1,900 research outputs found
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Research Status and Prospect for CT Imaging
Computed tomography (CT) is a very valuable imaging method and plays an important role in clinical diagnosis. As people pay more and more attention to radiation doses these years, decreasing CT radiation dose without affecting image quality is a hot direction for research of medical imaging in recent years. This chapter introduces the research status of low-dose technology from following aspects: low-dose scan implementation, reconstruction methods and image processing methods. Furthermore, other technologies related to the development tendency of CT, such as automatic tube current modulation technology, rapid peak kilovoltage (kVp) switching technology, dual-source CT technology and Nano-CT, are also summarized. Finally, the future research prospect are discussed and analyzed
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Computational Inverse Problems
Inverse problem typically deal with the identification of unknown quantities from indirect measurements and appear in many areas in technology, medicine, biology, finance, and econometrics. The computational solution of such problems is a very active, interdisciplinary field with close connections to optimization, control theory, differential equations, asymptotic analysis, statistics, and probability. The focus of this workshop was on hybrid methods, model reduction, regularization in Banach spaces, and statistical approaches
A Review on Deep Learning in Medical Image Reconstruction
Medical imaging is crucial in modern clinics to guide the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most
fundamental and important components of medical imaging, whose major objective
is to acquire high-quality medical images for clinical usage at the minimal
cost and risk to the patients. Mathematical models in medical image
reconstruction or, more generally, image restoration in computer vision, have
been playing a prominent role. Earlier mathematical models are mostly designed
by human knowledge or hypothesis on the image to be reconstructed, and we shall
call these models handcrafted models. Later, handcrafted plus data-driven
modeling started to emerge which still mostly relies on human designs, while
part of the model is learned from the observed data. More recently, as more
data and computation resources are made available, deep learning based models
(or deep models) pushed the data-driven modeling to the extreme where the
models are mostly based on learning with minimal human designs. Both
handcrafted and data-driven modeling have their own advantages and
disadvantages. One of the major research trends in medical imaging is to
combine handcrafted modeling with deep modeling so that we can enjoy benefits
from both approaches. The major part of this article is to provide a conceptual
review of some recent works on deep modeling from the unrolling dynamics
viewpoint. This viewpoint stimulates new designs of neural network
architectures with inspirations from optimization algorithms and numerical
differential equations. Given the popularity of deep modeling, there are still
vast remaining challenges in the field, as well as opportunities which we shall
discuss at the end of this article.Comment: 31 pages, 6 figures. Survey pape
Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)
In this paper, we consider the restoration and reconstruction of piecewise
constant objects in two and three dimensions using PaLEnTIR, a significantly
enhanced Parametric level set (PaLS) model relative to the current
state-of-the-art. The primary contribution of this paper is a new PaLS
formulation which requires only a single level set function to recover a scene
with piecewise constant objects possessing multiple unknown contrasts. Our
model offers distinct advantages over current approaches to the multi-contrast,
multi-object problem, all of which require multiple level sets and explicit
estimation of the contrast magnitudes. Given upper and lower bounds on the
contrast, our approach is able to recover objects with any distribution of
contrasts and eliminates the need to know either the number of contrasts in a
given scene or their values. We provide an iterative process for finding these
space-varying contrast limits. Relative to most PaLS methods which employ
radial basis functions (RBFs), our model makes use of non-isotropic basis
functions, thereby expanding the class of shapes that a PaLS model of a given
complexity can approximate. Finally, PaLEnTIR improves the conditioning of the
Jacobian matrix required as part of the parameter identification process and
consequently accelerates the optimization methods by controlling the magnitude
of the PaLS expansion coefficients, fixing the centers of the basis functions,
and the uniqueness of parametric to image mappings provided by the new
parameterization. We demonstrate the performance of the new approach using both
2D and 3D variants of X-ray computed tomography, diffuse optical tomography
(DOT), denoising, deconvolution problems. Application to experimental sparse CT
data and simulated data with different types of noise are performed to further
validate the proposed method.Comment: 31 pages, 56 figure
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