Back to Search Start Over

A Feasibility Study of Low-Dose Single-Scan Dual-Energy Cone-Beam CT in Many-View Under-Sampling Framework.

Authors :
Lee, Donghyeon
Lee, Jiseoc
Kim, Hyoyi
Lee, Taewon
Soh, Jeongtae
Park, Miran
Kim, Changhwan
Lee, Yeon Ju
Cho, Seungryong
Source :
IEEE Transactions on Medical Imaging. Dec2017, Vol. 36 Issue 12, p2578-2587. 10p.
Publication Year :
2017

Abstract

A single-scan dual-energy low-dose cone-beam CT (CBCT) imaging technique that exploits a multi-slit filter is proposed in this paper. The multi-slit filter installed between the x-ray source and the scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data. Since the high-energy projection data suffer from a substantially high noise level due to the beam filtration, we have developed a new algorithm that exploits the joint sparsity between the low- and high-energy CT images for image reconstruction of the high-energy CT image. The proposed image reconstruction algorithm uses a gradient magnitude image (GMI) of the low-energy CT image by regularizing the difference of GMIs of the low- and high-energy CT images to be minimized. The feasibility of the proposed technique has been demonstrated by the use of various phantoms in the experimental CBCT setup. Furthermore, based on the proposed dual-energy imaging, a material differentiation was performed and its potential utility has been shown. The proposed imaging technique produced promising results for its potential application to a low-dose single-scan dual-energy CBCT. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
02780062
Volume :
36
Issue :
12
Database :
Academic Search Index
Journal :
IEEE Transactions on Medical Imaging
Publication Type :
Academic Journal
Accession number :
126527324
Full Text :
https://doi.org/10.1109/TMI.2017.2765760