Papers Tomographic Reconstructions
“Tomographic Reconstructions” 태그가 달린 논문 15편 · 필터 해제
Revisiting $Ψ$DONet: microlocally inspired filters for incomplete-data tomographic reconstructions
In this paper, we revisit a supervised learning approach based on unrolling, known as $\Psi$DONet, by providing a deeper microlocal interpretation for its theoretical analysis, and extending its study to the case of spar…
Tomographic ReconstructionsNeural Vector Tomography for Reconstructing a Magnetization Vector Field
Discretized techniques for vector tomographic reconstructions are prone to producing artifacts in the reconstructions. The quality of these reconstructions may further deteriorate as the amount of noise increases. In thi…
Tomographic ReconstructionsScalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
Large neural networks trained on large datasets have become the dominant paradigm in machine learning. These systems rely on maximum likelihood point estimates of their parameters, precluding them from expressing model u…
Bayesian InferenceGaussian ProcessesSequential Decision MakingTomographic ReconstructionsTransient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Patient-specific hemodynamics assessment could support diagnosis and treatment of neurovascular diseases. Currently, conventional medical imaging modalities are not able to accurately acquire high-resolution hemodynamic …
Computational EfficiencyDeep LearningTomographic ReconstructionsPhysics-informed neural networks for diffraction tomography
We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can…
Tomographic ReconstructionsWNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been the main driver behind some of the cutt…
DecoderDenoisingTomographic ReconstructionsSHREC 2021: Classification in cryo-electron tomograms
Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low sig…
ClassificationElectron TomographyTemplate MatchingTomographic ReconstructionsAccelerated iterative tomographic reconstruction with x-ray edge illumination
Compared to standard tomographic reconstruction, iterative approaches offer the possibility to account for extraneous experimental influences, which allows for a suppression of related artifacts. However, the inclusion o…
Tomographic ReconstructionsDisassemblable Fieldwork CT Scanner Using a 3D-printed Calibration Phantom
The use of computed tomography (CT) imaging has become of increasing interest to academic areas outside of the field of medical imaging and industrial inspection, e.g., to biology and cultural heritage research. The pecu…
Computed Tomography (CT)Tomographic ReconstructionsA Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm Cone-bea…
AnatomyTomographic ReconstructionsFull-pulse Tomographic Reconstruction with Deep Neural Networks
Plasma tomography consists in reconstructing the 2D radiation profile in a poloidal cross-section of a fusion device, based on line-integrated measurements along several lines of sight. The reconstruction process is comp…
Tomographic ReconstructionsModel based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is s…
Tomographic ReconstructionsAdaptive Graph-based Total Variation for Tomographic Reconstructions
Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions. Local TV methods fail to preserve texture details and often crea…
Image ReconstructionTomographic ReconstructionsImproving analytical tomographic reconstructions through consistency conditions
This work introduces and characterizes a fast parameterless filter based on the Helgason-Ludwig consistency conditions, used to improve the accuracy of analytical reconstructions of tomographic undersampled datasets. The…
Tomographic ReconstructionsGraph Based Sinogram Denoising for Tomographic Reconstructions
Limited data and low dose constraints are common problems in a variety of tomographic reconstruction paradigms which lead to noisy and incomplete data. Over the past few years sinogram denoising has become an essential p…
DenoisingTomographic Reconstructions