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Volume 8, Issue 3
An Iterative Image Reconstruction for Differential X-ray Phase-contrast Computed Tomography

Fengrong Sun, Paul Babyn, Tetsuya Yuasa, Mingqiang Zhang, Feng Qin, Naoki Sunaguchi, Khan A. Wahid & Masami Ando

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 521-528.

Published online: 2015-08

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  • Abstract
The last decade saw the growth of the use of X-ray Phase-contrast Computed Tomography (PCT) imaging. Differential X-ray Phase-contrast CT (DPCT) has shown bright prospect for clinical use in recent years, but how to best reconstruct the phase-contrast tomographic image from incomplete or inconsistent projections remains challenging. We designed an iterative image reconstruction method for DPCT utilizing compressed sensing. This proposed method reconstructs the phase-contrast tomographic image of the refractive index decrement gradient with acceptable accuracy and image quality even when the projection data is undersampled or noisy. Real data experiments indicate this proposed method is accurate and practical and outperforms the analytic filtered back-projection reconstruction. This method shows potential for X-ray DPCT facilities in low dose tomography and fast acquisition imaging.
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@Article{JFBI-8-521, author = {}, title = {An Iterative Image Reconstruction for Differential X-ray Phase-contrast Computed Tomography}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {3}, pages = {521--528}, abstract = {The last decade saw the growth of the use of X-ray Phase-contrast Computed Tomography (PCT) imaging. Differential X-ray Phase-contrast CT (DPCT) has shown bright prospect for clinical use in recent years, but how to best reconstruct the phase-contrast tomographic image from incomplete or inconsistent projections remains challenging. We designed an iterative image reconstruction method for DPCT utilizing compressed sensing. This proposed method reconstructs the phase-contrast tomographic image of the refractive index decrement gradient with acceptable accuracy and image quality even when the projection data is undersampled or noisy. Real data experiments indicate this proposed method is accurate and practical and outperforms the analytic filtered back-projection reconstruction. This method shows potential for X-ray DPCT facilities in low dose tomography and fast acquisition imaging.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00125}, url = {http://global-sci.org/intro/article_detail/jfbi/4733.html} }
TY - JOUR T1 - An Iterative Image Reconstruction for Differential X-ray Phase-contrast Computed Tomography JO - Journal of Fiber Bioengineering and Informatics VL - 3 SP - 521 EP - 528 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00125 UR - https://global-sci.org/intro/article_detail/jfbi/4733.html KW - X-ray Phase-contrast Imaging KW - Computed Tomography (CT) KW - Compressed Sensing KW - Low Dose Tomography KW - Fast Acquisition Imaging AB - The last decade saw the growth of the use of X-ray Phase-contrast Computed Tomography (PCT) imaging. Differential X-ray Phase-contrast CT (DPCT) has shown bright prospect for clinical use in recent years, but how to best reconstruct the phase-contrast tomographic image from incomplete or inconsistent projections remains challenging. We designed an iterative image reconstruction method for DPCT utilizing compressed sensing. This proposed method reconstructs the phase-contrast tomographic image of the refractive index decrement gradient with acceptable accuracy and image quality even when the projection data is undersampled or noisy. Real data experiments indicate this proposed method is accurate and practical and outperforms the analytic filtered back-projection reconstruction. This method shows potential for X-ray DPCT facilities in low dose tomography and fast acquisition imaging.
Fengrong Sun, Paul Babyn, Tetsuya Yuasa, Mingqiang Zhang, Feng Qin, Naoki Sunaguchi, Khan A. Wahid & Masami Ando. (2019). An Iterative Image Reconstruction for Differential X-ray Phase-contrast Computed Tomography. Journal of Fiber Bioengineering and Informatics. 8 (3). 521-528. doi:10.3993/jfbim00125
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