title: Detection of mouse liver cancer via a parallel iterative shrinkage method in hybrid optical/microcomputed tomography imaging.
abstract: Liver cancer is one of the most common malignant tumors worldwide. In order to enable the noninvasive detection of small liver tumors in mice, we present a parallel iterative shrinkage (PIS) algorithm for dual-modality tomography. It takes advantage of microcomputed tomography and multiview bioluminescence imaging, providing anatomical structure and bioluminescence intensity information to reconstruct the size and location of tumors. By incorporating prior knowledge of signal sparsity, we associate some mathematical strategies including specific smooth convex approximation, an iterative shrinkage operator, and affine subspace with the PIS method, which guarantees the accuracy, efficiency, and reliability for three-dimensional reconstruction. Then an in vivo experiment on the bead-implanted mouse has been performed to validate the feasibility of this method. The findings indicate that a tiny lesion less than 3 mm in diameter can be localized with a position bias no more than 1 mm; the computational efficiency is one to three orders of magnitude faster than the existing algorithms; this approach is robust to the different regularization parameters and the lp norms. Finally, we have applied this algorithm to another in vivo experiment on an HCCLM3 orthotopic xenograft mouse model, which suggests the PIS method holds the promise for practical applications of whole-body cancer detection.
authors.affiliation: Chinese Academy of Sciences, Institute of Automation, Intelligent Medical Research Center, Beijing 100190, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Wu
authors.forename: Kai
authors.initials: K
authors.lastname: Liu
authors.forename: Qian
authors.initials: Q
authors.lastname: Zhang
authors.forename: Zhenwen
authors.initials: Z
authors.lastname: Xue
authors.forename: Yongbao
authors.initials: Y
authors.lastname: Li
authors.forename: Nannan
authors.initials: N
authors.lastname: Ning
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
authors.forename: Xingde
authors.initials: X
authors.lastname: Li
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
mesh_terms: D000465:Algorithms; D000818:Animals; D045744:Cell Line, Tumor; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008113:Liver Neoplasms / Q000175:diagnosis*; D008163:Luminescent Measurements / Q000379:methods*; D051379:Mice; D008819:Mice, Nude; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D041622:Tomography, Optical / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*