Comparison of Image Quality and Spatial Resolution Between 18F, 68Ga and 64Cu Phantom Measurements Using a Digital Biograph Vision PET/CT

Background: The PET nuclide and reconstruction method can have a considerable influence on spatial resolution and image quality of PET/CT scans, which can, for example, influence the diagnosis in oncology. The individual impact of the positron energy of 18F, 68Ga and 64Cu on spatial resolution and image quality of PET/CT scans acquired using a clinical, digital scanner was compared. Furthermore, the impact of different reconstruction parameters on image quality and spatial resolution was evaluated for 18F-FDG PET/CT scans acquired with a scanner of the newest generation. Methods: PET/CT scans of a Jaszczak phantom and a NEMA PET body phantom, filled with 18F-FDG, 68Ga-HCl and 64Cu-HCl, respectively, were performed on a Siemens Biograph Vision. Images were assessed using spatial resolution and image quality (Recovery Coefficients (RC), coefficient of variation within the background, Contrast Recovery Coefficient (CRC), Contrast-Noise-Ratio (CNR), and relative count error in lung insert). In a subsequent analysis, the scan of the NEMA PET body phantom filled with 18F-FDG was reconstructed applying different parameters (with/without the application of Point Spread Function (PSF), Time of Flight (ToF) or post-filtering; matrix size). Spatial resolution and quantitative image quality were compared between reconstructions. Results: We found that image quality was comparable between 18F-FDG and 64Cu-HCl PET/CT measurements featuring similar maximal endpoint energy. In comparison, RC, CRC and CNR were worse in 68Ga-HCl data, despite similar count rates. Spatial resolution was up to 18 % worse in 68Ga-HCl compared to 18F-FDG images. Post-filtering of 18F-FDG acquisitions changed image quality the most and reduced spatial resolution by 52 % if a Gaussian filter with 5 mm FWHM was applied. ToF measurements especially improved the recovery of the smallest lesion (RCmean = 1.07 compared to 0.65 without ToF) and improved spatial resolution by 29 %.Conclusions: The positron energy of PET nuclides influences spatial resolution and image quality of digital PET/CT scans. Image quality of 68Ga-HCl PET/CT images was worse compared to 18F-FDG and 64Cu-HCl, respectively, despite similar count rates. Reconstruction parameters have a high impact on image quality and spatial resolution and should be considered when comparing images of different scanners or centers.

Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (Rs) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET.

Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager

Land surface reflectance (LSR) is well known as an essential variable to understand land surface properties. The Geostationary Ocean Color Imager (GOCI) be able to observe not only the ocean but also the land with the high temporal and spatial resolution thanks to its channel specification. In this study, we describe the land atmospheric correction algorithm and present the quality of results through comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data for GOCI-II. The GOCI LSR shows similar spatial distribution and quantity with MODIS LSR for both healthy and unhealthy vegetation cover. Our results agreed well with in-situ-based reference LSR with a high correlation coefficient (>0.9) and low root mean square error (<0.02) in all 8 GOCI channels. In addition, seasonal variation according to the solar zenith angle and phenological dynamics in time-series was well presented in both reference and GOCI LSR. As the results of uncertainty analysis, the estimated uncertainty in GOCI LSR shows a reasonable range (<0.04) even under a high solar zenith angle over 70°. The proposed method in this study can be applied to GOCI-II and can provide continuous satellite-based LSR products having a high temporal and spatial resolution for analyzing land surface properties.

Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and -0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

<div>We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, (i) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and (ii) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately, but requires more time to solve. Both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.</div>

A dual-ended 400 km OFDR for vibration detection

Optical frequency-domain reflectometer (OFDR) has been widely used in vibration detection because of its unique advantages of simple configuration and high spatial resolution. Based on remote fiber amplification, an unrepeatered OFDR is experimentally investigated for vibration monitoring. To locate the vibration, we present an algorithm by calculating segmental cross-correlation between the beating signals with and without disturbances on the sensing fiber. It is shown that the OFDR demonstrates the ability of detecting the vibration over 222 km testing distance (112 km + 110 km). After sensing the first spool fiber of 112 km, the remnant laser is amplified by a remote-pumped EDFA before proceeding to probe the vibration in the second spool one of 110 km. To be specific, the PZT-induced vibrations positioned at z=110.9 km and z=220.9 km are both detected. More importantly, the OFDR system can be extended to operate in bi-directional sensing mode and to double detection range from 200 km to 400 km.