title: 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.
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, 210096, Nanjing, People's Republic of China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.forename: Xindao
authors.initials: X
authors.lastname: Yin
authors.forename: Luyao
authors.initials: L
authors.lastname: Shi
authors.forename: Huazhong
authors.initials: H
authors.lastname: Shu
authors.forename: Limin
authors.initials: L
authors.lastname: Luo
authors.forename: Jean-Louis
authors.initials: JL
authors.lastname: Coatrieux
authors.forename: Christine
authors.initials: C
authors.lastname: Toumoulin
mesh_terms: D000008:Abdominal Neoplasms / Q000000981:diagnostic imaging*; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011829:Radiation Dosage; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000009:adverse effects / Q000379:methods*