Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2889-2892. doi: 10.1109/EMBC.2016.7591333.
Cerenkov luminescence tomography (CLT) is a powerful imaging technique that allows dynamically and three-dimensionally resolving the metabolic process of radiopharmaceuticals. It uses optical method to detect radiopharmaceuticals with low cost and high sensitivity. However, because of the strong absorption and scatter of biological tissues, the reconstruction of CLT is always converted to an ill-posed linear system which is hard to solve. An accurate and fast reconstruct algorithm becomes a current issue. The traditional reconstruction algorithm based on l2 norm regularization is too smooth and with low accuracy. Some novel sparse reconstruction algorithm has satisfying accuracy and convergence rate, but lose its accuracy for multi-source situation. In this work, a novel CLT method based on forward-backward greedy algorithm is proposed to solve the ill-posed problem. Digital simulations and in vivo experiment were conducted to test the algorithm. The reconstruct results were compared with traditional orthogonal matching pursuit (OMP) algorithm and Tikhonov algorithm. Both the Digital simulations and in vivo experiment show that this approach can reconstruct the distribution of radiopharmaceuticals effectively and accurately.
切伦科夫发光断层扫描（CLT）是一种强大的成像技术，可动态、三维地解析放射性药物的代谢过程。它采用光学方法检测放射性药物，成本低且灵敏度高。然而，由于生物组织的强吸收和散射，CLT的重建总是转化为一个难以求解的不适定线性系统。一种准确且快速的重建算法成为当前的一个问题。基于l2范数正则化的传统重建算法过于平滑且精度较低。一些新颖的稀疏重建算法具有令人满意的精度和收敛速度，但在多源情况下会失去其精度。在这项工作中，提出了一种基于前后向贪婪算法的新型CLT方法来解决不适定问题。进行了数字模拟和体内实验来测试该算法。将重建结果与传统的正交匹配追踪（OMP）算法和蒂霍诺夫算法进行了比较。数字模拟和体内实验均表明，该方法能够有效且准确地重建放射性药物的分布。
