University of California, Merced, School of Engineering, Merced, California, United States.
University of California, Davis, Department of Biomedical Engineering, Davis, California, United States.
J Biomed Opt. 2017 May 1;22(5):55001. doi: 10.1117/1.JBO.22.5.055001.
Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and ill-conditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. An approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach’s feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false-positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method.
荧光分子断层成像(FMT)是一种重要的活体成像方式,可用于可视化小动物体内的生理和病理过程。然而,由于深层组织中强光学散射的存在,FMT 重建存在不适定性和不适定条件问题,导致空间分辨率较差。众所周知,通过在 FMT 重建中应用结构引导,可以显著改善 FMT 图像质量。本文提出了一种使用核方法将解剖学信息引入 FMT 重建的方法。与传统方法将解剖学信息与拉普拉斯型正则化矩阵相结合不同,所提出的方法将解剖学引导引入 FMT 的投影模型中。该方法的主要优点是不需要对解剖图像中的目标进行分割。已经进行了数值模拟和体模实验来验证所提出方法的可行性。数值模拟结果表明,所提出的核方法可以分离边缘到边缘距离为 1 毫米的两个 FMT 目标,并且对解剖图像中的假阳性引导和非均匀性具有鲁棒性。对于具有两个 FMT 目标的体模实验,核方法成功地重建了两个目标,这进一步验证了所提出的核方法。