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Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing.

Abstract : In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.
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https://www.hal.inserm.fr/inserm-00874944
Contributor : Christine Toumoulin <>
Submitted on : Tuesday, August 12, 2014 - 4:49:38 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM
Long-term archiving on: : Monday, November 17, 2014 - 3:58:41 PM

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Yang Chen, Xindao Yin, Luyao Shi, Huazhong Shu, Limin Luo, et al.. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing.. Physics in Medicine and Biology, IOP Publishing, 2013, 58 (16), pp.5803-20. ⟨10.1088/0031-9155/58/16/5803⟩. ⟨inserm-00874944⟩

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