TH-C-BRD-06: A Novel MRI Based CT Artifact Correction Method for Improving Proton Range Calculation in the Presence of Severe CT Artifacts

2014 ◽  
Vol 41 (6Part32) ◽  
pp. 551-551
Author(s):  
P Park ◽  
E Schreibmann ◽  
T Fox ◽  
J Roper ◽  
E Elder ◽  
...  
Keyword(s):  
Correction Method ◽  
Artifact Correction ◽  
Ct Artifact ◽  
Range Calculation

MRI-Based Computed Tomography Metal Artifact Correction Method for Improving Proton Range Calculation Accuracy

2015 ◽  
Vol 91 (4) ◽  
pp. 849-856 ◽  
Author(s):  
Peter C. Park ◽  
Eduard Schreibmann ◽  
Justin Roper ◽  
Eric Elder ◽  
Ian Crocker ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Correction Method ◽  
Metal Artifact ◽  
Artifact Correction ◽  
Range Calculation

scholarly journals Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2957 ◽  
Author(s):  
Gihyoun Lee ◽  
Sang Jin ◽  
Jinung An
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Motion Artifact ◽  
Correction Method ◽  
Motion Artifacts ◽  
Functional Near Infrared Spectroscopy ◽  
Artifact Correction ◽  
Performance Evaluation Index

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

WE-AB-207A-07: A Planning CT-Guided Scatter Artifact Correction Method for CBCT Images

2016 ◽  
Vol 43 (6Part39) ◽  
pp. 3799-3799 ◽  
Author(s):  
X Yang ◽  
T Liu ◽  
X Dong ◽  
E Elder ◽  
W Curran ◽  
...  
Keyword(s):  
Correction Method ◽  
Artifact Correction ◽  
Ct Guided ◽  
Planning Ct

CT artifact correction: an image-processing approach

1996 ◽  
Author(s):  
Hamid Soltanian-Zadeh ◽  
Joe P. Windham ◽  
Jalel Soltanianzadeh
Keyword(s):  
Image Processing ◽  
Artifact Correction ◽  
Processing Approach ◽  
Ct Artifact

The impact of artifact correction methods of RR series on heart rate variability parameters

2018 ◽  
Vol 124 (3) ◽  
pp. 646-652 ◽  
Author(s):  
Anderson Ivan Rincon Soler ◽  
Luiz Eduardo Virgilio Silva ◽  
Rubens Fazan ◽  
Luiz Otavio Murta
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Linear Time ◽  
Spline Interpolation ◽  
Moving Average ◽  
Linear Interpolation ◽  
Correction Method ◽  
Artifact Correction ◽  
The Impact

Heart rate variability (HRV) analysis is widely used to investigate the autonomic regulation of the cardiovascular system. HRV is often analyzed using RR time series, which can be affected by different types of artifacts. Although there are several artifact correction methods, there is no study that compares their performances in actual experimental contexts. This work aimed to evaluate the impact of different artifact correction methods on several HRV parameters. Initially, 36 ECG recordings of control rats or rats with heart failure or hypertension were analyzed to characterize artifact occurrence rates and distributions, to be mimicked in simulations. After a rigorous analysis, only 16 recordings ( n = 16) with artifact-free segments of at least 10,000 beats were selected. RR interval losses were then simulated in the artifact-free (reference) time series according to real observations. Correction methods applied to simulated series were deletion, linear interpolation, cubic spline interpolation, modified moving average window, and nonlinear predictive interpolation. Linear (time- and frequency-domain) and nonlinear HRV parameters were calculated from corrupted-corrected time series, as well as for reference series to evaluate the accuracy of each correction method. Results show that NPI provides the overall best performance. However, several correction approaches, for example the simple deletion procedure, can provide good performance in some situations, depending on the HRV parameters under consideration. NEW & NOTEWORTHY This work analyzes the performance of some correction techniques commonly applied to the missing beats problem in RR time series. From artifact-free RR series, spurious values were inserted based on actual data of experimental settings. We intend our work to be a guide to show how artifacts should be corrected to preserve as much as possible the original heart rate variability properties.

scholarly journals Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials

2016 ◽  
Vol 264 ◽  
pp. 94-102 ◽  
Author(s):  
Lena Trebaul ◽  
David Rudrauf ◽  
Anne-Sophie Job ◽  
Mihai Dragos Mălîia ◽  
Irina Popa ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Correction Method ◽  
Artifact Correction ◽  
Cortical Evoked Potentials
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