Tomographic Reconstructions
0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Tomographic Reconstructions
CBCT Walnut
Latest papers with no code
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
We apply the above methods to perform linearised neural network inference with ResNet-50 (25M parameters) trained on Imagenet (1. 2M observations and 1000 output dimensions).
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Nevertheless, the prediction of high-resolution transient CFD simulations for complex vascular geometries poses a challenge to conventional deep learning models.
Physics-informed neural networks for diffraction tomography
Our physics-informed neural networks can be generalized for any forward and inverse scattering problem.
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
We investigate the performance of the network on sparse-view chest CT scans, and we highlight the added benefit of having a trainable reconstruction layer over the more conventional fixed ones.
SHREC 2021: Classification in cryo-electron tomograms
To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms.
Accelerated iterative tomographic reconstruction with x-ray edge illumination
We take advantage of appropriately sampled illumination curves instead, which enables us to eliminate the corresponding parameter from the forward model and substantially increase computational speed.
Disassemblable 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.
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i. e. verification of screw placement.
Full-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.
Model 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.
