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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

460
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
460
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

402
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

219
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
219
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

359
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
359
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

336
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
336
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

362
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
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Stop moving: MR motion correction as an opportunity for artificial intelligence.

Zijian Zhou1,2, Peng Hu3,4, Haikun Qi5,6

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Magma (New York, N.Y.)
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Deep learning significantly improves magnetic resonance imaging (MRI) motion correction by reducing artifacts and estimating motion. This survey reviews neural networks for advanced MRI quality and future research directions.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Subject motion is a major challenge in magnetic resonance imaging (MRI), degrading image quality.
  • Numerous prospective and retrospective MRI motion correction techniques exist.
  • Deep learning methods have emerged as state-of-the-art for MRI motion correction.

Purpose of the Study:

  • To provide a comprehensive review of deep learning-based MRI motion correction.
  • To detail neural networks used for artifact reduction and motion estimation.
  • To explore the application of motion estimation in downstream tasks.

Main Methods:

  • Review of deep learning architectures for MRI motion correction.
  • Analysis of neural networks in image and frequency domains.
  • Discussion of motion estimation's role in MRI reconstruction and other applications.

Main Results:

  • Deep learning approaches demonstrate superior performance in MRI motion correction.
  • Various neural network strategies effectively reduce motion artifacts and estimate motion parameters.
  • Integration of motion estimation enhances downstream applications beyond reconstruction.

Conclusions:

  • Deep learning offers powerful solutions for MRI motion correction challenges.
  • Further research is needed to address current limitations and explore future directions.
  • Enhanced interaction between research areas can advance MRI technology.