title: Total variation based DCE-MRI decomposition by separating lesion from background for time-intensity curve estimation.
abstract: OBJECTIVE
  This study aims to obtain the accurate time intensity curve (TIC) of a dynamic contrast-enhanced magnetic resonance image (DCE-MRI) by eliminating the normal tissue enhancement and obtaining pure lesion information. The TIC of DCE-MRI is sometimes distorted because of the influence of normal tissue. In this paper, a new tracer-kinetic modeling based on total variation (DC-TV) is proposed to address this problem by decomposing the DCE-MRI into the normal tissue image and the lesion image. As TIC generation is not standardized and a credible program is expected, an accurate TIC generation is presented in this paper.
  
  
  METHODS
  We propose a new tracer-kinetic model DC-TV to decompose the lesion region in breast DCE-MRIs. The original image is decomposed into a normal tissue image and a lesion image to obtain the pure lesion enhancement information. The acquired lesion images are smooth and correspond to the diffusion of the contrast agent in the lesion. The normal tissue image sequences are stable and correspond to the enhanced normal tissue. To speed up the computational process of our convergent algorithm, the split Bregman iteration algorithm is applied. To compare the algorithm results, images generated by decomposed methods without normal tissue constraint based on total variation are compared with those generated by our method. The performance of the proposed method is evaluated by the correlation of normal tissue images with the lesion classification accuracy of lesion images.
  
  
  RESULTS
  Ninety-eight lesions, including 40 benign and 58 malignant, are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinoma in situ, tubular carcinoma, phyllodes tumor, hyperplasia, and fibroadenoma, among others. The area under the ROC for the pure lesion enhancement images acquired by DC-TV is greater than that acquired by the original DCE-MRIs.
  
  
  CONCLUSIONS
  The pure enhancement information from the original breast DCE-MRI lesions can be successfully obtained using our DC-TV. The TICs based on the acquired pure enhancement information closely conform to three-time-point model, which is a classic diagnosis rule. The experiment shows that DC-TV provide a credible TIC generation program.
authors.affiliation: Department of Biomedical Engineering, Dalian University of Technology, Dalian, 116024, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Information Science & Engineering and Institute of Life Sciences, Shandong Normal University, Jinan, 250014, China.
authors.forename: Yuanjie
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZheng, 518000, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Liang
authors.affiliation: Department of Biomedical Engineering, Dalian University of Technology, Dalian, 116024, China.
authors.forename: Pinpin
authors.initials: P
authors.lastname: Tang
authors.affiliation: Department of Biomedical Engineer, Dalian University of Technology, Dalian, 116024, China.
authors.forename: Fuquan
authors.initials: F
authors.lastname: Ren
authors.affiliation: Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, 116027, China.
authors.forename: Lina
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Second Affiliated Hospital, Dalian Medical University, Dalian, 116027, China.
authors.forename: Zuowei
authors.initials: Z
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D001940:Breast; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003287:Contrast Media; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D003557:Phyllodes Tumor / Q000000981:diagnostic imaging*
chemical_list: D003287:Contrast Media
keywords: Bregman iteration; DCE-MRI; decomposition algorithm; time intensity curve; tracer-kinetic model