Multienergy Cone-Beam Computed Tomography Reconstruction with a Spatial Spectral Nonlocal Means Algorithm



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Society for Industrial and Applied Mathematics Publications


Multienergy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multienergy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral nonlocal means (NLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type and NLM reconstruction methods. © 2018 Society for Industrial and Applied Mathematics.


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Cone-Beam Computed Tomography, Diagnosis, Image processing, Image reconstruction, Diagnostic imaging, X-rays, Tomography

The work of the authors was supported in part by grant R21EB017978 from the National Institute of Health, grant RP160661 from the Cancer Prevention and Research Institute of Texas, grant 81571771 from National Natural Science Foundation of China, and grant 2015BAI01B10 from Ministry of Science and Technology of China.


©2018 Society for Industrial and Applied Mathematics