Dehghani Hamid, Guggenheim James A, Taylor Shelley L, Xu Xiangkun, Wang Ken Kang-Hsin
School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK.
Department of Medical Physics & Biomedical Engineering, University College London, London, UK.
Biomed Opt Express. 2018 Aug 9;9(9):4163-4174. doi: 10.1364/BOE.9.004163. eCollection 2018 Sep 1.
Bioluminescence imaging (BLI) is a non-contact, optical imaging technique based on measurement of emitted light due to an internal source, which is then often directly related to cellular activity. It is widely used in pre-clinical small animal imaging studies to assess the progression of diseases such as cancer, aiding in the development of new treatments and therapies. For many applications, the quantitative assessment of accurate cellular activity and spatial distribution is desirable as it would enable direct monitoring for prognostic evaluation. This requires quantitative spatially-resolved measurements of bioluminescence source strength inside the animal to be obtained from BLI images. This is the goal of bioluminescence tomography (BLT) in which a model of light propagation through tissue is combined with an optimization algorithm to reconstruct a map of the underlying source distribution. As most models consider only the propagation of light from internal sources to the animal skin surface, an additional challenge is accounting for the light propagation from the skin to the optical detector (e.g. camera). Existing approaches typically use a model of the imaging system optics (e.g. ray-tracing, analytical optical models) or approximate corrections derived from calibration measurements. However, these approaches are typically computationally intensive or of limited accuracy. In this work, a new approach is presented in which, rather than directly using BLI images acquired at several wavelengths, the spectral derivative of that data (difference of BLI images at adjacent wavelengths) is used in BLT. As light at similar wavelengths encounters a near-identical system response (path through the optics etc.) this eliminates the need for additional corrections or system models. This approach is applied to BLT with simulated and experimental phantom data and shown that the error in reconstructed source intensity is reduced from 49% to 4%. Qualitatively, the accuracy of source localization is improved in both simulated and experimental data, as compared to reconstruction using the standard approach. The outlined algorithm can widely be adapted to all commercial systems without any further technological modifications.
生物发光成像（BLI）是一种非接触式光学成像技术，基于对内部光源发出的光的测量，而这种光通常与细胞活性直接相关。它广泛应用于临床前小动物成像研究，以评估癌症等疾病的进展，有助于新治疗方法的开发。对于许多应用而言，准确的细胞活性和空间分布的定量评估是很有必要的，因为这将能够进行直接监测以进行预后评估。这需要从BLI图像中获取动物体内生物发光源强度的定量空间分辨测量值。这就是生物发光断层扫描（BLT）的目标，其中将光在组织中传播的模型与优化算法相结合，以重建潜在源分布的地图。由于大多数模型仅考虑光从内部源传播到动物皮肤表面，另一个挑战是考虑光从皮肤传播到光学探测器（如相机）的情况。现有方法通常使用成像系统光学模型（如光线追踪、解析光学模型）或从校准测量得出的近似校正。然而，这些方法通常计算量很大或精度有限。在这项工作中，提出了一种新方法，即在BLT中不直接使用在几个波长下采集的BLI图像，而是使用该数据的光谱导数（相邻波长下BLI图像的差值）。由于相似波长的光遇到几乎相同的系统响应（通过光学器件等的路径），这就无需额外的校正或系统模型。该方法应用于具有模拟和实验体模数据的BLT，并表明重建源强度的误差从49%降低到了4%。定性地说，与使用标准方法进行重建相比，在模拟和实验数据中源定位的准确性都有所提高。所概述的算法无需任何进一步的技术修改即可广泛适用于所有商业系统。
