The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.

构建宽窄带隙同型异质结构的双层薄膜是提高透明导电薄膜光电性能的新思路。采用基于密度泛函的第一性原理，对本征和掺杂SnO2/SnSe2的电子结构、光学性质、载流子迁移率、电荷分布、能带排列进行计算。结果表明：本征和掺杂SnO2/SnSe2电子结构内部存在的电势差会使体系内部的电子向着界面处或SnSe2处转移，处于界面处的电子会在界面间隙内形成二维电子气并在界面处高速移动，从而提高了载流子的迁移率，而处于SnSe2处的电子由于没有杂质离子散射的影响迁移率也相应提高，4种不同掺杂类型异质结构的载流子迁移率分别为772.82、5 286.04、2 656.90 m2/(S·V)和17 724.60 m2/(S·V)，光学透过率在80%以上。

本文以单丁基三氯化锡(MBTC)为锡源，氟化铵(NH4F)为氟源，甲醇为溶剂，六水合氯化镍(NiCl2·6H2O)为镍源，采用气溶胶辅助化学气相沉积(AACVD)制备了镍掺杂FTO薄膜。利用分光光度计、四探针电阻仪及霍尔效应测试仪对镍掺杂FTO薄膜的光学性能、电学性能进行表征和分析，并基于第一性原理对掺杂体系的电子结构进行了计算。结果表明，Ni掺杂的FTO薄膜为四方金红石结构，导电性能有所提高。当Ni/Sn为2%(原子数分数)时，品质因数ΦTC达到3×10-2 Ω-1，电阻率ρ为3.79×10-4 Ω·cm，可见光平均透过率约为80%，载流子浓度n为6.88×1020 cm-3，迁移率μ为13.31 cm2·V-1·s-1。

Cone photoreceptor cell identification is important for the early diagnosis of retinopathy. In this study, an object detection algorithm is used for cone cell identification in confocal adaptive optics scanning laser ophthalmoscope (AOSLO) images. An effectiveness evaluation of identification using the proposed method reveals precision, recall, and F1-score of 95.8%, 96.5%, and 96.1%, respectively, considering manual identification as the ground truth. Various object detection and identification results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method. Overall, the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images, being comparable to manual identification.

设计了一种基于线扫描成像(LSI)的光片荧光显微镜(LSFM),旨在通过抑制样本散射达到提高成像质量的目的。该显微镜将数字扫描光片荧光显微镜(DSLM)与LSI两种方法结合,以前者为基础,在探测光路上增加了一个用于解扫描的扫描振镜。在控制过程中,将照明光路的扫描振镜与解扫描振镜同步,使得均匀运动的图像在相机前固定于同一位置,从而实现了LSI。在系统中,为了便于与传统方法对比,线阵探测器通过面阵相机模拟实现。另外,与常规的LSFM相比,改进后的系统,在高散射荧光微球样品和斑马鱼心脏样品的成像实验中,更有效地抑制了样本散射。因此,验证该方法具有可行性。

扫频源光学相干层析血管成像 (SS-OCTA)是一种基于分频幅去相关血管造影法 (SSADA)的新型血管成像技术, 在肿瘤等疾病的早期诊断方面拥有较大前景。本文在 5.12 mm×5.12 mm成像视场、标准图像最大信噪比 34.3 dB的 SS-OCTA成像平台, 对黑色素瘤 C57BL6小鼠进行皮肤结构和血管成像采集。结果表明在皮肤科疾病的早期诊断方面, 利用 SS-OCTA系统进行血管成像优于结构成像。