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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
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The energy transport per unit area per unit time, or the Poynting vector, gives the energy flux of an electromagnetic wave at any specific time. For a plane electromagnetic wave with E0 and B0 as the peak electric and magnetic fields and traveling along the x-axis, the time-varying energy flux can be given by the following equation:
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Enthalpy changes are typically tabulated for reactions in which both the reactants and products are at the same conditions. A standard state is a commonly accepted set of conditions used as a reference point for the determination of properties under other different conditions. For chemists, the IUPAC standard state refers to materials under a pressure of 1 bar and solutions at 1 M and does not specify a temperature. Many thermochemical tables list values with a standard state of 1 atm. Because...
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On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
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Pathology-preserving intensity standardization framework for multi-institutional FLAIR MRI datasets.

Brittany Reiche1, A R Moody2, April Khademi3

  • 1School of Engineering, University of Guelph, Guelph, Canada.

Magnetic Resonance Imaging
|May 19, 2019
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Summary
This summary is machine-generated.

Multi-centre studies analyzing brain white matter lesions (WML) using Fluid-Attenuated Inversion Recovery (FLAIR) MRI face image variability. This study introduces an intensity standardization framework to reduce this multi-centre effect, improving automated analysis.

Keywords:
Alzheimer's diseaseBrainFluid-attenuated inversion recoveryIntensity standardizationSegmentationVascular diseaseWhite matter lesions

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Radiology

Background:

  • Fluid-Attenuated Inversion Recovery (FLAIR) MRI is crucial for analyzing brain white matter lesions (WML) associated with neurodegenerative diseases.
  • Multi-centre (MC) studies are essential for understanding disease progression but suffer from image variability due to diverse acquisition parameters.
  • This variability, termed the MC effect, poses significant challenges for automated image analysis algorithms.

Purpose of the Study:

  • To investigate the variability in FLAIR MRI image properties across different institutions and scanner vendors in multi-centre studies.
  • To propose and evaluate an intensity standardization framework to mitigate the MC effect in FLAIR MRI.
  • To demonstrate the impact of intensity standardization on the performance of automated algorithms, such as brain extraction.

Main Methods:

  • Analysis of approximately 5000 multi-centre FLAIR MRI volumes to characterize image property variability.
  • Development and application of an intensity standardization framework designed to normalize FLAIR MRI intensities while preserving white matter lesion appearance.
  • Implementation and comparison of a threshold-based brain extraction algorithm against a classifier-based approach on standardized and original data.

Main Results:

  • Significant variations in image characteristics were observed across different scanner vendors and centres in the original FLAIR MRI data.
  • The proposed intensity standardization framework effectively reduced the observed variability between centres and vendors.
  • The threshold-based brain extraction algorithm achieved a competitive Dice Similarity Coefficient of 81% on 183 volumes after intensity standardization.

Conclusions:

  • Intensity non-standardness in multi-centre FLAIR MRI studies is a significant issue that impacts automated analysis.
  • The developed intensity standardization framework successfully reduces multi-centre variability, enabling more robust and simplified automated algorithms.
  • Optimized pre-processing through intensity standardization enhances the reliability of large-scale neuroimaging studies for analyzing white matter lesions.