR is
often based on the discrete forward model of X-ray transform and formulated as
a minimization problem with some appropriate image regularization method, so
that the reconstructed image corresponds to the minimizer of the optimization
problem. This work is to investigate the fused analytical and iterative
reconstruction (AIR) method.
Methods: Based on IR with L1-type image regularization, AIR is formulated
with a AR-specific preconditioner in the data fidelity term, which results in
the minimal change of the solution algorithm that replaces the adjoint X-ray
transform by the filtered X-ray transform. As a proof-of-concept 2D example of
AIR, FBP is incorporated into tensor framelet (TF) regularization based IR, and
the formulated AIR minimization problem is then solved through split Bregman
method with GPU-accelerated X-ray transform and filtered adjoint X-ray
transform.
Conclusion: AIR, the fused Analytical and Iterative Reconstruction method, is
proposed with a proof-of-concept 2D example to synergize FBP and TF-regularized
IR, with improved image resolution and contrast for experimental data. The
potential impact of AIR is that it offers a general framework to develop
various AR enhanced IR methods, when neither AR nor IR alone is sufficient
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
Medical image reconstruction: a brief overview of past milestones and future directions
This paper briefly reviews past milestones in the field of medical image
reconstruction and describes some future directions. It is part of an overview
paper on "open problems in signal processing" that will appear in IEEE Signal
Processing Magazine, but presented here with citations and equations.Comment: Part of a submission to IEEE Signal Processing Magazin
Deep artifact learning for compressed sensing and parallel MRI
Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is
one of the powerful ways to reduce the scan time of MR imaging with performance
guarantee. However, the computational costs are usually expensive. This paper
aims to propose a computationally fast and accurate deep learning algorithm for
the reconstruction of MR images from highly down-sampled k-space data.
Theory: Based on the topological analysis, we show that the data manifold of
the aliasing artifact is easier to learn from a uniform subsampling pattern
with additional low-frequency k-space data. Thus, we develop deep aliasing
artifact learning networks for the magnitude and phase images to estimate and
remove the aliasing artifacts from highly accelerated MR acquisition.
Methods: The aliasing artifacts are directly estimated from the distorted
magnitude and phase images reconstructed from subsampled k-space data so that
we can get an aliasing-free images by subtracting the estimated aliasing
artifact from corrupted inputs. Moreover, to deal with the globally distributed
aliasing artifact, we develop a multi-scale deep neural network with a large
receptive field.
Results: The experimental results confirm that the proposed deep artifact
learning network effectively estimates and removes the aliasing artifacts.
Compared to existing CS methods from single and multi-coli data, the proposed
network shows minimal errors by removing the coherent aliasing artifacts.
Furthermore, the computational time is by order of magnitude faster.
Conclusion: As the proposed deep artifact learning network immediately
generates accurate reconstruction, it has great potential for clinical
applications
Meaning of Interior Tomography
The classic imaging geometry for computed tomography is for collection of
un-truncated projections and reconstruction of a global image, with the Fourier
transform as the theoretical foundation that is intrinsically non-local.
Recently, interior tomography research has led to theoretically exact
relationships between localities in the projection and image spaces and
practically promising reconstruction algorithms. Initially, interior tomography
was developed for x-ray computed tomography. Then, it has been elevated as a
general imaging principle. Finally, a novel framework known as omni-tomography
is being developed for grand fusion of multiple imaging modalities, allowing
tomographic synchrony of diversified features.Comment: 47 pages, 14 figures, to appear in Physics in Medicine and Biolog
Tomographic Reconstruction using Global Statistical Prior
Recent research in tomographic reconstruction is motivated by the need to
efficiently recover detailed anatomy from limited measurements. One of the ways
to compensate for the increasingly sparse sets of measurements is to exploit
the information from templates, i.e., prior data available in the form of
already reconstructed, structurally similar images. Towards this, previous work
has exploited using a set of global and patch based dictionary priors. In this
paper, we propose a global prior to improve both the speed and quality of
tomographic reconstruction within a Compressive Sensing framework.
We choose a set of potential representative 2D images referred to as
templates, to build an eigenspace; this is subsequently used to guide the
iterative reconstruction of a similar slice from sparse acquisition data. Our
experiments across a diverse range of datasets show that reconstruction using
an appropriate global prior, apart from being faster, gives a much lower
reconstruction error when compared to the state of the art.Comment: Published in The International Conference on Digital Image Computing:
Techniques and Applications (DICTA), Sydney, Australia, 2017. The conference
proceedings are not out yet. But the result can be seen here:
http://dicta2017.dictaconference.org/fullprogram.htm
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
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