生物发光断层成像(BLT)是一种高灵敏度、高特异性的光学分子影像技术，可根据探测到的生物体表光强来重建发光光源在生物体内的三维分布。由于生物体表面测得的光强信息有限，光源重建面临巨大的挑战。为了在有限的测量条件下获得更精确的重建光源，结合BLT中光源稀疏分布的特征，将重建问题转化为L1范数优化问题，并采用迭代支撑检测(ISD)算法实现快速重建，该算法交替执行支撑集检测和信号重构两个模块，直至重建精度达到要求。为了评估ISD算法的光源定位能力，设计数字鼠仿真实验，并与三种典型的稀疏重建算法比较。仿真结果表明ISD算法对于单光源和双光源目标均可以实现准确的重建。

Bioluminescence tomography (BLT) is an optical molecular imaging technique with high sensitivity and specificity, and it can provide three-dimensional distribution of the internal source according to the detected boundary light intensity. However, the source reconstruction with limited measurements is a challenging problem. We take advantage of the sparsity feature of bioluminescence source and formulate the BLT source reconstruction into an L1 norm minimization problem. The iterative detection support (ISD) algorithm is used to realize more accurate and faster reconstruction with limited data. The reconstruction algorithm alternatively runs two operations, i.e., support detection and signal reconstruction, until the solution meets the accuracy requirement. Simulations based on a digital mouse are designed to assess the location ability of the ISD method, and the result is compared with those of other three representative sparse algorithms. The simulation results show that the ISD method can achieve accurate reconstruction in both single-source and double-source cases.