CT artifact reduction via U-net CNN

2018 ◽  
Author(s):  
Chengzhu Zhang ◽  
Yuxiang Xing
Keyword(s):  
Artifact Reduction ◽  
Ct Artifact

17 Impact of a CT artifact reduction algorithm in radiotherapy: preliminary qualitative and quantitative aspects

2018 ◽  
Vol 56 ◽  
pp. 48
Author(s):  
R. Villeneuve ◽  
S. Biteghe ◽  
O. Allegrini ◽  
R. Amblard ◽  
N. Garnier ◽  
...  
Keyword(s):  
Artifact Reduction ◽  
Reduction Algorithm ◽  
Qualitative And Quantitative ◽  
Ct Artifact

SU-FF-T-54: Quantification of CT Artifact Reduction of Second Generation Intracavitary Brachytherapy Applicator

2009 ◽  
Vol 36 (6Part9) ◽  
pp. 2531-2531
Author(s):  
Z Yu ◽  
P Balter ◽  
Y Zhang ◽  
A Klopp ◽  
M Price ◽  
...  
Keyword(s):  
Second Generation ◽  
Intracavitary Brachytherapy ◽  
Artifact Reduction ◽  
Ct Artifact

PO-1754: A stereoscopic CT artifact reduction method image quality comparison to current vendor solutions

2020 ◽  
Vol 152 ◽  
pp. S975-S976 ◽  
Author(s):  
D. Branco ◽  
S. Kry ◽  
T. Paige ◽  
J. Rong ◽  
X. Zhang ◽  
...  
Keyword(s):  
Image Quality ◽  
Reduction Method ◽  
Artifact Reduction ◽  
Ct Artifact

MR Imaging with Metal Artifact Reduction to Differentiate between Patients with and without Infected Total Hip Arthroplasty

2018 ◽  
Author(s):  
Benedikt Schwaiger ◽  
Alexandra Gersing ◽  
Daniela Muenzel ◽  
Julia Dangelmaier ◽  
Peter Prodinger ◽  
...  
Keyword(s):  
Total Hip Arthroplasty ◽  
Mr Imaging ◽  
Hip Arthroplasty ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Total Hip

scholarly journals Influence of iterative reconstruction and dose levels on metallic artifact reduction: A phantom study within four CT systems

2019 ◽  
Vol 100 (5) ◽  
pp. 269-277 ◽  
Author(s):  
J. Greffier ◽  
A. Larbi ◽  
J. Frandon ◽  
P.A. Daviau ◽  
J.P. Beregi ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Phantom Study ◽  
Artifact Reduction ◽  
Metallic Artifact ◽  
Dose Levels

Comparison of metal artifact reduction techniques in magnetic resonance imaging of carbon-reinforced PEEK and titanium spinal implants

2021 ◽  
pp. 028418512110290
Author(s):  
Georg Osterhoff ◽  
Florian A Huber ◽  
Laura C Graf ◽  
Ferdinand Erdlen ◽  
Hans-Christoph Pape ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Pedicle Screws ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Signal Loss ◽  
Resonance Imaging ◽  
Spinal Implants ◽  
Titanium Screws

Background Carbon-reinforced PEEK (C-FRP) implants are non-magnetic and have increasingly been used for the fixation of spinal instabilities. Purpose To compare the effect of different metal artifact reduction (MAR) techniques in magnetic resonance imaging (MRI) on titanium and C-FRP spinal implants. Material and Methods Rod-pedicle screw constructs were mounted on ovine cadaver spine specimens and instrumented with either eight titanium pedicle screws or pedicle screws made of C-FRP and marked with an ultrathin titanium shell. MR scans were performed of each configuration on a 3-T scanner. MR sequences included transaxial conventional T1-weighted turbo spin echo (TSE) sequences, T2-weighted TSE, and short-tau inversion recovery (STIR) sequences and two different MAR-techniques: high-bandwidth (HB) and view-angle-tilting (VAT) with slice encoding for metal artifact correction (SEMAC). Metal artifact degree was assessed by qualitative and quantitative measures. Results There was a much stronger effect on artifact reduction with using C-FRP implants compared to using specific MRI MAR-techniques (screw shank: P < 0.001; screw tulip: P < 0.001; rod: P < 0.001). VAT-SEMAC sequences were able to reduce screw-related signal loss artifacts in constructs with titanium screws to a certain degree. Constructs with C-FRP screws showed less artifact-related implant diameter amplification when compared to constructs with titanium screws ( P < 0.001). Conclusion Constructs with C-FRP screws are associated with significantly less artifacts compared to constructs with titanium screws including dedicated MAR techniques. Artifact-reducing sequences are able to reduce implant-related artifacts. This effect is stronger in constructs with titanium screws than in constructs with C-FRP screws.

scholarly journals Metal artifact reduction in computed tomography images based on developed generative adversarial neural network

2021 ◽  
Vol 24 ◽  
pp. 100573
Author(s):  
Goli Khaleghi ◽  
Mohammad Hosntalab ◽  
Mahdi Sadeghi ◽  
Reza Reiazi ◽  
Seied Rabi Mahdavi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Images

scholarly journals Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

2021 ◽  
Vol 13 (3) ◽  
pp. 409
Author(s):  
Howard Zebker
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Artifact Reduction ◽  
Interferometric Synthetic Aperture Radar ◽  
Spatial Properties ◽  
Model Free ◽  
Weather Model ◽  
Phase Variations ◽  
Source Of Error ◽  
Signal Correlation

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

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