Multi-centre, multi-vendor 7 Tesla fMRI reproducibility of hand digit representation in the human somatosensory cortex

Mapping Intimacies ◽

10.1101/2021.03.25.437006 ◽

2021 ◽

Cited By ~ 1

Author(s):

Ian D Driver ◽

Rosa M Sanchez Panchuelo ◽

Olivier Mougin ◽

Michael Asghar ◽

James Kolasinski ◽

...

Keyword(s):

Spatial Resolution ◽

Single Site ◽

7 Tesla ◽

Whole Body ◽

Localization Task ◽

Subject Recruitment ◽

Fine Spatial Resolution ◽

The Individual ◽

Hand Region ◽

Site Variability

AbstractWhilst considerable progress has been made in using ultra-high field fMRI to study brain function at fine spatial resolution, methods are generally optimized at a single site and do not translate to studies where multiple sites are required for sufficient subject recruitment. With a recent increase in installations of human 7 T systems, there is now the opportunity to establish a framework for multi-site 7 T fMRI studies. However, an understanding of the inter-site variability of fMRI measurements is required for datasets to be combined across sites. To address this, we employ a hand digit localization task and compare across-site and within-site reproducibility of 7 T fMRI to a hand digit localization task which requires fine spatial resolution to resolve individual digit representations. As part of the UK7T Network “Travelling Heads” study, 10 participants repeated the same hand digit localization task at five sites with whole-body 7T MRI systems to provide a measure of inter-site variability. A subset of the participants (2 per site) performed repeated sessions at each site for measurement of intra-site reproducibility. Dice’s overlap coefficient was used to assess reproducibility, with hand region inter-site Dice = 0.70±0.04 significantly lower than intrasite Dice = 0.76±0.06, with similar trends for the individual digit maps. Although slightly lower than intra-site reproducibility, the inter-site reproducibility results are consistent with previous single site reproducibility measurements, providing evidence that multi-site 7 T fMRI studies are feasible. These results can be used to inform sample size calculations for future multi-site somatomotor mapping studies.

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A Simple Method for the Evaluation of Internal Doses in Nuclear Medicine

Nuklearmedizin ◽

10.1055/s-0038-1624857 ◽

1974 ◽

Vol 13 (02) ◽

pp. 193-206

Author(s):

L. Conte ◽

L. Mombelli ◽

A. Vanoli

Keyword(s):

Nuclear Medicine ◽

Close Agreement ◽

Whole Body ◽

Simple Method ◽

Rapid Estimation ◽

Absorbed Doses ◽

The Mean ◽

The Individual ◽

Estimation Of Risk

SummaryWe have put forward a method to be used in the field of nuclear medicine, for calculating internally absorbed doses in patients. The simplicity and flexibility of this method allow one to make a rapid estimation of risk both to the individual and to the population. In order to calculate the absorbed doses we based our procedure on the concept of the mean absorbed fraction, taking into account anatomical and functional variability which is highly important in the calculation of internal doses in children. With this aim in mind we prepared tables which take into consideration anatomical differences and which permit the calculation of the mean absorbed doses in the whole body, in the organs accumulating radioactivity, in the gonads and in the marrow; all this for those radionuclides most widely used in nuclear medicine. By comparing our results with dose obtained from the use of M.I.R.D.'s method it can be seen that when the errors inherent in these types of calculation are taken into account, the results of both methods are in close agreement.

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A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

Remote Sensing ◽

10.3390/rs13152982 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2982

Author(s):

Richard Dworak ◽

Yinghui Liu ◽

Jeffrey Key ◽

Walter N. Meier

Keyword(s):

Sea Ice ◽

Spatial Resolution ◽

Infrared Imaging ◽

High Spatial Resolution ◽

Sea Ice Concentration ◽

Clear Sky ◽

Ice Concentration ◽

Best Linear Unbiased ◽

The Individual ◽

Sky Conditions

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.

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Arc-PET: Cost-Effective Whole-Body Scanner with High Sensitivity and High Spatial Resolution

2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) ◽

10.1109/nss/mic42677.2020.9507815 ◽

2020 ◽

Author(s):

Eric W. Petersen ◽

Wei Zhao ◽

Amir Goldan

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

High Sensitivity ◽

Cost Effective ◽

Whole Body ◽

Body Scanner

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Fog forecasting for Schiphol airport at sub-kilometre scale.

10.5194/egusphere-egu21-155 ◽

2021 ◽

Author(s):

Gert-Jan Steeneveld ◽

Roosmarijn Knol

Keyword(s):

Spatial Resolution ◽

Numerical Models ◽

Wrf Model ◽

Horizontal Resolution ◽

Physical Processes ◽

Radiation Fog ◽

Fog Forecasting ◽

Elevation Data ◽

The Individual ◽

The Impact

Fog is a critical weather phenomenon for safety and operations in aviation. Unfortunately, the forecasting of radiation fog remains challenging due to the numerous physical processes that play a role and their complex interactions, in addition to the vertical and horizontal resolution of the numerical models. In this study we evaluate the performance of the Weather Research and Forecasting (WRF) model for a radiation fog event at Schiphol Amsterdam Airport (The Netherlands) and further develop the model towards a 100 m grid spacing. Hence we introduce high resolution land use and land elevation data. In addition we study the role of gravitational droplet settling, advection of TKE, top-down diffusion caused by strong radiative cooling at the fog top. Finally the impact of heat released by the terminal areas on the fog formation is studied. The model outcomes are evaluated against 1-min weather observations near multiple runways at the airport.Overall we find the WRF model shows an reasonable timing of the fog onset and is well able to reproduce the visibility and meteorological conditions as observed during the case study. The model appears to be relatively insensitive to the activation of the individual physical processes. An increased spatial resolution to 100 m generally results in a better timing of the fog onset differences up to three hours, though not for all runways. The effect of the refined landuse dominates over the effect of refined elevation data. The modelled fog dissipation systematically occurs 3-4 h hours too early, regardless of physical processes or spatial resolution. Finally, the introduction of heat from terminal buildings delays the fog onset with a maximum of two hours, an overestimated visibility of 100-200 m and a decrease of the LWC with 0.10-0.15 g/kg compared to the reference.

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The NASA-TROPOMI Aerosol Algorithm: Evaluation of first results

10.5194/egusphere-egu21-8521 ◽

2021 ◽

Author(s):

Omar Torres ◽

Hiren Jethva ◽

Changwoo Ahn ◽

Glen Jaross ◽

Diego Loyola

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Spatial Resolution ◽

Single Scattering ◽

Initial Evaluation ◽

Continental Scale ◽

Related Information ◽

Fine Spatial Resolution ◽

Direct Measurements ◽

First Results

The NASA-TROPOMI aerosol algorithm (TropOMAER), is an adaptation of the currently operational OMI near-UV (OMAERUV & OMACA) inversion schemes, that take advantage of TROPOMI&#8217;s unprecedented fine spatial resolution at UV wavelengths, and the availability of ancillary aerosol-related information to derive aerosol loading in cloud-free and above-cloud aerosols scenes. In this presentation we will introduce the NASA TROPOMI aerosol algorithm and discuss initial evaluation results of retrieved aerosol optical depth (AOD) and single scattering albedo (SSA) by direct comparison to AERONET AOD direct measurements and SSA inversions. We will also demonstrate TropOMAER retrieval capabilities in the context of recent continental scale aerosol events.

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A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

Remote Sensing ◽

10.3390/rs12233900 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3900

Author(s):

Bingxin Bai ◽

Yumin Tan ◽

Gennadii Donchyts ◽

Arjen Haag ◽

Albrecht Weerts

Keyword(s):

Linear Regression ◽

Data Fusion ◽

Spatial Resolution ◽

Temporal Resolution ◽

Fusion Method ◽

Temporal Data ◽

Resolution Image ◽

Coarse Resolution ◽

Fine Spatial Resolution ◽

Spatio Temporal

High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.

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Changes in energy expenditure during the menstrual cycle

British Journal Of Nutrition ◽

10.1079/bjn19890108 ◽

1989 ◽

Vol 61 (2) ◽

pp. 187-199 ◽

Cited By ~ 84

Author(s):

J. T. Bisdee ◽

W. P. T. James ◽

M. A. Shaw

Keyword(s):

Menstrual Cycle ◽

Energy Expenditure ◽

Metabolic Rate ◽

Whole Body ◽

Basal Body Temperature ◽

Late Luteal Phase ◽

Excretion Rates ◽

The Individual ◽

Late Follicular Phase ◽

Exercise Efficiency

1. Eight women were studied under metabolic-ward conditions while consuming a constant diet throughout a single menstrual cycle. Basal body temperature, salivary and urinary hormone concentrations were used in monitoring the cycle and designing the study so that whole-body calorimetry for 36 h was conducted at four phases of the cycle in relation to the time of ovulation.2. The metabolic rate during sleep showed cyclical changes, being lowest in the late follicular phase and highest in the late luteal phase. The increase amounted to 6.1 (SD 2.7)%. Energy expenditure (24 h) also increased but the change was not statistically significant (P> 0.05). Exercise efficiency did not change during the cycle.3. There were no significant changes in plasma thyroxine, 3, 5, 3'-triiodothyronine or free 3, 5, 3'-triiodothyronine concentrations to explain the metabolic rate changes; nor did they relate to urinary luteinizing hormone, pregnanediol-3α-glucuronide or oestrone-3-glucuronide excretion rates. No link with salivary cortisol or progesterone concentrations was observed, but there was a small inverse relation between the individual increase in sleeping metabolic rate and the subjects' falling ratio of urinary oestrone-3-glucuronide: pregnanediol-3α-glucuronide.

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