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Results: 24
Workshop on reconstruction schemes for magnetic resonance data
Esin Ozturk-Isik, Ian Marshall, Patryk Filipiak, Arnold JV Benjamin, Valia Guerra Ones, Rafael Ortiz Ramón, Maria Del C Valdés Hernández
Sep 17, 2020
The high-fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the...
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction...
Equivariant neural networks for inverse problems
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
Aug 31, 2021
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research...
equivariance Image reconstruction Neural Networks variational regularisation /dk/atira/pure/subjectarea/asjc/2600/2614 name=Theoretical Computer Science /dk/atira/pure/subjectarea/asjc/1700/1711 name=Signal Processing /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/2600/2610 name=Mathematical Physics
Transmission/Reflection Dual-mode Ultrasonic Tomography using Weighted Least Square-Lagrange Joint Reconstruction
Industrial Ultrasonic Tomography (UT) possesses unique advantages in multiphase medium imaging and has received broad attention. In this work, a novel transmission/reflection dual-mode image reconstruction algorithm based on...
Attenuation Dual-modality tomography image fusion Image reconstruction Linear programming Sensors Tomographic inverse problem Tomography Transducers Ultrasonic process tomography Ultrasound reflection Ultrasound transmission /dk/atira/pure/subjectarea/asjc/2200/2207 name=Control and Systems Engineering /dk/atira/pure/subjectarea/asjc/1700/1710 name=Information Systems /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/2200/2208 name=Electrical and Electronic Engineering
Robust Shape Fitting for 3D Scene Abstraction
Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such as cuboids or cylinders. Inferring these...
estimation Image reconstruction Scene abstraction Shape Solid modeling surface reconstruction Three-dimensional displays Training cuboid fitting minimal solver multi-model fitting shape decomposition /dk/atira/pure/subjectarea/asjc/1700/1712 name=Software /dk/atira/pure/subjectarea/asjc/1700/1702 name=Artificial Intelligence /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics /dk/atira/pure/subjectarea/asjc/1700/1707 name=Computer Vision and Pattern Recognition /dk/atira/pure/subjectarea/asjc/1700/1703 name=Computational Theory and Mathematics
Robust Shape Fitting for 3D Scene Abstraction
Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such as cuboids or cylinders. Inferring these...
estimation Image reconstruction Scene abstraction Shape Solid modeling surface reconstruction Three-dimensional displays Training cuboid fitting minimal solver multi-model fitting shape decomposition /dk/atira/pure/subjectarea/asjc/1700/1712 name=Software /dk/atira/pure/subjectarea/asjc/1700/1702 name=Artificial Intelligence /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics /dk/atira/pure/subjectarea/asjc/1700/1707 name=Computer Vision and Pattern Recognition /dk/atira/pure/subjectarea/asjc/1700/1703 name=Computational Theory and Mathematics
Source-detector trajectory optimization in cone-beam computed tomography
S Hatamikia, A Biguri, G Herl, G Kronreif, T Reynolds, J Kettenbach, T Russ, A Tersol, A Maier, M Figl, JH Siewerdsen, W Birkfellner
Aug 16, 2022
Reduction of staircase effect with total generalized variation regularization for electrical impedance tomography
Image reconstruction in electrical impedance tomography is an ill-posed inverse problem. To address this problem, regularization methods such as Tikhonov regularization and total variation regularization have been adopted....
Reduction of staircase effect with total generalized variation regularization for electrical impedance tomography
Image reconstruction in electrical impedance tomography is an ill-posed inverse problem. To address this problem, regularization methods such as Tikhonov regularization and total variation regularization have been adopted....
Equivariant neural networks for inverse problems
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
Aug 31, 2021
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research...
equivariance Image reconstruction Neural Networks variational regularisation /dk/atira/pure/subjectarea/asjc/2600/2614 name=Theoretical Computer Science /dk/atira/pure/subjectarea/asjc/1700/1711 name=Signal Processing /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/2600/2610 name=Mathematical Physics
Discrete Shearlets as a Sparsifying Transform in Low-Rank Plus Sparse Decomposition for Undersampled (k, t)-Space MR Data.
The discrete shearlet transformation accurately represents the discontinuities and edges occurring in magnetic resonance imaging, providing an excellent option of a sparsifying transform. In the present paper, we examine the use...
Discrete Shearlets as a Sparsifying Transform in Low-Rank Plus Sparse Decomposition for Undersampled (k, t)-Space MR Data.
The discrete shearlet transformation accurately represents the discontinuities and edges occurring in magnetic resonance imaging, providing an excellent option of a sparsifying transform. In the present paper, we examine the use...
Enhancing joint reconstruction and segmentation with non-convex Bregman iteration
Veronica Corona, Martin Benning, Matthias J. Ehrhardt, Lynn F. Gladden, Richard Mair, Andi Reci, Andrew J. Sederman, Stefanie Reichelt, Carola-Bibiane Schoenlieb
Apr 26, 2019
All imaging modalities such as computed tomography, emission tomography and magnetic resonance imaging require a reconstruction approach to produce an image. A common image processing task for applications that utilise those...
Bregman iteration Image reconstruction image segmentation iterative regularisation Magnetic Resonance Imaging Non-convex optimisation Total variation /dk/atira/pure/subjectarea/asjc/2600/2614 name=Theoretical Computer Science /dk/atira/pure/subjectarea/asjc/1700/1711 name=Signal Processing /dk/atira/pure/subjectarea/asjc/2600/2610 name=Mathematical Physics /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics
Why chromatic imaging matters
J Sanchez-Bermudez, F Millour, F Baron, R van Boekel, L Bourgès, G Duvert, PJV Garcia, N Gomes, KH Hofmann, T Henning, JW Isbell, B Lopez, A Matter, JU Pott, D Schertl, E Thiébaut, G Weigelt, J Young
Dec 11, 2019
Abstract: During the last two decades, the first generation of beam combiners at the Very Large Telescope Interferometer has proved the importance of optical interferometry for high-angular resolution astrophysical studies in...
On the Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography (CCERT) with Entropy Priors
Regularization with priors is an effective approach to solve the ill-posed inverse problem of electrical tomography. Entropy priors have been proven to be promising in radiation tomography but have received less attention in the...
capacitively coupled electrical resistance tomography (CCERT) electrical tomography Entropy Image reconstruction regularization /dk/atira/pure/subjectarea/asjc/1700/1710 name=Information Systems /dk/atira/pure/subjectarea/asjc/2600/2610 name=Mathematical Physics /dk/atira/pure/subjectarea/asjc/3100/3101 name=Physics and Astronomy (miscellaneous) /dk/atira/pure/subjectarea/asjc/2200/2208 name=Electrical and Electronic Engineering
On the Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography (CCERT) with Entropy Priors
Regularization with priors is an effective approach to solve the ill-posed inverse problem of electrical tomography. Entropy priors have been proven to be promising in radiation tomography but have received less attention in the...
capacitively coupled electrical resistance tomography (CCERT) electrical tomography Entropy Image reconstruction regularization /dk/atira/pure/subjectarea/asjc/1700/1710 name=Information Systems /dk/atira/pure/subjectarea/asjc/2600/2610 name=Mathematical Physics /dk/atira/pure/subjectarea/asjc/3100/3101 name=Physics and Astronomy (miscellaneous) /dk/atira/pure/subjectarea/asjc/2200/2208 name=Electrical and Electronic Engineering
Equivariant neural networks for inverse problems.
Elena Celledoni, Matthias J Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
Sep 02, 2021
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research...
Published by: Inverse problems
Enhancing joint reconstruction and segmentation with non-convex Bregman iteration
Veronica Corona, Martin Benning, Matthias J. Ehrhardt, Lynn F. Gladden, Richard Mair, Andi Reci, Andrew J. Sederman, Stefanie Reichelt, Carola-Bibiane Schoenlieb
Apr 26, 2019
All imaging modalities such as computed tomography, emission tomography and magnetic resonance imaging require a reconstruction approach to produce an image. A common image processing task for applications that utilise those...
Bregman iteration Image reconstruction image segmentation iterative regularisation Magnetic Resonance Imaging Non-convex optimisation Total variation /dk/atira/pure/subjectarea/asjc/2600/2614 name=Theoretical Computer Science /dk/atira/pure/subjectarea/asjc/1700/1711 name=Signal Processing /dk/atira/pure/subjectarea/asjc/2600/2610 name=Mathematical Physics /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics
Transmission/Reflection Dual-mode Ultrasonic Tomography using Weighted Least Square-Lagrange Joint Reconstruction
Industrial Ultrasonic Tomography (UT) possesses unique advantages in multiphase medium imaging and has received broad attention. In this work, a novel transmission/reflection dual-mode image reconstruction algorithm based on...
Attenuation Dual-modality tomography image fusion Image reconstruction Linear programming Sensors Tomographic inverse problem Tomography Transducers Ultrasonic process tomography Ultrasound reflection Ultrasound transmission /dk/atira/pure/subjectarea/asjc/2200/2207 name=Control and Systems Engineering /dk/atira/pure/subjectarea/asjc/1700/1710 name=Information Systems /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/2200/2208 name=Electrical and Electronic Engineering
Equivariant neural networks for inverse problems.
Elena Celledoni, Matthias J Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
Sep 20, 2021
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research...