IEEE Trans Med Imaging. 2017 May;36(5):1086-1093. doi: 10.1109/TMI.2016.2646518. Epub 2016 Dec 29.
Diffuse Optical Tomography commonly neglects or assumes as insignificant the presence of optically clear regions in biological tissues, estimating their contribution as a small perturbation to light transport. The inaccuracy introduced by this practice is examined in detail in the context of a complete, based on realistic geometry, virtual fluorescence Diffuse Optical Tomography experiment where a mouse head is imaged in the presence of cerebral spinal fluid. Despite the small thickness of such layer, we point out that an error is introduced when neglecting it from the model with possibly reduction in the accuracy of the reconstruction and localization of the fluorescence distribution within the brain. The results obtained in the extensive study presented here suggest that fluorescence diffuse neuroimaging studies can be improved in terms of quantitative and qualitative reconstruction by accurately taking into account optically transparent regions especially in the cases where the reconstruction is aided by the prior knowledge of the structural geometry of the specimen. Thus, this has only recently become an affordable choice, thanks to novel computation paradigms that allow to run Monte Carlo photon propagation on a simple graphic card, hence speeding up the process a thousand folds compared to CPU-based solutions.
漫射光学断层成像通常忽略或假设生物组织中存在光学透明区域，或者认为这些区域的存在微不足道，将其对光传输的贡献估计为小的扰动。在一个完整的、基于真实几何形状的虚拟荧光漫射光学断层成像实验中，详细研究了这种做法的不准确性，该实验对存在脑脊液的老鼠头部进行了成像。尽管这种层的厚度很小，但我们指出，当从模型中忽略它时，会引入一个误差，这可能会降低重建的准确性，并导致大脑内荧光分布的定位不准确。本文提出的广泛研究结果表明，通过准确考虑光透明区域，特别是在重建过程中借助样本结构几何形状的先验知识的情况下，荧光漫射神经成像研究可以在定量和定性重建方面得到改善。因此，由于新的计算范例允许在简单的图形卡上运行蒙特卡罗光子传播，从而使该过程的速度比基于 CPU 的解决方案快一千倍，因此最近才成为一种可行的选择。
