Intratomo: self-supervised learning-based tomography via sinogram synthesis and prediction
We propose IntraTomo, a powerful framework that combines the benefits of learning-based
and model-based approaches for solving highly ill-posed inverse problems in the Computed …
and model-based approaches for solving highly ill-posed inverse problems in the Computed …
Neat: Neural adaptive tomography
In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive,
hierarchical neural rendering pipeline for tomography. Through a combination of neural …
hierarchical neural rendering pipeline for tomography. Through a combination of neural …
Ct2hair: High-fidelity 3d hair modeling using computed tomography
We introduce CT2Hair, a fully automatic framework for creating high-fidelity 3D hair models
that are suitable for use in downstream graphics applications. Our approach utilizes real …
that are suitable for use in downstream graphics applications. Our approach utilizes real …
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
T Cheslerean-Boghiu, FC Hofmann… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
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 …
applications. Most notably, clinical use-cases have gained an increased interest and have …
Scalarflow: a large-scale volumetric data set of real-world scalar transport flows for computer animation and machine learning
In this paper, we present ScalarFlow, a first large-scale data set of reconstructions of real-
world smoke plumes. In addition, we propose a framework for accurate physics-based …
world smoke plumes. In addition, we propose a framework for accurate physics-based …
[HTML][HTML] Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results
A recent PNAS paper reveals that several popular deep reconstruction networks are
unstable. Specifically, three kinds of instabilities were reported:(1) strong image artefacts …
unstable. Specifically, three kinds of instabilities were reported:(1) strong image artefacts …
[PDF][PDF] Stabilizing deep tomographic reconstruction networks
Tomographic image reconstruction with deep learning (DL) is an emerging field of applied
artificial intelligence, but a recent landmark study reveals that several deep reconstruction …
artificial intelligence, but a recent landmark study reveals that several deep reconstruction …
Sparse-view cone-beam CT reconstruction by bar-by-bar neural FDK algorithm
S Wang, T Yatagawa, Y Ohtake… - … Testing and Evaluation, 2024 - Taylor & Francis
ABSTRACT The Feldkamp, Davis and Kress algorithm is a computationally efficient
reconstruction method for three-dimensional cone-beam computed tomography. However, it …
reconstruction method for three-dimensional cone-beam computed tomography. However, it …
Tomofluid: Reconstructing dynamic fluid from sparse view videos
Visible light tomography is a promising and increasingly popular technique for fluid imaging.
However, the use of a sparse number of viewpoints in the capturing setups makes the …
However, the use of a sparse number of viewpoints in the capturing setups makes the …
Machine learning based sinogram interpolation for X-ray computed tomography validated on experimental data
S Bellens, P Guerrero, M Janssens, P Vandewalle… - Precision …, 2024 - Elsevier
The data driven industry 4.0 and increasing mass-customization of additive manufacturing
products require a flexible and high-throughput integration of a 100% quality inspection …
products require a flexible and high-throughput integration of a 100% quality inspection …