scholarly journals A novel iterative reconstruction algorithm based on discriminant adaptive-weighted TV regularization for fibrous biological tissues using in-line X-ray phase-contrast imaging

2021 ◽  
Author(s):  
Mengting Zheng ◽  
Yuqing Zhao ◽  
Shuo Han ◽  
Dongjiang Ji ◽  
Yimin Li ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Phase Contrast ◽  
Reconstruction Algorithm ◽  
Biological Tissues ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
X Ray ◽  
Iterative Reconstruction Algorithm ◽  
Tv Regularization ◽  
Fibrous Biological Tissues

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.

Evaluation of a new reconstruction algorithm for x-ray phase-contrast imaging

2016 ◽  
Author(s):  
Maria Seifert ◽  
Christian Hauke ◽  
Florian Horn ◽  
Sebastian Lachner ◽  
Veronika Ludwig ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Reconstruction Algorithm ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
X Ray

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

The Research of Cancer Imaging by X-Ray In-Line Phase Contrast Imaging Technique

2013 ◽  
Vol 718-720 ◽  
pp. 2099-2102
Author(s):  
Qiang Tao ◽  
Shu Qian Luo
Keyword(s):  
Nude Mice ◽  
Phase Contrast ◽  
Soft Tissues ◽  
Ccd Camera ◽  
Biological Tissues ◽  
Image Resolution ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
Noninvasive Technique ◽  
X Ray

The hard X-ray in-line phase contrast imaging (HXILPCI) is a phase contrast technique that generates excellent contrast of biological soft tissues compared to conventional X-ray absorption radiography. We explore the application of HXILPCI in the diagnosis of gastric cancer and pancreatic cancer. These nude mice cancer samples were checked by HXILPCI to obtain projection contrast images of 9μm image resolution with CCD camera. The texture extraction was based on gray level co-occurrence matrix (GLCM). The corresponding morphological features of abnormal and normal tissues are analyzed. The produced phase contrast images of nude mice cancer samples show clearly biological tissues architectures and the size of cancer. The paper results show that HXILPCI can be a potential noninvasive technique to diagnose early cancer.

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

Image Quality Analysis of X-ray Grating Interferometer Using Integrating-bucket Method

2020 ◽  
Vol 64 (2) ◽  
pp. 20503-1-20503-5
Author(s):  
Faiz Wali ◽  
Shenghao Wang ◽  
Ji Li ◽  
Jianheng Huang ◽  
Yaohu Lei ◽  
...  
Keyword(s):  
Image Quality ◽  
Phase Contrast ◽  
Quality Analysis ◽  
Phase Contrast Imaging ◽  
Phase Image ◽  
Contrast Imaging ◽  
High Quality ◽  
Image Quality Analysis ◽  
X Ray ◽  
Grating Interferometer

Abstract Grating-based x-ray phase-contrast imaging has the potential to enhance image quality and provide inner structure details non-destructively. In this work, using grating-based x-ray phase-contrast imaging system and employing integrating-bucket method, the quantitative expressions of signal-to-noise ratios due to photon statistics and mechanical error are analyzed in detail. Photon statistical noise and mechanical error are the main sources affecting the image noise in x-ray grating interferometry. Integrating-bucket method is a new phase extraction method translated to x-ray grating interferometry; hence, its image quality analysis would be of great importance to get high-quality phase image. The authors’ conclusions provide an alternate method to get high-quality refraction signal using grating interferometer, and hence increases applicability of grating interferometry in preclinical and clinical usage.

Numerical analysis of shift error in X-ray phase contrast imaging for large field of view

2017 ◽  
Vol 34 (1) ◽  
pp. 8
Author(s):  
Jianheng Huang ◽  
Yaohu Lei ◽  
Xin Liu ◽  
Jinchuan Guo ◽  
Ji Li ◽  
...  
Keyword(s):  
Numerical Analysis ◽  
Phase Contrast ◽  
Field Of View ◽  
Phase Contrast Imaging ◽  
Large Field ◽  
Contrast Imaging ◽  
X Ray
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