Iterative reconstruction algorithm for analyzer-based phase-contrast computed tomography of hard and soft tissue

2013 ◽  
Vol 103 (14) ◽  
pp. 143702 ◽  
Author(s):  
Naoki Sunaguchi ◽  
Tetsuya Yuasa ◽  
Masami Ando
Keyword(s):  
Computed Tomography ◽  
Soft Tissue ◽  
Iterative Reconstruction ◽  
Phase Contrast ◽  
Reconstruction Algorithm ◽  
Iterative Reconstruction Algorithm

scholarly journals Comparison of methods for sensitivity correction in Talbot–Lau computed tomography

2021 ◽  
Author(s):  
Lina Felsner ◽  
Philipp Roser ◽  
Andreas Maier ◽  
Christian Riess
Keyword(s):  
Iterative Reconstruction ◽  
Phase Contrast ◽  
Reconstruction Algorithm ◽  
General Purpose ◽  
Contrast Imaging ◽  
X Ray ◽  
Iterative Reconstruction Algorithm ◽  
Inhomogeneous Phase ◽  
The Cost ◽  
Residual Errors

Abstract Purpose In Talbot–Lau X-ray phase contrast imaging, the measured phase value depends on the position of the object in the measurement setup. When imaging large objects, this may lead to inhomogeneous phase contributions within the object. These inhomogeneities introduce artifacts in tomographic reconstructions of the object. Methods In this work, we compare recently proposed approaches to correct such reconstruction artifacts. We compare an iterative reconstruction algorithm, a known operator network and a U-net. The methods are qualitatively and quantitatively compared on the Shepp–Logan phantom and on the anatomy of a human abdomen. We also perform a dedicated experiment on the noise behavior of the methods. Results All methods were able to reduce the specific artifacts in the reconstructions for the simulated and virtual real anatomy data. The results show method-specific residual errors that are indicative for the inherently different correction approaches. While all methods were able to correct the artifacts, we report a different noise behavior. Conclusion The iterative reconstruction performs very well, but at the cost of a high runtime. The known operator network shows consistently a very competitive performance. The U-net performs slightly worse, but has the benefit that it is a general-purpose network that does not require special application knowledge.

scholarly journals Grating based X-ray phase contrast CT imaging with iterative reconstruction algorithm

2017 ◽  
Vol 66 (5) ◽  
pp. 054202
Author(s):  
Qi Jun-Cheng ◽  
Chen Rong-Chang ◽  
Liu Bin ◽  
Chen Ping ◽  
Du Guo-Hao ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Phase Contrast ◽  
Reconstruction Algorithm ◽  
Ct Imaging ◽  
X Ray ◽  
Iterative Reconstruction Algorithm ◽  
Contrast Ct

An ART iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging

2009 ◽  
Vol 33 (11) ◽  
pp. 975-980 ◽  
Author(s):  
Wang Zhen-Tian ◽  
Zhang Li ◽  
Huang Zhi-Feng ◽  
Kang Ke-Jun ◽  
Chen Zhi-Qiang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithm ◽  
Iterative Reconstruction Algorithm ◽  
Diffraction Enhanced Imaging ◽  
Enhanced Imaging

scholarly journals Low-dose computed tomography with 4th-generation iterative reconstruction algorithm in assessment of oncologic patients

2017 ◽  
Vol 9 (10) ◽  
pp. 423-430 ◽  
Author(s):  
Davide Ippolito ◽  
Alessandra Silvia Casiraghi ◽  
Cammillo Talei Franzesi ◽  
Davide Fior ◽  
Franca Meloni ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Iterative Reconstruction Algorithm ◽  
Oncologic Patients ◽  
Low Dose Computed Tomography

scholarly journals Statistical iterative reconstruction algorithm for X-ray phase-contrast CT

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Dieter Hahn ◽  
Pierre Thibault ◽  
Andreas Fehringer ◽  
Martin Bech ◽  
Thomas Koehler ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Phase Contrast ◽  
Reconstruction Algorithm ◽  
X Ray ◽  
Iterative Reconstruction Algorithm ◽  
Statistical Iterative Reconstruction ◽  
Contrast Ct
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