Chinese Academy of Sciences, Institute of Automation, Intelligent Medical Research Center, Beijing 100190, China.
J Biomed Opt. 2012 Dec;17(12):126012. doi: 10.1117/1.JBO.17.12.126012.
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.
肝癌是全球最常见的恶性肿瘤之一。为了能够实现对小鼠小肝癌的非侵入性检测，我们提出了一种双模态层析的并行迭代收缩（PIS）算法。它结合了微计算机断层扫描和多视角生物发光成像，提供解剖结构和生物发光强度信息，以重建肿瘤的大小和位置。通过结合信号稀疏性的先验知识，我们将一些数学策略（包括特定的光滑凸逼近、迭代收缩算子和仿射子空间）与 PIS 方法相关联，从而保证了三维重建的准确性、效率和可靠性。然后，我们在植入珠子的小鼠上进行了体内实验，以验证该方法的可行性。结果表明，直径小于 3 毫米的微小病变可以定位，位置偏差不超过 1 毫米；计算效率比现有算法快一个到三个数量级；该方法对不同的正则化参数和 lp 范数具有鲁棒性。最后，我们将该算法应用于另一个 HCCLM3 原位异种移植小鼠模型的体内实验中，表明 PIS 方法有望应用于全身癌症检测的实际应用。
