Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. - Université de Rennes Accéder directement au contenu
Article Dans Une Revue Physics in Medicine and Biology Année : 2013

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

Résumé

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.
Fichier principal
Vignette du fichier
paper_pmbR2.pdf (1.02 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inserm-00874944 , version 1 (12-08-2014)

Identifiants

Citer

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, 2013, 58 (16), pp.5803-20. ⟨10.1088/0031-9155/58/16/5803⟩. ⟨inserm-00874944⟩
387 Consultations
893 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More