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Related Concept Videos

Harmonic Mean01:09

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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
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To determine the energy of a simple harmonic oscillator, consider all the forms of energy it can have during its simple harmonic motion. According to Hooke's Law, the energy stored during the compression/stretching of a string in a simple harmonic oscillator is potential energy. As the simple harmonic oscillator has no dissipative forces, it also possesses kinetic energy. In the presence of conservative forces, both energies can interconvert during oscillation, but the total energy remains...
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Simple Harmonic Motion01:21

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Simple harmonic motion is the name given to oscillatory motion for a system where the net force can be described by Hooke's law. If the net force can be described by Hooke's law and there is no damping (by friction or other non-conservative forces), then a simple harmonic oscillator will oscillate with equal displacement on either side of the equilibrium position. To derive an equation for period and frequency, the equation of motion is used. The period of a simple harmonic oscillator is given...
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Phase Contrast and Differential Interference Contrast Microscopy01:26

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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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Characteristics of Simple Harmonic Motion01:17

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The key characteristic of the simple harmonic motion is that the acceleration of the system and, therefore, the net force are proportional to the displacement and act in the opposite direction to the displacement. Additionally, the period and frequency of a simple harmonic oscillator are independent of its amplitude. For example, diving boards move faster or slower based on their thickness. A stiff, thick diving board has a large force constant, which causes it to have a smaller period, while a...
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Simple Harmonic Motion and Uniform Circular Motion01:42

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While simple harmonic motion and uniform circular motion may be two separate concepts, they correlate and interlink with each other. Simple harmonic motion is an oscillatory motion in a system where the net force can be described by Hooke's law, while uniform circular motion is the motion of an object in a circular path at constant speed.
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DeepHarmony: A deep learning approach to contrast harmonization across scanner changes.

Blake E Dewey1, Can Zhao2, Jacob C Reinhold2

  • 1Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Kirby Center for Functional Brain Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA.

Magnetic Resonance Imaging
|July 14, 2019
PubMed
Summary
This summary is machine-generated.

DeepHarmony, a novel deep learning method, enhances magnetic resonance imaging (MRI) consistency across different protocols. This ensures reliable quantitative analysis in multi-site and longitudinal studies, even with hardware or software changes.

Keywords:
Contrast harmonizationDeep learningMagnetic resonance imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Magnetic resonance imaging (MRI) reproducibility is challenged by variations in protocols, hardware, and software.
  • Inconsistent quantitative results from MRI hinder multi-site and long-term research.
  • Standardization efforts often face limitations, impacting data comparability.

Purpose of the Study:

  • To introduce DeepHarmony, a deep learning-based method for MRI contrast harmonization.
  • To improve the consistency of quantitative MRI data across different scanning protocols.
  • To enable seamless hardware and protocol upgrades in long-term studies without compromising data integrity.

Main Methods:

  • Development of a U-Net-based deep learning architecture for contrast harmonization.
  • Utilizing a small overlap cohort (n=8) for generating training data.
  • Validation using a longitudinal MRI dataset from multiple sclerosis patients.

Main Results:

  • DeepHarmony significantly improved the consistency of volume quantification between scanning protocols.
  • Protocol changes substantially affected atrophy calculations in multiple sclerosis patients.
  • DeepHarmony substantially reduced the impact of protocol changes on atrophy calculations.

Conclusions:

  • DeepHarmony effectively harmonizes MRI contrast, reducing inconsistencies caused by scanner protocol changes.
  • The method allows for modernization of imaging hardware and protocols in long-term studies.
  • DeepHarmony ensures the validity of previously acquired data when implementing upgrades.