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Model-based X-ray CT Image and Light Field Reconstruction Using Variable Splitting Methods.

, 2014
Hung Nien
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LRM: Large Reconstruction Model for Single Image to 3D

International Conference on Learning Representations, 2023
We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category ...
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, Hao Tan   +9 more
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Image reconstruction

Physics in Medicine and Biology, 2006
We give an overview of the role of Physics in Medicine and Biology in the development of tomographic reconstruction algorithms. We focus on imaging modalities involving ionizing radiation, CT, PET and SPECT, and cover a wide spectrum of reconstruction problems, starting with classical 2D tomography in the 1970s up to 4D and 5D problems involving ...
Defrise, Michel, Gullberg, Grant
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Weighted microscopic image reconstruction

Discrete Applied Mathematics, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bar-Noy, Amotz, Böhnlein, Toni, Lotker, Zvi, Peleg, David, Rawitz, Dror   +4 more
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Image reconstruction by domain-transform manifold learning

Nature, 2017
Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio ...
Bo Zhu, Jeremiah Zhe Liu, B. Rosen, M. Rosen   +3 more
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Deep learning in magnetic resonance image reconstruction

Journal of Medical Imaging and Radiation Oncology, 2021
Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of deep learning in MR image reconstruction from different image ...
S. Chandra, Marlon Bran Lorenzana, Xinwen Liu, Siyu Liu, S. Bollmann, S. Crozier   +5 more
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Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.

AJR. American journal of roentgenology, 2020
OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS.
C. Jensen, Xinming Liu, E. Tamm, A. Chandler, Jia Sun, A. Morani, S. Javadi, N. Wagner-Bartak   +7 more
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Augmented Likelihood Image Reconstruction

IEEE Transactions on Medical Imaging, 2016
The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as
Maik, Stille, Matthias, Kleine, Julian, Hägele, Jörg, Barkhausen, Thorsten M, Buzug   +4 more
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Dual-Path Attention Network for Compressed Sensing Image Reconstruction

IEEE Transactions on Image Processing, 2020
Although deep neural network methods achieved much success in compressed sensing image reconstruction in recent years, they still have some issues, especially in preserving texture details.
Yubao Sun, Jiwei Chen, Qingshan Liu, Bo Liu, G. Guo   +4 more
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