title: Small-window parametric imaging based on information entropy for ultrasound tissue characterization.
abstract: Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.
authors.affiliation: Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.|Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.|Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
authors.forename: Po-Hsiang
authors.initials: PH
authors.lastname: Tsui
authors.affiliation: Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan.
authors.forename: Chin-Kuo
authors.initials: CK
authors.lastname: Chen
authors.affiliation: Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Wen-Hung
authors.initials: WH
authors.lastname: Kuo
authors.affiliation: Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan.|Taiwan Breast Cancer Foundation, Taipei, Taiwan.
authors.forename: King-Jen
authors.initials: KJ
authors.lastname: Chang
authors.affiliation: Ph.D. Program in Biomedical Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan.
authors.forename: Jui
authors.initials: J
authors.lastname: Fang
authors.affiliation: Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
authors.forename: Hsiang-Yang
authors.initials: HY
authors.lastname: Ma
authors.affiliation: Institute of Biomedical Engineering &Department of Engineering Science, University of Oxford, Oxford, UK.
authors.forename: Dean
authors.initials: D
authors.lastname: Chou
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D060787:Neoplasm Grading / Q000379:methods*; D012372:ROC Curve; D014463:Ultrasonography / Q000379:methods*