MO-D-332-09: Cardiac Function Measurements On An Interventional C-Arm System with Isotropic Spatial Resolution and High Temporal Resolution Using Prior Image Constrained Compressed Sensing (PICCS)

2008 ◽  
Vol 35 (6Part19) ◽  
pp. 2871-2871 ◽  
Author(s):  
J Tang ◽  
B Nett ◽  
S Leng ◽  
J Zambelli ◽  
H Rowley ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Cardiac Function ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Temporal Resolution ◽  
Isotropic Spatial Resolution

Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Arctic Sea

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.</p>

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data

2019 ◽  
Vol 23 (6) ◽  
pp. 2647-2663 ◽  
Author(s):  
Yingchun Huang ◽  
András Bárdossy ◽  
Ke Zhang
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Model Performance ◽  
Daily Rainfall ◽  
High Temporal Resolution ◽  
Dense Network ◽  
Spatially Distributed ◽  
Important Input ◽  
Temporal And Spatial ◽  
Improved Model

Abstract. Rainfall is the most important input for rainfall–runoff models. It is usually measured at specific sites on a daily or sub-daily timescale and requires interpolation for further application. This study aims to evaluate whether a higher temporal and spatial resolution of rainfall can lead to improved model performance. Four different gridded hourly and daily rainfall datasets with a spatial resolution of 1 km × 1 km for the state of Baden-Württemberg in Germany were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of sub-daily stations. Lumped and spatially distributed HBV models were used to investigate the sensitivity of model performance to the spatial resolution of rainfall. The four different rainfall datasets were used to drive both lumped and distributed HBV models to simulate daily discharges in four catchments. The main findings include that (1) a higher temporal resolution of rainfall improves the model performance if the station density is high; (2) a combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement in the tested models; and (3) for the present research, the increase in spatial resolution improves the performance of the model insubstantially or only marginally in most of the study catchments.

scholarly journals Fast in vivo detection of myocardial norepinephrine levels in the beating porcine heart

2020 ◽  
Vol 318 (5) ◽  
pp. H1091-H1099 ◽  
Author(s):  
Shyue-An Chan ◽  
Marmar Vaseghi ◽  
Nicholas Kluge ◽  
Kalyanam Shivkumar ◽  
Jeffrey L. Ardell ◽  
...  
Keyword(s):  
Cardiac Function ◽  
Temporal Resolution ◽  
High Performance ◽  
Cardiac Tissue ◽  
Enzyme Linked Immunosorbent Assay ◽  
High Temporal Resolution ◽  
Sample Collection ◽  
Significant Information ◽  
Porcine Heart

The sympathetic nervous system modulates cardiac function by controlling key parameters such as chronotropy and inotropy. Sympathetic control of ventricular function occurs through extrinsic innervation arising from the stellate ganglia and thoracic sympathetic chain. In the healthy heart, sympathetic release of norepinephrine (NE) results in positive modulation of chronotropy, inotropy, and dromotropy, significantly increasing cardiac output. However, in the setting of myocardial infarction or injury, sympathetic activation persists, contributing to heart failure and increasing the risk of arrhythmias, including sudden cardiac death. Methodologies for detection of norepinephrine in cardiac tissue are limited. Present techniques rely on microdialysis for analysis by high-performance liquid chromatography coupled to electrochemical detection (HPLC-ED), radioimmunoassay, or other immunoassays, such as enzyme-linked immunosorbent assay (ELISA). Although significant information about the release and action of norepinephrine has been obtained with these methodologies, they are limited in temporal resolution, require large sample volumes, and provide results with a significant delay after sample collection (hours to weeks). In this study, we report a novel approach for measurement of interstitial cardiac norepinephrine, using minimally invasive, electrode-based, fast-scanning cyclic voltammetry (FSCV) applied in a beating porcine heart. The first multispatial and high temporal resolution, multichannel measurements of NE release in vivo are provided. Our data demonstrate rapid changes in interstitial NE profiles with regional differences in response to coronary ischemia, sympathetic nerve stimulation, and alterations in preload/afterload. NEW & NOTEWORTHY Pharmacological, electrical, or surgical regulation of sympathetic neuronal control can be used to modulate cardiac function and treat arrhythmias. However, present methods for monitoring sympathetic release of norepinephrine in the heart are limited in spatial and temporal resolution. Here, we provide for the first time a methodology and demonstration of practice and rapid measures of individualized regional autonomic neurotransmitter levels in a beating heart. We show dynamic, spatially resolved release profiles under normal and pathological conditions.

scholarly journals A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1992 ◽  
Author(s):  
Zixi Xie ◽  
Weiguo Song ◽  
Rui Ba ◽  
Xiaolian Li ◽  
Long Xia
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Forest Fire ◽  
Forest Fires ◽  
Spatial Information ◽  
Fire Detection ◽  
High Temporal Resolution ◽  
Detection Accuracy ◽  
Contextual Model ◽  
Forest Fire Detection

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.

Compressed sensing cine imaging with high spatial or high temporal resolution for analysis of left ventricular function

2016 ◽  
Vol 44 (2) ◽  
pp. 366-374 ◽  
Author(s):  
Juliane Goebel ◽  
Felix Nensa ◽  
Haemi P. Schemuth ◽  
Stefan Maderwald ◽  
Marcel Gratz ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Left Ventricular Function ◽  
Ventricular Function ◽  
Temporal Resolution ◽  
Left Ventricular ◽  
Cine Imaging ◽  
High Temporal Resolution

scholarly journals Comparison of Landsat and Land-Based Phenology Camera Normalized Difference Vegetation Index (NDVI) for Dominant Plant Communities in the Great Basin

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1139 ◽  
Author(s):  
Keirith Snyder ◽  
Justin Huntington ◽  
Bryce Wehan ◽  
Charles Morton ◽  
Tamzen Stringham
Keyword(s):  
Great Basin ◽  
Plant Communities ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Plant Phenology ◽  
High Temporal Resolution ◽  
Spatial Coverage

Phenology of plants is important for ecological interactions. The timing and development of green leaves, plant maturity, and senescence affects biophysical interactions of plants with the environment. In this study we explored the agreement between land-based camera and satellite-based phenology metrics to quantify plant phenology and phenophases dates in five plant community types characteristic of the semi-arid cold desert region of the Great Basin. Three years of data were analyzed. We calculated the Normalized Difference Vegetation Index (NDVI) for both land-based cameras (i.e., phenocams) and Landsat imagery. NDVI from camera images was calculated by taking a standard RGB (red, green, and blue) image and then a near infrared (NIR) plus RGB image. Phenocam NDVI was calculated by extracting the red digital number (DN) and the NIR DN from images taken a few seconds apart. Landsat has a spatial resolution of 30 m2, while phenocam spatial resolution can be analyzed at the single pixel level at the scale of cm2 or area averaged regions can be analyzed with scales up to 1 km2. For this study, phenocam regions of interest were used that approximated the scale of at least one Landsat pixel. In the tall-statured pinyon and juniper woodland sites, there was a lack of agreement in NDVI between phenocam and Landsat NDVI, even after using National Agricultural Imagery Program (NAIP) imagery to account for fractional coverage of pinyon and juniper versus interspace in the phenocam data. Landsat NDVI appeared to be dominated by the signal from the interspace and was insensitive to subtle changes in the pinyon and juniper tree canopy. However, for short-statured sagebrush shrub and meadow communities, there was good agreement between the phenocam and Landsat NDVI as reflected in high Pearson’s correlation coefficients (r > 0.75). Due to greater temporal resolution of the phenocams with images taken daily, versus the 16-day return interval of Landsat, phenocam data provided more utility in determining important phenophase dates: start of season, peak of season, and end of season. More specific species-level information can be obtained with the high temporal resolution of phenocams, but only for a limited number of sites, while Landsat can provide the multi-decadal history and spatial coverage that is unmatched by other platforms. The agreement between Landsat and phenocam NDVI for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.

Abstract 94: Comparison of Ultrasound and Compact MRI for the Assessment of Cardiac Function in Mice

2014 ◽  
Vol 115 (suppl_1) ◽  
Author(s):  
Tonya Coulthard ◽  
Jun Wu
Keyword(s):  
Cardiac Function ◽  
Temporal Resolution ◽  
Cardiac Mri ◽  
High Performance ◽  
Myocardial Infarct ◽  
Strain Analysis ◽  
High Sensitivity ◽  
Whole Body ◽  
High Temporal Resolution ◽  
Phenotypic Analysis

Introduction: Ultrasound (US) is a standard for the assessment of cardiovascular function. Pre-clinically, this is due the modality’s translational potential, its high temporal resolution and the absence of infrastructure. Recent advancements in novel high-performance compact MRI has made cardiac MRI a more accessible technique for assessing a variety of pathologies in murine models of CVD while reducing the complexity and costs traditionally associated with superconducting MRI systems. These developments may allow compact MRI to overcome some of the limitations of US meanwhile producing viable solutions for quantification along with new capabilities for cardiovascular biomarker assessment. Hypothesis: This study explores the relative capabilities of novel compact high-performance MRI compared to HFU for phenotypic analysis of mouse models of CVD. Methods: Four normal and one induced myocardial infarct C57BL/6 mice were imaged using both US and MRI. MI mice were prepared by ligation of the left anterior descending artery. In order to assess the ability of each modality to qualitatively and quantitatively characterize cardiac function, CINE loops of long axis and short axis slices were acquired with both systems and analyzed offline. In addition to traditional measures of cardiac function, strain analysis was performed using the HARP method for compact MRI and using speckle tracking for US. Finally, a cardiac MRI method for mapping and measuring infarct extent using gadolinium (Gd) contrast agents is described. Results and Conclusions: Although compact MRI has lower temporal resolution, when compared with US, it still provides many advantages in pre-clinical CVD imaging. In particular, because MRI signal is not attenuated by dense tissue or air, compact MRI can successfully generate artefact-free imaging of pathologies difficult or impossible to image with US, such as clearly visualizing and quantifying endo and epicardial borders. It also provides a whole body image and a “whole heart” image, making pre-clinical CV imaging easier for biologists and helping to reduce inter-operator variability. Finally, high sensitivity to Gd-based contrast agent with compact MRI enable new applications such as infarct quantification.

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