ABSTRACT
Retinal microvascular disease has caused serious visual impairment widely in the world, which can be hopefully prevented via early and precision microvascular hemodynamic diagnosis. Due to artifacts from choroidal microvessels and tiny movements, current fundus microvascular imaging techniques including fundus fluorescein angiography (FFA) precisely identify retinal microvascular microstructural damage and abnormal hemodynamic changes difficulty, especially in the early stage. Therefore, this study proposes an FFA-based multi-parametric retinal microvascular functional perfusion imaging (RM-FPI) scheme to assess the microstructural damage and quantify its hemodynamic distribution precisely. Herein, a spatiotemporal filter based on singular value decomposition combined with a lognormal fitting model was used to remove the above artifacts. Dynamic FFAs of patients (n = 7) were collected first. The retinal time fluorescence intensity curves were extracted and the corresponding perfusion parameters were estimated after decomposition filtering and model fitting. Compared with in vivo results without filtering and fitting, the signal-to-clutter ratio of retinal perfusion curves, average contrast, and resolution of RM-FPI were up to 7.32 ± 0.43 dB, 14.34 ± 0.24 dB, and 11.0 ± 2.0 µm, respectively. RM-FPI imaged retinal microvascular distribution and quantified its spatial hemodynamic changes, which further characterized the parabolic distribution of local blood flow within diameters ranging from 9 to 400 µm. Finally, RM-FPI was used to quantify, visualize, and diagnose the retinal hemodynamics of retinal vein occlusion from mild to severe. Therefore, this study provided a scheme for early and precision diagnosis of retinal microvascular disease, which might be beneficial in preventing its development.
ABSTRACT
To discuss the coupling coordination relationship among tourism carbon emissions, economic development and regional innovation it is not only necessary to realize the green development of tourism economy, but also great significance for the tourism industry to take a low-carbon path. Taking the 30 provinces of China for example, this paper calculated the tourism carbon emission efficiency based on the super-efficiency Slacks based measure and Data envelope analyse (SBM-DEA) model from 2007 to 2017, and on this basis, defined a compound system that consists of tourism carbon emissions, tourism economic development and tourism regional innovation. Further, the coupling coordination degree model and dynamic degree model were used to explore its spatiotemporal evolution characteristics of balanced development, and this paper distinguished the core influencing factors by Geodetector model. The results showed that (1) during the study period, the tourism carbon emission efficiency showed a reciprocating trend of first rising and then falling, mainly due to the change of pure technical efficiency. (2) The coupling coordination degree developed towards a good trend, while there were significant differences among provinces, showing a gradient distribution pattern of decreasing from east to west. Additionally, (3) the core driving factors varied over time, however, in general, the influence from high to low were as follows: technological innovation, economic development, urbanization, environmental pollution control, and industrial structure. Finally, some policy recommendations were put forward to further promote the coupling coordination degree.