肺动脉栓塞检测的可视中国人蒙特卡罗模拟分析 
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Pulmonary embolism (PE) is a clinical and pathophysiological syndrome in which an endogenous or exogenous embolus occludes the pulmonary artery or its branches, causing pulmonary circulatory impairment that can lead to severe pulmonary infarction, usually accompanied by cardiovascular disease. Currently, the most accurate method for detecting PE is digital subtraction angiography (DSA) of the pulmonary arteries. However, this detection method is invasive and can easily damage blood vessels or surrounding tissues and often causes additional complications. Therefore, non-invasive, real-time, and accurate detection of pulmonary artery hemodynamics is highly desirable. Near-infrared spectroscopy (NIRS) is a simple, rapid, portable, and relatively inexpensive technique for non-invasive deep-tissue hemodynamic monitoring, which is based on the spectral characteristics of oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb), which are characteristics used to quantify tissue hemoglobin oxygen saturation for disease measurement. In recent years, an increasing number of research groups have chosen to use NIRS to monitor human diseases, and it has been validated for use in studies involving many tissue sites in the body. However, the study of photons in the human pulmonary artery site has not been conducted in depth. Therefore, the feasibility of using NIRS to detect pulmonary artery embolism is investigated in this study.
In this study, we used 80 photographic images from the Visual Chinese Human (VCH) dataset slices to map the thoracic tissue region of the pulmonary artery from top to bottom to build a 436×420×80 voxel thoracic photographic model (Figure 1). Each voxel is defined as a 0.4 mm×0.4 mm×0.4 mm cube. The entire model is divided into eight parts based on tissue type, including skin, muscle, bone, subcutaneous fat, lung, pulmonary artery, arterial blood, and venous blood. The constructed model is then loaded into a software program developed by our team for Monte Carlo simulation of light propagation in Voxelated Media (MCVM). MCVM generates two files: a photon absorption data file and a photon escape data file at the body surface, from which we can calculate a series of evaluation metrics to better evaluate the relationship between photon propagation and the separation of the light source and detector (LSD) in the VCH chest model.
According to photon absorption data, photons pass successively through the skin, fat, muscle, adipose tissue, and pulmonary artery to the venous blood. The intensity when reaching the pulmonary artery is estimated to be 45 voxel (Figure 3), which is sufficient to demonstrate that photons can cross the thoracic cavity and penetrate the experimental region of interest. Four indicators are introduced to study the optimized light source and detector distance: differential optical path-length factor (DPF), partial path-length factor (PPF), the ratio of PPF to DPF, and the proportion of photon absorption in the pulmonary artery. DPF increases exponentially with the increase of LSD,whereas the three other indicators (PPF, the ratio of PPF to DPF, and proportion of photo absorption in the pulmonary artery) trend upward and then downward, with the peak occurring in the 2.8-3.1 cm LSD range (Figure 4). Based on the above results, we selected 2.9 cm to study the effects of different degrees of pulmonary embolism on SSD (Table 3) and on the intensity of light detected on the body surface (Figure 5). As pulmonary embolism progressively worsens, the percentage of SSD present in the pulmonary arteries decreases (0.0131%, 0.0129%, 0.0127%, and 0.0126% in healthy, Level Ⅰ, Level Ⅱ, and Level Ⅲ subjects, respectively). The SSD proportions in arteriovenous blood and lungs were then evaluated for different degrees of pulmonary embolism. The results indicate that the proportion of SSD in the lung and venous blood gradually decreases as the degree of pulmonary embolism increases. However, the proportion of SSD in the arterial blood is almost zero. These results suggest that venous blood and lung tissue influence the sensitivity of the total assay. A one-dimensional linear regression analysis was performed by standardizing the light intensity of the measurement in the normal state and different embolic levels as independent variables. The results indicate that the standardized light intensity decreases with increasing levels of pulmonary embolism. Thus, the level of pulmonary embolism can be approximated using the detected light intensity.
This study implements noninvasive optical monitoring of pulmonary artery embolism based on the Visible Chinese Human dataset and use of Monte Carlo simulations. By simulating light migration in human thoracic tissues, the number of absorbed photons and the light fluence distribution in the thoracic tissues are derived, and the evidence suggests that some of the photons can reach the pulmonary artery, indicating that the proposed technique is feasible. In addition, this study proposes an optimized separation distance between the light source and the detector and concludes that a separation of 2.8-3.1 cm may be optimal based on two path length factors, PPF and DPF. The effect of pulmonary artery embolism on photon detection is verified based on SSD estimates at the pulmonary artery and the output photon intensity. The results indicate that the SSD at the pulmonary artery decreases with increasing embolization. Furthermore, light intensity directly correlates with the degree of embolization, and the presence of embolization can significantly decrease the light intensity, therefore indicating the presence of emboli in the pulmonary artery. Overall, this study demonstrates the great potential of NIRS for continuous bedside monitoring of pulmonary embolism.
1 引言
肺动脉栓塞(PE)是一种临床和病理生理综合征，通常伴随心血管疾病发生[1]。肺动脉栓塞的发病率仅次于冠心病和心血管疾病中的高血压，死亡率排名第三，临床观察急性肺动脉栓塞死亡率不低于心肌梗死[2]。目前，最准确的肺动脉栓塞检测方法是肺动脉数字减影血管造影(DSA)[3]，准确率接近100%。然而，这种检查方法具有侵入性，易对血管或周围组织造成损伤。此外，检查后经常出现并发症，从而限制了DSA的应用。因此，肺动脉栓塞的无创监测仍是心血管医学面临的一项挑战。
近红外光谱(NIRS)[4]技术基于氧合血红蛋白(HbO2)和脱氧血红蛋白(Hb)的光谱特征[5]来定量测定组织中血红蛋白氧饱和度，从而实现疾病的诊断。自20世纪70年代发明以来，NIRS技术凭借非接触、无创和在线快速监测等优势被用于一系列临床人体研究[6]。NIRS技术可以检测到深度高达几厘米处的物体，足以穿透胸部到达肺动脉[7]。然而，研究人员尚未对人体肺动脉区域的光子迁移进行深入研究。无创光学技术在临床肺动脉栓塞检测方面具有广阔的应用前景。
之前的研究显示，在出现肺动脉栓塞时通常会首先发生血液动力学异常，其次是发生与低氧血症相关的特征性事件。血流动力学的变化为直接检测低氧血症下的肺栓塞提供了生理基础[8]。目前，近红外光在肺动脉中的迁移规律尚不清楚，因此无法确定光源和检测器的放置位置、光源和探测器的间距(LSD)以及空间灵敏度分布(SSD)。这也是实现肺动脉栓塞无创监测应用于临床亟待解决的问题。
肺动脉栓塞检测研究是在三维结构上模拟的，中国数字化可视人体(VCH)数据集[9-10]是开展模拟的最佳选择。VCH数据集是一种个性化的三维异质结构模型，在众多研究中发挥着重要作用[11-12]。精确的组织建模可以增加光子迁移在更广泛应用中的效用。本团队开发了一个用于模拟光子在体素化介质中传播的软件程序(MCVM)[11]，通过该程序可以更好地评估VCH胸部模型中光子传播与LSD之间的关系。目前，MCVM＋VCH已被成功用于研究脑[13]、乳腺[14]和心脏[8]中光子的传输分布，而且众多研究已证明了MCVM＋VCH方法的可靠性。
本课题组采用VCH数据集中的胸部组织模型来提高胸部结构建模的精度，并结合三维体素化介质进行蒙特卡罗模拟，建立量化、可视化的光子迁移人体胸部模型，并计算了一系列SSD、部分路径长度因子(PPF)[15]和差异路径长度因子(DPF)[16]。结果显示，肺栓塞程度与检测到的信号强度之间有很强的线性关系。本研究验证了无创光学检测肺动脉栓塞的潜力，为其临床落地提供了可行性。
2 研究方法
2.1 由VCH数据集构建的胸部模型
在VCH数据集建立的胸部模型上进行模拟。VCH数据集是全身冰冻切片的一组彩色照片，收集自一具病理变化较小的中国男性尸体(一具捐赠尸体)。在本研究中，使用VCH切片的80张摄影图像从上到下映射出肺动脉及其附近的胸部组织，首次建立了436×420×80个体素的胸部摄影模型(如
图 1. VCH胸部模型的描述。(a)由436×420×80个体素的三维矩阵构建而成的胸部模型；(b)胸部模型分为8部分
Fig. 1. Description of VCH thoracic model. (a) Thoracic model built with 3D matrix of 436×420×80 voxels; (b) thoracic model is divided into 8 tissue parts
2.2 蒙特卡罗模拟
蒙特卡罗模拟可用于推断光在胸部组织VCH数据集中的传播。针对三维体素化介质，本文使用的蒙特卡罗建模算法已在之前的文献中进行了详细描述[11]。具体来说，当光子包(或单个光子)进入组织后，它会随机散射或被组织介质持续吸收，直至死亡。对探测器采集的存活光子进行分析可以获得有价值的信息，如强度和传播路径长度[17]。在本研究中，光源被放置于第二和第三前肋骨之间，检测器被连接到光源水平左侧的皮肤上，轴方向如
图 2. X-Y-Z视场下胸部组织中的光通量分布。(a)X-Y平面上的光通量；(b)Y-Z平面上的光通量；(c)X-Z平面上的光通量；(d)本研究中使用的三维空间坐标系
Fig. 2. Light fluence distributions in thoracic tissue under X-Y-Z field of view. (a) Light fluence in X-Y plane; (b) light fluence in Y-Z plane; (c) light fluence in X-Z plane; (d) three-dimensional space coordinate system used in this study
表 1. 800 nm波长下胸部组织的光学特性
Table 1. Optical properties of thoracic tissue at 800 nm
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本研究通过改变动脉血和静脉血之间的关系来改变血氧饱和度，进而根据动脉血氧饱和度(SaO2)和静脉血氧饱和度(SvO2)的变化设计了三个疾病级别来模拟患者。实验共分为4组，包括轻度(Ⅰ级)、中度(Ⅱ级)和严重(Ⅲ级)疾病的三个患者组，以及正常人的对照组。参数详情如
表 2. SaO2 和 SvO2在每个实验组中的参数设置
Table 2. Parameters of SaO2 and SvO2 for each experimental group
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2.3 SSD
本研究的目的是实现肺动脉组织的血液动力学检测，通过对血液成分进行分析得出低氧血症的程度，从而间接达到监测肺动脉栓塞的效果。所以，在体表检测到来自肺动脉组织的光子是本研究的关键。在这需要用到一个指标，即SSD。SSD反映的是组织在检测光中贡献的变化量，可通过式(1)计算获得。
3 结果分析
3.1 光通量分布
如方法部分所述，本文绘制了光通量分布图，以指示光子在胸部组织中的存在(如
图 3. 光通量在组织深度上的变化。(a)光通量从皮肤到静脉血(第40个切片)的变化；(b)第40切片上的光通量分布
Fig. 3. Variation of light fluence in tissue depth. (a) Variation of light fluence from skin to venous blood (the 40th slice); (b) light fluence distribution on the 40th slice
3.2 优化源探距离
本研究还探索了光源到探测器的距离(以下简称“源探距离”，记为LSD)的最佳值，并引入4个指标来研究源探距离。这4个指标分别为DPF、PPF、PPF与DPF的比值、肺动脉吸收光子的占比。DPF是将光子传输的平均路径与LSD联系起来的比例因子，表征的是所有组织体素的影响；PPF表征的是某一组织体素的影响。因此，肺动脉组织的PPFpa(pa为pulmonary artery的简写)反映的是检测光对肺动脉组织的灵敏度，而PPFpa与DPF的比值表示的是肺动脉组织对检测光的贡献。如
图 4. DPF、PPFpa、PPFpa与DPF的比值随LSD的变化以及肺动脉吸收光子的占比。(a)DPF随LSD增大呈指数级增长；(b)PPFpa随LSD的变化；(c)PPFpa与DPF的比值随LSD的变化；(d)肺动脉吸收光子的占比
Fig. 4. Variation of DPF, PPFpa, ratio of PPFpa to DPF with LSD. (a) DPF exponential growth with the increase of LSD; (b) variation of PPFpa with LSD; (c) variation of ratio of PPFpa to DPF with LSD; (d) proportion of photon absorption in pulmonary artery
3.3 肺栓塞对SSD的影响
4次仿真输出的吸收文件取均值，得到SSD的矩阵数据(
表 3. 不同等级的肺动脉栓塞下每种组织的SSD占比
Table 3. Proportion of SSD of each tissue at different levels of pulmonary embolism
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3.4 肺动脉栓塞对检测光强度的影响
在上述结果中，最佳的源探距离约为2.9 cm。因此，将LSD设置为2.9 cm，以检测不同肺栓塞等级下体表处的光强。以正常状态下的检测光强度为标准进行归一化，然后以归一化的检测光强为因变量，不同的栓塞水平为自变量进行一元线性回归分析。分析结果表明，归一化光强度随着肺栓塞程度的增加而降低。因此，肺动脉组织部位的光子吸收随着肺栓塞程度的增加而增加。虽然更高水平的肺动脉栓塞吸收了更多的光子，但探测器仍能检测到逃逸的光子，证明了本文检测方法可用于肺栓塞检测。归一化光强的线性拟合函数为y＝－0.00871x＋1.002(R2＝0.998, p<10－5)。这种线性关系表明，用检测到的光强度可以近似估计肺栓塞水平[23]。
图 5. 归一化光强度与肺栓塞水平的线性拟合
Fig. 5. Linear fitting of normalized light intensity and pulmonary embolism level
4 结论
本课题组基于VCH数据集并采用蒙特卡罗模拟方法实现了肺动脉栓塞的无创光学监测。通过模拟光子在人体胸部组织中的迁移，得出胸腔组织中吸收光子的数量和光通量分布，这些证据表明部分光子可以到达肺动脉。此外，本课题组还研究了光源与探测器之间的优化距离，根据PPF和DPF两个路径长度因素，得出2.8～3.1 cm是最佳距离。通过肺动脉处的SSD和输出光子强度验证了肺动脉栓塞对光子检测的影响，结果表明，肺动脉处的SSD随着栓塞程度的增大而减小。研究还发现，检测光强度与栓塞程度直接相关，严重栓塞对应着较低的光强度，这可以作为肺动脉栓塞程度的反映。总的来说，本研究证实了NIRS技术在床边连续监测肺动脉栓塞方面的巨大潜力。
[1] Gribbin J, Hubbard R B, Le Jeune I, et al. Incidence and mortality of idiopathic pulmonary fibrosis and sarcoidosis in the UK[J]. Thorax, 2006, 61(11): 980-985.
[2] Bae K, Jeon K N, Cho S B, et al. Improved opacification of a suboptimally enhanced pulmonary artery in chest CT: experience using a dual-layer detector spectral CT[J]. American Journal of Roentgenology, 2018, 210(4): 734-741.
[3] 蒋晴. 宽体能谱CT成像检测肺动脉栓塞的应用研究[J]. 现代医用影像学, 2021, 30(10): 1863-1865, 1869.
Jiang Q. Application of wide-spectrum CT imaging in detecting pulmonary embolism[J]. Modern Medical Imageology, 2021, 30(10): 1863-1865, 1869.
[4] Boushel R, Langberg H, Olesen J, et al. Monitoring tissue oxygen availability with near infrared spectroscopy (NIRS) in health and disease[J]. Scandinavian Journal of Medicine & Science in Sports, 2001, 11(4): 213-222.
[5] 徐斌, 林漫漫, 姚辉璐, 等. 拉曼光谱技术测量单个红细胞的血红蛋白浓度[J]. 中国激光, 2016, 43(1): 0115003.
[6] Li T, Wang P B, Qiu L N, et al. Optimize illumination parameter of low-level laser therapy for hemorrhagic stroke by Monte Carlo simulation on visible human dataset[J]. IEEE Photonics Journal, 2018, 10(3): 6100409.
[7] 李昊澄, 宁旭, 谭立文, 等. 基于中国可视人数据集的时域有限差分法人体头部电磁仿真模型构建[J]. 中国医学物理学杂志, 2021, 38(7): 837-841.
Li H C, Ning X, Tan L W, et al. Construction of FDTD electromagnetic simulation model of human head based on Chinese visible human data set[J]. Chinese Journal of Medical Physics, 2021, 38(7): 837-841.
[8] Liu W C, Chen W J, Fang X, et al. Monte Carlo modeling of photon migration in realistic human thoracic tissues for noninvasive monitoring of cardiac hemodynamics[J]. Journal of Biophotonics, 2019, 12(12): 201900148.
[9] Zhang S X, Heng P A, Liu Z J, et al. The Chinese Visible Human (CVH) datasets incorporate technical and imaging advances on earlier digital humans[J]. Journal of Anatomy, 2004, 204(3): 165-173.
[10] 吴兰蕙, 林宇, 李婷. 基于中国可视化人体数据集人脑水肿光子传输Monte Carlo模拟[J]. 国际生物医学工程杂志, 2018, 41(2): 111-117.
Wu L H, Lin Y, Li T. Monte Carlo simulation of photon transmission in brain edema based on Chinese Visual Human data set[J]. International Journal of Biomedical Engineering, 2018, 41(2): 111-117.
[13] Pan B A, Huang C, Fang X, et al. Noninvasive and sensitive optical assessment of brain death[J]. Journal of Biophotonics, 2019, 12(3): 201800240.
[14] Dunn A W, Zhang Y, Mast D, et al. In-vitro depth-dependent hyperthermia of human mammary gland adenocarcinoma[J]. Materials Science and Engineering: C, 2016, 69: 12-16.
[15] Li T, Gong H, Luo Q M. Visualization of light propagation in visible Chinese human head for functional near-infrared spectroscopy[J]. Journal of Biomedical Optics, 2011, 16(4): 045001.
[16] 储宝, 黄尧, 倪敬书, 等. 基于GS-SVM的脑组织差分路径因子定量方法研究[J]. 中国激光, 2022, 49(5): 0507303.
[18] Nishidate I, Mizushima C, Yoshida K, et al. In vivo estimation of light scattering and absorption properties of rat brain using a single-reflectance fiber probe during cortical spreading depression[J]. Journal of Biomedical Optics, 2015, 20(2): 027003.
[19] Boas D A, Dale A M. Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function[J]. Applied Optics, 2005, 44(10): 1957-1968.
[20] Page M J, Lourenço A L, David T, et al. Non-invasive imaging and cellular tracking of pulmonary emboli by near-infrared fluorescence and positron-emission tomography[J]. Nature Communications, 2015, 6: 8448.
[21] Wu L H, Lin Y, Li T. Effect of human brain edema on light propagation: a Monte Carlo modeling based on the visible Chinese human dataset[J]. IEEE Photonics Journal, 2017, 9(5): 6101810.
[22] 高晨阳, 修嘉, 李婷. 运动想象任务下近红外光谱脑激活程度适应性研究[J]. 中国激光, 2022, 49(5): 0507301.
[23] Xiong W, Zhao Y F, Xu M, et al. The relationship between tumor markers and pulmonary embolism in lung cancer[J]. Oncotarget, 2017, 8(25): 41412-41421.
Article Outline
王敏, 孟硕, 郭江辉, 李婷. 肺动脉栓塞检测的可视中国人蒙特卡罗模拟分析[J]. 中国激光, 2022, 49(24): 2407103. Min Wang, Shuo Meng, Jianghui Guo, Ting Li. Visible Chinese Human for Pulmonary Embolism Detection of Monte Carlo Simulation Analysis[J]. Chinese Journal of Lasers, 2022, 49(24): 2407103.
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