Researchers and physicians rely on functional imaging to better understand tumors and other structures within the human body. However, imaging technologies that capture deep structures have poor resolution, while those that provide high resolution have limited depth. Positron emission tomography (PET), for example, reveals details deep within tissue but suffers from poor spatial resolution, with each voxel of a PET scan representing thousands or even millions of cells. In contrast, optical microscopy can deliver subcellular spatial resolution but is usually limited to a depth of tens of microns.
光学相干断层扫描（OCT）有助于弥合低分辨率/高渗透和高分辨率/低渗透技术之间的差距，通过在一到两毫米的深度下提供微米尺度的空间分辨率。传统OCT仅提供结构信息；它缺乏提供功能或分子信息的必要对比度。
我们在斯坦福大学的研究小组通过开发莫扎特（在细胞分辨率上无创的分子成像和非侵入性表征的分子成像和表征）来解决这一缺点，这种方法使用大型金纳米棒（LGNR）来改善OCT图像的对比度in vivo(Figure 1).
Using spectral processing algorithms developed in MATLAB®, we have analyzed the backscattering from these LGNRs to noninvasively image blood vessels as small as 20 μm in diameter and up to 750 μm deep in tumor tissue. The MATLAB algorithms in MOZART adaptively correct for dispersion and depth-related aberrations in every image, enabling us to identify individual capillaries as well as the locations and functional states of the valves that control fluid flow in lymphatic vessel networks. This information could help scientists detect and develop treatments for certain forms of cancer and blindness.
我们本可以使用Python开发算法®或另一种脚本语言，但我们选择了MATLAB，因为我们需要的所有功能和功能（基本矩阵操作，图像处理，信号处理等）都可以随时可用。此外，MATLAB包括方便的调试功能，可以更深入地探索我们的分析算法。
我们的MATLAB代码可用于下载.
Figure 1. Top: A conventional OCT image, showing the tissue structure of a tumor in the ear pinna of a live mouse. Bottom: A MOZART image of the same tissue region. The spectral analysis reveals LGNRs in the blood vessels, which are shown in yellow-green.
设置实验并收集数据
We demonstrated the capabilities of MOZART in two types of experiments in which we imaged the ears of living mice. For the first type of experiment, we injected LGNRs intravenously and imaged blood vessels in tumors and healthy tissue before and after the injection. In healthy subjects, the LGNRs circulate until they are processed by the liver and spleen. In subjects with tumors, the LGNRs tend to accumulate in the tumor due to the enhanced permeability and retention (EPR) effect.
For the second type of experiment, we injected the LGNRs subcutaneously (below the skin) and imaged their clearance into the lymph vessels. To study the performance of lymphatic valves, we sequentially injected two distinct types of LGNRs and then tracked each type as it passed through the lymphatic system. We were able to distinguish between the two types of LGNRs owing to their distinct scattering spectra and to our spectral algorithms, which were able to differentiate between them.
Figure 2. A junction in the lymph network. The white arrow on the left points to a valve between adjacent lymphangions. The unidirectional flow of the valve is indicated by the blue area to the right of the valve (showing the presence of one type of LGNR) and the green area to the left (showing a second type).
在两种类型的实验中，我们都使用宽阔的超发光二极管（SLD）来照亮组织和光谱仪以测量来自组织和LGNR的反向散射光，该光大约为100 nm×30 nm。光谱仪记录了一项干涉图，从样品中的每个点捕获了近红外散射光谱。
Developing Spectral Processing Algorithms
Once we had captured the raw interferogram data from thein vivoOCT scans, we developed algorithms to automate data processing. The algorithms reconstruct a conventional OCT image (like the one shown in Figure 1) from the recorded interferogram. They apply a discrete Fourier transform, implemented with matrix multiplication, to map the sample’S散射器，which include both LGNRs and organic tissue in the sample.
接下来，我们使用LGNR的独特散射光谱将LGNR与周围组织区分开。我们更新了算法，应用了Hann过滤器将记录的频谱分为两个频段（图3）。
在重建了这两个频段的图像并应用了来自图像处理工具箱™的中间过滤器以减少噪声之后，该算法通过执行直接的减法来比较这两个图像。当不存在LGNR时，从两个频段重建的图像实际上是相同的，并且此减法的结果接近零。但是，当存在LGNR时，这两个图像由于LGNR的独特光谱散射特性而显着差异。
图3.记录的干涉图分为两个频段。
We found that our ability to evaluate the differences between the images with this simple subtraction was hampered by two physical phenomena. The first was optical dispersion, which can be caused by optical elements in the OCT system and the sample itself. To compensate for dispersion, we added an iterative MATLAB algorithm that optimizes the alignment between the two reconstructed images for each sample we analyze.
The second issue we identified was due to depth-dependent spectral artifacts in the reconstructed difference image. These artifacts were primarily caused by chromatic aberrations introduced by the optical setup. To correct for this issue, we added an algorithm that measures the color gradient in a spectrally neutral region of the image and calculates a depth-dependent gain by fitting the gradient to a polynomial using the MATLAB polyfit function. This approach adaptively calibrates the depth-dependent spectral shift for each image.
After applying dispersion compensation and depth correction, the LGNRs showed up much more clearly because they significantly and consistently produced a higher spectral signal than the surrounding tissue.
Unexpected Results and Next-Generation Algorithms
我们的MATLAB算法产生的图像so clear that they revealed a number of details that we did not fully anticipate seeing when we began our research. For example, we discovered instances in which LGNRs moved from blood vessels to lymph vessels. In a healthy subject such movement is unexpected, but this effect may have been due to porous blood vessels or an immune response. We were also surprised by how well we could see the lymph vessels, including the valves that control one-directional lymph flow in healthy subjects. To our knowledge, researchers have been unable to visualize lymph vessels and their functionality in this way until now.
We are currently enhancing our algorithms to support studies in which the LGNRs are coated with antibodies or peptides so that they target specific proteins in tumors. In our current studies, LGNRs are in motion as they flow through blood and lymph vessels. This flow makes it easy for our algorithms to average out noise. However, in studies where targeting is used, the LGNRs will remain localized in the tumor, making noise a more significant problem. We have already begun improving the noise-reduction capabilities of our next-generation algorithms in MATLAB to better visualize static LGNRs in preparation for future molecular targeting studies.
Acknowledgement
我们非常感谢同事Debasish Sen博士的工作及其对这项研究的贡献。
