Results 21 to 30 of about 1,826,590 (358)

Uniqueness of radiomic features in non‐small cell lung cancer

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Purpose The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). Approach Two cohorts of non‐small cell lung cancer (
Gary Ge, Jie Zhang
wiley   +1 more source

A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images

Tomography, 2023
Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions.
Rohan Nadkarni, Darin P. Clark, Alex J. Allphin, Cristian T. Badea   +3 more
doaj   +1 more source

An iterative reconstruction algorithm for Faraday tomography [PDF]

Monthly Notices of the Royal Astronomical Society, 2020
ABSTRACT Faraday tomography offers crucial information on the magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters, by observing its magnetoionic media. The observed linear polarization spectrum is inverse Fourier transformed to obtain the Faraday dispersion function (FDF), providing us a tomographic ...
Suchetha Cooray, Tsutomu T Takeuchi, Takuya Akahori, Yoshimitsu Miyashita, Shinsuke Ideguchi, Keitaro Takahashi, Kiyotomo Ichiki   +6 more
openaire   +4 more sources

The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Background and purpose For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non‐correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial‐, region‐, and intensity‐based) were jointly used in DIR algorithm for improved accuracy ...
Xin Xie, Yuchun Song, Feng Ye, Hui Yan, Shulian Wang, Xinming Zhao, Jianrong Dai   +6 more
wiley   +1 more source

Image quality improvement in low‐dose chest CT with deep learning image reconstruction

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Objectives To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening ...
Qian Tian, Xinyu Li, Jianying Li, Yannan Cheng, Xinyi Niu, Shumeng Zhu, Wenting Xu, Jianxin Guo   +7 more
wiley   +1 more source

Iterative initial condition reconstruction [PDF]

Physical Review D, 2017
Motivated by recent developments in perturbative calculations of the nonlinear evolution of large-scale structure, we present an iterative algorithm to reconstruct the initial conditions in a given volume starting from the dark matter distribution in real space.
Tobias Baldauf, Matias Zaldarriaga, Marcel Schmittfull   +2 more
openaire   +3 more sources

Is Iterative Reconstruction Ready for MDCT? [PDF]

Journal of the American College of Radiology, 2009
Although the very first computed tomographic scanners used the iterative algebraic reconstruction technique, the filtered back-projection (FBP) method soon became the gold standard for computed tomographic reconstruction. Image quality has dramatically improved over the past 30 years thanks to advances in x-ray tubes, detector technologies, and overall
Jingyan Xu, Benjamin M. W. Tsui, Mahadevappa Mahesh   +2 more
openaire   +3 more sources

Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures

Entropy, 2021
The problem of tomographic image reconstruction can be reduced to an optimization problem of finding unknown pixel values subject to minimizing the difference between the measured and forward projections. Iterative image reconstruction algorithms provide
Ryosuke Kasai, Yusaku Yamaguchi, Takeshi Kojima, Omar M. Abou Al-Ola, Tetsuya Yoshinaga   +4 more
doaj   +1 more source

Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction [PDF]

, 2022
Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-consistent (DC) deep learning can accelerate reconstruction with good image quality, but has not been formulated for non-Cartesian subspace imaging. In this study, we propose
arxiv   +1 more source

Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction

Korean Journal of Radiology, 2020
Objective To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction.
J. Hong, Eun-Ah Park, Whal Lee, C. Ahn, Jong Hyo Kim   +4 more
semanticscholar   +1 more source