scholarly journals Joint Motion Estimation and Layer Segmentation in Transparent Image Sequences—Application to Noise Reduction in X-Ray Image Sequences

2009 ◽  
Vol 2009 (1) ◽  
Author(s):  
Vincent Auvray ◽  
Patrick Bouthemy ◽  
Jean Liénard
Keyword(s):  
Motion Estimation ◽  
Noise Reduction ◽  
Image Sequences ◽  
Joint Motion ◽  
X Ray ◽  
Layer Segmentation

Analysis in Sensibility of a Motion Detection Algorithm for Selecting Noise Reduction Methods in X-Ray Image Sequences

2012 ◽  
Author(s):  
Lucas Souza e Silva ◽  
Filipe Ribeiro Rocha ◽  
Lorena Borges Amaral ◽  
Cassio Alves Carneiro ◽  
Flavia Maglhaes Freitas Ferreira ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Motion Detection ◽  
Detection Algorithm ◽  
Image Sequences ◽  
X Ray ◽  
Reduction Methods

HIT NOISE REDUCTION IN SOME X-RAY IMAGES

2009 ◽  
Vol 12 (4) ◽  
pp. 78-86 ◽  
Author(s):  
Ali Abid D. Al-Zuky ◽  
◽  
Ahmed Asal Kzar ◽  
Keyword(s):  
Noise Reduction ◽  
X Ray

syris: a flexible and efficient framework for X-ray imaging experiments simulation

2017 ◽  
Vol 24 (6) ◽  
pp. 1283-1295 ◽  
Author(s):  
Tomáš Faragó ◽  
Petr Mikulík ◽  
Alexey Ershov ◽  
Matthias Vogelgesang ◽  
Daniel Hänschke ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Data Processing ◽  
Graphics Processing Units ◽  
High Speed ◽  
Real Data ◽  
Estimation Accuracy ◽  
Processing Parameter ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Conditions

An open-source framework for conducting a broad range of virtual X-ray imaging experiments,syris, is presented. The simulated wavefield created by a source propagates through an arbitrary number of objects until it reaches a detector. The objects in the light path and the source are time-dependent, which enables simulations of dynamic experiments,e.g.four-dimensional time-resolved tomography and laminography. The high-level interface ofsyrisis written in Python and its modularity makes the framework very flexible. The computationally demanding parts behind this interface are implemented in OpenCL, which enables fast calculations on modern graphics processing units. The combination of flexibility and speed opens new possibilities for studying novel imaging methods and systematic search of optimal combinations of measurement conditions and data processing parameters. This can help to increase the success rates and efficiency of valuable synchrotron beam time. To demonstrate the capabilities of the framework, various experiments have been simulated and compared with real data. To show the use case of measurement and data processing parameter optimization based on simulation, a virtual counterpart of a high-speed radiography experiment was created and the simulated data were used to select a suitable motion estimation algorithm; one of its parameters was optimized in order to achieve the best motion estimation accuracy when applied on the real data.syriswas also used to simulate tomographic data sets under various imaging conditions which impact the tomographic reconstruction accuracy, and it is shown how the accuracy may guide the selection of imaging conditions for particular use cases.

scholarly journals Motion correction for routine X-ray lung CT imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Doil Kim ◽  
Jiyoung Choi ◽  
Duhgoon Lee ◽  
Hyesun Kim ◽  
Jiyoung Jung ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Motion Correction ◽  
Ct Imaging ◽  
Motion Artifacts ◽  
Ct Scans ◽  
Breath Hold ◽  
X Ray ◽  
Wide Range ◽  
Thorax Ct ◽  
Lung Ct

AbstractA novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

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