119 research outputs found
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging
techniques available for clinical applications. However, the rather slow speed
of MRI acquisitions limits the patient throughput and potential indi cations.
Compressive Sensing (CS) has proven to be an efficient technique for
accelerating MRI acquisition. The most widely used CS-MRI model, founded on the
premise of reconstructing an image from an incompletely filled k-space, leads
to an ill-posed inverse problem. In the past years, lots of efforts have been
made to efficiently optimize the CS-MRI model. Inspired by deep learning
techniques, some preliminary works have tried to incorporate deep architectures
into CS-MRI process. Unfortunately, the convergence issues (due to the
experience-based networks) and the robustness (i.e., lack real-world noise
modeling) of these deeply trained optimization methods are still missing. In
this work, we develop a new paradigm to integrate designed numerical solvers
and the data-driven architectures for CS-MRI. By introducing an optimal
condition checking mechanism, we can successfully prove the convergence of our
established deep CS-MRI optimization scheme. Furthermore, we explicitly
formulate the Rician noise distributions within our framework and obtain an
extended CS-MRI network to handle the real-world nosies in the MRI process.
Extensive experimental results verify that the proposed paradigm outperforms
the existing state-of-the-art techniques both in reconstruction accuracy and
efficiency as well as robustness to noises in real scene
Nonparametric neighborhood statistics for MRI denoising
technical reportThis paper presents a novel method for denoising MR images that relies on an optimal estimation, combining a likelihood model with an adaptive image prior. The method models images as random fields and exploits the properties of independent Rician noise to learn the higher-order statistics of image neighborhoods from corrupted input data. It uses these statistics as priors within a Bayesian denoising framework. This paper presents an information-theoretic method for characterizing neighborhood structure using nonparametric density estimation. The formulation generalizes easily to simultaneous denoising of multimodal MRI, exploiting the relationships between modalities to further enhance performance. The method, relying on the information content of input data for noise estimation and setting important parameters, does not require significant parameter tuning. Qualitative and quantitative results on real, simulated, and multimodal data, including comparisons with other approaches, demonstrate the effectiveness of the method
A Convex Semi-Definite Positive Framework for DTI Estimation and Regularization
International audienceIn this paper we introduce a novel variational method for joint estimation and regularization of diffusion tensor fields from noisy raw data. To this end, we use the classic quadratic data fidelity term derived from the Stejskal-Tanner equation with a new smoothness term leading to a convex objective function. The regularization term is based on the assumption that the signal can be reconstructed using a weighted average of observations on a local neighborhood. The weights measure the similarity between tensors and are computed directly from the diffusion images. We preserve the positive semi-definiteness constraint using a projected gradient descent. Experimental validation and comparisons with a similar method using synthetic data with known noise model, as well as classification of tensors towards understanding the anatomy of human skeletal muscle demonstrate the potential of our method
Noise Estimation, Noise Reduction and Intensity Inhomogeneity Correction in MRI Images of the Brain
Rician noise and intensity inhomogeneity are two common types of image degradation that manifest in the acquisition of magnetic resonance imaging (MRI) system images of the brain. Many noise reduction and intensity inhomogeneity correction algorithms are based on strong parametric assumptions. These parametric assumptions are generic and do not account for salient features that are unique to specific classes and different levels of degradation in natural images.
This thesis proposes the 4-neighborhood clique system in a layer-structured Markov random field (MRF) model for noise estimation and noise reduction. When the test image is the only physical system under consideration, it is regarded as a single layer Markov random field (SLMRF) model, and as a double layer MRF model when the test images and classical priors are considered.
A scientific principle states that segmentation trivializes the task of bias field correction. Another principle states that the bias field distorts the intensity but not the spatial attribute of an image. This thesis exploits these two widely acknowledged scientific principles in order to propose a new model for correction of intensity inhomogeneity.
The noise estimation algorithm is invariant to the presence or absence of background features in an image and more accurate in the estimation of noise levels because it is potentially immune to the modeling errors inherent in some current state-of-the-art algorithms. The noise reduction algorithm derived from the SLMRF model does not incorporate a regularization parameter. Furthermore, it preserves edges, and its output is devoid of the blurring and ringing artifacts associated with Gaussian and wavelet based algorithms. The procedure for correction of intensity inhomogeneity does not require the computationally intensive task of estimation of the bias field map. Furthermore, there is no requirement for a digital brain atlas which will incorporate additional image processing tasks such as image registration
Mumford-Shah and Potts Regularization for Manifold-Valued Data with Applications to DTI and Q-Ball Imaging
Mumford-Shah and Potts functionals are powerful variational models for
regularization which are widely used in signal and image processing; typical
applications are edge-preserving denoising and segmentation. Being both
non-smooth and non-convex, they are computationally challenging even for scalar
data. For manifold-valued data, the problem becomes even more involved since
typical features of vector spaces are not available. In this paper, we propose
algorithms for Mumford-Shah and for Potts regularization of manifold-valued
signals and images. For the univariate problems, we derive solvers based on
dynamic programming combined with (convex) optimization techniques for
manifold-valued data. For the class of Cartan-Hadamard manifolds (which
includes the data space in diffusion tensor imaging), we show that our
algorithms compute global minimizers for any starting point. For the
multivariate Mumford-Shah and Potts problems (for image regularization) we
propose a splitting into suitable subproblems which we can solve exactly using
the techniques developed for the corresponding univariate problems. Our method
does not require any a priori restrictions on the edge set and we do not have
to discretize the data space. We apply our method to diffusion tensor imaging
(DTI) as well as Q-ball imaging. Using the DTI model, we obtain a segmentation
of the corpus callosum
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Statistical Region Based Segmentation of Ultrasound Images
Segmentation of ultrasound images is a challenging problem due to speckle, which
corrupts the image and can result in weak or missing image boundaries, poor signal to
noise ratio, and diminished contrast resolution. Speckle is a random interference pattern
that is characterized by an asymmetric distribution as well as significant spatial correla-
tion. These attributes of speckle are challenging to model in a segmentation approach, so
many previous ultrasound segmentation methods simplify the problem by assuming that
the speckle is white and/or Gaussian distributed. Unlike these methods, in this paper
we present an ultrasound-specific segmentation approach that addresses both the spatial
correlation of the data as well as its intensity distribution. We first decorrelate the image
and then apply a region-based active contour whose motion is derived from an appropri-
ate parametric distribution for maximum likelihood image segmentation. We consider
zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed
to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and
display (log compressed) images, respectively. We present experimental results demon-
strating the effectiveness of our method, and compare the results to other parametric
and non-parametric active contours
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