Iterative image reconstruction using modified non-local means filtering for limited-angle computed tomography |
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| Affiliation: | 1. Laboratory of Radiation Biology, Joint Institute for Nuclear Research, 6 Joliot-Curie St., 141980 Dubna, Moscow Region, Russia;2. Biophysics Department, “Dubna” University, 19 Universitetskaya St., 141980 Dubna, Moscow Region, Russia;3. Academy of Sciences of Moldova, Chișinău, Moldova;4. Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia;5. Zakusov Institute of Pharmacology, Moscow, Russia;6. Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia;1. Key Laboratory for Earth Surface Processes, Department of Geography, Peking University, Beijing 100871, China;2. Institute of Ocean Research, Peking University, Beijing 100871, China;3. Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China;4. Institute of Heavy Ion Physics & State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China;5. Keck Carbon Cycle AMS Laboratory, Department of Earth System Science, University of California, Irvine, CA 92697-3100, USA;1. Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Germany;2. Siemens Healthcare, Division CT, Forchheim, Germany |
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| Abstract: | PurposeLimited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed.MethodsSince the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV.ResultsSimulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm.ConclusionThis paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods. |
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| Keywords: | Computed tomography Image reconstruction Limited-angle projection Non-local means filtering Artifacts nearby edges |
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