Objectives: To quantify spinal cord perfusion by using contrast-enhanced ultrasound (CEUS) in a porcine model with acute spinal cord injury. Methods: Microcirculatory changes of acute spinal cord injury were shown by CEUS in a porcine model with spinal cord contusion at three selected time points, coupling with conventional ultrasound (US) and Color Doppler US (CDFI). Time-intensity curves and perfusion parameters were also obtained by autotracking contrast quantification (ACQ) software in the epicenter of contusion site, adjacent region and distant region, respectively. Neurologic and histologic examinations were used to confirm the severity of injury. Results: Conventional US revealed the spinal cord was hypoechoic and homogeneous, whereas the dura mater, pia mater and cerebral aqueduct were hyperechoic. On CDFI intramedullary blood vessels were displayed as segmental and columnar. It was homogeneous on CEUS. After spinal cord contusion, the injured region on gray scale US was hyperechoic. CDFI demonstrated intramedullary blood vessels of adjacent region had increased and dilated during the observation period. On CEUS the epicenter of contusion site was hypoperfusion, whereas its adjacent region was hyperperfusion compared with the distant region. Quantitative analysis showed that peak intensity decreased in epicenters of contusion but increased in adjacent regions significantly at all time points (Po0.05). Evaluation of neurological function for post-contusion demonstrated significantly deterioration in comparison before injury (Po0.05). Conclusions: CEUS is a practical technique that provides overall views for evaluating microcirculatory pattern in spinal cord injury. Quantitative analysis shows the efficacy in assessment of perfusion changes after spinal cord injury.

Accurate determination of crop phenology information is essential for effective field management and decision-making processes. Remote sensing time series analyses are widely employed to extract the phenological stages. Each crop’s phenological stage has its unique characteristic on the crop plant, while the satellite-derived crop phenology refers to some key transition dates in time series satellite observations. Current techniques primarily estimate specific phenological stages by detecting points with distinctive features on the remote sensing time series curve. But these stages may be different from the Biologische Bundesanstalt, Bundessortenamt and CHemical Industry (BBCH) scale, which is commonly used to identify the phenological development stages of crops. Moreover, when aiming to extract various phenological stages concurrently, it becomes necessary to adjust the extraction strategy for each unique feature. This need for distinct strategies at each stage heightens the complexity of simultaneous extraction. In this study, we utilize the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data and propose a phenology extraction framework based on the Derivative Dynamic Time Warping (DDTW) algorithm. This method is capable of simultaneously extracting complete phenological stages, and the results demonstrate that the Root Mean Square Errors (RMSEs, days) of detected phenology on the BBCH scale for corn were less than 6 days overall.