Data Consistency Based Rigid Motion Artifact Reduction in Fan-Beam CT

2007 ◽  
Vol 26 (2) ◽  
pp. 249-260 ◽  
Author(s):  
Hengyong Yu ◽  
Ge Wang
Keyword(s):  
Rigid Motion ◽  
Motion Artifact ◽  
Data Consistency ◽  
Artifact Reduction

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.

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

Rigid Motion Artifact Reduction in CT Using the Phase Correlation Method

2020 ◽  
Vol 13 (8) ◽  
pp. 1119-1128
Author(s):  
Yuan Zhang ◽  
Liyi Zhang ◽  
Yunshan Sun
Keyword(s):  
Rigid Motion ◽  
Correlation Method ◽  
Reconstruction Algorithm ◽  
Human Head ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Phase Correlation ◽  
Artifact Reduction ◽  
Patient Motion ◽  
Reconstruction Quality

Background: In Computed Tomography (CT), it is often not possible for the subject to remain stationary during a scan. Unfortunately, a patient motion would result in degraded spatial resolution and image artifacts. It is desirable to improve reconstruction quality and reduce motion artifacts caused by patient motion. Methods: In this work, a method was proposed to eliminate the influence of the motion on image quality, based on the phase correlation method. Based on our previous work, projections were first taken by Radon transform and motion parameters were estimated by the phase-only correlation of projections in the Radon domain. In addition, an improved image reconstruction algorithm was performed to compensate for the motion effects. Results: Experimental results proved that the proposed method could not only obtain high precision and good real-time performance but also ensure a superior artifact reduction. Conclusion: Besides, the efficacy of the proposed method has been demonstrated in both simulated and human head experiments.

Data consistency based translational motion artifact reduction in fan-beam CT

2006 ◽  
Vol 25 (6) ◽  
pp. 792-803 ◽  
Author(s):  
Hengyong Yu ◽  
Yuchuan Wei ◽  
Jiang Hsieh ◽  
Ge Wang
Keyword(s):  
Translational Motion ◽  
Motion Artifact ◽  
Data Consistency ◽  
Artifact Reduction
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