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Author Xu, Q. ♦ Xing, L. ♦ Xiong, G. ♦ Elmore, K. ♦ Min, J.
Source United States Department of Energy Office of Scientific and Technical Information
Content type Text
Language English
Subject Keyword APPLIED LIFE SCIENCES ♦ BIOMEDICAL RADIOGRAPHY ♦ COMPUTERIZED TOMOGRAPHY ♦ DATASETS ♦ IMAGES ♦ ITERATIVE METHODS ♦ LEARNING ♦ RADIATION DOSES
Abstract Purpose: DECT collects two sets of projection data under higher and lower energies. With appropriates composition methods on linear attenuation coefficients, quantitative information about the object, such as density, can be obtained. In reality, one of the important problems in DECT is the radiation dose due to doubled scans. This work is aimed at establishing a dictionary learning based reconstruction framework for DECT for improved image quality while reducing the imaging dose. Methods: In our method, two dictionaries were learned respectively from the high-energy and lowenergy image datasets of similar objects under normal dose in advance. The linear attenuation coefficient was decomposed into two basis components with material based composition method. An iterative reconstruction framework was employed. Two basis components were alternately updated with DECT datasets and dictionary learning based sparse constraints. After one updating step under the dataset fidelity constraints, both high-energy and low-energy images can be obtained from the two basis components. Sparse constraints based on the learned dictionaries were applied to the high- and low-energy images to update the two basis components. The iterative calculation continues until a pre-set number of iteration was reached. Results: We evaluated the proposed dictionary learning method with dual energy images collected using a DECT scanner. We re-projected the projection data with added Poisson noise to reflect the low-dose situation. The results obtained by the proposed method were compared with that obtained using FBP based method and TV based method. It was found that the proposed approach yield better results than other methods with higher resolution and less noise. Conclusion: The use of dictionary learned from DECT images under normal dose is valuable and leads to improved results with much lower imaging dose.
ISSN 00942405
Educational Use Research
Learning Resource Type Article
Publisher Date 2015-06-15
Publisher Place United States
Journal Medical Physics
Volume Number 42
Issue Number 6


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