5,612 research outputs found
Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI
Super-resolved image enhancement is of great importance in medical imaging. Conventional methods often require multiple low resolution (LR) images from different views of the same object or learning from large amount of training datasets to achieve success. However, in real clinical environments, these prerequisites are rarely fulfilled. In this paper, we present a self-learning based method to perform superresolution (SR) from a single LR input. The mappings between the given LR image and its downsampled versions are modeled using support vector regression on features extracted from sparse coded dictionaries, coupled with dual-tree complex wavelet transform based denoising. We demonstrate the efficacy of our method in application of cardiac MRI enhancement. Both quantitative and qualitative results show that our SR method is able to preserve fine textural details that can be corrupted by noise, and therefore can maintain crucial diagnostic information
Sampling and Super-resolution of Sparse Signals Beyond the Fourier Domain
Recovering a sparse signal from its low-pass projections in the Fourier
domain is a problem of broad interest in science and engineering and is
commonly referred to as super-resolution. In many cases, however, Fourier
domain may not be the natural choice. For example, in holography, low-pass
projections of sparse signals are obtained in the Fresnel domain. Similarly,
time-varying system identification relies on low-pass projections on the space
of linear frequency modulated signals. In this paper, we study the recovery of
sparse signals from low-pass projections in the Special Affine Fourier
Transform domain (SAFT). The SAFT parametrically generalizes a number of well
known unitary transformations that are used in signal processing and optics. In
analogy to the Shannon's sampling framework, we specify sampling theorems for
recovery of sparse signals considering three specific cases: (1) sampling with
arbitrary, bandlimited kernels, (2) sampling with smooth, time-limited kernels
and, (3) recovery from Gabor transform measurements linked with the SAFT
domain. Our work offers a unifying perspective on the sparse sampling problem
which is compatible with the Fourier, Fresnel and Fractional Fourier domain
based results. In deriving our results, we introduce the SAFT series (analogous
to the Fourier series) and the short time SAFT, and study convolution theorems
that establish a convolution--multiplication property in the SAFT domain.Comment: 42 pages, 3 figures, manuscript under revie
Structural biology: a century-long journey into an unseen world
© Institute of Materials, Minerals and Mining 2015.When the first atomic structures of salt crystals were determined by the Braggs in 1912–1913, the analytical power of X-ray crystallography was immediately evident. Within a few decades the technique was being applied to the more complex molecules of chemistry and biology and is rightly regarded as the foundation stone of structural biology, a field that emerged in the 1950s when X-ray diffraction analysis revealed the atomic architecture of DNA and protein molecules. Since then the toolbox of structural biology has been augmented by other physical techniques, including nuclear magnetic resonance spectroscopy, electron microscopy, and solution scattering of X-rays and neutrons. Together these have transformed our understanding of the molecular basis of life. Here I review the major and most recent developments in structural biology that have brought us to the threshold of a landscape of astonishing molecular complexity
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images
Cross-modality synthesis (CMS), super-resolution (SR), and their combination
(CMSR) have been extensively studied for magnetic resonance imaging (MRI).
Their primary goals are to enhance the imaging quality by synthesizing the
desired modality and reducing the slice thickness. Despite the promising
synthetic results, these techniques are often tailored to specific tasks,
thereby limiting their adaptability to complex clinical scenarios. Therefore,
it is crucial to build a unified network that can handle various image
synthesis tasks with arbitrary requirements of modality and resolution
settings, so that the resources for training and deploying the models can be
greatly reduced. However, none of the previous works is capable of performing
CMS, SR, and CMSR using a unified network. Moreover, these MRI reconstruction
methods often treat alias frequencies improperly, resulting in suboptimal
detail restoration. In this paper, we propose a Unified Co-Modulated Alias-free
framework (Uni-COAL) to accomplish the aforementioned tasks with a single
network. The co-modulation design of the image-conditioned and stochastic
attribute representations ensures the consistency between CMS and SR, while
simultaneously accommodating arbitrary combinations of input/output modalities
and thickness. The generator of Uni-COAL is also designed to be alias-free
based on the Shannon-Nyquist signal processing framework, ensuring effective
suppression of alias frequencies. Additionally, we leverage the semantic prior
of Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authentic
preservation of anatomical structures during synthesis. Experiments on three
datasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, and
CMSR tasks for MR images, which highlights its generalizability to wide-range
applications
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Variational Multi-Task Models for Image Analysis: Applications to Magnetic Resonance Imaging
This thesis deals with the study and development of several variational multi-task models for solving inverse problems in imaging, with a particular focus on Magnetic Resonance Imaging (MRI). In most image processing problems, one usually deals with the reconstruction task, i.e., the task of reconstructing an image from indirect measurements, and then performs various operations, one after the other (i.e. sequentially), to improve the quality of the reconstruction and to extract useful information.
However, recent developments in a variational context, have shown that performing those tasks jointly (i.e. in a multi-task framework) offers great benefits, and this is the perspective that we follow in this thesis. We go beyond traditional sequential approaches and set a new basis for variational multi-task methods for MRI analysis. We demonstrate that by sharing representation between tasks and carefully interconnecting them, one can create synergies across challenging problems and reduce error propagation.
More precisely, firstly we propose a multi-task variational model to tackle the problems of image reconstruction and image segmentation using non-convex Bregman iteration. We describe theoretical and numerical details of the problem and its optimisation scheme. Moreover, we show that our multi-task model achieves better results in several examples and MRI applications than existing approaches in the same context.
Secondly, we show that our approach can be extended to a multi-task reconstruction and segmentation model for the nonlinear inverse problem of velocity-encoded MRI. In this context, the aim is to estimate not only the magnitude from MRI data, but also the phase and its flow information, whilst simultaneously identify regions of interest through the segmentation task.
Finally, we go beyond two-task frameworks and introduce for the first time a variational multi-task model to handle three imaging tasks. To this end, we design a variational multi-task framework addressing reconstruction, super-resolution and registration for improving the quality of MRI reconstruction. We demonstrate that our model is theoretically well-motivated and it outperforms sequential models whilst requiring less computational cost. Furthermore, we show through experimental results the potential of this approach for clinical applications
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