Signal and Image Processing Institute, Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA 90089, USA.
Phys Med Biol. 2012 Mar 21;57(6):1459-76. doi: 10.1088/0031-9155/57/6/1459. Epub 2012 Mar 5.
Fluorescence molecular tomography (FMT) is an imaging modality that exploits the specificity of fluorescent biomarkers to enable 3D visualization of molecular targets and pathways in vivo in small animals. Owing to the high degree of absorption and scattering of light through tissue, the FMT inverse problem is inherently ill-conditioned making image reconstruction highly susceptible to the effects of noise and numerical errors. Appropriate priors or penalties are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, fluorescent probes are locally concentrated within specific areas of interest (e.g., inside tumors). The commonly used L(2) norm penalty generates the minimum energy solution, which tends to be spread out in space. Instead, we present here an approach involving a combination of the L(1) and total variation norm penalties, the former to suppress spurious background signals and enforce sparsity and the latter to preserve local smoothness and piecewise constancy in the reconstructed images. We have developed a surrogate-based optimization method for minimizing the joint penalties. The method was validated using both simulated and experimental data obtained from a mouse-shaped phantom mimicking tissue optical properties and containing two embedded fluorescent sources. Fluorescence data were collected using a 3D FMT setup that uses an EMCCD camera for image acquisition and a conical mirror for full-surface viewing. A range of performance metrics was utilized to evaluate our simulation results and to compare our method with the L(1), L(2) and total variation norm penalty-based approaches. The experimental results were assessed using the Dice similarity coefficients computed after co-registration with a CT image of the phantom.
荧光分子断层成像（FMT）是一种成像方式，利用荧光生物标志物的特异性，能够在小动物体内对分子靶标和途径进行 3D 可视化。由于光在组织中的高度吸收和散射，FMT 反问题本质上是病态的，使得图像重建非常容易受到噪声和数值误差的影响。需要适当的先验或惩罚项来促进重建，并将搜索空间限制在特定的解集内。通常，荧光探针在特定的感兴趣区域（例如，肿瘤内部）中局部集中。常用的 L(2)范数惩罚项生成最小能量解，该解在空间上往往会扩散。相反，我们在这里提出了一种涉及 L(1)范数和全变差范数惩罚项的组合的方法，前者用于抑制虚假的背景信号并强制稀疏性，后者用于保持重建图像中的局部平滑性和分段恒定性。我们已经开发了一种基于代理的优化方法来最小化联合惩罚项。该方法使用从模拟组织光学特性并包含两个嵌入式荧光源的鼠形体模获得的模拟和实验数据进行了验证。荧光数据是使用具有 EMCCD 相机进行图像采集和锥形镜进行全面观察的 3D FMT 设备收集的。利用一系列性能指标来评估我们的模拟结果，并将我们的方法与 L(1)、L(2)和全变差范数惩罚项方法进行比较。使用与体模 CT 图像配准后计算的骰子相似系数评估实验结果。
