Results 1 to 10 of about 264,517 (319)

GNSS troposphere tomography based on two-step reconstructions using GPS observations and COSMIC profiles [PDF]

Annales Geophysicae, 2013
Traditionally, balloon-based radiosonde soundings are used to study the spatial distribution of atmospheric water vapour. However, this approach cannot be frequently employed due to its high cost. In contrast, GPS tomography technique can obtain water
P. Xia, P. Xia, C. Cai, Z. Liu
doaj   +3 more sources

Iterative Reconstruction of Memory Kernels [PDF]

Journal of Chemical Theory and Computation, 2017
In recent years, it has become increasingly popular to construct coarse-grained models with non-Markovian dynamics to account for an incomplete separation of time scales.
Hanke, Martin, Jung, Gerhard, Schmid, Friederike   +2 more
core   +3 more sources

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction [PDF]

Heliyon
Background: Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction.
Varin Jaruvongvanich, Kobkun Muangsomboon, Wanwarang Teerasamit, Voraparee Suvannarerg, Chulaluk Komoltri, Sastrawut Thammakittiphan, Wimonrat Lornimitdee, Witchuda Ritsamrej, Parinya Chaisue, Napapong Pongnapang, Piyaporn Apisarnthanarak   +10 more
doaj   +2 more sources

Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease. [PDF]

BMC Med Inform Decis Mak
Zhu L, Shi X, Tang L, Machida H, Yang L, Ma M, Ha R, Shen Y, Wang F, Chen D.   +9 more
europepmc   +3 more sources

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

Photoacoustics, 2023
Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-crafted parameters ...
Ko-Tsung Hsu, Steven Guan, Parag V. Chitnis   +2 more
doaj   +1 more source

Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction

Journal of Integrative Neuroscience, 2021
To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative ...
Chuluunbaatar Otgonbaatar, Jae-Kyun Ryu, Seonkyu Kim, Jung Wook Seo, Hackjoon Shim, Dae Hyun Hwang   +5 more
doaj   +1 more source

High performance Savu software for fast 3D model-based iterative reconstruction of large data at Diamond Light Source

SoftwareX, 2022
The challenge of processing big data effectively and efficiently is crucial for many synchrotron facilities which can collect up to several petabytes of data annually.
Daniil Kazantsev, Nicola Wadeson, Mark Basham   +2 more
doaj   +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

Multi-Scale Learned Iterative Reconstruction

IEEE Transactions on Computational Imaging, 2020
Model-based learned iterative reconstruction methods have recently been shown to outperform classical reconstruction algorithms. Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models.
Andreas Hauptmann, Jonas Adler, Simon Arridge, Ozan Oktem   +3 more
openaire   +5 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