IMRT QA and Gamma Comparisons: The impact of detector geometry, spatial sampling, and delivery technique on gamma comparison sensitivity

2021 ◽  
Author(s):  
Jennifer M. Steers ◽  
Benedick A. Fraass
Keyword(s):  
Spatial Sampling ◽  
Imrt Qa ◽  
Delivery Technique ◽  
Detector Geometry ◽  
The Impact

Acquiring and Imaging Ultra High Density Land Seismic Data - Practical Challenges and the Impact of Spatial Sampling

2014 ◽  
Author(s):  
A. Ourabah ◽  
M. Grimshaw ◽  
J. Keggin ◽  
M. Kowalczyk-Kedzierska ◽  
J. Stone ◽  
...  
Keyword(s):  
Seismic Data ◽  
High Density ◽  
Spatial Sampling ◽  
The Impact

The Impact of Spatial Sampling and Polarisation in GPR Survey for Utility Mapping

2019 ◽  
Author(s):  
F. Lombardi ◽  
M. Lualdi
Keyword(s):  
Spatial Sampling ◽  
The Impact

WE-C-BRB-06: Analyzing the Impact of Intra-Fraction Motion: Correlation of IMRT QA Metrics with D95%

2011 ◽  
Vol 38 (6Part31) ◽  
pp. 3805-3805
Author(s):  
B Waghorn ◽  
S Meeks ◽  
K Langen
Keyword(s):  
Imrt Qa ◽  
The Impact

Spatial sampling approach to unravel the impact of soil texture and root genotype on maize root gene expression profiles

2020 ◽  
Author(s):  
Minh Ganther ◽  
Marie-Lara Bouffaud ◽  
Lucie Gebauer ◽  
François Buscot ◽  
Doris Vetterlein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Soil Texture ◽  
Expression Profiles ◽  
Soil Column ◽  
Column Experiment ◽  
Spatial Sampling ◽  
Marker Genes ◽  
Sampling Depth ◽  
Plant Growth Hormones ◽  
The Impact

<p>The complex interactions between plant roots and soil microbes enable a range of beneficial functions such as nutrient acquisition, defense against pathogens and production of plant growth hormones. The role of soil type and plant genotype in shaping rhizosphere communities has been explored in the past, but often without spatial context. The spatial resolution of rhizosphere processes enables us to observe pattern formation in the rhizosphere and investigate how spatial soil organization is shaped through soil–plant–microbiome interactions.</p><p>We applied spatial sampling in a standardized soil column experiment with two maize genotypes (wildtype vs. <em>roothairless3</em>) and two different soil textures (loam vs. sand) in order to investigate how in particular functions of the maize roots relating to nutrient/water uptake, immunity/defense, stress and exudation are affected. RNA sequencing and differential gene expression analysis were used to dissect impact of soil texture, root genotype and sampling depth. Our results indicate that variance in gene expression is predominantly explained by soil texture as well as sampling depth, whereas genotype appears to play a less pronounced role at the analyzed depths. Gene Ontology enrichment analysis of differentially expressed genes between soil textures revealed several functional categories and pathways relating to phytohormone-mediated signaling, cell growth, secondary metabolism, and water homeostasis. Community analysis of rhizosphere derived ACC deaminase active (acdS gene including) plant beneficial bacteria, which suppress the phytohormone ethylene production, suggests that soil texture and column depth are the major factors that affect acdS community composition.</p><p>From the comprehensive gene expression analyses we aim to identify maize marker genes from the relevant core functional groups. These marker genes will be potentially useful for future experiments; such as field plot experiments for investigation of later-emerging plant properties.</p><p>This research was conducted within the research program “Rhizosphere Spatiotemporal Organisation – a Key to Rhizosphere Functions” of the German Science Foundation (TA 290/5-1).</p>

scholarly journals Will a perfect model agree with perfect observations? The impact of spatial sampling

2016 ◽  
Author(s):  
N. A. J. Schutgens ◽  
E. Gryspeerdt ◽  
N. Weigum ◽  
S. Tsyro ◽  
D. Goto ◽  
...  
Keyword(s):  
Black Carbon ◽  
Measurement Errors ◽  
Spatial Scales ◽  
Global Model ◽  
Spatial Sampling ◽  
Model Data ◽  
Temporal Sampling ◽  
Fold Reduction ◽  
Perfect Model ◽  
The Impact

Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid-spacings of ∼ 200 km, while satellite observations of aerosol use so-called pixels of ∼ 10 km. Ground site or air-borne observations concern even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid-spacing) over either the large areas of global model grid-boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field-of-view of ground-sites ("perfect" observations). Our analysis suggests that instantaneous RMS differences between these perfect observations and perfect global models can easily amount to 30–160%, for a range of observables like AOT (Aerosol Optical Thickness), extinction, black carbon mass concentrations, PM2.5, number densities and CCN (Cloud Condensation Nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a three-fold reduction i.c. AOT), than for others (e.g. a two-fold reduction for surface black carbon concentrations), but significant RMS differences remain (10-75%). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

scholarly journals Pragmatic spatial sampling for wearable MEG arrays

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tim M. Tierney ◽  
Stephanie Mellor ◽  
George C. O’Neill ◽  
Niall Holmes ◽  
Elena Boto ◽  
...  
Keyword(s):  
New Technologies ◽  
Signal To Noise Ratio ◽  
Sensor Arrays ◽  
Spatial Sampling ◽  
Spatial Discrimination ◽  
Optically Pumped ◽  
Deep Sources ◽  
Gain Errors ◽  
The Impact ◽  
Sensor Gain

AbstractSeveral new technologies have emerged promising new Magnetoencephalography (MEG) systems in which the sensors can be placed close to the scalp. One such technology, Optically Pumped MEG (OP-MEG) allows for a scalp mounted system that provides measurements within millimetres of the scalp surface. A question that arises in developing on-scalp systems is: how many sensors are necessary to achieve adequate performance/spatial discrimination? There are many factors to consider in answering this question such as the signal to noise ratio (SNR), the locations and depths of the sources, density of spatial sampling, sensor gain errors (due to interference, subject movement, cross-talk, etc.) and, of course, the desired spatial discrimination. In this paper, we provide simulations which show the impact these factors have on designing sensor arrays for wearable MEG. While OP-MEG has the potential to provide high information content at dense spatial samplings, we find that adequate spatial discrimination of sources (< 1 cm) can be achieved with relatively few sensors (< 100) at coarse spatial samplings (~ 30 mm) at high SNR. After this point approximately 50 more sensors are required for every 1 mm improvement in spatial discrimination. Comparable discrimination for traditional cryogenic systems require more channels by these same metrics. We also show that sensor gain errors have the greatest impact on discrimination between deep sources at high SNR. Finally, we also examine the limitation that aliasing due to undersampling has on the effective SNR of on-scalp sensors.

scholarly journals On the influence of spatial sampling on climate networks

2014 ◽  
Vol 21 (3) ◽  
pp. 651-657 ◽  
Author(s):  
N. Molkenthin ◽  
K. Rehfeld ◽  
V. Stolbova ◽  
L. Tupikina ◽  
J. Kurths
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Voronoi Cell ◽  
Spatial Sampling ◽  
Series Data ◽  
Cell Size Distribution ◽  
Reconstructed Network ◽  
Climate Time Series ◽  
The Impact ◽  
Climate Networks

Abstract. Climate networks are constructed from climate time series data using correlation measures. It is widely accepted that the geographical proximity, as well as other geographical features such as ocean and atmospheric currents, have a large impact on the observable time-series similarity. Therefore it is to be expected that the spatial sampling will influence the reconstructed network. Here we investigate this by comparing analytical flow networks, networks generated with the START model and networks from temperature data from the Asian monsoon domain. We evaluate them on a regular grid, a grid with added random jittering and two variations of clustered sampling. We find that the impact of the spatial sampling on most network measures only distorts the plots if the node distribution is significantly inhomogeneous. As a simple diagnostic measure for the detection of inhomogeneous sampling we suggest the Voronoi cell size distribution.

Modeling radiation dose to circulating lymphocytes during brain tumor treatment:  Effects of target volume, dose rate, and treatment technique.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 2017-2017
Author(s):  
Susannah G. Yovino ◽  
Stuart A. Grossman ◽  
Lawrence Kleinberg ◽  
Eric C Ford
Keyword(s):  
Radiation Dose ◽  
Dose Rate ◽  
Target Volume ◽  
Blood Pool ◽  
Treatment Technique ◽  
Delivery Technique ◽  
Circulating Lymphocytes ◽  
Target Volumes ◽  
The Mean ◽  
The Impact

2017 Background: Severe treatment-related lymphopenia (TRL) occurs in 40% of glioblastoma patients despite minimal radiation (RT) doses to bone marrow or nodal sites. In glioblastoma, TRL is associated with decreased survival. To explain the lymphopenia, we sought to estimate radiation doses received by circulating lymphocytes during partial brain RT. Methods: An in-house computer program linked to treatment planning software was used to calculate the mean radiation dose to circulating blood (DCB) and the fraction of blood receiving >0.5 Gy. The model also studied the impact of different target volumes (PTV), dose rates (DR), and delivery techniques (IMRT, 3D-CRT). Results: The mean DCB for a 60-Gy course (8-cm diameter PTV, dose rate 600 MU/minute) was 2.2 Gy. With this the entire blood pool receives a lymphotoxic dose of >0.5 Gy. DCB is correlated with fraction number, PTV size, and DR. Regardless of dose rate or delivery technique, the percent of circulating blood receiving >0.5 Gy approached 100% as the number of fractions increased. Changing dose rate had minimal effects on mean DCB (3.1Gy for 300 MU/min vs 2.2 Gy for 1200 MU/min). Smaller PTV size reduced the percent of blood receiving >0.5 Gy (15% for 2-cm diameter PTV vs 100% for 8-cm PTV). Conclusions: Standard RT for brain tumors delivers a lymphotoxic radiation dose to circulating blood. Altering dose rate may initially affect DCB, but advantages disappear over the course of 30 fractions. Marked reductions in target size appear to be the best way to avoid radiation injury to normal circulating lymphocytes. Other novel approaches are needed to limit radiation exposure to circulating lymphocytes given evidence associating lymphopenia with poorer outcomes in cancer patients.

scholarly journals Will a perfect model agree with perfect observations? The impact of spatial sampling

2016 ◽  
Vol 16 (10) ◽  
pp. 6335-6353 ◽  
Author(s):  
Nick A. J. Schutgens ◽  
Edward Gryspeerdt ◽  
Natalie Weigum ◽  
Svetlana Tsyro ◽  
Daisuke Goto ◽  
...  
Keyword(s):  
Black Carbon ◽  
Measurement Errors ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Global Model ◽  
Spatial Sampling ◽  
Model Data ◽  
Temporal Sampling ◽  
Perfect Model ◽  
The Impact

Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid spacings of  ∼ 200 km, while satellite observations of aerosol use so-called pixels of  ∼ 10 km. Ground site or airborne observations relate to even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid spacing) over either the large areas of global model grid boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field of view of ground sites ("perfect" observations). Our analysis suggests that instantaneous root-mean-square (RMS) differences of perfect observations from perfect global models can easily amount to 30–160 %, for a range of observables like AOT (aerosol optical thickness), extinction, black carbon mass concentrations, PM2.5, number densities and CCN (cloud condensation nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a threefold reduction for AOT), than for others (e.g. a twofold reduction for surface black carbon concentrations), but significant RMS differences remain (10–75 %). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

Designing and Testing of Backflow-Free Catheters

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
O. Ivanchenko ◽  
V. Ivanchenko
Keyword(s):  
Porous Media ◽  
Hydraulic Conductivity ◽  
Flow Direction ◽  
Drug Distribution ◽  
Fluid Injection ◽  
Conductivity Anisotropy ◽  
Convection Enhanced Delivery ◽  
Delivery Technique ◽  
Catheter Design ◽  
The Impact

Convection-enhanced delivery (CED) is a drug delivery technique used to target specific regions of the central nervous system (CNS) for the treatment of neurodegenerative diseases and cancer while bypassing the blood–brain barrier (BBB). The application of CED is limited by low volumetric flow rate infusions in order to prevent the possibility of backflow. Consequently, a small convective flow produces poor drug distribution inside the treatment region, which can render CED treatment ineffective. Novel catheter designs and CED protocols are needed in order to improve the drug distribution inside the treatment region and prevent backflow. In order to develop novel backflow-free catheter designs, the impact of the micro-fluid injection into deformable porous media was investigated experimentally as well as numerically. Fluid injection into the porous media has a considerable effect on local transport properties such as porosity and hydraulic conductivity because of the local media deformation. These phenomena not only alter the bulk flow velocity distribution of the micro-fluid flow due to the changing porosity, but significantly modify the flow direction, and even the volumetric flow distribution, due to induced local hydraulic conductivity anisotropy. These findings help us to design backflow-free catheters with safe volumetric flow rates up to 10 μl/min. A first catheter design reduces porous media deformation in order to improve catheter performance and control an agent volumetric distribution. A second design prevents the backflow by reducing the porosity and hydraulic conductivity along a catheter’s shaft. A third synergistic catheter design is a combination of two previous designs. Novel channel-inducing and dual-action catheters, as well as a synergistic catheter, were successfully tested without the occurrence of backflow and are recommended for future animal experiments.

Sign in / Sign up

Export Citation Format

Share Document