Motion artifact reduction with predictive gating

1996 ◽  
Author(s):  
Jiang Hsieh
Keyword(s):  
Motion Artifact ◽  
Artifact Reduction

scholarly journals Study of motion artifact reduction by mspatial frequency DSA techniques

1996 ◽  
Vol 52 (10) ◽  
pp. 1505
Author(s):  
Shigeto Kawase ◽  
Shinsuke Yano ◽  
Yutaka Yukawa ◽  
Akio Nakamori ◽  
ken'ichi Ogawa ◽  
...  
Keyword(s):  
Motion Artifact ◽  
Artifact Reduction

Motion artifact reduction by Randomized Phase Encoding (RPE) Turbo Spin Echo (TSE)

2009 ◽  
Author(s):  
Takashi Moroi ◽  
Nobuo Mizuuchi ◽  
Katsuya Maruyama ◽  
Shohei Takemoto ◽  
Toshinori Sueyoshi ◽  
...  
Keyword(s):  
Spin Echo ◽  
Motion Artifact ◽  
Artifact Reduction ◽  
Turbo Spin Echo ◽  
Phase Encoding ◽  
Turbo Spin

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

scholarly journals Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

2020 ◽  
Vol 30 (11) ◽  
pp. 5923-5932
Author(s):  
M.-L. Kromrey ◽  
D. Tamada ◽  
H. Johno ◽  
S. Funayama ◽  
N. Nagata ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Arterial Phase ◽  
Artifact Reduction ◽  
Gadoxetate Disodium ◽  
Mr Images ◽  
Liver Mri ◽  
Lesion Conspicuity

Abstract Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

scholarly journals Use of an Enhanced Gradient System for Diffusion MR Imaging with Motion-Artifact Reduction

1995 ◽  
Vol 36 (6) ◽  
pp. 662-670 ◽  
Author(s):  
Sara Brockstedt ◽  
C. Thomsen ◽  
R. Wirestam ◽  
J. de Poorter ◽  
C. de Wagter ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Motion Artifact ◽  
Gradient System ◽  
Artifact Reduction ◽  
Diffusion Mr ◽  
Diffusion Mr Imaging

A Motion Artifact Reduction Method in Cerebrovascular DSA Sequence Images

2018 ◽  
Vol 32 (08) ◽  
pp. 1854022
Author(s):  
Guanglei Wang ◽  
Pengyu Wang ◽  
Yan Li ◽  
Tianqi Su ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Heart Disease ◽  
Image Enhancement ◽  
Vascular System ◽  
Motion Artifact ◽  
Artifact Reduction ◽  
Artifact Removal ◽  
Guided Filtering ◽  
Fully Automatic ◽  
Automated Processes

Digital Subtraction Angiography (DSA) can be used for diagnosing the pathologies of vascular system including systemic vascular disease, coronary heart disease, arrhythmia, valvular disease and congenital heart disease. Previous studies have provided some image enhancement algorithms for DSA images. However, these studies are not suitable for automated processes in huge amounts of data. Furthermore, few algorithms solved the problems of image contrast corruption after artifact removal. In this paper, we propose a fully automatic method for cerebrovascular DSA sequence images artifact removal based on rigid registration and guided filter. The guided filtering method is applied to fuse the original DSA image and registered DSA image, the results of which preserve clear vessel boundary from the original DSA image and remove the artifacts by the registered procedure. The experimental evaluation with 40 DSA sequence images shows that the proposed method increases the contrast index by 24.1% for improving the quality of DSA images compared with other image enhancement methods, and can be implemented as a fully automatic procedure.

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
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