scholarly journals Progress on building a laboratory based x-ray phase contrast imaging computed tomography system

2019 ◽  
Author(s):  
Kyle R. Thompson ◽  
Amber Dagel ◽  
Ryan N. Goodner ◽  
Collin Epstein
Keyword(s):  
Computed Tomography ◽  
Phase Contrast ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
X Ray ◽  
Tomography System

Characterization of Osteoarthritic and Normal Human Patella Cartilage by Computed Tomography X-ray Phase-Contrast Imaging

2010 ◽  
Vol 45 (7) ◽  
pp. 437-444 ◽  
Author(s):  
Paola Coan ◽  
Fabian Bamberg ◽  
Paul C. Diemoz ◽  
Alberto Bravin ◽  
Kirsten Timpert ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Phase Contrast ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
X Ray ◽  
Normal Human

scholarly journals Influence of Data Acquisition Algorithms on X-Ray Phase Contrast Imaging Computed Tomography

2020 ◽  
Vol 3 (4) ◽  
Author(s):  
Collin J. C. Epstein ◽  
Ryan N. Goodner ◽  
R. Derek West ◽  
Kyle R. Thompson ◽  
Amber L. Dagel
Keyword(s):  
Computed Tomography ◽  
Data Acquisition ◽  
Phase Contrast ◽  
Three Dimensional ◽  
Quality Data ◽  
Phase Contrast Imaging ◽  
Ct Reconstruction ◽  
Contrast Imaging ◽  
X Ray ◽  
Phase Stepping

Abstract X-ray phase contrast imaging (XPCI) is a nondestructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating-based Talbot-Lau XPCI system to three-dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of the image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.

scholarly journals Convolutional neuronal networks combined with X-ray phase-contrast imaging for a fast and observer-independent discrimination of cartilage and liver diseases stages

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Johannes Stroebel ◽  
Annie Horng ◽  
Marco Armbruster ◽  
Alberto Mittone ◽  
Maximilian Reiser ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Classification Accuracy ◽  
Liver Diseases ◽  
Phase Contrast ◽  
Neuronal Networks ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
X Ray ◽  
Validation Test ◽  
Using Data

AbstractWe applied transfer learning using Convolutional Neuronal Networks to high resolution X-ray phase contrast computed tomography datasets and tested the potential of the systems to accurately classify Computed Tomography images of different stages of two diseases, i.e. osteoarthritis and liver fibrosis. The purpose is to identify a time-effective and observer-independent methodology to identify pathological conditions. Propagation-based X-ray phase contrast imaging WAS used with polychromatic X-rays to obtain a 3D visualization of 4 human cartilage plugs and 6 rat liver samples with a voxel size of 0.7 × 0.7 × 0.7 µm3 and 2.2 × 2.2 × 2.2 µm3, respectively. Images with a size of 224 × 224 pixels are used to train three pre-trained convolutional neuronal networks for data classification, which are the VGG16, the Inception V3, and the Xception networks. We evaluated the performance of the three systems in terms of classification accuracy and studied the effect of the variation of the number of inputs, training images and of iterations. The VGG16 network provides the highest classification accuracy when the training and the validation-test of the network are performed using data from the same samples for both the cartilage (99.8%) and the liver (95.5%) datasets. The Inception V3 and Xception networks achieve an accuracy of 84.7% (43.1%) and of 72.6% (53.7%), respectively, for the cartilage (liver) images. By using data from different samples for the training and validation-test processes, the Xception network provided the highest test accuracy for the cartilage dataset (75.7%), while for the liver dataset the VGG16 network gave the best results (75.4%). By using convolutional neuronal networks we show that it is possible to classify large datasets of biomedical images in less than 25 min on a 8 CPU processor machine providing a precise, robust, fast and observer-independent method for the discrimination/classification of different stages of osteoarthritis and liver diseases.

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.

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