173 research outputs found
Joint Image Reconstruction and Segmentation Using the Potts Model
We propose a new algorithmic approach to the non-smooth and non-convex Potts
problem (also called piecewise-constant Mumford-Shah problem) for inverse
imaging problems. We derive a suitable splitting into specific subproblems that
can all be solved efficiently. Our method does not require a priori knowledge
on the gray levels nor on the number of segments of the reconstruction.
Further, it avoids anisotropic artifacts such as geometric staircasing. We
demonstrate the suitability of our method for joint image reconstruction and
segmentation. We focus on Radon data, where we in particular consider limited
data situations. For instance, our method is able to recover all segments of
the Shepp-Logan phantom from angular views only. We illustrate the
practical applicability on a real PET dataset. As further applications, we
consider spherical Radon data as well as blurred data
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods
Low-rank and sparse reconstruction in dynamic magnetic resonance imaging via proximal splitting methods
Dynamic magnetic resonance imaging (MRI) consists of collecting multiple MR images in time, resulting in a spatio-temporal signal. However, MRI intrinsically suffers from long acquisition times due to various constraints. This limits the full potential of dynamic MR imaging, such as obtaining high spatial and temporal resolutions which are crucial to observe dynamic phenomena. This dissertation addresses the problem of the reconstruction of dynamic MR images from a limited amount of samples arising from a nuclear magnetic resonance experiment. The term limited can be explained by the approach taken in this thesis to speed up scan time, which is based on violating the Nyquist criterion by skipping measurements that would be normally acquired in a standard MRI procedure. The resulting problem can be classified in the general framework of linear ill-posed inverse problems. This thesis shows how low-dimensional signal models, specifically lowrank and sparsity, can help in the reconstruction of dynamic images from partial measurements. The use of these models are justified by significant developments in signal recovery techniques from partial data that have emerged in recent years in signal processing. The major contributions of this thesis are the development and characterisation of fast and efficient computational tools using convex low-rank and sparse constraints via proximal gradient methods, the development and characterisation of a novel joint reconstruction–separation method via the low-rank plus sparse matrix decomposition technique, and the development and characterisation of low-rank based recovery methods in the context of dynamic parallel MRI. Finally, an additional contribution of this thesis is to formulate the various MR image reconstruction problems in the context of convex optimisation to develop algorithms based on proximal splitting methods
SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation
High-resolution is a key trend in the development of synthetic aperture radar
(SAR), which enables the capture of fine details and accurate representation of
backscattering properties. However, traditional high-resolution SAR imaging
algorithms face several challenges. Firstly, these algorithms tend to focus on
local information, neglecting non-local information between different pixel
patches. Secondly, speckle is more pronounced and difficult to filter out in
high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging
generally involves high time and computational complexity, making real-time
imaging difficult to achieve. To address these issues, we propose a Superpixel
High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in
high-resolution SAR mode. Based on the concept of superpixel techniques, we
initially combine non-convex and non-local total variation as compound
regularization. This approach more effectively despeckles and manages the
relationship between pixels while reducing bias effects caused by convex
constraints. Subsequently, we solve the compound regularization model using the
Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into
a Deep Unfolded Network (DUN). The network's parameters are adaptively learned
in a data-driven manner, and the learned network significantly increases
imaging speed. Additionally, the Deep Unfolded Network is compatible with
high-resolution imaging modes such as spotlight, staring spotlight, and sliding
spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net
through experiments in both simulated and real SAR scenarios. The results
indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from
raw echo data, producing accurate imaging results
Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns
Purpose: To achieve automatic hyperparameter estimation for the joint
recovery of quantitative MR images, we propose a Bayesian formulation of the
reconstruction problem that incorporates the signal model. Additionally, we
investigate the use of complementary undersampling patterns to determine
optimal undersampling schemes for quantitative MRI.
Theory: We introduce a novel nonlinear approximate message passing framework,
referred to as ``AMP-PE'', that enables the simultaneous recovery of
distribution parameters and quantitative maps.
Methods: We employed the variable flip angle multi-echo (VFA-ME) method to
acquire measurements. Both retrospective and prospective undersampling
approaches were utilized to obtain Fourier measurements using variable-density
and Poisson-disk patterns. Furthermore, we extensively explored various
undersampling schemes, incorporating complementary patterns across different
flip angles and/or echo times.
Results: AMP-PE adopts a model-based joint recovery strategy, it outperforms
the -norm minimization approach that follows a decoupled recovery
strategy. A comparison with an existing joint-recovery approach further
demonstrates the advantageous outcomes of AMP-PE. For quantitative
mapping using VFA-ME, employing identical k-space sampling patterns across
different echo times produced the best performance. Whereas for and
proton density mappings, using complementary sampling patterns across different
flip angles yielded the best performance.
Conclusion: AMP-PE is equipped with built-in parameter estimation, and works
naturally in clinical settings with varying acquisition protocols and scanners.
It also achieves improved performance by combining information from the MR
signal model and the sparse prior on images
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