A frequency domain based rigid motion artifact reduction algorithm

2009 ◽  
Author(s):  
Hai Luo ◽  
Xiaojie Huang ◽  
Wenyu Pan ◽  
Heqin Zhou ◽  
Huanqing Feng
Keyword(s):  
Frequency Domain ◽  
Rigid Motion ◽  
Motion Artifact ◽  
Artifact Reduction ◽  
Reduction Algorithm

Clinical impact of a new cone beam CT angiography respiratory motion artifact reduction algorithm during hepatic intra-arterial interventions

2019 ◽  
Vol 30 (1) ◽  
pp. 163-174 ◽  
Author(s):  
Marco Dioguardi Burgio ◽  
Thomas Benseghir ◽  
Vincent Roche ◽  
Carmela Garcia Alba ◽  
Jean Baptiste Debry ◽  
...  
Keyword(s):  
Ct Angiography ◽  
Respiratory Motion ◽  
Cone Beam Ct ◽  
Motion Artifact ◽  
Clinical Impact ◽  
Cone Beam ◽  
Artifact Reduction ◽  
Reduction Algorithm ◽  
Respiratory Motion Artifact

Rigid and non-rigid motion artifact reduction in X-ray CT using attention module

2021 ◽  
Vol 67 ◽  
pp. 101883
Author(s):  
Youngjun Ko ◽  
Seunghyuk Moon ◽  
Jongduk Baek ◽  
Hyunjung Shim
Keyword(s):  
Rigid Motion ◽  
Motion Artifact ◽  
Artifact Reduction ◽  
X Ray

scholarly journals A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 155 ◽  
Author(s):  
Yuan Zhang ◽  
Liyi Zhang
Keyword(s):  
Iterative Process ◽  
Degrees Of Freedom ◽  
Blind Deconvolution ◽  
Pearson Correlation ◽  
Rigid Motion ◽  
Structural Similarity ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Artifact Reduction ◽  
Clinical Experiments

In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.

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