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精确测量视网膜血氧饱和度(oxygen saturation, SO2)可为糖尿病视网膜病变、青光眼及视网膜静脉阻塞等眼部疾病提供有价值的早期指示. 虽然可见光光学相干层析成像能够通过光谱拟合直接获取SO2, 但受限于可见光的刺激性及其对视网膜生理状态的影响. 近红外一区对眼部的刺激较小, 尽管血红蛋白在此波段的吸收效应较弱, 但其散射特性也与SO2相关. 基于此, 本研究提出了一种光学相干血流造影(optical coherence tomography angiography, OCTA)引导的近红外一区视网膜血氧饱和度测量技术, 利用氧敏感波长(855 nm)与氧等消光波长(805 nm)光衰减的比值结合标定实现了SO2的测量. 该方法利用OCTA生成的三维血流图自动识别血管以及周围组织区域的三维位置, 避免了手动选取区域引起的测量误差, 将视网膜动静脉分类的平均准确率由82.1%提高到96.7%. 测量结果显示, 动脉和静脉的平均血氧饱和度分别为94% ± 21%, 56% ± 13%, 符合正常视网膜血氧饱和度范围. 该方法有效提升了测量的准确性和效率, 为眼科疾病的早期诊断、病情评估及疗效监测提供了可靠的工具, 具有广阔的应用前景.Accurate measurement of retinal blood oxygen saturation (SO2) provides valuable early insights into the pathophysiology of ocular diseases such as diabetic retinopathy, glaucoma, and retinal vein occlusion. Visible-light optical coherence tomography (OCT) can directly measure SO2 through spectral fitting, but its application is limited due to the irritability of visible light and its influence on the physiological state of the retina. Near-infrared band 1 (NIR-I) causes less ocular stimulation. Even though the hemoglobin absorption effect is weaker in this region, its scattering property is also related to SO2. According to this principle, we propose a novel optical coherence tomography (OCTA) guided NIR-I technique for retinal blood oxygen saturation measurement. This method is used to calculate SO2 through calibrating the optical density ratio (ODR) of oxygen-sensitive wavelength (855 nm) to isosbestic wavelength (805 nm). By utilizing the three-dimensional (3D) blood flow maps generated by the OCTA, this technique can automatically identify retinal vessels and surrounding tissue regions, thereby minimizing the errors caused by manual selection. Consequently, the classification accuracy of arteries and veins increases from 82.1% to 96.7%. The calibrated average retinal blood oxygen saturation is 94%±21% for arteries and 56%±13% for veins, which aligns with normal physiological range. The representative result of artery-vein classification is presented in the following figure. This method greatly improves the accuracy and efficiency of measurement, and provides a reliable tool for early diagnosis, disease assessment, and treatment monitoring of ophthalmic diseases, which has broad application prospects.
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Keywords:
- optical coherence tomography angiography /
- optical coherence tomography /
- blood oxygen saturation /
- optical density
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图 2 (a)近红外宽带光源光谱强度图; (b) HbO2和Hb在近红外波段的衰减光谱, 数据来源于Nienke等[23]的研究; 蓝色和绿色矩形分别代表氧等消光波长805 nm, 氧敏感波长855 nm
Fig. 2. (a) Spectral intensity of the near-infrared (NIR) SLD light source; (b) extinction spectra of HbO2 and Hb at NIR band, the blue and green rectangles represent the oxygen-isosbestic wavelength (around 805 nm) and oxygen-sensitive wavelength (around 855 nm), respectively.
图 3 ODR分析算法流程图, ID-OCTA为基于逆信噪比和退相关的OCTA算法
Fig. 3. Flowchart of the ODR analysis algorithm, ID-OCTA is OCTA based on inverse SNR and decorrelation features.
图 4 自动识别血管边缘和组织区域三维位置算法流程 (a)自动识别血管边缘和组织区域X-Y平面上位置, 其中(I), (II), (III)分别为小鼠视网膜SVP层血管的造影图, 掩模图和退相关系数图, (IV), (V)分别为小鼠视网膜SVP层大血管的掩模图和轮廓图, (VI)代表了用于ODR计算的血管区域, (VII), (VIII)分别为不同膨胀系数(10, 8)的大血管膨胀图和大血管周围组织区域图, (IX) 代表了用于ODR计算的组织区域; (b)小鼠视网膜血流断层示意图, 其位置由(I)中白色虚线示意, 黄色虚线表示深度方向上用于ODR计算的区域
Fig. 4. Workflow of the algorithm for automatically identifying the 3D locations of vascular edges and adjacent tissues: (a) Automatic identification of vessel edges and tissue regions in the x-y plane, where (I)—(III) show the OCTA image, vascular mask, and decorrelation map of the mouse retina SVP, respectively, (IV) and (V) depict the mask and edge map of large vessels in the SVP, respectively, (VI) represents the vascular region used for ODR calculation. (VII) and (VIII) display images of the dilated large vessels with different dilation coefficients (10, 8) and the surrounding tissue region, (IX) shows the tissue region used for ODR calculation; (b) representative tomogram of mouse retinal blood flow, with its position indicated by the white dashed line in (I), the yellow dashed lines in (b) represent the depth range used for the ODR calculation.
图 5 小鼠视网膜动静脉分类结果 (a)OCT均值投影图, 白色虚线为血管周围组织区域示意; (b)SVP层OCTA图; (c)图(a), (b)中大血管对应的ODR值; (d)ODR动静脉分类的ROC分析曲线; (e)代表性的10组研究对象的所有大血管的ODR值, 黑色实线表示该组中所有大血管ODR值的平均值, 图中百分比数据代表各组相应的动静脉ROC分类准确率, 所有图中红色代表动脉, 蓝色代表静脉
Fig. 5. Classification results of arteries and veins in mouse retina: (a) Mean projection of OCT, with the white dashed lines indicating the tissue regions around vessels; (b) SVP OCTA image; (c) ODR values of large vessels shown in panels (a) and (b); (d) ROC analysis curve of the ability of ODR index to classify the arteries and veins; (e) ODR values of all large vessels in the representative ten subjects, with the black solid line representing the average ODR value for each subject, the percentage data in the figure represents the classification accuracy of the corresponding arteriovenous ROC in each group, red represents arteries, and blue represents veins in all panels.
图 6 手动选取血管和组织区域得到的小鼠视网膜动静脉分类代表性结果. 图中百分比数据代表各组相应的动静脉ROC分类准确率
Fig. 6. Representative arteriovenous classification results in mouse retina based on manual selection of vascular and tissue regions. the percentage data in the figure represents the classification accuracy of the corresponding arteriovenous ROC in each group.
表 1 近红外OCT视网膜血氧测量代表性结果
Table 1. Representative results of NIR OCT retinal oximetry.
研究对象 #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 动脉血氧/% 91±12 90±21 92±18 99±9 91±16 90±20 98±10 91±11 90±18 91±18 静脉血氧/% 57±7 51±14 54±15 58±19 50±19 42±12 57±16 51±12 43±13 57±15 
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