MO-D-330A-02: Motion Artifact Correction Using a Novel Data Consistency Condition

2006 ◽  
Vol 33 (6Part14) ◽  
pp. 2158-2158
Author(s):  
S leng ◽  
B Nett ◽  
G Chen
Keyword(s):  
Consistency Condition ◽  
Motion Artifact ◽  
Data Consistency ◽  
Artifact Correction

An imperceptible incorrectness in deriving data consistency condition from John’s equation for cone-beam CT imaging

Optik ◽  
2020 ◽  
Vol 202 ◽  
pp. 163603
Author(s):  
Shaojie Tang ◽  
Baolei Li ◽  
Zhiwei Qiao ◽  
Yining Zhu ◽  
Cong Guo ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Consistency Condition ◽  
Ct Imaging ◽  
Data Consistency ◽  
Cone Beam

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.

Numerical method for axial motion artifact correction in retinal spectral-domain optical coherence tomography

2020 ◽  
Author(s):  
Sergey Yu. Ksenofontov ◽  
Pavel A. Shilyagin ◽  
Dmitry A. Terpelov ◽  
Valentin M. Gelikonov ◽  
Grigory V. Gelikonov
Keyword(s):  
Optical Coherence Tomography ◽  
Numerical Method ◽  
Motion Artifact ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Axial Motion ◽  
Artifact Correction
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